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In this study, we explored the pharmaceutically underexploited mycobacterial gyrase ATPase (GyrB) domain as a template for a structure-based virtual screening of our in-house (BITS Pilani) compound collection to discover new inhibitors targeting Mycobacterium tuberculosis (M.tb.) The hit identified was further customized by using a combination of molecular docking and medicinal chemistry strategies to obtain an optimized analogue displaying considerable in vitro enzyme efficacy and bactericidal properties against the M.tb. H37 Rv strain. The binding affinity of the ligand toward the GyrB domain was reascertained by differential scanning fluorimetry experiments. Further evaluation of the hERG toxicity (a major limitation among the previously reported N-linked aminopiperidine analogues) indicated these molecules to be completely devoid of cardiotoxicity, a significant achievement within this class.

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TPS3164 Background: Despite longstanding national recommendations, uptake of lung cancer screening in the US remains low. Barriers include access to lung cancer screening, costs, and concerns over potential harms like false-positives and radiation exposure. The DELFI technology evaluates fragmentation patterns of cfDNA using supervised machine learning to distinguish cancer from non-cancer [PMID30943338; 34417454; 31142840]. Methods: The DELFI-L101 is a case-control observational study (NCT04825834) prospectively enrolling at academic and community sites. Eligible participants (≥50 years of age) are individuals who currently smoke or previously smoked, with smoking histories of ≥20 pack-years. Individuals are ineligible if they had cancer treatment in the prior year or a history of hematologic malignancies or myelodysplasia. Cases are individuals with pathologically confirmed cancers (group A – lung, group C – non-lung). Controls are those without cancer (group B) as determined by low-dose computed tomography screening completed within 6 weeks of enrollment. Cases and controls are identified among enrollees from all participating sites. Total enrollment is estimated to be ̃2500 participants across all groups. Blood samples are collected at enrollment for DELFI analyses, which involves cfDNA isolation from plasma, low-coverage, whole-genome, next-generation sequencing, and machine learning methods. Clinical data (medical history, demographics, and diagnostic, surgery, imaging, and pathology reports, and/or other diagnostic information) are collected at enrollment and at 12 months post-enrollment. The primary objective is to train and test a classifier for the detection of lung cancer using the DELFI technology with other biomarkers and clinical features. Secondary objectives include the evaluation of classifiers to distinguish lung cancer from other cancers, modeling benefits and harms using performance estimates of these classifiers, and description of the analytical performance (eg, repeatability and reproducibility) of the DELFI technology and classifiers. The primary endpoint is the accuracy of lung cancer detection as measured by sensitivity, specificity, and the area under the receiver operating characteristic curve. Secondary endpoints include accuracy of tissue of origin classification, and adverse events associated with blood sample collection. The training and performance characterization of a DELFI classifier will further the development of an affordable, accessible, blood-based cancer detection tool with potential to overcome barriers to lung cancer screening. Clinical trial information: NCT04825834.