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Does Pharmaceutical Predictivity Translate To Productivity in Drug
Development? If So, How?
Thorir D. Bjornsson, MD, PhD
Saint Davids, PennsylvaniaMay-2010
2
“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
4
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
6
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
8
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
26
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
27
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
28
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
30
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
31
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