Substituted Aminoacetamides as Novel Leads for Malaria Treatment

Activity refinement of aryl amino acetamides that target the P. falciparum STAR-related lipid transfer 1 protein

Malaria is a devastating disease that causes significant morbidity worldwide. The development of new antimalarial chemotypes is urgently needed because of the emergence of resistance to frontline therapies. Independent phenotypic screening campaigns against the Plasmodium asexual parasite, including our own, identified the aryl amino acetamide hit scaffold. In a prior study, we identified the STAR-related lipid transfer protein (PfSTART1) as the molecular target of this antimalarial chemotype. In this study, we combined structural elements from the different aryl acetamide hit subtypes and explored the structure-activity relationship. It was shown that the inclusion of an endocyclic nitrogen, to generate the tool compound WJM-715, improved aqueous solubility and modestly improved metabolic stability in rat hepatocytes. Metabolic stability in human liver microsomes remains a challenge for future development of the aryl acetamide class, which was underscored by modest systemic exposure and a short half-life in mice. The optimized aryl acetamide analogs were cross resistant to parasites with mutations in PfSTART1, but not to other drug-resistant mutations, and showed potent binding to recombinant PfSTART1 by biophysical analysis, further supporting PfSTART1 as the likely molecular target. The optimized aryl acetamide analogue, WJM-715 will be a useful tool for further investigating the druggability of PfSTART1 across the lifecycle of the malaria parasite.

Classification models for predicting the antimalarial activity against Plasmodium falciparum

Support vector machine (SVM) and general regression neural network (GRNN) were used to develop classification models for predicting the antimalarial activity against Plasmodium falciparum. Only 15 molecular descriptors were used to build the classification models for the antimalarial activities of 4750 compounds, which were divided into a training set (3887 compounds) and a test set (863 compounds). For the SVM model, its prediction accuracies are 89.5% for the training set and 87.3% for the test set. For the GRNN model, the prediction accuracies for the two sets are 99.7% and 88.9%, respectively. Both SVC and GRNN models have better prediction ability than the classification model based on binary logistic regression (BLR) analysis. Compared with previously published classification models both SVC and GRNN models are satisfactory in predicting antimalarial activities of compounds with in addition of fewer descriptors.

QSAR studies on benzothiophene derivatives as Plasmodium falciparum N-myristoyltransferase inhibitors: Molecular insights into affinity and selectivity

Malaria is an infectious disease caused by protozoan parasites of the genus Plasmodium and transmitted by Anopheles spp. mosquitos. Due to the emerging resistance to currently available drugs, great efforts must be invested in discovering new molecular targets and drugs. N-myristoyltransferase (NMT) is an essential enzyme to parasites and has been validated as a chemically tractable target for the discovery of new drug candidates against malaria. In this work, 2D and 3D quantitative structure-activity relationship (QSAR) studies were conducted on a series of benzothiophene derivatives as P. falciparum NMT (PfNMT) and human NMT (HsNMT) inhibitors to shed light on the molecular requirements for inhibitor affinity and selectivity. A combination of Quantitative Structure-activity Relationship (QSAR) methods, including the hologram quantitative structure-activity relationship (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) models, were used, and the impacts of the molecular alignment strategies (maximum common substructure and flexible ligand alignment) and atomic partial charge methods (Gasteiger-Hückel, MMFF94, AM1-BCC, CHELPG, and Mulliken) on the quality and reliability of the models were assessed. The best models exhibited internal consistency and could reasonably predict the inhibitory activity against both PfNMT (HQSAR: q² /r² /r² _pred = 0.83/0.98/0.81; CoMFA: q² /r² /r² _pred = 0.78/0.97/0.86; CoMSIA: q² /r² /r² _pred = 0.74/0.95/0.82) and HsNMT (HQSAR: q² /r² /r² _pred = 0.79/0.93/0.74; CoMFA: q² /r² /r² _pred = 0.82/0.98/0.60; CoMSIA: q² /r² /r² _pred = 0.62/0.95/0.56). The results enabled the identification of the polar interactions (electrostatic and hydrogen-bonding properties) as the major molecular features that affected the inhibitory activity and selectivity. These findings should be useful for the design of PfNMT inhibitors with high affinities and selectivities as antimalarial lead candidates.

Substituted Aminoacetamides as Novel Leads for Malaria Treatment

Herein we describe the optimization of a phenotypic hit against Plasmodium falciparum based on an aminoacetamide scaffold. This led to N-(3-chloro-4-fluorophenyl)-2-methyl-2-{[4-methyl-3-(morpholinosulfonyl)phenyl]amino}propanamide (compound 28) with low-nanomolar activity against the intraerythrocytic stages of the malaria parasite, and which was found to be inactive in a mammalian cell counter-screen up to 25 μm. Inhibition of gametes in the dual gamete activation assay suggests that this family of compounds may also have transmission blocking capabilities. Whilst we were unable to optimize the aqueous solubility and microsomal stability to a point at which the aminoacetamides would be suitable for in vivo pharmacokinetic and efficacy studies, compound 28 displayed excellent antimalarial potency and selectivity; it could therefore serve as a suitable chemical tool for drug target identification.