Artificial Intelligence Enabled Pharmacometrics

Dear Colleagues,

The goal of this Special Issue is to present the latest advances in the field of pharmacometrics enhanced by artificial intelligence (AI), with the ultimate aim to further improve drug discovery and development as well as personalized medicine.

Pharmacometrics and quantitative systems pharmacology plays a vital role in the development of new drugs and personalized medicine. With the current scientific advancements, increasingly vast amounts of medical data become available, which we believe creates attractive opportunities for computationally more efficient methods such as AI technologies.

AI can support pharmacometric approaches and can be integrated in, e.g., model building and selection of predictive covariates; therapy optimization; or selection and inclusion of innovative biomarkers through, e.g., imaging. AI technologies can add value to modeling and simulation workflows through higher computationally efficiency, faster analysis of big data such as imaging and -omics data, and improvement of complex model performance, whereas classical modeling and simulation methods can be used to improve interpretability of AI approaches in the context of clinical pharmacology and provide hypothesis testing, which is crucial for regulatory interactions. To utilize the great potential of both methods, we propose pharmacometrics and AI to join forces in order to improve model performance, predictivity, and confidence in clinical pharmacological models compared with either approach alone.

For this research topic, we welcome original research, reviews, and opinion letters on AI-enabled pharmacometrics. Both technical and clinical implementation research falls within the scope.

Prof. Dr. Ulrika Simonsson
Guest Editor

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