A New Product Development Partnership Model for Antibiotic Resistance

A machine learning model trained on a high-throughput antibacterial screen increases the hit rate of drug discovery

Screening for novel antibacterial compounds in small molecule libraries has a low success rate. We applied machine learning (ML)-based virtual screening for antibacterial activity and evaluated its predictive power by experimental validation. We first binarized 29,537 compounds according to their growth inhibitory activity (hit rate 0.87%) against the antibiotic-resistant bacterium Burkholderia cenocepacia and described their molecular features with a directed-message passing neural network (D-MPNN). Then, we used the data to train an ML model that achieved a receiver operating characteristic (ROC) score of 0.823 on the test set. Finally, we predicted antibacterial activity in virtual libraries corresponding to 1,614 compounds from the Food and Drug Administration (FDA)-approved list and 224,205 natural products. Hit rates of 26% and 12%, respectively, were obtained when we tested the top-ranked predicted compounds for growth inhibitory activity against B. cenocepacia, which represents at least a 14-fold increase from the previous hit rate. In addition, more than 51% of the predicted antibacterial natural compounds inhibited ESKAPE pathogens showing that predictions expand beyond the organism-specific dataset to a broad range of bacteria. Overall, the developed ML approach can be used for compound prioritization before screening, increasing the typical hit rate of drug discovery.

Nano-enabled strategies to combat methicillin-resistant Staphylococcus aureus

The emergence of methicillin-resistant Staphylococcus aureus (MRSA) has become a threat to global health because of limited treatments. MRSA infections are difficult to treat due to increasingly developing resistance in combination with protective biofilms of Staphylococcus aureus (S. aureus). Nanotechnology-based research revealed that effective MRSA treatments could be achieved through targeted nanoparticles (NPs) that withstand biological films and drug resistance. Thus, the principal aim towards improving MRSA treatment is to advance drug delivery tools, which successfully address the delivery-related problems. These potential delivery tools would also carry drugs to the desired sites of therapeutic action to overcome the adverse effects. This review focused on different types of nano-engineered carriers system for antimicrobial agents with improved therapeutic efficacy of entrapped drugs. The structural characteristics that play an essential role in the effectiveness of delivery systems have also been addressed with a description of recent scientific advances in antimicrobial treatment, emphasizing challenges in MRSA treatments. Consequently, existing gaps in the literature are highlighted, and reported contradictions are identified, allowing for the development of roadmaps for future research.