Use of Bioinformatics in Pharmacogenetics to Optimize Drug Efficacy

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Background

There is no doubt today that efficacy and/or adverse events of drugs are highly influenced by the genotype of the patient. Thus, understanding the relationship between the patient’s genotype and their response to a given drug could increase the efficacy and decrease the toxicity and the adverse effects of this drug. Pharmacogenomics and Pharmacogenetics, a science that studies the interaction of the genome-drug response, can evaluate the associations between drug efficacy/toxicity and variation in drug pharmacokinetics and pharmacodynamics characteristics, especially the metabolism (biotransformation), the transport, and the interactions with drug targets.

These mechanisms are controlled by what are so-called pharmacogenes. Any alteration in the expression of these pharmacogenes (by mutation) could lead to the failure of the treatment or adverse effects. Since there is a huge number of pharmacogenes and drugs to study in this sense, pharmacogenomic researchers need sophisticated tools such as Bioinformatics to be able to analyse the important quantity of data generated by genetic sequencing. Bioinformatic tools play a key role in the extraction, annotation, aggregation, and integration of data. It also can help in clinical interpretation and implementation. The goal is to optimize and improve drug efficacy within the framework of what is called personalized medicine, which means "giving the right drug to the right patient at the right dose."

Recently, the technology of next generation sequencing discovered a huge number of genetic variants involved in the drug response. Furthermore, scientists estimate that the number of variants in pharmacogenes, like cytochrome P450 genes, ATP-binding cassette (ABC) transporters, and others, would be much greater than predicted, and it has been reported that these variants play an important role in the pharmacogenomic and pharmacogenetics prediction of the drug response. Thus, the use of computational tools such as bioinformatics could help researchers in an efficacious way to find out how to use the drug-gene interactions to optimize the drug efficacy and decrease toxicity and adverse effects.

The goal of this Research Topic is to provide an overview of recent discoveries in the field of using bioinformatics in pharmacogenomics and pharmacogenetics to optimize drug efficacy. We welcome submissions of various types of manuscripts, including high-quality original research papers, reviews, and methods. We welcome topics including, but not limited to:
- The study of gene-drug interaction
- The use of bioinformatics to help pharmacogenetic researchers to understand drug-gene interactions
- Use of computational tools for extraction, annotation, aggregation, and integration of data.
- Use of bioinformatics in Clinical interpretation and implementation

Please note that:
- If patient data are analyzed, a comprehensive description of the patients including sex, age, diagnostic criteria, inclusion and exclusion criteria, disease stage, therapy received, comorbidities, and additional clinical information and assessment of clinical response/effects should be included.
- If genetic, proteomics, metabolomics, or other omics data are analyzed, a comprehensive description of the methods and the rationale for selecting the specific data studied should be provided.
- Studies related to natural compounds, herbal extracts, or traditional medicine products, will not be included in this Research Topic.
- Original studies analyzing published data or public databases, with no further experimental confirmation or replication, will not be included in this Research Topic.

Keywords: Pharmacogenetics, Bioinformatics, Drug, Efficacy, Adverse Effects

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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