1. 1 Use of Biomarkers in Clinical Development and Labeling: An Industry Perspective Douglas Mayers, MD International Head, Therapeutic Area Virology Boehringer Ingelheim Pharmaceuticals, Inc.

2. 2 Overview  General Overview  Use of Biomarkers in APTIVUS Clinical Development  Use of Biomarkers in Label  Use of TDM to Optimize Individual Patient Therapy

3. 3 Biomarkers  “A characteristic that is objectively measured and evaluated as an indicator of normal processes, pathogenic processes, or pharmacological responses to a therapeutic intervention”  Not a new concept but more aggressively pursued in current drug development due to increased understanding of disease processes  Often used in label to describe drug mechanism of action or describe target population of patients  Examples: IC50/EC50 against drug target Hgb A1c Her 2 Receptor or CEA-125

4. 4 Surrogate Endpoints  “Biomarker intended to substitute for a clinical endpoint, expected to predict clinical benefit/harm based on epidemiological, therapeutic, pathophysiologic or other scientific evidence”  Often used in label for documentation of efficacy  Requires prior validation process  Examples: Blood Pressure Antihypertensives CholesterolStatins Prothrombin timeCoumadin HIV RNA, CD4 cellsAntiretroviral Drugs

5. 5 Validation of Surrogate Endpoints – Example: CD4 T cells and HIV RNA in HIV-1  A collaborative effort involving multiple academic groups, industry, NIH and FDA  Required widespread availability of quality assured, well characterized, quantitative assays  Assays were biologically plausible and results were related to the natural history of HIV disease in several natural history cohorts  Changes in surrogate endpoints were related to clinical outcomes (AIDS progression and death) in several large clinical trials of antiviral drugs  Single sponsor or group unlikely to have the data needed to validate a surrogate endpoint

6. 6 Potential Uses of Biomarkers in Clinical Development Preclinical  Predicts relevant drug toxicities in man  Predicts efficacy in man Clinical  Dose selection for initial human studies  Target Validation/ Proof of Principle  Patient Selection  Phase 2/3 Trial Primary or Secondary Endpoints  Clinical Monitoring  Treatment Guidance  Individual patient dose selection or adjustment

7. 7 Linking PK  PD  Therapeutic Drug Monitoring Need to know the relationship between drug concentration and effect to successfully use therapeutic drug monitoring Direct relationship: plasma drug concentrations related directly to pharmacologic effect Indirect relationship: plasma drug concentrations related directly to a peripheral compartment (intracellular levels) or to a hypothetical effect compartment (receptor level) H. Derendorf & B. Meibohm Pharmaceutical Research 16(2): 176-185 (1999)

8. 8 Variability in PK and PD Estimates Dosage form variability <5% due to compendial requirements Assay variability <15% using internal standards and quality control samples PK data variability is controlled by experimental design to <50% Biomarker PD data variability depends on relationship to PK with %CV  100% Outcome data is highly variable requiring large trials and a search for biomarkers Dosage Form Assays PK PD Outcome PK/PD Variability H. Derendorf & B. Meibohm Pharmaceutical Research 16(2): 176-185 (1999)

9. 9 Use of Biomarkers and Surrogate Markers in HIV Drug Development – TPV Clinical Development  Development of HIV drugs has been greatly accelerated by the use of plasma HIV RNA levels and CD4 cell counts as surrogate endpoints for trials  Tipranavir phase 2/3 program was data-rich for biomarker and drug level data  All patients had baseline genotypic resistance, VL, CD4 cells  All patients had several TPV Cmin determinations with serial VL and CD4 data  Approximately 860 patients had baseline phenotypic drug resistance determined for their HIV isolates  TPV Cmin, baseline genotypic and phenotypic drug resistance, inhibitory quotient were related to viral load response in both phase 2 and phase 3 studies and results were used in clinical trial designs

10. 10  Novel nonpeptidic protease inhibitor developed to provide a new treatment option for highly treatment experienced patients or patients with virus resistant to multiple PIs  Potent in vitro activity against both WT HIV-1 and HIV-2, and the majority of multiple PI-resistant HIV-1 CH 3 OH NH O O SO 2 N F3CF3C H3CH3C Tipranavir Overview

11. 11 Tipranavir Co-Administration with Ritonavir  TPV exposure markedly enhanced with RTV co- administration  Cytochrome P450 3A is the major metabolic pathway  TPV induces CYP 3A  TPV and RTV co- administration results in net inhibition of CYP 3A Mean Plasma Tipranavir Concentration (  M) 125 100 75 50 25 0 Target 024681012 4x Cmax ss 9x greater exposure at steady-state Time (h) 48x Cmin ss TPV/r 500/200mg TPV 500mg alone

12. 12 -2 -1.5 -0.5 0 3581115 Days on Therapy Change (log 10 HIV-1 RNA) TPV 300 mg bid + RTV 200 mg bid (N = 10) TPV 1,200 mg bid + RTV 200 mg bid (N = 11) TPV 1,200 mg bid (N = 10) Protocol 1182.3: Wang Y et al. 7th Conference on Retroviruses and Opportunistic Infections. San Francisco, 2000. Abstract 673.Protocol 1182.3: Wang Y et al. 7th Conference on Retroviruses and Opportunistic Infections. San Francisco, 2000. Abstract 673. Early Study in Treatment-Naïve Patients

13. 13 Tipranavir Dose Finding Study Conclusions  3 TPV/r doses (500/100, 500/200, 750/200) BID studied in 216 patients with 3-class and 2 PI-regimen experience  500/200 dose selected for the Phase III trial program  500/100 dose eliminated due to inferior efficacy in patients with drug resistant viruses and more variable PK results  500/200 and 750/200 doses had similar efficacy and PK profiles  750/200 dose eliminated due to a higher rate of Grade 3 / 4 ALT/AST elevations and treatment discontinuations

14. 14 Tipranavir Key Mutations  Mutations at codons 33, 82, 84 and 90 of HIV-1 protease  Were either selected in early in vitro or in vivo studies or seen in HIV-1 isolates with decreased susceptibility to tipranavir  In the Phase II program multiple mutations at these sites:  Associated with decreased TPV/r responses  Associated with broad, high level resistance to other PIs (SQV, IDV, LPV, APV)  Used to select patients unlikely to get a durable response to any single PI-based regimen who were offered dual-boosted PI regimens containing TPV  < 3 key mutations into single boosted PI pivotal trials  > 3 key mutations into dual boosted PI phase 2 study

15. 15 Dose Finding BI 1182.52 NA, EU, AUS RESIST-1 BI 1182.12 North America Australia N=620, Safety N=620, Efficacy RESIST-2 BI 1182.48 Europe Latin America N=865, Safety N=539, Efficacy Companion Study BI 1182.51 All RESIST Countries Rollover Study, BI 1182.17, All RESIST Countries Pediatrics, Naïve Adults Emergency Use Expanded Access Tipranavir Phase II-III Study Program Optimal Dose TPV/r 500/200

16. 16 Efficacy

17. 17 RESIST Studies Efficacy Endpoints (Week 24) All Patients in 24-Week Analysis Set TPV/r N=582 CPI/r N=577P Value ≥ 1 log 10 VL Reduction (%)240 (41.2%)109 (18.9%)<0.0001 Total VL Reduction (log 10 )-0.80 -0.25<0.0001 VL <400 Copies/mL199 (34.2%)86 (14.9%)<0.0001 VL <50 Copies/mL139 (23.9%)54 (9.4%)<0.0001 Median CD4+ Cell Change+ 34+ 4<0.0001 AIDS Progression Events25 (3.4%)34 (4.6%) NS

18. 18 TPV Mutation Score Development  Uni- and multivariate regression analyses used to correlate 291 Phase II baseline genotypes to TPV phenotype, and Week 2 or 24 VL reduction  Uni- and multivariate regression analyses then applied to 569 Phase III baseline genotypes (RESIST) with phenotype and genotype to validate mutations selected by Phase II datasets  Reduced TPV susceptibility or reduced response associated with 21 mutations at 16 positions:  10V, 13V, 20M/R/V, 33F, 35G, 36I, 43T, 46L, 47V, 54A/M/V, 58E, 69K, 74P, 82L/T, 83D, and 84V  TPV Score = numbers of HIV Protease positions with a TPV- associated mutation

19. 19 Predictors of Antiviral Response to TPV/r Regimens: Multiple Regression Model P ValueEstimate <0.010.17 TPV Score (per mutation) <0.01-0.24 Per Available Drug in OBR <0.01-0.91Enfuvirtide Use <0.01-1.25Tipranavir/r 24 Weeks Parameter

20. 20 RESIST Trials 2-Week Viral Load Reduction According to TPV Trough (µMol) N=23305253 42 52 43 42 110  

21. 21 RESIST Trials 24-Week VL Reductions vs TPV Trough Geom. Mean TPV Trough Levels (  M) VL Change From Baseline (log copies/mL)

22. 22 Determination of Inhibitory Quotient IQ = C min /(3.75 x 0.058 x fold WT IC 50 )  C min = trough plasma TPV concentration (determined at days 7 and 14)  3.75 = protein binding adjustment factor  0.058  M = TPV IC 50 for HIV-1 wild type The reported TPV IQ is obtained exclusively from highly treatment-experienced (HTE) patients

23. 23 Tipranavir Phase 2 Trial Impact of IQ on 14-Day Viral Load Response 0.05 -0.13 -1.03 -1.16 -0.98 -1.25 -1.4 -1.2 -0.8 -0.6 -0.4 -0.2 0 ≤5≤5>5–30>30–50>50–100>100–150>150 Inhibitory quotient HIV RNA log 10 change from baseline 7 2714342558 0.2

24. 24 Sustained (Week 24) Viral Load Change by IQ and T20 use n = 61 42 26 101 26 16 13 44 + + + + IQ interval Viral load change from baseline (log 10 ) + + + + Actual Enfuvirtide Use No Yes Bars show range, box plots show IQR with median (—) and mean (+)

25. 25 Safety

26. 26 RESIST Trials (24 weeks) Grade 3/4 Lab Abnormalities >0.5% LabAbnormality TPV/r (n=748) CPI/r (n=737) HematologyWBC decrease4.9%5.5% Platelets1.1%1.0% Prothrombin Time1.1% ChemistryALT9.0%2.3% AST6.0%1.9% Bilirubin0.7%0.6% Amylase5.7%6.9% Lipase2.6%2.5% Cholesterol4.0%0.4% Triglycerides23.2%12.2% Glucose1.8%1.18%

27. RESIST Trials Patients with Grade 3 or 4 ALT, AST or Bilirubin by TPV Trough 0 10 20 30 40 50 <2020 - <4040 - <8080 - <120  120 TPV Concentration (µMol) Percentage Gr 3-412262064 At Risk148234205429 27

28. TPV Trough Comparison in Patients With vs Without Grade 3 or 4 ALT, AST or Bilirubin 28

29. 29 Use of TPV Drug Levels in Clinical Management  TPV trough levels > 6.5 uM associated with > 1 log VL response at 2 weeks  TPV trough levels > 120 uM associated with increased risk of ALT/AST elevations  93% of patients have TPV trough levels between 6.5  120 uM  Weak trends associating TPV trough levels with hepatic events and treatment responses  Large inter-patient variability could limit the utility of these measurements in clinical practice  The exception is the potential use of TDM to optimize dual- boosted regimens of TPV/r with a second PI which is being explored in small pilot studies at this time

30. 30 “The median Inhibitory Quotient (IQ) determined from 301 highly treatment- experienced patients was about 75 (inter-quartile range: 29-189), from pivotal clinical trials 1182.12 and 1182.48. The IQ is defined as the tipranavir trough concentration divided by the viral IC50 value, corrected for protein binding. There was a relationship between the proportion of patients with a ≥ 1 log10 reduction of viral load from baseline at week 24 and their IQ value. Among the 206 patients receiving APTIVUS/ritonavir without enfuvirtide, the response rate was 23% in those with an IQ value < 75 and 55% in those with an IQ value ≥ 75. Among the 95 patients receiving APTIVUS/ritonavir with enfuvirtide, the response rates in patients with an IQ value < 75 versus those with an IQ value ≥ 75 were 43% and 84%, respectively. These IQ groups are derived from a select population and are not meant to represent clinical breakpoints.” Clinical Pharmacology Section, APTIVUS US Package Insert

31. 31 Use of Biomarkers and Surrogate Markers in HIV Drug Development  Development of HIV drugs has been greatly accelerated by the use of plasma HIV RNA levels and CD4 cell counts as surrogate endpoints for trials  Phenotypic and genotypic drug resistance testing has been validated and integrated into clinical drug development and patient management  IQ calculations are used to determine target drug levels for phase 1 drug development and are then clinically validated in phase 2/3 trials  Genetic assessments are being conducted to understand PK variability and risk of drug-specific toxicity

32. 32 Use of Biomarkers in Labeling  Biomarkers are already integrated into product labeling:  Mechanism of Action  Patient Selection  Dose selection/adjustment  Clinical monitoring of therapy (safety and efficacy)  Validated surrogate endpoints are also integrated into current labeling:  Blood Pressure, Cholesterol  Prothrombin time  HIV or HCV RNA levels

33. 33 Use of TDM to optimize clinical management  Therapeutic drug monitoring to adjust doses to optimize patient management is more common in Europe than the US  A correlation between drug concentration and clinical effect needs to be established  Adjustment of drug levels must result in a clinical benefit  Use of TDM requires widely available, rapid turnaround, quality assured, clinically validated drug level assays with a clear algorithm for dose adjustment  Examples in clinical management:  Antiepileptic drugs  Aminoglycosides  Antiarrhythmic drugs

34. 34 Use of ARV drug levels or IQ measurements to individualize therapy  Mixed results from prospective clinical trials of use of drug levels or IQ to individualize ARV therapy with no clear demonstration of clinical benefit  Challenges:  Significant variability of drug levels in individual patients  Absence of standardized drug level measurements  Laboratory infrastructure does not currently exist  Absence of consensus on target drug concentrations or inhibitory quotients  Often only determined for one drug in a 3 – 4 drug regimen  2 to 4 week lag in obtaining drug levels and drug resistance results  Continued evolution of drug resistance in clinical HIV-1 with incompletely suppressive drug regimens such that high level resistance can emerge in 14 days for some drugs (NNRTI or lamivudine), partial resistance can emerge to PIs during the 14 – 28 day turnaround time for resistance results

35. 35 Labeling use of Therapeutic Drug Monitoring to optimize clinical management should require prospective validation  Biological plausibility is not a high enough standard  Use of TDM requires a certain level of infrastructure: widely available assay with rapid turnaround; quality assured, quantitative drug level measurements  The drug must have a large enough safe and effective range to allow reasonable dose adjustments (it is unclear what path should be taken to support drug doses giving drug levels above those tested in pivotal trials)  An algorithm for drug level adjustment must be prospectively validated with demonstrated clinical benefit – since dose adjustment could introduce new toxicities, loss of therapeutic effect, or decreased adherence  This level of proof does not exist for the majority of drugs (including antiretroviral drugs in clinical use today).