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1 Therapeutic Bench to Beside: Art & Science Of Drug Discovery and Development (and everyone’s role in it) Theodore F. Reiss, M.D.

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Presentation on theme: "1 Therapeutic Bench to Beside: Art & Science Of Drug Discovery and Development (and everyone’s role in it) Theodore F. Reiss, M.D."— Presentation transcript:

1 1 Therapeutic Bench to Beside: Art & Science Of Drug Discovery and Development (and everyone’s role in it) Theodore F. Reiss, M.D.

2 2 Nancy Whorf

3 Message/Objectives Therapeutic innovation : impactful, practical, real world application –Integrated, complex system, non-linear thinking, iterative, learning, collaborative, team centered. An overview approach –Model –Some specifics –Challenges and opportunities for the future 3

4 The 3T’s Dougherty, D. et al. JAMA 2008;299: Copyright restrictions may apply. The Innovation Interface Basic/clinical science Technology focus Public health Policy/ Culture “Language” of Biomedical Innovation Certainty of Benefit/Risk --vs- Partially justified new knowledge

5 5 “Bench To Bedside” : Interdependence Centered on collaborative, synergistic scientific efforts Resource intensive, necessitating efficiency Innovation or Stagnation: FDA March 2004 [ ” Bench to Bedside ” ]

6 6 SD/MD safety PK/PD characterization Target engaged Biologic effects Traditional Concept: Novel Mechanism Efficacy confirmation Outcomes Safety confirmation Detailed benefit/risk profile Health economic data for reimbursement authorization Post marketing requirements Post marketing safety surveillance New indications Understand pathobiology Target ID/validation Molecule ID Start formulation development In vitro In vivo Initial regulatory interactions Phase III Phase I Phase II Pre-clinical Toxicology Phase IV/V Discovery POC Dose selection Safety in patients Initial benefit/risk profile Formulation completed Finalize alignment with regulatory agencies

7 7 Strategic Development Metaphor: Financial Planning Define the goal Identify the component parts Develop a plan working backwards from the goal Plan depends on many factors: –Define level of benefit/risk –Determine interim steps that have to be achieved –Flexibility to adjust to environmental changes

8 8 “Bench to Bedside”: Principles Neither simple nor linear Begin with the goal: Unmet medical needs-public health value –Data: Clinical, regulatory, and health economic Demonstrate clear, population specific, benefit / risk –Efficient and timely as possible Dynamic, responding to new knowledge

9 9 Principles: Optimize Potential for Success Disease area focus –Multiple targets and/or molecules within a target Strategic scientific development plan –Begin with goal and design backwards Failure the norm –Go/no go criteria to exit early if risk/benefit unacceptable –Kill early Critical importance of predictive safety & efficacy biomarkers –Patient identification –Response prediction Apply learning iteratively

10 10 Drug Development Paradigm (Better!) Discovery Goal COMMERCIALIZATIONCOMMERCIALIZATION Phase IIIPhase IPhase IIToxicology “ System” Approach: –Neither simple, nor linear –Each component is part of a “whole” strategy –“Goal” driving earlier development steps: Iterative Address unmet medical need Demonstrate clear, population specific benefit/risk Efficient and timely as possible –Dynamic, responding to new knowledge –Collaborative : Integrated Project Team - Many Experts “Thinking as One”

11 11 “Begin With The End (Goal) In Mind” First Principle – Clinical Vision Patient population (or sub population) Efficacy “threshold” Tolerability profile Method of delivery Potency Dosing interval Drug Interaction Create public health value with optimum benefit /risk in a defined patient population Could refocus goal further during Phase I-III For example: optimal responder population identified

12 12 Unoptimized Strategy Phase I Phase II Phase III Preclinical Toxicology Patent life Regulatory Filing Phase III Phase II Phase I Preclinical Toxicology Discovery Time (Years) Efficiency Gain From A Thoughtful Scientific/Regulatory Strategy

13 13 Efficiency Gain From A Thoughtful End-to-End Strategy: Kill Early Time (Years) Patent life Regulatory Filing Phase I Phase III Preclinical Toxicology Phase II Discovery If don’t meet POC or ePOC Kill

14 14 Less Optimal Approaches Therapeutic goal not clear No planning: Stumbling forward one experiment to the next Single molecule focused only Why? Time/resource limitations Design to goal –Solving unmet need with acceptable benefit/risk –Molecules are means to ends

15 15 Advanced Concepts Development strategy optimizes for: –Fewest resources –Least time –Most information (scientific, clinical, regulatory, health economic) Milestones Go/no go –Scientific probability of achieving “goal” –Investment decision points Key regulatory agency interactions –Strategy aligned with regulatory agency perspective

16 16 Conceptual Model Linking Drug Development with Value GOALTPPVALUE DISCOVERYPH IPH III NO GO INTERNAL NPV EXTERNAL PUBLIC HEALTH Unmet Medical Needs Cost vs. Effectiveness (or Utilities) BIOLOGY PRECLINICAL TOXICOLOGY PH II

17 17 Advanced Concepts: Target Discovery What makes development programs efficient? Why are some disease areas easer than others for drug discovery and development? What are optimal characteristics of a “molecular target” for a therapy?

18 18 Pathobiology Of Disease: Clear Understanding Critical To Optimize Development Foundation for basic-preclinical-clinical consistency Facilitate safety and efficacy prediction –Provides potential for clearly defining “sub-populations” If clear: –Target rich (osteoporosis) If speculative: –Targets are high risk –Predicting efficacy and safety and defining “sub- populations” much more challenging

19 19 Osteoporosis: Available Knowledge Allows for “Efficient” Development Pathobiology of disease reasonably well understood Animal models available Animal models predictive of human disease Natural history of disease known –Large cohort studies –Clinically relevant endpoint defined and outcomes known –Establish and improving clinical disease biomarkers Investigational therapies affect biology of disease “Efficient” system to develop therapies, investigate new tools to predict response, and identify sub-populations

20 20 Respiratory Development Is Challenging Pathobiology is less well understood (relative to osteoporosis) Disease definition is imprecise Animal models are imprecisely predictive Natural history, outcomes less are defined Criteria defining clinical response difficult to determine

21 21 Example: Failure of Prediction DP 1 Antagonist in Asthma/Allergic Rhinitis Receptor for PGD 2 PGD 2 released from mast cells with histamine/CysLT’s –Pharmacologic activity in airways of asthmatics DP 1 knock-out – blocks response in mouse OVA model DP 1 blockade inhibits inflammation in guinea pigs Polymorphism in DP 1 receptor associated with asthma However: –No effect of receptor antagonist in asthma/allergic rhinitis

22 22 Target Identification and Validation Address unmet medical needs? Understand pathobiology/pathway regulation –Pathology, genetic pathways linked –Pharmacologic studies in disease models Knockouts, transgenic, pathway interruption (siRNA, antibodies, small molecules) –Predict benefit/risk Link to man for prediction (altered expression of target or biomarker) Probability of activity different from the target –Safety –Biomarker development Determine molecular approach and delivery optimization –Clone target /tractable Design molecules –Iterative process

23 23 Pre-Clinical Biology Generate and select optimal compound –Potency –PK in animals –“Probe” safety study –Determine if formulation possible Unique challenges by type and route Difficulty under appreciated Alternate strategy: unoptimized molecule to POC in man ASAP –Other strategies Target Discovery Pre-clinical Toxicology

24 24 Pre-clinical Toxicology General Concepts In vitro and in vivo studies to predict tolerability in man Examples: In vitro toxicology –Carcinogenic potential / metabolic profile/ P450 studies Examples: In vivo (at least two species) –Single dose, multiple dose: 1 week – 1 year, carcinogenicity studies –Dose or time related toxicities Component of strategic development plan –Facilitate strategy: Phase I-III trials (length, sequence) –Must provide adequate dose exposure margin

25 25 Phase I – IIa: Critical Bridge - Iterative Optimization Demonstrate: –PK, target engagement, biological activity, initial clinical benefit /risk –Go/no go “Learning phase” –Clinical experimental models to optimize target molecules –“Hypothesis” generating trial(s) to: Optimize subsequent clinical experiments Identify and validate predictive markers or sub-populations Time intensive, speed not primary concern –Biomarker(s) to optimize dose selection/prediction of benefit or risk Target engagement (example: receptor occupancy: NK1) Target engagement and biologic effect (example: urinary LTE 4 : 5LO) Target engagement and biologic effect and clinical surrogate endpoint (example: reticulocytes - EPO)

26 26 40/25 125/80 375/125 Imaging as a Biomarker Target Engagement and Dose NK 1 Antagonist Binding of PET tracer to NK 1 receptors Blockade of NK 1 receptors after aprepitant dosing Aprepitant Plasma Trough Concentration (ng/mL) Brain NK 1 Receptor Occupancy (%) Mean (± SE) Plasma Trough Concentrations Low High Tracer Binding Hargreaves J Clin Psych 63: (suppl 11): 18-24, 2003

27 27 Example: Biomarker 5-LO FLAP Inhibition Urinary LTE Hours Post dose LTE 4 % Predose (Mean  SEM) Placebo 25 mg 50 mg 125 mg 250 mg 500 mg

28 28 Dose Selection: The Phase II Activity Identify dose-response relationship Benefit/risk: must be determined in a defined population, through an adequate dose range - Must demonstrate minimal or no effect Goals: Identify minimal dose achieving maximal response without evidence of dose limiting toxicity

29 29 Example: Biomarker Leading to Dose Ranging CysLT 1 Antagonist Clinical Pharmacology & Therapeutics 1997; 61:(1) 83-92

30 30 Phase III: Characteristics Program design considerations –Sufficient to address clinical questions in targeted population (use in clinical practice) Multiple or few trials? If worldwide, special considerations One dose optimal –All measurements must have been previously “validated” and “qualified” according to stringent standards –Placebo vs. comparative designs –Tolerability: Pooling data – continued “signal” detection –Endpoint: outcome or surrogate? (CASS Study example) Health economic data: public health / reimbursement issues Cost - Effectiveness/ Cost - Utility

31 31 Regulatory Considerations Consultations –“ Buy-in” to Phase III plans before starting Endpoints/validation/statistical plan Data submissions –Worldwide submissions to regulatory agencies Content: scientific/clinical rationale, individual trial data, “integrated summaries” (safety and efficacy) –Biggest grant proposal you ever submitted! (5-15 trials [4,000-20,000 patients]) –Draft label included –Recommendation for patient information/post marketing surveillance Labeling –Scientific and risk/benefit data summarized –Negotiated separately with agencies worldwide –Draft label early in development: target product profile/promotion

32 32 Post Marketing Safety Surveillance Major emphasis for the future : –Example: “Sentinel” initiative Spontaneous reports –Different rigor among countries –Claims databases –Data very difficult to interpret Causal relationships difficult to determine New methods of “signal detection” Pooled clinical trial database (including Phase IV)

33 33 Efficient, Timely Execution Of Development Programs: Project Team Forms early: target discovery –Describe goal, develop plan, iteratively manage plan –Efficient, fast, scientifically excellent –Effectiveness: integration of scientific/regulatory information in a hypothesis driven, sequenced plan (No go decision points) High performance team (synergism) Individually experts in separate disciplines –Broad scientific/regulatory knowledge Members know all roles and responsibilities Anticipate others’ needs/thoughts Always think two steps ahead for self and other team members Leadership

34 34 Research & Discovery Process FDA Review 1 5,000 – 10,000 Compounds 250 Compounds 5 Compounds Drug Discovery Clinical Trials Post- Marketing Preclinical 2 Years Phase III n= Phase II n= Phase I n= It takes ~10-15 years and $802 million to develop one new medicine 1 1 DiMasi JA, Hansen RW, Grabowski HG Journal of Health Economics2003, 22,

35 35 Despite Substantial Investment, New Products Infrequent No. NCE ’ s Approved R&D Expenditure PhRMA Members (in $B) Source: PhRMA, FDA

36 What are Today’s Challenges? Greater, efficient output –Increasingly complex system/environment No consensus on efficient process improvement Poor predictive ability to identify targets with adequate benefit/risk Development knowledge/world view is siloed Art of development, regulatory, translational science not broadly known Collaborative efforts stifled Investment capital disappearing

37 37 Some Causes For Fewer New Therapies Simple, “low hanging ” targets have been developed –Biology is complex –Multiple, difficult to dissect pathways Accurate prediction of safety and efficacy based on the molecular target has proven elusive Better benefit/risk required, difficult to predict –Unmet medical needs more narrowly defined - phenotypes –To demonstrate efficacy, “Clinical Outcome” frequently required Rather than surrogate endpoint Greater need for validation –Less tolerance of side effects Common and infrequent Need to demonstrate value to multiple stakeholders (payers)

38 38 Challenges In Discovery & Development Greater efficacy and better safety necessitate: –Pathobiology of disease must be accurately understood (pick the right targets) –New tools for better safety and efficacy prediction Must increase probability of success at each step –Larger trials required Costs increasing rapidly, resources limited Demonstrate cost/effectiveness benefit –Specific “sub-populations” to enhance benefit risk Population identification Response prediction

39 39 How Should “Bench to Bedside” Evolve And Who Will Lead The Way? Bayh-Dole Act –Allowed University to patent research discoveries –Potential to create “wealth” for the universities (institutes) –Have had a effect on the conceptual models of the future of translational science

40 40 Path Forward: Improving “Bench to Bedside” – Position Statements National Institute of Medicine –Clinical Research Round Table NIH –“Road map for medical research” National Academy of Sciences –“Exploring strategies for future research” FDA –Critical Path Initiative

41 41 Key Elements of Position Statements Collaboration among Pharma, academic, government –Share knowledge (FDA, NIH & Pharma) to develop new science through consortiums –Extensive and complementary databases –Similar initiatives overseas “Bench to Bedside” is complex, true progress will require integrated solutions Need to teach team collaboration science

42 42 Conflict of Interest (COI) COI issues are real –Focus on financial only –Tone: non – collaborative –Unintended consequences – effecting collaborative efforts Debate needs broader view: goal - improve public health Alternative position –Include all types of conflicts –Improve public health is a primary interest –Checks and balances in place –Trust can’t be developed unless balance achieved Collaboration/ cooperation/ learning /teaching Examples in the public good: Bell Labs / IMI

43 Where are We Going? Vision? Scientific “paradigm shift”? Increasing realization that institutions can’t go it alone Collaborative efforts in complex systems Translational development science Focus on unmet medical need/public health value Sharing the rewards –Bayh/Dole New commercial models

44 Future Vision: Integration/Collaboration Sci Transl Med 7 April 2010: Vol. 2, Issue 26, p. 26cm12

45 Models and Policy Choices Shared science Cross-institution development & business models Shared continuous improvement Broad as well as deep scientific knowledge More transparency More phenotype specific therapies Cost /effectiveness Broader, balanced ethics discussions

46 46 Discovery & Development Complex system, goal oriented, integrated –Optimally, create public health value & maximize benefit/ risk –Non-linear –Iterative/responsive to new information Many moving parts: constant problem-solving/management Optimally, development phases predicatively linked Resource intensive: optimally efficient, disciplined; predictable biomarkers & surrogate outpatients to determine POS and Go/No Rules Collaborative, experienced project team Progress will require cross-institutional collaboration

47 47 Nancy Whorf

48 48 Concepts: Reviewed Neither simple nor linear Goal: Unmet medical needs – public health value –Data: Clinical, regulatory and health economic –Demonstrate clear, population specific benefit / risk –Efficient and timely as possible Dynamic, responding to new knowledge Disease area focus –Multiple targets and/or molecules within a target Strategic scientific development plan –Begin with goal and design backwards Failure the norm –Go/no go criteria to exit early if risk/benefit unacceptable –Kill early Critical importance of predictive safety & efficacy biomarkers –Patient identification –Response prediction Apply learning iteratively

49 49 Formulation Research and Development Is it feasible to synthesize the compound and develop a formulation? Difficulty under appreciated Unique challenges: By type: “small molecule”, vaccine, protein antibody, siRNA, gene By route: Oral, inhaled, IV, topical, etc.

50 50 Phase I-II: General Concepts Phase I: –Tolerability/pharmacokinetics/efficacy Tolerability issues affect benefit /risk or limit dose determination - Possible No Go –Biomarker(s) to optimize dose selection/prediction of benefit or risk Target engagement (example: receptor occupancy: NK1) Target engagement and biologic effect (example: urinary LTE 4 : 5LO) Target engagement and biologic effect and clinical surrogate endpoint (example: reticulocytes - EPO) Phase II –Significant strategy choices: selection of surrogate biomarkers and endpoints –Help in bracketing doses for final clinical dose ranging study –Predictive of ultimate clinical endpoint (outcome) –Help to identify responder populations and biomarkers –Experimental models in humans

51 51 Project Teams: Additional Principles High performance team –In order to facilitate collaboration/synergism Members know all roles and responsibilities Anticipate others’ needs/thoughts Always think two steps ahead for self and other team members –Listen to others’ points of view –Debate and dialogue respected and valued

52 52 Future Approaches: Systems Biology Nature Genetics 2005: 37;

53 53 Dose Ranging: M 3 Antagonist Example European Respiratory Journal 2006; 28: p

54 54 Phase I – IIa: Critical Bridge Pharmacodynamics/Proof Of Concept Demonstrate: –PK, target engagement, biological activity, initial clinical benefit Identify benefit/risk –Go/no go Understanding safety profile important –Strategy about commitment illness, medicines Significant strategy choices: selection of surrogate biomarkers and endpoints –Help in bracketing doses for final clinical dose ranging study –Predictive of ultimate clinical endpoint (outcome) –Help to identify responder populations and biomarkers –Experimental models in humans

55 55 Major Goals: Phase III and Beyond In population(s) of interest –Document tolerability in more patients for longer time –Confirm benefit/risk –Potentially outcome / health economic data Plan for post approval safety monitoring Plan for and execute new indication(s)

56 56 Dose Selection: Phase II Activity Necessary inputs to formal dose ranging study(ies ) –Tolerability response curve or known limitations (Phase I/IIa) –Pharmacokinetics, biomarker of target engagement or activity –Dose spacing Strategic options to obtain data –Standard dose ranging study/dose adaptive design/modeling Other strategic options –Combine dose ranging and POC –Dose ranging on surrogate endpoint or outcome

57 57 Other Phase III Considerations High costs –Example: 15K patient outcome study: $400M over 4 years –Resources becoming limited Recruitment of patients globally –Different approaches to medicine What is the basis for a Phase III Program “go” –Strategic options Importance of achieving profile Partnering Funding

58 58 Example: Serendipity Anti-Leukotriene (CysLT 1 ) Program History: Nobel prize: classical physiology, pharmacology Many attempts before final candidate “evolved” – Receptor Antagonists 100 Compounds 10 Animal Toxicology 5 Compounds in Man 5-LO Program (Both Direct and Indirect Inhibitors) – 50 Compounds – 5 Animal Toxicology – 2 Compounds in Man Took too long/too many iterations Efficient development program

59 59 Example: Dose Ranging - CysLT 1 Antagonist European Respiratory Journal (6)

60 60 Phase III: Characteristics (con’t) Trial design consideration –Usually single dose from Phase IIb –Statistically rigorous: clear hypothesis, pre-established data analysis plan, no post hoc data dredging –“Clinically important” treatment effect targeted –Safety monitoring boards becoming standard –Non-inferiority designs –Real world designs/health economic data –Inclusion criteria (generalizability) –Exposure to as many patients and for as long as possible


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