Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry
Model-based approaches are instrumental for successful drug development and use. Anchored within pharmacological principles, through mathematical modeling they contribute to the quantification of drug response variability and enables precision dosing. Reinforcement learning (RL)-a set of computation...
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Published in | Frontiers in pharmacology Vol. 13; p. 1094281 |
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Format | Journal Article |
Language | English |
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Frontiers Media S.A
17.02.2023
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Abstract | Model-based approaches are instrumental for successful drug development and use. Anchored within pharmacological principles, through mathematical modeling they contribute to the quantification of drug response variability and enables precision dosing. Reinforcement learning (RL)-a set of computational methods addressing optimization problems as a continuous learning process-shows relevance for precision dosing with high flexibility for dosing rule adaptation and for coping with high dimensional efficacy and/or safety markers, constituting a relevant approach to take advantage of data from digital health technologies. RL can also support contributions to the successful development of digital health applications, recognized as key players of the future healthcare systems, in particular for reducing the burden of non-communicable diseases to society. RL is also pivotal in computational psychiatry-a way to characterize mental dysfunctions in terms of aberrant brain computations-and represents an innovative modeling approach forpsychiatric indications such as depression or substance abuse disorders for which digital therapeutics are foreseen as promising modalities. |
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AbstractList | Model-based approaches are instrumental for successful drug development and use. Anchored within pharmacological principles, through mathematical modeling they contribute to the quantification of drug response variability and enables precision dosing. Reinforcement learning (RL)—a set of computational methods addressing optimization problems as a continuous learning process—shows relevance for precision dosing with high flexibility for dosing rule adaptation and for coping with high dimensional efficacy and/or safety markers, constituting a relevant approach to take advantage of data from digital health technologies. RL can also support contributions to the successful development of digital health applications, recognized as key players of the future healthcare systems, in particular for reducing the burden of non-communicable diseases to society. RL is also pivotal in computational psychiatry—a way to characterize mental dysfunctions in terms of aberrant brain computations—and represents an innovative modeling approach forpsychiatric indications such as depression or substance abuse disorders for which digital therapeutics are foreseen as promising modalities. |
Author | Ribba, Benjamin |
AuthorAffiliation | Roche Pharma Research and Early Development (pRED) , F. Hoffmann-La Roche Ltd , Basel , Switzerland |
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BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36873047$$D View this record in MEDLINE/PubMed |
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Cites_doi | 10.1002/psp4.12588 10.1126/science.1102384 10.1002/cpt.1036 10.1002/psp4.12607 10.1002/cpt.1782 10.1016/j.tics.2011.11.018 10.1007/s12160-016-9830-8 10.1186/2045-5380-3-12 10.1561/2200000070 10.1097/MD.0000000000023930 10.1200/JCO.2002.02.140 10.1176/appi.ajp.2010.09091379 10.1037/met0000283 10.1145/3381007 10.1016/j.annonc.2022.02.005 10.1007/s10549-016-3760-9 10.1201/b17203 10.1016/j.isci.2021.102804 10.1002/psp4.12786 10.1037/hea0000305 10.1287/moor.2014.0650 10.1214/19-sts720 10.1126/science.aab2358 10.1002/cpt.2064 10.1002/psp4.12478 10.1158/1078-0432.CCR-12-0084 10.3389/fnhum.2013.00261 10.1016/S0140-6736(12)61031-9 10.1001/jama.2021.1004 |
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Keywords | computational psychiatry precision dosing digital health pharmacometrics reinforcement learning |
Language | English |
License | Copyright © 2023 Ribba. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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Title | Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry |
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