Model‐Informed Artificial Intelligence: Reinforcement Learning for Precision Dosing. Issue 4 (23rd February 2020)

• Record Type: Journal Article
• Authors: Ribba, Benjamin; Dudal, Sherri; Lavé, Thierry; Peck, Richard W.
• Is Part Of: Clinical pharmacology & therapeutics. Volume 107:Issue 4(2020)
• Page Start: 853
• Page End: 857
• Publication Date: 2020-02-23
• Subjects: Pharmacology -- Periodicals; Therapeutics -- Periodicals; 615.5
• DOI: 10.1002/cpt.1777
• Languages: English
• ISSNs: 0009-9236

Abstract

Abstract : The availability of multidimensional data together with the development of modern techniques for data analysis represent an exceptional opportunity for clinical pharmacology. Data science—defined in this special issue as the novel approaches to the collection, aggregation, and analysis of data—can significantly contribute to characterize drug‐response variability at the individual level, thus enabling clinical pharmacology to become a critical contributor to personalized healthcare through precision dosing. We propose a minireview of methodologies for achieving precision dosing with a focus on an artificial intelligence technique called reinforcement learning, which is currently used for individualizing dosing regimen in patients with life‐threatening diseases. We highlight the interplay of such techniques with conventional pharmacokinetic/pharmacodynamic approaches and discuss applicability in drug research and early development.