the distribution of sales revenues from pharmaceutical innovation

The Distribution of Sales Revenuesfrom Pharmaceutical InnovationHenry G. Grabowski and John Vernon

Department of Economics, Duke University, Durham, North Carolina, USA

Abstract Objective: This report updates our earlier work on the returns to pharmaceuticalresearch and development (R&D) in the US (1980 to 1984), which showed thatthe returns distributions are highly skewed. It evaluates a more recent cohort ofnew drug introductions in the US (1988 to 1992) and examines how the returnsdistribution is emerging for drugs with life cycles concentrated in the 1990sversus the 1980s.

Design and setting:Methods were described in detail in our earlier reports. Thecurrent sample included 110 new drug entities (including 28 orphan drugs), andsales data were obtained for the period 1988 to 1998, which represented between7 and 11 years of sales for the drugs included. 20 years was chosen as the expectedmarket life for this cohort, and a 2-step procedure was used to project future salesfor the drugs – during the period until patent expiry and then beyond patent expiryuntil the 20-year time-horizon was completed. Thus, the values in the first halfof the life cycle are essentially based on realised sales, while those in the secondhalf are projected using information on patent expiry and other inputs.

Main outcome measures and results: Peak annual sales for the top decile ofdrugs introduced between 1988 and 1992 in the US amounted to almost $US1.1billion compared with peak sales of less than $US175 million (1992 values) forthe mean compound. In particular, the top decile accounted for 56% of overallsales revenue. Although the sales distributions were skewed in both our earlierand current analysis, the top decile in the later time-period exhibited more rapidrates of growth after launch, a peak that was more than 50% greater in real termsthan for the 1980 to 1984 cohort, and a faster rate of expected decline in salesafter patent expiry. One factor contributing to the distribution of sales revenuesbecoming more skewed over time is the orphan drug phenomenon (i.e. most ofthe orphan drugs are concentrated at the bottom of the distribution).

Conclusion:The distribution of sales revenues for new drug compounds is highlyskewed in nature. In this regard, the top decile of new drugs accounts for morethan half of the total sales generated by the 1988 to 1992 cohort analysed.Furthermore, the distribution of sales revenues for this cohort is more skewedthan that of the 1980 to 1984 cohort we analysed in previous research.

ORIGINAL RESEARCH ARTICLE Pharmacoeconomics 2000; 18 Suppl.1: 21-321170-7690/00/0001-0021/$20.00/0

© Adis International Limited. All rights reserved.

In this study, we examine the distribution ofsales revenues for a comprehensive sample of newdrugs introduced into the US during the period1988 to 1992. In earlier research, we examined thereturns to research and development (R&D) on USnew drug introductions during the 1970s and early1980s.[1,2] One of the key findings was that the topdecile of new drugs accounted for a large share ofthe total market value generated by these entities.In this regard, the returns to R&D projects inpharmaceuticals have properties similar to those ofventure capital investments. This has importantimplications for both private and public deci-sion- makers.A new analysis of this issue is warranted by a

number of important changes on both the demandand supply sides of the market for new drugs. Inparticular, there has been significant new entry andindustry restructuring since our last analysis of thereturns to R&D. In addition, managed care hasgrown dramatically during the 1990s, and now ac-counts for a dominant proportion of drug prescrip-tions. These factors can significantly affect the lifecycles of sales and the distribution of revenuesacross new drug introductions.

Background

The 1980 to 1984 Cohort of New DrugIntroductions

In this section, we summarise some of the corefindings from our previous work on pharmaceuti-cals and relate them to recent work on the returnsfor venture capital investment. Our last analysisfocused on a comprehensive sample of 64 newchemical entities (NCEs) introduced into the USmarket between 1980 and 1984.[1] In this regard,figure 1 shows the sales profiles over the marketinglife cycle for the top 2 deciles of NCEs (ranked bytenth-year US sales) and the mean and mediancompound. The figure indicates that there is a highdegree of variability in the sales performance ofNCEs. In particular, the peak annual US sales weremore than $US700 million for the top decile com-pounds, approximately $US300 million for the

second decile compounds, $US150 million for themean compound, and only $US50 million for themedian compound (1990 values). In our analysis,we also estimated the ‘quasi-profits’ for each entity– the surplus of global sales revenues over produc-tion and distribution costs – and discounted themto the date of market launch. The top decile, themost profitable 10% of the compounds, contrib-uted 48% of the quasi-profits realised by the fullsample of NCE introductions during this period.By contrast, the bottom half of the distribution(deciles 6 through 10, encompassing the entitieswith peak sales below $US50 million) accountedin total for only 8% of the quasi-profits.

Returns for Venture Capital Investments andInitial Public Offerings (IPOs)

Recent work by Scherer et al.[3,4] has shown thatmany other innovational activities are charac-terised by skewed outcome distributions. Of partic-ular interest are 2 of their data samples involving alarge number of investments by US venture capitalfirms in start-up companies between 1969 and1988. The first sample was compiled by VentureEconomics Incorporated and involved a portfolioof investments in 383 start-up companies made by13 venture capital firms. The second sample in-volved a similar data set assembled by Horsley-Keough Associates of 670 distinct investmentsmade by 16 venture capital companies.Scherer’s analysis indicates that investment re-

turns from venture-financed start-ups are highlyskewed. As shown in table I, a relatively smallnumber of start-up firms generate a large share ofthe total investment value, as measured by the cap-ital appreciation or loss at the time of investor exitfrom each investment. In the case of the VentureEconomics sample, the most profitable decile ofprojects accounted for 62% of the total value gen-erated by all 383 investments. For the Horsley-Keough sample, 59% of the overall value was at-tributed to the top decile of start-up companyinvestments. This can be compared with our sam-ples of 1980 to 1984 NCEs, where the top decile ofNCEs accounted for 48% of the quasi-profits.

22 Grabowski & Vernon

© Adis International Limited. All rights reserved. Pharmacoeconomics 2000; 18 Suppl. 1

Scherer et al.[3] also examined the stock marketperformance of a comprehensive sample of 110venture-funded high-technology companies thathad their IPOs between 1983 and 1986. A decadelater, he examined the returns from an equal dollarinvestment in each of these companies at the timeof their IPO. An investment in a full bundle of theseIPO companies would have slightly outper-formed a comparable dollar investment in theNASDAQ index over the same period.1 However,the market performance of these IPO firms alsoexhibited the same tendency toward extreme val-ues as the samples involving venture-financedstart-up investments discussed earlier in this sec-tion. As shown in table I, the 11 firms that consti-tuted the most profitable decile of these IPO com-panies accounted for 62% of the overall marketvalue in 1995. Correspondingly, the other 99 high-technology firms in this sample accounted for theremaining 38%.

Implications for R&D Investments

The data shown in table I indicate that R&Dinvestments in pharmaceuticals have much in

common with private investments by venture cap-ital firms in start-up companies as well as publicmarket investments in high-technology IPO com-panies. All of these innovative investment activi-ties are characterised by a high degree of risk. Thisresults from the fact that a few extreme values ac-count for a large share of the cumulative realisedreturns. As Scherer and others have observed,[4] thelaw of large numbers doesn’t work very well whenthe probability distribution of outcomes is highlyskewed. One important consequence for pharma-ceutical R&D is that considerable variability inportfolio outcomes can be expected, even for thosepharmaceutical companies with large diversifiedportfolios of R&D pipeline drugs.In the case of pharmaceuticals, the blockbuster

compounds, which constitute the top decile ofNCEs in figure 1, generally represent significanttherapeutic advances in treating a particular dis-

Table I. Distribution of returns for selective innovative samples

Data set Percent of valuein top decile

Venture Economics (383 start-upinvestments) [Scherer et al.[3]]

62

Horsley-Keough (670 start-up investments)[Scherer et al.[3]]

59

Scherer et al.[3] (1983-1986 IPOs: marketvalue in 1995)

62

Grabowski and Vernon[1]

(1980-1984 NCEs)48

IPOs = initial public offerings; NCEs = new chemical entities.

1 Returns were based on the market values of these com-panies approximately 1 decade later (December 31, 1995).This analysis takes account of the market values of thesurviving IPO companies, those that merged with otherfirms, and those deleted because of bankruptcies and failureto meet NASDAQ financial criteria.

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Fig. 1. US sales profiles for 1980 to 1984 new chemical entities.[1]

Revenue Distribution from Pharmaceuticals 23


ease, usually one with significant market size. Inmost instances, these therapies are the first or sec-ond introductions in a new chemical class of com-pounds, and offer a novel approach to treating aparticular disease.[5] The pharmaceutical industryhas also been characterised historically by signifi-cant first-mover advantages.[6,7] Other things beingequal, later market entrants tend to capture substan-tially lower market shares.The most novel compounds face the greatest

risks – from a scientific, regulatory and commer-cial perspective. In this regard, the therapeutic pro-files of these compounds are the most difficult topredict on the basis of preclinical screens and leads.In addition, the long lag time and R&D activitiesof competitors magnify these scientific and techni-cal risks. Accordingly, unforeseen clinical outcomes,the introduction of rival products and other changesin the market, and regulatory problems and lagtimes can dramatically affect a new drug’s eco-nomic prospects during the development process.These factors help to explain why so many of

the compounds in figure 1 are marketed despitevery small peak sales revenues and quasi-profits thatare a small fraction of mean R&D costs.2 If signifi-cant uncertainties surrounding a compound’s eco-nomic prospects are not resolved until clinical de-velopment is largely complete, most of the R&Dcosts are then sunk. At this point, as long as a com-pound’s expected revenues cover the incrementalor variable costs on a prospective basis, it is ra-tional to market or license out the compound,even if this doesn’t cover any of the compound’s

large fixed R&D costs. Of course, in the longrun, the firm also must have its share of winnersfor itsR&D programme to be profitable and remainviable.

Recent Market Developments

The basic sample to be investigated comprises110 new drug entities developed for the USmarket,approved by the FDA, and introduced into the USmarket between 1988 and 1992. This is a compre-hensive sample of the new drug entities introducedinto the US market during this period. In this paper,we focus on the US sales performance of these en-tities. In future papers, we will examine the returnson R&D of these entities and integrate global salesand costs into the analysis.In our past work, we have found that differences

in sales revenues constitute the major driving forceunderlying the skewed distribution of quasi-profitsacross NCEs.[1,2] An analysis of sales performancein the US is therefore interesting in its own right. Inthis regard, the US is also the largest market forpharmaceuticals, accounting for roughly half of thesales relating to new drug introductions studied inpast samples. We also found that sales of these newdrugs in other major markets (Europe and Japan)were significantly positively correlated with theirUS sales revenues.

Managed Care and Demand Side Changes

As noted in the introduction, the demand sideof the market for new pharmaceuticals has beenundergoing substantial change during the past de-cade. Pharmacy benefit management firms (PBMs)have emerged as the main overseers of the prescrip-tion drug plans of employers and managed-careinstitutions.[9,10] PBMs have implemented drugformularies to encourage more price competitionand incentive programmes for generic drug usagewhen brand products come off patent. At the sametime, managed-care institutions have broadenedinsurance coverage for prescription drugs, and unitsales have grown as drug therapies and compliancehave been encouraged as a way of avoiding moreexpensive medical treatments.

2 We did not have R&D costs on an individual NCE basis.Another factor could be that R&D costs are also lower fordrug entities with smaller sales and quasi-profits. While thismay be the case, an analysis of R&D costs for a repre-sentative sample of NCEs at different stages of the R&Dprocess by DiMasi et al.[8] indicated that there is much lessvariability in R&D costs than in revenues across NCEs. Thisis plausible, given the fact that all FDA approved drugs mustmeet stringent regulatory requirements. Approved drugs alsoshare in common pre-project discovery costs and the costs offailures. These components account for more than 50% ofthe mean estimated R&D cost of $US202 million in themid-1980s.



PBMs and health maintenance organisations(HMOs) can have differing effects on the sales rev-enue for a new drug introduction over the market-ing life cycle. New drugs that represent novel thera-peutic interventions for particular diseases andconditions have generally received broad coverageand speedy approvals for inclusion on drug formu-laries. However, as follow-on drugs are introducedinto the same class, price discounting and compe-tition usually occur in order to obtain formularyaccess. The growth of managed care has also beenan important factor contributing to a more rapiderosion of sales when drugs come off patent.Therefore, as a new drug proceeds through its mar-keting life cycle, and as competition develops in agiven therapeutic class, the influence of the PBMsof managed-care providers on sales revenues issubject to important shifts over time.

Biopharmaceuticals, Orphan Drugs andSupply Side Changes

There have also been important changes in thesupply side of the market. In this regard, the num-ber of new drug entities introduced onto the USmarket during the 1988 to 1992 period is signifi-cantly larger than during the earlier 1980 to 1984period. This reflects some important industry de-velopments. First, the current sample includes newbiopharmaceutical entities aswell asNCEs. The bio-technology industry was essentially in its infancyin the early 1980s. However, by the early 1990s, ithad become a significant source of new therapeuticentities.Another important event was the passage of the

Orphan Drug Act by Congress in 1983. This pro-vided incentives in the form of tax credits, marketexclusivity, and regulatory assistance for the devel-opment of drugs targeted to diseases and condi-tions involving small patient populations.[11] Inparticular, a drug is eligible for orphan drug statusunder the law if it is approved for an indicationinvolving a population of <200 000 patients.Roughly one-quarter of the drugs in our currentsample were granted orphan drug status for at leastone approved indication.

In our sample, there is also a high degree ofoverlap between the biopharmaceutical and orphandrug sets. This phenomenon has been discussedelsewhere and is the result of several factors.[11]First, many of the initial biotechnology drugs wererecombinant versions of natural hormones with ap-proved indications for small patient populations.In addition, many biopharmaceutical firms soughtthe market exclusivity protection of orphan drugstatus, given the initial uncertainties surroundingbiopharmaceutical patents.It is important to point out that there is wide

variability in the sales revenues realised by orphandrugs in our sample. In particular, some of thenovel biotechnology drugs granted orphan drugstatus were able to achieve blockbuster status byobtaining relatively large reimbursements per drugtreatment. In addition, some of these drugs re-ceived orphan drug status for some indications aswell as approval for other non-orphan indications.Conversely, many of the orphan drug approvals inthe 1988 to 1992 period were for very rare condi-tions and, by historical standards, these drugs hadvery small sales (i.e. annual sales of only a fewmillion dollars). Hence, the group of orphan drugcompounds is very heterogeneous in nature.

Data Samples and Methodology

Annual drugstore and hospital sales in the USwere obtained from IMS America for each of the110 new drug entities in our sample. The sales datacovered the period 1988 to 1998. This providedbetween 7 and 11 years of sales data for the drugsin our sample cohort, depending on a drug’s yearof introduction.20 years was chosen as the expected market life

for this cohort. We felt this was a reasonable value,since virtually all of the drugs in our sample hadpatent lifetimes of significantly less than 20 years,and products with substantial market sales wouldbe expected to face strong generic competition andsales losses after patent expiry. While some prod-ucts may have positive sales after year 20, thesesales would be expected to be small and to have



very low weights in any type of discounted presentvalue analysis.We used a 2-step procedure to project future

sales values for the products in this sample. A keytime-point in the life cycle of sales is the year ofpatent expiry. This is clustered between years 10and 14 for the current sample. In our approach, thefirst step involved projection to the year of patentexpiry and the second step projection of the post-patent expiry values.To project sales to the point of patent expiry, we

utilised an approach similar to our past analy-ses.[1,2] In particular, we constructed a referencelife cycle curve based on the sales of products in-troduced in the mid-1980s (i.e. the new drug cohortimmediately preceding the current one). We usedthis as the basic framework to project sales valuesfor most of the NCEs. However, to take account ofrecent market and competitive developments af-fecting demand for the leading compounds andtherapeutic groups, we also utilised the sales fore-casts from a group of security analysts to makeadjustments when there was a significant deviationfrom the reference case.The estimated sales for the period after patent

expiry were based on an analysis of generic com-petition in the mid-1990s.[12,13] When this analysiswas used, the percentage decline in average salesduring the first 2 years after patent expiry for prod-ucts with annual sales of $US50 million or more atthe time of patent expiry were computed to be 43and 42%, respectively.3 Thereafter, a 10% annualdecline was utilised over the remaining years ofmarket life. In our analysis of generic competitionsince the passage of the 1984 Waxman-Hatch Act,we have observed a strong trend over time towardan increased erosion of sales after patent expiry.[13]Since most of the products in our sample will ex-perience patent expiry in the early part of this de-cade, the rates of erosion of sales after patent expiry

for these drugs will tend to be understated if currenttrends persist into the future.

Empirical Results

Sales of Orphan versus Non-Orphan DrugsThe first issue we examined was the sales per-

formance of the orphan versus non-orphan drugs inour sample. Figure 2 shows a plot of the life cycleof sales profiles for the mean compound in these 2subsamples of drugs. As discussed in the previoussection, Data Samples and Methodology, the val-ues in the first half of the life cycle are essentiallybased on realised sales, while those in the secondhalf are projected using information on patent ex-piry and other inputs.4The non-orphan drugs exhibit the general char-

acteristics observed in prior work: rapid growth af-ter launch, maturation about 10 to 11 years into thelife cycle, and then a rapid decline in sales afterpatent expiry and generic entry. By contrast, theorphan compounds exhibit more moderate growthrates after launch, a much lower expected peaksales level, but also slower expected rates of de-clines in sales in the later stages of the life cycle.The last-mentioned phenomenon is due to longeraverage patent protection periods as well as lessgeneric exposure for the orphan drug population,given their smaller average sales levels.As discussed in the section entitled Biopharma-

ceuticals, Orphan Drugs and Supply Side Changes,there are different economic incentives in terms ofthe R&D and regulatory process for orphan drugscompared with non-orphan drugs. In future work,we plan to investigate their economic returns in aseparate study. Nevertheless, the 28 orphan drugsin our current sample are very heterogeneous andinclude some of the leading biopharmaceuticalproducts such as epoetin-α (erythropoietin) and

3 The probability of generic competition is low for drugswith annual sales at the time of patent expiry that are below$US50million. Accordingly, we assumed no generic compe-tition would occur in the case of these smaller selling drugs.

4 Since our sample involves a basket of new drugs intro-duced between 1988 and 1992, years 8 to 11 of the life cycleare a blend of actual and forecasted sales. For example, year8 involves the first year of forecasted sales for the 1992cohort and actual sales for the 1988 to 1991 cohorts. Simi-larly, year 11 involves actual sales for the 1988 cohort andprojected sales for the other cohorts.



human growth hormone. Because of this, we havechosen not only to retain these orphan drugs in oursample but also to analyse the distribution of saleswith and without these drugs present. The resultsdo not change in a qualitative manner.

Distribution of Sales for 1988 to 1992 IntroductionsFigure 3 provides a plot of the expected sales

profiles for the full sample of new drugs for the1988 to 1992 period. This provides an exact counter-part of figure 1, and shows the life cycle of salespatterns for the top 2 deciles and the median andmean drug compounds. The main observed differ-ence between figures 1 and 3 is associated with thetop decile of drugs. In particular, the ‘mountain’type profile of the top decile in figure 3 has growntaller and steeper compared with the other profilesdisplayed in these figures. In this respect, the top

decile in the later time-period exhibits more rapidrates of growth after launch, a peak more than 50%greater in real terms than for the 1980 to 1984 co-hort, and a faster rate of expected decline in salesafter patent expiry.The definite impression from figure 3 is that the

distribution of revenue has become more skewedover time. One factor contributing to this trend isthe orphan drug phenomenon. Most of the orphandrugs are concentrated at the bottom of the distri-bution, with a few blockbuster drugs in the topdecile. This tends to make the overall distributionmore skewed. However, the basic findings are notaltered in a qualitative manner when the orphandrugs are omitted from the sample.The movement toward more skewness over

time, as indicated in figure 3, was confirmed by amore detailed analysis that we performed. This is

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Fig. 3. US sales profiles of 1988 to 1992 new chemical entities.

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Fig. 2. Mean US sales for 1988 to 1992 new chemical entities (NCEs). Orphan versus non-orphan drugs.



presented in figure 4. In this figure, we plot the fulldistribution of sales by decile for the 1980 to 1984and the 1988 to 1992 cohorts. Here sales data arebased on the seventh year after launch, so that thisanalysis is based completely on actual sales values.In particular, the top decile of new drugs for the1988 to 1992 period accounts for 56% of the over-all sales revenue for the full sample of 110 drugs.If we omit the 28 orphan drugs, the top decile ac-counts for 52% of the sales revenue. By contrast,the top decile of NCEs for the 1980 to 1984 periodaccounted for 48% of overall sales (and the samepercentage of quasi-profits).The top decile in figures 3 and 4 is dominated

by new drug introductions that are pioneers or earlyentrants in a new therapeutic class of compounds.In particular, this group includes the world’s largestselling drug in 1998, Prilosec® (omeprazole), thefirst drug in the proton pump inhibitor class, whichis used to treat ulcers. It also includes the first 2selective serotonin re-uptake inhibitors, Prozac®(fluoxetine) and Zoloft® (sertraline), used to treatdepression. Also in the top decile of drugs are the2 largest selling biopharmaceutical therapies –Epogen® (epoetin-α), which is used for treatinganaemia, and Neupogen® (filgrastim), which isused as an adjunctive chemotherapeutic agent. Inaddition, the top-selling decile includes the follow-ing: Taxol® (paclitaxel), the leading chemothera-

peutic drug for ovarian cancer; Norvasc® (amlo-dipine), a new kind of calcium antagonist for treat-ing hypertension; Biaxin® (clarithromycin) andZithromax® (azithromycin), 2 semi-synthetic mac-rolide anti-infective agents; and Pravachol® (prava-statin) and Zocor® (simvastatin), 2 leading statindrugs for cholesterol reduction.

Changes in Mean Sales over TimeIn the case of skewed distributions, the revenue

performance of the mean compound is dis-proportionately affected by the realised values inthe upper tail of the distribution. Accordingly, wewould expect the mean sales to be significantlygreater in the 1988 to 1992 cohort, compared withthe earlier 1980 to 1984 cohort. In order to see howsales of the mean compound have changed overtime, we plot the mean curves for the 2 time cohortson a separate graph in figure 5. This graph is basedon the entire sample of 1988 to 1992 drugs, includ-ing the orphan compounds. The case with the or-phan drugs excluded is shown in figure 6.In both cases, there is a significant upward shift

in the mean sales curves through the period of prod-uct maturity. However, the faster rate of genericcompetition expected for the later time cohortscauses a projected convergence of the 2 curves af-ter year 15 of the life cycle. Nevertheless, the ex-pected present value of sales revenues will behigher for the more recent time cohort, given the

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Fig. 4. Distribution of US sales by decile. Data reflect actual sales in the seventh year after marketing. NCEs = new chemical entities.



positive differences in the earlier years of the lifecycle. As noted, this is driven in large part by thesales performance of the top decile products.

Sales of New Drug Introductions versus R&DOutlays by CompanyAs discussed earlier, one important conse-

quence of a skewed distribution is that even firmswith sizeable portfolios of R&D projects can ex-pect considerable variability in portfolio out-comes. In order to gain some further insights intothis issue, we aggregated each company’s sales (inthe seventh year of market life) for all of its newdrug introductions during the 1988 to 1992 period.We then plotted these portfolio outcomes againstthe company’s pharmaceutical R&D expenditures

in the 1983 to 1985 period. We utilised an averagelag time of 6 years between R&D expenditures andnew drug introductions to reflect the long gestationperiod in pharmaceutical R&D.[8]Figure 7 shows the resulting plot of new drug

sales versus R&D expenditures for a total of 18firms for which R&D expenditure data were avail-able. There is a considerable range in the size ofthese firms, but all can be characterised as multi-national companies that are also vertically inte-grated across all types of pharmaceutical activities(i.e. R&D, manufacturing and marketing). The an-nual R&D expenditures of these 18 firms in themid-1980s was between $US100 million and$US500 million (measured in 1992 dollars).

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Fig. 5. Comparison of mean US sales for the different new chemical entity (NCE) cohorts, with orphan drugs included in the 1988to1992 cohort.

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Fig. 6. Comparison of mean US sales for the different new chemical entity (NCE) cohorts, with orphan drugs excluded in the 1988to 1992 cohort.



Figure 7 shows that there is a positive relation-ship between a company’s R&D expenditures andits subsequent sales from new drug introductions.5However, there is also much variation in the scatterof points around the best fitted least-squares re-gression line, as shown in figure 7. This is espe-cially true for the group of large firms with R&Doutlays between $US300 and $US500 million. Thetotal seventh-year sales for the new drugs portfo-lios of these 9 firms range from <$US100 millionto more than $US3 billion.6In previous work, we examined the temporal

pattern of new drug sales outcomes across firms.[14]

One of the main findings was that there was a highdegree of variability from one period to the next inthe performance of new drug sales for individualfirms. This result is also consistent with the skeweddistribution that we have observed for new drugintroduction samples dating back to the early1970s.

Comparison with Global NCE IntroductionsIn a recent analysis, William Machtiger of IMS

International examined the 1997 worldwide saleslevels of 315 drugs that were first introduced ontothe worldwide market over the period 1986 to1992.[15] This provides an interesting benchmark tocompare with our sample of US drugs. One wouldexpect a global sample of NCE introductions toexhibit more skewness than a US based sample be-cause many of these global introductions are‘localised’ NCEs. In particular, many of theseNCEs are introduced in only a few countries withmuch smaller markets than the US. By contrast, USNCE introductions tend to include a greater per-centage of international or ‘consensus’ NCEs.[16]In fact, the global sample does exhibit more

skewness. In this regard, the drugs were groupedaccording to their worldwide sales levels. The topgroup comprised new drug introductions achieving

5 An alternative lag structure of 5 years yielded similarresults.

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Fig. 7. Sales for 1988 to 1992 new drug introductions (data from the seventh year post-launch) plotted against 1983 to 1985 meanresearch and development (R&D) expenditures for 18 multinational pharmaceutical companies. Both sales revenue and R&D outlaysare expressed in 1992 dollars.

6 We did not include the biotechnology firms in figure 7,because annual R&D expenditures were not available for thesefirms for the 1983 to 1985 period. However, we do know thatthese firmswere in their formative period and had annual R&Dexpenditures below $US100 million. Inclusion of these bio-technology firmswould have producedmuchmore scatter thanshown in figure 7, becausemost of the biotechnology firms hadvery small sales from new drug introductions; however, onefirm in particular (Amgen) had annual sales in excess of$US1.5 billion in the seventh year of market life from its 2blockbuster products introduced in the 1988 to 1992 period.



worldwide sales of $US500 million in 1997. Ap-proximately 10% of the 315 global introductionswere in this category. This top decile accounted for66% of the $US60 billion in total sales achievedby this group of 315 drug introductions. At theother end of the distribution were products thatachieved <$US50 million in worldwide sales.These products accounted for 57% of the introduc-tions but only 4% of the total sales in 1997. This isa significantly greater degree of skewness thanthat observed for our sample of US introductions(fig. 4).

Discussion and Conclusions

The analysis presented in this paper indicatesthat the distribution of sales revenues for new drugcompounds is highly skewed in nature. In this re-gard, the top decile of new drugs accounts for morethan half of the total sales generated by the 1988 to1992 cohort analysed in this paper. Furthermore,the distribution of sales revenues for this cohort ismore skewed than for the 1980 to 1984 cohort thatwe analysed in previous research.[1] This increasedskewness can be explained in part by the emer-gence during the past decade and a half of orphandrug compounds as a result of the incentives pro-vided by the 1983 Orphan Drug Act. However,even when the orphan drug compounds are omittedfrom the current sample, some tendency towardgreater skewness is still observed.One important implication of this distribution

observed in pharmaceuticals is that even very largefirms with sizeable portfolios of R&D projectsare subject to substantial volatility in the sales per-formance of their new drug introductions. In thisregard, we found that firms spending between$US300 and $US500 million for R&D in the mid-1980s (measured in 1992 dollars) had aggregatesales from their new product introductions in the1988 to 1992 period ranging from $US100 millionto $US3 billion in the seventh year of market life.Moreover, as one would expect in this situation,there is a high degree of volatility across compa-nies in the historical sales performance of new drugcohorts from different time-periods.

Because pharmaceutical firms and venturecapital firms confront outcome distributions withsimilar properties, they face similar strategic issuesin managing the extreme risks and rewards thatcharacterise their investment activities. In particu-lar, timing and adaptability are critical elements forboth entities. On the one hand, it is important torecognise and accelerate the development of po-tential winners to get them to the market as quicklyas possible. At the same time, it is important toterminate and cut one’s losses on unsuccessfulprojects before large development costs becometotally sunk costs. This is a difficult balancing actthat is subject to alternative types of errors. Venturecapital firms employ external financial controls,including staged finance and milestones as risk-hedging strategies in this regard.[17,18] Pharmaceuti-cal firms employ internal controls that embodysome of these same principles.During recent years, pharmaceutical firms have

placed increased emphasis on reducing the devel-opment time to market by use of various strategies,including parallel path development of activitiesthat are along the critical path. In addition, firmsare incorporating competitive and pharmaco-economic analysis earlier in the development lifecycle, well prior to the go-no-go decision for phaseIII development. In this regard, many drug firmstraditionally placed primary control of their R&Ddecisions in the hands of scientific and technicalpersonnel until clinical tests were nearly com-pleted. However, DiMasi[19] has recently shownthat the main reason new drugs are abandoned inphase III is not concern for safety or efficacy, butfor economic reasons. Hence, prioritisation ofR&D projects, using economic modelling as wellas technical milestones, can increase the prob-ability of economic success and reduce the likeli-hood of expensive late-stage failures.[20] Manyfirms appear to be moving in this direction.From a public decision-making perspective, the

winners among new drug introductions with block-buster sales make a tempting target for price con-trols and profit constraints. However, as we haveshown in prior analyses, expected returns are



highly sensitive to the performance of these topdecile drugs.[21] Imposing controls on today’s drugwinners will have adverse effects for future R&Dinvestments, especially for the very risky projectsthat involve the most novel approaches to thera-peutic advancement.

References1. Grabowski H, Vernon J. Returns to R&D on new drug introduc-

tions in the 1980s. J Health Econ 1994; 13: 383-4062. Grabowski H, Vernon J. A new look at the returns and risks to

pharmaceutical R&D. Manage Sci 1990; 36 (7): 167-853. Scherer FM, Harkoff D, Kudies J. Uncertainty and the size dis-

tribution of rewards from technological innovation. J EvolutEcon 2000; 10: 175-200

4. Scherer FM. New perspectives on economic growth and tech-nological innovation. Ch 5. Washington, DC: Brookings In-stitution Press, 1999: 53-88

5. Grabowski H, Vernon J. The determinants of pharmaceuticalresearch and development expenditures. J Evolut Econ 2000;10: 201-15

6. Bond R, Lean D. Sales, promotion and product differentiationin two prescription drug markets, Washington, DC: FederalTrade Commission Staff Report, Government PrintingOffice,Feb 1977

7. Berndt E, Bui L, Lucking-Reily D, et al. The roles of marketing,product quality and price competition in the growth and com-position of the US anti ulcer drug industry. In: Bresnahan T,Gordon RJ, editors. The economics of new goods. Chicago:University of Chicago Press, 1997: 277-328

8. DiMasi J, Hansen R, Grabowski H, et al. The cost of innovationin the pharmaceutical industry, J Health Econ 1991; 10:107-42

9. Grabowski H, Mullins CD. Pharmacy benefit management,cost-effectiveness analysis and drug formulary decisions, SocSci Med 1997; 45 (4): 535-54

10. Shulman S, Healy EM, Lasagna L, editors. PBMs: reshapingthe pharmaceutical distribution network, NewYork: HaworthPress, 1998

11. Schulman S, Bienz-Tadmor B, Seo PS, et al. Implementation ofthe orphan act: 1983-91. Food Law J 1992; 47 (4): 363-404

12. Grabowski H, Vernon J. Longer patents for increased genericcompetition in the US: the Waxman-Hatch Act after one de-cade. Pharmacoeconomics 1996; 10 Suppl. 2: 110-23

13. Grabowski H, Vernon J. Effective patent life in pharmaceuti-cals. Int J Technol Manag 2000; 19: 98-120

14. Grabowski H, Vernon J. Innovation and structural change inpharmaceuticals and biotechnology. Indust Corp Changes1994; 3 (2): 435-49

15. Machtiger WS. The strategic management review of the worldpharmaceutical market presentation to Drug Information As-sociation Workshop, London, Oct 13, 1998

16. Thomas III LG. Industrial policy and international competitive-ness in the pharmaceutical industry. In: Helms R, editor. Com-petitive strategies in the pharmaceutical industry, WashingtonDC: AEI Press, 1996: 107-29

17. Sahlman W. The structure and governance of venture capitalorganizations. J Financ Econ 1990; 27: 473-521

18. Simpson H. Biotechnology and the economics of discovery inthe pharmaceutical industry. Ch. 3. London: Office of HealthEconomics, 1998

19. DiMasi JA. Success rates for new drugs entering clinical testingin the United States. Clin Pharmacol Ther 1995; 58 (1): 1-14.

20. Grabowski H. The effect of pharmacoeconomics on companyresearch and development decisions. Pharmacoeconomics1997 May; 11 (5): 389-97

21. Grabowski H, Vernon J. Prospects for returns to pharmaceuticalR&D under health care reform. In: Helms R, editor. Compet-itive strategies in the pharmaceutical industry, WashingtonDC: AEI Press, 1996: 194-207

Correspondence and offprints: Prof. Henry G. Grabowski,Department of Economics, 314 Social Sciences, Box 90097Duke University, Durham, NC 27708, USA.



the distribution of sales revenues from pharmaceutical innovation

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