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.

39New chemical matter is needed to target the divergent biology associated with the different 40 life cycle stages of Plasmodium. Here, we report the parallel screening of the Medicines for 41 Malaria Venture Pandemic Response Box to identify multistage-active and stage-specific 42 compounds against various life cycle stages of Plasmodium parasites (asexual parasites, 43 stage IV/V gametocytes, gametes, oocysts and liver stages) and for endectocidal activity. Hits 44 displayed unique chemotypes and included two multistage-active compounds, 16 asexual-45 targeted, six with prophylactic potential and ten gametocyte-targeted compounds. Notably, 46 four structurally diverse gametocyte-targeted compounds with potent transmission-blocking 47 activity were identified: the JmjC inhibitor ML324, two azole antifungals including 48 eberconazole, and the antitubercular clinical candidate SQ109. Besides ML324, none of these 49 have previously attributed antiplasmodial activity, emphasizing the success of de novo parallel 50 screening against different Plasmodium stages to deliver leads with novel modes-of-action. 51Importantly, the discovery of such transmission-blocking targeted compounds covers a 52 previously unexplored base for delivery of compounds required for malaria elimination 53 strategies. 55 56Malaria treatment solely relies on drugs that target the parasite but current treatment options 57 have a finite lifespan due to resistance development. Moreover, whilst current antimalarials 58 are curative of asexual blood stage parasitemia and associated malaria symptoms, they 59 cannot all be used prophylactically and typically do not effectively block transmission. This 60 limits their utility in malaria elimination strategies, where the latter dictates that chemotypes 61 should block human-to-mosquito (gametocyte and gametes) and mosquito-to-human 62 (sporozoites and liver schizonts) transmission. 63The transmission stages of malaria parasites are seen as parasite population 64 bottlenecks, 1 with as few as 100 sporozoites able to initiate an infection after migrating to the 65 liver where exoerythrocytic schizogony occurs. The subsequent release of thousands of 66 daughter cells, which in turn infect erythrocytes, initiates the extensive population expansion 67 that occurs during asexual replication. A minor proportion (~1%) 2 of the proliferating asexual 68 parasites will undergo sexual differentiation to form mature stage V gametocytes, a 10-14 day 69 process in the most virulent parasite Plasmodium falciparum. Only ~10 3 of these falciform-70 shaped mature gametocytes are taken up by the next feeding mosquito to transform into male 71 and female gametes in the mosquito's midgut. 3 Fertilization results in zygote development, 72 and a motile ookinete that passes through the midgut wall forms an oocyst from which 73 sporozoites develop, making the mosquito infectious.74 The sporozoite and gametocyte population bottlenecks have been the basis of enticing 75 arguments towards the development of chemotypes able to targe...

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.

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.

Efficacy data from diverse chemical libraries, screened against the various stages of the malaria parasitePlasmodium falciparum, including asexual blood stage (ABS) parasites and transmissible gametocytes, serves 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 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 analogues. 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 predicts 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 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.