Role of modelling and simulation in Phase I drug development

Although the use of pharmacokinetic/pharmacodynamic modelling and simulatio n (M&S) in drug development has increased during the last decade, this has most notably occurred in patient studies using the population approach. The role of M&S in Phase II although of longer history, does not presently hav e the same impact on drug development. However, trends such as the increase d use of biomarkers and clinical trial simulation as well as adoption of th e learn/confirm concept can be expected to increase the importance of model ling in Phase I. To help identify the role of M&S, its main advantages and the obstacles to its rational use. an expert meeting was organised by COST B15 in Brussels, January 10-11, 2000, This article presents the views expre ssed at that meeting. Although it is clear that M&S occurs in only a minori ty of Phase I clinical trials. it is used for a large number of different p urposes. In particular. M&S is considered valuable in the following situati ons: censoring because of assay limitation, characterisation of non-lineari ty, estimating exposure-response relationship. combined analyses, sparse sa mpling studies, special population studies. integrating PK/PD knowledge for decision making, simulation of Phase II trials, predicting multiple dose p rofile from single dose, bridging studies and formulation development. One or more of the following characteristics of M&S activities are often presen t and severely impede its successful integration into clinical drug develop ment: lack of trained personnel, lack of protocol and/or analysis plan, abs ence of pre-specified objectives, no timelines or budget, low priority, ina dequate reporting, no quality assurance of the modelling process and no eva luation of cost-benefit. The early clinical drug development phase is chang ing and if these implementation aspects can be appropriately addressed, M&S can fulfill an important role in reshaping the early trials by more effect ive extraction of information from studies, better integration of knowledge across studies and more precise predictions of trial outcome, thereby allo wing more informed decision making. (C) 2001 Elsevier Science B.V. All righ ts reserved.