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Pharmaceutical Predictivity May 2010
 

Pharmaceutical Predictivity May 2010

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  • Candidate Attrition Rates Very High Attrition rates typically measured from candidate declaration to commercial launch Attrition rates average 90% (1:10) - 95% (1:20) across the industry Attrition Rates Vary Across Different Therapeutic Areas* Highest attrition rates for CNS, Oncology, Urology, Women’s Health Medium attrition rates for Metabolic Diseases, Ophthalmology Lowest attrition rates for Arthritis/Pain, Cardiovascular, Infectious Disease Attrition Rates Vary by Phase of Drug Development Highest attrition rates during Phase 2, similar for Phase 1 and Phase 3 Causes of Termination Vary Depending of Phase of Development Safety/Compound Properties dominate in Phase 0 and Phase 1 Efficacy dominates in Phase 2 and Phase 3 Attrition Rates Vary Depending on Targets Highest attrition rates for new and untested target
  • where scientific reasons include preclinical and clinical efficacy, preclinical and clinical safety, preclinical and clinical pharmacokinetics and bioavailability, and benefit to risk ratio; technical reasons include formulation and patent issues; commercial reasons include cost of goods, budget/resource constraints, portfolio rationalization and potential value; and regulatory reasons include regulatory hurdles and decisions.
  • where scientific reasons include preclinical and clinical efficacy, preclinical and clinical safety, preclinical and clinical pharmacokinetics and bioavailability, and benefit to risk ratio; technical reasons include formulation and patent issues; commercial reasons include cost of goods, budget/resource constraints, portfolio rationalization and potential value; and regulatory reasons include regulatory hurdles and decisions.

Pharmaceutical Predictivity May 2010 Pharmaceutical Predictivity May 2010 Presentation Transcript

  • Does Pharmaceutical Predictivity Translate To Productivity in Drug Development? If So, How? Thorir D. Bjornsson, MD, PhD Saint Davids, Pennsylvania May-2010
  • The Art of Making Predictions “ It’s hard to make predictions, especially about the future.” Lawrence Peter Berra ("Yogi" Berra) American Baseball Legend (born 1925)
    • 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 Predictions Drug Discovery and Development
  • From Preclinical to Clinical Development Discovery & Preclinical Efficacy Safety Compound Properties Efficacy Safety Compound Properties Clinical Development DATA DATA PLANNING
    • 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
    Preclinical Information Needed for Development Efficacy Related
    • Acute and chronic studies
    • Target organ toxicity
    • NOAEL/NOEL
    • Safety pharmacology
    • Special toxicity studies
    • Repro/genotoxicity
    • Carcinogenicity
    Preclinical Information Needed for Development Safety Related
    • Preclinical pharmacokinetics
    • Drug metabolizing enzymes
    • Drug metabolites
    • Drug interaction potential
    • Absorption & bioavailability
    • Pharmaceutical properties
    • Physiochemical properties
    Preclinical Information Needed for Development Compound Properties Related
  • Clinical Development Plans Discovery & Preclinical Efficacy Safety Compound Properties Efficacy Safety Compound Properties Clinical Development PREDICTIVITY DATA DATA
    • 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 Frameworks Reasonable Uniformity Across The Industry
    • 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
  • Attrition is a Key Challenge Attrition Rates Vary Depending on Different Attributes Vary by therapeutic areas Vary by phase of development Vary depending on targets Small Molecules > Biopharmaceuticals > Vaccines
  • 90 - 95% Average attrition rate across the industry Rate of Attrition: Unacceptably High
  • Compound Terminations 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 Commonly Cited Causes
  • Attrition Categories* Scientific Reasons Technical Reasons Commercial Reasons Regulatory Reasons Preclinical and Clinical Efficacy Preclinical and Clinical Safety Preclinical and Clinical Pharmacokinetics Bioavailability Formulation Issues Patent Issues Cost of Goods Budget/Resource Constraints Portfolio Rationalization Potential Value Regulatory Hurdles Regulatory Requirements Regulatory Decisions * CMR Categories
  • Fundamental Causes of Termination (Scientific) Bjornsson et al., Pharmaceutical Predictivity (msc), 2010 * Compound properties are defined as determinants and descriptors of acceptable exposure, including variability and time course Efficacy Safety Compound Properties*
    • 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 Analyses Why Don’t We Have Reliable Data?
  • 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
  • Pharmaceutical Predictivity P T = (1 – A T ) p i = [1 – (a i x A T )] P T = p e x p s x p c Just a Few Equations ….. Total predictivity, P T , is derived from total attrition, A T Total predictivity equals the product of the individual three key predictivities, ie, of efficacy, safety and compound properties, p e , p s and p c , respectively, and assumes these are independent of each other Each individual predictivity, p i , is related to the proportion of that attrition, a i, relative to total attrition Bjornsson et al., Pharmaceutical Predictivity (msc), 2010
  • Pharmaceutical Predictivity P T = p e x p s x p c Scientific Determinants of Success Rates What Do We Know About These Individual Predictivities?
    • 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
    • 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
    Examples of Efficacy Failures Lack of Predictivity * C oncentration- D ose- E xposure- R esponse
    • 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
    • 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
    • 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
    • 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
  • 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 0.5 0.5 0.2 0.050 (5.0%) 0.6 0.6 0.3 0.108 (10.8%) 0.5 0.5 0.5 0.125 (12.5%) 0.67 0.67 0.67 0.30 (30.0%) p c p s p e P T
    • 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 Diseases Neglected Diseases
    • 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 Tried These Have Not Involved Systematic Improvements or Been Based on Solid Data Demonstrating Lower Attrition
  • 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
  • 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
  • In Conclusion Predictivity = Productivity
  • A Quote on Predictivity “ 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