Use of Biomarker Information in Drug Product Use of Biomarker Information in Drug Product Labels to Individualize PharmacotherapyLabels to Individualize Pharmacotherapy

Lawrence J. Lesko, Ph.D., FCPDirector of the Office of Clinical Pharmacology and

BiopharmaceuticsCenter for Drug Evaluation and Research

Food and Drug Administration

Clinical Pharmacology Subcommittee (CPSC) of the Advisory Committee for Pharmaceutical Sciences

November 15, 2005Rockville, Maryland

Recap of Yesterday and Recap of Yesterday and Introduction to TodayIntroduction 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?

Critical Path Initiative: Seeking Critical Path Initiative: Seeking Solutions to Productivity ProblemSolutions 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.”

Biomarker Definition and Biomarker Definition and PharmacodiagnosticsPharmacodiagnostics

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)

Biomarker Discovery Programs Biomarker Discovery Programs Growing at a Rapid PaceGrowing 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?

Heavy Emphasis on Disease Pathways and Heavy Emphasis on Disease Pathways and Drug MOA in Drug DevelopmentDrug 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

Randomized Controlled Trials: Integrating Randomized Controlled Trials: Integrating Biomarkers* Into Drug DevelopmentBiomarkers* 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

Types of Questions To Be Types of Questions To Be AnsweredAnswered

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

Model-Based Drug Development: A Critical Model-Based Drug Development: A Critical Path Innovation to Integrate Data AnalysisPath 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

Conceptual Framework for MBDDConceptual Framework for MBDD

L.B. Sheiner, Learning vs. Confirming in Clinical Drug

Development, Clin Pharmacol Therap 61:275-

291 (1967)

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

“ 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

Many ADRs Are Avoidable: Many ADRs Are Avoidable: High Incidence, Less SevereHigh Incidence, Less Severe

• Most occur within range of approved doses

• 70% ~ extension ofpharmacological effects

• 90% ~ differences in exposure (PK) or response (PD)

Nebeker et al, Arch Intern Med, 2005, 165:1111 – 1116Korn, 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.

Risk Management Cannot Be Risk Management Cannot Be Monitored In – Monitored In – It Must Be Built InIt Must Be Built In

Continuum of Benefit/Risk

Critical Path Tool-Kit: Model-Based Drug Development

Biomarkers: Prognostic, PD and Predictive Patient Selection: All or Subset

Labeling: Information for Individualization

Drug and Disease Modeling Dose Selection and Risk Quantitation

Clinical Trial Simulation

Drug Product LabelDrug 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

“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 Assessment of Pharmacometrics in Regulatory DecisionsRegulatory Decisions

October 3, 2005, Volume 67, Number 40, Page 15

** Number not higher because sponsor application lacked necessary data

Perspective on Current SituationPerspective 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

D/R or PK/PD DataD/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

Development of Development of Exposure-Response Exposure-Response

ModelingModeling Throughout Drug Development 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

Very Few Labels Contain E/R Very Few Labels Contain E/R Relationships: Two ExamplesRelationships: 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

Example: Development of Tipranavir Example: Development of Tipranavir for Treatment of HIVfor Treatment of HIV

• Plasma drug levels: remain above IC50 or IC90 to achieve sustained viral suppression and avoid resistance development

• Inhibitory quotient (IQ): Cmin / IC50 proposed as a metric to predict efficacy (% responders at week 24)

• Potential basis for individualizing dosing in patients receiving tipranavir/ritonavir

Clinical Efficacy and Safety: Clinical Efficacy and Safety: Relationship to IQ and CminRelationship to IQ and Cmin

0 200 400 600 800 1000

Inhibitory Quotient

0%20

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phase 3 without T20 (n=200)phase 3 with T20 (n=91)phase 2 (n=160)

10 20 30 40 50Cmin in ug/mL

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Benefit Risk

From Dr. Jenny Zheng (OCPB), FDA Antiviral Drug AC Meeting, May 19, 2005

Issues With Use of IQ and Cmin to Issues With Use of IQ and Cmin to Guide DosingGuide Dosing

Protein binding adjustment factor ~ lack of consensus on estimate to calculate IC50

Estimate of IC50 ~ patient-dependent on prior treatment history, generalizability

Sensitivity and specificity of Cmin to predict grade 3-4 ALT toxicity

Relative variability in Cmin/IC50

Future studies of drugs in class designed to determine usefulness of IQ and Cmin

Final Tipranavir LabelFinal 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

Example: Optimizing B/R of Example: Optimizing B/R of Zoledronic Acid in Cancer PatientsZoledronic 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

Step 1: Collecting Prior Information Step 1: Collecting Prior Information on PK to Characterize on PK to Characterize ExcretionExcretion

• 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

Step 2: Step 2: Bone Turnover Response Bone Turnover Response BiomarkersBiomarkers as a Function of Dose 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

Step 3: Construct a Step 3: Construct a Drug-Disease-Drug-Disease-Outcome ModelOutcome Model to Guide Dosing 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

Potential Value of MBDD: Potential Value of MBDD: Informative Informative LabelingLabeling for Risk Management for Risk Management

Summary: Critical Path Summary: Critical Path Opportunity for InnovationOpportunity 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

AcknowledgmentsAcknowledgments

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