title: Jointly handling potency and toxicity of antimicrobial peptidomimetics by simple rules from desirability theory and chemoinformatics.
abstract: Today, emerging and increasing resistance to antibiotics has become a threat to public health worldwide. Antimicrobial peptides have unique action mechanisms making them an attractive therapeutic prospect to be applied against resistant bacteria. However, the major drawback is related with their high hemolytic activity which cancels out the safety requirements for a human antibiotic. Therefore, additional efforts are needed to develop new antimicrobial peptides that possess a greater potency for bacterial cells and less or no toxicity over erythrocytes. In this paper, we introduce a practical approach to simultaneously deal with these two conflicting properties. The convergence of machine learning techniques and desirability theory allowed us to derive a simple, predictive, and interpretable multicriteria classification rule for simultaneously handling the antibacterial and hemolytic properties of a set of cyclic β-hairpin cationic peptidomimetics (Cβ-HCPs). The multicriteria classification rule exhibited a prediction accuracy of about 80% on training and external validation sets. Results from an additional concordance test have shown an excellent agreement between the multicriteria classification rule predictions and the predictions from independent classifiers for complementary antibacterial and hemolytic activities, respectively, evidencing the reliability of the multicriteria classification rule. The rule was also consistent with the general mode of action of cationic peptides pointing out its biophysical relevance. We also propose a multicriteria virtual screening strategy based on the joint use of the multicriteria classification rule, desirability, similarity, and chemometrics concepts. The ability of such a virtual screening strategy to prioritize selective (nonhemolytic) antibacterial Cβ-HCPs was assessed and challenged for their predictivity regarding the training, validation, and overall data. In doing so, we were able to rank a selective antibacterial Cβ-HCP earlier than a biologically inactive or nonselective antibacterial Cβ-HCP with a probability of ca. 0.9. Our results thus indicate that promising chemoinformatics tools were obtained by considering both the multicriteria classification rule and the virtual screening strategy, which could, for instance, be used to aid the discovery and development of potent and nontoxic antimicrobial peptides.
authors: CIQ, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal. maikelcm@uclv.edu.cu
authors: Maykel
authors: M
authors: Cruz-Monteagudo
authors: Fernanda
authors: F
authors: Borges
authors: M Natália D S
authors: MN
authors: Cordeiro
mesh_terms: D000890:Anti-Infective Agents / Q000737:chemistry* / Q000493:pharmacokinetics / Q000633:toxicity*; D001185:Artificial Intelligence; D015195:Drug Design; D006090:Gram-Negative Bacteria / Q000378:metabolism; D006461:Hemolysis / Q000187:drug effects; D006801:Humans; D008954:Models, Biological; D057786:Peptidomimetics / Q000737:chemistry* / Q000493:pharmacokinetics / Q000633:toxicity*; D021281:Quantitative Structure-Activity Relationship
chemical_list: D000890:Anti-Infective Agents; D057786:Peptidomimetics