Rakičević Ljiljana1*, Kovač Mirjana2,3, Radojković Dragica1
1 Institute of molecular genetics and genetic engineering, University of Belgrade, Belgrade, Serbia
2 Faculty of Medicine, University of Belgrade, Belgrade, Serbia
3 Blood transfusion institute of Serbia, Hemostasis department, Belgrade, Serbia
lili [at] imgge.ac.bg.rs
Abstract
Pharmacogenetics, as a discipline which correlates genetics of an individual and the effects of drugs, has given new possibilities for personalized approaches in medicine. It is possible to design algorithms to predict the effects of a certain therapeutic by analysing relevant genetic variants as well as non-genetic factors which may influence therapy. It has been shown that algorithms designed in this way allow for better prediction in comparison to traditional trial and error method and represent a more cost-effective approach for health systems. Additionally, the contribution of factors affecting therapy may vary markedly between different ethnic groups. One of the most considered drugs in pharmacogenetics are coumarins (warfarin, acenocoumarol, phenprocoumon), anticoagulation drugs, used in treating and preventing thromboses.
This work was aimed to design pharmacogenetics-based algorithm for acenocoumarol. Assuming that population-specific algorithm may take advantage over models used in a generalized manner, we aimed to design a mathematical model for predicting individual drug dosage in the Serbian population based on clinical-demographic and genetic data.
Patients with stable acenocoumarol maintenance dose (N = 200) were divided into two cohort – derivation cohort (N = 100) and testing cohort (N = 100) – on a random basis. On the derivation cohort multiple regression analysis was applied in order to select predictors to be used for estimating the individual dose of acenocoumarol and to derive a model for dose prediction. The testing cohort was used for assessing the quality of the derived model.
Mathematical model for predicting individual acenocoumarol dose was designed and its unadjusted R2 was 61.8. In addition to genetic factors (VKORC1*2, CYP2C9*2, CYP2C9*3), we identified age, weight and gender of the patients as significant predictors of drug dosage. In comparison with the model given by other authors our model showed better prediction of individual acenocoumarol dose for patients in the Serbian population.
Keywords: pharmacogenetics, algorithms, personalized medicine
Acknowledgement: This work was supported by the Ministry of science, technological development and innovation of the Republic of Serbia (contract No: 451-03-66/2024-03/200042).