Does Pharmaceutical Predictivity Translate To Productivity in Drug

Development? If So, How?

Thorir D. Bjornsson, MD, PhD

Saint Davids, PennsylvaniaMay-2010

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“It’s hard to make predictions, especially about the future.”

Lawrence Peter Berra ("Yogi" Berra) American Baseball Legend (born 1925)

The Art of Making Predictions

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What is the scientific basis for predicting compound success in man based on preclinical data?

How good are such predictions? If such predictions are not good, what approaches can

be considered for making them better? What has or is being tried to improve the situation? Are there any quick fixes?

The Science of Making PredictionsDrug Discovery and Development

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Discovery &Preclinical

Efficacy

Safety

CompoundProperties

Efficacy

Safety

CompoundProperties

Clinical Development

DA

TA

DA

TA

From Preclinical to Clinical Development

PLANNING

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Biologic target and pathways Potency and selectivity Intended indication In vivo efficacy models Time course of effect Dose/concentration/exposure

vs. response (“CDE-R”) Biomarkers/bioimaging

EfficacyRelated

Preclinical Information Needed for Development

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Acute and chronic studies Target organ toxicity NOAEL/NOEL Safety pharmacology Special toxicity studies Repro/genotoxicity Carcinogenicity

SafetyRelated

Preclinical Information Needed for Development

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Preclinical pharmacokinetics Drug metabolizing enzymes Drug metabolites Drug interaction potential Absorption & bioavailability Pharmaceutical properties Physiochemical properties

CompoundPropertiesRelated

Preclinical Information Needed for Development

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Discovery &Preclinical

Efficacy

Safety

CompoundProperties

Efficacy

Safety

CompoundProperties

Clinical Development

PREDICTIVITY

DA

TA

DA

TA

Clinical Development Plans

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Different variations relative to timing of non-critical path studies, relative to lead indication

Different approaches relative to when to address potential compound “risk” issues, eg, drug interactions, QTc

Different approaches to go/no-go advancement criteria to late-stage development, eg, based on biomarkers, clinical experimental models, or clinical endpoint assessment

Different tactical cost-savings approaches, eg, exploratory IND, descending dose Phase IIa

Current Early Development FrameworksReasonable Uniformity Across The Industry

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Good compounds, ie, those that succeed brilliantly, are relatively easy to deal with and essentially take care of themselves

Bad compounds, ie, those that don’t succeed and fail, take up a lot of time and cost a lot of money

Two Simple Lessons That I Have Learned

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Attrition Rates Vary Depending on Different Attributes

Vary by therapeutic

areas

Vary by phase of

development

Vary dependingon targets

Small Molecules > Biopharmaceuticals > Vaccines

Attrition is a Key Challenge

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90 - 95% Average attrition rate across the industry

Rate of Attrition: Unacceptably High

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Clinical safety

Lack of efficacy

Formulation

PK/bioavailability

Commercial

Toxicology

Cost of goods

Unknown/other

Kola & Landis, Nature Review Drug Discovery, 3:711-715, 2004

Compound TerminationsCommonly Cited Causes

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Scientific Reasons

Technical Reasons

Commercial Reasons

Regulatory Reasons

Preclinical and Clinical EfficacyPreclinical and Clinical SafetyPreclinical and Clinical PharmacokineticsBioavailability

Formulation IssuesPatent Issues

Cost of GoodsBudget/Resource ConstraintsPortfolio RationalizationPotential Value

Regulatory HurdlesRegulatory RequirementsRegulatory Decisions

* CMR Categories

Attrition Categories*

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Bjornsson et al., Pharmaceutical Predictivity (msc), 2010

* Compound properties are defined as determinants and descriptors of acceptable exposure, including variability and time course

Fundamental Causes of Termination (Scientific)

Efficacy

Safety

Compound Properties*

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Most analyses are post-hoc, using different definitions (surveys, companies)

Time from development track declaration to failure for individual compounds may vary from <0.5 to >10 years

Different companies have different mix of therapeutic area focus and strategic approaches likely to result in different rates of attrition

Attrition AnalysesWhy Don’t We Have Reliable Data?

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Need For New Designs

“When things aren’t working the way they should be, you have the makings of a great design project.”

Bruce Mau, design thinker

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PT = (1 – AT)

pi = [1 – (ai x AT)]

PT = pe x ps x pc

Just a Few Equations …..

Total predictivity, PT, is derived from total attrition, AT

Total predictivity equals the product of the individual three key predictivities, ie, of efficacy, safety and compound properties, pe, ps and pc, respectively, and assumes these are independent of each other

Each individual predictivity, pi, is related to the proportion of that attrition, ai, relative to total attrition

Bjornsson et al., Pharmaceutical Predictivity (msc), 2010

Pharmaceutical Predictivity

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PT = pe x ps x pc

Pharmaceutical PredictivityScientific Determinants of Success Rates

What Do We Know About These Individual Predictivities?

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What Are These Preclinical Models? Target-related, pharmacology or disease models in the most

appropriate species

What Do We Know About Their Predictivities? Wide range in predictivity, from reasonably high to very low No comprehensive or systematic analyses; thus, predictivity of

the different preclinical efficacy models is not well characterized or understood, and likely to vary by target and indication

What Are Examples of Ongoing Research? EU’s Innovative Medicine Initiative; Sage Bionetworks;

bioinformatics/systems biology; industry and other working groups, eg, PhRMA/PISC

Preclinical Models of Efficacy

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Low compound potency (relative to dose range available for human testing) Target inappropriate for indication Undesirable PK/PD relationships Trial design inappropriate (eg, endpoints selected; measurement

methodology used; duration of trial; dose range tested; population studied) Low fraction of patients responding due to target/biopathway characteristics

or heterogeneity in CDE-R* relationships

Lack of Predictivity

*Concentration-Dose-Exposure-Response

Examples of Efficacy Failures

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What Are These Preclinical Models? A variety of regulatory mandated in vivo and in vitro preclinical

safety and toxicology studies

What Do We Know About Their Predictivities? A widely quoted comprehensive retrospective study of the

concordance between clinical and preclinical safety findings of 150 approved and marketed drugs

• Olson et al., Regul Toxicol Pharmacol, 32:56-67, 2000 Predictivity of safety thought to average about 0.7 and vary

between approx. 0.4 and 0.8 (depending on organ systems)

What Are Examples of Ongoing Research? C-Path Institute; SAE Consortium; various in silico approaches;

industry and other working groups

Preclinical Models of Safety

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Poor safety and tolerability profile Narrow clinical safety margin Specific organ toxicities (eg, cardiovascular, QT,

hepatic, renal, CNS, gastrointestinal, immunological) Geno/reprotoxicity or carcinogenicity Rare and unexpected SAE

Lack of Predictivity

Example of Safety/Toxicology Failures

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What Are These Preclinical Models? A variety of in vivo and in vitro preclinical studies studies

characterizing drug disposition and exposure Various allometric methods and models have been used over

the past few decades

What Do We Know About Their Predictivities? Considerable advances in recent years using physiologically-

based pharmacokinetics predictions, eg, SimCyp; GastroPlus; PK-Sim; Cloe-PK

Predictivity of compound properties of small molecules thought to average about 0.6, and vary from approx. 0.5 to 0.7

What Are Examples of Ongoing Research? In silico approaches; PKPB groups; industry and other working

groups, eg, PhRMA/PISC

Preclinical Models of Compound Properties

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Poor absorption and bioavailability Unacceptable PK profile (eg, half-life, exposure) Unacceptable food effect Toxic metabolite(s) Unacceptable drug-drug interaction(s)

Lack of Predictivity

Examples of Compound Properties Failures

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PT pe ps pc

0.30

(30.0%)

0.67 0.67 0.67

0.125

(12.5%)

0.5 0.5 0.5

0.108

(10.8%)

0.3 0.6 0.6

0.050

(5.0%)

0.2 0.5 0.5

Examples of different likelihoods of success depending on different mix of predictivities of efficacy, safety and compound properties in man

Pharmaceutical Predictivity

Is this what we

are talking about on

average? Maybe, but

need data

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Portfolio A

TA mix A

Single cause

Single mechanism

Non-degenerative diseases

Symptomatic treatment

Portfolio B TA mix B Multiple causes Multiple mechanisms Degenerative

diseases Curative treatment

Some Of The Things That Have Changed“The Low-Hanging Fruit Has Already Been Picked”

6000 Known DiseasesNeglected Diseases

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Experimental Medicine Translational Medicine Adaptive Clinical Trials Exploratory IND Biomarker Development Learn – Confirm Pharmacometrics Model-based Drug Development Bioinformatics and Systems Biology

Some Of The Things That Have Been TriedThese Have Not Involved Systematic Improvements or Been Based on Solid Data Demonstrating Lower Attrition

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Next Steps

Considering….. the current unacceptably high attrition rate, the fundamental role predictivities play in determining

success rates, and the current limited knowledge we have about these

predictivities

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Next Steps

...comprehensive coordinated efforts are needed by the collective biopharmaceutical community to raise awareness and promote the need for markedly better

understanding of current core scientifically-based predictivities, to define and implement approaches and means to quantify

underlying scientifically-based predictivities, to standardize methodologies and data collection, to establish criteria for predictive models, to prioritize and coordinate needed assessments, and to apply sophisticated bionetworks and systems modeling

approaches

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Predictivity = Productivity

In Conclusion

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“No branch of science can be called truly mature until it has developed some form of predictive capability.”

Sir Peter Medawar (1915 - 1987) Nobel Laureate in Physiology and Medicine, 1960

A Quote on Predictivity