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Machine learning models were developed and externally validated to predict whether dalbavancin plasma levels stay above PK/PD targets after two standard 1,500 mg doses (day 1/day 8 or day 1/day 15). Predictions were binary for adequacy versus subtherapeutic concentrations, directly supporting individualized redosing decisions.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/predicting-prolonged-dalbavancin-exposure-using-machine-learning-a-validated-strategy-for-individualized-redosing/455935/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/predicting-prolonged-dalbavancin-exposure-using-machine-learning-a-validated-strategy-for-individualized-redosing/455935.png","ImageObject",300,407,{"name":92,"@type":93},"Quinn Holloway","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What clinical problem does the study address for dalbavancin dosing?","Question",{"text":112,"@type":113},"It addresses the uncertainty in determining whether dalbavancin plasma concentrations remain above PK/PD targets after standard dosing, given interindividual PK variability and pathogen MIC heterogeneity.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How did the machine learning models define their predictions?",{"text":117,"@type":113},"Predictions were binary (adequate vs subtherapeutic concentration) and correspond to the clinical decision to readminister a 1,500 mg dose.",{"name":119,"@type":110,"acceptedAnswer":120},"What inputs were used for the model-based individualized redosing decision?",{"text":121,"@type":113},"Inputs included age, body weight, creatinine clearance, MIC, and a single plasma concentration measured before the second dose.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},455935,1790906777,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},2336474466712,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","| Pharmacology | Full-Length Text  \nPredicting prolonged dalbavancin exposure using machine learning: a validated strategy for individualized redosing  \nHamza Sayadi,1,2 Matthieu Gregoire,3,4 Yeleen Fromage,1 Mohamed Ksentini,1 Marc Labriffe,1,2 Caroline Monchaud,1,2 Cyrielle Codde,2,5 Jean-François Faucher,2,5 David Boutoille,6 Pierre Marquet,1,2 Jean-Baptiste Woillard1,2  \nAUTHOR AFFILIATIONS See affiliation list on p. 12.  \nABSTRACT Dalbavancin is a long-acting lipoglycopeptide increasingly used off-label for complex Gram-positive infections requiring prolonged therapy. Its extended halflife enables simplified regimens, but interindividual pharmacokinetic variability and pathogen MIC heterogeneity complicate dosing. We developed and externally validated machine learning (ML) models to predict whether dalbavancin plasma concentrations remain above predefined pharmacokinetic/pharmacodynamic targets after two standard 1,500 mg doses (day 1/day 8 or day 1/day 15) . Predictions were binary (adequate vs subtherapeutic concentration), directly reflecting the clinical decision to readminister a 1,500 mg dose. Models were trained on simulated PK profiles from a published population PK (popPK) model and evaluated in three independent settings: (i) simulated validation data sets from two alternative published popPK models, (ii) a real-world cohort from Limoges University Hospital (n = 31), and (iii) a secondary cohort from Nantes University Hospital (n = 7) . Input features included age, body weight, creatinine clearance, MIC, and a single plasma concentration obtained before the second dose. Support vector machine models achieved high accuracy (>88%) and sensitivity (>90%) across testing sets and clinical validation cohorts. In clinical data sets, no false negatives were observed (limited by sample size), with overall accuracy approaching 95% . Compared with maximum a posteriori Bayesian estimation, ML achieved higher accuracy and sensitivity across validation cohorts, particularly by reducing false negatives. Predictions remained reliable through week 8, the clinically relevant exposure window. This ML-based approach enables early individualized redosing decisions using minimal clinical inputs. By complementing Bayesian forecasting and reducing reliance on serial sampling, it represents a pragmatic strategy to support model-informed precision dosing of dalbavancin.  \nKEYWORDS Monte Carlo simulations, population pharmacokinetics, therapeutic drug monitoring, model-informed precision dosing, dalbavancine, machine learning  \nD albavancin is a semisynthetic lipoglycopeptide antibiotic derived from teicoplanin  \n(1) . Initially developed to simplify the treatment of skin and soft tissue infections, particularly those caused by methicillin-resistant Staphylococcus aureus (MRSA), dalbavancin was approved by the U.S. Food and Drug Administration in 2014 and by the European Medicines Agency in 2015 (2, 3) . The pivotal DISCOVER 1 and 2 trials demonstrated its non-inferiority to standard vancomycin–linezolid therapy, and these findings have since been corroborated by multiple real-world studies (4–8) .  \nDalbavancin’s prolonged half-life (~15 days) enables simplified dosing either as two injections (1,000 mg on day 1 and 500 mg on day 8) or as a single 1,500 mg dose. By reducing the need for prolonged intravenous therapy, dalbavancin can shorten hospital stays and mitigate adherence issues associated with extended oral regimens (2, 3, 5, 9, 10) . It exhibits broad bactericidal activity against Gram-positive pathogens, notably  \nEditor James E. Leggett, Providence Portland Medical Center, Portland, Oregon, USA  \nAddress correspondence to Jean-Baptiste Woillard, [jean-baptiste.woillard@unilim.fr](jean-baptiste.woillard@unilim.fr).  \nauthors declare conflict of interest  \nMRSA, vancomycin-intermediate Staphylococcus aureus, multidrug-resistant Streptococcus pneumoniae, and vancomycin-susceptible Enterococcus spp. (10–13) . Additional advantages,","cbCaig3yTL7DDPKS","https://ap.wps.com/l/cbCaig3yTL7DDPKS","pdf",425827,15,"English","# Abstract\n## Background and clinical challenge\n## Model development and validation approach\n## Performance in simulated and real-world cohorts\n## Clinical implications for individualized redosing","[{\"question\":\"What clinical problem does the study address for dalbavancin dosing?\",\"answer\":\"It addresses the uncertainty in determining whether dalbavancin plasma concentrations remain above PK/PD targets after standard dosing, given interindividual PK variability and pathogen MIC heterogeneity.\"},{\"question\":\"How did the machine learning models define their predictions?\",\"answer\":\"Predictions were binary (adequate vs subtherapeutic concentration) and correspond to the clinical decision to readminister a 1,500 mg dose.\"},{\"question\":\"What inputs were used for the model-based individualized redosing decision?\",\"answer\":\"Inputs included age, body weight, creatinine clearance, MIC, and a single plasma concentration measured before the second dose.\"}]","Predicting prolonged dalbavancin exposure using machine learning - a validated strategy for individualized redosing | PDF",1790744722,38]