Eliminating malaria transmission requires targeting immature and mature gametocytes through lipoidal uptake of antimalarials.

Novel antimalarial compounds targeting both the pathogenic and transmissible stages of the human malaria parasite, Plasmodium falciparum, would greatly benefit malaria elimination strategies. However, most compounds affecting asexual blood stage parasites show severely reduced activity against gametocytes. The impact of this activity loss on a compound's transmission-blocking activity is unclear.

Genetic complexity alters drug susceptibility of asexual and gametocyte stages of to antimalarial candidates.

Malaria elimination requires interventions able to target both the asexual blood stage (ABS) parasites and transmissible gametocyte stages of . Lead antimalarial candidates are evaluated against clinical isolates to address key concerns regarding efficacy and to confirm that the current, circulating parasites from endemic regions lack resistance against these candidates. While this has largely been performed on ABS parasites, limited data are available on the transmission-blocking efficacy of compounds with multistage activity.

Machine Learning Approaches Identify Chemical Features for Stage-Specific Antimalarial Compounds.

Efficacy data from diverse chemical libraries, screened against the various stages of the malaria parasite , including asexual blood stage (ABS) parasites and transmissible gametocytes, serve as a valuable reservoir of information on the chemical space of compounds that are either active (or not) against the parasite. We postulated that this data can be mined to define chemical features associated with the sole ABS activity and/or those that provide additional life cycle activity profiles like gametocytocidal activity. Additionally, this information could provide chemical features associated with inactive compounds, which could eliminate any future unnecessary screening of similar chemical analogs.

Chemogenomic Fingerprints Associated with Stage-Specific Gametocytocidal Compound Action against Human Malaria Parasites.

Kinase-focused inhibitors previously revealed compounds with differential activity against different stages of gametocytes. MMV666810, a 2-aminopyrazine, is more active on late-stage gametocytes, while a pyrazolopyridine, MMV674850, preferentially targets early-stage gametocytes. Here, we probe the biological mechanisms underpinning this differential stage-specific killing using in-depth transcriptome fingerprinting.

Machine Learning Uses Chemo-Transcriptomic Profiles to Stratify Antimalarial Compounds With Similar Mode of Action.

The rapid development of antimalarial resistance motivates the continued search for novel compounds with a mode of action (MoA) different to current antimalarials. Phenotypic screening has delivered thousands of promising hit compounds without prior knowledge of the compounds' exact target or MoA. Whilst the latter is not initially required to progress a compound in a medicinal chemistry program, identifying the MoA early can accelerate hit prioritization, hit-to-lead optimization and preclinical combination studies in malaria research.

Multistage and transmission-blocking targeted antimalarials discovered from the open-source MMV Pandemic Response Box.

Chemical matter is needed to target the divergent biology associated with the different life cycle stages of Plasmodium. Here, we report the parallel de novo screening of the Medicines for Malaria Venture (MMV) Pandemic Response Box against Plasmodium asexual and liver stage parasites, stage IV/V gametocytes, gametes, oocysts and as endectocides. Unique chemotypes were identified with both multistage activity or stage-specific activity, including structurally diverse gametocyte-targeted compounds with potent transmission-blocking activity, such as the JmjC inhibitor ML324 and the antitubercular clinical candidate SQ109.