Novel Pyrimidines as Antitubercular Agents

Abstract

infection is responsible for a global pandemic. New drugs are needed that do not show cross-resistance with the existing front-line therapeutics. A triazine antitubercular hit led to the design of a related pyrimidine family. The synthesis of a focused series of these analogs facilitated exploration of their activity, cytotoxicity, and physiochemical and absorption-distribution-metabolism-excretion properties. Select pyrimidines were then evaluated for their pharmacokinetic profiles in mice. The findings suggest a rationale for the further evolution of this promising series of antitubercular small molecules, which appear to share some similarities with the clinical compound PA-824 in terms of activation, while highlighting more general guidelines for the optimization of small-molecule antitubercular agents.

Pruned Machine Learning Models to Predict Aqueous Solubility

“…This external test set has 197 unique compounds, of which 35 compounds (17.8%) were defined as soluble; (b) The external validation set of our laboratory’s internal compounds (External JSF set) is composed of hits and leads synthesized during the course of our different infectious disease projects, which have been assayed for kinetic aqueous solubility. 23 In this set, 14 of the 52 compounds (26.9%) were soluble. Evaluations of the external statistics for these models with these two test sets are shown in Tables 1 , S2, and S3 .…”

“…In the present study, we systematically tested several different extents of pruning of the MLSMR set, the AZ set, and the fused MLSMR + AZ set to examine how different levels of pruning impact the ability to predict whether a compound has a solubility ≥100 μM (a typical goal value of this property 23 ). Since we utilized a Bayesian classifier model with this 100 μM cutoff, our goal was to differentiate “good” or sufficiently soluble compounds, defined as having a solubility ≥100 μM, from “bad” or insufficiently soluble compounds, which have a solubility <100 μM.…”

Optimization of N-benzyl-5-nitrofuran-2-carboxamide as an antitubercular agent

“…For instance, nitrofuranylamides, nitrofuranylpiperazines, and nitrofuranylisoxazolines have demonstrated in vitro and in vivo efficacy (Figure 2), [11][12][13][14][15][16] but to the best of our knowledge have yet to transition to clinical studies. Inspired by these compounds and our efforts with distantly related triazine 17 and pyrimidine 18 series, we decided to further probe the structure-activity relationship (SAR) of nitrofuranylamides as antitubercular agents. We commenced with the hit compound JSF-3449 from our internal screening efforts, 19 and pursued its evolution to an α,αdimethyl benzylamide JSF-4088 ( Figure 2).…”

“…The SI value (CC 50 /MIC) of 33, acceptable for a hit compound (≥ 10), was also targeted for improvement by not only a decrease in MIC but also by an increase in CC 50 . 18 JSF-3449 was profiled versus a panel of ESKAPE pathogens and was selective for M. tuberculosis (Supplemental Information, Table S1). Due to the potency of JSF-3449, we further assayed JSF-3449 for in vitro mouse liver microsome (MLM) stability and kinetic solubility (S) in pH 7.4 PBS as well as mouse pharmacokinetic (PK) profile.…”

“…Ideally, the t 1/2 would be greater than or equal to 60 min, but the solubility was more than satisfactory (>100 μM). 18 Oral dosing (po) of JSF-3449 in CD-1 female mice, at a level of 25 mg/kg in 5% DMA/60% PEG300/35% D5W, showed average (n = 2 mice) values of the maximum plasma concentration (C max ) of 285 ng/mL (1160 nM) and AUC 0-5h of 547 h × ng/mL (Supplemental Information, Figure S1). Thus, the PK profile reinforced the needs for improving MIC and t 1/2 , as a correlate of oral exposure in the mouse.…”