Literature DB >> 29313952

How to Consider Rare Genetic Variants in Personalized Drug Therapy.

Volker M Lauschke1, Magnus Ingelman-Sundberg1.   

Abstract

Personalized drug therapy aims to optimize the efficacy of pharmacological treatments by considering genetic, pathophysiological, dietary, and environmental factors as well as comedications and compliance. A multitude of associations between the specific genetic constitution of the patient and drug pharmacokinetics and pharmacodynamics has been identified in the last decades that encompass mainly common single nucleotide variants (SNVs) and gene copy number variations (CNVs) of importance for the function of genes encoding drug-metabolizing enzymes and transporters involved in drug absorption, distribution, metabolism, and excretion (ADME). In addition, genetic variation in factors encoding the major histocompatibility complex have been helpful to predict immune-mediated drug toxicity. This knowledge has been translated into clinical applications through the implementation of pharmacogenomic biomarkers. Over 230 of such markers that can, to a certain extent, predict drug efficacy or the likelihood of adverse drug reactions (ADRs) are recognized by the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) and are included in the drug labels of the respective medication. They are of particular value in cancer therapy to provide information of use to avoid ADRs and lack of response.
© 2018 American Society for Clinical Pharmacology and Therapeutics.

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Year:  2018        PMID: 29313952     DOI: 10.1002/cpt.976

Source DB:  PubMed          Journal:  Clin Pharmacol Ther        ISSN: 0009-9236            Impact factor:   6.875


  12 in total

Review 1.  Novel genetic and epigenetic factors of importance for inter-individual differences in drug disposition, response and toxicity.

Authors:  Volker M Lauschke; Yitian Zhou; Magnus Ingelman-Sundberg
Journal:  Pharmacol Ther       Date:  2019-01-22       Impact factor: 12.310

Review 2.  Sequencing XMET genes to promote genotype-guided risk assessment and precision medicine.

Authors:  Yaqiong Jin; Geng Chen; Wenming Xiao; Huixiao Hong; Joshua Xu; Yongli Guo; Wenzhong Xiao; Tieliu Shi; Leming Shi; Weida Tong; Baitang Ning
Journal:  Sci China Life Sci       Date:  2019-05-20       Impact factor: 6.038

3.  SWAAT Bioinformatics Workflow for Protein Structure-Based Annotation of ADME Gene Variants.

Authors:  Houcemeddine Othman; Sherlyn Jemimah; Jorge Emanuel Batista da Rocha
Journal:  J Pers Med       Date:  2022-02-11

4.  Pharmacogene Sequencing of a Gabonese Population with Severe Plasmodium falciparum Malaria Reveals Multiple Novel Variants with Putative Relevance for Antimalarial Treatment.

Authors:  Leyre Pernaute-Lau; Ayola Akim Adegnika; Yitian Zhou; Jeannot F Zinsou; Jose Pedro Gil; Sanjeev Krishna; Peter G Kremsner; Volker M Lauschke; Thirumalaisamy P Velavan
Journal:  Antimicrob Agents Chemother       Date:  2021-06-17       Impact factor: 5.191

5.  Integrating rare genetic variants into pharmacogenetic drug response predictions.

Authors:  Magnus Ingelman-Sundberg; Souren Mkrtchian; Yitian Zhou; Volker M Lauschke
Journal:  Hum Genomics       Date:  2018-05-25       Impact factor: 4.639

6.  Translating genotype data of 44,000 biobank participants into clinical pharmacogenetic recommendations: challenges and solutions.

Authors:  Sulev Reisberg; Kristi Krebs; Maarja Lepamets; Mart Kals; Reedik Mägi; Kristjan Metsalu; Volker M Lauschke; Jaak Vilo; Lili Milani
Journal:  Genet Med       Date:  2018-10-16       Impact factor: 8.822

7.  Ethnogeographic and inter-individual variability of human ABC transporters.

Authors:  Qingyang Xiao; Yitian Zhou; Volker M Lauschke
Journal:  Hum Genet       Date:  2020-03-23       Impact factor: 4.132

8.  Pharmacogenomic network analysis of the gene-drug interaction landscape underlying drug disposition.

Authors:  Yitian Zhou; Volker M Lauschke
Journal:  Comput Struct Biotechnol J       Date:  2019-12-05       Impact factor: 7.271

9.  Delineating significant genome-wide associations of variants with antipsychotic and antidepressant treatment response: implications for clinical pharmacogenomics.

Authors:  Maria Koromina; Stefania Koutsilieri; George P Patrinos
Journal:  Hum Genomics       Date:  2020-01-15       Impact factor: 4.639

10.  Population-scale predictions of DPD and TPMT phenotypes using a quantitative pharmacogene-specific ensemble classifier.

Authors:  Yitian Zhou; Carolina Dagli Hernandez; Volker M Lauschke
Journal:  Br J Cancer       Date:  2020-09-25       Impact factor: 7.640

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