new frontiers in manufacturing technology, regulatory

Vision of a Pharmaceutical Scientist:

Drug Development and Regulation

Leslie Z. Benet, PhD

Department of Bioengineering and Therapeutic Sciences

Schools of Pharmacy and Medicine

University of California San Francisco

New Frontiers in Manufacturing

Technology, Regulatory Sciences and

Pharmaceutical Quality Systems

Brasilia June 26, 2012

Kola and Landis, 2004

Was there a major scientific advance

between 1991 and 2000 that led to

this significant decrease in failure

rates due to PK/Bioavailability?

In fact, No.

The advance was academicians

being able to convince chemists

that drug exposure, i.e., systemic

concentrations, were important.

Historical New Chemical Entity (NCE)

Success Rate (Slide I first made about 18 years ago for FDA lectures at UCSF)

Traditional Med Chem

Rational Drug Design

Combinatorial Chemistry & High Throughput Screening

NCEs prepared

500

5,000

5,000,000

Evaluate PK/Metab/Tox

50

500

5,000

Clinical

3

3

3

Marketed Drugs

1

1

1 (an overestimate)

Although combinatorial chemistry and high

throughput screening of drug targets enabled

many new chemical entities to be identified as

potential therapeutic agents, these techniques

did not address the rate limiting step in drug

development, preclinical in vivo studies of

metabolism, pharmacokinetics and toxicity.

These are the areas of academic science that

I and my colleagues have tried to address

over the past 40 years.

What about the new “combinatorial

chemistries” that drug discovery and

development scientists are enamoured

with today and with which the regulatory

agencies must grapple?

1. In silico drug discovery and development

2. Pharmacogenomics

3. Biomarkers

4. Systems biology

5. Transporters

6. Nanomedicine and targeted drug delivery

7. Preclinical alternatives to animal studies

High Solubility Low Solubility H

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y

Amidon et al., Pharm Res 12: 413-420, 1995

Class 2 Low Solubility

High Permeability

Class 1 High Solubility

High Permeability

Rapid Dissolution

Class 3 High Solubility

Low Permeability

Class 4 Low Solubility

Low Permeability

This morning, Dr. Shah described BCS

Biopharmaceutics Classification System


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Rate

Class 1

Metabolism

Class 3 Renal & Biliary

Elimination of

Unchanged Drug

Class 4 Renal & Biliary

Elimination of

Unchanged Drug

Major Routes of Drug Elimination

Class 2

Metabolism

Wu and Benet, Pharm. Res. 22: 11-23 (2005)

Biopharmaceutics Drug Disposition Classification System

BDDCS

High Solubility Low SolubilityE

xte

ns

ive

Me

tab

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Po

or

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sm

Class 2Low SolubilityExtensive Metabolism

Class 1High SolubilityExtensive Metabolism

(Rapid Dissolution and ≥70% Metabolism for Biowaiver)

Class 3High SolubilityPoor Metabolism

Class 4Low SolubilityPoor Metabolism

Wu and Benet, Pharm. Res. 22: 11-23 (2005)

Major Differences Between

BDDCS and BCS

Purpose: BCS – Biowaivers of in vivo

bioequivalence studies.

BDDCS – Prediction of drug disposition

and potential DDIs in the intestine & liver.

BDDCS– Predictions based on intestinal

permeability rate

BCS – Biowaivers based on extent of

absorption, which in a number of cases

does not correlate with jejunal permeability

rates

BDDCS Applied to

Over 900 Drugs

Leslie Z. Benet, Fabio Broccatelli,

and Tudor I. Oprea

AAPS Journal

13: 519-547 (2011)

Here we compile the BDDCS classification for 927 drugs, which

include 30 active metabolites. Of the 897 parent drugs, 78.8%

(707) are administered orally. Where the lowest measured

solubility is found this value is reported for 72.7% (513) of

these orally administered drugs and a Dose Number is

recorded. Measured values are reported for percent excreted

unchanged in urine, Log P and Log D7.4 when available. For

all 927 compounds, the in silico parameters for predicted Log

solubility in water, calculated Log P, Polar Surface Area and

the number of hydrogen bond acceptors and hydrogen bond

donors for the active moiety are also provided, thereby

allowing comparison analyses for both in silico and

experimentally measured values. We discuss the potential use

of BDDCS to estimate disposition characteristics of novel

chemicals (new molecular entities, NMEs) in the early stages of

drug discovery and development.


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y Class 1 Marketed Drugs

40%

NMEs: 18%

Class 3 Marketed Drugs

21%

NMEs: 22%


6%

NMEs: 6%

Distribution of Drugs on the Market

(698 oral IR) vs. Small Molecule NMEs


33%

NMEs: 54%

NME percentages

from a data set of

28,912 medicinal

chemistry

compounds tested

for at least one target

and having affinities

at μM or less

concentrations.

[LZ Benet, F Broccatelli and TI Oprea, AAPS J. 13: 519-547 (2011)]

[Broccatelli et al., Mol. Pharmaceut. 9: 570-580 (2012)]


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40%

NMEs: 18% 79.3% (55.8%)


21%

NMEs: 22% 90.1% (74.8%)


6%

NMEs: 6% 39.5% (82.7%)


(698 oral IR) vs. Small Molecule NMEs [LZ Benet, F Broccatelli and TI Oprea, AAPS J. 13: 519-547 (2011)]


33%

NMEs: 54% 78.1% (85.1%)

Best in silico

solubility vs

measured

VolSurf+

r2=0.33

(ALOGPS

r2 = 0.24)


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40%

NMEs: 18%


21%

NMEs: 22%


6%

NMEs: 6%


(698 oral IR) vs. Small Molecule NMEs [LZ Benet, F Broccatelli and TI Oprea, AAPS J. 13: 519-547 (2011)]


33%

NMEs: 54%

For either

measured or

calculated

Log P >2, the

probability of

extensive

metabolism is

79.9 and 81.0 %,

respectively.

For Log P

values < 0 the

probability of

poor metabolism

is 84.0 % for

M Log P and

83.4 % for

CLogP.

Figure 7 When Log P

values range

from 0-2

(31.4% of

drugs for

M Log P

and 27.5%

of drugs for

CLogP)







2. Pharmacogenomics--Reviewed yesterday

3. Biomarkers

4. Systems biology

5. Transporters









2. Pharmacogenomics

3. Biomarkers

4. Systems biology

5. Transporters




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Class 1 Transporter

effects minimal in

gut and liver

Class 3 Absorptive

transporter effects

predominate (but can

be modulated by efflux

transporters)

Class 4 Absorptive and

efflux transporter

effects could be

important

Oral Dosing Transporter Effects

Class 2 Efflux transporter

effects predominate in

gut, but both uptake &

efflux transporters

can affect liver

S. Shugarts and L. Z. Benet. Pharm. Res. 26, 2039-2054 (2009).

Class 1

highly soluble, high permeability,

extensively metabolized drugs

• Transporter effects will be minimal

in the intestine and the liver

• Even compounds like verapamil that

can be shown in certain cellular

systems (MDR1-MDCK) to be a

substrate of P-gp will exhibit no

clinically significant P-gp substrate

effects in the gut and liver

Class 2

poorly soluble, highly permeable,

extensively metabolized drugs • Efflux transporter effects will be important

in the intestine and the liver

• In the intestine efflux transporter –enzyme

(CYP 3A4 and UGTs) interplay can markedly

affect oral bioavailability

• In the liver the efflux transporter-enzyme

interplay will yield counteractive effects to

that seen in the intestine.

• Uptake transporters can be important for the

liver but not the intestine.

Class 3

highly soluble, low permeability,

poorly metabolized drugs

• Uptake transporters will be

important for intestinal absorption

and liver entry for these poor

permeability drugs

• However, once these poorly

permeable drugs get into the

enterocyte or the hepatocyte efflux

transporter effects can occur.

Recent Transporter Interplay

Reviews from the Benet Lab

The Role of Transporters in the Pharmacokinetics

of Orally Administered Drugs. S. Shugarts and

L. Z. Benet. Pharm. Res. 26, 2039-2054 (2009).

The Drug Transporter-Metabolism Alliance:

Uncovering and Defining the Interplay. L. Z. Benet.

Mol. Pharmaceut. 6, 1631-1643 (2009).

Predicting Drug Disposition via Application of a

Biopharmaceutics Drug Disposition Classification

System. L. Z. Benet. Basic Clin. Pharmacol. Toxicol.

106, 162-167 (2010).

Improving the Prediction

of the Brain Disposition

of Orally Administered Drugs

Using BDDCS

F. Broccatelli, C.A. Larregieu, G. Cruciani,

T.I. Oprea and L.Z. Benet

Advanced Drug Delivery Reviews

64: 95-109 (2012)

Prior to our paper earlier this year, the best

methodologies for predicting central effects of

drug candidates had an accuracy of ~80%,

based on lipophilicity, molecular structure and

susceptibility to transport by brain efflux

transporters (P-glycoprotein and BCRP).

With our recognition that BDDCS class 1

compounds would exert central effects

even if they are substrates for efflux

transporters, we have been able to cut the

lack of predictability in half.

Class 1 Drugs A major proposition of

BDDCS is that Class 1,

P450/UGT metabolized drugs

are not substrates of clinical

relevance for transporters

in the intestine, liver,

kidney and brain.

Another Implication

Class 1 compounds will

achieve brain concentrations

whether this is desired or not

for an NME, which could be

the rationale for not always

wanting Class 1 NMEs.

How can the concepts presented be

used in predicting DMPK of an NME?

• In silico methodology, at present, is not sufficient (except possibly for Vss). We cannot predict clearance & bioavailability

• Permeability measures in Caco-2, MDCK or PAMPA vs metoprolol or labetalol will predict Class 1 & 2 vs. 3 & 4 and thus major route of elimination in humans.

• Is solubility over the pH range 1-7.5 more than 0.2 mg/ml ( i.e., 50 mg highest dose strength) as proposed by Pfizer scientists, defining Class 1 & 3 vs. 2 & 4?

Potential DDIs Predicted by BDDCS

• Class 1: Only metabolic in the intestine and liver

• Class 2: Metabolic, efflux transporter and efflux transporter-enzyme interplay in the intestine. Metabolic, uptake transporter, efflux transporter and transporter-enzyme interplay in the liver.

• Class 3 and 4: Uptake transporter, efflux transporter and uptake-efflux transporter interplay







2. Pharmacogenomics

3. Biomarkers

4. Systems biology

5. Transporters



Yesterday, we heard Dr. Stavchansky describe

the exciting potential of nanotechnololgy in drug

delivery, diagnostics and nanomedicine, as well

as our ambition to achieve targeted drug

delivery. However, it is important to recognize

that these techniques must be far more

successful than even proposed by Dr.

Stavchansky if we are to solve major drug

delivery issues.

This leads me to comment on the very

unrealistic projections being made for

macromolecular drugs.

We are all well aware of the concern

with the limited number of new drug

approvals, and with the statistics

suggesting that of the approvals in the

last few years, macromolecular drug

products range from 25 to 40%.

This has led some observers, particularly

venture capitalists, to predict that the future

of drug development is in macromolecular

products and to estimate that 60-70% of future

drug approvals will be macromolecules.

But in my opinion this is a poor

prediction and can only be true if we can

discover how to deliver macromolecules

orally and relatively inexpensively.

The monoclonal antibody products being

approved now are the low hanging fruit that

address primarily cancer treatments where we

can tolerate expensive parenteral or complex

drug delivery processes.

In fact, I predict that the fraction of new drug

approvals for macromolecules will decrease

rather than increase.







2. Pharmacogenomics

3. Biomarkers

4. Systems biology

5. Transporters



Regulatory pressure, public

clamor against animal testing,

especially in Europe:

“REACH” initiative: Comprehensive EU legislation regulating

chemical substances has brought controversy because it

portends massive increases in animal testing unless suitable

non-animal alternatives are implemented.

EU also passed sanctions against products relying on animal

testing

Sanctions become effective in tranches: 2009, 2013

I did not provide a Financial Disclosure

but I want to discuss some recent

advancements by Hμrel Corporation,

a company that I co-founded and for

which I serve as Chair of the SAB

PETA honored Hμrel with its “Proggy”

Award for Best Scientific Achievement 2010

Liver

Lung Gas Exchange

Other Tissues (Non-metabolizing, non-

accumulating)

Adipose

In

Out

The original vision: an “human on a chip”

(i.e., an in vitro, multi-tissue, microfluidic, cell-

based assay platform for improved

pharmacological / toxicological prediction)

Working together with bioengineers and

experts in liver microstructure we

developed microfluidic, cell-based biochips

Individual compartments contain cultures of living cells of different organs

Heterogeneous cell types mimic different organs or tissues of an animal (and humans)

Compartments fluidically interconnected

Fluid and compounds recirculate

as in a living system

(See Nature, 435: 12-13, May 5, 2005;

Forbes, August 15, 2005, pp. 53-54;

The Observer, September 25, 2005, p.7;

Newsweek, October 10, 2005, p.59

Nature, 471: 661-665, March 31, 2011)

Photo of early prototype silicon biochip

From the web site: www.hurelcorp.com

The metabolic competency of Hμrel®’s patented flow-based biochips

and instrumentation enables parent compound clearance and

metabolite generation significantly greater than that afforded by

hepatocytes cultured under static conditions, when presented

to compounds that represent a broad range of Phase 1 and

Phase 2 enzymes. Hμrel®’s flow-based metabolic competency

has been shown stable for 14 days and longer.

Before I close, I would like to present our

laboratories latest efforts to improve and

shorten the drug development process.

There has been a marked increase in the utilization

of pharmacokinetic-pharmacodynamic modeling in

the drug development process, with a strong

interest in this area from the regulatory

authorities.

Historically, such PKPD modeling has considered

the hysteresis between measured concentrations

and PD measures, as characterizing

“direct” and “indirect” relationships.

Direct Indirect

Pharmacokinetic- Pharmacodynamic Modeling Direct vs. Indirect PKPD

distribution to the site of action

slow turnover and transduction

processes

site of action is the central circulation

rapid receptor binding, turnover, and transduction

mechanisms

increasing t1/2,ke0

increasing t1/2,kout

Can a Single Framework Describe Benefit-Toxicity

Relationships for Many Diverse Drugs? A. Grover & L.Z. Benet, J. Clin. Pharmacol., in revision

Today each NME, particularly a first-in-class therapeutic, is investigated de novo with respect to choosing the appropriate dose and dosing regimen. We reasoned that for drugs showing a direct and rapid response to drug levels (both benefit and adverse effects) there should be a general relationship between drug levels above an effective (or toxic) concentration measure (e.g., EC50) and the appropriate dosing interval and dose. Many drugs will not be direct effect, but we reasoned that a continuum should exist between direct and indirect response models.

Therapeutic index concepts are exclusively

Can a Single Framework Describe

Benefit-Toxicity Relationships

for Many Diverse Drugs? A. Grover & L.Z. Benet, J. Clin. Pharmacol., in revision

Here we show that for 17 diverse drugs (19 evaluations) that a ED50 Relevance Parameter defined as the fraction of the dosing interval (τ) when drug levels are not above EC50:

(τ - Time concentrations over EC50) / τ

can be related to keo or kout

Results

5 10 150.0

0.5

1.0

Prednisolone

LorazepamEtodolac

LevodopaTerazosin

Dexamethasone

Terbutaline

AtropineRocuronium

Dexamethasone

Rosuvastatin

Ibuprofen

PrednisoloneEtodolac

Bilastine

Mizolastine

Ranitidine

Tocilizumab

kout, kout regression

ke0, ke0 regression

ke0 & kout combined regression

[EC50RP = 1.1 exp(-0.46 (ke0 or kout)) - 0.17]

ke0 or kout (hr-1)

EC

50 R

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Pa

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(Ta

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r E

C5

0)/

Ta

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Results

0.001 0.01 0.1 10 100

0.5

1.0

kout, kout regressionke0, ke0 regression

Prednisolone

LorazepamEtodolac

LevodopaTerazosin

Dexamethasone

Terbutaline

AtropineRocuronium

Dexamethasone

Rosuvastatin

Ibuprofen

PrednisoloneEtodolac

Bilastine

Mizolastine

Ranitidine

Tocilizumab

ke0 & kout combined regression

[EC50RP = 1.1 exp(-0.44 (ke0 or kout)) - 0.17]

ke0 or kout (hr-1)

EC

50 R

ele

va

nce

Pa

ram

ete

r

(Ta

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tim

e o

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r E

C5

0)/

Ta

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Can a Single Framework Describe Benefit-

Toxicity Relationships for Many Diverse

Drugs? A. Grover & L.Z. Benet, J. Clin. Pharmacol., in revision

Here we show that for 17 diverse drugs (19 evaluations) that a

ED50 Relevance Parameter defined as the fraction of the dosing interval (τ) when drug levels are not above EC50:

(τ - Time concentrations over EC50) / τ

can be related to keo or kout

But we also have identified 3 drugs that do not fit the relationship. In two of those cases we believe that an inappropriate PD measure was chosen, but the third drug may indicate that the relationship is not general. We need others to confirm or invalidate the relationship with company data.

Benefit-Toxicity Relationships

Dosing regimens that fall along the log-linear regression balance between over-dosing [below the regression line] and under-dosing [above the regression line]

0.001 0.01 0.1 10 100

1.0

over-dosed:increased potential for

adverse eventsassociated with highdrug concentrations

under-dosed: effects(therapeutic or toxic) are not

evident because concentrationsare not above the EC50 for

sufficient time

ke0 or kout (hr-1)

EC

50 R

ele

va

nce

Pa

ram

ete

r

(Ta

u -

tim

e o

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r E

C5

0)/

Ta

u

Thank you for your attention

and very kind hospitality

[email protected]

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