Novel Dual-Stage Antimalarials: Machine learning prediction, validation and evolution

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PROJECT SUMMARY Specifically, this proposal focuses on novel new small molecules that inhibit both the blood and liver stages of malaria infection. The causative pathogen – Plasmodium spp. – was responsible for 241,000,000 cases that resulted in 627,000 deaths in 2020. Plasmodium spp. drug-resistant infections leave few good choices for physicians and put at risk the productivity and the lives of those infected. A clear case has been made for new drugs to treat these infections through the discovery and development of novel therapeutic strategies. These strategies would optimally be dual stage, targeting the blood stage for treatment and the liver stage for prophylaxis. The innovative strategy in this proposal builds on the technology of machine learning models for the prediction of novel dual-stage antimalarial small molecules with significant potential as drug discovery entities. Such a computational approach to seed the discovery of small molecule malaria parasite inhibitors with dual- stage efficacy has only been reported by us in 2022. The approach begins with preliminary data around two novel antimalarial small molecules with demonstrated in vitro efficacy versus both blood and liver stages of Plasmodium spp. infection and a lack of significant cytotoxicity to cultured liver cells. These molecules were derived from a set of hits discovered with a random forest model trained with high-throughput screening data. The molecules are representative of novel chemotypes for dual-stage antimalarials and, thus, offer a high probability of modulating new targets that are critical throughout the parasite’s lifecycle. This initial machine learning effort will be significantly expanded with a range of model types and a different and larger commercial library to predict a set of new hit compounds. Two validated hits, meeting in vitro efficacy and cytotoxicity criteria and maintaining wild type in vitro efficacy versus a set of drug-resistant parasite strains, will be profiled for key molecular properties such as mouse liver microsomal stability, aqueous solubility, and mouse pharmacokinetic profile. These data along with the existing in vitro efficacy and cytotoxicity evaluations will guide the evolution of each hit with a goal of preparing one or more analogs with a composite profile to enable downstream in vivo efficacy evaluation in infection models. A novel combination of medicinal chemistry and machine learning will be leveraged to afford such molecules.