Artificial intelligence coupled to pharmacometrics modelling to tailor malaria and tuberculosis treatment in Africa.

ABSTRACT: Africa's vast genetic diversity poses challenges for optimising drug treatments in the continent, which is exacerbated by the fact that drug discovery and development efforts have historically been performed outside Africa. This has led to suboptimal therapeutic outcomes in African populations and overall scarcity of relevant pharmacogenetic data, including characteristic genotypes as well as drugs prescribed in the continent to treat infectious diseases. Here, we propose a general approach to identify drug-gene pairs with potential pharmacogenetic interest. Our pipeline couples machine learning and artificial intelligence with physiologically-based pharmacokinetic (PBPK) and non-linear mixed effects (NLME) modelling to hypothesize which pharmacogenes could be of potential clinical interes

SUBMITTER: Turon G 

PROVIDER: S-EPMC12537934 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature