Exploring gene-drug interactions for personalized treatment of post-traumatic stress disorder

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2023-12-21 DOI:10.3389/fncom.2023.1307523
Konstantina Skolariki, Panagiotis Vlamos
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Abstract

Introduction

Post-Traumatic Stress Disorder (PTSD) is a mental disorder that can develop after experiencing traumatic events. The aim of this work is to explore the role of genes and genetic variations in the development and progression of PTSD.

Methods

Through three methodological approaches, 122 genes and 184 Single Nucleotide Polymorphisms (SNPs) associated with PTSD were compiled into a single gene repository for PTSD. Using PharmGKB and DrugTargetor, 323 drug candidates were identified to target these 122 genes. The top 17 drug candidates were selected based on the statistical significance of the genetic associations, and their promiscuity (number of associated genestargets) and were further assessed for their suitability in terms of bioavailability and drug-like characteristics. Through functional analysis, insights were gained into the biological processes, cellular components, and molecular functions involved in PTSD. This formed the foundation for the next aspect of this study which was to propose an efficient treatment for PTSD by exploring drug repurposing methods.

Results

The main aim was to identify the drugs with the most favorable profile that can be used as a pharmacological approach for PTSD treatment. More in particular, according to the genetic variations present in each individual, the relevant biological pathway can be identified, and the drug candidate proposed will specifically target said pathway, accounting for the personalized aspect of this work. The results showed that the drugs used as off-label treatment for PTSD have favorable pharmacokinetic profiles and the potential drug candidates that arose from DrugTargetor were not very promising. Clozapine showed a promising pharmacokinetic profile and has been linked with decreased psychiatric symptoms. Ambrucin also showed a promising pharmacokinetic profile but has been mostly linked with cancer treatment.

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探索基因与药物的相互作用,实现创伤后应激障碍的个性化治疗
导言创伤后应激障碍(PTSD)是一种可在经历创伤事件后发生的精神障碍。方法通过三种方法,将与创伤后应激障碍相关的122个基因和184个单核苷酸多态性(SNPs)编入创伤后应激障碍的单一基因库。通过使用 PharmGKB 和 DrugTargetor,确定了 323 种候选药物来靶向这 122 个基因。根据基因关联的统计学意义及其杂合性(关联基因靶点的数量),选出了前 17 种候选药物,并进一步评估了它们在生物利用度和类似药物特性方面的适用性。通过功能分析,我们深入了解了创伤后应激障碍所涉及的生物过程、细胞成分和分子功能。这为本研究的下一步工作奠定了基础,即通过探索药物再利用方法,提出治疗创伤后应激障碍的有效方法。更具体地说,根据每个人的基因变异,可以确定相关的生物通路,所提出的候选药物将专门针对所述通路,从而实现这项工作的个性化。研究结果表明,用于创伤后应激障碍标示外治疗的药物具有良好的药代动力学特征,而从 DrugTargetor 中产生的潜在候选药物并不十分理想。氯氮平显示出良好的药代动力学特征,并与精神症状的减少有关。安布鲁新也显示出良好的药代动力学特征,但主要与癌症治疗有关。
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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