Novel bacterial topoisomerase inhibitors derived from isomannide.

A series of Novel Bacterial Topoisomerase Inhibitors (NBTIs) employing a linker derived from isomannide were synthesized and evaluated. Reduced hERG inhibition was observed compared to structure-matched analogues with different linkers, and compound 6 showed minimal proarrhythmic potential using an in vitro panel of cardiac ion channels. Compound 6 also displayed excellent activity against fluoroquinolone-resistant MRSA (MIC90 = 2 µg/mL) and other Gram-positive pathogens.

STarFish: A Stacked Ensemble Target Fishing Approach and its Application to Natural Products.

Target fishing is the process of identifying the protein target of a bioactive small molecule. To do so experimentally requires a significant investment of time and resources, which can be expedited with a reliable computational target fishing model. The development of computational target fishing models using machine learning has become very popular over the last several years because of the increased availability of large amounts of public bioactivity data. Unfortunately, the applicability and performance of such models for natural products has not yet been comprehensively assessed. This is, in part, due to the relative lack of bioactivity data available for natural products compared to synthetic compounds. Moreover, the databases commonly used to train such models do not annotate which compounds are natural products, which makes the collection of a benchmarking set difficult. To address this knowledge gap, a data set composed of natural product structures and their associated protein targets was generated by cross-referencing 20 publicly available natural product databases with the bioactivity database ChEMBL. This data set contains 5589 compound-target pairs for 1943 unique compounds and 1023 unique targets. A synthetic data set comprising 107 190 compound-target pairs for 88 728 unique compounds and 1907 unique targets was used to train k-nearest neighbors, random forest, and multilayer perceptron models. The predictive performance of each model was assessed by stratified 10-fold cross-validation and benchmarking on the newly collected natural product data set. Strong performance was observed for each model during cross-validation with area under the receiver operating characteristic (AUROC) scores ranging from 0.94 to 0.99 and Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC) scores from 0.89 to 0.94. When tested on the natural product data set, performance dramatically decreased with AUROC scores ranging from 0.70 to 0.85 and BEDROC scores from 0.43 to 0.59. However, the implementation of a model stacking approach, which uses logistic regression as a meta-classifier to combine model predictions, dramatically improved the ability to correctly predict the protein targets of natural products and increased the AUROC score to 0.94 and BEDROC score to 0.73. This stacked model was deployed as a web application, called STarFish, and has been made available for use to aid in target identification for natural products.

An Isoquinoline Scaffold as a Novel Class of Allosteric HIV-1 Integrase Inhibitors.

Allosteric HIV-1 integrase inhibitors (ALLINIs) are a new class of potential antiretroviral therapies with a unique mechanism of action and drug resistance profile. To further extend this class of inhibitors via a scaffold hopping approach, we have synthesized a series of analogues possessing an isoquinoline ring system. Lead compound 6l binds in the v-shaped pocket at the IN dimer interface and is highly selective for promoting higher-order multimerization of inactive IN over inhibiting IN-LEDGF/p75 binding. Importantly, 6l potently inhibited HIV-1NL4-3 (A128T IN), which confers marked resistance to archetypal quinoline-based ALLINIs. Thermal degradation studies indicated that at elevated temperatures the acetic acid side chain of specific isoquinoline derivatives undergo decarboxylation reactions. This reactivity has implications for the synthesis of various ALLINI analogues.