Trosset Jean-Yves
BIRL-Sup'Biotech, Villejuif, France.
Methods Mol Biol. 2025;2905:137-151. doi: 10.1007/978-1-0716-4418-8_8.
Linking a drug's mechanism of action to a disease-related phenotype is the greatest challenge in pharmaceutical research. Target-based and phenotype-based screening are the two basic tools to identifying drug candidates which display efficiency in triggering disease-related phenotype through a known Mechanism of Action (MoA) via a predefined primary target. In this paper, we present a reverse engineering approach which identify drug candidates from active compounds in phenotypic-based assay while bypassing the biochemical assay on the primary target. This important information is deduced implicitly by taking a reference drug with known MoA, i.e., a known primary target as a template. An in silico protocol is developed to preferentially select from phenotypic-active compounds, those that share the same target profile (primary and secondary target) as the reference drug. This is realized by requiring the same pharmacophore pattern as the reference drug while staying within its molecular envelop. These two constraints ensure a similar action on the primary target, while limiting the risk of digression with respect to the profile of secondary targets of the reference drug. Target-based screening assay can be used later to validate this in silico-based drug candidate selection. This reverse engineering approach can be used to crosslink two therapeutic indications if the reference drug has itself been validated experimentally on both. In that case, we use compound activity data from a phenotypic-based high-throughput screening campaign carried out on the target indication, in our case: malaria. The primary target is chosen indirectly through a reference drug and validated in the first indication (here cancer). If we choose a reference drug which is also validated in the malaria field, this implies that the heterologous version of the primary target in cancer field is also a key player of parasite proliferation within humans. We are using BIX-01294, an inhibitor of human histone lysine methyltransferase (HKMT), as a reference drug against cancer which has been validated experimentally to prevent plasmodium proliferation with human red blood cells through the inhibition of HKMT. The developed in silico protocol extracts general cytotoxic compounds with innovative chemical classes, yet with similar MoA as BIX-01294, i.e., inhibiting the original human HKMT, the biochemical bioassay on the plasmodium version of HKMT being not available in a kit-format. This reverse engineering approach is well adapted to transfer the knowledge associated with drug-targets in one therapeutic area (e.g., cancer) to another therapeutic indication for which the target-based approach is way more difficult. The reference drug plays therefore the role of a chemical probe to investigate targets in this target therapeutic area.
将药物的作用机制与疾病相关表型联系起来是药物研发中最大的挑战。基于靶点和基于表型的筛选是识别候选药物的两种基本工具,这些候选药物通过预定义的主要靶点,以已知的作用机制(MoA)触发疾病相关表型,从而展现出有效性。在本文中,我们提出了一种逆向工程方法,该方法可从基于表型的试验中的活性化合物中识别候选药物,同时绕过对主要靶点的生化试验。通过将具有已知MoA的参考药物(即已知的主要靶点)作为模板,隐含地推导出这一重要信息。我们开发了一种计算机模拟方案,以便从表型活性化合物中优先选择那些与参考药物具有相同靶点谱(主要和次要靶点)的化合物。这是通过要求与参考药物具有相同的药效团模式,同时保持在其分子范围内来实现的。这两个限制条件确保了对主要靶点的类似作用,同时限制了相对于参考药物次要靶点谱偏离的风险。随后可使用基于靶点的筛选试验来验证这种基于计算机模拟的候选药物选择。如果参考药物本身已在两种治疗适应症上都经过实验验证,那么这种逆向工程方法可用于关联两种治疗适应症。在这种情况下,我们使用来自针对目标适应症(在我们的案例中为疟疾)进行的基于表型的高通量筛选活动的化合物活性数据。主要靶点是通过参考药物间接选择的,并在第一种适应症(此处为癌症)中得到验证。如果我们选择一种在疟疾领域也经过验证的参考药物,这意味着癌症领域中主要靶点的异源版本也是人类体内寄生虫增殖的关键因素。我们使用人类组蛋白赖氨酸甲基转移酶(HKMT)抑制剂BIX - 01294作为抗癌参考药物,该药物已通过实验验证可通过抑制HKMT来防止疟原虫在人类红细胞中增殖。所开发的计算机模拟方案提取出具有创新化学类别但与BIX - 01294具有相似MoA(即抑制原始人类HKMT)的一般细胞毒性化合物,因为针对疟原虫版本的HKMT的生化生物测定试剂盒不可用。这种逆向工程方法非常适合将与一个治疗领域(例如癌症)中的药物靶点相关的知识转移到另一个基于靶点的方法困难得多的治疗适应症中。因此,参考药物起到了化学探针的作用,用于研究该目标治疗领域中的靶点。