Machine learning algorithms for prediction of side effects development in patients with pharmacoresistance to antipsychotics and antidepressants

Rationale. Artificial intelligence and machine learning allow for development of predictive models using pharmacogenetic testing (PGT) data. It helps to predict the development of side effects (SE) in patients treated with antipsychotics (AP) and antidepressants (AD) and provides personalized approach for the treatment of patients with treatment resistance to antipsychotics and antidepressants. Aim: To compare machine learning algorithms for prediction of side effects development in patients with pharmacoresistance (PR) to antipsychotics and antidepressants.

Material and Methods. A retrospective study utilized PGT data of 144 patients (72 males and 72 females, mean age 33±8.4 years) with PR to AP and AD, treated on an outpatient basis for the period from 2016 to 2024. PGT assessed CYP2D6, CYP2C19, CYP1A2, and MDR1 (C3435T) gene polymorphisms conducted in medical laboratories in St. Petersburg (MedLab, Invitro). Machine learning algorithms Lasso, Ridge, Extra Tree (ET), k-Nearest Neighbors (KNN), Naive Bayes (NB), Random Forest (RF), and eXtreme Gradient Boosting (XGB) were used to build the predictive model for SE development.

Results. RF algorithm demonstrated the best performance as the predictive model in test sample parameters: ROC-AUC 75.5% [59.6; 89.9], sensitivity 72.2% [55.0; 88.9], and specificity 58.3% [33.3; 81.8]. The main predictors included age, sex, CYP2C19, CYP2D6, CYP1A2, MDR1 C3435T genotypes and alleles, smoking, presence of neurological diseases and substance abuse.

Conclusion. Random Forest model machine learning algorithm has demonstrated high efficiency in predicting side effects probability in treatment resistant patients to AP and AD. The model can serve as the basis for future research and development of personalized treatment approach for the patients treated with AP and AD, with the possibility of further integration into Medical Decision Support System.

1. Kam H., Jeong H. Pharmacogenomic Biomarkers and their applications in psychiatry. Genes (Basel). 2020;11(12):1445. https://doi.org/10.3390/genes11121445

2. Siskind D., Orr S., Sinha S., Yu O., Brijball B., Warren N. et al. Rates of treatment-resistant schizophrenia from first-episode cohorts: systematic review and meta-analysis. Br. J. Psychiatry. 2022;220(3):115–120. https://doi.org/10.1192/bjp.2021.61

3. Rush A.J., Trivedi M.H., Wisniewski S.R., Nierenberg A.A., Stewart J.W., Warden D. et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am. J. Psychiatry. 2006;163(11):1905–1917. https://doi.org/10.1176/ajp.2006.163.11.1905

4. Li G., Zhang L., DiBernardo A., Wang G., Sheehan J.J., Lee K. et al. A retrospective analysis to estimate the healthcare resource utilization and cost associated with treatment-resistant depression in commercially insured US patients. PLoS One. 2020;15(9):e0238843. https://doi.org/10.1371/journal.pone.0238843

5. El-Hage W., Leman S., Camus V., Belzung C. Mechanisms of antidepressant resistance. Front. Pharmacol. 2013;4:146. https://doi.org/10.3389/fphar.2013.00146

6. Glavinas H., Krajcsi P., Cserepes J., Sarkadi B. The role of ABC transporters in drug resistance, metabolism and toxicity. Curr. Drug Deliv. 2004;1(1):27–42. https://doi.org/10.2174/1567201043480036

7. Mrazek D. Psychiatric Pharmacogenomics: e-book. OUP USA; 2010:285. ISBN: 9780195367294.

8. Carvalho Henriques B., Yang E.H., Lapetina D., Carr M.S., Yavorskyy V., Hague J. et al. How can drug metabolism and transporter genetics inform psychotropic prescribing? Front. Genet. 2020;11:491895. https://doi.org/10.3389/fgene.2020.491895

9. Beunk L., Nijenhuis M., Soree B., de Boer-Veger N.J., Buunk A.M., Guchelaar H.J. et al. Dutch Pharmacogenetics Working Group (DPWG) guideline for the gene-drug interaction between CYP2D6, CYP3A4 and CYP1A2 and antipsychotics. Eur. J. Hum. Genet. 2024;32(3):278–285. https://doi.org/10.1038/s41431-024-01572-4

10. Clinical Pharmacogenetics Implementation Consortium (CPIC). Guideline for tricyclic antidepressants and CYP2D6 and CYP2C19. URL: https://cpicpgx.org/guidelines/guideline-for-tricyclic-antidepressantsand-cyp2d6-and-cyp2c19 (19.02.2025).

11. Zhiganova T.A., Shkadova S.S., Sergeeva T.A., Radkova E.A., Petrova E.V. Genotyping of CYP2D6, CYP2C19, CYP1A2 and p-glycoprotein MDR1 (C3435T) in patients with treatment resistance to antipsychotics and antidepressants: step to implementation of personalized approach into clinical practice. Experimental and Clinical Pharmacology. 2024;87(11):13-19. (In Russ). https://doi.org/10.30906/0869-2092-2024-87-11-13-19

12. Course on machine learning from Vorontsov K. http://www. machinelearning.ru (10.02.2025).

13. Miwakeichi F., Galka A. Comparison of bootstrap methods for estimating causality in linear dynamic systems: a review. Entropy (Basel). 2023;25(7):1070. https://doi.org/10.3390/e25071070

14. Lin E., Kuo P.H., Liu Y.L., Yu Y.W., Yang A.C., Tsai S.J. A deep learning approach for predicting antidepressant response in major depression using clinical and genetic biomarkers. Front. Psychiatry. 2018;9:290. https://doi.org/10.3389/fpsyt.2018.00290

15. Lin E., Kuo P.H., Lin W.Y., Liu Y.L., Yang A.C., Tsai S.J. Prediction of probable major depressive disorder in the Taiwan biobank: An integrated machine learning and genome-wide analysis approach. J. Pers. Med. 2021;11(7):597. https://doi.org/10.3390/jpm11070597

16. Kautzky A., Baldinger P., Souery D., Montgomery S., Mendlewicz J., Zohar J. et al. The combined effect of genetic polymorphisms and clinical parameters on treatment outcome in treatment-resistant depression. Eur. Neuropsychopharmacol. 2015;25(4):441–453. https://doi.org/10.1016/j.euroneuro.2015.01.001

17. Athreya A.P., Neavin D., Carrillo-Roa T., Skime M., Biernacka J., Frye M.A. et al. Pharmacogenomics-driven prediction of antidepressant treatment outcomes: A machine-learning approach with multi-trial replication. Clin. Pharmacol. Ther. 2019;106(4):855–865. https://doi.org/10.1002/cpt.1482

18. Smart S.E., Agbedjro D., Pardiñas A.F., Ajnakina O., Alameda L., Andreassen O.A. et al. Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium. Schizophr. Res. 2022;250:1–9. https://doi.org/10.1016/j.schres.2022.09.009

19. Guo L.K., Su Y., Zhang Y.Y., Yu H., Lu Z, Li W.Q. et al. Prediction of treatment response to antipsychotic drugs for precision medicine approach to schizophrenia: randomized trials and multiomics analysis. Mil. Med. Res. 2023;10(1):24. https://doi.org/10.1186/s40779-02300459-7

20. Ivashchenko D.V., Buromskaya N.I., Shimanov P.V., Deitch D.V., Ryzhykova K.A., Grishina Е.A. et al. Pharmacogenetics biomarkers of antipsychotics’ safety in adolescents with acute psychotic episode. V.M. Bekhterev review of psychiatry and medical psychology. 2019;(4-1):75–77. (In Russ.). https://doi.org/10.31363/2313-7053-2019-4-1-75-77