Evolutionary druggability: leveraging low-dimensional fitness landscapes towards new metrics for antimicrobial applications.
Evolutionary druggability: leveraging low-dimensional fitness landscapes towards new metrics for antimicrobial applications.
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进化成药性：利用低维适应度景观来制定抗菌应用的新指标。
DOI：
10.1101/2023.04.08.536116
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Ogbunugafor,CBrandon
中科院分区：
文献类型：
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作者：
Guerrero,RafaelF;Dorji,Tandin;Harris,Ra'MalM;Shoulders,MatthewD;Ogbunugafor,CBrandon
术语“可药物性”描述药物或药物干预靶点的分子特性，通常用于临床应用的药物开发工作。目前还没有类似的概念来量化药物-靶标相互作用，即给定靶标变体在一组药物的宽度上的敏感性，或者给定药物在靶蛋白等位基因上的有效性范围。利用由16个β-内酰胺酶等位基因和7种β-内酰胺药物组成的低维经验适应度景观数据，我们引入了两个指标，用于捕获(i)药物靶标的等位基因变体对给定面板中任何可用药物的平均易感性（“变异脆弱性”），以及（ii）药物（或混合物）在药物靶标的等位基因变体中的平均适用性（“药物适用性”）。最后，我们（iii）根据突变与环境（G × G × E）的相互作用，解开了药物靶标中基因座与七种药物环境之间相互作用的质量和程度，为变体变异性和药物适用性指标提供了机制见解。总之，我们建议我们的框架可以应用于其他数据集和病原体-药物系统，以了解临床环境中哪些病原体变异是最受关注的（低变异脆弱性），以及一组药物中哪些药物最有可能在病原体药物靶点的遗传变异定义的感染中有效（高药物适用性）。
The term “druggability” describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variant’s sensitivity across a breadth of drugs in a panel, or a given drug’s range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16 β-lactamase alleles and seven β-lactam drugs, we introduce two metrics that capture (i) the average susceptibility of an allelic variant of a drug target to any available drug in a given panel (“variant vulnerability”), and (ii) the average applicability of a drug (or mixture) across allelic variants of a drug target (“drug applicability”). Finally, we (iii) disentangle the quality and magnitude of interactions between loci in the drug target and the seven drug environments in terms of their mutation by mutation by environment (G × G × E) interactions, offering mechanistic insight into the variant variability and drug applicability metrics. Summarizing, we propose that our framework can be applied to other datasets and pathogen-drug systems to understand which pathogen variants in a clinical setting are the most concerning (low variant vulnerability), and which drugs in a panel are most likely to be effective in an infection defined by standing genetic variation in the pathogen drug target (high drug applicability).