AI in Oncology: Predicting Pharmacokinetics for Dose Adjustment and Therapeutic Optimization

Dear Colleagues,

In oncology, the emergence of targeted therapies administered by the oral route has enabled the chronicization of cancer treatments. A large number of patients can now be treated for several years with these anti-cancer agents. They receive several tablets or capsules every day. These drugs are very often co-administered with others for the treatment of various pathologies in the same patients. The oral route has the disadvantage of higher variability in the fate of the active substance in the body, which leads to significant inter- and intra-individual pharmacokinetic variability. Furthermore, the oral intake and the metabolism of these active substances cause significant risks of drug–drug interactions. Finally, oral intake and the associated side-effects or toxicities are responsible for poor compliance. Therapeutic drug monitoring (TDM) developed for antibiotherapy, neuroleptics, or immunosuppressive therapy is also being developed in oncology. The emergence of artificial intelligence and, more generally, mathematical modelling of the large amount of data collected in this context should make it possible to propose optimized and personalized treatments with adjusted dosage in order to improve the effectiveness and limit the side-effects of these targeted therapies.

The theme of this Special Issue concerns all aspects of the mathematical pharmacokinetic, pharmacogenetic, and pharmacometric modelling of targeted therapies and more generally of anti-cancer drugs, in order to predict exposure and the pharmacodynamic effects (efficacy or toxicities) of these treatments.

Prof. Dr. Angelo Paci
Guest Editor

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• oncology
• pharmacokinetics
• artificial intelligence
• mathematical modelling
• therapeutic drug monitoring
• optimization
• personalized medicine