Meta-analysis of pharmacogenetic clinical decision support systems for the treatment of major depressive disorder.

The study aimed to conduct a meta-analysis of studies comparing pharmacogenetically guided dosing of antidepressants with empiric standard of care. Publications referring to genotype-guided antidepressant therapy were identified via PubMed, Google Scholar, Scopus, Web of Science, Embase, and Cochrane databases from the inception of the databases to 2021. In addition, bibliographies of all articles were manually searched for additional references not identified in primary searches. Studies comparing clinical outcomes between two groups of patients who received antidepressant treatment were included in meta-analysis. Analysis of the data revealed statistically significant differences between the experimental group receiving pharmacogenetically guided dosing and the empirically treated controls. Specifically, genotype-guided treatment significantly improved response and remission of patients after both eight and twelve weeks of therapy, whereas no effect on the development of adverse drug reactions was observed. This meta-analysis indicates that the use of preemptive genotyping to guide dosing of antidepressants might increase treatment efficacy.

Using a personalized clinical decision support system for bromdihydrochlorphenylbenzodiazepine dosing in patients with anxiety disorders based on the pharmacogenomic markers.

INTRODUCTION: Although pharmacogenetic tests provide the information on a genotype and the predicted phenotype, these tests themselves do not provide the interpretation of data for a physician. There are currently approximately two dozen pharmacogenomic clinical decision support systems used in psychiatry. Implementation of clinical decision support systems capable of forming recommendations on drug and dose selection according to the results of pharmacogenetic testing is an urgent task. Fulfillment of this task may allow increasing the efficacy of therapy and decreasing the risk of undesirable side effects. MATERIALS AND METHODS: The study included 51 male patients (21 in the main group and 30 in the control group) with alcohol withdrawal syndrome. To evaluate the efficacy and safety of therapy, several international psychometric scales and rating scales to measure side effects were used. Genotyping was performed using real-time polymerase chain reaction with allele-specific hybridization. Pharmacogenetic test results were interpreted using free software PGX2 (www.pgx2.com). RESULTS: Statistically significant differences between the scores derived from all psychometric scales were revealed. For instance, the total score on CIWA-Ar scale by day 3 was 13.5 [11.2; 16.0] for the main group and 18.0 [17.0; 22.0] (p < 0.001) for the control group; by day 5, it was 6.5 [4.2; 8.0] for the main group and 15.0 [14.0; 16.0] (p < 0.001) for the control group. The UKU side effect rating scale (UKU) also revealed a statistically significant difference. The total score on UKU scale by day 3 was 6.0 [5.0; 7.0] for the main group and 7.0 [6.0; 8.0] (p < 0.001) for the control group; by day 5, this difference grew significantly: 5.5 [3.0; 9.0] for the main group and 14.0 [12.0; 19.0] (p < 0.001) for the control group. The groups were representative (there was no difference between the scores at the inclusion of patients). CONCLUSION: Pharmacogenetic-guided personalization of drug dose in patients with alcohol withdrawal syndrome can reduce the risk of undesirable side effects and pharmacoresistance. It allows recommending the use of pharmacogenomic clinical decision support systems for optimizing drug dosage.