Integrated computational strategies for the identification of Plasmodium falciparum dihydrofolate reductase inhibitors
Dhrubajyoti Gogoi, Partha Pratim Dutta, Bhaskarjyoti Gogoi, Vishwa Jyoti Baruah, Rasna Paul, Bhaswati Sarmah, Anupaul Baruah, Bibhuti Bhusan Kakoti, Bulumoni Kalita, Prafulla Dutta, Rasana Yadav · Computers in Biology and Medicine · 2025
BACKGROUND: The development of drug resistance in Plasmodium falciparum is predominantly associated with the mutations in Plasmodium falciparum dihydrofolate reductase (pfDHFR) enzyme, a crucial target for antifolate antimalarial medications such as pyrimethamine and cycloguanil. Specific nucleotide substitutions in the pfDHFR gene, occurring either singly or in various combinations, substantially reduce the effectiveness of antifolate treatments, thus intensifying the worldwide struggle against malaria. METHODS: The present investigation, pharmacophore modeling assisted virtual screening, and, in vitro investigations were conducted to address this resistance issue by identifying novel inhibitors targeting mutant pfDHFR. A ligand-oriented pharmacophore model (r = 0.94) was developed utilizing a dataset of 17 compounds exhibiting inhibitory activity spanning four orders of magnitude. RESULT: The most statistically robust hypothesis designated Hypo1, was subsequently employed as a three-dimensional query to conduct screening of cheminformatics repositories. The screening process incorporated drug-likeness parameters, ADME/Tox assessment, and pharmacophore mapping and validation techniques. Through molecular docking analyses and molecular dynamics (MD) simulations, we have identified 28 compounds that exhibited robust binding affinities to resistant variants of pfDHFR. The most promising candidates displayed in vitro antimalarial efficacy, indicating their potential to circumvent anti-folate drug resistance. CONCLUSION: values of 30.822 μg/mL and 126.077 μg/mL against the 3D7 and Dd2 strains of P. falciparum, respectively, significantly outperforming other compounds (p < 0.05). This comprehensive approach underscores the critical role of integrating pharmacophore modeling with experimental methods to discover alternative antimalarial therapies.