JSPS. Modeling & Simulation: A Tool to Enable Efficient Clinical Drug Development. March 29, 2005

Transcription

1 Dr. Kunihiro Sasahara, Ph.D. Dr. Russell Wada, Ph.D. Dr. Yuying Gao, M.D., Ph.D. Modeling & Simulation: A Tool to Enable Efficient Clinical Drug Development JSPS March 29, 2005

2 Agenda Overview of Clinical Trial Simulations The Pharsight Trial Simulator software Application to the efficient approval of etanercept in pediatric patients with rheumatoid arthritis Conclusions 2

3 Population pharmacokinetic modeling and exposure-response modeling are essential ingredients in drug development today. Exposure-Response (FDA Guidance: 2003) Dose PK Cp PD Efficacy/Safety Pop PK (FDA Guidance: 1999) How can we incorporate these models into clinical trial simulations? How might clinical trial simulations be useful? 3

4 In 1999, the CDDS developed good practices for simulations in drug development, with three guiding principles: clarity, completeness, and parsimony. Simulation in Drug Development: Good Practices Draft Publication of the Center for Drug Development Science (CDDS) Draft version 1.0, July 23, 1999 Copyright: CDDS, 1999 Editors: Holford NHG, Hale M, Ko HC, Steimer J-L, Sheiner LB, Peck CC Contributors : Bonate P, Gillespie WR, Ludden T, Rubin DB, Stanski D CLARITY: The report of the simulation should be understandable in terms of scope and conclusions by intended users such as those responsible for committing resources to a clinical trial. COMPLETENESS: The assumptions, methods and critical results should be described in sufficient detail to be reproduced by an independent team. PARSIMONY: The complexity of the models and simulation procedures should be no more than necessary to meet the objectives of the simulation project 4

5 Here s an example using clinical trial simulations to avoid a Phase 3 trial studying dose-intensification of docetaxel in NSCLC patients with high α1-acid glycoprotein levels. Docetaxel was approved at a dose of 100 mg/m2 in patients with non-smallcell lung cancer. Patients with high α1-acid glycoprotein levels (AAG) had shorter time to progression and death. Exposure-response analysis of 151 Phase 2 patients showed that cumulative dose, first cycle AUC and AAG were predictive of progression and survival. 125 mg/m2 was selected as the optimal dose because of dose-limiting hematologic toxicity. Clinical trial simulations demonstrated a slight survival benefit that had a 6% likelihood to be significant in Phase 3. Veyrat-Follet CPT 2000; 68:

6 Here s an example of using Phase 3 data to demonstrate similar clinical outcome for weight-based vs. fixed-dose regimens of ARANESP, an erythropoietin stimulating protein. Jumbe et al. Supplement Oncology 2002, p. 37 6

7 Agenda Overview of Clinical Trial Simulations The Pharsight Trial Simulator software Application to the efficient approval of etanercept in pediatric patients with rheumatoid arthritis Conclusions 7

8 Before using Trial Simulation, equations describing population variability must be determined using modeling. Clearance/F Population Mean ± SE Variability BSA Population Mean Variability Female Male ± (L/h) ± (L/h) 29% (m 2 ) 30% CL/F = (Population Mean ± SE) (BSA/1.071) 1.41 exp(η) Variability due to BSA Variability due to uncertain parameter estimates 8 Variability (unexplained)

9 The trial simulator software organizes the clinical trial simulation into seven sections. 9

10 The drug model is organized using a graphical model editor. Formulations Responses PK Model 10

11 Demographic variables and pharmacokinetic parameters are viewed by opening the blocks. 11

12 Monte-Carlo simulations generate distributions of patient characteristics. These virtual patients are enrolled into a clinical trials, and drug response is determined. Count ID SEX BSA (m2) CLF (L/h) 1 F M F M F F M F M F BSA (m2) Count Count Count Count Male, BSA=1.071 m Clearance (L/h) Male Clearance (L/h) Female Clearance (L/h) All Children Clearance (L/h) 12

13 Dosing schedules are specified and displayed in a graphical user interface. 13

14 Agenda Overview of Clinical Trial Simulations The Pharsight Trial Simulator software Application to the efficient approval of etanercept in pediatric patients with rheumatoid arthritis This example is based on a recent publication by Yim et al, Journal Clinical Pharmacology, 2005; 45: It was selected because it illustrates clearly how clinical trial simulations bridge from data to new trials using models. Conclusions 14

15 Development strategy Tumor necrosis factor alpha is elevated in synovial joints in rheumatoid arthritis. Etanercept inactivates tumor necrosis factor alpha Received FDA approval in adults for RA, psoriatic arthritis, ankylosing spondylitis and polyarticular course of juvenile rheumatoid arthritis and psoriasis in adults. Recommended dose in JRA patients was 0.4 mg/kg SQ twice weekly Etanercept has a 5-day half-life, so a doubled dose of a once-weekly injection was sought. Key Question Using PK data from twice-weekly SQ injections in JRA patients, could population PK models and clinical trial simulations be used to support the approval of the once-weekly regimen? Yim et al, Journal Clinical Pharmacology, 2005; 45:

16 Description of patients studied with twice-a-week dosing used in the modeling. Yim et al, Journal Clinical Pharmacology, 2005; 45:

17 Simulation strategy Develop demographic model and population PK based on 69 juvenile RA patients. Trial simulations 100 subjects per BIW and QIW regimen. 100 trial replications of the trial Display graphically the mean, median, 5 th and 95 th percentile concentration values at each time point. Compare BIW vs. QIW regimens Utilize previously developed exposure-response model for efficacy in adults, and dose-response information for safety in juveniles and adults. Yim et al, Journal Clinical Pharmacology, 2005; 45:

18 Population PK model parameters Yim et al, Journal Clinical Pharmacology, 2005; 45:

19 Here is an overlay of measured and predicted concentrations. Yim et al, Journal Clinical Pharmacology, 2005; 45:

20 Simulations of BIW and QIW dosing show 11% greater peak concentrations and 18% lower trough concentrations on QIW dosing. Yim et al, Journal Clinical Pharmacology, 2005; 45:

21 Conclusions of the clinical trial simulation Concentration simulations for the 0.4 mg/kg twice-weekly and 0.8 mg/kg once weekly dose regimens showed overlapping profiles that support interchangeability between the dosing regimens. It is possible that the increased peak concentrations by 11% may cause a safety risk. On the basis of simulation analysis, FDA approved the dosing regimen of etanercept 0.8 mg/kg SC once weekly for pediatric patients with JRA along with 50 mg SC once weekly in adults. Key Bridging Assumptions Twice-weekly pharmacokinetics is predictive of once-weekly pharmacokinetics in pediatrics Exposure-response relationships in JRA are similar between pediatrics and adults 21

22 Agenda Overview of Clinical Trial Simulations The Pharsight Trial Simulator software Application to the efficient approval of etanercept in pediatric patients with rheumatoid arthritis Conclusions 22

23 Clinical trial simulation bridges from existing data to new trials using models. The predictability depends on the strength of the key assumptions. Phase 2 NSCLC to Phase 3 NSCLC for docetaxel Phase 2 population is similar to phase 3 population Exposure-response relationship extends to higher doses Phase 3 weight-based vs. fixed doses of ARANESP The effects of weight on hemoglobin response are captured in pharmacokinetics Twice-a-week to once-a-week dosing of etanercept in juvenile RA Once-a-week PK predicts twice-a-week PK Exposure-response similar in adults and children When assumptions are strong, clinical trial simulations can be used to support regulatory interactions. When assumptions are weaker, clinical trial simulations support internal decisions. 23

24 Other bridging applications of clinical trial simulations From immediate release to extended release formulations From approved statins to new statins in hypercholesteremia Similar relationship between LDL reduction and cardiovascular risk reduction For PPAR gammas to PPAR alphas in diabetes Animal models for glucose lowering are relevant to humans For novel neuroprotective Parkinson s drugs Animal models of neuroprotection are relevant to humans From Western populations to Japanese populations 24

25 Model-based drug development was broadly discussed by industry and FDA at the 2005 American Society of Clinical Pharmacology and Therapeutics Meeting. * 25

26 Conclusions Modeling and clinical trial simulation is a tool that is being used by pharmaceutical companies and FDA to improve the efficiency of drug development. Clinical trial simulation bridges from existing data to new trials using models. Pharsight s Trial Simulator is capable of implementing most Clinical Trial Simulations. 26