{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T15:08:27Z","timestamp":1791558507996,"version":"4.3.4"},"reference-count":259,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"\n Background<\/jats:title>\n The global rise of multidrug-resistant (MDR) infections has intensified the clinical reliance on last-resort antibiotics such as vancomycin, linezolid, and tigecycline, particularly in critically ill patients. These agents possess narrow therapeutic windows, complex pharmacokinetics, and substantial inter-patient variability, making therapeutic drug monitoring (TDM) a cornerstone of individualized therapy. Traditional TDM methodologies though accurate are often limited by centralized processing, slow turnaround times, and cost constraints, which hinder real-time clinical decision-making in intensive care settings.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This review aims to critically evaluate emerging bioanalytical platforms, modeling frameworks, and decision-support systems for optimizing TDM of last-resort antibiotics. It focuses on enhancing precision dosing, improving clinical outcomes, and addressing antimicrobial resistance through integration of innovative sensor technologies and artificial intelligence.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A comprehensive literature search was conducted across databases, including PubMed, Google Scholar, Scopus, and Nature. Relevant studies were analyzed for analytical techniques, matrix types, extraction strategies, assay validation parameters, and clinical applicability. Emphasis was placed on comparing high-performance liquid chromatography (HPLC), LC\u2013MS\/MS, and immunoassays with novel approaches such as microneedle-based biosensors, real-time urinary antibiotic monitoring, wearable devices, and luciferase-based bioluminescent sensors. Mechanism-based pharmacokinetic\/pharmacodynamic (PK\/PD) modelling and Bayesian forecasting frameworks were reviewed for their role in adaptive dosing.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n LC\u2013MS\/MS emerged as the most sensitive and specific platform, while immunoassays provided practical solutions for near-patient testing. Innovations such as microsampling, temperature-responsive two-dimensional chromatography, and bioluminescent sensor platforms demonstrated potential to overcome the limitations of conventional assays. Integration of population pharmacokinetic (PPK) models and AI-driven decision-support algorithms enhanced predictive precision, allowing dynamic dose optimization for \u03b2-lactam antibiotics, tetracyclines, vancomycin, linezolid, and tigecycline. A three-tiered TDM model was proposed, combining site-specific sensing, real-time analysis, and computational forecasting to improve antimicrobial stewardship.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n Emerging bioanalytical technologies and predictive PK\/PD modeling are transforming TDM from a static laboratory tool into a real-time, precision-guided clinical decision platform. The integration of minimally invasive sensing technologies with AI-enabled dose optimization offers a path toward personalized antibiotic therapy, optimized clinical outcomes, and the mitigation of antimicrobial resistance in critical care settings. This paradigm shift supports a more adaptive and responsive approach to TDM, ensuring last-resort antibiotics are used effectively and sustainably.<\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fphar.2026.1761582","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:19:56Z","timestamp":1783570796000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Next-generation hybrid bioanalytical platforms and clinical integration of TDM technologies for precision monitoring to optimize last-resort antibiotic therapy"],"prefix":"10.3389","volume":"17","author":[{"given":"Aparna","family":"Inamdar","sequence":"first","affiliation":[{"name":"Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research (JSS AHER)","place":["Mysuru, Karnataka, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Narasimha M.","family":"Beeraka","sequence":"additional","affiliation":[{"name":"Department of Human Anatomy and Histology, I.M. Sechenov First Moscow State Medical University (Sechenov University)","place":["Moscow, Russia"]},{"name":"Saveetha Institute of Basic Medical Sciences (SIBMS), Saveetha Institute of Medical and Technical Sciences, Saveetha University","place":["Chennai, Tamil Nadu, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P. R.","family":"Hemanth Vikram","sequence":"additional","affiliation":[{"name":"School of Medical Science and Technology, Indian Institute of Technology Kharagpur","place":["Kharagpur, West Bengal, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bannimath","family":"Gurupadayya","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research (JSS AHER)","place":["Mysuru, Karnataka, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. 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