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

Malaria elimination requires interventions able to target both the asexual blood stage (ABS) parasites and transmissible gametocyte stages of Plasmodium falciparum. 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. Here, we evaluated the efficacy of lead antimalarial candidates against both ABS parasites and late-stage gametocytes side-by-side, against clinical P. falciparum isolates from southern Africa. We additionally correlated drug efficacy to the genetic diversity of the clinical isolates as determined with a panel of well-characterized, genome-spanning microsatellite markers. Our data indicate varying sensitivities of the isolates to key antimalarial candidates, both for ABS parasites and gametocyte stages. While ABS parasites were efficiently killed, irrespective of genetic complexity, antimalarial candidates lost some gametocytocidal efficacy when the gametocytes originated from genetically complex, multiple-clone infections. This suggests a fitness benefit to multiclone isolates to sustain transmission and reduce drug susceptibility. In conclusion, this is the first study to investigate the efficacy of antimalarial candidates on both ABS parasites and gametocytes from P. falciparum clinical isolates where the influence of parasite genetic complexity is highlighted, ultimately aiding the malaria elimination agenda.

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 Plasmodium falciparum, 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. Therefore, we aimed to use machine learning to identify the chemical space associated with stage-specific antimalarial activity. We collected data from various chemical libraries that were screened against the asexual (126 374 compounds) and sexual (gametocyte) stages of the parasite (93 941 compounds), calculated the compounds' molecular fingerprints, and trained machine learning models to recognize stage-specific active and inactive compounds. We were able to build several models that predict compound activity against ABS and dual activity against ABS and gametocytes, with Support Vector Machines (SVM) showing superior abilities with high recall (90 and 66%) and low false-positive predictions (15 and 1%). This allowed the identification of chemical features enriched in active and inactive populations, an important outcome that could be mined for essential chemical features to streamline hit-to-lead optimization strategies of antimalarial candidates. The predictive capabilities of the models held true in diverse chemical spaces, indicating that the ML models are therefore robust and can serve as a prioritization tool to drive and guide phenotypic screening and medicinal chemistry programs.

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 Plasmodium falciparum 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. Compound-specific chemogenomic profiles were observed with MMV674850 treatment associated with biological processes shared between asexual blood stage parasites and early-stage gametocytes but not late-stage gametocytes. MMV666810 has a distinct profile with clustered gene sets associated primarily with late-stage gametocyte development, including Ca2+-dependent protein kinases (CDPK1 and 5) and serine/threonine protein kinases (FIKK). Chemogenomic profiling therefore highlights essential processes in late-stage gametocytes, on a transcriptional level. This information is important to prioritize compounds that preferentially compromise late-stage gametocytes for further development as transmission-blocking antimalarials.

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. The effects of drug treatment on a cell can be observed on systems level in changes in the transcriptome, proteome and metabolome. Machine learning (ML) algorithms are powerful tools able to deconvolute such complex chemically-induced transcriptional signatures to identify pathways on which a compound act and in this manner provide an indication of the MoA of a compound. In this study, we assessed different ML approaches for their ability to stratify antimalarial compounds based on varied chemically-induced transcriptional responses. We developed a rational gene selection approach that could identify predictive features for MoA to train and generate ML models. The best performing model could stratify compounds with similar MoA with a classification accuracy of 76.6 ± 6.4%. Moreover, only a limited set of 50 biomarkers was required to stratify compounds with similar MoA and define chemo-transcriptomic fingerprints for each compound. These fingerprints were unique for each compound and compounds with similar targets/MoA clustered together. The ML model was specific and sensitive enough to group new compounds into MoAs associated with their predicted target and was robust enough to be extended to also generate chemo-transcriptomic fingerprints for additional life cycle stages like immature gametocytes. This work therefore contributes a new strategy to rapidly, specifically and sensitively indicate the MoA of compounds based on chemo-transcriptomic fingerprints and holds promise to accelerate antimalarial drug discovery programs.

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. Mechanistic investigations prove that ML324 prevents histone demethylation, resulting in aberrant gene expression and death in gametocytes. Moreover, the selection of parasites resistant to SQ109 implicates the druggable V-type H+-ATPase for the reduced sensitivity. Our data therefore provides an expansive dataset of compounds that could be redirected for antimalarial development and also point towards proteins that can be targeted in multiple parasite life cycle stages.