Search

Multiple drug discovery initiatives for tuberculosis are currently ongoing to identify and develop new potent drugs with novel targets in order to shorten treatment duration. One of the drug classes with a new mode of action is DprE1 inhibitors targeting an essential process in cell wall synthesis of Mycobacterium tuberculosis. In this investigation, three DprE1 inhibitors currently in clinical trials, TBA-7371, PBTZ169, and OPC-167832, were evaluated side-by-side as single agents in the C3HeB/FeJ mouse model presenting with caseous necrotic pulmonary lesions upon tuberculosis infection. The goal was to confirm the efficacy of the DprE1 inhibitors in a mouse tuberculosis model with advanced pulmonary pathology and perform comprehensive analysis of plasma, lung, and lesion-centric drug levels to establish pharmacokinetic-pharmacodynamic (PK-PD) parameters predicting efficacy at the site of infection. Results showed significant efficacy for all three DprE1 inhibitors in the C3HeB/FeJ mouse model after 2 months of treatment. Superior efficacy was observed for OPC-167832 even at low-dose levels, which can be attributed to its low MIC, favorable distribution, and sustained retention above the MIC throughout the dosing interval in caseous necrotic lesions, where the majority of bacteria reside in C3HeB/FeJ mice. These results support further progression of the three drug candidates through clinical development for tuberculosis treatment.

Efforts to develop effective and safe drugs for treatment of tuberculosis require preclinical evaluation in animal models. Alongside efficacy testing of novel therapies, effects on pulmonary pathology and disease progression are monitored by using histopathology images from these infected animals. To compare the severity of disease across treatment cohorts, pathologists have historically assigned a semi-quantitative histopathology score that may be subjective in terms of their training, experience, and personal bias. Manual histopathology therefore has limitations regarding reproducibility between studies and pathologists, potentially masking successful treatments. This report describes a pathologist-assistive software tool that reduces these user limitations, while providing a rapid, quantitative scoring system for digital histopathology image analysis. The software, called 'Lesion Image Recognition and Analysis' (LIRA), employs convolutional neural networks to classify seven different pathology features, including three different lesion types from pulmonary tissues of the C3HeB/FeJ tuberculosis mouse model. LIRA was developed to improve the efficiency of histopathology analysis for mouse tuberculosis infection models, this approach has also broader applications to other disease models and tissues. The full source code and documentation is available from https://Github.com/TB-imaging/LIRA.