Use of statistical and pharmacokinetic-pharmacodynamic modeling and simulation to improve decision-making: A section summary report of the trends and innovations in clinical trial statistics conference.

The application of modeling and simulation (M&S) methods to improve decision-making was discussed during the Trends & Innovations in Clinical Trial Statistics Conference held in Durham, North Carolina, USA on May 1-4, 2016. Uses of both pharmacometric and statistical M&S were presented during the conference, highlighting the diversity of the methods employed by pharmacometricians and statisticians to address a broad range of quantitative issues in drug development. Five presentations are summarized herein, which cover the development strategy of employing M&S to drive decision-making; European initiatives on best practice in M&S; case studies of pharmacokinetic/pharmacodynamics modeling in regulatory decisions; estimation of exposure-response relationships in the presence of confounding; and the utility of estimating the probability of a correct decision for dose selection when prior information is limited. While M&S has been widely used during the last few decades, it is expected to play an essential role as more quantitative assessments are employed in the decision-making process. By integrating M&S as a tool to compile the totality of evidence collected throughout the drug development program, more informed decisions will be made.

Life Cycle Modeling and Simulation: Current Practice and Future Impact on Health Care Innovation and Delivery.

Modeling and simulation is poised to transform drug development across the entire life cycle from discovery to commercialization. For the biopharmaceutical industry, this transformation will enable knowledge-based decision making and foster new collaborative ways of working that will translate into more high-value treatments and increased development efficiencies. In the health care arena, where value for money is paramount, modeling and simulation will inform future health care planning and practice.

Rheumatoid Arthritis Disease Progression Modeling.

Time progression models provide a significant advantage in developing clinical trials and can also be used to elicit comparisons among therapeutic agents. The authors performed a meta-analysis to construct a time progression model for rheumatoid arthritis (RA), an area of significant interest for pharmaceutical development, using the ACR20 end point. Compounds studied were chiefly monoclonal antibodies that were used in conjunction with methotrexate. The study shows that an exponential time response model adequately fits the data. From the modeling, a distribution of effects for biological RA therapies can be provided.