Current status of antimicrobial resistance in Indian healthcare system: combating antimicrobial resistance with precision medicine

Abstract

This review provides a unique perspective by integrating antimicrobial resistance (AMR) data from Indian healthcare, with a particular emphasis on outpatient settings that are often overlooked in existing literature. Unlike previous reviews that primarily focus on hospital-acquired infections, this article explores the community dimension of AMR and its implications for public health. Furthermore, it introduces an innovative framework linking AMR mitigation strategies with precision medicine approaches, including pharmacogenomics, metabolomics, proteomics, and transcriptomics. By combining multi-omics insights with national surveillance data and stewardship initiatives, this review highlights the translational potential of personalized antimicrobial therapy tailored to the Indian healthcare ecosystem. This integrated perspective offers a novel direction for AMR research and policy, bridging the gap between genomic science and clinical application in resource-limited settings.

1 Introduction

Antimicrobial resistance (AMR) refers to the ability of microorganisms, including bacteria, viruses, fungi and parasites, to resist the therapeutic effects of previously effective antimicrobials (Tang et al., 2023). By 2050, this silent pandemic could cause 10 million mortalities globally, up from 700,00 per year (Ahmed et al., 2024c). High prevalence of AMR in India stems from the extensive use of antibiotics in clinical settings (Gandra et al., 2017). Antimicrobial – resistant pathogens such as Carbapenem-resistant Enterobacterales (CRE), Multi-drug-resistant Gram-negative bacteria (MDR-GNB) such as Acinetobacter baumanii, Pseudomonas aeruginosa and Gram-positive cocci (GPCs) such as MDR Staphylococcus aureus whose management have become complicated, contribute significantly to hospital-acquired infections (HAIs). In (Figure 1) we have provided a graphical representation of current trends in AMR. These infections result in treatment failures, extended length of stays, increased healthcare expenses, and socioeconomic burden, for healthcare facilities (Jean et al., 2022; Kumar et al., 2021). In addition to Hospital acquired infections (HAIs), AMR is a growing problem in outpatient settings, where the prevalence of drug-resistant bacteria in community-acquired infections continues to rise (Alsan et al., 2018). Alarming rise in prevalence of resistant pathogens in OPD settings can be inferred from (Table 1; Figure 2). The WHO published its 2024 priority bacterial pathogens list, which classifies Acinetobacter baumannii, Carbapenem and 3^rd generation Cephalosporin resistant Enterobacterales as critical priority pathogens, while Staphylococcus aureus, Pseudomonas, Enterococcus faecium, Shigella spp., and Salmonella spp. as high priority pathogens. It is important to note that these pathogens have consistently been ranked highly antimicrobial-resistant ever since the first WHO report in 2017 (Jesudason, 2024; World Health, 2024a).

Figure 1

Figure 2

Several factors contribute to increase in AMR in India which includes: various secondary hospitals’ poor antimicrobial stewardship program due to poor resource availability, the absence of educational programs to fill the knowledge gap of healthcare professionals (Mathew et al., 2020), over-the-counter (OTC) dispensing of antibiotics, and patients refilling antibiotics using older prescriptions (Jani et al., 2021). In addition, an important factor in the emergence and spread of AMR is the improper use of antibiotics and lack of monitoring. To address this growing issue, the Indian Council of Medical Research (ICMR) developed evidence-based treatment guidelines. The updated information on treatment guidelines together with the dosage, mode of administration, and length of therapy for skin infections, soft tissues, bones, and joints, are included in the second edition. To provide uniformity in the management of different infectious diseases, the “Treatment guidelines for antimicrobial use in common syndromes” seek to justify the use of antibiotics (Aggarwal et al., 2019). Before starting antimicrobial therapy, antimicrobial susceptibility testing must be carried out extensively, especially to estimate minimum inhibitory concentration (MIC). Together with personalized medicine approaches, such as pharmacogenomics, that is treatment based on genetic profiling of patients and therapeutic drug monitoring of antimicrobial drugs, should be included. The use of narrow-spectrum antibiotics provides a targeted antimicrobial effect against specific pathogens, thus limiting the development of antimicrobial resistance. This approach ensures that antibiotics are administered precisely, within the minimum inhibitory concentration (MIC) levels, achieving optimal therapeutic efficacy. It simultaneously prevents drug-related adverse effects, including drug-induced liver injuries (DILIs), nephrotoxicity, ulcers, rashes, vomiting, and gastric upset (Roberts et al., 2012). If appropriate solutions are not implemented, AMR could compromise the sustainable development goals (SDGs) and cause millions of people to continue living in poverty by 2030 (World Health, 2021). Strong measures to prevent the spread are crucial, as evidenced by the prevalence of resistant pathogens in India (Ravishankar et al., 2021).

A recent systematic review on Knowledge, Attitudes and Practices (KAP) related to AMR reported that knowledge levels were highest among doctors, followed by nurses and pharmacists. However, despite relatively good theoretical knowledge, a noticeable gap persists in the translation of this knowledge into appropriate clinical practice among healthcare providers in India. Moreover, the review highlighted that the majority of published literature predominantly focuses on hospital-based infections, indicating a clear gap in evidence related to AMR in outpatient and community settings (Rana et al., 2024).

This review provides a unique perspective by integrating antimicrobial resistance (AMR) data from Indian healthcare, with a particular emphasis on outpatient settings that are often overlooked in existing literature. Unlike previous reviews that primarily focus on hospital-acquired infections, this article explores the community dimension of AMR and its implications for public health. Furthermore, it introduces an innovative framework linking AMR mitigation strategies with precision medicine approaches, including pharmacogenomics, metabolomics, proteomics, and transcriptomics. By combining multi-omics insights with national surveillance data and stewardship initiatives, this review highlights the translational potential of personalized antimicrobial therapy tailored to the Indian healthcare ecosystem. This integrated perspective offers a novel direction for AMR research and policy, bridging the gap between genomic science and clinical application in resource-limited settings.

2 Prevalence and AMR patterns of infectious diseases

2.1 Urinary tract infections

A wide range of bacterial genera that cause urinary tract infections (UTIs) include: Escherichia, Klebsiella, Proteus, Pseudomonas, Enterococcus, Staphylococcus species, Enterobacter species, and Citrobacter species (Ronald, 2002). The most prevalent uropathogens are GNBs, amongst which E. coli is the most reported cause of UTI and is followed by Proteus spp., Klebsiella spp., and Pseudomonas spp. Whereas, around a quarter of infections are caused by gram-positive bacterium such as Enterococcus spp (Smita et al., 2019). A breakdown of prevalent bacterial genera in different types of urinary tract infections can be found in (Table 2) (Patel et al., 2021; Prabhu et al; Umesha et al., 2018; M et al., 2017; Srivastava et al., 2014).

Reportedly, MDR strains are highly prevalent in India (Mohapatra et al., 2022). Significant resistance to beta-lactam antibiotics, beta-lactamase inhibitor combinations such as piperacillin-tazobactam, third-generation cephalosporins, fluoroquinolones (e.g., ciprofloxacin), and an increasingly concerning rise in resistance to carbapenems which are usually saved for severe infections have been observed in E. coli. While Klebsiella and Proteus species have comparable patterns of resistance demonstrating strong resistance to third-generation cephalosporins, fluoroquinolones, and nitrofurans. High levels of resistance to beta-lactams, cephalosporins, fluoroquinolones, and aminoglycosides are seen in Pseudomonas spp., a challenging pathogen to treat. Beta-lactams, nitrofurans, aminoglycosides, tetracyclines, and some incidences of vancomycin resistance are among the antibiotics against which Enterococcus spp., exhibit alarming patterns of resistance (Smita et al., 2019; Mohapatra et al., 2022).

2.2 Respiratory tract infections

Airborne pathogens spread quickly, especially in crowded, poorly ventilated situations. Respiratory infections are the most prevalent infectious diseases in the world. From minor ailments like the common cold to potentially fatal ones like pneumonia, they can range in severity. These illnesses fall under the category of upper respiratory tract infections (URTIs), which impact the sinuses, pharynx, larynx, and nose. Airway and lung complications comprise lower respiratory tract infections (LRTIs). It is relatively rare for URTIs to have bacterial origins; instead, viruses, such as rhinoviruses, are usually the source (Jain et al., 2001). Common bacterial respiratory infections include pneumonia, bronchitis, community acquired pneumonia (CAP). Common causal bacterial genera for such diseases include Streptococcus pneumoniae, Staphylococcus spp., Mycoplasma spp., Haemophilus influenzae, Klebsiella pneumoniae, and Pseudomonas spp (Feldman and Shaddock, 2019).

Studies indicate GNB were the most common causes of infections, amongst which Pseudomonas aeruginosa, and Klebsiella pneumoniae were the most isolated organisms (Singh et al., 2020). Additionally, gram - positive bacteria Streptococcus pneumoniae is the leading cause of pneumonia in hospitalized patients (Sangale et al., 2021).

MDR Klebsiella pneumoniae and Pseudomonas aeruginosa strains show very high levels of resistance towards cephalosporins (ceftriaxone), fluoroquinolones (ciprofloxacin), beta lactam/beta lactamase inhibitors (piperacillin -tazobactam) have been reported (Kaleem Ullah et al., 2022; Sharma et al., 2023b). Streptococcus pneumoniae showed high levels of resistance for macrolides (Erythromycin), an increase in resistance for fluoroquinolones (Levofloxacin), and a concerning increase in resistance towards carbapenems (Meropenem) (Kulkarni et al., 2023).

2.3 Skin and soft tissue infection

Microorganisms that give rise to SSTIs are generally divided into transitory pollutants and resident skin flora. While resident bacteria on the skin typically coexist harmlessly, they might turn infectious in rare situations such as trauma or immunosuppression. Staphylococcus aureus, Staphylococcus warneri, Streptococcus pyogenes, and Corynebacterium species are typical transitory flora. Additionally, GNB are often implicated, including Proteus species, Escherichia coli, and Pseudomonas aeruginosa (Tognetti et al., 2012).

In India, studies show a predominance of Gram-positive cocci (GPC) in SSTIs in comparison to GNB (Roopashree et al., 2021). Amongst GPCs, S. aureus is the most prevalent, with studies pointing towards an increase in prevalence of Methicillin-resistant S. aureus (MRSA) strains. Many S. aureus isolates showed high levels of resistance towards penicillin and cephalosporin drugs (Roopashree et al., 2021; Shah et al., 2022). MDR P. aeruginosa and E. coli were the most prevalent GNBs, being highly resistant to penicillin and penicillin derivative drugs, cephalosporins, beta lactams/beta-lactamase inhibitors (Deka et al., 2020; Kakhandki et al., 2020; Ramakrishna et al., 2021).

2.4 Diarrhoeal infections

Diarrhoea can be caused by different infectious agents such as bacteria (E. coli, Salmonella, Shigella, Vibrio), viruses (Rotavirus, Adenovirus), and protozoans (Giardia, Cyclospora, Cryptosporidium, Isospora, Microsporidia) (Shrivastava et al., 2017).

Diarrheagenic E. coli (DEC) strains are most prevalent and show resistance towards beta-lactams (Ampicillin), macrolides (Azithromycin), fluoroquinolones (Ofloxacin, Ciprofloxacin), and tetracyclines (Doxycycline) (Biswas et al., 2024). Shigella species were also frequently seen, with S. flexneri being the most prevalent, followed by S. sonnei, S. dysentriae and S. boydii. Shigella spp., showed high resistance towards fluoroquinolones (Ofloxacin, Ciprofloxacin, Nalidixic acid, Norfloxacin), tetracyclines (Tetracycline), sulfonamides (Co-trimoxazole), aminoglycosides (Streptomycin) and a growing resistance towards amphenicols (chloramphenicol) and beta lactams (Ampicillin) (Bose et al., 2024; Jain et al., 2020).

Amongst Salmonella spp., S. typhi, the most common species showed high resistance towards fluoroquinolones (Ciprofloxacin, Nalidixic acid) and increasing resistance towards cephalosporins (Cefixime, Ceftriaxone) (Biswas et al., 2022). Nontyphoidal Salmonella (NTS) also were moderately prevalent, showing MDR patterns encompassing fluoroquinolones (Ciprofloxacin, Levofloxacin), aminoglycosides (Gentamycin), macrolides (Azithromycin), and sulphonamides (Trimethoprim/Sulfamethoxazole) (Mahindroo et al., 2024).

2.5 Hospital acquired infections

In India, GNBs such as P. aeruginosa, Klebsiella spp., Acinetobacter baumanii, Citrobacter spp., Aeromonas spp., are the most prevalent organisms causing ventilator associated pneumonia (VAP). Among GPC, Staphylococcus aureus is also a significant contributor to VAP cases. Enterococcus spp. showed very high prevalence in catheter-associated urinary tract infections (CAUTI) followed by Klebsiella spp., Citrobacter spp., showing significant prevalence. For catheter-related bloodstream infections (CRBSI), the leading pathogens are P. aeruginosa, Klebsiella spp., Acinetobacter baumanii, Citrobacter spp., and S. aureus (Kumar et al., 2021; Saikeerthana et al., 2021; Dr. Ranjeet et al., 2024). (Table 3) (Crank and O’Driscoll, 2015; Hassoun et al., 2017; Nandhini et al., 2022; Singhal et al., 2022) provides insights of treatment options for hard-to-treat bacterial diseases.

A concerning trend in Indian healthcare is the alarming rate of AMR among hospitalized patients in India. A significant number of bacterial isolates have been classified as MDR, and an even greater proportion are suspected to be extremely-drug resistant (XDR). Particularly, many strains of P. aeruginosa, Acinetobacter baumanii and Klebsiella spp., have shown XDR patterns. Furthermore, many isolates of S. aureus have also been reported as MDR (Vaithiyam et al., 2020). An alarming increase in carbapenem - resistant Enterobacteriaceae (CRE) is reported amongst which Klebsiella spp. is the most reported CRE showing high levels of resistance towards carbapenems (Modi et al., 2021).

3 Factors contributing to AMR in India

3.1 Antibiotic misuse and overuse in India

Global estimates indicate that only 50% of the global antibiotic consumption is appropriately justified (Iskandar et al., 2020). Dispensing antimicrobial drugs without prescription by pharmacies in the private sector in India within an urban setting was unacceptably high (around 67%) (Shet et al., 2015). Reports indicate that 44% of outpatient antibiotic prescriptions are intended to inappropriately treat acute respiratory conditions (e.g., viral upper respiratory tract infections, bronchitis, asthma, allergies, and influenza) (Laxminarayan and Chaudhury, 2016). In India nearly 60% mortality of children aged 5 to 14 years is due to infectious diseases such as diarrhea, pneumonia, measles, etc (Morris et al., 2011). The rate of infectious disease is also linked with increased sale and consumption of antibiotic in India. The lack of diagnostic facilities, irrational prescription, lack of knowledge, experience about antibiotic use and pharma companies’ incentives are the factors which cause misuse of antibiotics.

3.2 Unregulated OTC dispensing of antibiotics

A 2018 study found that more than 60% of fixed drug combinations (FDCs) had no regulatory approval (McGettigan et al., 2019). They found many formulations were pharmacologically incompatible, reinforcing the need for better regulation and oversight of the pharmaceutical industry. Schedule H1, an amendment to the Drugs and Cosmetics Rules Act of 1945 that imposed restrictions on OTC dispensing of certain antibiotics (mostly third and fourth-generation cephalosporins, carbapenems, and newer fluoroquinolones) was perceived as a vital policy with ineffective implementation and adherence (Nair et al., 2023). Universal coverage of vaccines is expected to prevent 11.4 million days of antibiotic use per year in children less than 5 years of age. Consequently shows an estimated 47% reduction in antibiotics used to treat these pneumococcal infections (Wu et al., 2021).

3.3 Economic and healthcare access barriers contributing to AMR

Due to healthcare costs, patients avoid diagnostic and purchase antibiotics without a prescription and self-medicate with older prescriptions to have a rapid recovery. These collectively promote the misuse of antibiotics and increase AMR (Jani et al., 2021). In rural areas, many healthcare providers are often inefficient and employ unqualified staff. Thereby, urging the patients to choose private healthcare, which leads to high costs, particularly for the middle-class and low-income earners. To cut this expense, patients turn to self-medication, which results in high consumption of antibiotics. The observation validates the finding of Wu et al (Wu et al., 2021), which highlighted a rise in parents self-medicating their children (below 5 yrs), posing a serious threat to child health. This behavior causes AMR to first-generation and broad-spectrum antibiotics (Gandra et al., 2017).

3.4 Environmental contribution to AMR

Antibiotics are extensively used to compensate for poor sanitation, prevent diseases in herds/flocks, treat bacterial infections, and to promote animal growth in the distinct agricultural sectors (Page and Gautier, 2012). The magnitude and contribution of cross-reservoir resistance transmission from animals to humans is difficult to quantify and track mainly because of globalized food trade (Mather et al., 2013). Wastewater is flagged as a significant environmental reservoir of drug-resistant bacteria; horizontal gene transfer of antimicrobial resistance genes (ARGs) results in the dissemination of AMR in microbial communities. India has become the center for MDR and XDR Mycobacterium tuberculosis, which complicates the control of tuberculosis. In addition, the genomic flexibility of many resistant organisms allows them to acquire resistance to reserved antibiotics like carbapenem and colistin (Jani et al., 2021).

4 Impact of AMR on clinical outcomes

4.1 Effect of AMR on patient morbidity and mortality

People’s health is directly impacted by antibiotic resistance. It impairs our capacity to create a successful treatment strategy to eradicate infectious diseases and lower rates of morbidity and death. Patients who become resistant to many drugs are more likely to experience higher morbidity or even mortality (Cornejo-Juárez et al., 2015). AMR poses a risk to the treatment of pre-operative and post-operative nosocomial infections, making treatments difficult for already debilitated patients. The burden of patient mortality is as high as 13.1% overall in MDR infections, with MDR A. baumanii infections resulting in 2–3 fold higher mortality (29%) (Barrasa-Villar et al., 2017; Massart et al., 2022; Figueiredo Costa, 2008). However, it to be noted that long course colistin treatment over short course for Carbapenem resistant Acinetobacter baumanii proved to be promising and beneficial for critically ill patients and helped in reduction of 30-day mortality rate (aOR = 0.46, 95% CI: 0.26 – 0.83, p = 0.009) amongst the long course colistin group with negligible differences in nephrotoxicity amongst the two groups (aOR 1.28, 95% CI: 0.74 – 2.22, p = 0.368) (Katip et al., 2023).

For hospitalized patients, increased length of ICU stay is higher in patients with HAIs, averaging at 13 days, and is also associated with a higher risk of mortality (Ramsamy et al., 2017). Similarly, for community-acquired infections, MDR infections were associated with inappropriate antibiotic use and were directly related to higher mortality (adjusted odds ratio (aOR), 6.06, 95% CI, 1.2 – 55.7) (Lohiya et al., 2020). Bacteraemia, being a life-threatening condition, often resulting in half of the patients succumbing to illness, requires exhaustive antimicrobial treatment. Due to ever-increasing AMR, antimicrobial therapy is often ineffective resulting in increased patient mortality (Figueiredo Costa, 2008; Ramsamy et al., 2017). Additionally, it makes the treatment plan more difficult for patients with long-term antibiotic-dependent conditions such as tuberculosis, whose prevalence is already rampant in India (Lohiya et al., 2020).

4.2 Rural AMR gaps

Antimicrobial resistance (AMR) has become a widespread public health concern across India; however, significant differences in resistance patterns and underlying drivers are observed between rural and urban settings. These variations are influenced by multiple socio-economic, environmental, and healthcare-related factors. Urban areas, in particular, are often densely populated and experience higher levels of environmental pollution, conditions that facilitate the rapid transmission of infectious diseases and potentially accelerate the emergence and spread of AMR (Balachandra et al., 2021).

In contrast, rural areas may have comparatively lower access to antibiotics due to limited healthcare infrastructure; however, inappropriate antibiotic use remains a significant concern. Limited awareness regarding proper antibiotic usage, coupled with widespread over-the-counter (OTC) availability, contributes to misuse in rural India (Sharma et al., 2023a). The situation is particularly alarming in states such as Bihar—India’s third most populous state—where scarce healthcare resources and inadequate infrastructure exacerbate the problem. Evidence indicates a substantial rise in antimicrobial resistance to β-lactams, carbapenems, and fluoroquinolones in Bihar. Methicillin-resistant Staphylococcus aureus (MRSA) prevalence is notably high at 65%, significantly exceeding the national average of 47.8% (Modgil et al., 2025). Furthermore, community-level comparative studies between urban and rural settings demonstrate a consistently higher prevalence of resistant pathogens in rural regions, with rural AMR trends surpassing corresponding data reported by ICMR.

4.3 Economic impact of AMR on healthcare

It is now evident that MDR infections take a significant toll on patient and hospital finances. Studies pointing towards increased length-of-stay (LOS) and increased patient mortality can be extrapolated to higher expenditures on patient health (Nathwani et al., 2014). AMR increases the cost of healthcare due to additional nursing and medical care needed for the patient (Pulingam et al., 2022). Treatment of drug-resistant pathogens inadvertently promotes further utilization of antimicrobials to curb the infection. This never-ending cycle results in the burden of purchasing costly antibiotics (Dadgostar, 2019). The median total cost of managing resistant infections in India is calculated at US$ 199 in comparison to US$ 109 for susceptible infections, the median cost of hospitalization per day is US$ 65 and US$ 35 for resistant and susceptible infections respectively. This resulted in heavy economic burdens as nearly half of the patients borrowed money for treatment expenses (Kadam et al., 2024).

4.4 Global significance

Outpatient AMR patterns in India provide valuable insights for healthcare professionals worldwide. India’s high antibiotic use and diverse resistance profiles mirror challenges faced by many low- and middle-income countries, offering a model for strengthening global surveillance and stewardship. Data from India support WHO-led initiatives such as GLASS, enhancing evidence-based AMR monitoring and policy development (World Health, 2014; Okeke et al., 2024). The AMR patterns observed in India can help healthcare professionals globally understand prescription trends, antibiotic guideline, and AMSP practice-related resistance mechanisms. Beyond national policy reforms, these insights can inform global antibiotic use strategies and guide the evolution of international frameworks for rational and precision-based antimicrobial therapy.

5 Current efforts and interventions to curb AMR

5.1 National AMR surveillance programs in India

According to the Global Burden of Disease study, bacterial infections cause around 37 million deaths globally, making AMR among dangerous bacteria a significant health problem (Murray et al., 2022). The 68^th World Health Assembly created and accepted the Global Action Plan on AMR (GAP–AMR) in 2015 after receiving support from several international organizations (World Health, 2024b). The AMR Political Declaration adopted by the 71^st UN General Assembly in 2016 affirmed the GAP-AMR and its five strategic objectives as the blueprint for addressing AMR globally and has informed the development of National Action Plans (NAPs). By the end of 2023, 178 countries had developed NAPs based on this framework with 68% implementing their plans, 25% having costed and budgeted NAPs and with Monitoring & Evaluation frameworks. However, so far only 10% of countries have responded that they have made specific financial provisions for implementing their NAPs which highlights the cross-cutting nature of AMR interventions.) (Gupta and Bhatia, 2023). In India, the 12th (2012-2017) five-year plan included the National Program on AMR Containment, which was started in 2013 and is managed by the National Centre for Disease Control (NCDC). The National Antimicrobial Resistance Surveillance Network (NARS-Net) was gradually extended to all states and Union Territories (UTs). (Figure 3) illustrates priority pathogens monitored under NARS - Net. Because of this effort, by March 2024, the program had 50 medical colleges and labs in 27 states and 6 UTs, guaranteeing geographic representation. After certification, “WHONET” an offline, open-source program for managing microbiological data, is used by the NCDC to evaluate surveillance data from NARS-Net sentinel sites. The 5-year NAP-AMR (2017–2021) was a crucial foundation for states to create action plans to address AMR locally because India’s healthcare system is state-governed (Nair et al., 2021). Similarly, ICMR created an AMR monitoring network (AMRSN) to gather nationally representative data on the trends and patterns of antibiotic resistance in pathogens of public health significance.

Figure 3

5.2 Precision medicine

Current treatment practices can be boiled down to a “one-size-fits-all” approach (Merker et al., 2020). Conventional antibiotic therapy comprises either a single dose or a combination of two or more antibiotics. Limitations of this approach relate to issues such as misuse, overuse and underuse of antibiotics. These increased risk of adverse effects and toxicities, also reducing the efficacy of antibiotics (Ahmed et al., 2024a; Wawruch et al., 2002).

Precision Medicine helps to make customized and informed medical decisions, tailoring therapies by considering patients’ genetic makeup, immune response, specific infection, and socio-environmental and lifestyle factors. Precision medicine affirms the belief that every patient has different demands rather than a “one-size-fits-all” approach (Naithani et al., 2021). Such informed therapies allow for better treatment outcomes by selecting the most appropriate antibiotics, quick modifications of antibiotic regimens, and allowing for better antimicrobial stewardship. These ultimately prevent the development of AMR (Ahmed et al., 2024a; Watkins, 2022).

5.2.1 Pharmacogenomics

Precision medicine’s patient-centric approach allows us to closely monitor drug levels using therapeutic drug monitoring (TDM). This helps maintain the peak therapeutic efficacy of drugs while mitigating adverse effects and drug-related toxicities (Cusumano et al., 2020). Incorporating pharmacogenomic testing for single nucleotide polymorphisms (SNPs) and mutations brings out insights about patients’ responses towards the antimicrobial drugs prescribed to them. Thereby preventing unnecessary adverse drug reactions (ADRs) and toxicities promoting safe, effective, and conserved use of antimicrobial drugs (Daly, 2023). (Table 4) gives a detailed breakdown of genes, their Single nucleotide polymorphisms (SNPs) and the associated negatory effects (Hahn et al., 2016; Cirulli et al., 2019; Naidoo et al., 2017, 2018; Weiner et al., 2007, 2018; Andrews et al., 2010; Yee et al., 2013; Allegra et al., 2018a; Franke et al., 2011; Lancaster et al., 2012; Baietto et al., 2015; Allegra et al., 2018b; Chen et al., 2024; Estivill et al., 1998; Prezant et al., 1993; Gervasoni et al., 2013; Wang et al., 2012). SNPs that are related to various ADRs in persons carrying those mutations. These adverse effects include drug induced liver injuries (DILI), drug reactions with systemic eosinophilia (DRESS), nephrotoxicity, ototoxicity resulting in hearing loss, neutropenia, neurological side effects and immunologic adverse reactions (such as cutaneous adverse reactions due to polymorphisms in MHC Class I and II) a detailed breakdown is given in (Table 5) (Lucena et al., 2011; Stephens et al., 2013; Urban et al., 2017; Konvinse et al., 2019; Yang et al., 2017). An in-depth review of the pharmacogenomics of antibiotics (Stocco et al., 2020). Pharmacogenomic interpretation and screening for such polymorphisms will be of immense clinical importance; further research to prove such SNPs as biomarkers are required.

5.2.2 Metabolomics

Carbapenem-resistant Acinetobacter baumannii (CRAB) strains are prevalent worldwide and a significant threat to public health. Orthogonal Partial Least- Squares discriminant analysis (OPLS-DA) identified key biomarkers distinguishing Carbapenem-resistant Acinetobacter baumannii (CRAB) and Carbapenem-sensitive A. baumannii (CSAB). A total of 16 differential metabolites, including AMP, guanine, glutamine, fumarate, succinic acid, and α-ketoglutarate, were detected. Among these, eight metabolites were downregulated in CRAB, with six associated with the tricarboxylic acid (TCA) cycle and purine metabolism pathways (Li et al., 2024). An interesting example of discovering a metabolite-based antibiotic adjuvant by comparing the metabolic profiles of Gentamicin-sensitive Salmonella Choleraesuis (SCH-S) and gentamicin-resistant Salmonella Choleraesuis (SCH-R). Remarkably, D-ribose was the most suppressed metabolite in SCH-R and enhanced the efficacy of gentamicin against SCH-R and clinical multidrug-resistant strains (Zhou et al., 2022). Glucose and alanine are prominent biomarkers whose abundances are significantly reduced in kanamycin-resistant Edwardsiella tarda (Peng et al., 2015).

The potential mechanisms of utilizing gas chromatography-mass spectrometry (GC-MS) based metabolomics approaches are explored to profile the metabolomes of Edwardsiella tarda in the presence or absence of serum stress. They identified exogenous succinate as promoting the TCA cycle and increasing serum resistance, while TCA cycle inhibitors (bromopyruvate and propanedioic acid) that inhibit the TCA cycle attenuated serum resistance (Cheng et al., 2017). The metabolic profiles of clinically isolated multidrug-resistant and susceptible Escherichia coli, identifying glutamine as a potential biomarker that is suppressed in drug-resistant strains (Zhao et al., 2021). The differential metabolomes of S. agalactiae evades serum-mediated killing. Through bioinformatics analysis, decreased malic acid and increased adenosine are identified as the most crucial biomarker. The findings reveal that S. agalactiae utilizes a metabolic trick to respond to plasma killing due to serum resistance (Wang et al., 2016). To determine resistance profiles of A. baumannii (ATCC strain), 26 clinical isolates were screened against ciprofloxacin, colistin, cefixime, gentamicin, and co-amoxiclav. Malleobactin, Endophenazine, pyochelin, and L-lysine were significantly associated with ciprofloxacin resistance while Cefixime resistance is associated with L-serine and cytidine (Ramzan et al., 2025). A total of 40 metabolites were differentiated between ciprofloxacin. Susceptible and resistant isolates of Klebsiella oxytoca. Univariate receiver operating characteristic (ROC) curve analyses revealed that six of these metabolites, glycerol-3-phosphate, O-phosphoethanolamine, asparagine dehydrate, maleimide, tyrosine, and alanine, have a crucial role in distinguishing susceptible from resistant isolates (AUC>0.84) contributing to antimicrobial resistance. The identified metabolites belong to central carbon metabolism, arginine and proline metabolism; alanine, aspartate, and glutamate metabolism; and pyruvate metabolism (Ahmed et al., 2024b).

5.2.3 Proteomics

Whole-genome sequencing and proteome analysis of Escherichia coli, have decoded the dominant Antimicrobial Resistance Genes bla_CTX-M-15, bla_CMY-42, bla_NDM-5, and aadA (Mat Ghani et al., 2025) and bla_TEM-1B, bla_OXA-232, bla_NDM-1, rmtB, and rmtC were dominant in Klebsiella pneumoniae. In contrast, Pseudomonas aeruginosa and Acinetobacter baumannii harbored bla_VEB, bla_VIM-2, aph(3’), strA/B, bla_OXA-23, aph(3′) variants, and amrA, respectively (Mehrotra et al., 2023). Acinetobacter baumannii strains revealed the role of bla_OXA-23 and bla_OXA-66 genes in carbapenem resistance. Through 16S rRNA gene sequence analysis and species-specific PCR targeting, the bla_OXA51-like gene has been identified. Comparative genome analysis revealed bla_OXA-66 as the most dominant variant of bla_OXA-51-like gene and a widespread distribution of bla_OXA-23 gene (Mat Ghani et al., 2025).

Multidrug-resistant E. coli proteome under antimicrobial pressure highlights a significant role for chromosomally encoded genes. Several proteins with altered abundance in response to ampicillin, cefotaxime, and imipenem, compared to untreated controls, have been identified. These include stress-related proteins such as ecotin (Eco) and methionine-R-sulfoxide reductase (MsrC) and detoxification proteins like SodA (ampicillin and cefotaxime), OsmC, GrxC, GrxD (cefotaxime), OxyR (imipenem), and PspE (cefotaxime and imipenem). Cold shock proteins (CspE, CspA) were elevated in cefotaxime, imipenem, and ampicillin-treated groups. Additionally, β-lactamase CTX-M-15 was significantly upregulated under cefotaxime exposure and TEM-1 under imipenem, but neither in response to ampicillin (Margalit et al., 2022). These findings partially differ from Møller et al. and can be attributed to differences in β-lactamase variants, antibiotic concentrations, and plasmid content (Møller et al., 2017). Bacteroides fragilis can be classified into division I (cfiA negative - chromosomal carbapenemase gene negative) and division II (cfiA positive- chromosomal carbapenemase gene positive) isolates. 14.9% proportion of cfiA-positive B. fragilis among blood culture isolates. Division II (cfiA positive) B. fragilis is more resistant to β-lactam antibiotics than division I (cfiA negative), making cfiA a valuable biomarker for antimicrobial therapy. Empiric treatment for Division II isolates should avoid carbapenems and co-amoxiclav (Jeverica et al., 2019).

An easy and rapid protocol for detecting KPC (Klebsiella pneumoniae carbapenemase) by MALDI-TOF MS is developed from the colony, and positive blood culture of Klebsiella pneumoniae and Escherichia coli. Statistical results showed 100% sensitivity, CI 95%: [94.0%; 100%] and 100% specificity, CI 95%: [94.6%; 100%], indicating a promising test with a high discriminative power. This approach offers a cost-effective and clinically impactful tool for early detection of KPC-2, supporting timely and targeted antibiotic stewardship in Enterobacterales infections (Figueroa-Espinosa et al., 2019). The “MALDIxin” test, a MALDI-TOF MS-based diagnostic that identifies polymyxin resistance in intact Escherichia coli within 15 minutes. The assay detects pETN-modified lipid A and distinguishes between chromosomal and plasmid-encoded resistance mechanisms. MALDIxin test accurately identified all mcr-1 producers in a blinded panel of carbapenemase-producing E. coli (Dortet et al., 2018).

5.2.3 Transcriptomics

There are various transcriptome biomarkers which actively promote Pseudomonas aeruginosa resistance to several drug classes. The algX gene, which stimulates alginate production, most likely enhances the protection for fluoroquinolones like ciprofloxacin by facilitating the formation of biofilms. In addition, the expression levels of the putative proteins like PA14_32290, PA14_48950, and PA14_59390 also indicated resistance against antibiotics. Furthermore, resistant strains showed an increased expression of the opdP gene, which codes for a glycine-glutamate dipeptide porin, and yedZ (PA14_62100), a sulfide oxidase subunit. One important factor in the development of multidrug resistance, particularly ciprofloxacin resistance, was the sensor kinase gene cbrA (Khaledi et al., 2016). The ampC gene showed a strong connection with ceftazidime resistance for β-lactam antibiotics such as ceftazidime and meropenem, with higher minimum inhibitory concentrations (MICs) correlating to greater expression levels (Libbrecht and Noble, 2015; Sundaramurthy and Eghbalnia, 2015).

The oprD gene, which codes for a carbapenem entry porin, downregulated in meropenem-resistant strains. The relevance of this downregulation in resistance was validated by protein studies and nonsense mutations, even though it did not reach statistical significance by AUC. In addition, the meropenem resistance pattern was influenced by the increased expression of genes such gbuA (a guanidinobutyrase involved in the arginine dehydrogenase pathway), cc4 (encoding the diheme protein cytochrome c4), and PA14_46110 (a putative sodium-solute symporter). When combining these indicators, it provides helpful targets for clinical testing and aid in the prediction of resistance phenotypes (Köhler et al., 1999).

5.3 Role of antimicrobial stewardship programs and infection control measures

AMR is a significant threat to public health, driven by several key issues. These include inappropriate and overuse of antibiotics in humans and agriculture, and poor infection control and management. This is further exacerbated by the inaccessibility of clean drinking water and proper sanitation, increased travel and globalization, and the slow development of novel or improved antimicrobial drugs (Mukherjee, 2024). The management of antimicrobial resistance now requires antimicrobial stewardship, which seeks to maximize antibiotic use through coordinated actions across healthcare settings, especially considering the slow development of new antibiotics (Ha et al., 2019). Studies reveal that integrating antimicrobial stewardship programs (ASPs) with infection prevention and control improves the efficacy of both approaches, as evidenced by notable decreases in infections, including Clostridium difficile infections, brought about by these coordinated initiatives (Knobloch et al., 2021). Moreover, in 2019, ICMR published the second version of its guidelines on using antibiotics in common syndromes, which includes updated information on how to treat skin infections, soft tissues, central nervous system, and bones and joints (Sandeep et al., 2020). So that ASPs are necessary for optimizing antibiotic use, improving patient safety, and lowering AMR (Ponto, 2010).

Antimicrobial stewardship methods can be applied in several ways:

5.3.1 Educating and screening of patients

Before prescribing antibiotics, screening and interacting with patients can help to reduce needless antibiotic use in healthcare settings. This can be of help to establish whether antibiotics are truly needed in a particular case. The Centers for Disease Control and Prevention have released a useful checklist to guide the healthcare facilities in more efficiently implementing the practice of screening patients for antibiotics (Sanchez, 2016).

5.3.2 Policy update

Updating hospital policy regarding antimicrobial stewardship initiatives is one of the most effective ways to enhance patient care and reduce needless medication use (Walia et al., 2019).

5.3.3 Limiting the use of antibiotics as a prevention strategy

The primary aim of antibiotics is to treat infections, not to prevent. However, there are some circumstances where antibiotics can be used for prophylaxis. Because of spread of AMR in hospitals, many institutions have begun to use antibiotics as a last resort rather than a first line of defense because there are other general prophylaxis such as hand hygiene which can help stop infections even before they start (Andersen, 2019).

Centers for Disease Control and Prevention (CDC) has list of steps that should be followed by healthcare professionals to ensure proper hand hygiene: Ensure that hand hygiene supplies are always available to all healthcare professionals. Antibacterial soap or 2% chlorhexidine gluconate solution is also a better option for proper hand hygiene. However, any ethanol-based hand rub (70% v/v) will also be equally effective (Lemmen and Lewalter, 2018).

Studies have shown that antimicrobial stewardship interventions in outpatient settings, particularly those involving pharmacist-led actions based on microbiology culture reports, yield significant clinical benefits. In a study by Wattengel et al. (2020), acceptance of pharmacist recommendations was associated with markedly lower rates of 30-day treatment failure (5% vs 28%) and 30-day hospital admission (0.7% vs 11%) compared with cases where the interventions were rejected.

5.3.4 De-escalation/modification

Modify empiric broad spectrum antibiotics depending on culture and antimicrobial susceptibility reports and patient status. Avoid double or redundant gram negative or anaerobic coverage - Discontinue antibiotics if a non-infectious mimic identified - De-escalate combination therapy to a single agent (Aggarwal et al., 2019).

5.3.5 Monitoring the path of every medication

Errors are expected whenever a drug is given to the patients. So, it is very crucial to track the path of every medication from the time it is prescribed until it is given to the patient. Maintaining records of medicine orders and administration is very crucial for tracking the path of medication order (Bankar et al., 2022).

5.3.6 Staff education

Limiting the use of antibiotics is only one component of effective antimicrobial stewardship. But it also includes giving training to staff on alternative treatment methods such as the use of gloves and hand sanitizer to prevent the spread of infections. In addition, it involves educating staffs about how infections are spread generally (Pereira et al., 2017).

By implementing these methods, infections can be reduced which will reduce the overall requirement of medicines.

6 Conclusion and future prospective

This narrative review provides an overview of the AMR status in Indian healthcare settings, with an emphasis on outpatient settings. We explore the impact of AMR on clinical outcomes and review the current efforts and interventions such as AMR Stewardship programs. In addition, we comprehensively discussed precision medicine, pharmacogenomics and other Omics based approaches aimed at curbing AMR. This study highlights the growing issue of antimicrobial resistance (AMR), particularly from multidrug-resistant (MDR) Gram-negative bacteria, which presents serious difficulties for healthcare systems. MDR in common pathogens like E. coli, Klebsiella pneumoniae, and Staphylococcus aureus is well-documented, but specific resistance mechanisms remain underexplored. AMR studies often reflect data from selected regions in India or isolated hospitals. A research gap exists in understanding how resistance patterns vary across rural vs. urban settings and in primary, secondary, and tertiary healthcare levels.

Few studies assess the direct impact of AMR on treatment outcomes, length of hospital stay, and mortality rates. Poor sanitation, inappropriate antibiotic usage in agriculture, and a lack of healthcare infrastructure in rural regions are examples of environmental variables that contribute to the spread of AMR, which are poorly understood. Research focusing on the clinical effects of drug-resistant infections in vulnerable populations, particularly in Indian settings, utilizes pharmacogenomics methods to provide personalized treatments through genetic profiling and therapeutic drug monitoring. Additionally, studies on the effectiveness of antimicrobial stewardship initiatives and community-based ASPs aim to reduce antimicrobial resistance. Our knowledge of the elements contributing to resistance in developing nations such as India would be improved further by research integrating environmental and socioeconomic factors with AMR patterns.

The emergence of multidrug-resistant organisms (MDROs) raises morbidity and death rates which complicates treatment regimens and raises healthcare expenses from a clinical standpoint (Nathwani et al., 2014; Kadam et al., 2024). Treatment of infections caused by resistant pathogens, including extensively carbapenem-resistant Enterobacterales (CRE), and methicillin-resistant Staphylococcus aureus (MRSA), is difficult because only few effective antibiotics are available. When pan-resistant strains appear, it is then especially concerning and highlights the seriousness of the issue because for that there are now no viable antibiotics.

Using antibiotics carelessly in aquaculture and agriculture has made the issue worse. The widespread use of antibiotics for both infection treatment and livestock growth promotion lead to the selection of resistant strains that can spread to humans through the food chain and environmental pathways (Page and Gautier, 2012). The necessity of a “One Health” approach that acknowledges the interdependence of environmental, animal, and human health is highlighted (Jani et al., 2021). Antimicrobial stewardship programs (ASPs) are essential for reducing AMR. Targeted therapy, which lessens the need for broad-spectrum antibiotics and the selection pressure for resistant bacteria, can be made possible by quick and precise diagnostic methods. In the same way, ASPs urge for careful consumption of antibiotics, supporting de-escalation techniques, suitable dosage, and treatment length to curb antimicrobial resistance.

The pharmaceutical pathway for novel antibiotics is still limited due to scientific, regulatory and financial obstacles. Developing new antibiotics requires modest commercial returns, significant time, investment and commitment. By promoting research in novel anti-infective therapy, we can revive the antimicrobial pipeline for which we would need development through public private collaborations, market entry incentives, simplified regulatory processes and international cooperation which is essential to effectively address AMR. International frameworks like World Health Organization (WHO) have goals on Global Action Plan (GAP) on AMR which are, are stronger surveillance, better infection prevention and control and easier access to high-quality antimicrobials. However, there have been major obstacles such as antimicrobial access and healthcare infrastructure of some nations.

A successful response towards curbing AMR requires addressing these inconsistencies through technology transfer, capacity building and fair access to diagnostics and antimicrobials. Precision medicine helps to make customized and informed medical decisions, tailoring therapies by considering patient’s genetic makeup, immune response, specific infection, and socio-environmental and lifestyle factors. By using precision medicine’s patient-centric approach we can closely monitor drug levels using therapeutic drug monitoring (TDM) (Roberts et al., 2012). This helps maintain the peak therapeutic efficacy of drugs while mitigating adverse effects and drug-related toxicities. In conclusion, we need more research focusing on the clinical consequences of drug-resistant infections in vulnerable populations especially in Indian Outpatient settings which could be achieved using pharmacogenomics methods to provide personalized treatments using genetic profiling and TDM.

References

• 1

  Aggarwal S. Walia K. J M. Gopalkrishnan R. Ohri V. Gangakhedkar R. (2019). Treatment Guidelines for Antimicrobial Use in Common Syndromes 2019 New Delhi, India i ICMR, New Delhi Treatment Guidelines for Antimicrobial Use in Common Syndromes. 2nd edition Available online at: https://www.researchgate.net/publication/336511625_Treatment_Guidelines_for_Antimicrobial_Use_in_Common_Syndromes_2019_New_Delhi_India_i_ICMR_New_Delhi_Treatment_Guidelines_for_Antimicrobial_Use_in_Common_Syndromes_2_nd_edition.

  • Google Scholar

• 2

  Ahmed S. K. Hussein S. Qurbani K. Ibrahim R. H. Fareeq A. Mahmood K. A. et al . (2024c). Antimicrobial resistance: Impacts, challenges, and future prospects. J. Medicine Surgery Public Health2, 100081. doi: 10.1016/j.glmedi.2024.100081

  • CrossRef
  • Google Scholar

• 3

  Ahmed F. Shamim N. J. Das A. Sharma H. K. Grewal A. S. Pandita D. et al . (2024a). Combating Antimicrobial Resistance: A paradigm shift from general to precision medicine. Chem. Biol. Lett.11, 662. doi: 10.62110/sciencein.cbl.2024.v11.662

  • CrossRef
  • Google Scholar

• 4

  Ahmed S. Shams S. Trivedi D. Lima C. Mcgalliard R. Parry C. M. et al . (2024b). Metabolic response of Klebsiella oxytoca to ciprofloxacin exposure: a metabolomics approach. Metabolomics21, 8. doi: 10.1007/s11306-024-02206-y

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 5

  Allegra S. Cardellino C. S. Fatiguso G. Cusato J. De Nicolò A. Avataneo V. et al . (2018a). Effect of ABCC2 and ABCG2 gene polymorphisms and CSF-to-serum albumin ratio on ceftriaxone plasma and cerebrospinal fluid concentrations. J. Clin. Pharmacol.58, 1550–1556. doi: 10.1002/jcph.1266

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 6

  Allegra S. Di Paolo A. Cusato J. Fatiguso G. Arrigoni E. Danesi R. et al . (2018b). A common mdr1 gene polymorphism is associated with changes in linezolid clearance. Ther. Drug Monit.40, 602–609. doi: 10.1097/ftd.0000000000000536

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 7

  Alsan M. Kammili N. Lakshmi J. Xing A. Khan A. Rani M. et al . (2018). Poverty and community-acquired antimicrobial resistance with extended-spectrum β-lactamase-producing organisms, Hyderabad, India. Emerg. Infect. Dis.24, 1490–1496. doi: 10.3201/eid2408.171030

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 8

  Andersen B. M. (2019). “ Prevention of postoperative wound infections,” in Prevention and Control of Infections in Hospitals: Practice and Theory ( Springer International Publishing, Cham). doi: 10.1007/978-3-319-99921-0_33

  • CrossRef
  • Google Scholar

• 9

  Andrews E. Armstrong M. Tugwood J. Swan D. Glaves P. Pirmohamed M. et al . (2010). A role for the pregnane X receptor in flucloxacillin-induced liver injury. Hepatology51, 1656–1664. doi: 10.1002/hep.23549

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 10

  Baietto L. D’avolio A. Cusato J. Pace S. Calcagno A. Motta I. et al . (2015). Effect of SNPs in human ABCB1 on daptomycin pharmacokinetics in Caucasian patients. J. Antimicrobial Chemotherapy70, 307–308. doi: 10.1093/jac/dku368

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 11

  Balachandra S. S. Sawant P. S. Huilgol P. G. Vithya T. Kumar G. Prasad R. (2021). Antimicrobial resistance (AMR) at the community level: An urban and rural case study from Karnataka. J. Family Med. Primary Care10, 1404–1411. doi: 10.4103/jfmpc.jfmpc_888_20

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 12

  Bankar N. J. Ugemuge S. Ambad R. S. Hawale D. V. Timilsina D. R. (2022). Implementation of antimicrobial stewardship in the healthcare setting. Cureus14, e26664. doi: 10.7759/cureus.26664

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 13

  Barrasa-Villar J. I. Aibar-Remón C. Prieto-Andrés P. Mareca-Doñate R. Moliner-Lahoz J. (2017). Impact on morbidity, mortality, and length of stay of hospital-acquired infections by resistant microorganisms. Clin. Infect. Dis.65, 644–652. doi: 10.1093/cid/cix411

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 14

  Biswas M. Biswas S. Gupta B. Mascellino M. T. Rakshit A. Chakraborty B. (2022). Changing paradigms in antibiotic resistance in salmonella species with focus on fluoroquinolone resistance: A 5-year retrospective study of enteric fever in a tertiary care hospital in Kolkata, India. Antibiotics11, 1308. doi: 10.3390/antibiotics11101308

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 15

  Biswas S. Khuntia H. K. Bal M. Pati S. Dixit S. Pattanaik S. S. S. et al . (2024). Prevalence and antimicrobial resistance patterns of diarrheagenic and uropathogenic Escherichia coli in Odisha, India. One Health Bull.4, 85–92. doi: 10.4103/ohbl.ohbl_42_23

  • CrossRef
  • Google Scholar

• 16

  Bose P. Chowdhury G. Halder G. Ghosh D. Deb A. K. Kitahara K. et al . (2024). Prevalence and changing antimicrobial resistance profiles of Shigella spp. isolated from diarrheal patients in Kolkata during 2011–2019. PloS Negl. Trop. Dis.18, e0011964. doi: 10.1371/journal.pntd.0011964

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 17

  Chen H.-K. Hsieh Y.-W. Hsu H.-Y. Liu T.-Y. Zhang Y.-T. Lin C.-D. et al . (2024). Increased risk of hearing loss associated with MT-RNR1 gene mutations: a real-world investigation among Han Taiwanese Population. BMC Med. Genomics17, 155. doi: 10.1186/s12920-024-01921-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 18

  Cheng Z.-X. Gong Q.-Y. Wang Z. Chen Z.-G. Ye J.-Z. Li J. et al . (2017). Edwardsiella tarda tunes tricarboxylic acid cycle to evade complement-mediated killing. Front. Immunol.8. doi: 10.3389/fimmu.2017.01706

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 19

  Cirulli E. T. Nicoletti P. Abramson K. Andrade R. J. Bjornsson E. S. Chalasani N. et al . (2019). A missense variant in PTPN22 is a risk factor for drug-induced liver injury. Gastroenterology156, 1707–1716.e2. doi: 10.1053/j.gastro.2019.01.034

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 20

  Cornejo-Juárez P. Vilar-Compte D. Pérez-Jiménez C. Ñamendys-Silva S. A. Sandoval-Hernández S. Volkow-Fernández P. (2015). The impact of hospital-acquired infections with multidrug-resistant bacteria in an oncology intensive care unit. Int. J. Infect. Dis.31, 31–34. doi: 10.1016/j.ijid.2014.12.022

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 21

  Crank C. O’Driscoll T. (2015). Vancomycin-resistant enterococcal infections: epidemiology, clinical manifestations, and optimal management. Infection Drug Resistance8, 217–30. doi: 10.2147/IDR.S54125

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 22

  Cusumano J. A. Klinker K. P. Huttner A. Luther M. K. Roberts J. A. Laplante K. L. (2020). Towards precision medicine: Therapeutic drug monitoring–guided dosing of vancomycin and β-lactam antibiotics to maximize effectiveness and minimize toxicity. Am. J. Health-System Pharm.77, 1104–1112. doi: 10.1093/ajhp/zxaa128

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 23

  Dadgostar P. (2019). Antimicrobial resistance: implications and costs. Infection Drug Resistance12, 3903–3910. doi: 10.2147/IDR.S234610

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 24

  Daly A. K. (2023). Relevance of pharmacogenomics to the safe use of antimicrobials. Antibiotics12, 425. doi: 10.3390/antibiotics12030425

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 25

  Deka S. Kalita D. Mahanta P. Baruah D. (2020). High prevalence of antibiotic-resistant gram-negative bacteria causing surgical site infection in a tertiary care hospital of northeast India. Cureus12, e12208. doi: 10.7759/cureus.12208

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 26

  Dortet L. Bonnin R. A. Pennisi I. Gauthier L. Jousset A. B. Dabos L. et al . (2018). Rapid detection and discrimination of chromosome- and MCR-plasmid-mediated resistance to polymyxins by MALDI-TOF MS in Escherichia coli: the MALDIxin test. J. Antimicrobial Chemotherapy73, 3359–3367. doi: 10.1093/jac/dky330

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 27

  Dr. Ranjeet S. Dr. Krishna Gopal S. Dr. Farha S. Dr. Harsha G. (2024). Prevalence and impact of nosocomial infections in the ICU of A tertiary care hospital in central India. Educ. Administration: Theory Pract.30, 1574–1578. doi: 10.53555/kuey.v30i6.5540

  • CrossRef
  • Google Scholar

• 28

  Estivill X. Govea N. Barceló A. Perelló E. Badenas C. Romero E. et al . (1998). Familial progressive sensorineural deafness is mainly due to the mtDNA A1555G mutation and is enhanced by treatment with aminoglycosides. Am. J. Hum. Genet.62, 27–35. doi: 10.1086/301676

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 29

  Feldman C. Shaddock E. (2019). Epidemiology of lower respiratory tract infections in adults. Expert Rev. Respir. Med.13, 63–77. doi: 10.1080/17476348.2019.1555040

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 30

  Figueiredo Costa S. (2008). Impact of antimicrobial resistance on the treatment and outcome of patients with sepsis. Shock30, 23–29. doi: 10.1097/SHK.0b013e3181818990

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 31

  Figueroa-Espinosa R. Costa A. Cejas D. Barrios R. Vay C. Radice M. et al . (2019). MALDI-TOF MS based procedure to detect KPC-2 directly from positive blood culture bottles and colonies. J. Microbiological Methods159, 120–127. doi: 10.1016/j.mimet.2019.02.020

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 32

  Franke R. M. Lancaster C. S. Peer C. J. Gibson A. A. Kosloske A. M. Orwick S. J. et al . (2011). Effect of ABCC2 (MRP2) transport function on erythromycin metabolism. Clin. Pharmacol. Ther.89, 693–701. doi: 10.1038/clpt.2011.25

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 33

  Gandra S. Singh S. K. Jinka D. R. Kanithi R. Chikkappa A. K. Sharma A. et al . (2017). Point prevalence surveys of antimicrobial use among hospitalized children in six hospitals in India in 2016. Antibiotics6, 19. doi: 10.3390/antibiotics6030019

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 34

  Gervasoni C. Cattaneo D. Falvella F. S. Vitiello P. Cheli S. Milazzo L. et al . (2013). Levofloxacin-induced seizures in a patient without predisposing risk factors: the impact of pharmacogenetics. Eur. J. Clin. Pharmacol.69, 1611–1613. doi: 10.1007/s00228-013-1515-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 35

  Gupta R. Bhatia R. (2023). “ Antimicrobial resistance in India: the road ahead,” in Handbook on Antimicrobial Resistance: Current Status, Trends in Detection and Mitigation Measures. Eds. MothadakaM. P.Nagarajrao RavishankarC.VaiyapuriM.BhatiaR.JenaJ.Rao BadireddyM. ( Springer Nature Singapore, Singapore). doi: 10.1007/978-981-16-9723-4_1-2

  • CrossRef
  • Google Scholar

• 36

  Ha D. R. Haste N. M. Gluckstein D. P. (2019). The role of antibiotic stewardship in promoting appropriate antibiotic use. Am. J. Lifestyle Med.13, 376–383. doi: 10.1177/1559827617700824

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 37

  Hahn A. Fukuda T. Hahn D. Mizuno T. Frenck R. W. Vinks A. A. (2016). Pharmacokinetics and pharmacogenomics of β-lactam-induced neutropenia. Pharmacogenomics17, 547–559. doi: 10.2217/pgs-2015-0008

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 38

  Hassoun A. Linden P. K. Friedman B. (2017). Incidence, prevalence, and management of MRSA bacteremia across patient populations—a review of recent developments in MRSA management and treatment. Crit. Care21, 211. doi: 10.1186/s13054-017-1801-3

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 39

  Iskandar K. Molinier L. Hallit S. Sartelli M. Catena F. Coccolini F. et al . (2020). Drivers of antibiotic resistance transmission in low- and middle-income countries from a “One health” Perspective—A Review. Antibiotics9, 372. doi: 10.3390/antibiotics9070372

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 40

  Jain P. A. Kulkarni R. D. Dutta S. Ganavali A. S. Kalabhavi A. S. Shetty P. C. et al . (2020). Prevalence and antimicrobial profile of shigella isolates in a tertiary care hospital of north karnataka: A 12-year study. Indian J. Med. Microbiol.38, 101–108. doi: 10.4103/ijmm.IJMM_20_107

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 41

  Jain N. Lodha R. Kabra S. K. (2001). Upper respiratory tract infections. Indian J. Pediatr.68, 1135–1138. doi: 10.1007/BF02722930

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 42

  Jani K. Srivastava V. Sharma P. Vir A. Sharma A. (2021). Easy access to antibiotics; spread of antimicrobial resistance and implementation of one health approach in India. J. Epidemiol. Global Health11, 444–452. doi: 10.1007/s44197-021-00008-2

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 43

  Jean S.-S. Harnod D. Hsueh P.-R. (2022). Global threat of carbapenem-resistant gram-negative bacteria. Front. Cell. Infection Microbiol.12. doi: 10.3389/fcimb.2022.823684

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 44

  Jesudason T. (2024). WHO publishes updated list of bacterial priority pathogens. Lancet Microbe5, 100940. doi: 10.1016/j.lanmic.2024.07.003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 45

  Jeverica S. Sóki J. Premru M. M. Nagy E. Papst L. (2019). High prevalence of division II (cfiA positive) isolates among blood stream Bacteroides fragilis in Slovenia as determined by MALDI-TOF MS. Anaerobe58, 30–34. doi: 10.1016/j.anaerobe.2019.01.011

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 46

  Kadam A. Mamulwar M. Bhambure G. Bembalkar S. Bapat S. Mane A. et al . (2024). Incremental cost of treating antimicrobial-resistant infections among hospitalised patients in India: a cohort study. BMJ Open14, e086505. doi: 10.1136/bmjopen-2024-086505

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 47

  Kakhandki L. Takpere A. Y. Bagali S. Wavare S. Karigoudar R. Shahapur P. R. (2020). Antibiogram of gram negative bacteria isolated from the skin and soft tissue infections a guide for empirical therapy to the clinicians. J. Pure Appl. Microbiol.14, 1353–1358. doi: 10.22207/jpam.14.2.32

  • CrossRef
  • Google Scholar

• 48

  Kaleem Ullah M. Malamardi S. Siddaiah J. B. A T. Prashant A. Vishwanath P. et al . (2022). Trends in the bacterial prevalence and antibiotic resistance patterns in the acute exacerbation of chronic obstructive pulmonary disease in hospitalized patients in south India. Antibiotics11, 1577. doi: 10.3390/antibiotics11111577

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 49

  Katip W. Rayanakorn A. Oberdorfer P. Taruangsri P. Nampuan T. (2023). Short versus long course of colistin treatment for carbapenem-resistant A. baumannii in critically ill patients: A propensity score matching study. J. Infection Public Health16, 1249–1255. doi: 10.1016/j.jiph.2023.05.024

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 50

  Khaledi A. Schniederjans M. Pohl S. Rainer R. Bodenhofer U. Xia B. et al . (2016). Transcriptome profiling of antimicrobial resistance in pseudomonas aeruginosa. Antimicrobial Agents Chemotherapy60, 4722–4733. doi: 10.1128/aac.00075-16

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 51

  Knobloch M. J. Mckinley L. Keating J. Safdar N. (2021). Integrating antibiotic stewardship and infection prevention and control programs using a team science approach. Am. J. Infection Control49, 1072–1074. doi: 10.1016/j.ajic.2021.01.020

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 52

  Köhler T. Epp S. F. Curty L. K. Pechère J.-C. (1999). Characterization of MexT, the regulator of the MexE-MexF-OprN multidrug efflux system of Pseudomonas aeruginosa. J. Bacteriology181, 6300–6305. doi: 10.1128/jb.181.20.6300-6305.1999

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 53

  Konvinse K. C. Trubiano J. A. Pavlos R. James I. Shaffer C. M. Bejan C. A. et al . (2019). HLA-A*32:01 is strongly associated with vancomycin-induced drug reaction with eosinophilia and systemic symptoms. J. Allergy Clin. Immunol.144, 183–192. doi: 10.1016/j.jaci.2019.01.045

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 54

  Kulkarni N. Routray A. Taur S. (2023). A multicenter evaluation of overall susceptibility and antimicrobial resistance among Streptococcus pneumoniae isolates. Cureus15, e41984. doi: 10.7759/cureus.41984

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 55

  Kumar A. Chaudhry D. Goel N. Tanwar S. (2021). Epidemiology of intensive care unit-acquired infections in a tertiary care hospital of north India. Indian J. Crit. Care Med.25, 1427–1433. doi: 10.5005/jp-journals-10071-24058

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 56

  Lancaster C. S. Bruun G. H. Peer C. J. Mikkelsen T. S. Corydon T. J. Gibson A. A. et al . (2012). OATP1B1 polymorphism as a determinant of erythromycin disposition. Clin. Pharmacol. Ther.92, 642–650. doi: 10.1038/clpt.2012.106

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 57

  Laxminarayan R. Chaudhury R. R. (2016). Antibiotic resistance in India: drivers and opportunities for action. PloS Med.13, e1001974. doi: 10.1371/journal.pmed.1001974

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 58

  Lemmen S. W. Lewalter K. (2018). Antibiotic stewardship and horizontal infection control are more effective than screening, isolation and eradication. Infection46, 581–590. doi: 10.1007/s15010-018-1137-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 59

  Li X. Feng D. Zhou J. Wu W. Zheng W. Gan W. et al . (2024). Metabolomics method in understanding and sensitizing carbapenem-resistant acinetobacter baumannii to meropenem. ACS Infect. Dis.10, 184–195. doi: 10.1021/acsinfecdis.3c00480

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 60

  Libbrecht M. W. Noble W. S. (2015). Machine learning applications in genetics and genomics. Nat. Rev. Genet.16, 321–332. doi: 10.1038/nrg3920

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 61

  Lohiya A. Suliankatchi Abdulkader R. Rath R. S. Jacob O. Chinnakali P. Goel A. D. et al . (2020). Prevalence and patterns of drug resistant pulmonary tuberculosis in India—A systematic review and meta-analysis. J. Global Antimicrobial Resistance22, 308–316. doi: 10.1016/j.jgar.2020.03.008

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 62

  Lucena M. I. Molokhia M. Shen Y. Urban T. J. Aithal G. P. Andrade R. J. et al . (2011). Susceptibility to amoxicillin-clavulanate-induced liver injury is influenced by multiple HLA class I and II alleles. Gastroenterology141, 338–347. doi: 10.1053/j.gastro.2011.04.001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 63

  M V. R P. M B. (2017). Prevalence of microorganisms causing catheter associated urinary tract infections (CAUTI) among catheterised patients admitted in a tertiary care hospital. Int. J. Res. Med. Sci.5, 2367–2372. doi: 10.18203/2320-6012.ijrms20172084

  • CrossRef
  • Google Scholar

• 64

  Mahindroo J. Thanh D. P. Kaur H. Nguyen T. H. T. Carey M. E. Verma R. et al . (2024). The genomic diversity and antimicrobial resistance of Non-typhoidal Salmonella in humans and food animals in Northern India. One Health19, 100892. doi: 10.1016/j.onehlt.2024.100892

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 65

  Margalit A. Carolan J. C. Walsh F. (2022). Global protein responses of multidrug resistance plasmid-containing Escherichia coli to ampicillin, cefotaxime, imipenem and ciprofloxacin. J. Global Antimicrobial Resistance28, 90–96. doi: 10.1016/j.jgar.2021.12.006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 66

  Massart N. Mansour A. Ross J. T. Piau C. Verhoye J.-P. Tattevin P. et al . (2022). Mortality due to hospital-acquired infection after cardiac surgery. J. Thorac. Cardiovasc. Surg.163, 2131–2140.e3. doi: 10.1016/j.jtcvs.2020.08.094

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 67

  Mat Ghani N. Kar-Wai H. Mei L. Y. J. Yin L. Y. Hoi-Sen Y. Keng T. K. et al . (2025). Whole genome analysis revealed the role of blaOXA-23 and blaOXA-66 genes in carbapenem resistance of Acinetobacter baumannii strains. Pathog. Global Health119, 10–21. doi: 10.1080/20477724.2024.2442194

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 68

  Mather A. E. Reid S. W. J. Maskell D. J. Parkhill J. Fookes M. C. Harris S. R. et al . (2013). Distinguishable epidemics within different hosts of the multidrug resistant zoonotic pathogen salmonella typhimurium DT. Science341, 1514–1517. doi: 10.1126/science.1240578

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 69

  Mathew P. Ranjalkar J. Chandy S. J. (2020). Challenges in implementing antimicrobial stewardship programmes at secondary level hospitals in India: an exploratory study. Front. Public Health8. doi: 10.3389/fpubh.2020.493904

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 70

  McGettigan P. Alonso Olmo C. Plueschke K. Castillon M. Nogueras Zondag D. Bahri P. et al . (2019). Patient Registries: An Underused Resource for Medicines Evaluation: Operational proposals for increasing the use of patient registries in regulatory assessments. Drug Saf.42, 1343–1351. doi: 10.1007/s40264-019-00848-9

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 71

  Mehrotra T. Konar D. Pragasam A. K. Kumar S. Jana P. Babele P. et al . (2023). Antimicrobial resistance heterogeneity among multidrug-resistant Gram-negative pathogens: Phenotypic, genotypic, and proteomic analysis. Proc. Natl. Acad. Sci.120, e2305465120. doi: 10.1073/pnas.2305465120

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 72

  Merker M. Tueffers L. Vallier M. Groth E. E. Sonnenkalb L. Unterweger D. et al . (2020). Evolutionary approaches to combat antibiotic resistance: opportunities and challenges for precision medicine. Front. Immunol.11. doi: 10.3389/fimmu.2020.01938

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 73

  Modgil V. Sahay S. Taneja N. Qayyumi B. Singh R. Mukherjee A. et al . (2025). Mapping the AMR infection landscape in bihar: implications for strengthening policy and clinical practice. Antibiotics14, 684. doi: 10.3390/antibiotics14070684

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 74

  Modi C. Singh S. Pandya Y. (2021). Prevalence of carbapenem resistant Enterobacteriaceae in a tertiary care hospital of Gujarat, India. Blood273, 19. doi: 10.7860/JCDR/2021/47332.14627

  • CrossRef
  • Google Scholar

• 75

  Mohapatra S. Panigrahy R. Tak V. J. V S. K. C S. Chaudhuri S. et al . (2022). Prevalence and resistance pattern of uropathogens from community settings of different regions: an experience from India. Access Microbiol.4, 000321. doi: 10.1099/acmi.0.000321

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 76

  Møller T. S. B. Liu G. Boysen A. Thomsen L. E. Lüthje F. L. Mortensen S. et al . (2017). Treatment with cefotaxime affects expression of conjugation associated proteins and conjugation transfer frequency of an IncI1 plasmid in Escherichia coli. Front. Microbiol.8. doi: 10.3389/fmicb.2017.02365

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 77

  Morris S. K. Bassani D. G. Awasthi S. Kumar R. Shet A. Suraweera W. et al . (2011). Diarrhea, pneumonia, and infectious disease mortality in children aged 5 to 14 years in India. PloS One6, e20119. doi: 10.1371/journal.pone.0020119

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 78

  Mukherjee K. (2024). Antimicrobial Resistance and its Impact on Public Health. (Bhopal, India: Med Research)

  • Google Scholar

• 79

  Murray C. J. L. Ikuta K. S. Sharara F. Swetschinski L. Aguilar G. R. Gray A. et al . (2022). Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet399, 629–655. doi: 10.1016/S0140-6736(21)02724-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 80

  Naidoo A. Naidoo K. Mcilleron H. Essack S. Padayatchi N. (2017). A review of moxifloxacin for the treatment of drug-susceptible tuberculosis. J. Clin. Pharmacol.57, 1369–1386. doi: 10.1002/jcph.968

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 81

  Naidoo A. Ramsuran V. Chirehwa M. Denti P. Mcilleron H. Naidoo K. et al . (2018). Effect of genetic variation in UGT1A and ABCB1 on moxifloxacin pharmacokinetics in South African patients with tuberculosis. Pharmacogenomics19, 17–29. doi: 10.2217/pgs-2017-0144

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 82

  Nair M. Engel N. Zeegers M. P. Burza S. (2023). Perceptions of effective policy interventions and strategies to address antibiotic misuse within primary healthcare in India: A qualitative study. J. Infection Prev.24, 113–118. doi: 10.1177/17571774231158778

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 83

  Nair M. Zeegers M. P. Varghese G. M. Burza S. (2021). India’s National Action Plan on Antimicrobial Resistance: a critical perspective. J. Global Antimicrobial Resistance27, 236–238. doi: 10.1016/j.jgar.2021.10.007

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 84

  Naithani N. Sinha S. Misra P. Vasudevan B. Sahu R. (2021). Precision medicine: Concept and tools. Med. J. Armed Forces India77, 249–257. doi: 10.1016/j.mjafi.2021.06.021

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 85

  Nandhini P. Kumar P. Mickymaray S. Alothaim A. S. Somasundaram J. Rajan M. (2022). Recent developments in methicillin-resistant staphylococcus aureus (MRSA) treatment: A review. Antibiotics11, 606. doi: 10.3390/antibiotics11050606

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 86

  Nathwani D. Raman G. Sulham K. Gavaghan M. Menon V. (2014). Clinical and economic consequences of hospital-acquired resistant and multidrug-resistant Pseudomonas aeruginosa infections: a systematic review and meta-analysis. Antimicrobial Resistance Infection Control3, 32. doi: 10.1186/2047-2994-3-32

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 87

  Okeke I. N. De Kraker M. E. A. Van Boeckel T. P. Kumar C. K. Schmitt H. Gales A. C. et al . (2024). The scope of the antimicrobial resistance challenge. Lancet403, 2426–2438. doi: 10.1016/S0140-6736(24)00876-6

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 88

  Page S. W. Gautier P. (2012). Use of antimicrobial agents in livestock. Rev. Sci. Tech31, 145–188. doi: 10.20506/rst.31.1.2106

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 89

  Patel C. Shah M. B. Singh S. Modi C. Shah P. (2021). Biofilm production and antimicrobial resistance in catheter associated urinary tract infection (CAUTI) pathogens isolated from ICU patients. Eur. J. Mol. Clin. Med. (EJMCM)8, 2021. Available online at: https://www.researchgate.net/publication/365038922 (Accessed December 11, 2024).

  • Google Scholar

• 90

  Peng B. Su Y.-B. Li H. Han Y. Guo C. Tian Y.-M. et al . (2015). Exogenous Alanine and/or Glucose plus Kanamycin Kills Antibiotic-Resistant Bacteria. Cell Metab.21, 249–262. doi: 10.1016/j.cmet.2015.01.008

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 91

  Pereira N. R. Castro-Sanchez E. Nathwani D. (2017). How can multi-professional education support better stewardship? Infect. Dis. Rep.9, 6917. doi: 10.4081/idr.2017.6917

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 92

  Ponto J. A. (2010). ASHP statement on the pharmacist’s role in antimicrobial stewardship and infection prevention and control. Am. J. Health-System Pharm.67, 575–577. doi: 10.2146/sp100001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 93

  Prabhu K. Bhat P. N. Boloor R. Shetty A. K. Nayak S. G. Sundarsingh V. (2024). Clinico-microbiological profile of urosepsis patients in a tertiary care hospital in India: A 1-year study. J. Lab. Physicians16, 387–92. doi: 10.25259/JLP_25_2024

  • CrossRef
  • Google Scholar

• 94

  Prezant T. R. Agapian J. V. Bohlman M. C. Bu X. Öztas S. Qiu W.-Q. et al . (1993). Mitochondrial ribosomal RNA mutation associated with both antibiotic–induced and non–syndromic deafness. Nat. Genet.4, 289–294. doi: 10.1038/ng0793-289

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 95

  Pulingam T. Parumasivam T. Gazzali A. M. Sulaiman A. M. Chee J. Y. Lakshmanan M. et al . (2022). Antimicrobial resistance: Prevalence, economic burden, mechanisms of resistance and strategies to overcome. Eur. J. Pharm. Sci.170, 106103. doi: 10.1016/j.ejps.2021.106103

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 96

  Ramakrishna M. S. Jeyamani L. Abimannan G. C. Vajravelu L. K. (2021). Microbial profile and antibiogram pattern analysis of skin and soft tissue infections at a tertiary care center in South India. J. Pure Appl. Microbiol.15, 915–925. doi: 10.22207/jpam.15.2.50

  • CrossRef
  • Google Scholar

• 97

  Ramsamy Y. Hardcastle T. C. Muckart D. J. J. (2017). Surviving sepsis in the intensive care unit: the challenge of antimicrobial resistance and the trauma patient. World J. Surg.41, 1165–1169. doi: 10.1007/s00268-016-3531-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 98

  Ramzan M. Ahmed A. Siddiqui A. J. Khan M. N. Nisa Z. U. Raza A. et al . (2025). Understanding the pattern of resistance in multi-drug resistant clinical isolates of Acinetobacter baumannii based on metabolomics approach. Microbial Pathogenesis200, 107307. doi: 10.1016/j.micpath.2025.107307

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 99

  Rana S. Kaur K. N. Narad P. Walia K. Saeed S. Chandra A. et al . (2024). Knowledge, attitudes and practices of antimicrobial resistance awareness among healthcare workers in India: a systematic review. Front. Public Health12. doi: 10.3389/fpubh.2024.1433430

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 100

  Ravishankar U. P S. Thayanidhi P. (2021). Antimicrobial resistance among uropathogens: surveillance report from south India. Cureus13, e12913. doi: 10.7759/cureus.12913

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 101

  Roberts J. A. Norris R. Paterson D. L. Martin J. H. (2012). Therapeutic drug monitoring of antimicrobials. Br. J. Clin. Pharmacol.73, 27–36. doi: 10.1111/j.1365-2125.2011.04080.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 102

  Ronald A. (2002). The etiology of urinary tract infection: traditional and emerging pathogens. Am. J. Med.113, 14–19. doi: 10.1016/S0002-9343(02)01055-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 103

  Roopashree S. Prathab A. Sandeep T. (2021). Bacteriological profile and antibiotic susceptibility patterns of wound infections in a tertiary care hospital in South India. Indian J. Microbiol. Res.8, 76–85. doi: 10.18231/j.ijmr.2021.016

  • CrossRef
  • Google Scholar

• 104

  Saikeerthana D. Prabha P. Vijayashree V. Krishna G. N. (2021). Overview of Pathogens causing Nosocomial Infections in Various Wards of a Tertiary Health Care Hospital and their Antimicrobial Susceptibility Pattern-A Prospective Surveillance Study. J. Pure Appl. Microbiol.15, 1940–6. doi: 10.22207/JPAM.15.4.15

  • CrossRef
  • Google Scholar

• 105

  Sanchez G. V. (2016). Core elements of outpatient antibiotic stewardship. MMWR. Recommendations Rep.65, 1–12. doi: 10.15585/mmwr.rr6506a1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 106

  Sandeep L. Rajesh R. Krunal D. Ashwini P. Yashpal C. Alap G. et al . (2020). Tackling AMR crisis in India: Changing paradigm. Asian J. Med. Sci.11, 129–137. doi: 10.71152/ajms.v11i6.3940

  • CrossRef
  • Google Scholar

• 107

  Sangale A. Vivek B. Kelkar R. Biswas S. (2021). Microbiology of ventilator-associated pneumonia in a tertiary care cancer hospital. Indian J. Crit. Care Med.25, 421–428. doi: 10.5005/jp-journals-10071-23790

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 108

  Shah S. Rampal R. Thakkar P. Poojary S. Ladi S. (2022). The prevalence and antimicrobial susceptibility pattern of gram-positive pathogens: three-year study at a tertiary care hospital in Mumbai, India. J. Lab. Physicians14, 109–114. doi: 10.1055/s-0041-1731136

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 109

  Sharma S. K. Bhadra S. Sarwat T. Kakru D. K. (2023b). Antibiotic Resistance Rate among Bacterial Pathogens Isolated from Bronchoalveolar Lavage Fluid at a Tertiary Care Center in Western Uttar Pradesh, India. J. Med. Microbiol. Infect. Dis.11, 110–116. doi: 10.61186/JoMMID.11.2.110

  • CrossRef
  • Google Scholar

• 110

  Sharma A. Thakur N. Thakur A. Chauhan A. Babrah H. (2023a). The challenge of antimicrobial resistance in the Indian healthcare system. Cureus15, e42231. doi: 10.7759/cureus.42231

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 111

  Shet A. Sundaresan S. Forsberg B. C. (2015). Pharmacy-based dispensing of antimicrobial agents without prescription in India: appropriateness and cost burden in the private sector. Antimicrobial Resistance Infection Control4, 55. doi: 10.1186/s13756-015-0098-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 112

  Shrivastava A. K. Kumar S. Mohakud N. K. Suar M. Sahu P. S. (2017). Multiple etiologies of infectious diarrhea and concurrent infections in a pediatric outpatient-based screening study in Odisha, India. Gut Pathog.9, 16. doi: 10.1186/s13099-017-0166-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 113

  Singh S. Sharma A. Nag V. L. (2020). Bacterial pathogens from lower respiratory tract infections: A study from Western Rajasthan. J. Family Med. Primary Care9, 1407–1412. doi: 10.4103/jfmpc.jfmpc_994_19

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 114

  Singhal T. Rodrigues C. Soman R. Wattal C. Swaminathan S. Nambi S. et al . (2022). Treatment of MRSA infections in India: Clinical insights from a Delphi analysis. Indian J. Med. Microbiol.40, 35–45. doi: 10.1016/j.ijmmb.2021.11.005

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 115

  Smita M. S. Wavare S. S. Gajul S. Sajjan A. G. (2019). Bacterial profile of urinary tract infections and antibiotic resistance pattern in a tertiary care hospital. Online J. Health Allied Scs18, 6. Available online at: https://www.ojhas.org/issue72/2019-4-6.html.

  • Google Scholar

• 116

  Srivastava R. Agarwal J. Srivastava S. Mishra B. (2014). Role of special pathogenicity versus prevalence theory in pathogenesis of acute cystitis caused by Escherichia coli. J. Med. Microbiol.63, 1038–1043. doi: 10.1099/jmm.0.073270-0

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 117

  Stephens C. López-Nevot M.-Á. Ruiz-Cabello F. Ulzurrun E. Soriano G. Romero-Gómez M. et al . (2013). HLA alleles influence the clinical signature of amoxicillin-clavulanate hepatotoxicity. PloS One8, e68111. doi: 10.1371/journal.pone.0068111

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 118

  Stocco G. Lucafò M. Decorti G. (2020). Pharmacogenomics of antibiotics. Int. J. Mol. Sci.21, 5975. doi: 10.3390/ijms21175975

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 119

  Sundaramurthy G. Eghbalnia H. R. (2015). A probabilistic approach for automated discovery of perturbed genes using expression data from microarray or RNA-Seq. Comput. Biol. Med.67, 29–40. doi: 10.1016/j.compbiomed.2015.07.029

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 120

  Tang K. W. K. Millar B. C. Moore J. E. (2023). Antimicrobial resistance (AMR). Br. J. Biomed. Sci.80. doi: 10.3389/bjbs.2023.11387

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 121

  Tognetti L. Martinelli C. Berti S. Hercogova J. Lotti T. Leoncini F. et al . (2012). Bacterial skin and soft tissue infections: review of the epidemiology, microbiology, aetiopathogenesis and treatment: A collaboration between dermatologists and infectivologists. J. Eur. Acad. Dermatol. Venereology26, 931–941. doi: 10.1111/j.1468-3083.2011.04416.x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 122

  Umesha L. Shivaprasad S. M. Rajiv E. N. Kumar M. M. S. Leelavathy V. Sreedhara C. G. et al . (2018). Acute pyelonephritis: A single-center experience. Indian J. Nephrol.28, 454–461. doi: 10.4103/ijn.IJN_219_16

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 123

  Urban T. J. Nicoletti P. Chalasani N. Serrano J. Stolz A. Daly A. K. et al . (2017). Minocycline hepatotoxicity: Clinical characterization and identification of HLA-B∗35:02 as a risk factor. J. Hepatol.67, 137–144. doi: 10.1016/j.jhep.2017.03.010

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 124

  Vaithiyam V. S. Rastogi N. Ranjan P. Mahishi N. Kapil A. Dwivedi S. N. et al . (2020). Antimicrobial resistance patterns in clinically significant isolates from medical wards of a tertiary care hospital in north India. J. Lab. Physicians12, 196–202. doi: 10.1055/s-0040-1721161

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 125

  Walia K. Ohri V. C. Madhumathi J. Ramasubramanian V. (2019). Policy document on antimicrobial stewardship practices in India. Indian J. Med. Res.149, 180–184. doi: 10.4103/ijmr.IJMR_147_18

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 126

  Wang D. Curtis A. Papp A. C. Koletar S. L. Para M. F. (2012). Polymorphism in glutamate cysteine ligase catalytic subunit (GCLC) is associated with sulfamethoxazole-induced hypersensitivity in HIV/AIDS patients. BMC Med. Genomics5, 32. doi: 10.1186/1755-8794-5-32

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 127

  Wang Z. Li M.-Y. Peng B. Cheng Z.-X. Li H. Peng X.-X. (2016). GC–MS-based metabolome and metabolite regulation in serum-resistant Streptococcus agalactiae. J. Proteome Res.15, 2246–2253. doi: 10.1021/acs.jproteome.6b00215

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 128

  Watkins R. R. (2022). Antibiotic stewardship in the era of precision medicine. JAC-Antimicrobial Resistance4, dlac066. doi: 10.1093/jacamr/dlac066

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 129

  Wattengel B. A. Sellick J. A. Mergenhagen K. A. (2020). Outpatient antimicrobial stewardship: Optimizing patient care via pharmacist led microbiology review. Am. J. Infection Control48, 189–193. doi: 10.1016/j.ajic.2019.07.018

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 130

  Wawruch M. Bozekova L. Krcmery S. Kriska M. (2002). Risks of antibiotic treatment. Bratislavske lekarske listy103, 270–275.

  • Pubmed Abstract
  • Google Scholar

• 131

  Weiner M. Burman W. Luo C.-C. Peloquin C. A. Engle M. Goldberg S. et al . (2007). Effects of rifampin and multidrug resistance gene polymorphism on concentrations of moxifloxacin. Antimicrobial Agents Chemotherapy51, 2861–2866. doi: 10.1128/AAC.01621-06

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 132

  Weiner M. Gelfond J. Johnson-Pais T. L. Engle M. Peloquin C. A. Johnson J. L. et al . (2018). Elevated plasma moxifloxacin concentrations and SLCO1B1 g.– 11187G> A polymorphism in adults with pulmonary tuberculosis. Antimicrobial Agents Chemotherapy62, 10.1128/aac. doi: 10.1128/AAC.01802-17

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 133

  World Health, O (2014). “ Antimicrobial resistance: global report on surveillance,” (Geneva: World Health Organization). Available online at: https://www.who.int/publications/i/item/9789241564748.

  • Google Scholar

• 134

  World Health, O (2021). Ending the neglect to attain the sustainable development goals: a global strategy on water, sanitation and hygiene to combat neglected tropical diseases 2021–2030. Available online at: https://coilink.org/20.500.12592/7703wm (Accessed December 24, 2024).

  • Google Scholar

• 135

  World Health, O (2024a). “ WHO bacterial priority pathogens list 2024: bacterial pathogens of public health importance, to guide research, development, and strategies to prevent and control antimicrobial resistance,” (Geneva: World Health Organization). Available online at: https://www.who.int/publications/i/item/9789240093461.

  • Google Scholar

• 136

  World Health, O (2024b). The Work of the World Health Organization in the African Region (Brazzaville: World Health Organization). Available online at: https://www.afro.who.int/sites/default/files/2024-08/WHO_AnnualReport_0.pdf (Accessed December 24, 2024).

  • Google Scholar

• 137

  Wu J. Yang F. Yang H. Zhang G. Mu K. Feng J. et al . (2021). Prevalence of antibiotic self-medication behavior and related factors among children aged 0 to 5 years. Expert Rev. Anti-infective Ther.19, 1157–1164. doi: 10.1080/14787210.2021.1882303

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 138

  Yang Y. Chen S. Yang F. Zhang L. Alterovitz G. Zhu H. et al . (2017). HLA-B*51:01 is strongly associated with clindamycin-related cutaneous adverse drug reactions. Pharmacogenomics J.17, 501–505. doi: 10.1038/tpj.2016.61

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 139

  Yee S. W. Nguyen A. N. Brown C. Savic R. M. Zhang Y. Castro R. A. et al . (2013). Reduced renal clearance of cefotaxime in asians with a low-frequency polymorphism of OAT3 (SLC22A8). J. Pharm. Sci.102, 3451–3457. doi: 10.1002/jps.23581

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 140

  Zhao X.-L. Chen Z.-G. Yang T.-C. Jiang M. Wang J. Cheng Z.-X. et al . (2021). Glutamine promotes antibiotic uptake to kill multidrug-resistant uropathogenic bacteria. Sci. Trans. Med.13, eabj0716. doi: 10.1126/scitranslmed.abj0716

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 141

  Zhou Y. Yong Y. Zhu C. Yang H. Fang B. (2022). Exogenous D-ribose promotes gentamicin treatment of several drug-resistant Salmonella. Front. Microbiol.13. doi: 10.3389/fmicb.2022.1053330

  • Pubmed Abstract
  • CrossRef
  • Google Scholar