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Current Neuropharmacology

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ISSN (Print): 1570-159X
ISSN (Online): 1875-6190

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Review Article

Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry

Author(s): Antonio Del Casale*, Giuseppe Sarli, Paride Bargagna, Lorenzo Polidori, Alessandro Alcibiade, Teodolinda Zoppi, Marina Borro, Giovanna Gentile, Clarissa Zocchi, Stefano Ferracuti, Robert Preissner, Maurizio Simmaco and Maurizio Pompili

Volume 21, Issue 12, 2023

Published on: 09 August, 2023

Page: [2395 - 2408] Pages: 14

DOI: 10.2174/1570159X21666230808170123

Price: $65

Abstract

Traditional medicine and biomedical sciences are reaching a turning point because of the constantly growing impact and volume of Big Data. Machine Learning (ML) techniques and related algorithms play a central role as diagnostic, prognostic, and decision-making tools in this field. Another promising area becoming part of everyday clinical practice is personalized therapy and pharmacogenomics. Applying ML to pharmacogenomics opens new frontiers to tailored therapeutical strategies to help clinicians choose drugs with the best response and fewer side effects, operating with genetic information and combining it with the clinical profile. This systematic review aims to draw up the state-of-the-art ML applied to pharmacogenomics in psychiatry. Our research yielded fourteen papers; most were published in the last three years. The sample comprises 9,180 patients diagnosed with mood disorders, psychoses, or autism spectrum disorders. Prediction of drug response and prediction of side effects are the most frequently considered domains with the supervised ML technique, which first requires training and then testing. The random forest is the most used algorithm; it comprises several decision trees, reduces the training set's overfitting, and makes precise predictions. ML proved effective and reliable, especially when genetic and biodemographic information were integrated into the algorithm. Even though ML and pharmacogenomics are not part of everyday clinical practice yet, they will gain a unique role in the next future in improving personalized treatments in psychiatry.

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