Wang Tianduanyi, Pulkkinen Otto I, Aittokallio Tero
Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
Department of Computer Science, Aalto University, Espoo, Finland.
Front Pharmacol. 2022 Sep 23;13:1003480. doi: 10.3389/fphar.2022.1003480. eCollection 2022.
Most drug molecules modulate multiple target proteins, leading either to therapeutic effects or unwanted side effects. Such target promiscuity partly contributes to high attrition rates and leads to wasted costs and time in the current drug discovery process, and makes the assessment of compound selectivity an important factor in drug development and repurposing efforts. Traditionally, selectivity of a compound is characterized in terms of its target activity profile (wide or narrow), which can be quantified using various statistical and information theoretic metrics. Even though the existing selectivity metrics are widely used for characterizing the overall selectivity of a compound, they fall short in quantifying how selective the compound is against a particular target protein (e.g., disease target of interest). We therefore extended the concept of compound selectivity towards target-specific selectivity, defined as the potency of a compound to bind to the particular protein in comparison to the other potential targets. We decompose the target-specific selectivity into two components: 1) the compound's potency against the target of interest (absolute potency), and 2) the compound's potency against the other targets (relative potency). The maximally selective compound-target pairs are then identified as a solution of a bi-objective optimization problem that simultaneously optimizes these two potency metrics. In computational experiments carried out using large-scale kinase inhibitor dataset, which represents a wide range of polypharmacological activities, we show how the optimization-based selectivity scoring offers a systematic approach to finding both potent and selective compounds against given kinase targets. Compared to the existing selectivity metrics, we show how the target-specific selectivity provides additional insights into the target selectivity and promiscuity of multi-targeting kinase inhibitors. Even though the selectivity score is shown to be relatively robust against both missing bioactivity values and the dataset size, we further developed a permutation-based procedure to calculate empirical -values to assess the statistical significance of the observed selectivity of a compound-target pair in the given bioactivity dataset. We present several case studies that show how the target-specific selectivity can distinguish between highly selective and broadly-active kinase inhibitors, hence facilitating the discovery or repurposing of multi-targeting drugs.
大多数药物分子会作用于多种靶蛋白，从而产生治疗效果或不良副作用。这种靶点多效性在一定程度上导致了高淘汰率，造成了当前药物研发过程中的成本和时间浪费，也使得化合物选择性评估成为药物开发和重新利用工作中的一个重要因素。传统上，化合物的选择性是根据其靶点活性谱（宽或窄）来表征的，可以使用各种统计和信息理论指标进行量化。尽管现有的选择性指标被广泛用于表征化合物的整体选择性，但它们在量化化合物对特定靶蛋白（如感兴趣的疾病靶点）的选择性方面存在不足。因此，我们将化合物选择性的概念扩展到靶点特异性选择性，定义为化合物与其他潜在靶点相比结合特定蛋白的效力。我们将靶点特异性选择性分解为两个组成部分：1）化合物对感兴趣靶点的效力（绝对效力），以及2）化合物对其他靶点的效力（相对效力）。然后，将最大选择性化合物 - 靶点对确定为一个双目标优化问题的解，该问题同时优化这两个效力指标。在使用代表广泛多药理学活性的大规模激酶抑制剂数据集进行的计算实验中，我们展示了基于优化的选择性评分如何提供一种系统方法来寻找针对给定激酶靶点的强效且选择性的化合物。与现有的选择性指标相比，我们展示了靶点特异性选择性如何为多靶点激酶抑制剂的靶点选择性和多效性提供额外的见解。尽管选择性评分被证明对缺失的生物活性值和数据集大小都相对稳健，但我们进一步开发了一种基于排列的程序来计算经验p值，以评估在给定生物活性数据集中观察到的化合物 - 靶点对选择性的统计显著性。我们展示了几个案例研究，说明靶点特异性选择性如何区分高选择性和广泛活性的激酶抑制剂，从而促进多靶点药物的发现或重新利用。
