Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Anticancer Agents Med Chem. 2023;23(9):1085-1101. doi: 10.2174/1871520623666230125090815.
Targeting mutated isocitrate dehydrogenase 1 (mIDH1) is one of the key therapeutic strategies for the treatment of glioma. Few inhibitors, such as ivosidenib and vorasidenib, have been identified as selective inhibitors of mIDH1. However, dose-dependent toxicity and limited brain penetration of the blood-brain barrier remain the major limitations of the treatment procedures using these inhibitors.
In the present study, computational drug repurposing strategies were employed to identify potent mIDH1- specific inhibitors from the 11,808 small molecules listed in the DrugBank repository.
Tanimoto coefficient (Tc) calculations were initially used to retrieve compounds with structurally similar scaffolds to ivosidenib. The resultant compounds were then subjected to molecular docking to discriminate the binders from the non-binders. The binding affinities and pharmacokinetic properties of the screened compounds were examined using prime Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) and QikProp algorithm, respectively. The conformational stability of these molecules was validated using 100 ns molecular dynamics simulation.
Together, these processes led to the identification of three-hit molecules, namely DB12001, DB08026, and DB03346, as potential inhibitors of the mIDH1 protein. Of note, the binding free energy calculations and MD simulation studies emphasized the greater binding affinity and structural stability of the hit compounds towards the mIDH1 protein.
The collective evidence from our study indicates the activity of DB12001 against recurrent glioblastoma, which, in turn, highlights the accuracy of our adapted strategy. Hence, we hypothesize that the identified lead molecules could be translated for the development of mIDH1 inhibitors in the near future.
针对突变型异柠檬酸脱氢酶 1(mIDH1)是治疗神经胶质瘤的关键治疗策略之一。一些抑制剂,如ivosidenib 和 vorasidenib,已被鉴定为 mIDH1 的选择性抑制剂。然而,这些抑制剂的治疗方法仍然存在剂量依赖性毒性和血脑屏障通透性有限的主要限制。
本研究采用计算药物再利用策略,从 DrugBank 存储库中列出的 11808 种小分子中筛选出有效的 mIDH1 特异性抑制剂。
最初使用 Tanimoto 系数(Tc)计算来检索与ivosidenib 结构相似的化合物。然后将得到的化合物进行分子对接,以区分结合物和非结合物。使用 prime 分子力学-广义 Born 表面面积(MM-GBSA)和 QikProp 算法分别检查筛选化合物的结合亲和力和药代动力学特性。使用 100ns 分子动力学模拟验证这些分子的构象稳定性。
这些过程共同确定了三个命中分子,即 DB12001、DB08026 和 DB03346,作为 mIDH1 蛋白的潜在抑制剂。值得注意的是,结合自由能计算和 MD 模拟研究强调了命中化合物对 mIDH1 蛋白的更大结合亲和力和结构稳定性。
我们的研究综合证据表明 DB12001 对复发性神经胶质瘤具有活性,这反过来又突出了我们适应策略的准确性。因此,我们假设鉴定出的先导分子可能在不久的将来被转化为 mIDH1 抑制剂的开发。