In Journal of molecular modeling
Mutation in isocitrate dehydrogenase 2 (mIDH2) is an oncogenic driver prevalently reported in various cancer types including gliomas. To date, enasidenib is the only FDA-approved drug widely used as a mIDH2 (R140Q) inhibitor. However, dose-limiting toxicity and modest brain penetrating capability restrict its use as a plausible mIDH2 inhibitor. Furthermore, secondary site mutations (Q316E and I319M) were identified in patients with enasidenib treatments resulting in acquired therapeutic resistance. Hence, in the present investigation, we aimed to identify novel and potent drug-like compounds to overcome the existing drawbacks using an integrated in-silico strategy. A sum of 1574 natural compounds from the naturally occurring plant-based anti-cancerous compound activity target (NPACT) database was proclaimed and subjected to molecular docking. The binding affinities of the resultant natural compounds were rescored using MM-GBSA scoring functions. The resultant lead molecules were subjected to anticancer activity prediction using the machine-learning model. Furthermore, the toxicity and drug-likeliness of the lead compounds were investigated using ADMET properties. Eventually, the integrated in silico approach resulted in a lead molecule, namely squalene (NPACT00954) against mIDH2 protein. The screened compound was subjected to mutational analysis accomplishing second-site mutations. Interestingly, squalene exhibited appreciable binding affinity alongside good brain penetrating potential than enasidenib. Indeed, the reproducibility and significance of our results are examined by running 3 replicas of 100-ns simulations per system using the random initial velocities of the atoms generated by Maxwell distribution at a given temperature. Thus, we hypothesize from our results that further optimization of squalene could be beneficial for the treatment and management of glioma in the near future.
Murali Poornimaa, Karuppasamy Ramanathan
2022-Dec-09
MM-GBSA, Molecular docking, Molecular dynamics simulation, Mutant isocitrate dehydrogenase 2 (mIDH2)
