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QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data

ABSTRACT: Pseudomonas aeruginosa is a Gram-negative bacillus included among the six “ESKAPE” microbial species with an outstanding ability to “escape” currently used antibiotics and developing new antibiotics against it is of the highest priority. Whereas minimum inhibitory concentration (MIC) values against Pseudomonas aeruginosa have been used previously for QSAR model development, disk diffusion results (inhibition zones) have not been apparently used for this purpose in the literature and we decided to explore their use in this sense. We developed multiple QSAR methods using several machine learning algorithms (support vector classifier, K nearest neighbors, random forest classifier, decision tree classifier, AdaBoost classifier, logistic regression and naïve Bayes classifier). We used four sets of molecular descriptors and fingerprints and three different methods of data balancing, together with the “native” data set. In total, 32 models were built for each set of descriptors or fingerprint and balancing method, of which 28 were selected and stacked to create meta-models. In terms of balanced accuracy, the best performance was provided by KNN, logistic regression and decision tree classifier, but the ensemble method had slightly superior results in nested cross-validation.

SUBMITTER: Bugeac C

PROVIDER: S-EPMC8003670 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCeftazidime-avibactam was approved in China in 2019 for treating complicated intra-abdominal infections, hospital-acquired pneumonia, ventilator-associated pneumonia, and infections caused by Enterobacterales and Pseudomonas aeruginosa for which treatment options are limited. However, no currently available commercial systems have been approved for antimicrobial susceptibility testing of ceftazidime-avibactam in China. Here, we evaluated the Etest and disk diffusion method for detecting the activity of ceftazidime-avibactam against Enterobacterales and P. aeruginosa in China.ResultsIn total, 194 Enterobacterales and 77 P. aeruginosa isolates, which were divided into a random selection group (140 Enterobacterales and 46 P. aeruginosa isolates) and stock group (54 Enterobacterales and 31 P. aeruginosa isolates), were assessed by the Etest, disk diffusion and broth microdilution methods. Minimum inhibitory concentrations and zone diameters were interpreted according to the CLSI supplement M100 30th edition. For all 271 tested isolates, no very major errors were found by using Etest, whereas the overall major error rate was 2.0% (4/203). The overall categorical agreement rates of Etest for Enterobacterales and P. aeruginosa were 99.5% (193/194) and 96.1% (74/77), respectively, and the essential agreement rates were 95.9% (186/194) and 94.8% (73/77), respectively. The disk diffusion method showed that the very major error and major error rates were 1.5% (3/204) and 2.5% (5/203), respectively. Overall categorical agreement rates values of the disk diffusion method for Enterobacterales and P. aeruginosa were 98.5% (191/194) and 93.5% (72/77) compared with broth microdilution, respectively.ConclusionsFor Enterobacterales and P. aeruginosa, both the Etest and disk diffusion method showed acceptable performance as alternatives to the standard broth microdilution method for clinical treatment interpretation. Application of the disk diffusion method in Enterobacterales was slightly better than that in P. aeruginosa.

Project description:Objectives: Ceftazidime-avibactam is a novel synthetic beta-lactam + beta-lactamase inhibitor combination. We evaluated the performance of the gradient diffusion strip method and the disk diffusion method for the determination of ceftazidime-avibactam against Enterobacterales and Pseudomonas aeruginosa. Methods: Antimicrobial susceptibility testing of 302 clinical Enterobacterales and Pseudomonas aeruginosa isolates from two centers were conducted by broth microdilution (BMD), gradient diffusion strip method, and disk diffusion method for ceftazidime-avibactam. Using BMD as a gold standard, essential agreement (EA), categorical agreement (CA), major error (ME), and very major error (VME) were determined according to CLSI guidelines. CA and EA rate > 90%, ME rate < 3%, and VME rate < 1.5% were considered as acceptable criteria. Polymerase chain reaction and Sanger sequencing were performed to determine the carbapenem resistance genes of all 302 isolates. Results: A total of 302 strains were enrolled, among which 182 strains were from center 1 and 120 strains were from center 2. A percentage of 18.21% (55/302) of the enrolled isolates were resistant to ceftazidime-avibactam. The CA rates of the gradient diffusion strip method for Enterobacterales and P. aeruginosa were 100% and 98.65% (73/74), respectively, and the EA rates were 97.37% (222/228) and 98.65% (73/74), respectively. The CA rates of the disk diffusion method for Enterobacterales and P. aeruginosa were 100% and 95.95% (71/74), respectively. No VMEs were found by using the gradient diffusion strip method, while the ME rate was 0.40% (1/247). No MEs were found by using the disk diffusion method, but the VME rate was 5.45% (3/55). Therefore, all the parameters of the gradient diffusion strip method were in line with acceptable criteria. For 31 bla KPC , 33 bla NDM , 7 bla IMP , and 2 bla VIM positive isolates, both CA and EA rates were 100%; no MEs or VMEs were detected by either method. For 15 carbapenemase-non-producing resistant isolates, the CA and EA rates of the gradient diffusion strips method were 100%. Whereas the CA rate of the disk diffusion method was 80.00% (12/15), the VME rate was 20.00% (3/15). Conclusion: The gradient diffusion strip method can meet the needs of clinical microbiological laboratories for testing the susceptibility of ceftazidime-avibactam drugs. However, the VME rate > 1.5% (5.45%) by the disk diffusion method. By comparison, the performance of the gradient diffusion strip method was better than that of the disk diffusion method.

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QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data

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