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In silico functionality prediction analysis of pharmacogenes and development of a pharmacogenetic score for Brazilian Familial Hypercholesterolemia patients

Grant number: 19/19009-4
Support type:Scholarships abroad - Research Internship - Doctorate (Direct)
Effective date (Start): January 03, 2020
Effective date (End): October 02, 2020
Field of knowledge:Health Sciences - Pharmacy
Principal Investigator:Rosario Dominguez Crespo Hirata
Grantee:Carolina Dagli Hernandez
Supervisor abroad: Volker Lauschke
Home Institution: Faculdade de Ciências Farmacêuticas (FCF). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Local de pesquisa : Karolinska Institutet, Sweden  
Associated to the scholarship:16/25637-0 - Pharmacogenomic and pharmacoepigenomic analysis in individuals with familial hypercholesterolemia, BP.DD


Familial hypercholesterolemia (FH) is a primary dyslipidemia resulting from functional mutations in cholesterol homeostasis genes. Statins are the first-line treatment, but the response is highly variable among individuals. Pharmacogenetic scores have been developed to predict this variability, but none is specific to the Brazilian FH population. Also, the impact of variants found on product functionality is not precisely known. Recently, Dr. Volker M. Lauschke's group of the Karolinska Institute, Sweden, developed a framework for predicting in silico functionality of variants in pharmacogenes. This study aims 1) to predict the functionality of variants in genes associated with the response to statins; and 2) to develop a pharmacogenetic score for statin response in Brazilian FH patients for LDL-c reduction and statin-related adverse events (SRAE). The in silico functionality prediction will be performed using the framework developed by Dr. Volker M. Lauschke's group for variants identified in genes involved in the pharmacokinetics and pharmacodynamics of statins in our study population. Using this result, a pharmacogenetic algorithm for dose prediction will be developed for better therapeutic response and reducing SRAE. FH patients will be grouped into a generation cohort (G) (80% of patients) and a validation cohort (V) (20% of patients). The analysis will be performed by univariate linear regressions using G cohort and significant variables will be tested by multiple linear regression to predict the patient's therapeutic response. The algorithm will be validated in the cohort V. A correlation analysis will be performed to determine the correlation between the actual statin dose and that predicted with the algorithm. With this study, we hope to develop a pharmacogenetic score that could help Brazilian FH patients to achieve the best response to statin treatment.