Modeling and simulation for medical product development and evaluation: highlights from the FDA-C-Path-ISOP 2013 workshop.

Medical-product development has become increasingly challenging and resource-intensive. In 2004, the Food and Drug Administration (FDA) described critical challenges facing medical-product development by establishing the critical path initiative [1]. Priorities identified included the need for improved modeling and simulation tools, further emphasized in FDA's 2011 Strategic Plan for Regulatory Science [Appendix]. In an effort to support and advance model-informed medical-product development (MIMPD), the Critical Path Institute (C-Path) [www.c-path.org], FDA, and International Society of Pharmacometrics [www.go-isop.org] co-sponsored a workshop in Washington, D.C. on September 26, 2013, to examine integrated approaches to developing and applying model- MIMPD. The workshop brought together an international group of scientists from industry, academia, FDA, and the European Medicines Agency to discuss MIMPD strategies and their applications. A commentary on the proceedings of that workshop is presented here.

Fostering culture and optimizing organizational structure for implementing model-based drug development.

Model-based drug development (MBDD) is a promising approach to improve decision making in drug development. The pharmaceutical industry has made substantial progress from engaging in empirical decision making to increasingly using pharmacometrics (ie, modeling and simulation [M&S]) as a quantitative decision-making tool. Focusing on culture and an organizational structure perspective, this commentary summarizes experiences and vision from industry M&S leaders on implementing MBDD. A culture for MBDD needs to have wide acceptance of MBDD, enhanced decision making with probability-based evidence and transparent rationale, quantitative impact metrics, and a brand that emphasizes cross-disciplinary collaboration and ownership. An organizational structure for MBDD needs to have a dedicated pharmacometrics function, fine balance between quick wins and impact on long-term R&D goals, and collaborative MBDD teams among clinical pharmacologists, statisticians, pharmacometricians, and clinicians. Pharmaceutical companies with these characteristics are prepared to fully embrace and implement MBDD.

Model-based drug development: the road to quantitative pharmacology.

High development costs and low success rates in bringing new medicines to the market demand more efficient and effective approaches. Identified by the FDA as a valuable prognostic tool for fulfilling such a demand, model-based drug development is a mathematical and statistical approach that constructs, validates, and utilizes disease models, drug exposure-response models, and pharmacometric models to facilitate drug development. Quantitative pharmacology is a discipline that learns and confirms the key characteristics of new molecular entities in a quantitative manner, with goal of providing explicit, reproducible, and predictive evidence for optimizing drug development plans and enabling critical decision making. Model-based drug development serves as an integral part of quantitative pharmacology. This work reviews the general concept, basic elements, and evolving role of model-based drug development in quantitative pharmacology. Two case studies are presented to illustrate how the model-based drug development approach can facilitate knowledge management and decision making during drug development. The case studies also highlight the organizational learning that comes through implementation of quantitative pharmacology as a discipline. Finally, the prospects of quantitative pharmacology as an emerging discipline are discussed. Advances in this discipline will require continued collaboration between academia, industry and regulatory agencies.

Population dose-response model for tadalafil in the treatment of male erectile dysfunction.

PURPOSE: To determine the population dose-response relationship for tadalafil during on-demand (as-needed) administration for treatment of erectile dysfunction (ED). METHODS: A total of 212 male patients with mild, moderate, or severe ED participated in a multicenter, randomized, double-blind, placebo-controlled, parallel-group study. Patients were randomized to receive placebo or 2, 5, 10, or 25 mg tadalafil, taken on demand over an 8-week period. Efficacy was assessed on the basis of questions 2 and 3 of the Sexual Encounter Profile (SEP) and questions 3 and 4 of the International Index of Erectile Function (IIEF) questionaires. These scores were modeled using logistic regression. A fifth patient response, the IIEF EF (erectile function) domain score, was modeled as a continuous variable. RESULTS: The dose-response relationship for each efficacy variable was best described with an Emax model, in which maximum effect increased with ED severity at baseline. Response scores increased substantially between 10 and 25 mg tadalafil doses, and the dose-response parameter estimates suggested possibly higher responses at even higher doses. CONCLUSIONS: Population dose-response modeling of all five oucome measures indicated that efficacy in all ED severity groups in the studied population generally increased across the 2 to 25 mg tadalafil dose range. Estimates of maximal improvement (Emax) in the IIEF EF domain score were 7.5, 11.4, and 16.3 points for patients with mild, moderate, and severe ED, respectively. Corresponding tadalafil doses to attain half-maximal improvement (ED50 estimates) were 4.7 mg, 7.1 mg, and 10.1 mg.