Membrane-targeting, ultrashort lipopeptide acts as an antibiotic adjuvant and sensitizes MDR gram-negative pathogens toward narrow-spectrum antibiotics.

Globally, infections due to multi-drug resistant (MDR) Gram-negative bacterial (GNB) pathogens are on the rise, negatively impacting morbidity and mortality, necessitating urgent treatment alternatives. Herein, we report a detailed bio-evaluation of an ultrashort, cationic lipopeptide 'SVAP9I' that demonstrated potent antibiotic activity and acted as an adjuvant to potentiate existing antibiotic classes towards GNBs. Newly synthesized lipopeptides were screened against ESKAPE pathogens and cytotoxicity assays were performed to evaluate the selectivity index (SI). SVAP9I exhibited broad-spectrum antibacterial activity against critical MDR-GNB pathogens including members of Enterobacteriaceae (MIC 4-8 mg/L), with a favorable CC50 value of ≥100 mg/L and no detectable resistance even after 50th serial passage. It demonstrated fast concentration-dependent bactericidal action as determined via time-kill analysis and also retained full potency against polymyxin B-resistant E. coli, indicating distinct mode of action. SVAP9I targeted E. coli's outer and inner membranes by binding to LPS and phospholipids such as cardiolipin and phosphatidylglycerol. Membrane damage resulted in ROS generation, depleted intracellular ATP concentration and a concomitant increase in extracellular ATP. Checkerboard assays showed SVAP9I's synergism with narrow-spectrum antibiotics like vancomycin, fusidic acid and rifampicin, potentiating their efficacy against MDR-GNB pathogens, including carbapenem-resistant Acinetobacter baumannii (CRAB), a WHO critical priority pathogen. In a murine neutropenic thigh infection model, SVAP9I and rifampicin synergized to express excellent antibacterial efficacy against MDR-CRAB outcompeting polymyxin B. Taken together, SVAP9I's distinct membrane-targeting broad-spectrum action, lack of resistance and strong in vitro andin vivopotency in synergism with narrow spectrum antibiotics like rifampicin suggests its potential as a novel antibiotic adjuvant for the treatment of serious MDR-GNB infections.

Applying Machine Learning for Antibiotic Development and Prediction of Microbial Resistance.

Antimicrobial resistance (AMR) poses a serious threat to human health worldwide. It is now more challenging than ever to introduce a potent antibiotic to the market considering rapid emergence of antimicrobial resistance, surpassing the rate of antibiotic drug discovery. Hence, new approaches need to be developed to accelerate the rate of drug discovery process and meet the demands for new antibiotics, while reducing the cost of their development. Machine learning holds immense promise of becoming a useful tool, especially since in the last two decades, exponential growth has occurred in computational power and biological big data analytics. Recent advancements in machine learning algorithms for drug discovery have provided significant clues for potential antibiotic classes. Apart from discovery of new scaffolds, the machine learning protocols will significantly impact prediction of AMR patterns and drug metabolism. In this review, we outline power of machine learning in antibiotic drug discovery, metabolic fate, and AMR prediction to support researchers engaged and interested in this field.