Abstract
Resistance of bacteria to current antibiotics is an alarming health problem. In this sense, Pseudomonas represents a genus of Gram-negative pathogens, which has emerged as one of the most dangerous species causing nosocomial infections. Despite the effort of the scientific community, drug resistant strains of bacteria belonging to Pseudomonas spp. prevail. The high costs associated to drug discovery and the urgent need for more efficient antimicrobial chemotherapies envisage the fact that computeraided methods can rationalize several stages involved in the development of a new drug. In this work, we introduce a chemoinformatic methodology devoted to the construction of a multitasking model for quantitative-structure biological effect relationships (mtk-QSBER). The purpose of this model was to perform simultaneous predictions of anti-Pseudomonas activities and ADMET (absorption, distribution, metabolism, elimination, and toxicity) properties of organic compounds. The mtk-QSBER model was created from a large and heterogeneous dataset (more than 54000 cases) and displayed accuracies higher than 90% in both training and prediction sets. In order to demonstrate the applicability of our mtk-QSBER model, we used the investigational antibacterial drug delafloxacin as a case of study, for which experimental results were recently reported. The predictions performed for many biological effects of this drug exhibited a remarkable convergence with the experimental assays, confirming that our model can serve as useful tool for virtual screening of potent and safer anti-Pseudomonas agents.
Keywords: Antibacterial, ADMET, delafloxacin, linear discriminant analysis, mtk-QSBER, Pseudomonas spp., TOMOCOMD.
Combinatorial Chemistry & High Throughput Screening
Title:Computer-Aided Discovery in Antimicrobial Research: In Silico Model for Virtual Screening of Potent and Safe Anti-Pseudomonas Agents
Volume: 18 Issue: 3
Author(s): Alejandro Speck-Planche and Maria N.D.S. Cordeiro
Affiliation:
Keywords: Antibacterial, ADMET, delafloxacin, linear discriminant analysis, mtk-QSBER, Pseudomonas spp., TOMOCOMD.
Abstract: Resistance of bacteria to current antibiotics is an alarming health problem. In this sense, Pseudomonas represents a genus of Gram-negative pathogens, which has emerged as one of the most dangerous species causing nosocomial infections. Despite the effort of the scientific community, drug resistant strains of bacteria belonging to Pseudomonas spp. prevail. The high costs associated to drug discovery and the urgent need for more efficient antimicrobial chemotherapies envisage the fact that computeraided methods can rationalize several stages involved in the development of a new drug. In this work, we introduce a chemoinformatic methodology devoted to the construction of a multitasking model for quantitative-structure biological effect relationships (mtk-QSBER). The purpose of this model was to perform simultaneous predictions of anti-Pseudomonas activities and ADMET (absorption, distribution, metabolism, elimination, and toxicity) properties of organic compounds. The mtk-QSBER model was created from a large and heterogeneous dataset (more than 54000 cases) and displayed accuracies higher than 90% in both training and prediction sets. In order to demonstrate the applicability of our mtk-QSBER model, we used the investigational antibacterial drug delafloxacin as a case of study, for which experimental results were recently reported. The predictions performed for many biological effects of this drug exhibited a remarkable convergence with the experimental assays, confirming that our model can serve as useful tool for virtual screening of potent and safer anti-Pseudomonas agents.
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Cite this article as:
Speck-Planche Alejandro and N.D.S. Cordeiro Maria, Computer-Aided Discovery in Antimicrobial Research: In Silico Model for Virtual Screening of Potent and Safe Anti-Pseudomonas Agents, Combinatorial Chemistry & High Throughput Screening 2015; 18 (3) . https://dx.doi.org/10.2174/1386207318666150305144249
DOI https://dx.doi.org/10.2174/1386207318666150305144249 |
Print ISSN 1386-2073 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5402 |
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