Observations from the Antiviral Information Management System (AIMS) Database

1. Observations from the Antiviral Information Management System (AIMS) Database Jeffry Florian, Ph.D. CDER/OTS/OCP Division of Pharmacometrics, Reviewer The opinions and information in this presentation are those of the author, and do not represent the views and/or policies of the U.S. Food and Drug Administration.

2. Outline • Overview of the Antiviral Information Management System (AIMS) • Observations and analyses from AIMS • EOP2 • NDA • Trial design • Future project considerations with the database

3. 40+ HCV drugs in development BOTTLENECK: Lack of available database & data standards A system to archivedata and assistanalysis for new anti-HCV agents is needed to inform dose selection. • With more than forty new anti-HCV drugs in the pipeline, we must keep pace with development. • Modeling and simulation can help inform dosing and trial design issues for efficient development. • Systematic archival of data and analysis will help leverage prior knowledge in this emerging therapeutic area. • Project started in 2008 – Critical Path Initiative • Dr. Gobburu and Dr. Jadhav

4. Mean viral load : 1a 1b Modeling codes and analyses log10 HCV RNA Analysis Data Modeling codes and analyses 1.0 0.5 0 Fraction achieving SVR time (weeks) 0 12 24 48 Time (weeks) AIMS database relies on : (1) Database standards, (2) Data requests (3) Internal analysis codes, and (4) Shared experience AIMS Database Standards

5. Implementation of the relational database structure, data templates and controlled terms requires forward thinking and planning. Sponsors will receive a data template and a list of controlled terms to guide data submission. • Templates: definitions and examples for all data fields. • Controlled Terms: specific listing of acceptable inputs for each data field to ensure identical formatting for all sponsors. AIMS Database Relationship Diagram: Relational structure supports efficient queries CM CM EX EX concomitant meds exposure PC PC pharmacokinetics DRUG DRUG STUDY STUDY DM DM MB MB demographics virology VS VS Analysis data specific for HCV vital signs Raw data in abbreviated CDISC format EP EP LB LB endpoints lab measures

6. The available information depends on the active and willing collaboration of sponsors developing HCV drugs • Original information request was issued in July 2010 to all companies with an active HCV IND • New data request issued whenever a sponsor submits a new IND • Voluntary data submission of completed trials according to the provided data standard. • Request data at the time of End-of-Phase 2, End-of-Phase 2A, or other key early development meetings

7. Every day is like drinking from a fire hose http://www.phdcomics.com/comics/archive.php?comicid=820

8. The AIMS database contains demographic, PK, virologic, and treatment data from previous, recent, and ongoing trials. 9 drug development programs 29 studies 10K+ subjects • Legacy data were converted using internal resources to include in the database. • Recent Phase III trials were submitted according to AIMS standard. • Many EOP2 meetings have accompanying AIMS datasets • Multiple treatment regimens (PR, PR+DAA) and durations were included in the dataset.

9. The AIMS database assists reviewers in analyses across all HCV submissions In addition ... • Use archived HCV data to generate research hypotheses across multiple studies and drugs. • Archive data from sponsors without additional formatting. • Generate analysis datasets, plots and reports using automated scripts. • Archive analysis results (data, models, plots, reports). • Access historic data to inform decisions about new submissions.

10. Outline • Overview of the Antiviral Information Management System (AIMS) • Observations and analyses from AIMS • EOP2 • NDA • Trial design • Future project considerations with the database

11. AIMS database has aided reviewers in dose selection and treatment duration during early drug development. Involved in 16+ EOP2/EOP2A/Type C meetings

12. Sponsors are submitting materials to support and justify doses, treatment durations, and patient population • A majority of EOP2 submission packages are accompanied by modeling and simulation results • Supportive analyses for regimen(s) selected for registrational trials • Viral kinetic modeling including resistance and viral subtypes • Predictions of SVR using studied and/or exploratory regimens • Exposure-response safety analyses for key signals identified in Phase II • AIMS datasets have been provided for ~50% of early phase meetings • Data conversion and standardization is time consuming • not commonly performed until later in drug development • Sponsors provide datasets for modeling and simulation whenever available

13. General observations over multiple EOP2 submissions • As regimens are becoming better (↑SVR, ↓treatment duration), the need for earlier assessments increases • Intrinsic patient factors remain important for treatment outcome (e.g., IL28B, cirrhosis, baseline HCV RNA) • Genotype subtype is becoming more important (ref: Dr. Harrington) • P/R-regimens: time to HCV RNA not detected remains predictive of response • The impact of shortening treatment duration may require even earlier metrics (eRVR or even ‘Week 1’VR) • IFN-free regimens: antiviral activity ≠ SVR • Predictive factors based on viral kinetics remain to be identified • Much easier to identify when something is not optimal

14. Outline • Overview of the Antiviral Information Management System (AIMS) • Observations and analyses from AIMS • EOP2 • NDA • Trial design • Future project considerations with the database

15. Two new HCV therapies characterized by drastically different drug development programs Two programs, same story

16. “Bridging” Observations Through Interferon Responsiveness • Similar response with first or second round of P/R treatment • Data for previously treated subjects are “bridged” with data from untreated subjects • Previously treated subjects are represented within untreated subjects • P/R treatment for HCV is unlike HIV treatment which frequently leads to resistance and does not yield similar virologic response on subsequent courses of treatment

17. Similar Virologic Response at Week 4 with First or Second PR treatment (pooled analysis) 1416 468 597 219 507 112 548 Liu et al. CID 2012

18. Standardized datasets facilitated similar analyses and led to novel dosing recommendations. • A successful trial in TE subjects can serve as evidence of effectiveness to support dosing and approval in TN subjects.

19. Outline • Overview of the Antiviral Information Management System (AIMS) • Observations and analyses from AIMS • EOP2 • NDA • Trial design • Future project considerations with the database

20. TREATMENT Follow-up Wk 24 Follow-up Wk 24 TREATMENT 32 8 16 24 40 48 60 72 WEEK 0 SVR24 was the surrogate endpoint used in original Peg-IFN/RBV and recent DAA+Peg-IFN/RBV trials 60-70% 30-40% • Endpoint is assessed 24 weeks after the end of treatment • Follow-up duration may be as long as treatment • SVR12 (HCV not detected at 12 weeks post treatment) is evidence of effectiveness in Phase II • Can a similar assessment be used in Phase III?

21. Concordance was observed between SVR12 and SVR24 for all Peg-IFN/RBV and DAA+Peg-IFN/RBV treatments ~2% of patients with SVR12 relapse by SVR24 assessment (false positive) PPV: 98% NPV: 99% Sensitivity: 99% Specificity: 98.0% • Less agreement between SVR4 and SVR24 • SVR4 may be useful for guiding dose selection PPV: 91.1% NPV: 98.2% 21 Sensitivity: 98.7% Specificity: 87.7%

22. Overall 2% Sensitivity analyses support that SVR12 and SVR24 are concordant for Peg-IFN/RBV containing regimens • No matter how the analysis was performed 1-3% of patients relapse between SVR12 and SVR24

23. The analysis of SVR12/SVR24 for genotype 1 subjects motivated similar analyses for other populations and different regimens • Subsequent application of the same analysis demonstrated concordance for Pediatrics Genotype 2/3 G2/3 • IFN-free regimens: Additional data is required • Provide all available SVR12 and SVR24 data from drug development program and discuss (all regimens)

24. Outline • Overview of the Antiviral Information Management System (AIMS) • Observations and analyses from AIMS • EOP2 • NDA • Trial design • Future project considerations with the database

25. eDISH (Evaluation of Drug-Induced Serious Hepatotoxicity) – FDA reviewer tool • eDISH is a tool developed to assist reviewers in analyzing/explaining DILI in an IND/NDA • Compatible with AIMS datasets submitted by sponsors http://www.fda.gov/downloads/Drugs/ScienceResearch/ResearchAreas/ucm076777.pdf

26. eDISH (Evaluation of Drug-Induced Serious Hepatotoxicity) – FDA reviewer tool (cont.) eDISH includes time plots of key laboratory values • Data is linked to individual patient narratives • May assist in the safety analyses for IFN-free regimens 26 http://www.fda.gov/downloads/Drugs/ScienceResearch/ResearchAreas/ucm076777.pdf

27. Conclusions • Sponsors are submitting datasets for AIMS • These datasets are assisting in the review of submissions at EOP2 meetings • Information from these submissions has provided insight regarding subsequent HCV trial design • Future projects will continue to be evaluated as additional data becomes available

28. Acknowledgements • Division of Antiviral Products • Debra Birnkrant • Jeff Murray • Many supportive medical reviewers and project managers • DAVP Clinical Virology Team • Patrick Harrington • Jules O’Rear • Lisa Naeger • NCTR • Steve Hodge • Edward Bearden • OTS • Chuck Cooper • Office of Biometrics • Ted Guo • Many subjects, investigators, and sponsors who have provided data • Critical Path Initiative and ORISE • Lauren Neal • Jianmeng Chen • OCP (Division of Clinical Pharmacology IV) • John Lazor • Kellie Reynolds • Sarah Robertson • Vikram Arya • Stanley Au • Ruben Ayala • Shirley Seo • Jenny Zheng • OCP (Division of Pharmacometrics) • Joga Gobburu • Pravin Jadhav • Yaning Wang • Ying Chen

29. Questions