Chuleeporn Phanus-Umporn

Antimicrobial resistance in gram-negative bacteria has renewed reliance on colistin, yet resistance to this last-line agent is increasing. In Klebsiella pneumoniae , activation of the PmrA/PmrB two-component system remodels lipid A and reduces colistin binding, motivating the sensor kinase PmrB as an adjuvant target. Here, we combined computational drug repurposing with experimental validation to identify colistin potentiators. High-throughput virtual screening of DrugBank using integrated ligand- and structure-based approaches shortlisted five approved drugs (mebendazole, flurbiprofen, tirbanibulin, flufenamic acid, and netarsudil) for experimental evaluation. In vitro assays against colistin-resistant K. pneumoniae showed flufenamic acid most robustly resensitized resistant isolates and suppressed the emergence of additional resistance under combination exposure. Mechanistic studies indicated that the colistin-flufenamic acid combination increased membrane permeability and significantly downregulated PmrA/PmrB-regulated genes ( pmrC and arnT ). Molecular docking and molecular dynamics simulations further supported a plausible interaction of flufenamic acid with the PmrB ATPase region, and representative PmrB substitutions (D150Y, T157P, and R256G) did not alter the predicted binding mode. Together, these data suggest that modulation of the PmrA/PmrB pathway may contribute to colistin potentiation and demonstrate a target-guided repurposing-to-validation framework for antibiotic adjuvant discovery in colistin-resistant K. pneumoniae . IMPORTANCE: Colistin is one of the last remaining treatment options for multidrug-resistant Klebsiella pneumoniae infections, but resistance to this drug is rising worldwide. In this study, we used a PrmB-informed, computer-guided drug repurposing workflow followed by in vitro validation to identify approved compounds that can potentiate colistin activity against colistin-resistant K. pneumoniae . This approach independently prioritized flufenamic acid as the most robust colistin enhancer among the screened candidates, restoring colistin activity and reducing the emergence of additional resistance under combination exposure. Mechanistically, our findings are consistent with modulation of the PmrB/PmrA resistance pathway, supporting bacterial histidine-kinase signaling as a promising direction for antibiotic adjuvant development. Overall, this study highlights a practical and scalable framework for discovering resistance-targeted adjuvants to help protect last-resort antibiotics and improve treatment options for difficult-to-treat infections.

Osteoanabolic agents, or drugs that promote bone formation, have gained considerable attention for osteoporosis management due to their curative and preventive potentials. Sphingosine-1-phosphate receptor 2 (S1PR2) is an attractive drug target, in which its activation leads to osteogenesis-promoting effect. Nitrogen-containing heterocyclic scaffolds (i.e., quinoxaline and indole) are promising pharmacophores possessing diverse bioactivities and were reported as S1PR2 activators. Quantitative structure-activity relationship (QSAR) modeling is a computational approach well-known as a fundamental tool for facilitating successful drug development. This study demonstrates the discovery of new S1PR2 activators using computational-driven rational design. Herein, an original dataset of nitrogen-containing S1PR2 activators was collected from ChEMBL database. The retrieved dataset was separated into two datasets according to their core scaffolds (i.e., quinoxaline and indole). QSAR modeling was performed using multiple linear regression (MLR) algorithm to successfully obtain two models with good predictive performance. The constructed models also revealed key properties playing essential roles for potent S1PR2 activation, such as Van der Waals volume (R2v+ and E3v), mass (MATS5m and Km), electronegativity (H3e), and number of 5-membered rings (nR05). Subsequently, the constructed models were further employed to guide rational design and predict S1PR2 activating effects of an additional set of 752 structurally modified compounds. Most of the modified compounds were predicted to have higher potency than their parents, and a set of promising potent newly designed compounds was highlighted. Additionally, drug-likeness prediction was performed to reveal that most of the newly designed compounds are druggable compounds with possibility for further development.

Correction for ‘Privileged substructures for anti-sickling activity via cheminformatic analysis’ by Chuleeporn Phanus-umporn et al., RSC Adv., 2018, 8, 5920–5935.

Sickle cell disease (SCD), an autosomal recessive genetic disorder, has been recognized by the World Health Organization (WHO) as a major public health problem as it affects 300 000 individuals worldwide. Complications arising from SCD include anemia, microvascular occlusion, severe pain, stokes, renal dysfunction and infections. A lucrative therapeutic strategy is to employ anti-sickling agents that can disrupt the formation of the HbS polymer. This study therefore employed cheminformatic approaches, encompassing classification structure–activity relationship (CSAR) modeling, to deduce the privileged substructures giving rise to the anti-sickling activity of an investigated set of 115 compounds, followed by substructure analysis. Briefly, the compiled compounds were described by fingerprint descriptors and used in the construction of CSAR models via several machine learning algorithms. The modelability of the data set, as exemplified by the MODI index, was determined to be in the range of 0.70–0.84. The predictive performance was deduced by the accuracy, sensitivity, specificity and Matthews correlation coefficient, which was found to be statistically robust, whereby the former three parameters afforded values in excess of 0.7 while the latter statistical parameter provided a value greater than 0.5. An analysis of the top 20 important substructure descriptors for anti-sickling activity revealed that 10 important features were significant in the differentiation of actives from inactives, as illustrated by aromaticity/conjugation (e.g. SubFPC287, SubFPC171 and SubFPC5), carbonyl groups (e.g. SubFPC137, SubFPC139, SubFPC49 and SubFPC135) and miscellaneous groups (e.g. SubFPC303, SubFPC302 and SubFPC275). Furthermore, an analysis of the structure–activity relationship revealed that the length of alkyl chains, choice of functional moiety and position of substitution on the benzene ring may affect the anti-sickling activity of these compounds. Thus, this knowledge is anticipated to be useful for guiding the design of robust compounds against the gelling activity of HbS, as preliminarily demonstrated in the data-driven compound design presented herein.