PhRMA White Paper on ADME Pharmacogenomics: Survey Results Lisa A. Shipley, Ph.D. Advisory Committee for Pharmaceutical Science and Clinical Pharmacology.

PhRMA White Paper on ADME Pharmacogenomics: Survey Results Lisa A. Shipley, Ph.D. Advisory Committee for Pharmaceutical Science and Clinical Pharmacology March 18, 2008

Goals of the White Paper Present a pharmaceutical industry perspective of the recent and future utility of pharmacogenomics (PGx) related to the ADME properties of drugs for drug development and utilization Offer perspectives on the current state of practices, strategies, knowledge and key information gaps that need to be addressed in order to fulfill the promise of PGx in targeted medicine Does not intend to provide best practice for ADME PGx in drug development or utilization, nor to address ethical ramifications of ADME PGx Second half of paper discusses current understanding of clinically significant polymorphisms of drug metabolism enzymes and transporters

Approach To establish a cross-industry perspective on the current utility of ADME PGx, PhRMA conducted a survey of recent (2003-2005) major pharmaceutical company practices

The Survey Assembled a series of questions to elicit broad information about current pharmaceutical company ADME PGx practices Survey respondents were instructed to base all answers on clinical trials that initiated during the period 2003-2005 Companies were also asked to provide citations for peer- reviewed original PGx ADME research published by industry scientists (up to 3/company), and examples of how PGx ADME information has been used for internal decision-making and regulatory interactions PhRMA staff solicited responses from PhRMA member companies and aggregated data, to preserve companies’ anonymity (except for citations of published papers) Not every company answered every question Some questions have been reordered for ease of presentation

Participating Companies Abbott Laboratories Amgen Astra-Zeneca Bristol Myers Squibb GlaxoSmithKline Johnson & Johnson Lilly Merck Millennium Novartis Pfizer Sanofi-Aventis Schering-Plough Wyeth

1. How often has your company collected DNA w/ consent for ADME-related genotyping in: Study Type# of Responses AlwaysUsuallySometimesNever First in human2111 Multiple rising dose 212 Drug-drug interaction 1112 Special population851 Other clin pharm113 Proof of concept77 Dose ranging77 Pivotal68 Other481

2. How often has your company performed ADME-related genotyping in: Study Type# of Responses AlwaysUsuallySometimesNever First in human1112 Multiple rising dose 1112 Drug-drug interaction 1121 Special population1112 Other clin pharm1112 Proof of concept122 Dose ranging185 Pivotal104 Other95

3. Has your company used ADME-related genotype(s) in study design? Inclusion Criterion – 10 yes, 4 no Exclusion Criterion – 10 yes, 4 no Gene # of Responses CYP2D6 11 CYP3A51 UGT1A14 CYP2C197 CYP2C94 MGMT0 GSTM10

AlwaysUsuallySometimesNever 4. How often has your company specified PG-PK analysis in study protocols? 212 7. How often does your company have a written PG-PK plan or strategy for a compound in development? 2192 12. How often has your company used phenotyping to ensure that genotype- assigned phenotypes are correct? 68 15. How often has the statistical power of PG-PK analysis entered into study design criteria at your company? 95 17. How often has your company used any FDA-approved in vitro diagnostic (UGT1A1 kit or CYP2D6/2C19 chip) for PG-PK for clinical trial applications? 68

5. Breadth of genotyping. Please check whether your company currently genotypes each gene. GENE # of Responses YesNo CYP1A274 CYP2A665 CYP2B665 CYP2C875 CYP2C9121 CYP2C19130 CYP2D6140 CYP3A484 CYP3A5113 Other phase 1 enzyme 63 GENE # of Responses YesNo UGT1A1131 TPMT38 Other phase 2 enzyme 83 OATP1B174 BCRP74 MDR193 Other transporter 75 Other PK- related 75

6. Has you company only genotyped when preclinical data indicate a role for the gene in a compound’s PK, or do you genotype a broader range of genes? With preclinical support only:8 Broader range:6 8. Has genotyping been done within your company or outsourced? In-house5 Outsourced4 Both8 9. How has your company coded samples collected for PG-PK research (ok to check more than one)? Single Coded: 8 Double Coded:10 Anonymized/Anonymous 3

YESNO 10. Has your company kept/banked DNA beyond the initial period of the clinical trial? 130 13. Has your company combined samples across studies of a single compound to enhance the statistical power of PG-PK analysis? 111 16. Has your company used large-scale (e.g., multigene chip- based) exploratory PG-PK analysis? 68 18. Does your company apply the following standards for human DNA sample collection and generation of human genotype data that might be used in regulatory submissions? GCP GLP GMP 14 10 2 3 10

11. When PG-PK research has been included in a trial, has it been a required study activity or optional for each subject in: (ok to check both boxes in a row) STUDY TYPE # of Responses YES (Required) NO (Optional) Phase 1 studies12 Drug interaction studies 108 Phase 2 studies412 Phase 3 studies313

14. How important has replication of a PG-PK finding been in your company? (please check only one box) # of Responses An independent replication has always been necessary 1 An unreplicated result has been used for internal decision-making but not in a regulatory submission 7 An unreplicated result based on a known valid biomarker has been used in a regulatory submission 2 An unreplicated result based on another biomarker has been used in a regulatory submission 0 PG-PK results have not been used2

YESNO 19. Have scientists from your company published original PG-PK research in peer-reviewed journals? 86 20. Has PG-PK information been used in decision-making at your company? 113 21. Has your company interacted with FDA or other regulators regarding PG- PK? 103

Summary of Survey PGx has already significantly impacted drug development and beginning to influence drug utilization PGx allows the identification, confirmation or exclusion of clearance pathways PGx analyses have utility in  Explaining PK variability  Ensure trial population appropriately balanced  Ensure the safety of volunteers and patients  Provide mechanistic information PGx studies may support labeling claim concerning PK dosing; ethnic variability and safety A significant effort remains in the education of the public, prescribers, ethics committees and investigators

Questions or Comments?

Incorporation of Pharmacogenetic in Healthcare Eric Lai, Ph.D., VP, Pharmacogenetics March 18, 2008

19 This presentation represents my personal views and does not necessarily reflect the policies or endorsement of GlaxoSmithKline.

20 What is Pharmacogenetics (PGx)?  The field of Pharmacogenetics deals with the effect of individual gene variants on the action of a given drug.  “Right Drug for the right patient at the right time” or personalized medicine.

21 Misconceptions about PGx  There is no such things as a personalized medicine (The right medicine to the right patient at the right dosage at the right time). This is a perfect example of marketing talk (Drugs are not like cars or computers).  Clinical trials are done on populations, effects are observed in a group of patients.  PGx increase the probability whether the drug is going to be beneficial for you or may have major adverse effort(s) against you.  The drug is not specifically designed for you but for a group of targeted individuals (Targeted or Informed Medicine). So it is more like clothing sizes (whether you are size 0 or 14).  Not all clinical trials or drugs have PGx components

22 PGx results are used to support post-marketing risk management, product differentiation in market place & asset progression  Support post-marketing risk management and product differentiation by identifying treatment and management opportunities using patient’s marker status (Phase III - IV) –Improve safety profile: Safety PGx  Support asset progression (Phase II – III) –Improve efficacy profile (Efficacy PGx) –Improve safety profile  Account for variability (Phase I) –Drug safety & efficacy varies between individuals and ethnic groups.  Explain etiology (Disease understanding) –Genetics influences susceptibility to adverse drug reactions. Understanding these mechanisms can be used to minimize risk. –Genetics influences efficacy of medicines– understanding mechanisms can be used to select back-up compounds or identify best treatment population.

23 Is PGX ready for prime time?  Is the Science of PGx robust enough for routine application?  Is the Pharmaceutical industry ready to incorporate PGx in post-market drug safety management and/or to improve the probability of success in drug development?  Are the Physicians ready to order PGx testing?  Are the Patients ready for PGx testing?  Are the payers (e.g. Government, insurance industry, etc) ready to pay for PGx testing?  Are the regulatory agencies up to the task of implementing PGx in drug development and drug safety?

24 Safety PGx - abacavir hypersensitivity

25 What is abacavir hypersensitivity?  Abacavir (ABC) commonly used in treatment of HIV-1 infection  Approved products include Ziagen, Trizivir and Epzicom / Kivexa  Generally well tolerated  Key limitation: abacavir hypersensitivity reaction (ABC HSR) –affects ~5% of clinical trial patients  Features of ABC HSR: –Multi-organ clinical syndrome – typically fever and/or rash ± constitutional, GI and/or respiratory symptoms –Symptoms usually (>90%) occur within first 6 weeks of therapy –The symptoms worsen with continued therapy and can be life- threatening but usually resolve upon permanent discontinuation of abacavir –Re-challenge is contraindicated and can be fatal

26 GSK research on ABC HSR  Post-approval commitments in 1999 to regulatory authorities to conduct research to understand ABC HSR and develop tests to confirm diagnosis  Pooled analysis of studies identified some risk factors for HSR: race, sex, CDC class, prior HIV drug treatment, NNRTI co-introduction  Comprehensive & effective risk management program created (educational materials, labelling, pharmacovigilance, etc.)

27 Cumulative patient-years of exposure to ABC products & cumulative spontaneous reports of ABC HSR-associated mortality …but can we determine an individual’s risk for ABC HSR?

28 GSK Abacavir-HSR project timeline  1994 Initiate clinical development program  1998/9 Marketing Approval for first ABC product – Ziagen  1999 Initiation of the GSK Abacavir-HSR project team  2001 Association of HLA-B*5701 with ABC HSR  Caucasian males (50-94% sensitivity, 98% selectivity)  2003 HLA-B*5701 extended to Caucasian females  2004/5 LabCorp offers HLA-B*5701 screening assay –Response to requests by US HIV clinicians  2006 First reports of prospective HLA-B*5701 screening on incidence of HSR  2007 Prospective trial (Predict -1) results presented at IAS

30 PGx can be used to detect ADRs: Number of Cases Required to Achieve 80% Statistical Power Adverse Drug ReactionGenetic Risk Factor Cases Required DrugReactionPrevalenceRisk AlleleFreq. 1 Effect 2 GefitinibDiarrhea0.28ABCG2 Q141K0.07547 (>150) IsoniazidHepatotoxicity0.15CYP2E1*1 & NAT2 Slow Ac 0.13 3 7122 (>150) IrinotecanNeutropenia0.20UGT1A1*280.322826 (58) AbacavirHypersensitivity reaction 0.05HLA-B*57010.043615 (19) TranilastHyperbilirubinemia0.12UGT1A1*280.304842 (54) AllopurinolSevere cutaneous adverse reactions <0.001HLA-B*58010.1567819 (19) CarbamazepineStevens-Johnson Syndrome <0.001HLA-B*15020.0410239 (9) 1 Allele frequency of the ADR susceptibility variant 2 Genetic effect is the estimate of the genotype relative risk for those homozygous for the susceptible genotype compared to the low risk homozygotes 3 Frequency of the CYP2E1*1 and NAT2 slow acetylator homozygous genotype in Europeans 4 Number of cases required to achieve 80% power to reject the null hypothesis with 200 clinical matched (population) controls. Bonferroni adjusted α = 0.05. Assumes LD between genetic risk factor and best SNP marker is r 2 = 0.7.

31 Is PGX ready for prime time?  Is the Science of PGx robust enough for routine application?  Is the Pharmaceutical industry ready to incorporate PGx in post-market drug safety management and/or to improve the probability of success in drug development? Yes -> if they are forced to.  Are the Physicians ready to order PGx testing?  Are the Patients ready for PGx testing?  Are the payers (e.g. Government, insurance industry, etc) ready to pay for PGx testing?  Are the regulatory agencies up to the task of implementing PGx in drug development and drug safety?

32 0.0 0.05 0.10 0.15 0.20 1998/19992000/20012002/20032004 to July 2005 Proportion of ABC-naïve patients discontinuing ABC within 6 weeks n=68n=131n=102n=49 1 Before PGx screening After PGx screening 2 P<0.05 Non-GSK study: Prospective HLA-B*5701 screening reduces early discontinuation of ABC Non-GSK study – Western Australia Rauch et al. Clin Infect Dis 2006; 43: 99 Discontinuation due to any reason Discontinuation due to ABC HSR 11 22 7 2 11 5

33 Determining Clinical Utility: GSK’s Prospective Trial (PREDICT-1) study objectives  To determine whether prospective screening for HLA-B*5701, prior to abacavir treatment, resulted in: –a significantly lower incidence of clinically suspected ABC HSR –a significantly lower incidence of immunologically confirmed ABC HSR as determined by ABC skin patch testing Mallal S et al. NEJM, 2008

34 PREDICT-1 results published Feb 7, 2008 Proportion of patients with ABC HSR (%) Control Screen Clinically suspected ABC HSR P<0.0001 Odds Ratio 0.4 (CI: 0.25, 0.62) P<0.0001 Odds Ratio 0.03 (CI: 0.00, 0.18) Clinically suspected ABC HSR plus skin patch test positive n=847 n=803 n=842n=802

35 PREDICT-1 conclusions  Prospective HLA-B*5701 screening and avoidance of ABC in patients who were HLA-B*5701 positive –significantly reduced the incidence of clinically suspected ABC HSR –eliminated immunologically confirmed (i.e., patch test-positive) ABC HSR  HLA-B*5701 positive patients are at increased risk for ABC HSR  HLA-B*5701 negative patients are at reduced risk for ABC HSR Mallal S et al. NEJM, 2008

37 Volume of HLA-B*5701 testing: Experience of one clinical laboratory LabCorp communication Presentation of PREDICT-1 and SHAPE data at AIDS conference PREDICT-1 publication in NEJM Growth

39 Common Sense and Predictably Irrational behavior  Odds of winning in a casino. –Will you pay for a test if the result can tell you whether you can improve your odd of winning from 5% to 50%?  >80% of the people will say yes  Odds of getting an adverse drug reaction. –Will you let your physician order for a test if the result can tell you whether you have a higher chance of getting an adverse drug reaction (from 5% to 50%)?  Only about 50% of the people will say yes

41 Efficacy PGx

42 Current Drug Development Process  Current drug development and approval processes center on data collected from research participants  Most drugs are effective in a majority of patients (Spear, B. Trends Mol Med May 2001 7 (5) 201-204) : Alzheimer30% Asthma60% Cardiac Arr.60% Depression62%  All drugs have side effects and some drugs produce major adverse reactions in small subset of patients

43 Examples of Efficacy PGx DrugIndicationEfficacy measureGene-alleleFDA guidelines CetuximabColorectal cancerDisease-free survivalEGFR+Required TrastuzumabBreast CancerDisease-free survivalHer2 overexpression Required LapatinibBreast cancerDisease-free survivalHer2+Required TamoxifenBreast cancer (high risk of recurrence) Reduced relapse-free timeCYP2D6*4/*4For information only BusulfanMyelogenous leukemiaDisease-freePhiladelphia chromosome + For information only CarvedilolHeart failureEjection fraction; survival  1-AR (arg389) none PravastatinDyslipidemiaHDL and total cholesterol levels, atherosoclerosis progression CETP-B1 HMGCR (HAP7) none DonepezilAlzheimer’s DiseaseImprovement in ADAS-CogApoE4+none RosiglitazoneAlzheimer’s DiseaseImprovement in ADAS-CogApoE4-none

45 Are current drug development process and regulatory thinking ready for Efficacy PGx?

46 Last thought: what is most important?

47 History of mobile phone development  Importance of the regulatory agency: –The basic concept of cellular phones began in 1947, when researchers looked at crude mobile (car) phones and realized that by using small cells (range of service area) with frequency reuse they could increase the traffic capacity of mobile phones substantially. However at that time, the technology to do so was nonexistent. –Anything to do with broadcasting and sending a radio or television message out over the airwaves comes under Federal Communications Commission (FCC) regulation. A cell phone is a type of two-way radio. In 1947, AT&T proposed that the FCC allocate a large number of radio- spectrum frequencies so that widespread mobile telephone service would become feasible and AT&T would have a incentive to research the new technology. We can partially blame the FCC for the gap between the initial concept of cellular service and its availability to the public. The FCC decided to limit the amount of frequencies available in 1947, the limits made only twenty-three phone conversations possible simultaneously in the same service area - not a market incentive for research.

48 1968: Change in regulatory landscape  The FCC reconsidered its position in 1968.  AT&T and Bell Labs proposed a cellular system to the FCC of many small, low-powered, broadcast towers, each covering a 'cell' a few miles in radius and collectively covering a larger area.  Dr Martin Cooper, a former general manager for the systems division at Motorola, is considered the inventor of the first modern portable handset. Cooper made the first call on a portable cell phone in April 1973.

49 1983: First commercial cellular phone Dr.Cooper Motorola is first to ship a commercial portable cellular phone, the DynaTAC, with a suggested retail price of $3,995 The DynaTac phone weighs 28 ounces, is 13 x 1.75 x 3.5 inches in dimension, boasts one hour of talk time and eight hours of standby time and has the nickname of “brick phone”.

50 $299 with better and more reliable coverage Cell Phone Subscribers in the U.S., 1985–2005

51 So what did the cell phone industry teach us?  Regulatory decisions are critical  A common standard (cellular transmission)  Reasonable cost for the general public  Reliable and good coverage of services (US cell phone companies still have a lot of work to improve on this issue).

52 PGx results are used to support post-marketing risk management, product differentiation in market place & asset progression  Support post-marketing risk management and product differentiation by identifying treatment and management opportunities using patient’s marker status (Phase III - IV) –Improve safety profile: Safety PGx  Support asset progression (Phase II – III) –Improve efficacy profile (Efficacy PGx) –Improve safety profile  Account for variability (Phase I) –Drug safety & efficacy varies between individuals and ethnic groups.  Explain etiology (Disease understanding) –Genetics influences susceptibility to adverse drug reactions. Understanding these mechanisms can be used to minimize risk. –Genetics influences efficacy of medicines– understanding mechanisms can be used to select back-up compounds or identify best treatment population.

53 The Long Road to P450 testing  Cytochrome P450 proteins with well established common polymorphisms that affect drug metabolism have been described since 1950s and molecular basis for the polymorphisms have been known since 1980s.  Potential predictors of optimum dose, drug choice and side effect response –Eg. CYP2D6 & codeine activation, CYP2C9 & warfarin inactivation  Why have they not been taken up into clinical practice? –Complicated gene families and difficult assays –Lack of a standard and “agreed” panel –Lack of regulatory input and guidance –Limited awareness –Feasibility  Access to test  Genetic information required at point of prescribing decision?

54 Agreements among 9 Pharma Companies on ADME genes  Core List - “Must Have Genes” - FDA validated - Significant burden of proof - DMET scientist guided - 33 genes (213 markers)  Extended List - “Need to Have Genes” - Probable involvement - Lacking burden of proof of Core - Mostly addition of Transporters - 143 genes (~2,500 markers)  Investigative List - “Like to Have Genes” - Unknown but possible involvement - Very little literature - Modifiers of metabolism - 333 genes (~9,500 markers)

56 Core List CYP2C19CYP2C9CYP3A5CYP1A2CYP3A4CYP2D6 CYP1A1CYP2A6CYP2B6CYP3A7DPYDCYP2C8 CYP2E1CYP2J2UGT1A1NAT1NAT2GSTP1 TPMTGSTM1UGT2B7GSTT1ABCB1ABCC2 ABCG2SLCO1B1SLC22A1SLC22A2SLC22A6SLCO1B3 SLC15A2CDAVKORC1

57 PGx: Key Stakeholders Patients Regulators Diagnostics and biotech industry Healthcare Providers Bioethics & Policy Organizations Payers Government Drug safety and efficacy are shared responsibilities Pharma Central Testing Laboratories

Feb. 1, 2008 © Pharsight Corporation All Rights Reserved On the Use and Value of Drug-Independent Survival Models to Support Clinical Drug Development in Oncology Rene Bruno, Laurent Claret Pharsight corporation FDA Clinical Pharmacology Advisory Committee Meeting: Quantitative Clinical Pharmacology: Critical Path Opportunities Washington DC, March 18, 2008

© Pharsight Corporation All Rights Reserved March, 2008 Outline Drug development in oncology A drug-disease modeling framework to support drug development in Oncology ●Tumor growth model ●Survival model Support to end-of-Phase II development decisions: A retrospective project with capecitabine (Roche) On the use the FDA NSCLC survival model ●A case study based on erlotinib data Value of the survival simulations Conclusions

© Pharsight Corporation All Rights Reserved March, 2008 slide 60 Drug development in oncology Lots of new drug candidates with new mechanisms of action ●Major advances in understanding the molecular biology and genetics of the disease have led to the so-called “targeted therapies” ●Highly competitive market Empirical selection of dose and dosing schedules ●MTD vs. biologically active dose paradigm in Phase I ●Phase II studies not designed to assess dose-response Typical randomized Phase IIb dose-ranging studies are not conducted in oncology ●Analysis of clinical trial data poorly informative (e.g. response rate, neutropenia grade…) Limit the ability to learn from early clinical trials High failure rate in Phase III

© Pharsight Corporation All Rights Reserved March, 2008 A drug-disease modeling framework to predict clinical endpoints and support oncology drug development Models / Endpoints DoseExposure Survival Tumor size dynamics Dose-reductions PFS ORR DLT Biomarkers PK / MOA / Resistance covariates, prognostic factors, gene expression, protein profile

© Pharsight Corporation All Rights Reserved March, 2008 The exposure - tumor size – survival model: A bridge from Phase II to Phase III endpoints DoseExposureSurvival Tumor size dynamics Drug specific Disease/Patient specific Disease specific Claret L et al. Model-based predictions of expected anti-tumor response and survival in Phase III studies based on phase II data of an investigational agent. Proc ASCO, 24 (18S), 307s (Abs 6025), 2006. Phase 2 Phase 3 To predict phase 3 endpoint based on phase 2 endpoint and prognostic factors

© Pharsight Corporation All Rights Reserved March, 2008 The tumor-size model incorporates tumor growth and drug effect Claret et al. PAGE 15, (Abstract 1004), 2006 [www.page-meeting.org/?abstract=1004] K D, : drug specific Y 0, K L : disease/patient specific y(t): Larger diameter at time t (mm), y(0): baseline tumor size Exposure(t): Exposure at time t (dose, AUC…)) R(t): Resistance function decreasing with time, ranging from 1 (no resistance) to 0 (no more drug action) : Rate constant of resistance appearance K L : Tumor growth rate K D : Drug constant-cell-kill rate

© Pharsight Corporation All Rights Reserved March, 2008 slide 64 Goal: To support early drug development decisions ●Go/No go ●Design of Phase III studies Simulate expected survival difference in Phase III ●Comparing a new drug (X) to a reference drug (R) ●Based on Phase II data of X and historical data of R Retrospective project: ●To simulate: Phase III of capecitabine (X) + docetaxel (R) vs. docetaxel in MBC Phase III of capecitabine (X) vs. 5-Fu (R) in CRC Claret L et al. Model-based predictions of expected anti-tumor response and survival in Phase III studies based on phase II data of an investigational agent. Proc ASCO, 24 (18S), 307s (Abs 6025), 2006. Support to end-of-Phase II development decisions: A retrospective project with capecitabine (Roche)

© Pharsight Corporation All Rights Reserved March, 2008 slide 65 Model parameter estimation ●Capecitabine data Phase II (2 studies, 170 patients) ●Docetaxel data Phase III (docetaxel arm, 223 patients) Simulation ●Phase III study of capecitabine + docetaxel vs. docetaxel (443 patients, 1000 replicates) Assumes additive effect for the combination Capecitabine scaled from Phase II to Phase III using disease specific parameters Focus on efficacy, no model for dose-limiting side-effects ●Simulations conditioned on observed dose intensity (dosing history) ●Drug effect driven by dose Simulation of a Phase III study comparing docetaxel to docetaxel + capecitabine in MBC

© Pharsight Corporation All Rights Reserved March, 2008 Tumor size reduction relative to baseline -0.20.00.20.40.6 0 100 200 300 -0.20.00.20.40.6 0 100 200 300 -0.20.00.20.40.6 0 50 100 200 300 Docetaxel + Capecitabine Arm Number of replicates Median25 th Quantile75 th Quantile Simulation of tumor size reduction at week 6 vs. observed in the Phase III study (1000 replicates)

© Pharsight Corporation All Rights Reserved March, 2008 slide 69 The structure of the tumor size model was robust to predict tumor growth and anti-tumor effect of: ●Three cytotoxic drugs in two tumor types Change in tumor size was a good predictor of survival ●Modeling of longitudinal tumor size data is much more informative than response rate determination ●Poor predictor of survival (primary endpoint in Phase III) Study-level correlations (Buyse Lancet 2000, Shanafelt JCO 2004) Even more problematic with new targeted therapies (cytostatic rather than cytotoxic) The combined tumor size and survival models: ●Successfully predicted expected treatment differences ●Is a useful approach to support early development decisions: Does the expected survival benefit of the new drug warrant further development? If yes, which Phase III study need be designed to show non-inferiority, superiority? Capecitabine project conclusions

© Pharsight Corporation All Rights Reserved March, 2008 slide 70 Pharsight uses the FDA NSCLC model Availability of data to develop the survival model is problematic in many companies The availability of generic public-domain models is critical We used the FDA NSCLC model (Wang et al, DIA, 2007) in one of our projects ●Pharsight Uses FDA Disease Model to Support Oncology Drug Development: http://media.corporate-ir.net/media_files/irol/12/121504/Release112007.pdf http://media.corporate-ir.net/media_files/irol/12/121504/Release112007.pdf The company was interested in getting expectations of survival for a NCE in combination ●To support decision to start a large Phase III study ●They had a Phase Ib combination study in NSCLC (less than 30 patients) We used the FDA model and simulated expected survival based on: ◦ Observed tumor shrinkage ◦ Patient’s prognostic factors ●The Pharmacometry team (Drs. Wang and Gobburu) provided us the information we needed The model will be used in several other projects soon

© Pharsight Corporation All Rights Reserved March, 2008 slide 71 A case study to illustrate the use of the FDA NSCLC model with erlotinib data Tumor shrinkage data are generally not reported in papers Karrison, Maitland, Stadler and Ratain recently proposed to use change in tumor size as the primary endpoint in randomized Phase II trials (J Natl Cancer Inst, 99, 1455-1461, 2007) ●In table 1 they report data for change in tumor size from 4 trials ●We used data from the pivotal erlotinib trial in 2 nd line patients (Shepherd et al. New Engl J Med, 353, 123-132) 488 patients in the erlotinib arm ORR: 8.9% Survival: 6.7 months

© Pharsight Corporation All Rights Reserved March, 2008 slide 72 A case study to illustrate the use of the FDA NSCLC model with erlotinib data (cont.) Data used for the simulation ●Week 8 fractional change data based on Karrison reported log ratio The ratio of tumor size at week 8 to baseline size is normally distributed Log ratio was sampled from normal distribution (Table 1): Mean 0.048, SD: 0.340 Shrinkage = exp(logratio)-1 (distribution given in backup) ●Baseline tumor size distribution Sampled from lognormal distribution Mean: 100 mm, SD: 57 mm (distribution in backup) ●EGOG 0, 1 proportion (80/20%) based on observed in Shepherd Shepherd trial included 66 % ECOG 0, 1 and 34 % ECOG 2, 3 patients (Table 1) Among ECOG 0, 1 patients, 20% had ECOG 1 and 80% ECOG 2 We simulated 1000 replicates of a virtual treatment arm of 300 patients (second–line, ECOG 0 or 1) ●C and D models (assumed to be second line treatments) were used to simulate 1000 replicates 25% of the replicates with C1, C2, D1 and D2 Parameters for each of the replicates were sampled in uncertainty of estimates ●Adjusted with early dropouts

© Pharsight Corporation All Rights Reserved March, 2008 The FDA SSCLC model can be used to simulate expected survival based on tumor shrinkage data Expected median survival: 7.2 months (95% PI: 5.2 to 10.1 months) ●Slightly longer than in Shepherd (was 6.7 months) ●But only concerns ECOG 0, 1 patients 1000 replicates of 300 patients Median 95% prediction interval

© Pharsight Corporation All Rights Reserved March, 2008 slide 74 The survival probability distribution of an investigational treatment can be quantified based on early tumor shrinkage clinical data (typically available in Phase Ib or II) ●Can be a new NCE ●Can be a new combination treatment An arm of the investigational treatment can be simulated conditional on a sample size ●To mimic a clinical trial arm These simulations can be compared to a survival distribution from a reference treatment ●Expected treatment arm difference can support Go/no go decision Phase III clinical trial design Phase III clinical trials can be simulated to assess probability of success Value of the survival simulations

© Pharsight Corporation All Rights Reserved March, 2008 slide 75 Change in tumor size is a good predictor of survival ●Response rate (the primary endpoint in Phase II) is a poor predictor of survival Study-level correlations (Buyse Lancet 2000, Shanafelt JCO 2004) ●Modeling of longitudinal tumor size data is much more informative than response rate determination to predict clinical benefit ●Supports use of change in tumor size as a primary endpoint in Phase II studies Drug-independent survival models allow to predict survival expectations or simulate Phase III trials based on early Phase Ib or Phase II data ●Availability of these models is limited ●FDA is in a unique position to develop such models without disclosing proprietary information ●Models for other endpoints (e.g. PFS) might be needed A modeling framework combining longitudinal tumor size models and drug independent survival models can be used ●To predict expected treatment efficacy (ORR, Survival, PFS) ●To simulate clinical trials ●To support Drug development decisions Clinical trial design Drug registration Conclusions

© Pharsight Corporation All Rights Reserved March, 2008 slide 76 Contributors to the Roche project ●P. Girard, Pharsight corporation, now with INSERM, University of Lyon, France ●K. Zuideveld, K. Jorga, J. Fagerberg, F. Sirzen, M. Abt, F. Hoffmann-La Roche J. O’Shaughnessy, Baylor-Sammons Cancer Center ●P. Hoff, MD Anderson Cancer Center ●E. Van Cutsem, University Hospital Gasthuisberg ●J. Blum, US Oncology Dallas FDA pharmacometrics team ●Y. Wang and J. Gobburu Acknowledgements

PhRMA White Paper on ADME Pharmacogenomics: Survey Results Lisa A. Shipley, Ph.D. Advisory Committee for Pharmaceutical Science and Clinical Pharmacology.

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PhRMA White Paper on ADME Pharmacogenomics: Survey Results Lisa A. Shipley, Ph.D. Advisory Committee for Pharmaceutical Science and Clinical Pharmacology.

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