ABSTRACT
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.
