Prediction of antimicrobial minimal inhibitory concentrations for Neisseria gonorrhoeae using machine learning models

Muhammad Yasir; Asad Mustafa Karim; Sumera Kausar Malik; Amal A Bajaffer; Esam I Azhar

doi:10.1016/j.sjbs.2022.02.047

Prediction of antimicrobial minimal inhibitory concentrations for Neisseria gonorrhoeae using machine learning models

Saudi J Biol Sci. 2022 May;29(5):3687-3693. doi: 10.1016/j.sjbs.2022.02.047. Epub 2022 Mar 4.

Authors

Muhammad Yasir^{1

2}, Asad Mustafa Karim³, Sumera Kausar Malik³, Amal A Bajaffer¹, Esam I Azhar^{1

2}

Affiliations

¹ Special Infectious Agents Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
² Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
³ Department of Bioscience and Biotechnology, The University of Suwon, Hwaseong City, Gyeonggi-do 18323, South Korea.

Abstract

The lowest concentration of an antimicrobial agent that can inhibit the visible growth of a microorganism after overnight incubation is called as minimum inhibitory concentration (MIC) and the drug prescriptions are made on the basis of MIC data to ensure successful treatment outcomes. Therefore, reliable antimicrobial susceptibility data is crucial, and it will help clinicians about which drug to prescribe. Although few prediction studies based on strategies have been conducted, however, no single machine learning (ML) modelling has been carried out to predict MICs in N. gonorrhoeae. In this study, we propose a ML based approach that can predict MICs of a specific antibiotic using unitigs sequences data. We retrieved N. gonorrhoeae genomes from European Nucleotide Archive and NCBI and analysed them combined with their respective MIC data for cefixime, ciprofloxacin, and azithromycin and then we constructed unitigs by using de Brujin graphs. We built and compared 35 different ML regression models to predict MICs. Our results demonstrate that RandomForest and CATBoost models showed best performance in predicting MICs of the three antibiotics. The coefficient of determination, R², (a statistical measure of how well the regression predictions approximate the real data points) for cefixime, ciprofloxacin, and azithromycin was 0.75787, 0.77241, and 0.79009 respectively using RandomForest. For CATBoost model, the R² value was 0.74570, 0.77393, and 0.79317 for cefixime, ciprofloxacin, and azithromycin respectively. Lastly, using feature importance, we explore the important genomic regions identified by the models for predicting MICs. The major mutations which are responsible for resistance against these three antibiotics were chosen by ML models as a top feature in case of each antibiotics. CATBoost, DecisionTree, GradientBoosting, and RandomForest regression models chose the same unitigs which are responsible for resistance. This unitigs-based strategy for developing models for MIC prediction, clinical diagnostics, and surveillance can be applicable for other critical bacterial pathogens.

Keywords: Antimicrobial resistance; Machine learning; Minimum inhibitory concentration; Neisseria gonorrhoeae.