Predicting Antibiotic Resistance and Assessing the Risk Burden from Antibiotics: A Holistic Modeling Framework in a Tropical Reservoir

Xuneng Tong; Shin Giek Goh; Sanjeeb Mohapatra; Ngoc Han Tran; Luhua You; Jingjie Zhang; Yiliang He; Karina Yew-Hoong Gin

doi:10.1021/acs.est.3c10467

Predicting Antibiotic Resistance and Assessing the Risk Burden from Antibiotics: A Holistic Modeling Framework in a Tropical Reservoir

Xuneng Tong
, Shin Giek Goh
, Sanjeeb Mohapatra
, Ngoc Han Tran
, Luhua You
, Jingjie Zhang^*
, Yiliang He
, Karina Yew-Hoong Gin^*

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

22 Citations (Scopus)

3 Downloads (CityUHK Scholars)

Abstract

Predicting the hotspots of antimicrobial resistance (AMR) in aquatics is crucial for managing associated risks. We developed an integrated modeling framework toward predicting the spatiotemporal abundance of antibiotics, indicator bacteria, and their corresponding antibiotic-resistant bacteria (ARB), as well as assessing the potential AMR risks to the aquatic ecosystem in a tropical reservoir. Our focus was on two antibiotics, sulfamethoxazole (SMX) and trimethoprim (TMP), and on Escherichia coli (E. coli) and its variant resistant to sulfamethoxazole-trimethoprim (EC_SXT). We validated the predictive model using withheld data, with all Nash-Sutcliffe efficiency (NSE) values above 0.79, absolute relative difference (ARD) less than 25%, and coefficient of determination (R²) greater than 0.800 for the modeled targets. Predictions indicated concentrations of 1-15 ng/L for SMX, 0.5-5 ng/L for TMP, and 0 to 5 (log₁₀ MPN/100 mL) for E. coli and −1.1 to 3.5 (log₁₀ CFU/100 mL) for EC_SXT. Risk assessment suggested that the predicted TMP could pose a higher risk of AMR development than SMX, but SMX could possess a higher ecological risk. The study lays down a hybrid modeling framework for integrating a statistic model with a process-based model to predict AMR in a holistic manner, thus facilitating the development of a better risk management framework. © 2024 The Authors. Published by American Chemical Society.

Original language	English
Pages (from-to)	6781-6792
Journal	Environmental Science and Technology
Volume	58
Issue number	15
Online published	1 Apr 2024
DOIs	https://doi.org/10.1021/acs.est.3c10467
Publication status	Published - 16 Apr 2024
Externally published	Yes

Funding

This research grant is supported by the National University of Singapore PhD Research Scholarship and the Singapore National Research Foundation (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) program (E2S2-CREATE project ES-2: Detection, Assessment, and Modelling of Emerging Contaminants in the Urban Environment). We also acknowledge support from the National Natural Science Foundation of China (No. 42077356). We thank the Public Utilities Board (PUB), Singapore, for supporting our work.

Research Keywords

antibiotics
antimicrobial resistance
bacteria
integrated modeling
risk assessment

Publisher's Copyright Statement

This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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10.1021/acs.est.3c10467

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title = "Predicting Antibiotic Resistance and Assessing the Risk Burden from Antibiotics: A Holistic Modeling Framework in a Tropical Reservoir",

abstract = "Predicting the hotspots of antimicrobial resistance (AMR) in aquatics is crucial for managing associated risks. We developed an integrated modeling framework toward predicting the spatiotemporal abundance of antibiotics, indicator bacteria, and their corresponding antibiotic-resistant bacteria (ARB), as well as assessing the potential AMR risks to the aquatic ecosystem in a tropical reservoir. Our focus was on two antibiotics, sulfamethoxazole (SMX) and trimethoprim (TMP), and on Escherichia coli (E. coli) and its variant resistant to sulfamethoxazole-trimethoprim (EC\_SXT). We validated the predictive model using withheld data, with all Nash-Sutcliffe efficiency (NSE) values above 0.79, absolute relative difference (ARD) less than 25\%, and coefficient of determination (R2) greater than 0.800 for the modeled targets. Predictions indicated concentrations of 1-15 ng/L for SMX, 0.5-5 ng/L for TMP, and 0 to 5 (log10 MPN/100 mL) for E. coli and −1.1 to 3.5 (log10 CFU/100 mL) for EC\_SXT. Risk assessment suggested that the predicted TMP could pose a higher risk of AMR development than SMX, but SMX could possess a higher ecological risk. The study lays down a hybrid modeling framework for integrating a statistic model with a process-based model to predict AMR in a holistic manner, thus facilitating the development of a better risk management framework. {\textcopyright} 2024 The Authors. Published by American Chemical Society.",

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doi = "10.1021/acs.est.3c10467",

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Predicting Antibiotic Resistance and Assessing the Risk Burden from Antibiotics: A Holistic Modeling Framework in a Tropical Reservoir. / Tong, Xuneng; Goh, Shin Giek; Mohapatra, Sanjeeb et al.
In: Environmental Science and Technology, Vol. 58, No. 15, 16.04.2024, p. 6781-6792.

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

TY - JOUR

T1 - Predicting Antibiotic Resistance and Assessing the Risk Burden from Antibiotics

T2 - A Holistic Modeling Framework in a Tropical Reservoir

AU - Tong, Xuneng

AU - Goh, Shin Giek

AU - Mohapatra, Sanjeeb

AU - Tran, Ngoc Han

AU - You, Luhua

AU - Zhang, Jingjie

AU - He, Yiliang

AU - Gin, Karina Yew-Hoong

PY - 2024/4/16

Y1 - 2024/4/16

N2 - Predicting the hotspots of antimicrobial resistance (AMR) in aquatics is crucial for managing associated risks. We developed an integrated modeling framework toward predicting the spatiotemporal abundance of antibiotics, indicator bacteria, and their corresponding antibiotic-resistant bacteria (ARB), as well as assessing the potential AMR risks to the aquatic ecosystem in a tropical reservoir. Our focus was on two antibiotics, sulfamethoxazole (SMX) and trimethoprim (TMP), and on Escherichia coli (E. coli) and its variant resistant to sulfamethoxazole-trimethoprim (EC_SXT). We validated the predictive model using withheld data, with all Nash-Sutcliffe efficiency (NSE) values above 0.79, absolute relative difference (ARD) less than 25%, and coefficient of determination (R2) greater than 0.800 for the modeled targets. Predictions indicated concentrations of 1-15 ng/L for SMX, 0.5-5 ng/L for TMP, and 0 to 5 (log10 MPN/100 mL) for E. coli and −1.1 to 3.5 (log10 CFU/100 mL) for EC_SXT. Risk assessment suggested that the predicted TMP could pose a higher risk of AMR development than SMX, but SMX could possess a higher ecological risk. The study lays down a hybrid modeling framework for integrating a statistic model with a process-based model to predict AMR in a holistic manner, thus facilitating the development of a better risk management framework. © 2024 The Authors. Published by American Chemical Society.

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KW - antibiotics

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KW - bacteria

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KW - risk assessment

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DO - 10.1021/acs.est.3c10467

M3 - RGC 21 - Publication in refereed journal

C2 - 38560895

SN - 0013-936X

VL - 58

SP - 6781

EP - 6792

JO - Environmental Science and Technology

JF - Environmental Science and Technology

IS - 15

ER -

Predicting Antibiotic Resistance and Assessing the Risk Burden from Antibiotics: A Holistic Modeling Framework in a Tropical Reservoir

Abstract

Funding

Research Keywords

Publisher's Copyright Statement

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