Water contamination by commonly used antimicrobials around a tertiary care centre in South Kerala, India – environmental risk and antimicrobial resistance perspective

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The level of water contamination caused by antimicrobials in water sources and its consequences such as environmental risk and antimicrobial resistance is less explored in Kerala, India. The study aims to find the concentration of antimicrobials in water sources and to analyze the environmental risk and antimicrobial resistance that can arise due to the prevailing drug concentration in water sources. Five commonly used antimicrobials in a tertiary care centre were identified with the help of case records and purchase records for a period of one year- Azithromycin, Cefotaxime, Ciprofloxacin, Meropenem and Metronidazole Thirty-one samples of surface and ground water including drinking water sources collected were preconditioned by solid phase extraction technique and passed through Liquid Chromatography Mass Spectrometry (LCMS-MS) instrument to quantify the concentrations of antimicrobials. Measured median environmental concentration (MEC) were- Azithromycin (0.60 µg/L), Cefotaxime (1.0 µg/L), Ciprofloxacin (0.07 µg/L), Meropenem (0.05 µg/L and Metronidazole (1.73 µg/L). Using Predicted no effect concentration- environment (PNEC-ENV), environmental risk (R) calculated for Azithromycin (97.67), Cefotaxime (25.83), Ciprofloxacin (1.76), Meropenem (2.28) and Metronidazole (78.0) are above the value of 1, which shows that they are at a level to cause environmental risk. Highest MECs of the five antibiotics against PNEC-MIC (minimum inhibitory concentration) – Azithromycin (2.93, 0.25), Cefotaxime (3.10, 0.13), Ciprofloxacin (0.80, 0.06), Meropenem (3.42, 0.06) and Metronidazole (2.35, 0.13) shows that MECs are above the PNEC- MIC values used for determining antimicrobial resistance. These points to the occurrence of grave consequence of selection of antimicrobial resistant organisms in the environment. The concentration of antimicrobials in the water sources are in the range of posing environmental risk and for selection of antimicrobial resistant organisms in the environment. Water purification methods have to be developed to remove these drugs from water sources. Modification of policy regulations for proper and safe disposal of drugs have to be done and implemented to prevent pharmaceuticals from entering the water sources. water contamination environmental risk antimicrobial resistance pharmaceuticals predicted no effect concentration water purification Figures Figure 1 Figure 2 Introduction Environmental contamination by pharmaceuticals is a major concern these days. Improper disposal of pharmaceuticals by manufacturers, hospitals and households reach our water sources. Presence of these pharmaceuticals in water sources and their long-term ingestion can lead many many health hazards to living organisms (Gwenzi et. al, 2020) Among the pharmaceuticals, antimicrobials in the environment can cause serious public health hazard due to the emergence of antimicrobial resistance (Burnham et al. 2025, Sambaza et. al 2023 ). Overuse and misuse of antibiotics has led to the development of antimicrobial resistance in the community. It is estimated that by 2050, 10 million deaths per year are expected world wide due to antimicrobial resistant infections (CDC 2024). Sustained presence of antimicrobials in subtherapeutic concentrations is the cause of emergence and spread of antimicrobial resistant genes in promiscuous organisms (Singh et al.2022, Barathe et al,2024). Antimicrobials reach water sources through drug manufacturing unit outlets (Gonzalez Plaza et al. 2019; Mheidli et al. 2022). They also reach the water sources from hospitals and households through sewage and reach waste water treatment plants (Kumari et al.2020, Khan et al. 2021). Water purification process are insufficient to extract and remove many pharmaceuticals including antimicrobials (Alfonso-Muniozguren et al. 2021, Skalska Tuomi et al. 2025) Thus, they reach the natural water sources from where they reach humans and other living organisms. The presence of these low concentrations of antimicrobials in the environment as well as in the body of living organisms cause the emergence of antimicrobial resistant genes in microorganisms and thus antimicrobial resistant infections in humans (Hayward et. al 2020, Duarte et al. 2022). Widespread use of antimicrobials in animal husbandry (Delgado et al. 2023,Charuaud et al. 2019), aquaculture and agriculture (Manyi Loh et al. 2018, Miller et al. 2022) is a serious concern apart from its use in humans. Policy regulators and administrators should take meticulous steps to mitigate this devastating problem by imposing regulations on the safe and judicious use of antimicrobials as well as proper disposal of used, unused and left-over drugs including antimicrobials (Kinrys et al. 2018). Denmark model of antimicrobial usage is a set example to follow with respect to judicious use of antimicrobials (Levy et al. 2014)They have imposed policies on judicious prescription of antimicrobials by veterinarians and right usage practices by cattle and poultry farmers. Unavailability of the antimicrobials without prescription and also health education and awareness generation among farmers regarding the importance of right usage practices of antimicrobials stand a long way in preventing emergence of antimicrobial resistant infections (Belay et al. 2020; Emes et al.2024). Nowadays zoonotic diseases are causing serious concerns as it spreads to humans. Covid caused by SARS- Cov2 virus and monkeypox disease caused by human metapneumo virus are recent examples of zoonotic diseases transmitted to humans causing epidemics and even pandemics. Virus, bacteria, protozoans and fungi can cause serious systemic infections in humans due to drug resistant strains (Dafale et al. 2020). Though studies are done in some parts of the world to find the concentration of antimicrobials in the environment, such an exploratory research in Kerala, India is lacking. The presence of antimicrobials in water sources and the quantification of them helps to gather information on the potential risk of emergence of antimicrobial resistant organisms in the environment as well as the health hazards that may arise by the consumption of these subtherapeutic concentrations of antimicrobials for a long time (Izah et al. 2025; Macedo et al. 2025; Sun et al. 2020). Long term consumption of water with these low concentrations of antimicrobials can alter the gut flora in humans which can predispose to a series of health consequences indirectly such as obesity, diabetes, metabolic dysfunctions and endocrine abnormalities. Recently much focus is given on the concept of gut- brain axis where gut is considered the second brain, as any alteration in the gut flora can alter the release of neurotransmitters in the Central Nervous System and thus affect the higher mental functions like cognition which includes memory, intelligence, motivation, attention and concentration (O’ Riordan et al. 2025; Rusch et al.2020; Carabotti et al. 2015 ). Materials and Methods Baseline information of the selected drugs- Five commonly used antimicrobials in a tertiary care centre were identified from the data obtained from case records and purchase records for a period of one year Sample identification and collection-To find the contamination of water sources due to antimicrobial agents, a two-kilometer radius area was identified in Thiruvananthapuram district of South Kerala, India where many tertiary care hospitals are situated. Thirty-one water sources were identified from this area using Google Earth software. These water sources included natural sources like ponds, rivers, lakes and man-made sources like bore wells. pH, Temperature, Total dissolved solutes (TDS) and Electrical Conductivity (EC) of the water samples were checked at the site of collection itself using TDS meter and pH meter. One litre water was collected from these water sources in HDPE bottles and transported in ice bags to the centre for analysis. The samples were stored at -20 degree Celsius at the centre before it was taken for analysis. Solid Phase Extraction- The samples were preconcentrated using solid phase extraction technique before quantifying the drugs. For preconcentration of samples the water samples were passed through Whatman filter paper 41 and filtered under vacuum to remove sediments. The filtered water samples were passed through Oasis HLB 600/ 5ml cartridge at the rate of 2ml/ minute. The cartridge was then eluted with 5ml of 100% methanol for drug extraction. This 5ml sample was taken for analysis using Schimadzu Liquid Chromatography Mass Spectrometry – Mass Spectrometry (LCMS- MS) instrument. LCMS- MS analysis – Drug standards of LCMS grade were procured from drug manufacturing companies. The column used for the analysis of antimicrobials was C18 Shim-pack GIST 3µm C18 Material 3 x 150mm. The mobile phases used were Mobile phase A − 0.1% formic acid in water- aqueous phase and Mobile phase B − 0.1% formic acid in methanol- organic phase. The preconcentrated samples were run in the LCMS-MS instrument. Calibration curves were plotted and the area of the curve was found to determine the concentration of the test drug. Calibration curve was plotted with five concentrations of a single drug and the concentration in the test samples were calculated. Predicted no effect concentration- Environment, (PNEC- ENV) is an indicator used for calculating the environmental risk associated with a particular concentration of the drug in the environment. The PNEC- ENV value of each of the five drugs were obtained from literature and was used to find if the prevalent drug concentration in water sources is potential to cause environmental hazard using the formula Environmental Risk, R = Measured environmental concentration (MEC)/ PNEC- ENV. If the value is more than one, it carries risk of causing hazard to environment. Similarly, Predicted no effect concentration- Minimum Inhibitory Concentration (PNEC- MIC) is used to calculate the potential to cause antimicrobial resistance in the environment due to a particular concentration of the drug using the formula, Risk of developing antimicrobial resistance, R = MEC/ PNEC-MIC. PNEC- MIC value for each of the drugs is obtained from literature which is used to calculate the risk (Quadra et al. 2023 ; Vestel et al. 2016 , 2022 ). Results and Discussions Five commonly used antimicrobials in a tertiary care centre were identified with the help of case records and purchase records for a period of one year- Azithromycin, Cefotaxime, Ciprofloxacin, Meropenem and Metronidazole. Measured median environmental concentration (MEC) of these antimicrobials in water sources were- Azithromycin (0.60 µg/L), Cefotaxime (1.0 µg/L), Ciprofloxacin (0.07 µg/L), Meropenem (0.05 µg/L and Metronidazole (1.73 µg/L) (Table 1 ). Using Predicted no effect concentration- environment (PNEC-ENV), environmental risk (R) calculated for Azithromycin (97.67), Cefotaxime (25.83), Ciprofloxacin (1.75), Meropenem (2.28) and Metronidazole (78.0) are above the value of 1, which shows that they are at a level to cause environmental risk (Table 2 ). As the measured environmental concentration increases or the PNEC- ENV value decreases, R becomes greater than 1 (Fig. 1 ). MECs of the five antibiotics against PNEC-MIC (minimum inhibitory concentration) (Table 3 )– Azithromycin (2.93, 0.25), Cefotaxime (3.10, 0.13), Ciprofloxacin (0.80, 0.06), Meropenem (3.42, 0.06) and Metronidazole (2.35, 0.13) shows that MECs are above the PNEC- MIC values used for determining risk of developing antimicrobial resistance, R AMR ( Fig. 2 ). Many studies on the determination of concentration of various antibiotics like tetracyclines, quinolones, sulfonamides and macrolides in water sources are done in different parts of the world. The concentration of macrolides, quinolones, tetracyclines, sulfonamides were up to 3847 ng/L, 660.13 ng/L, 20 ng/L, 20.82 ng/ L respectively.Studies related to the detection of the risk of a particular concentration of antimicrobial in the environment as related to R ENV and risk of developing antimicrobial resistance R AMR is seen only in very few parts of the world. Surface and ground water studies have showed the presence of antimicrobials in them. Many studies from sewage treatment plants has pointed to the ineffective water treatment practices that let out unremoved antimicrobials into drinking water sources. In a study sulfamethoxazole was found in a concentration of 4.36 µg/L, and other antibiotics such as clarithromycin, trimethoprim, ciprofloxacin, sulfamethoxazole and azithromycin were found in water treatment plants in the range of 1.86 to 4.47 µg/L (Juarez et al.2021). This information is very crucial as it points to the risk of causing environmental hazard and emergence of antimicrobial resistance in the community. Environmental risk assessment calculated from measured environmental concentration and predicted no effect concentration of the antimicrobials gives an extrapolated information on how much toxicity it can cause to the primary trophic levels of aquatic organisms like green algae, crustaceans and vertebrates (Quadra et al. 2023 ; Vestel et al. 2016 , 2022 ). Green algae, daphnia and zebrafish are study models that represent the three trophic levels of organisms. It adversely affects their growth, mobility and reproductive rate of the aquatic organisms (Zhou et al. 2024 ; Ninomiya et al. 2024 ). Zebra fish is a model that has almost 70% homology to humans (Adhish and Manjubala 2023 ; Mlnaříková et al. 2024 ). Hence the toxicity to these organisms throws light to the toxicity that can happen in humans on long term use of sub-therapeutic concentrations of these antimicrobials. Table 1 – Measured median environmental concentration of antimicrobials Drugs Measured Environmental Concentration (MEC) Cefotaxime 1.0 µg/L Azithromycin 0.60 µg/L Metronidazole 1.73 µg/L Meropenem 0.05 µg/L Ciprofloxacin 0.07 µg/L Table 2 – Environmental risk R > 1 Drug Highest MEC PNEC- ENV R Cefotaxime 3.10 µg/L 0.12 µg/L 25.83 Azithromycin 2.93 µg/L 0.03 µg/L 97.67 Metronidazole 2.35 µg/L 0.03 µg/L 78.0 Meropenem 3.42 µg/L 1.5 µg/L 2.28 Ciprofloxacin 0.80 µg/L 0.45 µg/L 1.76 Table 3 – Risk of developing AMR (R AMR ) > 1 Drug Highest MEC PNEC- MIC R AMR Cefotaxime 3.10 µg/L 0.13 µg/L 23.85 Azithromycin 2.93 µg/L 0.25 µg/L 11.72 Metronidazole 2.35 µg/L 0.13 µg/L 18.0 Meropenem 3.42 µg/L 0.06 µg/L 57.0 Ciprofloxacin 0.80 µg/L 0.06 µg/L 13.33 Conclusions The findings reveal that the concentration of antimicrobials in the water sources are in the range of posing environmental risk and for selection of antimicrobial resistant organisms in the environment. Water purification methods have to be developed to remove these drugs from water sources. Modification of policy regulations for proper and safe disposal of drugs have to be done and implemented to prevent pharmaceuticals from entering the water sources. Declarations Acknowledgements- We express our gratitude to the Kerala University of Health Sciencesand theAdministrator, staff and students of Inter University Instrumentation Centre and Sophisticated Analytical Instrumentation Facility at Mahatma Gandhi University, Kerala who have helped with the technical support in the completion of the study. Funding – No financial support was obtained for this study. It was a self-funded work. Author information- Authors and affiliations- Dr. Jitha S, Associate Professor, Department of Pharmacology, Government Medical College, Thiruvananthapuram, Kerala, India Dr. Harikumaran Nair G S, Professor, Department of Radiodiagnosis, SreeGokulam Medical College, Thiruvananthapuram, Kerala, India Dr. Annapurna Y, Professor, Department of Pharmacology, Government Medical College, Thiruvananthapuram, Kerala, India Dr. Scaria Thomas P, Associate Professor, Department of Pharmacology, Government Medical College, Kottayam, Kerala, India Dr. Pradeep S, Professor, Department of Pharmacology, Dr Somervell Memorial CSI Medical College, Thiruvananthapuram, Kerala, India Author’s Contributions- Dr. Jitha S- conceptualization, data curation, formal analysis, writing—original draft writing and editing. Dr. Harikumaran Nair G S- review, analysis, supervision and editing. Dr. Annapurna Y- supervision, review and editing. Dr. Scaria Thomas P – supervision, technical support, review and editing Dr. Pradeep S - supervision, review and editing. Corresponding author- Dr. Jitha S, Associate Professor, Department of Pharmacology, Government Medical College, Thiruvananthapuram, Kerala, India email- [email protected] Ethics declaration Ethical approval- The study was approved by the Institutional Review Board of the institution vide order No: A2/ 119/2022/GMCT Consent to participate- Not applicable. Consent for publication- Not applicable. There are no conflicts of interest in the study Data availability - The original data obtained in the study are included in the article; further inquiries can be communicated to the corresponding author. Notes - The PNEC- ENV and PNEC- MIC values are compared with median measured environmental concentration to find the risk associated as an environmental hazard as well as a cause for emergence of antimicrobial resistance. References Alfonso-Muniozguren, P., Serna-Galvis, E. 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Scientific reports , 14 (1), 9401. https://doi.org/10.1038/s41598-024-59971-y Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6558909","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":455804450,"identity":"e74094e7-fe27-4acf-b735-bb9d43a5d8dc","order_by":0,"name":"Jitha Sushama","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACxmYGNgiLvQFIGFgQo4UZqoXnAEiLBDH2wLRIJIBJIjS08x978IOhLnH+zOdXN/wokGDgb+9OIOQwdsMehsOJG27nlN3sATpM4szZDYS0sEnwMBxI3CCdk3aDB6jFQCKXsBbJP2CHnUm7+YdYLdI8DMyJDTfYj90m1hYzaRmDw8YbzuSw3ZYxkOAh6BfD/oPPJN9U1MnObz/+7OabPzZy/O29BLQ0gEgDEMEDIfEqBwF5BJP9AUHVo2AUjIJRMDIBADO1Qbq4oA5ZAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-8406-1796","institution":"Government Medical College Thiruvananthapuram","correspondingAuthor":true,"prefix":"","firstName":"Jitha","middleName":"","lastName":"Sushama","suffix":""},{"id":455804451,"identity":"73e1066b-438e-4abb-b79f-d37d93be36ac","order_by":1,"name":"Harikumaran Nair","email":"","orcid":"","institution":"Sree Gokulam Medical College and Research Foundation","correspondingAuthor":false,"prefix":"","firstName":"Harikumaran","middleName":"","lastName":"Nair","suffix":""},{"id":455804452,"identity":"a2bd32e5-2b8a-42c2-bf5f-92c49c0a1594","order_by":2,"name":"Annapurna Yadavalli","email":"","orcid":"","institution":"Government Medical College Thiruvananthapuram","correspondingAuthor":false,"prefix":"","firstName":"Annapurna","middleName":"","lastName":"Yadavalli","suffix":""},{"id":455804453,"identity":"7b091313-4cd0-4bc7-9945-940bc8be3d04","order_by":3,"name":"Scaria Thomas Pulikkunnel","email":"","orcid":"","institution":"Government Medical College Kottayam","correspondingAuthor":false,"prefix":"","firstName":"Scaria","middleName":"Thomas","lastName":"Pulikkunnel","suffix":""},{"id":455804454,"identity":"fafed562-39ce-458c-943c-103725a271fe","order_by":4,"name":"Pradeep Sadasivan Pillai","email":"","orcid":"","institution":"Dr Somervell Memorial CSI Medical College and Hospital","correspondingAuthor":false,"prefix":"","firstName":"Pradeep","middleName":"Sadasivan","lastName":"Pillai","suffix":""}],"badges":[],"createdAt":"2025-04-29 18:38:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6558909/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6558909/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82887908,"identity":"63a1a7c5-6be2-454b-b503-e4bad1b212c9","added_by":"auto","created_at":"2025-05-16 11:59:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40497,"visible":true,"origin":"","legend":"\u003cp\u003eMEC, PNEC- ENV and R of the five antimicrobials\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6558909/v1/d2cdc3bcae80d9edbaf2452a.png"},{"id":82887909,"identity":"0af102b4-1c6a-4d77-b17e-98524e4db5d5","added_by":"auto","created_at":"2025-05-16 11:59:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53957,"visible":true,"origin":"","legend":"\u003cp\u003eMEC, PNEC- MIC and RAMR of the five antimicrobials\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6558909/v1/bfafb3c1755737622a48d439.png"},{"id":90368565,"identity":"2e1ed29c-5cfc-461c-8a7e-910e01c065cd","added_by":"auto","created_at":"2025-09-02 03:27:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":563792,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6558909/v1/dd922bd2-5442-435b-b09d-0e726ae65f7b.pdf"}],"financialInterests":"","formattedTitle":"Water contamination by commonly used antimicrobials around a tertiary care centre in South Kerala, India – environmental risk and antimicrobial resistance perspective","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEnvironmental contamination by pharmaceuticals is a major concern these days. Improper disposal of pharmaceuticals by manufacturers, hospitals and households reach our water sources. Presence of these pharmaceuticals in water sources and their long-term ingestion can lead many many health hazards to living organisms (Gwenzi et. al, 2020) Among the pharmaceuticals, antimicrobials in the environment can cause serious public health hazard due to the emergence of antimicrobial resistance (Burnham et al. 2025, Sambaza et. al 2023 ).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOveruse and misuse of antibiotics has led to the development of antimicrobial resistance in the community. It is estimated that by 2050, 10 million deaths per year are expected world wide due to antimicrobial resistant infections (CDC 2024). Sustained presence of antimicrobials in subtherapeutic concentrations is the cause of emergence and spread of antimicrobial resistant genes in promiscuous organisms (Singh et al.2022, Barathe et al,2024). Antimicrobials reach water sources through drug manufacturing unit outlets (Gonzalez Plaza et al. 2019;\u0026nbsp;Mheidli et al. 2022). They also reach the water sources from hospitals and households through sewage and reach waste water treatment plants (Kumari et al.2020, Khan et al. 2021). Water purification process are insufficient to extract and remove many pharmaceuticals including antimicrobials (Alfonso-Muniozguren et al. 2021, Skalska Tuomi et al. 2025) Thus, they reach the natural water sources from where they reach humans and other living organisms. The presence of these low concentrations of antimicrobials in the environment as well as in the body of living organisms cause the emergence of antimicrobial resistant genes in microorganisms and thus antimicrobial resistant infections in humans (Hayward et. al 2020, Duarte et al. 2022).\u003c/p\u003e\n\u003cp\u003eWidespread use of antimicrobials in animal husbandry (Delgado et al. 2023,Charuaud et al. 2019), aquaculture and agriculture (Manyi Loh et al. 2018, Miller et al. 2022) is a serious concern apart from its use in humans. Policy regulators and administrators should take meticulous steps to mitigate this devastating problem by imposing regulations on the safe and judicious use of antimicrobials as well as proper disposal of used, unused and left-over drugs including antimicrobials (Kinrys et al. 2018). Denmark model of antimicrobial usage is a set example to follow with respect to judicious use of antimicrobials (Levy et al. 2014)They have imposed policies on judicious prescription of antimicrobials by veterinarians and right usage practices by cattle and poultry farmers. Unavailability of the antimicrobials without prescription and also health education and awareness generation among farmers regarding the importance of right usage practices of antimicrobials stand a long way in preventing emergence of antimicrobial resistant infections (Belay et al. 2020; Emes et al.2024).\u003c/p\u003e\n\u003cp\u003eNowadays zoonotic diseases are causing serious concerns as it spreads to humans. Covid caused by SARS- Cov2 virus and monkeypox disease caused by human metapneumo virus are recent examples of zoonotic diseases transmitted to humans causing epidemics and even pandemics. Virus, bacteria, protozoans and fungi can cause serious systemic infections in humans due to drug resistant strains (Dafale et al. 2020).\u003c/p\u003e\n\u003cp\u003eThough studies are done in some parts of the world to find the concentration of antimicrobials in the environment, such an exploratory research in Kerala, India is lacking. The presence of antimicrobials in water sources and the quantification of them helps to gather information on the potential risk of emergence of antimicrobial resistant organisms in the environment as well as the health hazards that may arise by the consumption of these subtherapeutic concentrations of antimicrobials for a long time (Izah et al. 2025; Macedo et al. 2025; Sun et al. 2020). Long term consumption of water with these low concentrations of antimicrobials can alter the gut flora in humans which can predispose to a series of health consequences indirectly such as obesity, diabetes, metabolic dysfunctions and endocrine abnormalities. Recently much focus is given on the concept of gut- brain axis where gut is considered the second brain, as any alteration in the gut flora can alter the release of neurotransmitters in the Central Nervous System and thus affect the higher mental functions like cognition which includes memory, intelligence, motivation, attention and concentration (O\u0026rsquo; Riordan et al. 2025; Rusch et al.2020; Carabotti et al. 2015 ).\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eBaseline information of the selected drugs- Five commonly used antimicrobials in a tertiary care centre were identified from the data obtained from case records and purchase records for a period of one year\u003c/p\u003e \u003cp\u003eSample identification and collection-To find the contamination of water sources due to antimicrobial agents, a two-kilometer radius area was identified in Thiruvananthapuram district of South Kerala, India where many tertiary care hospitals are situated. Thirty-one water sources were identified from this area using Google Earth software. These water sources included natural sources like ponds, rivers, lakes and man-made sources like bore wells. pH, Temperature, Total dissolved solutes (TDS) and Electrical Conductivity (EC) of the water samples were checked at the site of collection itself using TDS meter and pH meter. One litre water was collected from these water sources in HDPE bottles and transported in ice bags to the centre for analysis. The samples were stored at -20 degree Celsius at the centre before it was taken for analysis.\u003c/p\u003e \u003cp\u003eSolid Phase Extraction- The samples were preconcentrated using solid phase extraction technique before quantifying the drugs. For preconcentration of samples the water samples were passed through Whatman filter paper 41 and filtered under vacuum to remove sediments. The filtered water samples were passed through Oasis HLB 600/ 5ml cartridge at the rate of 2ml/ minute. The cartridge was then eluted with 5ml of 100% methanol for drug extraction. This 5ml sample was taken for analysis using Schimadzu Liquid Chromatography Mass Spectrometry \u0026ndash; Mass Spectrometry (LCMS- MS) instrument.\u003c/p\u003e \u003cp\u003eLCMS- MS analysis \u0026ndash; Drug standards of LCMS grade were procured from drug manufacturing companies. The column used for the analysis of antimicrobials was C18 Shim-pack GIST 3\u0026micro;m C18 Material 3 x 150mm. The mobile phases used were Mobile phase A \u0026minus;\u0026thinsp;0.1% formic acid in water- aqueous phase and Mobile phase B \u0026minus;\u0026thinsp;0.1% formic acid in methanol- organic phase. The preconcentrated samples were run in the LCMS-MS instrument. Calibration curves were plotted and the area of the curve was found to determine the concentration of the test drug. Calibration curve was plotted with five concentrations of a single drug and the concentration in the test samples were calculated.\u003c/p\u003e \u003cp\u003ePredicted no effect concentration- Environment, (PNEC- ENV) is an indicator used for calculating the environmental risk associated with a particular concentration of the drug in the environment. The PNEC- ENV value of each of the five drugs were obtained from literature and was used to find if the prevalent drug concentration in water sources is potential to cause environmental hazard using the formula Environmental Risk, R\u0026thinsp;=\u0026thinsp;Measured environmental concentration (MEC)/ PNEC- ENV. If the value is more than one, it carries risk of causing hazard to environment. Similarly, Predicted no effect concentration- Minimum Inhibitory Concentration (PNEC- MIC) is used to calculate the potential to cause antimicrobial resistance in the environment due to a particular concentration of the drug using the formula, Risk of developing antimicrobial resistance, R\u0026thinsp;=\u0026thinsp;MEC/ PNEC-MIC. PNEC- MIC value for each of the drugs is obtained from literature which is used to calculate the risk (Quadra et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Vestel et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e "},{"header":"Results and Discussions","content":"\u003cp\u003eFive commonly used antimicrobials in a tertiary care centre were identified with the help of case records and purchase records for a period of one year- Azithromycin, Cefotaxime, Ciprofloxacin, Meropenem and Metronidazole. Measured median environmental concentration (MEC) of these antimicrobials in water sources were- Azithromycin (0.60 \u0026micro;g/L), Cefotaxime (1.0 \u0026micro;g/L), Ciprofloxacin (0.07 \u0026micro;g/L), Meropenem (0.05 \u0026micro;g/L and Metronidazole (1.73 \u0026micro;g/L) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Using Predicted no effect concentration- environment (PNEC-ENV), environmental risk (R) calculated for Azithromycin (97.67), Cefotaxime (25.83), Ciprofloxacin (1.75), Meropenem (2.28) and Metronidazole (78.0) are above the value of 1, which shows that they are at a level to cause environmental risk (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As the measured environmental concentration increases or the PNEC- ENV value decreases, R becomes greater than 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). MECs of the five antibiotics against PNEC-MIC (minimum inhibitory concentration) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u0026ndash; Azithromycin (2.93, 0.25), Cefotaxime (3.10, 0.13), Ciprofloxacin (0.80, 0.06), Meropenem (3.42, 0.06) and Metronidazole (2.35, 0.13) shows that MECs are above the PNEC- MIC values used for determining risk of developing antimicrobial resistance, R AMR ( Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany studies on the determination of concentration of various antibiotics like tetracyclines, quinolones, sulfonamides and macrolides in water sources are done in different parts of the world. The concentration of macrolides, quinolones, tetracyclines, sulfonamides were up to 3847 ng/L, 660.13 ng/L, 20 ng/L, 20.82 ng/ L respectively.Studies related to the detection of the risk of a particular concentration of antimicrobial in the environment as related to R \u003csub\u003eENV\u003c/sub\u003e and risk of developing antimicrobial resistance R \u003csub\u003eAMR\u003c/sub\u003e is seen only in very few parts of the world. Surface and ground water studies have showed the presence of antimicrobials in them. Many studies from sewage treatment plants has pointed to the ineffective water treatment practices that let out unremoved antimicrobials into drinking water sources. In a study sulfamethoxazole was found in a concentration of 4.36 \u0026micro;g/L, and other antibiotics such as clarithromycin, trimethoprim, ciprofloxacin, sulfamethoxazole and azithromycin were found in water treatment plants in the range of 1.86 to 4.47 \u0026micro;g/L (Juarez et al.2021). This information is very crucial as it points to the risk of causing environmental hazard and emergence of antimicrobial resistance in the community.\u003c/p\u003e \u003cp\u003eEnvironmental risk assessment calculated from measured environmental concentration and predicted no effect concentration of the antimicrobials gives an extrapolated information on how much toxicity it can cause to the primary trophic levels of aquatic organisms like green algae, crustaceans and vertebrates (Quadra et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Vestel et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Green algae, daphnia and zebrafish are study models that represent the three trophic levels of organisms. It adversely affects their growth, mobility and reproductive rate of the aquatic organisms (Zhou et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ninomiya et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Zebra fish is a model that has almost 70% homology to humans (Adhish and Manjubala 2023 ; Mlnař\u0026iacute;kov\u0026aacute; et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Hence the toxicity to these organisms throws light to the toxicity that can happen in humans on long term use of sub-therapeutic concentrations of these antimicrobials.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Measured median environmental concentration of antimicrobials\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrugs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasured Environmental Concentration (MEC)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCefotaxime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAzithromycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetronidazole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.73 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeropenem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCiprofloxacin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Environmental risk R\u0026thinsp;\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHighest MEC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNEC- ENV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCefotaxime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.10 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAzithromycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.93 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetronidazole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.35 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeropenem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.42 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCiprofloxacin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Risk of developing AMR (R AMR )\u0026thinsp;\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHighest MEC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNEC- MIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR AMR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCefotaxime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.10 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAzithromycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.93 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetronidazole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.35 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeropenem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.42 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCiprofloxacin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06 \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"Conclusions","content":"\u003cp\u003eThe findings reveal that the concentration of antimicrobials in the water sources are in the range of posing environmental risk and for selection of antimicrobial resistant organisms in the environment. Water purification methods have to be developed to remove these drugs from water sources. Modification of policy regulations for proper and safe disposal of drugs have to be done and implemented to prevent pharmaceuticals from entering the water sources.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements-\u003c/strong\u003e We express our gratitude to the Kerala University of Health Sciencesand theAdministrator, staff and students of Inter University Instrumentation Centre and Sophisticated Analytical Instrumentation Facility at Mahatma Gandhi University, Kerala who have helped with the technical support in the completion of the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e – No financial support was obtained for this study. It was a self-funded work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information-\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors and affiliations-\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDr. Jitha S, Associate Professor, Department of Pharmacology, Government Medical College, Thiruvananthapuram, Kerala, India\u003c/p\u003e\n\u003cp\u003eDr. Harikumaran Nair G S, Professor, Department of Radiodiagnosis, SreeGokulam Medical College, Thiruvananthapuram, Kerala, India\u003c/p\u003e\n\u003cp\u003eDr. Annapurna Y, Professor, Department of Pharmacology, Government Medical College, Thiruvananthapuram, Kerala, India\u003c/p\u003e\n\u003cp\u003eDr. Scaria Thomas P, Associate Professor, Department of Pharmacology, Government Medical College, Kottayam, Kerala, India\u003c/p\u003e\n\u003cp\u003eDr. Pradeep S, Professor, \u0026nbsp;Department of Pharmacology, Dr Somervell Memorial CSI Medical College, Thiruvananthapuram, Kerala, India\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s Contributions-\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDr. Jitha S- conceptualization, data curation, formal analysis, writing—original draft writing and editing.\u003c/p\u003e\n\u003cp\u003eDr. Harikumaran Nair G S- review, analysis, supervision and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDr. Annapurna Y- supervision, review and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDr. Scaria Thomas P – supervision, technical support, review and editing\u003c/p\u003e\n\u003cp\u003eDr. Pradeep S - supervision, review and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCorresponding author- Dr. Jitha S, Associate Professor, Department of Pharmacology, Government Medical College, Thiruvananthapuram, Kerala, India email- [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval- The study was approved by the Institutional Review Board of the institution vide order No: A2/ 119/2022/GMCT\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent to participate- Not applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication- Not applicable.\u003c/p\u003e\n\u003cp\u003eThere are no conflicts of interest in the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e- The original data obtained in the study are included in the article; further inquiries can be communicated to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes\u003c/strong\u003e- The PNEC- ENV and PNEC- MIC values are compared with median measured environmental concentration to find the risk associated as an environmental hazard as well as a cause for emergence of antimicrobial resistance.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlfonso-Muniozguren, P., Serna-Galvis, E. 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The gut-brain axis: interactions between enteric microbiota, central and enteric nervous systems. \u003cem\u003eAnnals of gastroenterology\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(2), 203\u0026ndash;209.\u003c/li\u003e\n\u003cli\u003eCharuaud, L., Jard\u0026eacute;, E., Jaffr\u0026eacute;zic, A., Liotaud, M., Goyat, Q., Mercier, F., \u0026amp; Le Bot, B. (2019). Veterinary pharmaceutical residues in water resources and tap water in an intensive husbandry area in France. \u003cem\u003eThe Science of the total environment\u003c/em\u003e, \u003cem\u003e664\u003c/em\u003e, 605\u0026ndash;615. https://doi.org/10.1016/j.scitotenv.2019.01.303\u003c/li\u003e\n\u003cli\u003eDelgado, N., Orozco, J., Zambrano, S., Casas-Zapata, J. C., \u0026amp; Marino, D. (2023). Veterinary pharmaceutical as emerging contaminants in wastewater and surface water: An overview. \u003cem\u003eJournal of hazardous materials\u003c/em\u003e, \u003cem\u003e460\u003c/em\u003e, 132431. https://doi.org/10.1016/j.jhazmat.2023.132431\u003c/li\u003e\n\u003cli\u003eDuarte, A. C., Rodrigues, S., Afonso, A., Nogueira, A., \u0026amp; Coutinho, P. (2022). Antibiotic Resistance in the Drinking Water: Old and New Strategies to Remove Antibiotics, Resistant Bacteria, and Resistance Genes. \u003cem\u003ePharmaceuticals (Basel, Switzerland)\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(4), 393. https://doi.org/10.3390/ph15040393\u003c/li\u003e\n\u003cli\u003eEmes, E., Belay, D., \u0026amp; Knight, G. M. (2024). 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Biological toxicity of sulfamethoxazole in aquatic ecosystem on adult zebrafish (Danio rerio). \u003cem\u003eScientific reports\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 9401. https://doi.org/10.1038/s41598-024-59971-y\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"water contamination, environmental risk, antimicrobial resistance, pharmaceuticals, predicted no effect concentration, water purification","lastPublishedDoi":"10.21203/rs.3.rs-6558909/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6558909/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWater contamination due to pharmaceuticals is a serious health concern and antimicrobials in particular pose a major challenge pertaining to the development of antimicrobial resistance. The level of water contamination caused by antimicrobials in water sources and its consequences such as environmental risk and antimicrobial resistance is less explored in Kerala, India. The study aims to find the concentration of antimicrobials in water sources and to analyze the environmental risk and antimicrobial resistance that can arise due to the prevailing drug concentration in water sources. Five commonly used antimicrobials in a tertiary care centre were identified with the help of case records and purchase records for a period of one year- Azithromycin, Cefotaxime, Ciprofloxacin, Meropenem and Metronidazole Thirty-one samples of surface and ground water including drinking water sources collected were preconditioned by solid phase extraction technique and passed through Liquid Chromatography Mass Spectrometry (LCMS-MS) instrument to quantify the concentrations of antimicrobials. Measured median environmental concentration (MEC) were- Azithromycin (0.60 \u0026micro;g/L), Cefotaxime (1.0 \u0026micro;g/L), Ciprofloxacin (0.07 \u0026micro;g/L), Meropenem (0.05 \u0026micro;g/L and Metronidazole (1.73 \u0026micro;g/L). Using Predicted no effect concentration- environment (PNEC-ENV), environmental risk (R) calculated for Azithromycin (97.67), Cefotaxime (25.83), Ciprofloxacin (1.76), Meropenem (2.28) and Metronidazole (78.0) are above the value of 1, which shows that they are at a level to cause environmental risk. Highest MECs of the five antibiotics against PNEC-MIC (minimum inhibitory concentration) \u0026ndash; Azithromycin (2.93, 0.25), Cefotaxime (3.10, 0.13), Ciprofloxacin (0.80, 0.06), Meropenem (3.42, 0.06) and Metronidazole (2.35, 0.13) shows that MECs are above the PNEC- MIC values used for determining antimicrobial resistance. These points to the occurrence of grave consequence of selection of antimicrobial resistant organisms in the environment. The concentration of antimicrobials in the water sources are in the range of posing environmental risk and for selection of antimicrobial resistant organisms in the environment. Water purification methods have to be developed to remove these drugs from water sources. Modification of policy regulations for proper and safe disposal of drugs have to be done and implemented to prevent pharmaceuticals from entering the water sources.\u003c/p\u003e","manuscriptTitle":"Water contamination by commonly used antimicrobials around a tertiary care centre in South Kerala, India – environmental risk and antimicrobial resistance perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 11:59:38","doi":"10.21203/rs.3.rs-6558909/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4c7a408a-0d31-4561-aa7f-4436e6114f22","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-02T03:19:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 11:59:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6558909","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6558909","identity":"rs-6558909","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}