Why Highly Variable Drugs are Safer Leslie Z. Benet, Ph.D. Professor of Biopharmaceutical Sciences University of California San Francisco FDA Advisory Committee for Pharmaceutical Science Rockville, MD October 6, 2006

I have made two previous presentations on this topic to ACPS November 29, 2001 Individual Bioequivalence: Have the Opinions of the Scientific Community Changed? April 14, 2004 Bioequivalence of Highly Variable (HV) Drugs: Clinical Implications Why HV Drugs are Safer Many of the slides today are the same as presented in my previous appearances

The Current U.S. Procrustean Bioequivalence Guidelines The manufacturer of the test product must show using two one-sided tests that a 90% confidence interval for the ratio of the mean response (usually AUC and C max ) of its product to that of the reference product is within the limits of 0.8 and 1.25 using log transformed data. (Procrustean  marked by an arbitrary, often ruthless disregard for individual differences or special circumstances.) Note: BCS is a non-Procrustean advance We are considering another non-Procrustean advances

Bioequivalence Issues What are we trying to solve? For all drugs, but particularly for NTI drugs, practitioners need assurance that transferring a patient from one drug product to another yields comparable safety and efficacy (switchability). For wide-therapeutic index, highly variable drugs we should not have to study an excessive number of patients to prove that two equivalent products meet preset (one size fits all) statistical criteria. To give patients and clinicians confidence that a generic equivalent approved by the regulatory authorities will yield the same outcome as the innovator product. (Nov. 29, 2001 & April 14, 2004)

Why is meeting bioequivalence criteria a relatively minor concern for drugs with narrow therapeutic indices? By definition, approved drugs By definition, approved drugs with narrow therapeutic with narrow therapeutic indices exhibit small indices exhibit small intrasubject variability. intrasubject variability. If this were not true, patients If this were not true, patients would routinely experience would routinely experience cycles of toxicity and lack of cycles of toxicity and lack of efficacy, and therapeutic efficacy, and therapeutic monitoring would be useless. monitoring would be useless.

NTIDrugs Frequently Proposed NTI Drugs Frequently Proposed to Limit Generic Substitution CV% CV% Inter Intra Inter Intra Subject Subject Subject Subject Carbamazepine 38 Conjugated Estrogens Cyclosporine (Neoral Package Insert) Digoxin 52 Furosemide Levothyroxine sodium 20 <20 Phenytoin sodium Theophyllin sustained release Warfarin sodium Black numbers Black numbers (Benet, Transplant. Proc. 31: )

(µ T - µ R ) 2 +  D 2 + (  WT 2 -  WR 2 ) <  (µ T - µ R ) 2 +  D 2 + (  WT 2 -  WR 2 ) <   WR 2 Individual Bioequivalence (IBE) Initial Promises for IBE Initial Promises for IBE Addresses the correct question (switchability) Addresses the correct question (switchability) Considers subject by formulation interaction (  D ) Considers subject by formulation interaction (  D ) Incentive for less variable test product Incentive for less variable test product Scaling based on variability of the reference product Scaling based on variability of the reference product both for highly variable drugs and for certain both for highly variable drugs and for certain agency-defined narrow therapeutic range drugs agency-defined narrow therapeutic range drugs Encourages use of subjects more representative of Encourages use of subjects more representative of the general population the general population

Re-examination of the Initial Promises for IBE Re-examination of the Initial Promises for IBE Addresses the correct question (switchability)— Necessity questionable and proof nonexistent Addresses the correct question (switchability)— Necessity questionable and proof nonexistent Considers subject by formulation interaction— Unintelligible parameter Considers subject by formulation interaction— Unintelligible parameter Incentive for less variable test product—ABE with scaling could also solve this issue Incentive for less variable test product—ABE with scaling could also solve this issue Scaling based on variability of the reference product both for highly variable drugs and for certain agency- defined narrow therapeutic range drugs– ABE with scaling could also solve this issue Scaling based on variability of the reference product both for highly variable drugs and for certain agency- defined narrow therapeutic range drugs– ABE with scaling could also solve this issue Encourages use of subjects more representative of the general population—Failed Encourages use of subjects more representative of the general population—Failed

Highly Variable Drugs (CV>30%) For wide-therapeutic index highly variable drugs we should not have to study an excessive number of patients to prove that two equivalent products meet preset (one size fits all ) statistical criteria. This is because,by definition, highly variable approved drugs must have a wide therapeutic index, otherwise there would have been significant safety issues and lack of efficacy during Phase 3 Highly variable narrow therapeutic index drugs are dropped in Phase 2 since it is not possible to prove either efficacy or safety.

“Drug A” The Poster Drug for High Variability A repeat measures study of “Drug A” 2x200 mg capsules in 12 healthy post-menopausal females yielded: Intrasubject CV for AUC of 61% Intrasubject CV for C max of 98% A generic company calculated that a 2 period crossover BE study for “Drug A” Capsules, 200 mg would require dosing in 300 postmenopausal women to achieve adequate statistical power

Pharmacogenetics and Highly Variable Drug Safety Should pharmacogenetics be considered in setting the criteria? For some drugs, high variability may be the result of genetic polymorphisms

Can we make some general conclusions as to when metabolic and transporter genetic polymorphisms will be important clinically in terms of drug disposition?  CYP 2D6 For sure  MDR1 No  CYP 2C19 Yes  OATPs Yes  CYP 2C9 Yes  OCTs Yes  CYP 3A4 No  OATs Probably  CYP 1A2 Maybe  MRP2 Maybe  UGT 1A1 Maybe  Other ABC  NAT2 No transporters ??

What are the Substrate Characteristics that Result in Pharmacogenetic Variance Affecting Pharmacokinetics? Substrate is BCS Class 1 Genetic variants exhibit very wide differences in phenotype activity, preferable at one extreme marked effect and the other extreme no effect For an enzyme, protein is not present or not active extrahepatically, especially not present in the gut. For a Class 2, 3 or 4 substrate, efflux transporter effects are minimal. Compounds are primarily a substrate for a single metabolic enzyme, a single uptake transporter or a single efflux transporter The primary genetic variable potentially affecting substrate pharmacokinetics is not embedded.

Cytochrome P450 2D6 Substrates Appear to be predominantly Class 1 substrates Therefore there will be no transporter interplay (We are unaware of a CYP2D6 substrate that has been shown to be an efflux transporter substrate) Therefore they will exhibit good absorption The enzyme shows marked genetic differences in enzyme activity between EMs and PMs (i.e., marked activity vs. no activity) There is no significant gut CYP2D6 activity Many CYP2D6 substrates have minimal metabolism by other enzymes All factors that minimize nongenetic variability

Yet, many CYP 2D6 substrates have qualified generic substitutes on the market The question should not be if such drugs are eligible for scaling in bioequivalence assessment or even if such drugs should be eligible for approval as generic equivalents Rather this is a labeling issue. If genetic polymorphisms are critical to drug dosing this should be true for the innovator as well as the generic

Recommendations of the FDA Expert Panel on Individual Bioequivalence to this Advisory Committee (April 2001) Sponsors may seek bioequivalence approval using either ABE or IBE (with SxF deleted) Scaling of ABE should be considered. If an IBE study is carried out and the test product fails, the data or a subset of the data may not be reanalyzed by ABE for approval A point estimate criteria on mean AUCs of ±15% and on mean C max of ±20% should be required for both ABE and IBE. Consideration should be given for narrower point estimate criteria for NTI drugs (e.g., AUC ±10%, C max ±15%)

My Recommendations April 14, 2004 Methodology should be developed to allow approvals based on weighting of average bioequivalence analyses for highly variable drugs (i.e.,  WR > 30%). A point estimate criteria on mean AUCs of ±10% and on mean C max of ±15% should be required for NTI drugs where  WR  20%. A point estimate criteria on mean AUCs of ±15% and on mean C max of ±20% should be required for all other drugs, including NTI drugs where  WR > 20%.

It is important to note that when I presented the recommendations of the Expert Panel (2001) as well as my own recommendations (2004) to the ACPS, the following were also stated: 1.There is no scientific basis or rationale for the point estimate recommendations 2. There is no belief that addition of the point estimate criteria will improve the safety of approved generic drugs 3. The point estimate recommendations are only “political” to give greater assurance to clinicians and patients who are not familiar (don’t understand) the statistics of highly variable drugs

Conclusions Highly variable narrow therapeutic index drugs are limited, at most, to a few cancer treatments but I am unaware of any documentation of highly variable narrow therapeutic index drugs. Highly variable drugs on the market are the safest drugs because marked swings in systemic drug levels have been shown to not affect safety and efficacy in individual patients High variability can result from a number of environmental and genetic factors, none of which appear to require any special considerations not already found in the labeling of the innovator drug