No abstract

Patients with clinical manifestations of leishmaniasis, including cutaneous leishmaniasis, have limited treatment options, and existing therapies frequently have significant untoward liabilities. Rapid expansion in the diversity of available cutaneous leishmanicidal chemotypes is the initial step in finding alternative efficacious treatments. To this end, we combined a low-stringency Leishmania major promastigote growth inhibition assay with a structural computational filtering algorithm. After a rigorous assay validation process, we interrogated ∼200,000 unique compounds for L. major promastigote growth inhibition. Using iterative computational filtering of the compounds exhibiting >50% inhibition, we identified 553 structural clusters and 640 compound singletons. Secondary confirmation assays yielded 93 compounds with EC50s ≤ 1 µM, with none of the identified chemotypes being structurally similar to known leishmanicidals and most having favorable in silico predicted bioavailability characteristics. The leishmanicidal activity of a representative subset of 15 chemotypes was confirmed in two independent assay formats, and L. major parasite specificity was demonstrated by assaying against a panel of human cell lines. Thirteen chemotypes inhibited the growth of a L. major axenic amastigote-like population. Murine in vivo efficacy studies using one of the new chemotypes document inhibition of footpad lesion development. These results authenticate that low stringency, large-scale compound screening combined with computational structure filtering can rapidly expand the chemotypes targeting in vitro and in vivo Leishmania growth and viability.

Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications including basic biomedical research, drug discovery, diagnostics and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell level data, as well as the need for standard metrics of the spatial, temporal and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics and the design of optimal therapeutic strategies for individual patients.