Adding Model-Informed Precision Dosing to Precision Medicine to Improve Patient Drug Treatment Outcomes

Dear Colleagues,

Providing the right treatment at the right dose to the right patient is the ultimate goal of individualized (personalised) medicine. Although innovative breakthroughs in personalised medicine have led to the identification of mostly genetic biomarkers, improving treatment selection (the right treatment for the right patient), this process often stops without providing optimised personalised dosing (right dose). Thus, a large research gap remains.

Despite the complex pharmacokinetics of many clinically used drugs, and known physiological and pharmacological variabilities, fixed dosing, as determined by tightly controlled clinical trials, is still used. This often results in large variability in drug exposure between individuals and can lead to a significant proportion of patients experiencing toxicity or therapeutic failure, leading to poor patient outcome and a significant health and economic burden.

Model-informed precision dosing is an advanced mathematical approach, which integrates existing clinical and individual patient characteristics to determine the optimal dose. This approach has been shown to reduce the large inter-individual variability in drug exposure observed with many clinically used drugs. At present, patients are in an advantageous position to benefit from their treatment due to the advances in treatment selection, but the commonly used “one dose fits all” approach needs to be replaced by model-informed precision dosing, putting patients in an ideal position to receive the optimal treatment together with the optimal dose. The need to combine model-informed precision dosing with precision medicine is undeniable.

This Special Issue's theme focuses on using pharmacokinetic, pharmacodynamic, pharmacogenetic or any biomarker knowledge in a model-informed precision-dosing strategy to further enhance precision medicine for therapeutic agents by improving toxicity, efficacy, or survival.

Prof. Dr. Kishor Wasan
Dr. Madele Dyk
Dr. Robin Michelet
Guest Editors

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