Predicting Pseudomonas aeruginosa drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra

Nguyen, H.-A.; Peleg, A. Y.; Song, J.; Antony, B.; Webb, G. I.; Wisniewski, J. A.; Blakeway, L. V.; Badoordeen, G. Z.; Theegala, R.; Zisis, H.; Dowe, D. L.; Macesic, N.

doi:10.1101/2023.10.25.563934

bioRxiv · 10.1101/2023.10.25.563934

Predicting Pseudomonas aeruginosa drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra

Nguyen, H.-A.·Peleg, A. Y.·Song, J.·Antony, B.·Webb, G. I.·Wisniewski, J. A.·Blakeway, L. V.·Badoordeen, G. Z.·Theegala, R.·Zisis, H.·Dowe, D. L.·Macesic, N.

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Abstract

Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used in clinical microbiology laboratories for bacterial identification but its use for prediction of antimicrobial resistance (AMR) remains limited. Here, we used MALDI-TOF MS with artificial intelligence (AI) approaches to successfully predict AMR in Pseudomonas aeruginosa, a priority pathogen with complex AMR mechanisms. The highest performance was achieved for modern {beta}-lactam/{beta}-lactamase inhibitor drugs, namely ceftazidime/avibactam and ceftolozane/tazobactam, with area under the receiver operating characteristic curve (AUROC) of 0.86 and 0.87, respectively. As part of this work, we developed dynamic binning, a feature engineering technique that effectively reduces the high-dimensional feature set and has wide-ranging applicability to MALDI-TOF MS data. Compared to conventional methods, our approach yielded superior performance in 10 of 11 antimicrobials. Moreover, we showcase the efficacy of transfer learning in enhancing the performance for 7 of 11 antimicrobials. By assessing the contribution of features to the models prediction, we identified proteins that may contribute to AMR mechanisms. Our findings demonstrate the potential of combining AI with MALDI-TOF MS as a rapid AMR diagnostic tool for Pseudomonas aeruginosa.

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Nguyen, H.-A., Peleg, A. Y., Song, J., Antony, B., Webb, G. I., Wisniewski, J. A., Blakeway, L. V., Badoordeen, G. Z., Theegala, R., Zisis, H., Dowe, D. L., Macesic, N.. 2023-10-26. Predicting Pseudomonas aeruginosa drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra. https://doi.org/10.1101/2023.10.25.563934

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