1. Use of Biomarker Information in Drug Product Labels to Individualize Pharmacotherapy Lawrence J. Lesko, Ph.D., FCP Director of the Office of Clinical Pharmacology and Biopharmaceutics Center for Drug Evaluation and Research Food and Drug Administration Clinical Pharmacology Subcommittee (CPSC) of the Advisory Committee for Pharmaceutical Sciences November 15, 2005 Rockville, Maryland

2. Recap of Yesterday and Introduction to Today Biomarkers: - 2C9 and VKORC1 - Viral load - Blood glucose Purpose: - Matching patients to dosing - Maximizing success in clinical trials Innovation: - How can biomarkers be better utilized? - What types of innovation can help?

3. Critical Path Initiative: Seeking Solutions to Productivity Problem “The goal of critical path research is to develop new, publicly available scientific and technical tools – including assays, standards, computer modeling techniques, biomarkers and clinical trial endpoints…..” “The emerging techniques of PGx and proteomics show great promise for contributing biomarkers to target responders, monitor clinical response and serve as biomarkers of drug effectiveness.”

4. Biomarker Definition and Pharmacodiagnostics A characteristic that is objectively measured and evaluated as an indicator of normal processes, pathogenic processes or pharmacological responses to a therapeutic intervention  Most common biomarkers use a single feature - INR (confirm activity), s-warfarin levels (predict activity)  More accurate biomarkers use multiple features - multivariate analysis of warfarin co-factors (prognostic of disease outcome)  More precise biomarkers possible with genomics - haplotypes, gene expression (type of diagnostic)

5. Biomarker Discovery Programs Growing at a Rapid Pace Explosive growth in the number and scope of biomarker knowledge in drug development. Related to new technologies such as PGx and imaging and better understanding of diseases. Creates potential for individualization of treatment, i.e., scientific basis to the “art of medicine” Question is how to obtain biomarker information effectively and efficiently, and when is it critical to translate into labels for directing patient treatment?

6. Heavy Emphasis on Disease Pathways and Drug MOA in Drug Development Target plasma drug conc in POC trials Risk factors to select patients for trials Individual measurements to direct therapy Dose finding data to select phase 3 doses Plasma drug conc associated with PD Derived parameters to adjust dosing Identify patients at risk for AEs Qualification of biomarkers through disease-drug-statistical models has been done infrequently thus limiting potential use of biomarkers as tests or diagnostics in clinical practice

7. Randomized Controlled Trials: Integrating Biomarkers* Into Drug Development Provide best evidence for rejecting null hypothesis of no treatment effect –Goal to demonstrate efficacy (and safety) –Assumes homogeneous population Not designed to qualify relevant efficacy and safety biomarkers prospectively –Need to address a different set of questions –Focus on heterogeneity of patients How can “better” biomarkers/diagnostics be incorporated into drug development? –Prospective hypothesis-testing beyond early trials * Not referring to biomarkers as surrogate endpoints

8. Types of Questions To Be Answered D/R for benefit and risk –Inherent variability, changes with patient co- factors and impact of dosing regimens Biomarkers most suitable to use to adjust doses in clinical practice –Basis for TDM monitoring and diagnostic test development Quantitative models to qualify relevant biomarker associations with clinical outcomes

9. Model-Based Drug Development: A Critical Path Innovation to Integrate Data Analysis Quantitative pharmacology ~ mathematical explanation of relationships to explain clinical outcomes over timeframe of interest –Extensive use of biomarkers –E/R modeling (D/R and/or PK/PD) –Disease progression modeling –Clinical trial simulation (e.g., Phase III) –Enrichment and other innovative study designs Goal ~ to improve decision-making on dosing, trial design options, risk/benefit and transfer knowledge to label

10. Conceptual Framework for MBDD L.B. Sheiner, Learning vs. Confirming in Clinical Drug Development, Clin Pharmacol Therap 61:275- 291 (1967)

11. Drug Safety in the Post-Vioxx Era: What Can Be Done About This?

12. “ New drugs are inherently more risky because of the relatively small amount of data about their effects.” Goodman and Gilman, The Pharmacological Basis of Therapeutics, 2001 Origin of ADRs Is No Secret: Inherent Predisposition and Environmental Factors

13. Many ADRs Are Avoidable: High Incidence, Less Severe Most occur within range of approved doses 70% ~ extension of pharmacological effects 90% ~ differences in exposure (PK) or response (PD) Nebeker et al, Arch Intern Med, 2005, 165:1111 – 1116 Korn, Drug Development Science, AAMC, 2005 What is needed are new pre-marketing approaches to using biomarkers in trial design, dose selection and translation of information into labels.

14. Risk Management Cannot Be Monitored In – It Must Be Built In Continuum of Benefit/Risk Critical Path Tool-Kit: Model-Based Drug Development Biomarkers: Prognostic, PD and PredictivePatient Selection: All or Subset Labeling: Information for Individualization Drug and Disease ModelingDose Selection and Risk Quantitation Clinical Trial Simulation

15. Drug Product Label Information in a drug product label is an integral part of the drug development process. Goal of labeling is to assure that prescribers have access to useful data than suggest ways to optimize efficacy and manage risk. Information on D/R or PK/PD relationships are of particular importance to individualizing therapy

16. “Pharmacometrics Can Guide Future Trials, Minimize Risk -- FDA Analysis” 244 ~ number of NDAs surveyed in cardio-renal, oncology and neuropharmacology 42 ~ NDAs with pharmacometric (PM) analysis** 26 ~ PM pivotal or supportive of NDA approval 32 ~ PM provided evidence for label language Assessment of Pharmacometrics in Regulatory Decisions October 3, 2005, Volume 67, Number 40, Page 15 ** Number not higher because sponsor application lacked necessary data

17. Perspective on Current Situation  Much of the value inherent in the development of a new drug product is lost in uninformative labels “A physician without information cannot take responsibility; a physician who is given information cannot help but take responsibility” - Paraphrased from Wilbert Leo Gore (Founder of Gore-Tex)  Put knowledge (information + skills + experience) in the value chain of drug development

18. D/R or PK/PD Data Data obtained from phase 1 and 2 trials –D/R relationships for efficacy (sometimes safety) used to pick phase 3 dose(s) and design trials Data obtained from phase 2/3 trials –Extensive data on PD biomarkers and PK drug levels associated with clinical outcomes Data obtained from targeted special population studies –PK drug level rich with changes in levels associated with dosing adjustments

19. Development of Exposure-Response Modeling Throughout Drug Development Integrate D/R across trials more complete use of data Use to interpret and predict differences in exposure between patients Use to design and simulate clinical trials Use to estimate the probabilities of clinical outcomes

20. Very Few Labels Contain E/R Relationships: Two Examples Irbesartan: D/R for effects on DBP “…the maximal beta blocking effects have been estimated to produce a 30% reduction in exercise HR. Beta blocking effects in the 30-80% of maximal effect occur at metoprolol plasma ranging from 30-540 nmol/L. The conc-effect curve plateaus at 200-300 nmol/L.” Metoprolol CR: PK/PD effects on exercise HR

21. Example: Development of Tipranavir for Treatment of HIV Plasma drug levels: remain above IC 50 or IC 90 to achieve sustained viral suppression and avoid resistance development Inhibitory quotient (IQ): C min / IC 50 proposed as a metric to predict efficacy (% responders at week 24) Potential basis for individualizing dosing in patients receiving tipranavir/ritonavir

22. Clinical Efficacy and Safety: Relationship to IQ and Cmin BenefitRisk From Dr. Jenny Zheng (OCPB), FDA Antiviral Drug AC Meeting, May 19, 2005

23. Issues With Use of IQ and Cmin to Guide Dosing Protein binding adjustment factor ~ lack of consensus on estimate to calculate IC 50 Estimate of IC 50 ~ patient-dependent on prior treatment history, generalizability Sensitivity and specificity of Cmin to predict grade 3-4 ALT toxicity Relative variability in C min /IC 50 Future studies of drugs in class designed to determine usefulness of IQ and C min

24. Final Tipranavir Label Approved for combination antiretroviral treatment of HIV-1 in treatment-experienced patients or those having resistance to PTs –Large interindividual variability in PK, many DDIs and other uncertainties (28 post-marketing studies) –Some reports of clinical hepatitis and hepatic decompensation Label advocates genotype/phenotype testing a priori for viral resistance FDA recommended individualized dosing based on the relationship between IQ and probability of efficacy (pilot study will be conducted in 2006) See The Pink Sheet, June 30, 2005

25. Example: Optimizing B/R of Zoledronic Acid in Cancer Patients Pain and fracture reduction in patients metastatic bone disease Major clinical concern about risk what dose would optimized clinical benefit without causing unnecessary renal deterioration Apply quantitative pharmacology to understand trade-offs and select dosing

26. Step 1: Collecting Prior Information on PK to Characterize Excretion Constructed dose-PK model that included intersubject variability Used to identify significant co-variates affecting clearance of zoledronic acid Renal function identified as only major co-variate of interest

27. Step 2: Bone Turnover Response Biomarkers as a Function of Dose Urinary excretion of deoxypyridinoline, corrected for creatinine clearance, as a measure of bone turnover Response biomarker previously shown to be predictive of metastasis and fracture risk reduction Predicted dose-response was flat from 2 – 16 mg

28. Step 3: Construct a Drug-Disease- Outcome Model to Guide Dosing Risk of renal deterioration in any given patient is dependent on exposure to zoledronic acid (dose-dependent) Risk of renal deterioration at any given dose is dependent on renal function at baseline pre-dosing NR 4 mg 3 mg

29. Potential Value of MBDD: Informative Labeling for Risk Management

30. Summary: Critical Path Opportunity for Innovation Systematic effort should be made to include biomarker data in labeling (translational science) when: - It is available from drug development - Validated assays can be available - Associated with meaningful clinical outcome - Potentially useful in guiding dose adjustments - Adjunct to clinical monitoring of patient

31. Acknowledgments Dr. Joga Gobburu Dr. Bob Powell Dr. Don Stanski Dr. Jenny Zheng Pharmacometrics Division Regulatory Review Staff Jadhav P et al, “How Biomarkers Can Improve Clinical Drug Development?, Amer.Pharm.Res., July 2004