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Adaptive dosing strategies extend the precision dosing concept to time\u2010varying treatments which require sequential dose adjustments based on evolving patient conditions. Reinforcement learning (RL) naturally fits this paradigm: it perfectly mimics the sequential decision\u2010making process where clinicians adapt dose administration based on patient response and evolution monitoring. This paper aims to investigate the potentiality of coupling RL with population PK\/PD models to develop precision dosing algorithms, reviewing the most relevant works in the field. Case studies in which PK\/PD models were integrated within RL algorithms as simulation engine to predict consequences of any dosing action have been considered and discussed. They mainly concern propofol\u2010induced anesthesia, anticoagulant therapy with warfarin and a variety of anticancer treatments differing for administered agents and\/or monitored biomarkers. The resulted picture highlights a certain heterogeneity in terms of precision dosing approaches, applied methodologies, and degree of adherence to the clinical domain. In addition, a tutorial on how a precision dosing problem should be formulated in terms of the key elements composing the RL framework (i.e., system state, agent actions and reward function), and on how PK\/PD models could enhance RL approaches is proposed for readers interested in delving in this field. Overall, the integration of PK\/PD models into a RL\u2010framework holds great promise for precision dosing, but further investigations and advancements are still needed to address current limitations and extend the applicability of this methodology to drugs requiring adaptive dosing strategies.<\/jats:p>","DOI":"10.1002\/cpt.3356","type":"journal-article","created":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T04:34:37Z","timestamp":1720672477000},"page":"619-636","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Model\u2010Informed Reinforcement Learning for Enabling Precision Dosing Via Adaptive Dosing"],"prefix":"10.1002","volume":"116","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9801-2135","authenticated-orcid":false,"given":"Elena Maria","family":"Tosca","sequence":"first","affiliation":[{"name":"Electrical, Computer and Biomedical Engineering University of Pavia  Pavia Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7055-2140","authenticated-orcid":false,"given":"Alessandro","family":"De Carlo","sequence":"additional","affiliation":[{"name":"Electrical, Computer and Biomedical Engineering University of Pavia  Pavia Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Davide","family":"Ronchi","sequence":"additional","affiliation":[{"name":"Electrical, Computer and Biomedical Engineering University of Pavia  Pavia Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8931-4676","authenticated-orcid":false,"given":"Paolo","family":"Magni","sequence":"additional","affiliation":[{"name":"Electrical, Computer and Biomedical Engineering University of Pavia  Pavia Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,7,11]]},"reference":[{"key":"e_1_2_9_1_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.2064"},{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-statistics-022513-115553"},{"key":"e_1_2_9_3_1","volume-title":"Reinforcement Learning: An Introduction","author":"Sutton R.S.","year":"2018"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101964"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.1777"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2022.1094281"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13184624"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107280"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101836"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2014.07.004"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.02.041"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2005.06.020"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0213-5"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/psp4.12353"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/bcp.14801"},{"key":"e_1_2_9_16_1","article-title":"Artificial intelligence and machine learning: just a hype or a new opportunity for pharmacometrics?","volume":"28","author":"Bartolucci R.","year":"2019","journal-title":"PAGE"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2015.05.013"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102227"},{"key":"e_1_2_9_19_1","unstructured":"Yauney G.&Shah P.Reinforcement learning with action\u2010derived rewards for chemotherapy and clinical trial dosing regimen selection. 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