DISCUSSION / CONCLUSIONS

1. Literature Data Survival Models: Case Studies for Anticoagulants in Atrial Fibrillation and Acute Coronary Syndrome Farkad Ezzet Pharsight, A Certara Company, St. Louis, MO, USA INTRODUCTION OBJECTIVE Due to small event rates of efficacy and safety outcomes, phase III clinical trials of anticoagulants require thousands of patient and thus are very costly. Learning from competitor drugs is key to improving trial design and potentially reducing development costs of new treatments. Public literature when appropriately captured, analyzed and interpreted helps meet these goals. In indications such as atrial fibrillation (AF) and acute coronary syndrome (ACS) efficacy (e.g. events of strokes) and safety (events of major bleeds) are typically reported as time course of cumulative hazard rates (CHR) or of cumulative incidence rates (CIR). CHR and CIR provide sufficient information to characterize disease and treatment effects. Using properties of survival analysis further insight is gained in the disease areas allowing characterization of competitive landscape, optimization of trial designs and exploration of alternative drug development strategies. • To effectively use public literature of large clinical trials of anticoagulants to characterize • Events of stroke or systemic embolism in patients with Atrial Fibrillation (AF) • Event of Major Bleeding in patients with Acute Coronary Syndrome (ACS) RESULTS RESULTS • Study 1: • CHR of stroke was found to be linear with time. Rates were 0.09%, 0.12%, and 0.14% per month for dabigatran 150 mg, dabigatran 110 mg and warfarin, respectively. • Study 2: • CHR of stroke was found to be linear with time. Rates were 0.14% and 0.31% per month for apixaban 5 mg bid and aspirin, respectively. • Study 3: • CIR of MB was best described by an Emax model with a Hill parameter =0.7, Figure 2 (left panel). The ET50 was 0.9 months. Emax of clopidogrel was 12.9% while that of ticagrelor was slightly higher at 13.5% Because of rapid ET50 most MB events occurred early. At 3, 6, 9 or 12 months, MB event rates were estimated at 10%, 11.3%, 11.8% and 12% respectively. • Using the Weibul CH function provided a reasonable fit to the data. The parameters λ and θ where estimated equal to 6.3 and 0.26, respectively. The advantage of this model is the ability to use model parameters to simulate time to event data. A useful tool to explore trial design and dose optimization. METHODS The primary efficacy endpoint in two AF studies was stroke or systemic embolism (SSE) [1,2]. The primary safety endpoint in a third study in ACS indication was major bleeding (MB) [3] Figures of Cumulative hazard rates (CHR) or of cumulative incidence rates (CIR) reported in the respective publications were captured digitally providing rates and times, Figure 1. Figure 1 suggests a linear hazard rates of SSE (top panels) and a non-linear hazard rate for MB (bottom panel of Figure 1) . A linear model with zero intercept and a slope was fitted to SSE for each AF study separately. The slope provides an estimate of the hazard rate. An Emax type function with zero intercept was fitted to MB data of study 3. ET50 represents the time (m) to reach 50% of maximal bleed rate (Emax). Alternatively, treating CIR as CHR, the data were fitted using cumulative hazard with time assumed to follow a Weibul distribution, according to CH(time) = λ time θ, λ>0, θ>0 [3] [3] Observed Cumulative Hazard Figure 2: Observed and fitted CIR for MB using Emax (left) and using Weibul CH (right) DISCUSSION / CONCLUSIONS Use of publicly available literature for the characterization of safety and efficacy profiles of competing therapies is becoming key in the evaluation and assessment of new treatments. Integration of data extracted from publicly available literature on competitor treatments is a convenient and highly cost-effective method of gathering data. Despite conceptual and technical challenges, implementation has been largely successful, as evidenced by the number of publications appearing under the heading “Literature Based Meta-analysis”, especially for outcomes on the continuous [4, 5] or binary scales [6] I n the case of time to event (TTE) response [7], results are typically reported as plots of Cumulative Hazard Rates (CHR) [1,2] or Cumulative Incidence Rates (CIR) [3]. CHR and CIR must be dealt with and interpreted using properties of TTE variables and survival models. When appropriately analyzed and interpreted, CHR provides insight on safety and efficacy profiles for classes of drugs and of comparators. A further benefit of this effort is a comprehensive characterization of the clinical outcome, significantly improving trial design and potentially reducing development cost [2] [1] [3] Figure 1: Observed CHR for Stroke or systemic embolism [1], N=18100, and [2], N=5600 (top panels) and CIR for MB [3], N=18600 (bottom panel) REFERENCES [1] Connolly SJ, Ezekowitz MD, Yusuf S, et al (2009) Dabigatran versus Warfarin in Patients with Atrial Fibrillation, N Engl J Med; 361:1140-51 [2] Connolly SJ, Eikelboom J, Joyner C, et al (2011) Apixaban in Patients with Atrial Fibrillation, N Engl J Med; 364:806-817 [3] Wallentin, L., Richard C. RC, Budaj, et al (2009) Ticagrelor versus Clopidogrel in Patients with Acute Coronary Syndromes, N Engl J Med; 361:1045-57 [4] Ahn JE, French JL (2010) Longitudinal aggregate data model-based meta-analysis with NONMEM: approaches to handling within treatment arm correlation. J Pharmacokinet Pharmacodyn 37: 179-201 [5] Ezzet, F. (2008) The Role of Literature-Based Disease Progression Models to Support Knowledge Management and Decision-Making in Clinical Drug Development, AAPS National Biotechnology Conference, Toronto, Canada [6] Ezzet, F., Prins, K, Boucher, M. (2010) Modeling Adverse Event rates of Opioids for the Treatment of Osteoarthritis Pain using Literature Data. PAGE 19 (2010) Abstr 1771 [www.page-meeting.org/?abstract=1797] [7] Cox E, Wada DR, Zhang N, Wiegand F (2010) Meta- Analysis of Retention Rates of Post-Marketing Trials to Compare Effectiveness of Second Generation Antiepileptic Drugs. PAGE 19 (2010) Abstr 1797 [www.page-meeting.org/?abstract=1797]