SUMMARY Fatty acid biosynthesis has been viewed as an important biological function of and therapeutic target for Plasmodium falciparum asexual blood stage infection. This apicoplast-resident type II pathway, distinct from the mammalian type I process, includes FabI. Here, we report synthetic chemistry and transfection studies concluding that Plasmodium FabI is not the target of the antimalarial activity of the bacterial FabI inhibitor triclosan. Disruption of fabI in P. falciparum or the rodent parasite P. berghei does not impede blood stage growth. In contrast, mosquito-derived fabI-deficient P. berghei sporozoites are markedly less infective for mice and typically fail to complete liver stage development in vitro. This is characterized by an inability to form intra-hepatic merosomes that normally initiate blood stage infections. These data illuminate key differences between liver and blood stage parasites in their requirements for host versus de novo synthesized fatty acids, and create new prospects for stage-specific antimalarial interventions.

Bacterial survival requires an intact peptidoglycan layer, a 3-dimensional exoskeleton that encapsulates the cytoplasmic membrane. Historically, the final steps of peptidoglycan synthesis are known to be carried out by d,d-transpeptidases, enzymes that are inhibited by the β-lactams which constitute >50% of all antibacterials in clinical use. Here, we show that the carbapenem subclass of β-lactams is distinctly effective not only because they inhibit d,d-transpeptidases and are poor substrates for β-lactamases, but primarily because they also inhibit non-classical transpeptidases, namely the l,d-transpeptidases, that generate the majority of linkages in the peptidoglycan of mycobacteria. We have characterized the molecular mechanisms responsible for inhibition of l,d-transpeptidases of M. tuberculosis and a range of bacteria, including ESKAPE pathogens, and utilized this information to design, synthesize and test simplified carbapenems with potent antibacterial activity.

Persistence, manifested as drug tolerance, represents a significant obstacle to global tuberculosis control. The bactericidal drugs isoniazid and rifampicin kill greater than 99% of exponentially growing () cells, but the remaining cells are persisters, cells with decreased metabolic rate, refractory to killing by these drugs, and able to generate drug-resistant mutants. We discovered that the combination of cysteine or other small thiols with either isoniazid or rifampicin prevents the formation of drug-tolerant and drug-resistant cells in cultures. This effect was concentration- and time-dependent, relying on increased oxygen consumption that triggered enhanced production of reactive oxygen species. In infected murine macrophages, the addition of-acetylcysteine to isoniazid treatment potentiated the killing of Furthermore, we demonstrate that the addition of small thiols to drug treatment shifted the menaquinol/menaquinone balance toward a reduced state that stimulates respiration and converts persister cells to metabolically active cells. This prevention of both persister cell formation and drug resistance leads ultimately to mycobacterial cell death. Strategies to enhance respiration and initiate oxidative damage should improve tuberculosis chemotherapies.

SUMMARY Identification of unique leads represents a significant challenge in drug discovery. This hurdle is magnified in neglected diseases such as tuberculosis. We have leveraged public high-throughput screening (HTS) data, to experimentally validate virtual screening approach employing Bayesian models built with bioactivity information (single-event model) as well as bioactivity and cytotoxicity information (dual-event model). We virtually screen a commercial library and experimentally confirm actives with hit rates exceeding typical HTS results by 1-2 orders of magnitude. The first dual-event Bayesian model identified compounds with antitubercular whole-cell activity and low mammalian cell cytotoxicity from a published set of antimalarials. The most potent hit exhibits the in vitro activity and in vitro/in vivo safety profile of a drug lead. These Bayesian models offer significant economies in time and cost to drug discovery.

Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with Mycobacterium tuberculosis ( Mtb) has led to many large-scale phenotypic screens and many thousands of new active compounds identified in vitro. However, with limited funding, efforts to discover new active molecules against Mtb needs to be more efficient. Several computational machine learning approaches have been shown to have good enrichment and hit rates. We have curated small molecule Mtb data and developed new models with a total of 18,886 molecules with activity cutoffs of 10 μM, 1 μM, and 100 nM. These data sets were used to evaluate different machine learning methods (including deep learning) and metrics and to generate predictions for additional molecules published in 2017. One Mtb model, a combined in vitro and in vivo data Bayesian model at a 100 nM activity yielded the following metrics for 5-fold cross validation: accuracy = 0.88, precision = 0.22, recall = 0.91, specificity = 0.88, kappa = 0.31, and MCC = 0.41. We have also curated an evaluation set ( n = 153 compounds) published in 2017, and when used to test our model, it showed the comparable statistics (accuracy = 0.83, precision = 0.27, recall = 1.00, specificity = 0.81, kappa = 0.36, and MCC = 0.47). We have also compared these models with additional machine learning algorithms showing Bayesian machine learning models constructed with literature Mtb data generated by different laboratories generally were equivalent to or outperformed deep neural networks with external test sets. Finally, we have also compared our training and test sets to show they were suitably diverse and different in order to represent useful evaluation sets. Such Mtb machine learning models could help prioritize compounds for testing in vitro and in vivo.