Clinical Pharmacology Worldwide: A Global Health Perspective.

Low- and middle-income countries (LMICs) have the highest rates of mortality and morbidity globally, but lag behind high-income countries in the number of clinical trials and trained researchers, as well as research data pertaining to their populations. Lack of local clinical pharmacology and pharmacometrics expertise, limited training opportunities, and lack of local genomic data may contribute to health inequalities and limit the application of precision medicine. Continuing to develop health care infrastructure, including well-designed clinical pharmacology training and data collection in LMICs, can help address these challenges. International collaboration aimed at improving training and infrastructure and encouraging locally driven research and clinical trials will be of benefit. This review describes several examples where clinical pharmacology expertise could be leveraged, including opportunities for pharmacogenomic expertise that could drive improved recommendations for clinical guidelines. Also described are clinical pharmacology and pharmacometrics training programs in Africa, and the personal experience of a Tanzanian researcher currently on a training sabbatical in the United States, as illustrative examples of how training in clinical pharmacology can be effectively implemented in LMICs. These training efforts will benefit from advocacy for employment opportunities and career development pathways for clinical pharmacologists that are gradually being recognized and developed in LMICs. Clinical pharmacologists have a key role to play in global health, and development of training and research infrastructure to advance this expertise in LMICs will be of tremendous benefit.

Non-linear mixed effects modeling - from methodology and software development to driving implementation in drug development science.

Few scientific contributions have made significant impact unless there was a champion who had the vision to see the potential for its use in seemingly disparate areas-and who then drove active implementation. In this paper, we present a historical summary of the development of non-linear mixed effects (NLME) modeling up to the more recent extensions of this statistical methodology. The paper places strong emphasis on the pivotal role played by Lewis B. Sheiner (1940-2004), who used this statistical methodology to elucidate solutions to real problems identified in clinical practice and in medical research and on how he drove implementation of the proposed solutions. A succinct overview of the evolution of the NLME modeling methodology is presented as well as ideas on how its expansion helped to provide guidance for a more scientific view of (model-based) drug development that reduces empiricism in favor of critical quantitative thinking and decision making.