boston consulting group report 2001

8/13/2019 Boston Consulting Group Report 2001

http://slidepdf.com/reader/full/boston-consulting-group-report-2001 1/64

A Revolution in R&D

H O W G E N O M I C S A N D G E N E T I C S A R E T R A N S F O R M I N G

T H E B I O P H A R M A C E U T I C A L I N D U S T R Y

B C G R E P O RT



The Boston Consulting Group is a general management consulting firmthat is a global leader in business strategy. BCG has helped companiesin every major industry and market achieve a competitive advantage bydeveloping a nd implementing unique strategies. Founded in 196 3, thefirm now operates 51 offices in 34 countries. For further information,pleas e visit our Web site a t w ww. bcg.com.



w w w. b c g . c o m

P ETER TOLLMAN

PHILIPPE GUY

J ILL ALTSHU LER

ALASTAIR FLANAGAN

MICHAEL STEINER

A Revolution in R&DH O W G EN O M I C S A N D G EN E T I C S A R E T RA N S F O R M I N GT H E B I O P H A R M A C E U T I C A L I N D U S T RY

N O VE M B E R 2 0



2

© The B oston Consulting Group, Inc. 2 001 . All rights reserved.

For information or permission to reprint, please contact BCG at:E-mail: imc-info@ bcg.comFax: 617-973-1339, a t tent ion IMC/Permiss ionsMa il: IMC /P erm is sio ns

The B oston Cons ulting Group, Inc.Exchange PlaceBoston, MA 02109USA

Credits: Left cover photo by Bob Wa terston, Was hington Un iversity, St . Louis, Missouri. Us ed by permission.The photo sh ow s a b ird’s-eye view of one room in the DNA seq uencing fa cility at th e Whitehea d Institute Cente r forGenome Research.



Ta b l e o f Co n t e n t s

ABOUT THE AUTHORS

FOREWORD 5

EXECUTIVE SUMMARYINTRODUCTION

CHAPTER 1: THE IMPACT OF GENOMICSPrefa ce 11

The Opportunities 12

The Cha llenges 18

A Fina l Word 21

CHAPTER 2: THE IMPACT OF GENETICSPrefa ce 24

Disea se Genetics 27

P ha rma cogenetics 33

A Fina l Word 39

CHAPTER 3: MANAGERIAL CHALLENGESPrefa ce: Looking B a ck a nd Looking Forw a rd 41

Stra tegy—Sea rching for Genomic Competitive Adva nta ge 41

Putting the Stra tegy into Opera tion 49

A Fina l Word 56

CONCLUSION 57

METHODOLOGY 5



About the AuthorsPeter Tollman is a vice president in the B oston of fice and leads BCG 's biopha rma ceutical R&D bu siness.Philippe Guy is a senior vice president in the Paris office and leads the worldwide Health Care practice. JillAltshuler is a manager in the Boston office and a key contributor to BCG’s genomics initiative. AlastairFlanagan is a vice president in the London office and leads the U.K. Health Care practice. Michael Steineris a senior vice president in the Munich office and leads the German Health Care practice.

AcknowledgmentsSarah C airns-Smith (Boston) pioneered B CG ’s investigation of geno mics. Samanth a G ray (Boston) has mad esignificant contributions throughout the research and writing phases of the report.

The aut hors would like to th an k the ad visor y team: O liver Fetzer (B oston) , H amilton Moses (Washington ,D.C.) , Niko Vrettos (D üsseldor f), a nd Cra ig Wheeler (B oston). The a utho rs would a lso like to a cknowledgethe contributions of the project team: Dierk Beyer (Frankfurt), Markus Hildinger (Boston), Raphael Lehrer(Washington D.C.), Nan cy Macmillan (Boston), Jona than Montagu (Lon don) , and Joanne Smith-Farrell(Washington , D.C.).

For Further ContactThe authors welcome your questions and comments. For inquiries about this report or BCG’s Health Carepractice, please contact:

Alastair Flan aga n, Lond on e-mail: flana gan [email protected] ilippe G uy, Pa ris e-ma il: guy.ph [email protected] mMa r k L ub ke ma n , L os An g el es e -m a il : lu bke ma n .m a rk@b cg .c omMichae l Steiner, Munich e-ma il: steiner.micha [email protected] mMart in Reeves, Tokyo e-ma il: reeves.ma rtin @bcg.co mPeter Tollman , Boston e-mail: tollman [email protected]

Abo u t t h e Au t h o r s

4



Fo r e wo r d

To meet growth tar gets, phar maceutical com pan ies are go ing to ha ve to increa se R&D pr oductivity. By a fo r-tunate coincidence, that crisis in expectation is being counterbalanced by a surge of opportunity. Recentyears have seen astonishing advances in technology and explosions of data, which are driving two waves ofchan ge thro ugh th e industry—a genom ics wave and a genet ics wave—and ra dically resha ping R&D meth odsand econom ics in the pr ocess. Biopha rma ceutical R&D is moving int o a n ew era: almost ever y link in thevalue cha in ha s the potent ial for trem endo us boosts in efficiency or success.

But t hese advances are no t assured. Techno logical hurd les ha ve yet to be overcome, par ticularly in th e genet-ics wave. Moreover, because the productivity boosts are likely to be unequal and uncoordinated, the valuechain itself will demand reconfiguring. And so too, in consequence, will many traditional operational pro-cedures and or gan izational structures. The repercussions of genomics, in oth er words, are going to rea ch thefurthest recesses of corporate constitution and culture. A true revolution, in short—and one that is alreadywell under way.

BCG ha s evaluat ed d eeply the econ omic an d b usiness implications of these disruptions. To bo lster our in ter-nal understanding, we gathered information and perspectives in an extensive program of interviews withleading R&D scientists and executives. O ur find ings—based on the co mbina tion o f these inter views, eco-nomic modeling, and client casework—form the substance of this report. Its three sections are devotedrespectively to the impact of genomics, the impact of genetics, and some of the strategic and operationalimplications for biopharmaceutical firms.

The first two sections ha ve alrea dy been published separately. They genera ted considerable pu blicity, a nd —

more impor tan t—considera ble comment . We now look forwar d to your furth er responses to th e report as awhole.

Philippe G uySenior Vice President



In th e phar maceutical industry’s struggle to rea chthe levels of growth expected of it, one of its keyaim s will be to in crea se R&D pr od uctivity. And a keymean s of meeting th is challenge is to ad opt some ofthe new technologies and approaches broadlydefined as genomics. 1 That is bound to be a com-plicated, perilous, and often painful process, but ifcompanies get their strategy right and overcomethe obstacles, they could, in the best case, as muchas halve the cost of drug development.

The repor t is divided in to th ree part s.

The Impact of Genomics

The first great advance of the genomics era is intechnology—above all, the integration of new high-throughput techniques with powerful new comput-ing capabilities. The new technologies are activethro ugho ut R&D, most immediat ely at th e drug d is-covery stage, and promise to enhance productivityby boosting efficiency.

The staggering investment needed to develop adrug—$880 million and 15 years is the pre-genom ics average—could be reduced b y as much a s$300 million and two years by applying genomics

technologies. Productivity gains would be realizedat every step in the value chain. Potential obstacles

abound, however. In particular, two broad chal-lenges must be met to r ealize the savings:

• Target quality must be maintained. Pursuing new target classes could involve unfamiliar costs ini-

tially, and these could delay the reward s—thou ghonly temporarily. But to jeopardize target qualityby withholding that early investment would be torisk higher failure rates downstream, and thatwould involve far gr eater co sts in th e end.

• Bottlenecks must be eased. Owing to the uneven-ness of the efficiency gains at different steps inthe value chain, the pipeline’s flow will beimpeded at various chokepoints. If the requisiteaction is taken, an even flow should be restored

and the promised rewards should b e safeguarded.

The Impact of Genetics

The second grea t ad van ce of the gen omics era is inthe quantity and quality of data. From the data,invaluable information about individuals’ geneticvariation can be extracted and exploited. In phar-maceutical R&D, g enetics will be applied part icu-larly to two tasks: identifying genes whose carriersare susceptible to specific diseases (disease genet-

ics); and subdividing patients in clinical trialsaccording to variations in drug response (pharma -

6

Ex e c u t i ve Su mma r y

1. Genomics in its narrow sense contrasts with geneti cs . Roughly, the former con cerns itself with the common “standard” genetic makeup, the latter withthe distinctive genetic makeup of individuals. But in its broader sense, genomics includes genetics . In this report, the context makes clear which sense isintended.



cogenetics). The productivity gains will be realizedmostly in later phases of the value chain, throughthe b oosting o f success rates.

This genetics wave is still gathering strength, but in

due course could make an even greater impact onR&D th an the gen omics wave. In an ideal scenario,the savings would exceed half a billion dollars perdrug. Several troubling hurdles would have to benegotia ted first, h owever. These include:

• Scientific and technical hurdles. For geneticsapproaches to work, the disease susceptibility ordrug response has to be genetic in nature. Thegene in question has to be identifiable and mustlead to a dr ugable target and/or be found in time

to streamline tr ials.

• Economic and market hurdles. The cost of con-ducting genetics studies will need to drop, andthe opportunity cost of a restricted label couldoffset the potential market upside of pharmaco-genetics.

Beyond t hese hurdles, other cha llenges will need tobe addressed:

• Difficult investment d ecisions will have to be

made, weighing high risk against potentially highrewards. Companies will need to decide exactly

how to participate in genetics—whether to investin genetics approaches, and how deeply, consis-tent with their level of risk tolerance.

• Unprecedented coordinat ion between market ingan d R&D will be necessar y. Marketing will need tohave a say in deciding which markets and whichgenetic diseases R&D should concen trat e on, a ndwill need to b ecome involved ea rlier tha n ever.

• Careful a t tent ion will need to be given to e thicalconsiderations. Companies will have to ensureprivacy of genetic material, and be prepared toaddress any concerns the public may have.

Managerial Challenges

With the new wealth of options and the increasedinterdependencies across the value chain, strategicissues will prove more complex than in the past.Likewise operational issues: many traditional waysof doing business will be disrupted by genomicstechnologies, and companies may need to restruc-ture fairly drastically.

The range of s t ra tegic opt ions avai lable to acompany will be dictated by the company’s starting

position—its size, beliefs, a spiration s, and capab ili-ties. Given the magnitude of the opportunities and



the risks involved, momentous investment decisionswill need to be made, and at the very highest levelsof th e org anization . And R&D executives will face adaunting new set of management responsibilitiesand challenges. These include:

• Selec t ing an appropr ia te research focus—nolonger just the th erapeutic area or d isease state o finterest, but also such dimensions as target classand treatment modality

• Choosing which technologies to implement andwhen an d ho w to implement th em—in-house, orthrough partnering or licensing

• Rebalancing the value chain—partly by reallocat-ing resources but mainly by redesigning processesand more actively planning an d man aging capacity

• Estab l ish ing a un i fi ed in format ics in f rast ruc-ture—including a centralized knowledge manage-ment system

• Establishing the new organization—creating new inter faces within th e R&D d epartm ent, betweendepartments, and even between corporations

• Revising d ecision-making pro cedures—fully

exploiting the latest data in order to select themost promising targets and compounds to movethrough the pipeline and to optimize their rela-tive resourcing

• Reinforcing these various reforms by engagingthe emotional and behavioral issues as keenly asthe operational ones

All things considered, companies cannot standaside. Certa inly there are risks in signing up fo r th erevolution, b ut ther e is also a grea t risk in ignorin git—the risk of becoming uncompetitive. The revo-lution is real, and will leave no one untouched.

8



In t r o d u c t io n

Throughout the pharmaceutical industry, execu-tives are worried. They fear th ey will not be able tomeet the doub le-digit ann ual growth expectation simplied by high market capitalizations. The requi-

site new d rugs will not be forth coming: R&D justcannot deliver them all.

One standard response to this problem is to scaleup—that has been the basis of many a recentmerger—but while scale can pa y off in com mercial-ization, global development, marketing, and distri-bution, it is unlikely that scale alone can solve theR&D pro blem. Anot her stan da rd r esponse is to bu yin dr ug cand idates. Such a B and -Aid approach can-not work indefinitely, and is a risky one anyway,

given that the price of these deals will continue torise as demand for them grows.

The only sure way to address the problem is toincrea se R&D pr od uctivity. And th e way to ensurethat is either to increase efficiency (lower cost orhigher speed) or reduce failure rates along thevalue chain. Many companies have increased pro-ductivity over the past decade, specifically byreengineering the development phase. That opti-mization may be reaching its limits, however. As for

the d iscover y phase, it has long been less amenab leto such improvements. So the problem of produc-

tivity persists. Traditional approaches cannot pro-vide an an swer, but genomics can. ( See Exhibit 1.)

It will not be easy, of course. There are some diffi-cult obstacles en route—difficult, but not insur-

mountable. By making informed strategic choices,compa nies can overcome the obstacles an d reap theproductivity rewards. Those that embrace the revo-lution most boldly could potentially halve the costand time it takes to develop a new drug—if theymeet certain challenges successfully.

EXHIBIT 1GENOMICS IMPROVES R&D PRODUCTIVITY

Total cost (time) per step

Cost (time)spent

Failed targets/candidates

Successfuldrug

Reducefai lure

Improveeff iciency

Total co st to develop a drug

SOURCE : BCG analysis.



Ch a pt e r 1 : Th e Impa c t o f Ge n o mi c s

Preface

As the science of genom ics ha s advanced, so ha s thedefinition. When the term was coined in 1986, itreferred mainly to the study of the mammaliangenome—specifically, the mapping, sequencing,and analyzing of all its genes. The scope soonexpanded, focusing not just on the genes’ structurebut on their function as well. More recently, thescope of the term h as broadened further, focusingno lon ger just on knowledge of th e genome bu t alsoon the exploitation of that knowledge, especiallyfor health care.

Going beyond dictionary definitions, our interest isin what geno mics means for the economics of pha r-

maceutical R&D. O n the b asis of o ur exten siveresearch an d ma ny discussions with pro minent peo-ple throughout the industry, we suggest character-izing genom ics, for the purposes of this study, as th econfluence of two interdependent trends that arefund amen tally cha nging th e way R&D is cond ucted:industrialization (creating vastly higher through-puts, and hence a huge increase in data) , and informatics (computerized techniques for managing andanalyzing those data). The surge of data —gener-ated by the former, and processed by the latter—isof a d ifferent ord er from the d ata yields of the pre-genomics era.

To ela bor at e. The n ew high -tech industrialization has increased the efficiency of certain activitiesbeyond recognition. Instead of assigning individualscientists to work manually on modest individualexperiments, companies now invoke automation

and parallel processing to conduct experimentsmuch larger in scale and complexity, an d a t a muchfaster pace.

L ook around th is lab—you have to search hi gh and l ow to find a human hear tbeat. Now robots can do the menial thi ngs we di d in grad school.

—Research leader,leading biotech company

The data that emerge are immensely greater bothin q uantity and in richness. Enorm ous databa ses—detailing gene expression, for example, or homolo-gous genes across species, or protein structures—afford unprecedented comprehensive views ofbiological processes. Increasingly, researchers can

understand properties of the system rather thanjust individua l parts, and th at ho lds out the promiseof a m ore rational approa ch to d rug d iscovery.

The new technology of informatics serves to handleand process all these data. Without it, the datawould remain raw material. Informatics was nur-ture d by several coincid ing fa ctor s: th e ever-acceler -ating power of computers, refined algorithms, theintegration of data and technology platforms, andthe versatility of the Internet. The effect is thatoverwhelming masses of information can now bemarshaled, mana ged, and ana lyzed as never before.Data are tran sformed into knowledge.

We could never have achi eved drug development that fast wi th t radi ti onal techni ques. N o way— wi thout the comput ers we di dn’t have a chance.

—VP of chem istr y,biotech company



The Opportunit ies

What is the impact of genomics on the economicsof R&D? To wha t exten t will geno mics impro ve pro -ductivity overall, and what will its effects be whenapplied at various points of the value chain? Whatother incidental advantages might genomics bringin its wake?

These crucial questions have received a great dealof att ention o f late, and a wide variety of responses.To ad dress the q uestions in a rigorous, fa ct-basedway, we built an econom ic mod el of th e entire R&Dvalue chain, grounded in a program of discussionswithin the industry (more than 100 meetings withmore than 60 scientists and executives from nearly50 companies and academic institutions.) (See themethodology section a t the end o f this report.)

Realizing SavingsBefore genomics technology, developing a new drug has cost companies on average $880 million,and has taken about 15 years from start to finish,that is, from target identification 2 through regula-tor y approval. (See Exhibit 2.) Of th is cost, about 75percent can be attributed to failures along the way.

By applying g enom ics techno logy, compa nies could

on average realize savings of nearly $300 millionand two years per drug, largely as a result of effi-ciency gains. That represents a 35 percent cost and15 percent time savings. (And tho se are th e savingspossible with technologies that are available today;when new or improved genomics technologiesemerge, th e savings will be even gr eater.) If co mpa-nies wish to stay competitive, they have no choice:they must implement genomics technologies. (SeeExhibit 3.)

Doing so, however, will hardly produce such hugesavings immediately, or automatically. It will take afew years, and many deft decisions, for the savingsto be realized. The early years of implementationmay in fa ct involve an increase in costs as the lear n-ing curve is negotiated for novel targets—specifi-

cally, as the necessary quality controls are estab-lished—and as major strategic decisions (aboutpersonnel and processes, for instance) are con-firmed or r evised.

More on these challenges later. But first, we willtake a closer look at t he lon g-term upside, d etailingthe savings at various steps along the value chain .

12

EXHIBIT 2DRUG R&D IS EXPENSIVE AND TIME-CONSU

Cost : $880 mil l ion tota l

Approximate cost ($M)

165

20540

12090

260

Time: 14.7 years to ta l

Approximate time (yrs)

1

2 0 .4

2.7

1 .6

7

DevelopmentPrecl ini cal Cli ni cal

ChemistrySc re en in g Op ti mi za ti on

BiologyTarge t ID Targe t Va l idat ion

SOURCES : BCG analysis; industry interviews; scientific literature; publicfinancial data; Lehman Brothers; PAREXEL’S Pharmaceutical R&D

Statistical Sourcebook 20 00 .

NOTE : Cost to drug includes fa ilures. Target identifica tion includes initialexperiments that companies may have outsourced to academic researchinstitutions.

2. Includes initial experiments to identify potential targets. Traditionally, companies have sourced much of this research from academia.



Target Discovery/B iol ogy

The identification of targets is being industrial-ized—through the use of technology such as genechips to perform gene expression analysis, forexample—and then further enhanced by bioinfor-mat ics. Scientists can n ow use a single gene ch ip tocompare the expression of thousands of genes, indiseased and healthy tissue alike, all at once, andcan then use informatics technology to find follow-

up information, on these or related genes, in data -bases aro und t he world. (Target validation , how-ever, seems difficult to industrialize, owing to the“slow” biology of whole-animal systems stil linvolved, a nd is not yet showing significant prod uc-tivity gains.)

In a ll, the potential savings per drug a re on a verageabo ut $140 million and just under o ne year of timeto ma rket, achieved entirely through imp roved effi-ciency. That would add about $100 million in valueper drug (assuming an “average” drug with peakan nual sales of $500 million) . So for this step in thevalue chain, productivity would increase vastly: itwould be six times as high as before, assuming thesame level of investment. A sixfold increase in thenumber of potential targets!

Several companies have already benefited hand-somely from this windfall . Take the case ofMillennium, which was an ear ly ad opter of in dustri-alized biology. The company, anticipating an over-abundance of targets, established a business modelin which it sells off much its output and uses thatincome to fund internal research. Starting from itsearly genomics platform, Millennium has strategi-cally acquired or partnered with other platformcompanies to establish an integrated drug discov-

ery value chain. From the other perspective, phar-maceutical companies such as Bayer and Aventishave made deals with Millennium, in the expecta-tion of profiting from the n ew abundance of targetsthey can choose to pursue.

Lead Discovery/Chemist ry Chemistry is being revolutionized by in silico (thatis, computer-aided) technology—specifically, vir-tual screening supported by chemoinformatics. Invirtual screening, potential lead chemicals are

assessed with com puter a lgorithms to test how likelythey are to interact with a target. Chemoinform aticsprovides the necessary platform for virtual screen-ing, using d ata a nd a na lysis from h igh-thro ughputscreening (HTS) and other chemistry activities.This appro ach increases efficiency by focusing com -pound synthesis, reducing the number of assays,increasing the parallelization of screening steps,

EXHIBIT 3GENOMICS CAN YIELD SIGNIFICANT SAVINGS

Cost t o drug

Cost ($M)

Time to dr ug

880Pre-genomics

740Post-genomicstarget ID

610Plus in silicochemistry

590Plus preclinical andclinical advances1

Time (years)

Pre-genomics

13.8Post-genomicstarget ID

13.0Plus in silicochemistry

12.7Plus preclinical andclinical advances1

ID

14.7

1,00080 06004002000

151050

Development

Prec li nic al Cl inic al

Chemistry

Sc re en in g Op ti mi za ti on

Biology

Targe t ID Targe t Va l ida t ion

SOURCES : BCG analysis; industry interviews; scientific literature; publicfinancial da ta; Lehman Brot hers; PAREXEL’S Pharmaceutical R&D

Statistical Sourcebook 20 00 .

1Includes surrogate marker savings from early elimination of unpromisingcandidates, not from early FDA approval; does not include potential savingsfrom pharmacogenetics.



and generally helping to optimize screening. Thepower of this approach is expected to increase dra-matically with the availability of larger data sets forrefining the pred ictive algorithm s. (At the mo ment,however, in silico chemistry has one notable short-coming: it looks as if it will be suitable for onlyabout 30 percent of targets—the rest fail to yieldthe requisite structural information—and eventhen might prove difficult to apply until lead opti-mization. Our savings are calculated for those tar-gets where in silico techn ology can b e applied.)

The potential savings are on average about $130million and nearly one year per drug. That wouldadd about $90 million in value per drug. For thisstep of the value chain, then, productivity woulddoub le, assuming the same level of in vestment.

As a beneficiary of these advances, a good case inpoint is Vertex. Start ing from an I DD (in silico dr ugdesign) platform in chemistry, the company hasgone on to develop an integrated value chain in itsown right. In silico models have allowed more effi-cient design of small-molecule dr ugs than a pur elytraditional approach , and the compan y’s discoveryfocus has been on certain target classes that benefitmost from proprietary in silico technologies. Thisappro ach ha s met with considerab le success, culmi-

nating in one of the biggest biotech alliances so far(with No var tis, and wo rth $813 million ). Vert ex canfair ly claim to h ave the stro nge st sma ll-mo leculedr ug pipeline within th e biotech ind ustry. With on edrug on the market and twelve candidates in devel-opmen t, it compa res favora bly with some of the b igpharmaceutical pipelines.

Serious money can be saved for the tar get classes where in si l ico chemistr y works.

—Director of chemistry,major pharmaceutical company

Development Three key genomics advances look set to increasecapacity here. In silico ADME/tox ( absorption , dis-tribution, metabolism, and excretion/toxicity) andhigh-thro ughput in vitro t oxicology are revolution-izing the preclinical phase through their power topredict drug properties. And surrogate markers

(physiological markers that correlate with elementsof d rug response), applied in both preclinical an dclinical trials, evaluat e dr ug effects more ef ficientlythan before: they are quick to identify failing com-pounds, and once regulatory approval is granted,will be used to iden tify passing com pound s too.

In combination, the potential savings available inthe short term are on the order of $20 million and0.3 years per drug. That would add about $15 mil-lion in value per drug. But these approaches willbecome even more valuable as clinical data on therelationship between genes, gene expression, anddisease accumulate and regulatory agencies beginto accept clinical-mar ker data : the pot ential savingscould rise to $70 million.

These technologies are being adopted by forward-looking chemistry companies, and are enablingthem to pull certain preclinical activities into thechemistry part of the value chain. For example,ArQule has recently acquired Camitro to incorpo-rate an integrated in vitro and in silico ADME/toxplatfor m into its own set of capa bilities.

These are no t th e only ad van ces likely to tr an sformproductivity during the development phase. Phar-macogenomics—through its power to identify sub-groups of patients who respond differently to adrug under study—offers the promise of streamlin-ing clinical trials; we explore this topic in moredetail later. Beyond genomics (and beyond thescope of th e current report ), “e-techn ologies,” suchas electronic patient recruitment and monitoringvia the Internet, are expected to speed up thelaunch and completion of clinical trials.

Beyond the Tradit ional Value Chain:Chemical Genomics

The various productivity gains just outlined occurwithin specific steps of the value cha in. B ut supposeyou could tra nscend th e trad itional value chain, orrefashion it to stream line R&D. Tha t is one of therevolutiona r y prospects now o pening up. The key ischemical gen omics, and the wa y it will dissolve theold boundaries is by introducing into the valuechain a kind of parallel processing. (See sidebar,“Chemical Genomics—Forward or Reverse.”)

14



One immediate result would be to process the glutof identified targets more quickly: instead of join-ing the logjam a t the validation stage, a great m anyof them can now be diverted directly to screening.If they fail there, they can be discarded right away,and thus simply bypass most of the validation stage

altogether. In other words, screening moves up thevalue chain to rest alongside validation, in a paral-lel rather than consecutive position. By bracketingthe industrialized steps of target identification andchemical screening, chemical genomics has giventhe value chain a remar kable makeover.

The key is to move lengthy, messy biology far down- str eam where you kn ow i t’ s wor th pu rsuing. M any tar gets aren’t dr ugable, so just val idate the smal ler dr ugable subset.

—SVP of discovery,leading biotech company

The effect o f this new value cha in is drama tic: timeto dr ug is cut by a further two years (that’s on topof the year already saved by using genomic targetidentification). On the other ha nd, th ere is a large

increase in cost, offsetting a ll cost savings from t ar-get identification. But the tradeoff is still positive.In a h ighly competitive market, where new entra ntsare continuously eroding share, chemical genomicscan add more than $200 million in value per drug.(In less competitive conditions, the value added

may be as little as $20 million.)

No doubt chemical genomics costs more—but you take the loss to gain the speed. Time is money.

—SVP of discovery and technology,major pharmaceutical company

One important drawback of chemical genomics isthis: it is limited mainly to known target classes.With targets of unknown function, results becomever y difficult to interpret. The p roxy assays used fo rscreenin g—hea t-stability assays, for insta nce—ten dto yield b oth false positives and false negat ives.

Nevertheless, chemical genomics is already beingpursued throughout the industry. Several big phar-maceutical companies have adopted it , and geno-mics companies such as Aurora Biosciences 3 and

C H E M I C A L G EN O M I C S — F O R WA R D O R R E V ER S E

When companies say they are pursuing chemicalgenomics, they are usually referring to large-scalereverse chemical genetics. (Tha t is how the term isused in our report. ) This a pproac h involves findingchemical compounds that bind to a known target.Companies often perform this task for entire targetclasses; it is especially popular for protein classesthat are known to be highly drugable, such as G-protein coupled receptors (GP CRs). The a ssa y forbinding does not need to provide functional informa -tion relevant to a specific disease state—biologicalfunction can be assessed in validation experiments.

The a lterna tive is forw a rd chemica l genetics. This

approach begins with functional knowledge. Alibrary of compounds is screened in an assay thattests for changes in a specific biological function.

The intention is to screen a library a ga inst a ll ex-pressed genes in the system under investigation.This approach ha s the tremendous adva nta ge of al-lowing the identification of targets without any pre-sumptions as to their function. Additionally, thesetargets can help to elucidate the mechanism of dis-eas e, thereby revealing other potentia l ta rgets in rel-evant pathw ays. The drawb ack is that forwa rd chem-ical genetics has not yet been industrialized, andthroughput levels are therefore very low. Accordingto our model, implementing it today would increasecosts to more than $1 billion per drug, owing to theuse of “slow” biology, which is needed to set up the

screening a ssa ys in chemistry. The expert estima te isthat forward chemical genetics is still as much asfive years away from being economically feasible.

3. In July 2001, Aurora Bio sciences was acquired b y Vertex Ph arma ceuticals.



Cellomics are well positioned to exploit the ex-pected resulting demand for screening resources.Auror a is a likely winn er in th e race t o resolve chem -ical geno mics-relat ed bo ttlen ecks, since it boa stssome of the most advanced screening and assaytechno logies in the in dustry. It has an unusual busi-ness model, in that it provides tools and discoveryservices but does not engage in any drug discoveryof its own.

* * *

So much fo r the imm inent ef ficiency savings acrossthe R&D value cha in. They are h ard ly the end of

the story, of course. Other technological advancesare b ound to impr ove R&D pro ductivity further indue course. Important emerging technologiesinclude proteomics, partial target inhibition, andstruct ura l biology. (See sideba r, “Techn olog ies inWaitin g.” )

Improving Decision MakingThe eco no mics of R&D h inge o n success ra tes, an dsuccess rates depend largely on a cascade of deci-s ions that have to be made again and again:whether or not to pursue a target or lead, and if so,how—to what extent and with what a pproach.

16

In this report we have focused on the technologiesand approaches that are having the greatest impacton R&D economics t oda y. S everal other exciting ad-vances appear likely to make a comparable impactbeyond the next three to five years (too far ahead forinclusion in our analysis for this report), in particu-lar, the use of proteomics in target identification,conditional gene inhibition in target validation, andindustrialized structural biology in screening anddrug design.

Proteomics is the study of protein expression andprotein-protein interactions. Its aim is an understand-ing, and ultimately exploitation, of protein function.

Identifying proteins through sequence or structurehomology ha s recently become much more efficient,thanks to bioinformatics’ role in analyzing large-scale experiments. One example of a genomics com-pany applying proteomics is Oxford Glycosciences,which is engaged in identifying targets and surrogatemarkers, both in collaboration with pharmaceutical

companies and in an independent pipeline. But pro-teomics is not really industrialized yet, and has highhurdles to overcome before it is.

We examined the economics of proteomic expressionstudies using two-dimensional gel analysis, followed

by identification of interesting proteins through ma ssspectrometry.

Under optimal conditions today, this approach hasthe potential to save about as much in cost asgenomics-based approaches do, though not as muchin time (about six months less). As the technologybecomes industrialized, proteomics could well sur-pass genomics-based approaches, but that is stillseveral years a wa y.

The a im of the second promising techn ology weinvestigated, conditional gene inhibition , is to over-come a common problem in target validation. Hereis the background. A standard technique for targetvalidat ion uses “ta rget knockouts.” The potential ta r-get is removed, or “knocked out,” from an animal atconception; this results in the total inhibition of theta rget’s function from embryo to a dult. The trouble istha t drugs w ork differently. Very seldom d o theyinhibit target function fully, and they are taken onlya fter genes ha ve already fulfilled their developmenta lrole in utero. So the use of target knockouts as a tar-get valida tion technique does run the risk of creatingfalse negatives (in some cases indicated by death,because of the unnatural disruption of embryonicdevelopment). What is needed instead of total gene

T EC H N O L O G I E S I N WA I T I N G — O TH E R TE C H N O L O G I E S EX AM I N E D ,

B U T O M I T T E D F R O M O U R R E P O R T



trials, if the com pany were able to d ecide in just oneout of t en such cases to aband on d evelopment ear-lier, it could save an additional $100 million perdrug.

Improving decision making to that extent will takemore than simply acquiring and implementing thenew genomics technologies and approaches. It willtake some serious strategic reth inking too, and pos-sibly major organ izational cha nges. Whether to keepall act ivities in-ho use, or seek par tner s, or buy in ta r-gets or leads. H ow to red istribut e resources, reassignpersonnel, and revise lines of communication andchains of command. Such operational an d organ iza-tional quandaries will be addressed in detail in thefinal chapter of t his report.

We implemented a fast-in/ fast-out decision poli cy about pr ojects—if we didn’ t have opti mal condi- ti ons met i n 18 mont hs, we ki ll ed i t. That made all the di fference.

—Form er execu tive,leading pharmaceutical company

Even the basic business skill of decision making,then, is not immune to the influence of genomicstechnology. Whatever other benefits i t brings,gen omics serves as a wake-up ca ll acro ss th e ind us-try, even for companies trying to shelter from thegenom ics revolution.

The Chall enges

Although implementing genomics offers compa-nies great opportunities, it also presents them withformidable challenges. One of these is to ensurethat the q uality of the pipeline remains uncompro-mised. Another is to put the new technologies intoefficient operation.

Maintaining QualityIf the potential productivity gains are to be fullyrea lized, th e post-geno mics R&D pip eline will needto retain or improve its pre-genomics quality. Anydecl ine in qual i ty—the qual i ty of targets andleads—would obviously have an adverse effect onproductivity. The main threat to quality derivesfrom the unorthod oxy, the un familiar n ature, of so

many new targets. Entire target classes, previouslyunknown, will need investigating. The temptationto pursue leads prematurely is bound to arise, andquality control will need to be rigorously enforcedto u phold the pipeline’s usual success rates.

In any given experi ment , 70 percent of what I see i s completely new. I t could be a gold r ush, or i t could be ju nk—-there’s no way to tell un ti l I si t at the bench and do more work.

—Director of research,leading biotech company

To appr eciate the th reat a ccurately, we need aproper definition of the term q uality.

The “intrinsic quality” of a target or lead amountsto its likelihood of success, which is based on factor ssuch as clinical relevance and drugability.Companies can do little to alter this type of quality.The “provisional quality” (or “informational qual-ity”) of a target or lead is based on the amount ofdata available on it at any given time—how much isknown about its clinical relevance, drugability, andso on. (This informational quality helps to predictsuccess rates, but does not influence them.)Companies can alter this type of quality, by spend-ing appro priately, and in tha t way can impr ove theirability to pred ict do wnstream success rates.

This distinction is crucial. But it has at times beenoverlooked, resulting in some confusion in theindustr y. A widely publicized con cern ha s been tha tnovel targets identified through genomics wouldtend to be of inherently lower quality than pre-genomics targets, and thus more likely to fail atsome costly pha se downstream. Tha t inferen ce is anoversimplification , an d is misleading .

Certainly genomics proposes many more novel tar-

gets (as much as 60 to 70 percent of potential tar-gets, in our interviewees’ experience, may belongto previously unknown target classes), and theirinfor mation al qua lity at tha t early stage is duly mod-est. But that says nothing about their intrinsic qual-ity. Any prud ent co mpan y, no mat ter ho w bold, willstrive to learn more about novel targets beforedeciding to pursue them downstream. In our an aly-sis, investment s made to r aise a no vel target ’s infor-

18



mational quality to the level of a known target’swould be more tha n recouped in d ue course.

The overall cost of these novel targets—raisingtheir informational q uality and then pursuing themdown the value chain—is bound to rise initially.

However, within three to five years from the initialdiscovery of a target in a novel class, according toour m od el, the overall cost increa se per no vel-classdrug could return to average.

Where do the added costs come from? And whatmust happen to offset them?

The Cost of Quali ty Control Our mod el predicts that th e typical increase will beabout $200 million and more than one year per

drug (that is, a total cost of $790 million versus$590 million, a nd a t otal t ime to d rug of 13.8 yearsversus 12.7 years). The increase is mainly attributa-ble to the extra time needed to understand targetfunction and develop appropriate assays in targetvalidat ion an d screening; also, to the need to screena higher proportion o f compounds, since an appro-priate subset of a larger library cannot be selectedin a dvance.

Chemical optimization costs would increase only if

the novel target required a novel compound ( by nomeans a necessary requirement, though certainly apossible one occasionally). Our model examinesthis worst-case scenario explicitly. If a novel targetdoes happen to require a novel compound, or acompound unfamiliar to the medicinal chemists,the potential efficiency loss causes a furtherincrease of $290 million and more than two yearsper drug (that is, a total cost of about $1.1 billionversus $590 million, and a total time to drug of 15years versus 12.7 years). The additional increases

here would be d ue to the extra time needed now formedicinal chemists to learn h ow to mod ify the com-pound and attain specific properties through trialand error. But this worst-case scenario should no tbe very common .

Moving further still down the value chain, to theprecl inical and cl inical phases , costs are notexpected to increase. The downstream success rate

for novel compounds or targets should turn out tobe much the same as that for known compounds ortargets, as long as the same standards are applied.There should be no significant increase in toxicityor decrease in efficacy, other than in very unlikelycircumstances—for instance, if existing animalmodels somehow proved less suitable, or if drugsfor novel target classes were to interact with meta-bolic path ways in utter ly unfamiliar ways.

Offsettin g the Costs

Raising the informational quality of novel targetsinvolves a heavy investment, but it is a wise in-vestment. And a fairly quick one: knowledge aboutone novel target quickly elucidates other poten-tial targets in the same class. Thanks to feedback

loops, knowledge increases geometrically. As moreis learned, the level of investment can tail offaccordingly.

In any case, the alternatives to making that earlyinvestment in informational quality are far fromattractive. On the one hand, dropping the targetswould be terribly short-sighted : com pan ies wouldbe forgoing the opportunity to discover and exploituntapped sources of revenue. On the other hand,pushing novel targets onward without adequate

information on them would almost certainly resultin a higher failure rate downstream, with all theassociated implications for cost. An increased fail-ure rate of just 10 percent across chemical opti-mization a nd all of development would on averageincrease costs by about $200 million per d rug.

To sum up, th en: costs incurred early in th e valuechain (by information gathering) look preferableto those that would other wise be incurred later (asthe result of a higher downstream failure rate). All

the more so, given that the early costs should soonbegin falling (investment in information is almostalways associated with an experience curve): asnovel targ et classes become increasingly familiar, itwill become increasingly efficient and economicalto pursue new targets within those classes. So withproper handling, the burden of that early costincrease is just a short -term one, and the p rod uctiv-ity of geno mics-dr iven R&D shou ld soon retur n



almost to t ha t of m ore fa miliar tar get classes. Weestimate the time required for this is about three tofive years from the discovery of a novel target,which is the amount of time it should take to com-plete validat ion a nd early screening (a ssay develop-ment). (See Exhibit 4.)

Putting the New Technology into OperationIt is one thing to acquire and install new capabili-ties and anoth er to get them to function as they aremeant to. The challenge of making genomics tech-nologies operational has two major components:easing the b ottlenecks that will develop, an d r esolv-ing the personnel conundrums that are sure toarise.

The Problem of Bot tl enecksThe bottlenecks result, in effect, from the uneven-ness of the ef ficiency gains at d ifferent poin ts in th evalue cha in. (See Exhibit 5.)

Consider the sixfold increase in target identifica-tion described above. This escalating quantity oftargets could turn out to be n ot so much a gloriousprofusion as an exasperating glut. Unless there issome corresponding increase in the capacity toprocess them downstream , th ese ta rgets will simply

loiter at their source in a wasteful logjam. Or con-sider chemical genomics. Implementing thisapproach will build up huge pressure on screeningresources: sending unvalidated targets for screen-ing could involve a 120-fold increase. So too withefficiency gains at other points in the value chain:without the necessary downstream adjustments,bottlenecks will inevitably develop.

Our capacity to do functional experiments was

completely choked by potential targets.—VP of discovery,

major pharmaceutical company

But the problem is a dynamic one, and accordinglyvery awkward to deal with. Ease one bottleneck andyou often create another downstream. Or ease ittoo much and you convert it into a bulge—over-

20

EXHIBIT 4IMPACT OF QUALITY ON COST TO DRUG

Mix of novel andknown t argets

Approximate cost ($M)

Novel targets only

Novel targets andnovel compounds only

Benefits of experienceover 3-5 years

Pre-genomic s Post -genomics

1,080

880

590

590

790

SOURCES : BCG a nalysis; industry interviews.

EXHIBIT 5UNEVEN PRODUCTIVITY GAINS CREATE IMB

2,4001

108138

72

307

Increasedproduc t iv i ty

Number todayto get one drug

Requiredproduc t iv i ty2

Not to scale

Potentialtargets

Validatedtargets

Leadcandidates

Drugcandidates

INDs Drug

Targets Compounds

Poten-t ial

targets

40 0

ID Development

Precl ini cal Cl ini cal

Chemistry


Biology

Targe t ID Targe t Va l idat ion

SOURCES : BCG a nalysis; industry interviews.

NOTE : Does not include impact of pharmacogenetics, to be addressed innext installment.

1Number of targets identified by investing same resources post-genomics aspre-genomics.

2Productivity required to exploit all potential targets from target identification.



resourced in relation to the flow from upstream,and hence wasteful once again. It will take someadroit adjustment of resources and processes alongthe value chain to restore a smooth flow.

This imbalance will affect incumbents—integrated

companies with established value chains—worst ofall. They have resources and processes in place;chan ges are likely to be difficult an d disruptive. Toimplement the new genomics technologies is trou-blesome enough, but then to have to redistributeresources along the en tire value chain will take realdeter minat ion. (To other compa nies, by cont rast,bottlenecks might represent very favorable oppor-tunities. In particular, genomics companies couldbenef it . (See sideba r, “U pstart Sta rt-ups—theCompetitors Classified.”)

The Hum an Factor To flour ish in the n ew genom ics era, and possiblyeven to survive, companies are going to have toengag e the n ew realities. It will not b e easy. Some ofthe new technologies will tend to overstretch oreven defy existing capabilities and organizationalstructures. All along the value ch ain, processes andresources are going to have to be adjusted.

The resources in question include human resources,

and retrenching, reassigning, or supplementing tal-ented personnel is a far from straightfo r ward proce-dure. But it will have to be done. Organizationalrestructuring is likely to entail distressing upheavalsfor corporate culture and personnel alike. Thestrategies adopted for managing it will require con-stan t mon itoring an d fine-tuning. New modes ofcross-functiona l collabora tion m ay need to b e insti-tuted, new incentives offered, a nd so on .

I spend hal f my time looking for tal ent that i sn’ t out t here, an d the other half worr yin g where they would fi t i f I foun d them.

—Research director,leading biotech company

* * *

In sum, implementing genomics technology will bevery tricky. It will almost certainly require a holistic,

cro ss-value-ch ain per spective. We will discuss pot en -tial solutions to these operational challenges in thethird chapter.

A Final Word

By engaging aff i rmat ively with the brave new genomics world, companies are making it possibleto incre ase R&D pro duc tivity substan tially. They willbring to bear an array of industrialized processes,informatics, and rich data sets—a formidable com-bina tion th at pr omises to bo ost efficiency, an d evenimprove success rates, all alon g th e value chain.

Here we have discussed both the opportunities andthe challenges that arise when a company adoptsand implements genomics technologies that are

available tod ay.

The opportunities add up to potential savings ofnear ly $300 million per d rug—abo ut on e-third ofthe cost—and the prospect of bringing each drugto m arket two years sooner. The ch allenges includemana ging qua lity control and dealing with unfamil-iar operational predicaments: bottlenecks along thepipeline and a host of organizational difficulties.

But for companies that choose not to meet thegenom ics revolution h ead on, th e challenge is evengreater: they will be una ble to com pete. These com-panies do more than leave money on the table.They face th e inevitab ility of being left behind .

To reap ma ximum benefit from th e new technolo-gies, companies will need to scrutinize their re-sources, processes, and policies throughout thevalue chain. Pharmaceutical and biotech managerswill need to a sk themselves some t axing q uestions asthey begin to formulate their genomics strategy:

• Which specif ic genomics technologies andapproaches make the most sense for our com-pan y? Wha t investment s and capa bilities would beneeded to integrate these new technologies andapproaches successfully?

• What ca pabi l it ies do we already have? Whatinvestment are we prepared to make to acquirethose we lack?



22

U P S TA R T S TA R T- U P S — T H E C O M P E TI T O RS C L A S SI F I E D

As the e-commerce revolution has demonstrated,disruptive technologies tend to spawn start-ups thataim to exploit the disruption, either as suppliers to,or as replacements for, incumbents disoriented by achanging world. In the case of genomics, the in-cumbents are easy to identify: the traditional phar-maceutical companies and the larger fully integratedbiotech companies. But who are the start-ups?

Genomics companies can be classified in threebroad groups, on the basis of how closely or dis-tantly they are related to the actual developing andmarketing of drugs.

The group furthest aw a y conta ins the companiesthat supply enabling technologies , in the form ofha rdwa re or softw a re. These compa nies resemblethe merchants of the California gold rush who soldpickaxes to the miners, or more recently, companiessuch as Cisco and Sun Microsystems that have beenproviding the necessary infrastructure for the multi-tude of e-commerce practitioners. Examples of suchcompanies are PE Biosystems, the supplier of high-throughput sequencing machines, and Affymetrix,the preeminent gene-chip manufacturer and supplier.

The second broad group conta ins the companiessupplying information and knowledge , includingthose companies tha t generate proprieta ry da taba sesand offer access to them through subscriptions orfee-per-use business models. One of the best-knownexamples is Celera, which sells subscription-basedaccess to human and animal model-sequence data.The group a lso includes compa nies tha t a re attempt-ing to integrate and exploit those databases to con-duc t in silico R&D. An exam ple is LION Biosc ience ,which integra tes information from public a nd priva te

sources into a single platform to make targets andleads easier to identify and analyze.

Finally, there is the group of companies that developand sell more traditional “physical” drug intermedi-ates —targets a nd lead compounds. We ca ll theseplatform and orchestrator companies.

P latform compa nies deploy proprietary technology inthe quest for promising targets and leads. One suchcompany is Aurora Biosciences, which has devel-oped proprietary high-throughput screening technol-ogy to exploit a n opportunity in screening a nd chem-ical genomics. Another example is MorphoSys,

which has developed a platform for rapid develop-ment of high-a ffinity antibodies, for use in ta rget val-idation and therapeutic antibody discovery.

Going one step further are orchestrator companies,which string together adjacent platforms to createoptimized seg ments of the R&D value cha in. As th eorchestrators extend their value chain, they can selldrug candidates that have progressed further andfurther downstream—and have thus become moreand more valuable. Although these companies arestill selling only intermediates, they show every signof graduating into fully integrated drug companies.Millennium has already made that transition: con-centrating initially on genomics target discovery, itha s s ubseq uently developed a full R&D pipeline inits own right.

Wha t is t he outlook of eac h of thes e groups? Thefirst two (the pure suppliers, either of enabling tech-nologies or of information and knowledge) wouldappear to be well positioned if they target areas ofscarcity (that is, bottlenecks) with proprietary prod-



• Where is industr ywide scale to be found rather

than just company-level scale? Which capabilitiesshould we ther efore d evelop in-house, an d wh ichthrough par tners?

• Ho w will any new technologies affect the rest ofthe value chain? How can we optimize decisionmaking and information flow up and down thevalue chain?

• What are the implicat ions for the organizat ion ofthe ch an ges we wish to make? How feasible is thenecessary restructuring? And what would be themost efficient way to carry it out?

These questions can be addressed by thorough,

thoughtful analysis. Key investment decisions willbe required, as well as a carefully planned imple-mentation program to ensure that the value oftho se decisions is captured.

In th e next chapter, we turn to genetics and analyzeits likely impa ct on R&D pro du ctivity. In th e fina lchapt er, we will examine mo re closely the strat egicchoices and operational implications of the variouschanges in prospect.

ucts, or if they have enough clients to achieve scaleefficiencies reachable only by supplying multiplecompanies. But so far, most of these companieshave struggled to find a sustainable, profitable busi-ness model.

Mea nwhile, th e third group seems in the most prom-ising position. The pha rmaceutica l business rema insattractive, with margins averaging more than 80 per-cent, so it is easy to see why so many genomicscompanies aspire to become drug companies. But

there are many hurdles en route, and overambitiouscompanies risk tripping over them. Although many ofthese companies may fail, those that succeed willhave a transformational impact on the industry.

Moreover, the tra ditional drug companies s eem to b emoving in the opposite direction, increasingly out-sourcing portions of th eir R&D va lue cha ins. Wha t isgoing on? We w ill try to a nsw er that quest ion in thethird chapter, when we examine these trends inmore detail.



Preface

H aving discussed the genom ics wave in th e previouschapter, and the way that i t promises to enhanceR&D pr od uctivity, we now tur n to the g enet ics wave.Several broa d differen ces suggest them selves imme-diately. Where the genomics wave is technology-driven, the genetics wave is better viewed as data-driven, exploiting the known details of the humangenome and individual variations within it. Wherethe genomics wave brings benefits mainly at thedr ug-discovery a nd preclinical pha ses, the gen eticswave will prove its worth in bo th the ea rliest ph aseand the later phases of the value chain—target dis-covery and the clinic. Where the genomics wave

enh an ces R&D pr od uctivity mainly by securinggreat improvements in efficiency (with on ly mod estimprovements, if an y, in success rates), t he geneticswave could boost success rates dr ama tically as well.

One fur ther difference should be mentioned:where our mod el for the gen omics wave was put for-ward with considerable confidence, our model forthe genetics wave is more tentative. At this earlystage, any assessment of gen etics’ impa ct on the eco-nomics of R&D is boun d t o b e provisional. C ertainlygenetics has huge potential: if all goes according t oplan, it will chan ge R&D product ivity beyond recog-nition. But between that potential and its full real-ization lie several years and man y obstacles.

The potential consists in tremendous savings. First,genetics can bring about great efficiency gains bymaking it possible to shorten or even bypass varioussteps in the value chain. Second, gen etics holds the

prospect of transforming success rates: failures inthe R&D pipeline cur rently accoun t for 75 percentof the tota l cost to d rug. But o ffsetting such oppor-tunities, dangers loom large. Riding the geneticswave involves a greater risk than riding thegenomics wave alone—though it is more exhilarat-ing a nd , if th e risks are successfully negotiated , ulti-mat ely more reward ing. Ho w to choose between dis-cretion and valor is a crucial strategic decision thatcompa nies will have to ma ke.

In a na lyzing the econo mic implications of genetics,this chapter of our report considers the effect onlyon pha rma ceutical R&D. But genetics is likely toaffect health care far beyond R&D, in both theshort and the long term. In the short term, new market opportunities should arise in the formerlysleepy diagnostics sector. (Drug companies may ormay not be able to exploit these opportunities: seesideba r, “D iagno stics—an O pportu nity Too G ood toMiss… and P erhaps Too G ood to G rasp.”) In thelonger term, genetics is likely to transform thedeliver y of h ealth care. I ncrea singly, d iseases will beredefined into various subtypes—a refinement thatshould facilitate more appropriate care and more“rational” drug design. The combination of new diagnostics, new disease definitions, and new tai-lored drugs should prove a winning one, and maywell usher in an era of individua lized med icine.

R&D remain s the focus of our a na lysis here, ho w-ever: specifically, the wide range of economic reac-tions that R&D might show und er the impact of th enew genet ics infor mation . We discuss the trem en-dous opportunities as well as the accompanying

Ch a pt e r 2 : Th e Impa c t o f Ge n et i c s

24



risks inh eren t in gen etics-ba sed R&D, a nd explor evarious ways of managing them.

Two Kinds of Genetics ApproachesThere are two relevant approaches to considerwhen a ssessing th e econom ic impact of gen etics on

R&D: di sease genetics and pharmacogenetics . Theyoperate at different stages of the value chain.Di sease genetics is invoked earlier, during the discov-ery pha se: it involves the search for gen es that ma kepeople susceptible to particular diseases, with theaim of then finding targets. Pharmacogenetics is the

D I A G N O S TI C S — A N O P P O R T U N I T Y T OO G O OD T O M I S S …A N D P E R H A P S T O O G O O D T O G R A S P

It will be several years before genetics fulfills itspromise. In the meantime, however, companiesmight begin to enjoy a preliminary reward, in theform of diagnostics—essentially a byproduct of theirbroader genomics research programs. Certainly diag-

nostics is the subject of great expectations, thoughwhether and how soon it will meet them remains tobe seen.

Many research projects in genomics and geneticswill devise diagnostic tests as a matter of course—inparallel with research or simply as a preliminarystep, perhaps—without portraying them that way.Diagnostic tests can be understood in a fairly broadsense here. Disease genetics, for example, in identi-fying a target, is in effect finding a marker of diseasesusceptibility. Expression profiling, in identifying themolecular differences characterizing a disease’s dif-ferent subtypes, is pointing the way to differentiatedand fine-tuned therapies. And pharmacogenetics, inidentifying variations in drug response among vari-ous patients, could be helping to suggest the mostsuitable drug for them.

The opportunities inherent in dia gnostics w ill a ppea lto drug companies at several levels. First, costs arelow. The intellectua l capita l needed to develop a di-agnostic test comes free, courtesy of existing re-sea rch in drug discovery and d evelopment; va lida tionstudies ca n be run in para llel with drug efficacy stud-ies, or perhaps can even simply borrow their resultsand extrapolate from them; and as for safety studies,diagnostic tests don’t need any. All in all, then, theincremental spending required to develop a mar-keta ble diagnostic test is, rela tively speaking, pa ltry.

Second, rewards a re prompt. Diagnostics, in bypas s-ing most of the traditional steps of pharmaceuticalR&D, c a n be brought t o ma rket not only fa r morecheaply than drugs, but far more quickly too. Drugcompanies are thereby able to realize some unusu-a lly fa st pa yba ck on their R&D spending.

Third, th e ma rket outlook is fa vorable. As new thera -pies proliferate, more diagnostics will be demanded;and as technologies advance, new types of diagnos-tics w ill become a vailable. The signs a re good.

These opportunities a re to some extent offset, how -ever, if not by risks, then at least by challenges.

There is the cha llenge of novelty, for inst a nce: formany traditional companies, diagnostics wouldinvolve manufa cturing an unfa milia r kind of product—

a kit—and that in turn would involve developing new capabilities, or else partnering with a dedicated diag-nostics company. Companies that have an in-housediagnostics division, such as Hoffmann-La Roche,Abbott, and Bayer, will have an advantage here.

Then th ere is the c ha llenge of intellectua l-propertyrights: companies might find it more difficult toassert those rights over diagnostics than over theirfindings in pharmaceutical research.

Perhaps the most daunting challenge is timing: diag-nostic tests will tend to emerge too speedily, becom-ing available sooner than the therapies they indicate.So the chief appeal of investing in diagnostics—itsprompt availability—may be undercut. Drug compa-nies may have to delay marketing their diagnostics(and thus delay capitalizing on the opportunities)until their drug R&D pipelines ca tch up.



genetics-based for m of phar maco geno mics (seesidebar, “Pharmacogenomics—Some Definitions”),and comes into play later, in the developmentphase: it involves predicting the efficacy and sideeffects of can didate drugs.

The data explosion detonated by genomics tech-nology has created vast amounts of genetic infor-mation, ready for sifting. The findings of theHuman Genome Project and related endeavors aremerely the starting point. The ultimate goal is toelucidate the genetic basis of human disease anddrug response. In the short term, genetics researchwill enable scientists to predict disease susceptibilityand likely drug response in individuals; in thelonger term, it should help to improve the qualityof pha rmaceuticals and medical diagnoses.

Attain ing the short-term goa l is, conceptua lly, sim-ple enough . The gen etic codes of ind ividuals differin tin y, but sometimes decisive, d etails. By compa r-ing an individual’s genetic variations against the“stand ard ” geno me, scientists should be able to pre-dict whether that individual is at risk for a specificdisease, an d, if so, h ow well suited h e or she is to aparticular drug therapy—the work respectively ofdisease genetics and pharmacogenetics.

The two appro aches benefit R&D econ omics in dif-ferent ways. Disease genetics will improve efficiencyin target discovery and, by leading to the discoveryof par ticularly high-q uality targets, will bring ab outimproved success rates in validation and down-stream. Pharmacogenetics, by enabling scientists toselect patient s more appr opriately for clinical tria ls,

26

P H A R M A C O G E N OM I C S— S O M E D EF I N I T I O N S

Pharmacogenomics is the use of genomicsa pproac hes to elucida te drug response. There arethree relevant approaches: via DNA, via RNA, andvia proteins, and three corresponding forms of phar-macogenomics: pharmacogenomics using geneticapproaches (or pharmacogenetics ), expression pro-

filing (or expression pharmacogenomics ), and pro-teomics (or proteomic pharmacogenomics ).

Pharmacogenetics predicts patients’ drug responseby a nalyzing the genetic va ria tions in their DNA. It isthe form of pharmacogenomics discussed in themain text here.

Expression pharmacogenomics predicts patients’drug response by analyzing their RNA levels—specif-ically, by comparing the amounts of RNA found indifferent samples to determine which genes are

expressed a t d ifferent levels. An example: a researchgroup at The Whitehea d Institut e study ing tw o verysimilar leukemias (AML and ALL) has observed adistinct difference in expression levels of specificgenes, and thereby provided a quick and reliablemethod for differentiating them. Patients are now less at risk of being misdiagnosed and being given

an incorrect, and possibly lethal, drug treatment: ineffect, the test screens for adverse drug response.Expression pharmacogenomics seems to be movingfrom academic studies and biotechs into more main-strea m pha rma ceutica l R&D. Witness t he recent pur-chase by Merck and Co. of Rosetta Inpharmatics, abiotech founded specifically to develop expressionpharmacogenomics.

Finally, proteomic pharmacogenomics predictspatients’ drug response by analyzing their proteinlevels—specifically, by comparing protein readingsin different tissue samples to identify proteins thatdiffer either in structure or in expression levels.Consider the example of an aberrant fusion of twoproteins called Bcr and Abl, which occurs in morethan 95 percent of patients with CML (chronicmyeloid leukemia, which accounts for about 20 per-

cent of a ll cases of ad ult leukemia). This a berra ntfusion protein is present only in cancer cells. It dis-tinguishes itself from its normal counterparts by itsincreas ed size. It ca n be used not only to monitor theprogression of the disease but also to test whetherGleevec, a revolutionary new drug, would provide aneffective therapy.



will not only help to make the trials faster andcheaper, but also allow some drugs to pass thatwould otherwise have failed owing to poor efficacyor side effects.

High-Risk, High-Reward ResearchIn th e near-to-med ium ter m, genet ics prom ises toreduce R&D costs drama tically. O r d oes it? Ourmod el shows that t he application of disease geneticsand pharmacogenetics together could, in the verybest case, save as much as two-th irds of th e curr entcost to develop a drug and nearly two years. Ofthese potential savings, the vast majority comesfrom disease genetics. Of the remainder, some can-not be clearly apportioned to either disease genet-ics or pharmacogenetics but must be credited totheir joint efforts, being the product of synergiesrealized when disease genetics inform ation is incor-porat ed into phar macog enetics-driven clinical tri-als. (Although genetics can be combined with thegenomics technologies described in the previouschapter, the potential savings are not additive. Inthe n ext ch apt er, we will assess the tot al saving s real-istically achievable through the app lication of geno -mics and genetics.)

Our model also shows that realizing this potentialwill be far from straightforward and is far from

guara nteed. The high-end savings estimat e a ssumesa positive resolution of several scientific, tech nical,and market risks. With every setback, the savingsdiminish. Too man y setbacks, and the savings fall tozero. Implementing gen etics could even turn out tohave a negative impact on value, owing to adversemarket dynamics.

Such a d ouble-edged sword is awkward to wield. Ifcompanies fail to grasp it at all, they forfeit theopportunity to reap enormous rewards—nearly

double the savings possible with genomics tech-nologies. If th ey do gr asp it, they put themselves insome danger, and will need to develop a geneticsstrategy aligned with t heir risk profile and with spe-cific mar ket conditions. As an appro ach to researchand developmen t, gen etics remains risky, but full ofpromise, too. This chapter assesses the promise andthe risk alike.

Disease Genetics

Underlying many diseases are genetic variants, orpolymorphisms—alterations in the DNA sequenceof a given gen e tha t influence ind ividual risk of dis-ease. (The term polymorphism usually refers to acommon variant—one found in more than 1 per-cent of the population.) Often the genetic differ-ence consists of just a single altered letter in thegenetic code (the variant is then known as a single nucleotide polymorphi sm , or SNP). Such a tiny alter-ation ca n h ave fatal consequences; for example, thesubstitution of an A for a T in the hemoglobin ßgene is responsible for sickle cell anemia. The goalof disease genetics is to identify such DNA alter-ation s; so fa r, th is goal ha s proved elusive for all butthe most strongly inherited conditions. (See side-bar, “Disease Genetics—Various Approaches toVarian ts.”) But researchers persevere, since find ingthe causal variants can be a major step to finding atreatment—and potentially a cure.

Uncertainty persists: the technologies have yet toprove their full worth by producing the necessaryquota of practical results. But if all goes accordingto plan, the savings realized by pharmaceuticalcompa nies will be enormo us.

The PotentialThe savings would come partially from improvedefficiency in discovery, but principally fromimproved success in target validation and clinicaltrials. The improvements in efficiency result fromthe consolidation of target discovery into a singlestep; the improved success rates result from therefining of target identification, making it possibleto pinpoint the targets associated with disease sus-ceptibility in hum an s.

On ce the targets have been located, they can boa sta collective superiority in th ree pa rticular respects.

First, their relevance to human disease is certain.They have, after all, been validated in humans,showing directly that modulation of gene activityleads to alteration in the intensity or duration ofdisease. (By cont rast, other targ ets are usually iden -



28

D I S EA S E G E N E TI C S — VA R I O U S A P P R O A C H ES TO VA R I A N T S

When disease genetics is used to identify or testvariant genes, the investigation can take a variety offorms. There a re three key dimens ions in the d esignof such an investiga tion: na rrow/broa d, linkage/a sso-cia tion, a nd direct/indirect. These dime nsions a llhave a bearing on cost and the chances of success.

First, resea rchers ca n examine some of the genes (ina narrow study, or candidate-gene study ) or all ofthe genes (in a broad study, or genome-wide scan ).

Second, these genes ca n be examined through inher-itance patterns in families prone to the disease (in alinkage study) or by comparing individual patientswith healthy individua ls in the popula tion at large (ina n association study ).

Finally, within association studies, each variant genecan be studied directly or indirectly: researchers cantest each variant individually for any involvement inthe disease (in a direct study ) or test clusters ofclosely positioned variants for the presence of a cul-prit among them (in an indirect study ).

The ea rliest disea se genetics investigat ions, con-ducted prior to the 1 980 s, were a ssociation studies,and direct, a nd a s na rrow a s could be—studying justa single gene, which had been selected on the basisof biological knowledge about disease mechanisms.The resea rcher w ould seek polymorphisms in thegene, and compare their frequencies in patients andcontrols. This ea rly a pproac h did a chieve somenotable successes, including, in 1956, the first dis-covery of an inherited genetic variation found tocause disease—the variant underlying sickle cella nemia. The approach ha d a serious limitat ion, how -ever: it allowed very few genes to be examined, andit required a prior hypothesis.

In the 1980s and 1990s, attention turned to fami-lies show ing an inherited patt ern of disea se. Theinvestigat ions took the form of broad linka ge studies,and were tremendously successful in identifyingsome genes responsible for single-gene disorders,

notably the cystic fibrosis gene in 1989. Such stud-ies, being genome-wide, were now unbiased andcomprehensive. But the actual identification ofgenes remained a slow, painstaking laboratoryprocess. So the early versions of such studies werereally suitable only for monogenic diseases. Hopeswere raised in the early 1990s, when companiessuch as Millennium, Sequana, and Myriad were setup to develop and exploit these techniques in thequest to identify the genes implicated in commonpolygenic disea ses . Their initia tive seems to bestalled at the moment: although the localizing ofdisease-related genes has become more efficient,the actual locating of them remains discouraginglydifficult. Tha t ta sk would be better suited to a ssoci-ation studies.

Given the limitations in genome-wide linkage stud-ies, association studies have recently come back intofashion, fortified by the efforts of the HumanGenome Project, Celera, and the SNP Consortium.These st udies ha ve high-throughput tec hnologies toundergird them, as well as comprehensive da tab asesof gene sequences and SNPs.

Of the tw o possible approaches here, d irect a nd indi-rect, the former looks like a very formidable task.The researcher conducting a direct a ssocia tion study,a nd a iming to find the a ctua l polymorphism underly-ing the specified disease, is confronted by the entireset of common variants in the genome—expected tonumber som e ten million. To examine s uch a hordeof variants with current technology would be inordi-nat ely time-consuming a nd expensive.

Hence the hopes—and funds—now being invested inindirect studies. Since variants in close proximity

tend to form clusters (known as haplotypes), it maybe possible to track down the disease-related poly-morphisms using only a small proportion of allSNPs. In light of recent research findings, indirectassociation studies of this kind do look practicable,if not quite imminent.



tified through the use of animals and tissue cul-tures, and so their relevance to human disease islargely speculative.) In other words, there is no pos-sibility of failure in target validation, because iden-tified targets are ipso facto validated. (This is, ofcourse, no guarantee of their drugability—theirrespon siveness to small-mo lecule inter vent ion .)

I t would n ot be possible to overstat e the value of i n vi vo human vali dati on. M ost of what passes for target validation today is largely conjectural in rela ti on to the di sease in questi on.

—Diabetes researcher,Harvard Medical School

Second, the frequency of the causal polymorphismsis known at the o utset. If a study identifies multiplegenes associated with a particular disease, it willalso reveal their relative culpability. Consider theexample of Alzheimer’s disease, a heritable butgenetically complex disorder. On the one hand,there are variants in three genes—PS1, PS2, andAPP —that a re ver y rare but very potent: if a personhas any of them, he or she is almost certain todevelop Alzheimer’s. On the other hand, there isthe ApoE4 polymorph ism o f th e ApoE gen e, whichhas a more modest effect on disease susceptibilitybut is much more common in the population atlarge, and among pat ients with Alzheimer ’s .Informa tion of this kind can be useful for predict-ing a drug’s potential marketability: although itmight be equally feasible to develop a drug thatinfluences the rarer variants, a drug targetingApoE4 might expect broader effectiveness, andthus a larger market, and so might t ake precedencein furth er research. (The rarer variant s may still beworth pursuing, using path way ana lysis.)

Fina lly, th e na ture of the r elevant polymorphisms isknown—differ ent disease-ind ucing mecha nisms

among variant forms, for instance. Such informa-tion m ay help to streamline clinical trials if used byefficacy-based pha rma cogenetics to identify “n onre-sponders”—patients who lack the crucial DNAalterat ion, an d hen ce are unlikely to experience theintended effect of a candidate drug—and excludethem from th e trials. (In mo deling the potential of

disease genetics, we have included this effect ofefficacy-based pharmacogenetics. See the sectionon pharm acogenetics below for further details.)

Depending on the approach taken, cost savings perdrug could be as great as $420 million, with the

potent ial time savings rang ing from 0.7 to about 1.6years (producing an added $290 million of valueper drug). (See Exhibit 6.) Of the cost savings, thevast ma jority would be yielded by the impro vementsin success rates: $390 million, consisting of $110million in validation and $280 million in the clinic.

EXHIBIT 6DISEASE GENETICS OFFERS GREAT SAVINGS

Cost to drug

$M

Time to drug

880Pre-genomics

460Candidategene study

485Genome-wide scan

455Genome-wide scanplus pathway analysis

Pre-genomics

Candidategene study

Genome-wide scan

Genome-wide scanplus pathway analysis

Years

13.1

14

16.5

ID

14.7

1,0008006004002000

20151050

Development

Prec lini cal Cli ni cal

Chemistry


Biology


SOURCES : Industry interviews; scientific literature; public financial data;BCG analysis.



Efficiency improvements in target discovery ac-count for the remaining savings.

The UncertaintyFor these vast savings to materialize, two require-ments will have to be met. First, disease geneticsmust prove scientifically feasible for the relevantcommo n d iseases. Second , no t on ly must studies inhumans work; in addition, the targets they identifymust be drugable; failing that, identifying the dis-ease genes is pointless, and all the effort that hasgone int o find ing them will be wasted. ( See sideba r,“Drug-Resistant?—Are Disease Genes DrugableTargets?”)

Feasibil it y—th e Lim it ati ons of Technology

Funda menta l technological con cerns still hover o verdisease genetics. Can it a ctually be do ne? The resultsso far have been very modest. The bonanza ofclearly do cumen ted disease-susceptibility genes fo rcommon multigenic diseases has yet to materialize.Candidate gene studies, for instance, are by defini-tion limiting: they focus on a subset of genes definedby a prior hypothesis, and therefore risk excludingsome crucial culprits. And genome-wide linkagestudies, although highly successful in addressingsingle-gene diseases, have proved disappointing for

the mo re commo n m ultigenic kind o f disease: a d is-ease-related gen e might be a ccurately pinpointed inaffected families (such as the BRCA1 breast cancersusceptibility gene), o nly for it th en to show ver y low prevalence outside the families used in identifying

it. True, th ese two a pproaches might become mo retractable now, in the wake of the sequencing of thehuman genome and the development of compre-hensive SNP maps (catalogs of the characteristicsand locations of SNPs in the genome).

As for ge no me-wide associatio n stud ies, con sideredby many experts to be the most promising of all,they have only recently became practicable: all therequisite tools (a full genome sequence with a SNPmap to match, genotyping technologies, and so on)appear to be in place. But the a pproach remains vir-tually untested, owing to the still exorbitant cost ofgenot yping. The prefera ble form of gen ome-wideassociation studies would clearly be the indirectkind—still covering the entire genome, but geno-typing far fewer SNPs—yet even here the currentcost is a prohibitive $400 million or so for each dis-ease investigated. Within five years, however, geno-typing costs are expected to fall to $20 million orless, and the essential proof-of-concept tests canthen take place more routinely. (See sidebar,

30

D R U G - R E S I S TA N T? — A R E D I S E A SE G EN E S D R U G A B L E TA R G ET S?

The skeptics pose a n a w kwa rd q uestion: Will dis-ease-related genes ever prove to be drugable in sig-nificant numbers? The record so fa r is ha rdly encour-a ging. Some disea se genes, s uch a s CFTR in cysticfibrosis, were identified long ago, yet have failed togenera te th erapeutics. The infrequency of successstories, such as Ceredase—a drug for type I

Gaucher’s disease that was essentially a creation ofdisease genetics—only highlights the general trendof failure.

These long-identified disea se genes tend t o be forsingle-gene disorders, however. And such disordersa re by their nat ure difficult to cure. They a re binaryphenomena: the gene is broken, you get the disease.

Finding a small-molecule therapeutic to repair a com-pletely defective protein is a n extremely difficult cha l-lenge. (Indeed, Ceredase is a protein thera peutic.)

Most disorders, by contrast, are attributable not to asingle gene but to m ultiple genes, a nd perhaps ot herfactors too. This means tha t the system a s a wholecan still function, just hampered to a greater orlesser degree. Such cases benefit from patching up,so drugs can be beneficial without actually consti-tuting a cure. There is little reason t o doubt th a tsuch palliative drugs will soon emerge in abun-dance, as disease genetics becomes ever faster atidentifying some of the genes implicated in multi-genic d isorders.



duce tar gets high in qua lity but low in qua ntity, per-haps just one to five for an average disease. Thatwould produce at best a 25–30 percent chance ofyielding a drug, given that chemistry and develop-ment remain far fr om fail-safe. For d isease geneticsto live up to its promise, it will need to improvethose odd s considera bly. And to d o th at, it will haveto call on a supplementary technique: pathwayanalysis.

Disease-susceptibility genes, if identified throughdisease genetics, serve not only as targets them-selves, but also as guides to additional targets. Thegenes form part of broader disease pathways, andthese pathways contain other molecules that mayserve as targets (perhaps 10 to 15 targets per path-way, according to experts). These new targets areidentified by pathway ana lysis, often taking the for mof the study of “simple” experimental systems, suchas those of Drosophila melanogaster or C. elegans (a.k.a.fruitflies and worms). When studied in the labora-tory, these systems disclose the genetic componentsof a given pathway. (Other approaches to pathwayanalysis include expression profiling of tissue sam-ples and studies of pro tein-protein int eraction.)

Implementin g pa thway an alysis will result in a lowercost per drug on average: it improves efficiency.Costs are reduced because pathwa y studies are rela-tively inexpensive a nd fewer hum an-derived targ etsare required—pathway analysis expands the pool ofpotential targets tenfold or more. As targets, theyprove to be of high quality, moreover (in keepingwith the disease-susceptibility gene that inspiredtheir discovery), achieving good success rates inclinical trials. For a lthough they themselves are no tyet validated in humans or clearly implicated in thedisease, they participate in a pathway that is.

So pathway analysis should give human studies therequisite boost, with enough targets emerging toyield a drug more often than not. On the downside, there are the time an d cost of ad ditional ani-mal research and the loss of some advantages inclinical trials. Adding pathway analysis, via geneticstudies of Drosophila melanogaster , to indirectgen ome -wide association studies would result in a

tota l savings of $425 million for ea ch d rug o n a ver-age, regardless of the or iginal human s tudyapproach, th ough it would ad d n early two years ofadditional work (producing an additional $255 mil-lion of value per drug).

The fi rst one to do geneti c studi es takes a hu ge risk.I f i t works, you’ ll see ever yone run ni ng to join the crowd.

—R&D execu tive,leading pharmaceutical company

Implementing Disease GeneticsThe savings promised by disease genetics are enor-mous, and companies cannot ignore them. But theycann ot ignor e the risks either, and will need to exer-cise rigor ous selectivity and d iscipline when it co mesto p ursuing specific disease genet ics stud ies—whichapproaches to adopt, for instance, and which dis-eases to explore. Placing bets in this way is going tobe ner ve-racking enoug h. But com pan ies faceano ther d ifficult set of ch oices as well, in the variousoperation al issues that need to be a dd ressed.

Placing Bets Some diseases have clear appeal as objects ofgenetic research: asthma, Alzheimer’s disease, anddiabetes, for example, being complex diseases thatafflict large populat ions and o bviously conta in heri-table factors. They have already become competi-tive areas of study. Although opinion is still dividedover the likely impact of disease genetics, substan-tial bets are being placed by various companies—emerging biotechs and established pharm aceuticalcompanies alike. A few claim they are already see-ing benefits from their investments: following itsalliance with Roche, deCODE genetics claims tohave succeeded in identifying a gene contributingto cerebrovascular disease; GlaxoSmithKline hasanno unced finding genes associated with m igraine,Type II diabetes, and psoriasis; and Genset, aFrench biotech company, is reported to have iden-tified genes implicated in prostate cancer andschizophrenia.

But all companies embarked on, or intent on, pur-suing disease genetics must acknowledge that it is

32



still a scientifically risky endeavor. As they constructtheir customized portfolio of bets, they will have tokeep reviewing not just their inter na l capab ilities butalso th e degree of risk they are prepared to a ccept.

Putt ing Disease Geneti cs into OperationIf companies do opt to participate in disease genet-ics, they may still choose to ma intain some distanceby outsourcing the activity or licensing in theresults. Those tha t d ecide to laun ch a major d iseasegenetics program in-house will confro nt a numb erof significant challenges as they put the programinto operation.

Take the problem of ob taining t he req uired sam-ples, for instance. From whom should samples becollected? And how are samples to be stored? Theuse of huma n tissue raises ethical con sidera tions aswell. Wha t con stitutes consent? Ho w can privacy beprotected? Who “owns” the tissue material? Andwho should profit from it? (Several companies,such as Genomics Collaborative and the not-for-profit First Genetic Trust, are emerging to addressthese conundrums.)

And, of course, there are major organizationalquestions. What are the implications for humanresources an d labo r relations? For big pharm aceuti-

cal companies, new capabilities will be demanded,and new skills will have to be acquired—statisticalgeneticists, for instance, and experts on pathwaygenetic studies. And other capabilities may sud-denly be less in d eman d—perhaps even o bsolete.

We will discuss implem ent at ion issues such a s thesein more d etail in th e final chapter of th is report.

Pharmacogenetics

Just as some genetic variations among individualsmay influence their susceptibility to diseases, soothers may influence their responsiveness to drugtreatments for those diseases. It is the goal of phar-macogenetics to seek out and characterize some ofthese latter variation s.

The savings that pharmaceutical companies mighthope to harvest are considerable, though nothing

like as high as those that disease genetics stands toachieve in ideal circumstances.

The PotentialThe impact o f pha rma cogenetics on R&D pro duc-tivity will derive from the increased flexibility itintroduces into clinical development. Currently,dr ug-developmen t policy is dom inated by a binar yscenario in its later stages: either shepherd a com-pound through its clinical trials and out to market,or abandon it as unmarketable if it stumbles in thetrials. Pharmacogenetics provides a more nuancedscenario, with an expanded range of possible out-comes, by allowing th e exclusion of pa tients geneti-cally predisposed to respond poorly to the drug.Two particular benefits emerge: “standard” clinical

trials can now be streamlined; and “failing” com-pounds can now be salvaged. (See Exhibit 7.)

The streamlining of trials would apply only to com-pounds destined to proceed successfully throughthe clinical trial pr ocess an yway. Tha t pat h ca n n ow be ma de smooth er. The trials can be d esigned moresubtly. They can be smaller and quicker thanbefore, now that it is possible to preselect promis-ing patients—that is, patients whose geneticmakeup is likely to maximize the drug’s efficacyand minimize its side effects. So, patients lackingthe d rug-susceptible variat ion of t he ta rget gen e

EXHIBIT 7PHARM ACOGENETICS EXPANDS DEVELOPM E

High

P r o p o r t i o n o

f p a

t i e n

t s s

h o w

i n g

p o o r o r n o

r e s p o n s e

Lo w

Current opt ions

Abandon drugbefore market

Continue cl inicalt r ia ls to market

Options available withpharmacogenet ics

Continue trials safely byexcluding at-r isk pat ients

Optimize cl inic al t r ia ls ,making them

smaller and shorter

SOURCE : BCG analysis.



would be excluded from th e trial, in ord er to show high efficacy levels for the subset of patients whowould eventually use th e d rug. Also excluded wouldbe patients having a specific genetic variation asso-ciated with side effects.

To see the stream lining ef fect of such exc lusions,consider the case of Herceptin, a treatment forad van ced br east cancer. It is effective on ly in a sub-set of patients—the 25–30 percent whose tumorsoverexpress the HER2/neu oncogene. It is thisgene tha t ser ves as the dr ug tar get. By screening forHER2/neu expression, Genentech was able toexclude no nrespond ers—some two-third s of thesubjects originally tested—early in th e clinical t rial.Without this prescreening, Genentech would haveneeded nine times as many patients in phase III toachieve significant results. The cost of such a trialwould have made H erceptin econom ically unviable.

Turning to the second benefit, for failing com-pound s pharma cogenetics lowers the h urdle by eas-ing the conditions for market viability. Considerspecifically those candid ate d rugs tha t reveal seriousside effects in a significant proportion of the sub-jects (such as the 7 percent of Caucasians with low levels of CYP2D6, an enzyme that helps metabolizesome 25 percent of all drugs). Traditionally, anysuch d rug would be perceived a s too risky to ma rket,and would be abandoned in preclinical studies.Tod ay, ho wever, phar ma cogen etics makes it possibleto iden tify the at-risk patients, so th e dr ug would n otbe disqualified right away, and could go on to provemarketable—patients would just need to be testedfor vulnerability before being given prescriptions.

These are th e poten tial ben efits to R&D, t he pri-mary focus of this report. Even greater potential,some observers believe, may lie in market advan-tages. Three such advantages are possible: pricepremium, share shift, and new patients. In otherwords, if a perception emerges that the pha rmaco-genetics-assisted drug is distinctly less risky or dis-tinctly more efficacious for the (now restricted) tar-get patient populat ion, payers may tolerate a higher

price for the drug, physicians may favor it whenoffering new patients a prescription, and patientswho have shunned previous medications (owing toside effects, typically) may now choose to try it.Although it seems reasonable for pharmaceuticalcompanies to expect some market upside frommore efficacious, better tolerated therapies, i tremains to be seen to what extent they will be ableto reap these market rewards.

Putting figures on the cost savings pharmacogenet-ics benefits might achieve, our model estimates anaverage of $335 million in the cost to drug—ifpharmacogenetics were to work every time it wereapplied. But pharmacogenetics won’t work everytime. Given the set of cases where it is applied and

succeeds, the expected savings would average a bout$80 million, as discussed below. And of course, cor-responding to the potential market upside, there isthe coun terpart scenario—potential destruction ofvalue in the market. Why the uncertainty? (SeeExhibit 8.)

34

EXHIBIT 8PHARMACOGENETICS’ POTENTIAL IS CONTI

Cost to drug

$M

880Pre-genomics

545Pharmacogenetics:the promise 1

800

ID

1,0008006004002000

Pharmacogenetics:expected savings 2

Development

Prec lini cal Cli ni cal

Chemistry


Biology


SOURCES : Technica l literature; industry interviews ; publicly ava ilable infor-mation; BCG analysis.

1Savings per drug assuming pharmacogenetics can be applied across theR&D p ipeline.

2Average sa vings across R&D pipeline, given scientific and ma rket limitations.



The UncertaintyThe sizable potential savings are mirrored by siz-able risks—market a nd regulato r y risks this time, aswell as scientific and technical risks. In some cir-cumstances, market dynamics might be so unfavor-able that companies would be well advised to stepback and forgo the potential savings altogether.Once again, given the range of possible outcomes,the economic impact could ultimately be a negativeone—far from resulting in savings, applying phar-ma coge net ics could result in a net loss for R&D. Toassess pharmacogenetics realistically, companiesneed to ask two questions: How feasible is it? Andhow d esirable is it?

Feasibil it y—th e Technological Limi tat ions

Pharmacogenetics will not apply to all drugs. It willapply only where differing drug response is dueentirely to genetic variation, and where that rela-tionship can be elucidated.

It is fairly rare for both of these conditions to pre-vail. For one t hing, biology-based variation in d rugresponse can be due in part to environmental fac-tors: grapefruit juice, for example, is known tomodify the effect of certain drugs in certain indi-viduals, sometimes raising the uptake to dangerouslevels. For an oth er, drug -respon se variat ion willoften be th e work of multiple genes, acting severallyor jointly, and that compounds the statistical andtechnological difficulties of the search.

The business guys hear about thi s stu ff, and are li ke, “Great! M ake i t happen! ” We’r e left scrat ching our heads, l ooki ng li ke poor sport s, because a lot of i t j ust i sn’ t possible.

—Senior scientist,major biotech company

Even if the two conditions are fulfilled, a furtherchallenge lies in wait—to find the relevant gen es intime to streamline the trial. For pharmacogeneticsto effect this streamlining, you need to be able toscreen out nonresponders. And that means findingthose variants, or identifying the nonrespondergenotype before designing any streamlined trial.

Now, developing a robust pharmacogenetic testwould generally require more than 1,000 patients.A phase I trial is far too small for that purpose, sono streamlining would be possible for a phase IIt r ia l , despi te the exci tement surrounding that

prospect. Streamlining could be possible in time forphase III trials, but even that is far from assured.(See Exhibit 9.) (The other kind of screening test,for side effects, is less problematic, since the associ-ated metabolic variations are often determined inpreclinical trials. But th at kind o f test does not helpto streamline clinical trials, which have to be sizedto test for efficacy rather than for side effects.)

I t i s hard to see how these phase I I tr ials wi ll be used f or phar macogeneti cs, because most of t he

var iants are expected to be rela ti vely i nfr equent.Cer tai nl y, 30 percent preval ence [ whi ch fal ls within standard clin ical t ri al si zes] woul d be rela- ti vely rare.

—Geneticist,The Whiteh ead Institute

EXHIBIT 9STATISTICAL REQUIREMENTS LIM IT PH ARMPOTENTIAL TO STREAMLI NE TRIALS

Frequency of responder genotype

6050403020100

Number of pat ients required to develop a test

P reva lence > 30% needed todevelop a test prior to phase III trials

Typical ph ase II trial size

5,000

4,000

3,000

2,000

1,000

Typical phase I t r ial s ize

SOURCES : Technica l literature; industry interviews ; publicly ava ilable infor-mation; BCG analysis.

NOTE : Graph analyzes a scenario from Nature Biotechnology , vol. 18, May200 0. Scenario uses efficacy pharmacogenetics to identify the ApoEresponder genotype, a predictor of efficacy of Cognex (tacrine) forAlzheimer’s. Strength of effect, a different variable, is not considered here.



Given these technological limitations, we estimatethat less than 15 percent of drugs will be amenableto the application of pharma cogenetics.

Desirabil it y—Market Economic s

As already mentioned, there are circumstances inwhich a company might have an incentive to shunpharmacogenetics entirely. After all, by excludingpatients from trials, you are in effect giving thedrug a restricted label when trying to market it.

Gauging the likely effect of a restricted labelinvolves some complex analysis. For a start, youneed to consider two distinct groups of patients:those who take a prescription for the full course oftreatm ent (which cou ld last many years, or even the

remaining lifetime for those suffering from chronicdiseases), and those who embark on a prescriptionbut then discontinue it for reasons of inefficacy orside effects.

The pharmacogenetics test would shrink these twopotent ial patient groups in different ways. From th eformer, it would eliminate the “placebo respon-ders.” From the latter, it would eliminate some ofthe nonresponders and negative responders.

M arket fr agment ati on has happened in many industries—the marketing group can’t put their heads in the sand. We have to fi gure out what to do about pharmacogeneti cs.

—Genetics director,leading biotech company

Since pharmacogenetics seems to be chipping awayat a drug’s market base, why pursue it in the firstplace? The answer may lie, in part, in competitivedynamics and game th eory: companies may have toembrace pharmacogenetics because their competi-tors are d oing so. Merck & Co., for exa mple,according to a recent Wall Str eet Journal article, isbusy developing capabilities to reproduce pharma-cogenetic analyses conducted by its competitors, ifonly to disprove any claims that a rival drug mightbe superior to its own.

But the compensator y advanta ges can be more pos-

itive, too—the potential for market upside, once

aga in: price premium, sha re shift, an d new patients.What a company has to judge, before adopting

pharm acogenetics for an y drug in development, is

the likely breakeven point—the point at which aprice premium or increa sed ma rket shar e begins tooffset the volume loss. O ur mo del assumes a m odest

market premium of 20 percent, and calculates the

breakeven point in various scenar ios, based o n fo ur

different approaches to pharmacogenetics. (Seesidebar, “Pharmacogenetics—Four Applications,”

and Exhibit 10.)

Effi cacy-based phar macogeneti cs can reduce trial costsconsiderably. But the market dynamics could then

cast a cloud over that economic picture. If the

restricted label, by disqualifying placebo respon-

ders and some nonresponders and negative respon-ders, tra nslates into a n o verall revenue loss of just 2

percent, th at ca ncels out th e savings achieved in the

clinical tr ials.

36

EXHIBIT 10PHARM ACOGENETICS’ VALUE DEPENDS ON MDYNAMICS

Revenue increase required from market premium3002001000

Patients lacking good response (% )1

Pharmacogenet ics can opt imize cl inic al t r ia ls

Abandon drug

Conduct normal t r ia lsPharmacogenet ics

tr ia ls make drug viable

100

80

60

40

20

SOURCES : Industry interviews; BCG a nalysis.

1Example based on a scenario in Nature Biotechnology , vol. 18, May 2000of ApoE4 efficacy in tacrine response; a ssumes response rate of 41 percentamong patients with the SNP versus 20 percent among those without it;also as sumes 50 percent of nonresponders discontinue.



P H A R M A C O G EN E TI C S — F O U R A P P L I C ATI O N S

To evalua te pha rma cogenet ics properly, com pa niesneed to take an especially close look at the marketdyna mics. These dyna mics vary a ccording to how pharmacogenetics is used. We have identified foursuch applications (each associated with a differentca tegory of pa tient responding to a given drug). Thefirst three are used to exclude patients from trials;the fourth is used to expand the potential market forthe drug.

First, efficacy prediction identifies patients who willshow no real or significant response to the drug—perhaps because they metabolize the drug in anunusual way, or have an unusual form or combina-

tion of susceptibility genes. A typical drug producesthis negligible response in about a third of patients,but sometimes the proportion is far higher. Forexample, Cognex (tacrine), the first drug forAlzheimer’s, is inefficac ious in m ore than 50 percentof patients. The varying response is a ssocia ted w ithdiffering versions of the ApoE gene, and is thereforereadily predictable by a pharmacogenetic test.

Second, common-side-effect prediction identifiespatients likely to experience familiar side effects, asa result of metabolic difficulties caused by well-known enzymes. A test can screen out negativeresponders—“slow a cetylat ors,” for insta nce. Thea cetyla tion polymorphism in t he NAT2 g ene is one ofthe commonest genetic variations in drug metabo-lism; it has the effect of reducing the enzyme’s life-span and thus reducing the effective amount of theenzyme in cells at a ny one time. This polymorphismis present in more than 50 percent of Caucasians,who are thus at greater risk of drug toxicity.Knowledge of this polymorphism could save a drugin clinical trials that would otherwise be abandoned.

Third, very-rare-side-effect prediction identifies pa-tients at risk for unconventional side effects, butcomes into play only after the drug is on the market.Unlike most of the common side effects, which areassociated with metabolic pathways and usually

emerge in preclinical studies, these rare side effectstend to be provoked by nonmetabolic genes, and tobe overlooked a t first. They ca nnot ea sily be pre-dicted, since there are too many possible sources(modifications of the target or of the disease path-wa y, or unrelated pathw ays), a nd they may occur toorarely to show up in clinical trials.

A case in point is Lotronex, a drug for irritable bowelsyndrome, now withdrawn from the market. Onlyafter its ma rket launch, and 450 ,00 0 prescriptions,did its severe side effect (bowel impaction) becomeapparent. About one in 6,500 patients wasaffected—a frequency far too rare for a standard

clinical trial to detect beforehand. (A typical trialinvolves about 5,000 patients: for this side effect tohave been manifest in a statistically significant way,a trial of nearly 100,000 patients would have beenneeded.) Pharmacogenetics could in certain casescome to the rescue of such compromised drugs, bybelatedly devising a screening test.

Finally, market expansion identifies patients who arecurrently unsuited to the drug but potentially respon-sive to it. Since fine-tuning of dosages or formulationcan often reduce side effects and occasionallyimprove efficacy, pharmacogenetics could reassessand upgrade many of the supposedly ineligiblepa tients. The ma rket for the drug might expa nd con-siderably a s a result.

Ta ke the ca se of cyclophosphamide, a chemotherapydrug, which works only when metabolized by theenzymes CYP3A4 and CYP3A5. Some patientsappear underresponsive to it: a genetic variationsuppresses the activity of the enzymes, therebydecreasing the amount of active drug in the blood-

stream . The best course is not to d iscontinue thedrug, but to compensate by taking a higher dosage.A pharmacogenetic test could identify the appropri-ate patients prior to treatment, and their consump-tion of the drug, instead of declining to zero, wouldactually increase.



Side-effect-based pharmacogeneti cs for common side effects can save a candidate drug that would otherwise fail.This form of pharmacogenetics does not streamlinetrials; in fact, it imposes a m odera te increase in costs,since more patients have to be recruited initially forthe vetting process. It requires a smaller upside tobreak even th an efficacy-based pha rma cogenetics,however, since its powers of exclusion apply only tothe second type of patient (those who would ordi-narily try the dr ug an d then d iscontinue it). They donot apply to the first group (th ose who would t ake afull course of the drug), since placebo respondersdo not suffer from side effects.

Side-effect-based pharmacogeneti cs for very rare side effects is the type that would be applied for a d rug alreadyon the market. Once the number of adverse events

(instances of severe side effects) reaches a criticalmass, the drug’s reputation suffers, and its contin-ued marketability is jeopardized. (In severe cases,regulator y agencies require the dr ug to be removedfro m th e mar ket.) To salvag e it would involve imple-menting screening tests for all potential patients—a kind of postmarket surveillance. This type ofpharmacogenet ics would increase costs fa i r lysteeply, yet it could still make economic sense if thedr ug were saved.

The economics hinge on a paradox: the fewer theadverse events, the harder it might be to save thedr ug. To iden tify the culprit genetic ma rker for usein the screening test, you need a certain minimumnumber of patients who have experienced the sideeffect. That could be as low as 20 (assuming youachieved a 100 percent association with a singleSNP marker), and that would carry the modestprice tag of $100,000 (assuming the expected geno-typing cost of one cent per SNP). But the requirednumber could be 2,000 (assuming you achievedonly a 10 percent a ssociation ), an d th e likelihood istha t such a numb er of side-effect sufferers wouldsimply never emerge.

I t’ s a crime that a ver y smal l percentage of pati ent s can someti mes eliminate an other wise hi ghly bene- fi cial dr ug from the market. Pharmacogeneti cs ben- efi ts ever yone here.

—Research executive,leading pharmaceutical company

Finally, market-expansion pharmacogenetics for themost part has highly favorable economics. Since itseffect is to expand rather tha n contra ct the market,all it needs to ensure is that the expansion be largeenough to cover the cost of the req uired trial.

The prospects hinge to some extent on the inci-dence of the genetic variant. Consider two drugs,one producing side effects in poor CYP2D19metabolizers (including 20 percent of Asians) andthe other in poor CYP2D6 metabolizers (including7 percent of Caucasians); and suppose that dosageadjustments could resolve the side effects. It mightturn out that the former case warrants the invest-ment and the latter does not, given the differencein their potential market expansions.

All in a ll, these limitations reduce the expected sav-ings that pharmacogenetics would bestow on anaverage drug to about $75 million. But of coursethere is no such thing as a tr ue “average” d rug. Insome cases, pha rma cogenetics could yield potentia lsavings as high as $335 million an d potent ially cap-ture ad ditional upside through price premiums oran in crease in ma rket sha re. In oth er cases, it mightsave nothing, or even destroy value in the market.The key to success is to be selective.

Implementing PharmacogeneticsAlthough th e savings attainable through pharm aco-genetics appear less dramatic than those attainablethrough disease genetics, they are in the right cir-cumstances quite substantial. And the total valuead ded would be eno rmo us if the hoped-for mar ketadvantages were realized.

38



Realizing this value is a matter not only of marketdynamics, but also of various more speculative fac-tors: how acceptable pharmacogenetics will proveto payers, patients, physicians, and regulator y agen-cies; how readily physicians and patients willembra ce the screening tests to gen erate share shift;and so on. So the different types of pharmacoge-netics will probably come into effect at differenttimes. Detection of rare side effects will proba bly beintroduced first, as pharmaceutical companies arehighly motivated to save dr ugs from failure. Efficacypharmacogenetics will probably progress on aslower timetable, owing to concerns about marketfragmentation. It might even take FDA action toturn efficacy testing into a routine procedure.

When contemplating their pharmacogenetics pol-icy, companies will need to scrupulously analyzespecific drugs and markets. Deciding shrewdly justwhere and when to apply pharmacogenetics, forinstance, will mean assessing market dynamics ear-lier than ever before in the clinical trials phase. Andtha t in turn will dem an d new decision-makingprocesses and communication channels, includingstronger ties between research and development,and between R&D a nd commercia l activities. It ison operational and organizational issues of this

kind that the spotlight will fall in the next chapterof this report.

A Fina l Word

If the n ew genetics can r ealize its full potentia l, theeconom ics of pha rma ceutical R&D will undergo ametamorphosis. Efficiency will improve hand-somely an d success ra tes will surge. The sums savedcould exceed a half bill ion d ollars per dr ug, morethan halving the current cost.

That prospect is far from assured. There areenough risks and uncertainties to temper excite-

ment. The range of possible outcomes is wide, andcompa nies will ha ve to examin e minutely and a pplyselectively the various genetics opportunities.

Contrast genomics technology: the productivityimprovements promised by its implementa tion m ay

be more modest, but they are clearly achievable,despite the operational challenges. With genetics,the operational cha llenges are formidable too, b utthey are compounded by less distinct and possiblymore intractable challenges: technological limita-tions, scientific unknowns, and (in th e case of pha r-macogenetics) the vagaries of the marketplace.

So, companies determined to acquire and exploitgenetic informa tion need to know what th ey are let-ting themselves in for. They need to consider how

applicable genetics is to their current researchstrat egy. They need to spell out t he level of risk th eyare prepared to take on, and then plan how to man-age th at risk. In shor t, th ey need a gen etics strategy.

In the case of disease genetics, risk managementwould best begin by contemplating the sheer mag-nitude of the undertaking. Companies will beprompt ed to a sk themselves questions such as these:How feasible is it for us to establish an extensivedisease genetics progra m in -house? On which d is-

eases should our program focus? Are there oppor-tunities to share the r isk, perhaps by joining a “pre-competitive” industry consortium, along the linesof the SNP Con sortium? Or, should we ad opt a wait-an d-see stan ce, and then h ope to license in th efruits of others’ labor?

In the case of ph arma cogenetics, risk managementbegins by reevaluating the pipeline. On that basis,companies will try to determine the drugs to whichpharmacogenetics applications would add mostvalue. Co mpan ies will also want t o study intently themarket context an d competitor landscape, thinkingthrough potential competitor moves and counter-



moves, along with the relative scientific feasibilityfor each drug or therapeutic class. Such assess-ments will need to be revised continuously, as dif-ferent drugs present themselves for considerationand perhaps suggest different approaches, and asthe market and the regulatory environment con-tinue to evolve.

A genetics strategy would encompass all of theseissues, and would optimize any potential synergiesamong genetics approaches. If a company decidesto implement both disease genetics and pharm aco-genetics, it will need to decide ho w to integrate a ndharmonize them. Which diseases, for example,might be amenable to disease genetics on the onehand, and be likely to provide a market premium

on the other? If genetic redefinition of diseasesmakes it possible to develop suites of drugs andthereby address several smaller markets, how canresearch best collaborate with marketing to maxi-mize the impact? The answers to these questionswill generate still more questions: Do we have therequisite skills and capabilities to pursue the strat-egy? Do we have the right alliances in place, or theright alliance strategy?

Genetics is a risky endeavor. Companies cannotavoid the risk—but they cannot lightly ignore thepotential jackpot either. They need to be selectiveand smart in d eciding how an d where to place theirbets. With such vast winnings at stake, it seemsappropriate that the od ds should be fa irly long.

40



Ch a pt e r 3 : Ma n a g e r i a l Ch a l l e n g e s

Preface: Looking Back and Looki ng Forward

The Story So FarThe genomics revolution is poised to sweep aside

the o ld econo mics of phar maceutical R&D. Thebiotechnology and pharm aceutical industries—andperhaps health ca re deliver y in genera l—are on t hebrink of t ransformation, and companies thatembrace the revolution in the right way stand toreap enormous benefits. Developing a new drugshould become co nsiderably less unpredictable a ndmuch less expensive. Companies will recordimprovements both in efficiency and in successrates all along the value chain, and the average costand time needed to bring a n ew drug to market will

fa l l correspondingly. (See s idebar, “Potent ia lSavings—From Theoret ical to Pra ctical.”)

But this benign prospect is clouded by some warn-ings: great rewards will require comparably greateffort s; a new par ad igm in R&D econ omics maynecessitate pa rad igm shifts in R&D ma nag ement;abo ve all, th e grea t pro mise is offset by great risks—though, as in any revolution, the risks of standingaside may be greater th an t hose of getting involved.

Ensuring Your FutureAll biophar maceutical comp anies are, or should be,actively deciding how best to engage in the revolu-tion. Making such decisions is no easy matter. Thefamiliar bearings are no longer there, since thecompeti t ive and regulatory landscapes havechanged so much—and con tinue to change—in re-sponse to the pro mise tha t genom ics offers. Compa -

nies have been rushing to claim intellectual prop-erty rights (in th e so-called IP lan d gra b), no w thatthe sequencing of the human genome has beencompleted. Statutes and court decisions regulatingthose IP rights keep emerging and modifying thepicture. (See sidebar, “Intellectual Property—Lostand Found.”) And the corporate map is being re-drawn: the major mergers of recent years have cre-ated industry superpowers, and the pace of acquisi-tions and alliances is set to quicken, if anything.(See sidebar, “Industry Changes.”)

With so much change occurring, there are boundto be winners and losers. Although the decisionswill be unfamiliar and difficult, success will in theend be determined by traditional criteria. The win-ners will be those who make optimal strategicchoices and then implement them in an optimalway. The two components of the winning combina-tion will differ from company to company, accord-ing to each company’s size, aspirations, financialpower, capabilities, and so on. In this final chapterof our report, we identify the strategic and opera-tional issues and examine the various options thatdifferent companies might exercise.

To begin with the strategic issues, then—specifi-

cally, the challenge of defining a strategy in thegenomics era.

Strategy— Searching for GenomicCompet it i ve Advantage

Before genomics, biopharmaceutical companiesused t wo basic tools—chemistry and molecular biol-



42

In the first two chapters of this report, we assessedthe potential savings for the two waves of thegenomics revolution: first, the substantial savingsattainable through genomics technologies; second, thegreater but far less certain savings attainable throughgenetics approaches, notably disease genetics andpharma cogenetics. Those as sessments s how the highend of the achievable range, and they view the twowaves singly rather than jointly; that is, they indicatediscrete and best-case scenarios, which will be diffi-cult for companies to realize in practice and impossi-ble to combine.

A more integrated assessment needs to average out

the achievable range—to take account of worst-casescena rios too. And it ha s to a nalyze the various likelycombinations of approaches from the two waves,rather than treating genomics and geneticsapproaches in isolation.

According to t he comb inat ion selected, the R&Dvalue chain as a whole will assume a particular new form and favor a particular subset of potential drugs.Tha t is a crucia l consideration for a c ompa ny engagedin building a portfolio of technologies: the more com-

binations of approaches, the greater the company’sversatility in pursuing different drug subsets.

Drawing once again on our economic model, wehave examined the impact of each feasiblegenomics-based approach to target discovery, aug-mented by pharmacogenetics and genomics technol-ogy whenever possible. And we have estimated therealizable value in each case: first, by calculating thecost, time, and added value for each possible com-bination of approaches, and then—adjusting for thepercentage of targets each approach is able toprocess—by calculating a weighted average perdrug. The result is three broad s cena rios:

• A genomics-bas ed a pproach: industrialized targetidentification, supplemented where applicable bydownstream genomics technologies (in silicochemistry, in silico toxicology, in vitro ADME, sur-rogate markers, and pharmacogenetics)

• Chemical genomics: industria lized ta rget identifi-cation, followed by chemistry and traditional vali-dation conducted in parallel, and supplementedwhere possible by the downstream technologiesjust listed

P O T E N TI A L S AV I N G S — F R O M T H EO R ET I C A L T O P R A C TI C A L

Development

Precl ini cal Cl ini cal

Chemistry

Screening Optimizat ionOptimization

Genomics:In s i l ico/ in vi t ro tests , surrogate markers

Pharmacogenet ics

Genomics and pharmacogenetics

Traditional

73 %

25 %

< 1%

2%

30 %In s i l ico

70 %Traditional

Genomic target discovery

Chemical genomics target discovery

Genetic target discovery with pathway analysis

Biology

Target ID Target Val idat ion

DRUG R&D VALUE CHAIN ACTIVITIES



• Diseas e genetics supported by pathw ay a nalysis,combined where possible with the various down-stream technologies

The cha rt on page 42 show s the three scena rios,and the weighting applied to calculate the averagecost per drug. Here, by way of example, are realisticsavings estimates generated by our economic modelfor these three approaches.

First, a genomics-based approach in a traditionalvalue cha in structure. This provides a n idea l founda-tion for a portfolio perspective on drug discovery. Itapplies to both known and unknown target classes,and offers impressive potential savings of $200 mil-lion and 1.5 years per drug.

Next, chemical genomics is probably the most com-petitive approach for targets where there is an estab-lished high-throughput chemical screening assay.The t ime sa vings a re particularly important—nearly3.5 years—boosting drug revenues by means ofintellectual property rights and first-to-marketadvantages. When combined with other genomicsapproaches, chemical genomics offers potential sav-ings of a bout $1 00 million per drug. The a pproac h

lends itself particularly well to certain targets, suchas GPCRs, so a company electing not to pursuechemical genomics would be at a disadvantage if itretained such targets on its wish list.

Finally, disease genetics supported by pathwayanalysis is, theoretically, the most direct route tounderstanding huma n diseas e. This approach isapplica ble to known a nd unknown ta rget clas ses a ndto targets overlooked in animal-based studies. Andon the face of it, it is the most attractive approach

financially, w ith cost sa vings of $4 00 million. Thereis an extended time to drug, however, amounting toabout one year, though that drawback would often

be offset by the early securing of intellectual prop-erty rights. Unfortunately, this inviting approach isstill not affordable, and in fact its scientific feasibil-ity remains unproven. (See the graphs below for asummary of expected savings.)

REALIZABLE RESULTS BASED ON DISCOVERY

Cost to drug

Cost ($M)

Weighted average of approaches across value chain

Time to dr ug

880Pre-genomics

675Genomics-basedapproach

780Chemical genomics-based approach

475Genetics-basedapproach 1

Pre-genomics

Genomics-basedapproach

Chemical genomics-based approach

Genetics-basedapproach 1

Time (years)

13.3

11.3

15.8

14.7

1,0008006004002000

20151050

1With pathway a nalysis, 50% through genetic simple systems, 50% throughgenomics expression profiling. Assumes resolution of scientific and techno-logical questions.



44

ogy—to discover new drugs. Broadly speaking, thedrugs that emerged were much indebted toserendipity. Research strategy consisted mainly ofchoosing which thera peutic areas to investigate, an ddiscover y efforts focused on individual d rug t argets.

Development provided even fewer strategic choices:a promising compound emerging from chemistrywould be tested on animals and humans in largeand inefficient trials (inefficient because there wasno means of identifying in advance likely respon-ders or nonresponders). With the rise of genomics,there ha ve come new techn ologies, new approa ches,new information, and new ways of thinking about

research and development. These have broughtwith them a new opportun ity, or imperative, to turnresearch to com petitive ad vanta ge.

So companies now have weighty strategic issues to

ad dress. At th e corpor ate level, the q uestion is how

much to invest, given th e current environm ent. For

R&D lead ership, the qu estion ten ds to be where tofocus those investments—in wha t th erapy area s, on

what target classes, and so on—as well as which

technologies to adopt and how to adopt them (in-

house or externally, for example), and how to miti-gate the associated risks.

I N T EL L E C TU A L P R O P E R TY — L O S T A N D F O U N D

Gene patent applications are flourishing: in 2000alone, more than 20,000 were submitted to theUnited Sta tes P a tent a nd Tradema rk Office. Despitethe large number of applications, two central ques-tions have yet to be answered. What exactly can bepatented? And what rights does a patent actuallyconfer on its holder?

For a gene or gene fra gment (an expressed seq uence

ta g, or EST) to secure a pa tent, its “ utility” ha s to beestablished. In January 2001, the United StatesPa tent a nd Trad ema rk Office issued Ut ility Exami-nat ion Guidelines to cla rify the sta nda rd used. (Tha tin itself was encouraging to those in favor of genepatenting, reinforcing the view that genetic materialcan indeed be patented.) Following on the interimguidelines released in 1999, the new guidelinesadvise patent seekers to provide at least one “spe-cific, substantial, and credible” use for the gene orgene fragment in q uestion. This resta tement effec-

tively fixes the height of the hurdle for applicantsand disqualifies undersubstantiated applicationsfrom the outset. Some uncertainty remains, how-ever: whether it is necessary when presenting evi-dence of utility to understand the actual biologicalfunction of the genetic material, and whether gene

fragments, as distinct from full-length genes, are eli-gible for a patent.

If getting a patent approved seems daunting, all themore so is enforcing it, or sheltering confidently inits protective embra ce. The strength of protect ionafforded by a gene patent is still a developing legalissue. One recent court decision, though, clearlymarks a setback for patent holders— Festo Corp. v

Shoketsu Ki nzoku Kogyo Kabushik i , decided by thefederal circuit court in November 2000. It appearsto weaken many patents by precluding a broad inter-pretation of most patent claims; it does so by virtu-a lly excluding the “ doctrine of equivalents.” This is adoctrine that generally allows extension of a patent’sclaim beyond its literal language, so that a would-beinfringer who makes trivial changes to the patentedproduct is not thereby exempt from the pat ent’s con-straints. According to the Festo ruling, if the lan-guage of the legal claim diverges from that of the

patent itself, the doctrine no longer applies. Manypatents now look very narrow and vulnerable, andcompanies will have to plan their patent submis-sions even more carefully in the future. Or hope fora change of fortune: the U.S. Supreme Court isexpected to review Festo in the 2001–2002 term.



The Starting PositionAlthough these same broad questions will applyequally to all companies, there can be no stand ardanswers. The actual options available to any com-pany will depend on its starting position.

Company Size A key constraint on a co mpan y’s strategic option s issize. The largest pharmaceutical companies boastcapabilities and finances on a scale that allows fullparticipation in the new technologies, even whenthe risk is high. Not that this exempts them fromhaving to make choices. In fact, since scale givesthem so many options, they arguably carr y a greaterburden of strategic decision making. How to selectfrom such an embarra ssment of riches? In ad dition,

they face the challenge of managing complexity. Ifthey are not selective enough, and embrace toomany options, the operational problems couldprove over whelming.

The narrower capabilities and lesser scale of small-to-medium-sized ph arm aceutical compa nies and thelarger biotech companies could represent either asevere drawba ck or a d istinct ad vant age. O n the on ehand, there are reduced opportunities and even theprospect of being locked out by the big ph arm aceu-tical firms: with disease genetics, for instance, a com-pan y with insufficient scale to build a n in-ho usecapability would risk forfeiting potentially lucrativeintellectual property rights. On the other hand,since lesser scale often means lesser complexity,these mod est-sized comp an ies can co mpete mo reflexibly, cha ngin g their t actics quickly in response totechnological a dvances or competitor m oves.

To see how scale can affect a co mpan y’s option s,consider the differing ways in which large and mid-size companies approach the target land grab. The

larger companies have been able to take veryaggressive approaches—scaling up or pursuing bigdeals to secure intellectual property rights to tar-gets. The smaller companies, lacking in resources,have been unable to follow suit, but some of themhave compensated by choosing very focused strate-gies, concentrating on their special competenciesand imposing higher q uality stand ards.

Buil ding the Fact B ase Apart from company size, the two most importantfacets of a company’s starting point are the beliefsand hypotheses held by its leadership team (rough ly,its corpora te culture) an d its current R&D ca pabili-ties. Com panies need to scrutinize both.

It is crucial to und erstand a nd shape the beliefs andhypotheses of leaders throughout the o rganization,especially since, with genomics and genetics, thecontr ibutions and effects are cross-function al—thatis, the managers or sections that contribute mostare not necessarily those that benefit most. Allthose affected need to a rticulate th eir perceptionsof the value and applicability of genomics andgenetics to the company. Once tested, these per-ceptions should be given con sidera ble weight whenit comes to definin g compa ny strat egy.

An equally thorough assessment needs to be madeof the compa ny’s relevan t R&D capab ilities—itstechnologies, skills, specific knowledge of diseasesan d d isease mechan isms, and so on. Id eally, this willinclude an aud it of curren t R&D pro ductivity atevery step in the value chain, identifying bottle-necks and other constraints. The more accurateand detailed the assessment, the more effectivelythe company can address the strategic questions as

they perta in to its specific situation .

Corporate Decisions:How Much to Invest and WhereAs suggested above, even the largest pharmaceuti-cal companies will have to make choices. Considersome of the huge deals of recent times: the $500million deal between Bayer and Millennium for tar-gets, the $800 million deal between Novartis andVert ex for in silico chem istry, the $500 million dea lbetween Roche and deCOD E for disease genes.

Note that these deals concern discrete steps of thevalue chain: in each case, it appears likely that thecompanies concerned were acting on an explicitpreference—an established strategic preference.After all, given the magnitude of these deals, itseems unlikely that any one company would haveplaced all three bets. More to the point, such largedeals, alth ough essentially R&D ventures, ar e no t



46

I N D U S T R Y C H A N G ES

The genomics lands ca pe fea tures many sma ll sta rt-ups amid the larger genomics companies and thegenomics divisions of big pharmaceutical corpora-tions. But th a t lands ca pe is cha nging. The numberof deals—of genomics companies combining witheach other or being taken over by big pharmaceuti-cal firms—has been growing steadily. What is driv-ing this tendency toward consolidation?

The Pressure t o Extend Scope

Increasingly, genomics companies are aspiring tobecome full-fledged drug compa nies. No specializedcompany, it seems, has yet succeeded in building atruly stable competitive position as a drug-industrysupplier; intellectual-property statutes do notappear to be enough to guarantee long-term protec-tion; and the chances of proprietary advantage arebeing nullified by the trend toward public-privatepartnerships or consortia, underwritten by big phar-maceutical companies.

Wa ll Street appea rs to place a far higher value onintegrated drug producers than on pure technologycompanies (if only because the drug sector has tra-ditionally enjoyed such high profits and such high

regard among investors). According to a recent USBWa rburg study, the a vera ge integrated drug com-pany has been able to raise $870 million, as aga insta mere $330 million for the average technologycompa ny. (The s tudy noted a further interestingdivergence among technology companies them-selves: biology companies—those focused on targetidentification and validation—raised $480 millionon average, whereas companies in the chemistryarea—those focused on screening and lead opti-mization—raised on average only $170 million.)

In keeping with this expansionist aspiration, most ofthe recent deals have consisted of acquisitions ofdownstream drug-development capabilities. Witness

LION’s a cq uisition of Trega (for $35 million),Celera’s acquisition of AxyS (for $173 million), andLexicon’s acquisition of Coelacanth (for $32 million).

The Pressure to Ach ieve Scale As sections of the value chain have become indus-trialized, the va lue of scale in R&D ha s ga inedprominence. And for genomics platform companiesand pharmaceutical companies alike, it may appearquicker and neater to achieve scale through a mergerthan through painstaking in-house upscaling. (Ofcourse, pharmaceutical companies might have otherreasons to acquire genomics companies: to jump-start their genomics efforts, for instance, or to

acquire otherwise rare capabilities.)

Sure enough, most of the recent mergers and acqui-sitions have clearly been initiated for the sake ofincreasing scale: Sequenom’s acquisition of GeminiGenomics (for $238 million), for example, orSangamo’s acquisition of Gendaq (for $40 million).These sc a le deals ha ve been prima rily in target iden-tification and validation rather than in chemistry—areflection of the urgency of the land grab.

The Pressure t o Spend

Whatever the inducements to merge, there is a tra-ditiona l impediment—la ck of wherew itha l. The spiritis w illing but t he purse is wea k. Tha t is c ertainly nota constraint, however, on some of the largegenomics companies at the moment. In mid-2001,three top companies—Human Genome Sciences,Celera, and Millennium—boasted over $4 billion incash between them, representing about 25 percentof their combined market capitalizations. Idle moneycries out to be spent—probably, for these compa-nies, on diversification more than on scaling.

Market expectations, a sense of urgency, an abun-dance of funds: all signs pointing to continued con-solidation in the near term.



R&D d ecisions alone . Almost cert ain ly, the d eci-sions were thrashed out at the corporate level.

For mor e mo dest-sized com pan ies, strategic choicesoften go beyond mat ters of preference or emphasis.The question might be whether to concentrate all

their efforts on some value chain steps and forgoothers altogether. Certainly it no longer makessense for even midsized pharmaceutical companiesto compete in target identification. And at thesmaller end of the scale, companies with less than$400 million in R&D, say, may find themselves ask-ing even more radical questions: Can we affordresearch at all? Should we n ot focus exclusively onlicensing instead ? Again, it is at th e corpo rate level,rath er tha n within R&D alon e, that such questionswill eventually be settled.

It is not just through major partnerships andinvestment decisions, however, that the corporatelevel is impinging on R&D strategy. More a nd more,specific R&D activities are ha ving ram ificationsbeyon d R&D itself, an d in voking co rpo ra te-levelpar t ic ipat ion. Pharmacogenet ics , for instance,often touches on corporate strategy as much as onR&D strategy. Should the com pan y continue to pur -sue a promising compound, say, when the risk ofmarket fragmentation might outweigh the positive

market effects? Should the company attempt to res-urrect cand idate dr ugs previously killed b ecause ofrare side effects? And so on.

R&D Leadership Decisions:Where and How to CompeteWith genomics and genetics now part of the land-scape, R&D d ecision m aking ha s become mo recomplex. The options are far more numerous:there are more ways of gaining access to capabili-ties, more technologies to choose among, and even

new dimen sions in which to co mpete. R&D execu-tives must select a combination of options that notonly dovetail with the company’s starting positionand aspirations but can also be integrated smoothlywith o ne an other.

Choosin g a Research Focus The d imensions of competition includ e:

• Disease states. Some disease states have becomemore tractable, thanks to genomics approaches,and any company continuing to investigate them

will have to deploy genomics if it is to remaincompetitive. Just which therapeutic areas or dis-ease states are most amenable to genomics isdetermined by several factors: the degree towhich the d isease is genetic in na ture, the cur rentund erstand ing of d isease processes at a molecularor genetic level, and so on.

• Target class. Some genomics approaches are atodd s with tradition al therapeut ic-area bo rders,and favor a broader deployment—around target

class—rather than the old focus on disease state.(The targets within a class are usually similar instructure and biochemical function.)

• Therapeutic modalities. Small-molecule drugsstill dominate the market, but they no longermonopolize it. Some new therapeutic modalitieshave already established a foothold—injectibleprotein therapeut ics , for instance, based onsecreted factors and antibodies. Others remainvery much in the experimental stage—gene ther-apy an d ant i-sense techology, for example—thoughadventurous companies are pursuing themund aunt ed (as exemplified by Lilly’s recent $200million d eal with Isis to gain a ccess to a nt i-sensecapabilities).

These dimensions are interconnected, of course,an d even in terdepen den t. Take Novartis’s interestin oncology, for example—a broad disease state.Given that interest, it made sense for the companyto focus on kinases, a key target class in oncology.Kina ses constitute on e of th e few target cla sses that

are amena ble to a par ticular genomics approach, insilico drug design. Novartis has duly set about aug-menting i ts expert ise with the appropriategenomics technology, forming an alliance withVertex to th at en d.



institutions, banded together to identify 300,000

SNPs (in the end, the total was about one million)

and put them into the public domain. This joint

effort had two very beneficial effects for its partici-

pants. First, it enabled the companies to concen-

trate more on their core interest, finding drugs;second, it forestalled the efforts of genomics com-

panies, which would have sought to patent and

extract rents from these SNPs. Other candidates for

“coopetition” of this kind include protein structure

mod eling and broa d-scale sample collection for dis-

ease association studies.

Putting the Strategy into Operation

Defining a genomics strategy is a good start, but

even th e most brilliant strategy is futile if it remains

defined on paper only. The point is to put it into

operation. Putting a strategy into operation consistsessentially of making changes and managing them

effectively. In the case of genomics and genetics,

the changes that need to be made are profound,

affecting a ll aspects of the R&D or gan ization an d,

by extension, the corporation as a whole—core

processes, organizational structure, job descrip-

A midsized pharmaceutical company initiated anR&D st rat egy overhaul. With the b road goa l of in-creasing productivity, the effort was directed at re-ducing the number of and concentrating the areas ofinvestigation and boosting the company’s access torelevant technologies.

The CEO a nd R&D d irector comm issioned a review of the company’s research and development capabil-ities. A cross-functional project team then set about

defining those capabilities precisely at all steps ofthe value chain, rating their quality, aligning themwith the diseases and markets of interest, and iden-tifying gaps and synergies.

With specific therapeutic areas in mind, the com-pany next t urned t o optimizing its genomics technol-ogy portfolio. The project t ea m emb a rked on a three-stage assessment of the various strategic optionsa nd t heir corresponding technologies.

First, having audited the company’s capabilities and

research interests, the team identified relevantindustry trends, and from these generated a list ofthe strategic options. (Genetics was listed, for exam-ple, as a major target-discovery trend in a favoredresearch a rea—a therapeutic a rea with ma ny herita-ble diseas es.) The tea m then c ompiled a list of

matching technologies, whether currently owned ornot—those that would enhance the options’ chanceof success. (Against the genetics option, forinstance, were listed such matches as SNP mapsa nd genotyping technologies.) Finally, the tea m eva l-uated each technology’s likely impact on productiv-ity, using a sophisticated productivity model thecompany had established.

What emerged was a ranking of various strategic

options and their required technologies—in effect,the basis for a new, integrated technology strategyand the blueprint for an optimized technology port-folio. Company executives were now in a position toponder access arrangements—whether upgrading,licensing, or partnering.

More broa dly, t he result of this effort ha s been a dra-matic focusing and aligning of research activities,directed b y a w ell-a rticula ted s tra tegy. The R&Dmanagers have been able to commit to specific pro-

ductivity metrics a nd time frames. They ha ve beenable to agree on a distinct research focus: three keytherapeutic areas and limited modalities. And theynow have a specific technology strategy, with a clearplan for acquiring the necessary components and aninvestment program to make it happen.

C A SE S TU D Y: B R I N G I N G R E S EA R C H I N T O F O C U S



50

tions, interfa ces, an d so on . The n ecessary work canbe divided into th ree broad areas:

• Rebalancing the value chain

• Estab l i sh ing the new organ iza t ion and i t s

governance• Managing organ iza t iona l change

Rebalancing the Value ChainThe old ways of con ducting R&D a re often unsuitedto the new era. As the first chapter showed, the tra-ditiona l R&D value chain n o long er works. For onething, its smooth flow q uickly becomes disrupted bya series of bottlenecks, induced by the differentproductivity gains at different phases. For another,its sequence is unsustainable, since the new tech-nologies dan ce to a different schedule: much of thechemistry phase might now take place simultane-ously with tar get validation, for exa mple. To rein-state a smooth flow (while enjoying the new, muchaccelerated ra te of thro ughput) , R&D need s to easethe bottlenecks and adjust to a reconfigured valuechain. And that means redistributing resourcesand, more importantly, redesigning processes, aswell as keeping the n ew value cha in in ba lance.

Restoring B alance: Reall ocati on versus Redesign

At first sight, the bottleneck problem would seemfairly simple to resolve: scale up downstream stepsto meet the increased demand. But how feasiblewould that be? The number of targets identifiedcould increa se sixfold or more. To scale up to meetthat increase, a company accustomed to spending$1 billion on all of R&D would n ow ha ve to spendmore than $1.5 billion on target validation alone.

Another simple approach suggests itself: adjustresources along the value chain in order to bring

the uneven phases back into ba lance, shifting fundsfrom more efficient phases (notably target discov-ery) to less efficient downstream phases (such aspreclinical). But such reallocation of resources is,on its own, an overcautious measure, and will nothave a really dram atic impact on R&D econ omics. It

neglects, or even distracts from, the central oppor-tunity that genomics offers: the opportunity to“raise the game” by changing fundamentally theway R&D is cond ucted .

This tran sform at ion o f R&D will derive ab ove all

from the bold reconfiguring of processes, for thesake of both physical process flow and informationflow. For the former, new technology platformsneed to be integrated and optimized, both withinvalue chain steps and across the value chain. (Insome cases, this may require a discipline and arearran gement compara ble to the moving assemblyline introduced by Henry Ford in 1913.) As forinformation flow, the tremendous amount of datagenerated by the new technologies remains worth-less unless translated into functional information

and supplied punctually—that is, in time to influ-ence the decisions being made. (See sidebar,“Establishing a Un ified Informa tics Structure.”)

The extent of the redesign, and the particularshape the new flows take, depend very much on thecompa ny’s strat egy choices. Pr ocesses that are n ewlyindustrialized, but that still follow a traditionalR&D sequ ence, need to b e systematized. In somecases, however, the tr ad ition al R&D value cha in willneed to b e disrupted. To integra te chemical geno-mics and gen etics, for instan ce, would necessitate a

major restructuring of the value chain. Chemicalgenomics introduces a new parallelism, as targetvalidation and chemistry activities are conductedsimultaneously; the two processes now interactrather than just interface. And genetics introducesfeedba ck loops, where lat e-pha se findings (such asgenetic informa tion from the clinic) feed back intoearlier steps of the value chain (such as disease-genetics-based tar get discovery).

In anticipation of any process redesign, individual

function heads should be pondering the contin-gencies: how and when genomics might affectthem, a nd wh at act ions to take when it do es. As onebott leneck is relieved, a noth er is created : When willthe bottleneck reach their step in the chain, andwhat will its impact b e? What new techn ologies and



E STAB L I S H I N G A U N I F I E D I N F O R M AT I C S I N F R A S TR U C TU R E

For a company to extract full value from the new technologies and the copious data that will emerge

from them—that is, to transform data into knowl-edge—it will have to devise a comprehensive infor-ma tics vision and a rchitecture. The vision w ill artic-ulate the role of informatics as a potential source ofcompetitive a dvanta ge. The a rchitecture w ill havethree essential components

First, there must be an optimi zed information flow across the newly industrialized research and devel-opment value chain. Standards will need to beestablished to ensure that data are formatted, or-

ganized, and defined consistently. Hardware andapplications will have to be linked and networkedappropriately so that information can move where itneeds to, feeding subsequent steps in the process.Second, a central ized knowledge management sys- t em is required, to capture and store the data, inte-grate it with external data, and make it availablethroughout the company. Finally, powerful analytical tools w ill be required, to mine and ma ke sense of thedata—sophisticated algorithms, visualization tools,and so on.

To develop and integra te thes e components, mostcompanies w ill have t o invest h eavily. The costs ma ylook particularly high in relation to traditional costs,but that is partly because the industry has generallyunderinvested in IT. (One la rge biotec h c ompa ny, fol-lowing its informatics upgrade, reports a threefoldincreas e in its a nnua l ITb udget. ) To focus the invest-ment accurately, a coherent plan is once againessential. A critical decision is whether to developthe capabilities in-house, outsource to solutionsproviders, or purchase and integrate informaticspac kages. The choice or choices ma de w ill dependon such factors as available internal expertise, theamount of integration with legacy information sys-

tems required, and the availability of reliable inte-gration vendors, package suppliers, or solutions

providers.

Tha t la st fac tor ma y prove pa rticula rly difficult toassess. Who would provide the most reliable andsuita ble a ss ista nce? Toda y, no single provider ofapplication software provides all the functionalityneeded. The informa tics industry is c rowded w ithsmall start-ups offering niche products; the cumula-tive market capitalization of all publicly listed bioin-formatics companies scarcely amounts to one-eighthof Gla xoSmithKline’s a nnua l R&D budget . And w hile

larger IT solutions providers a re gea ring up to servethe burgeoning needs of this market, they are still inthe process of developing internal life-sciences c a pa-bilities. Given that each prospective solutionsprovider is likely to try to make its offering the cen-terpiece of the company's informatics architecture,and given that multiple solutions will need to beknitted together, companies would be wise to solicitindependent, unbiased advice before deciding onparticular vendors.

In addition to managing all this informatics com-plexity, companies will have to deal with a furtherchallenge if they are to implement the new informa-tion regime success fully. They w ill ha ve to find a w a yto resolve the human-resources and organizationalissues tha t a re bound to a rise. Ta lented, experiencedinformatics personnel are difficult to come by: how to find and keep the right people and how to fit theminto the organizational structure are questions thatcompanies will need to address more actively andimaginatively than ever. So too the question of how to change processes and behaviors generally,throughout the organization, to ensure fullest use ofthe new informatics tools—a question examined insome detail elsewhere in this chapter.



52

approaches will be available, and how effectivelywill they relieve the bottleneck? The head of devel-opment, for example, should already be contem-plating the inevitable increase in demand for clini-cal trial capacity and weighing the various options,such as pharmacogenetics, for meeting it.

Retaining Balance:Capacit y Planni ng and Management (CPM) After th e initial jolt of geno mics, supply and deman dshould get back into alignment, thanks to the com-bined forces of resource reallocation and processredesign. But this restored balance is a precariousone, and needs careful and regular maintenance.That is where capacity planning and management,or CP M, can play an invaluable role. By enabling anorganization to keep supply and demand aligned,CPM also enables it to make rationa l plans, linked tocapacities and resources, and thereby to manageprojects with optimal efficiency.

Though well established in high-profile corpora-tions such a s Genera l Electric, Hewlett-Pa ckard,and Cisco, CPM is conspicuously rare in biotechand pharmaceutical companies. For the genomicsrevolution to realize anything like its full productiv-ity potential, efficient C PM will be immensely bene-ficial if not imp erative.

Establishing the New Organization and itsGovernanceImplementing the process changes just mentionedwill entail a thorough review of a company’s exist-ing hierarchies and procedures. For the processchanges to yield optimal value, changes also needto be mad e in trad itional decision-making metho dsand in orga nizational structures.

New Linkages and Int erf aces To begin with or gan izationa l chan ges. With th evalue chain so much altered in appearance, and

In a global pharmaceutical company, the discoverydivision was slowly undergoing a change of charac-ter, from a pre-genomics one of small, independentefforts to an up-to-date one of highly defined andseq uenced processes. The company w a s a nxious tospeed up and optimize this inevitable transition. Ithad recently created several centers of excellence inresearch, each containing a particular combinationof technologies, resources, and expertise, but thesenew groupings remained in need of improvedprocess flow, both within and between them.

An “industrializing” approach was proposed: whynot treat each center of excellence as a factory, and

in that way rethink or refashion the discovery pro-gram as a whole?

A fac tory-ba sed structure imposes a strict discipline.Typica lly, fa ctories ha ve clear, mea sura ble objec-tives, with defined inputs and outputs, and specifiedresources and roles. Efficiency is closely regulated:internal processes and interfaces are optimized for

scale, quality, and productivity; external interactionsare monitored regularly for compatibility and costeffectiveness.

In keeping with this ethos, the company set aboutdefining processes within each potential factory asclearly as possible. The project tea m set ta rgets forinputs, outputs, and quality standards; it identifiedactivities that could be completed inside the factory,as distinct from those supplied as support from out-side; and it itemized links between factories them-selves, between factory and nonfactory research,and between research units and units outside re-search and development.

The result has b een a sub tly redesigned disc overydivision. Processes and functions are now clearlyassigned to specific factories, expectations andachievements are more transparent than before, andthe interactions throughout discovery research arenow easily tracked from factory to factory, with themap being constantly refined.

C A S E S T U D Y: R E D I S C O V E R I N G D I S C O V E R Y R E S E A R C H



processes now so different from before, manydisparities and stresses will inevitably develop inan unaltered managerial system. The old struc-tures will creak and strain under the unfamiliarnew pressures. To restore con gru ence, a co m-pany may need to undertake some bold organiza-tional reshaping—shifting or removing divisionalborders , reassigning personnel , redis t r ibut ingareas of responsibility, and so on—not just withinR&D, but a lso within th e compa ny as a whole andeven beyond, in the alliances the company mightenter into.

The R& D Department. Incorporating the requisitenew cap abilities, it goes without saying, repr esents aformidable organizational challenge: not only dothey have to mesh with existing capabilities, theyneed to coord inate with on e ano ther a s well. Toimplement in silico drug design, for example, itwould be almost essential to provide an informaticsinterface between structural biology and chemistrydata. Meanwhile, a comparable reorganization ofpersonnel has to be undertaken. Biologists andchemists, for example, can no longer proceed inisolat ion, but must now work alongside each

A large pharmaceutical company was facing a capac-ity crisis. Both development and staffing levels wereunder pressure, mainly as a result of productivityimprovements in basic research and competition forscientific talent. As a key part of the remedy, the com-pany undertook worldwide implementation of capac-ity planning and management—a considerable chal-lenge for such a complex organization, where demandwas uncertain and resources were not fungible.

Development of CPM had four main components:

• Quantif ying capacity and demand. Appropriateunits of capacity and demand were defined foreach function. In clinical departments, forinstance, the typical unit of capacity was definedas a team of monitors, coordinators, and supportpersonnel, and the unit of demand was a study.

• Busi ness processes. To exploit CP M fully and fos -ter cooperation among departments and projectteams, various new business processes were initi-ated—most importantly, the tracking and interpre-tation of demand and capacity information, andthe consequent adjustment of timelines andresource allocation. Appropriate linkages neededto be made to related functions such as facilitiesplanning and human resources.

• Change management . Since CPM tends to affectdeeply the way an organization operates—publi-

cizing the relative productivity and workload ofdifferent departments, for example—some man-a gers react more nega tively tha n others. The com-pany took steps, both before and during theimplementation of CPM, to ease the transition.The messa ge w a s consta ntly reinforced—that thechanged regimen was beneficial, essential, andpermanent.

• IT support . With CPM quickly generating a wealthof information, some centralized and some requir-ing broad dissemination, the company recognizedtha t its CPM initiat ive needed extra IT support. Itidentified suitable vendors with the requisite flexi-bility and pharmaceutical experience.

The CP M endeavor has been w idely ha iled. Nolonger is the question “Do we have the capacity todo these projects?” met w ith silence. Nowa days, theCPM team can provide a detailed, graphical depic-tion of capacity and d emand in each department andoverall, and an analysis of the capacity impact ofeach project.

Looking ahead, the company expects CPM to con-tribute to enhanced revenues, by speeding time tomarket and increasing the number of indications percompound. It also expects to use CPM to improveportfolio management, and to save costs throughmore rational investment in hiring and facilities.

C A SE S TU D Y : M A N A GI N G C A PA CI T Y — E M P O W E R M E N T T H R O U G H C P



other—often literally—on collaborative projects orin formal discovery partnerships. And geneticsrequires far closer collaboration between basicresearch and development than ever before.

One excellent example of rethinking traditional

organizat ional s t ructure and boundaries isGlaxoSmithKline. Alert to the impact of scale, thecompany has on the one hand consolidated func-tions where scale and coordination provide a clearadvantage, and on the oth er, engaged in decentral-izing wher e size and complexity could prove a dra w-back. Specifically, prompted by the scale benefits,the company decided to organize centrally both thefront end and the back end of R&D (th at is, targetdiscovery and full development). For the steps inbetween, conversely, where the company’s enor-mous scale would risk encumbering innovation, ithas established smaller, more autonomous centersof excel lence (based on different therapeut icarea s), which attempt t o simulate th e feel of smallerbiotech companies.

The Ent i re Corporati on. So, enha nced control of dataand increased cro ss-function ality of personnel a reset to cha nge th e structu re and tone o f the R&Ddepartment . But their sphere of operat ion isbroader than that. As with the strategic issues dis-

cussed earlier, the company as a whole is impli-cated. New lines of communication, and possiblynew chains of command, will need to be extendedbetween R&D an d ot her units. In par ticular, therelationship between R&D a nd marketing will befunda mental ly t ransformed: with R&D fa cinggreater choice and placing b igger bets earlier thanever, commercial input will be crucial. And phar-macogenetics will require new ways of thinkingabout markets, competitors, an d customers. (P har-macogenetics may also inspire new linkagesbetween pharmaceutical and diagnostic units forcorporations that have both).

Coordinat ing the commercial izat ion processbetween R&D a nd mar keting has always been a del-icate balancing act. Most biopharmaceutical com-panies have established product development proj-ect teams to drive the process. These cross-

functional teams are charged with d eveloping prod -uct strategy and coordinating the various functionsas prod ucts progress from R&D in to th e mar ket.The job has now become even more complex andtricky, owing to larger global efforts, greater infor-mation f low, more special ized funct ions, andincreased liaison with global strategic marketing(especially when companies consider the optionsfor applying pharmacogenetics to molecules indevelopment).

Beyond the Corporation. Finally, new partnershipmodels need to be considered. Although tradition-ally organized partnerships are still appropriate inman y cases, new and more flexible forms of alliancewill sometimes be required, notably when it comesto collaborating with academic or not-for-profitinstitutions and to joining horizontal networks orconsortia.

R&D Governanc e

On e poten tial source of ga in in R&D is improveddecision ma king. Consider again t he example at thestart of the value chain—the glut of identified tar-gets and the need to decide which on es should pro-ceed to the next phase. Genomics technologieshave created this quandary, but they have also pro-

vided the means for solving it. Using new genomicmetho ds of “a ptitude-testing,” decision makers canconfidently preselect the most promising targetsand forward them downstream.

Even decisions unrelated to genomics technologiesstand to improve, since the new genomics regimenfosters a culture of rigorous selection criteria. Infact, one of the most important, though perhapsleast noted, benefits of genomics is the way itencourages a thorough rethinking of decision-

making processes. New kinds of data now presentthemselves for interpretation, and they enter thecalculations earlier and in greater abundance thanthe old kind s did. And R&D decision makers haveto take into account a new set of factors too, be-yond th e confines of R&D, in or der to maximizevalue—factors such as marketing and IP implica-tions, for example.

54



Managing Organizational ChangeWith the advent of genomics, R&D personnel sud-

denly find themselves in alien territory. As the sci-ent i f ic methods change, the old inst inct iveapproaches and behaviors need to change as well.Amon g th e grea test challenges facing R&D execu-tives is managing the human side of change.

How the Scient ist ’s Job is Changing R&D science is shifting fr om an aren a o f experi-mentation to one increasingly concerned with th e-oretical biology. The challenge is now less how toget the data tha n what to do with the data collected.

Scientists who form erly could do their jobs virtuallyon their own—conduct their own experiments, an dgenerate and analyze the data themselves—now find they need to collabora te with others who havemore specialized techn ological skills, in area s suchas informatics , robot ics , or microfabricat ion.Ind eed, th e scientists of th e pre-genom ics era a redestined to evolve into two kinds of successors:

those who interpret the data and devise plans forexploiting it, and those who continue to develop

and optimize the technologies required for gener-ating the data. (Companies should be sure to rec-ognize and reward th e latter group for its contribu-tions, an d n ot r elegate it to second -class status.)

All scientists will need to b ecome co mfort able withnew ways of working togeth er—more sharing o r col-lectivist now, less conducive to solitary initiative.The scientists of the future will still take responsi-bility for th eir own work, but perha ps will no longertake the credit for it: that will be ascribed to team

effort.

Managin g the Transit ion Cha nging from bench-based to informa tion-basedwork in this way, and from favoring fairly independ-ent endeavors to promoting a more collaborativeethos, is bound to be awkward or even painful formost of t hose involved, scientists an d m an ager s alike.

A large drug company recently completed an inten-sive three-month project to redesign discovery gov-ernance a nd is a lread y reaping the benefits. Thecompany had always placed a high value on thequality of its scientists and their entrepreneurialdrive. Now, how ever, it w a s growing increasingly dis-satisfied with its existing system of allocatingresources: the decision-making procedures wereproving very troublesome to na viga te, decision ma k-ers were difficult to identify, communication waspoor, and the decisions themselves often seemedpolitically motivated rather than guided by scientificand commercial promise.

In redesigning the decision-making procedures, thecompany began with a thorough review of its currentgovernance process, both as espoused and as prac-ticed (the two were remarkably distinct in certaininstanc es). Va rious root ca uses of undesirable out-comes were identified: these included perverseincentives (that is, incentives encouraging behavior

at odds with company strategy); unclear criteria,which project champions were disinclined to clarify,let alone follow; and inadequate allocation of deci-sion rights (that is, too vague a definition of w ho wa sentitled to make which decisions), which oftenmeant that no decision was made at all.

From the lessons learned, a new governance processwas devised. Not just devised, but activated: bymodifying incentives, the company ensured thatpractice was now properly aligned with espousal.

The new process is w orking well: R&D ma na gersnavigate it easily, and decisions are being made and

communicated clearly and consistently. It allows sci-entists more time to focus on their projects, and itgives those projects appropriate funding and man-agement involvement. It has accordingly won theconfidence of those affected by it, and can claim aconsiderable contribution to the marked improve-ment in productivity that has followed its adoption.

C A SE S TU D Y: R E D E S I G N I N G R & D G O V ER N A N C E



The formidable operat ional and organizat ionalchanges will entail cultural changes too: in fact, thenew pro cesses and structures ma y prove far less diffi-cult to establish th an new habits and attitudes.

Consider informatics. It is not enough simply to

introduce powerful new IT tools within traditionalsilos—with in chem istry, for exa mple, wher e in silicoapproaches would boost the efficiency of screeningand optimization. To ach ieve their full impact,these IT tools need t o be d eployed a cross function s:to bring biologists and chemists together, to incor-porate data from the clinic into discovery, and soon. And that will require not just new software, oreven new managerial positions, but new ways ofthinking and of relating to colleagues.

Some idea of wha t lies in store can be glean ed fromthe history of another transformational technol-ogy—CAD/CAM for airplane design. Likegenomics, it promised to transform a costly andlabor-intensive R&D process into a highly auto-mated and efficient one. After languishing in nicheapplications in the 1970s and ’80s, it finally provedits worth in the 1990s, when Boeing used it indesigning the first “paperless” airplane, the Boeing777. To exploit t he tech no logy fully, the co mpa nyhad to break down departmental barr iers and

encourage collaboration across the full range offunctions. Jobs and job responsibilit ies had tochange. Cherished traditions were called into ques-tion. The company held quarterly meetings atwhich employees could ask questions and voicetheir concerns. The transformation was a struggle,but ultimately a great success: Boeing continues topush the envelope in “in silico” a irplane d esign.

When pharmaceutical companies convert to geno-mics, they will have to temper the discomforts of

transition in their turn. And that means engagingthe emotional and behavioral issues—the human

issues—as deeply as the o peration al o nes. 4 Attentiveman agemen t of the hum an issues, which has playedsuch a prominent role in so many industries in thethroes of reform, is going to be particularly crucialwhen it comes to the massive institutional changesdemanded by the genomics revolution.

A Final Word

To stake a claim in the chan ging biopha rma ceuticalland scape, let alone feature pro minent ly within it, acompa ny will have to ma ke itself rad ically amen ableto change. Defining a strategy is certainly a step inthat direction, and initiating that strategy is cer-tainly a gesture of commitment. But wholeheartedcommitm ent is evidenced n ot by initiating the strat-egy but rather b y maintaining it—that is, mon itor-ing the new structures and procedures constantly,responding to shifts in external and internal cir-cumstances, and introducing fur ther changesrepeated ly, a ggressive or defensive, a s new oppo rtu-nities or new cha llenges arise, tho ugh always in linewith the con trolling wisdom of th e strategy itself.

If the unfamiliar outer landscape provokes feelingsof unease, so too will a company’s inner landscape,once all the requisite operational and organiza-tional changes are in place. In particular, the in-crea se in cross-fun ction al activity ma y be disorien t-ing for some executives of the old school. Many ofthe ancient landmarks, t idy borders, and familiarcategor ies will no longer b e there to give them theirbearings. Short of attempting a counterrevolutionor withdrawing into obscurity, they will need tofamiliarize themselves with the new terrain fairlypromptly—and accept it affirmatively, not grudg-ingly. Changes in attitude will perhaps prove themost difficult changes of all to bring about, and a

company’s prosperity could be in jeopardy if theyfail to take effect.

56

4. For a fuller discussion of the emotional aspects of change, read T he Change M onster , by Jeanie Daniel Duck, published by Crown in 2001.



Co n c l u s io n

The international pharmaceutical industry is press-ing ahead in an unexpectedly difficult environ-ment. D rug companies face unfamiliar frustrations.On one side, pricing policies are coming increas-ingly under th reat ( witness the recent mo ves in var-ious U.S. states to restrict access to costlier drugsfor Medicaid patients). From the other side, thepressure of expectat ion increases too, with fina ncialanalysts continuing to count on triumphant prod-uct launches and enormous growth. In such anenviron ment, corpor ate well-being, or even sur-vival, d epends o n boosting prod uctivity.

It is against this background that the genomics rev-olution is unfolding. In their quest for improvedproductivity, companies should welcome the new technologies and approaches. Genomics promisesprodigious benefits: it will unlock storehouses ofinformation about the workings of huma n d isease,and greatly refine—perhaps even personalize—health care. More to the point, it promises to trans-form how pharmaceutical research is conducted.The paradigm will shift from small-scale andserendipitous to global, industrialized, and system-atic; and from methodical and compartmentalizedto f luid an d cross-function al. The imp act on R&Deconomics is likely to be tremendous: in the bestcase, productivity could as much a s double.

Looking beyond R&D, gen omics and genetics alsopromise to tra nsform the way pharm aceutical com-panies conduct their business in the coming years.

If genetics realizes its potential, for example, treat-ments will become m ore sophisticated , mar kets mayfragment, and the shape and value of marketingand sales organizations will change dramatically.The en tire system of h ealth care deliver y, alrea dy influx, will complete its metam orph osis.

The offer that genomics and genetics are holdingout is really an offer that companies cannot refuse.Companies that fail to accept the offer adequatelywill find themselves not simply uncompetitive butpossibly right out of contention. There is nowhereto hid e, and certainly no safety in inaction. To shunthe promise of phar macogenomics out of a fear ofmarket fragmentation, for instance, is not to avertthe fragmenta tion but simply to cede the ma rket toone’s rivals.

Embra cing the revolution appropr iately will requireboth boldness and finesse: managers will have tomake major strategic decisions, and to implementthem will have to radically reconfigure operations.The d ecisions take careful a nalysis to get righ t, a ndthe operational h urdles need nimble negotiation tosurmount. It all adds up to a formidable but by nomeans impossible task. And for companies that doit well, the rewar ds will be ha nd some.

The opportunities are unprecedented. So are thechallenges. The shrewd company will be one thatremains responsive to both, as it tries to keep itshead and to prosper in these revolutionary times.



Me t h o d o l o g y

The many diverse studies on drug developmenthave reached diverse conclusions. They have putthe cur rent pr ice tag at an ywhere between $350 mil-lion an d $800 million per dr ug (a ll underestimates,in our view: our own calculation is $880 million).Not surprisingly, when it comes to the likely impactof genomics and genetics on the economics of drugdevelopment, opinions diverge again.

For our study, we conducted an extensive programof discussions in an effort to compile accurate fig-ures for a ll the ma in act ivities in the R&D pr ocess,both pre- and post-genomics. The result is a robustbotto m-up mo del of R&D, b ased on the time, co st,and likely success rate for each step of the valuechain.

Our mod el goes beyond existing mod els of R&D inthree important ways:

• It is the product of primary research. Other esti-mates have tended to build on the findings of ear-lier work; our mod el also dr aws on more th an 100discussions at nearly 50 companies and academicinstitutions.

• It ana lyzes the discovery phase more closely thanhas previously been possible. Earlier models typi-cally assigned a conjectural figure to representthe sunk cost of discover y, but t he a rt of discoveryis becoming industrialized, and we have duly

been able to model its activities more scientifi-cally. A more detailed understanding of the eco-nom ics of discovery ha s resulted, a nd t ha t in turnhas allowed us to more accurately quantify theimpact of gen omics on R&D. (See the ch art o npage 60 for technologies modeled.)

• It is activity-based and flexible. The num berscited in this report represent an average drug,unless noted otherwise, but in our research weranged far wider than that, a nd assessed each stepof the value chain under a range of circum-stances. So th e mod el allows for scenario buildin gand sensitivity analysis, as well as enables us totailor inputs to match the unique circumstancesof individual companies.

As alread y mentioned, a ll numbers cited in the textare for an average drug. In any individual case, costan d time will var y according to factor s such as ther-apeutic area a nd target class.

All numbers cited in the text are for a relevantdrug, that is, one to which the technology underdiscussion could be applied. Various technologiesmay no t a pply to all tar gets or dr ugs. Where specificlimitations are likely to be a significant factor, that

is pointed out.When we discuss the “value added” to a drug, weare referring to its net present value: the current



value of expected profits, discount ed by a repr esen-tative hurdle rate, less the cost of developing thedrug. For the average drug, we assume peak annualsales of $500 million a nd 11 years to paten t expira-tion. Also, our num bers reflect the fact tha t R&D

dollars saved are pretax dollars; these “saved” dol-lars are subject to taxation, which explains in partwhy the expressed NPV calculations can be lowerth an th e R&D savings.

60

GENOMI CS TECHNOLOGY ASSUM PTIONS

Development

Pre-c l i nical Cl ini cal

Chemistry

Screening Opt imizat ion

Biology

Target ID Target Validat ion

Target ident i f icat ion

• L imi t ed number s o f genes

• Molecu la r b io logy and b iochemis t rytechniques

Target validation

• Ce ll and t i s sue s tud i e s

• Mouse knockou t s

Target ident i f icat ion

• La rge number s o f genes

• Indus t r ia l i zed t echn iques(e .g . , gene chip express ion)

• B io in fo rma t i c s(e .g . , da tabase searches for hom ologies)

Target validation

• Ce ll and t i s sue s tud i e s

• Mouse knockou t s

Screening

• Para l le l synthes is for l ibrary des ign

• Assay deve lopmen t for h igh - th roughpu tscreening ( HTS)

• HTS

Chemical opt imizat ion

• B e n ch s y n t h es i s

• Pa ra l l e l syn thes i s

Screening

• S t ruc tu ra l b io logy ( t a rge t s t ruc tu re)

• SAR p ro f i l ing o f l i b r a ry

• Assay developmen t fo r LTS1

• Vir tual screening and LTS1

Chemical opt imizat ion

• I n s i l i co - suppor t ed bench syn thes i s

• In s i l i co ea r ly ADME/ tox

Preclinical (ADME/tox)

• A n im a l t e st i n g

Clinical

• Pa ti en t tr i a l s

Preclinical (ADME/tox)

• A n im a l t e st i n g

• In s i l ico ADME/ tox

• In v i t ro t oxi co logy

• Su r roga t e marke r s

Clinical

• Pa t ien t t ri a l s

• Su r roga t e marke r s

P o s

t - g

e n o m

i c s

P r e

- g e n o m

i c s

SOURCES : BCG analysis; industry interviews; scientific literature.

1LTS = LOw-throughput screening; generally more information-rich, but less s tandardized, a ssays that cannot be used in HTS.



AmsterdamAthensAtlantaAucklandBangkokBerlinBostonBrusselsBudapestBuenos AiresChicagoCologneCopenhagen

DallasDüsseldorfFrankfurtHamburgHelsinki

Hong KongIstanbulJakartaKuala LumpurLisbonLondonLos AngelesMadridMelbourneMexico CityMilanMonterreyMoscow

MumbaiMunichNew DelhiNew YorkOslo

ParisSan FranciscoSão PauloSeoulShanghaiSingaporeStockholmStuttgartSydneyTokyoToro ntoViennaWarsaw

Washing tonZürich

boston consulting group report 2001

Documents