Malaria is a mosquito-borne parasitic disease that is serious and fatal, mostly affecting the young children and pregnant women living in the sub-Saharan Africa. In 2020, the World Health Organization reported that in 2019, there was an estimate of 229 million malaria cases, with approximately 409,000 deaths. Malaria transmission is caused by the genus, Plasmodium, where the two most prevalent species responsible for this disease in human are Plasmodium vivax and Plasmodium falciparum. Due to the ever-increasing rate of resistance, a new drug class is in urgent need to treat all stages of infection. In order to develop potent inhibitors of cytochrome bc1 complex, a validated antimalarial target, we utilized computational methodologies to generate predictive models. Here, we developed an AutoQSAR model based on existing 4(1H)-quinolones (n=342). The developed models demonstrated reliable predictive capabilities through the use of standard statistical parameters R2 and Q2, which displayed statistically meaningful values. The QSAR model developed here will be useful to develop novel potent, orally bioavailable antimalarial 4(1H)-quinolones and provide immense guidance in our ongoing SAR study of antimalarial quinolones, 1,2,3,4-tetrahydroacrin-9(10H)-ones, with antimalarial activity against erythrocytic and exoerythrocytic stages.

Development of quantitative structure: Activity relationship (QSAR) models to assist in the design and development of antimalarial 1,2,3,4 tetrahyroacrin-9(10H)-ones (THAs)

ACS Fall 2021

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