Pharmacotherapy最新文献

{"title":"Bayesian Dosing Simulator (BDS): A Pharmacokinetic Modeling Tool for Optimized Antibiotic Therapy.","authors":"Adrian Valadez, Marc H Scheetz, Michael N Neely, Helen K Donnelly, Erin Korth, Richard G Wunderink, Nathaniel J Rhodes","doi":"10.1002/phar.70148","DOIUrl":"10.1002/phar.70148","url":null,"abstract":"<p><strong>Background: </strong>Meropenem is widely used to treat hospital-acquired pneumonia in critically ill patients, with efficacy dependent on the time that free concentrations exceed the minimum inhibitory concentration (fT > MIC). In practice, single-sample therapeutic drug monitoring (TDM) may not ensure target attainment, particularly in patients requiring continuous renal replacement (CRRT). Model-informed precision dosing (MIPD) enables individualized, real-time adjustments, but implementation is limited. Herein, we developed a Shiny application for Bayesian meropenem dose optimization.</p><p><strong>Methods: </strong>A previously published nonparametric model was translated into a parametric Bayesian framework with maximum a posteriori (MAP) updating implemented through a Shiny interface. Plasma pharmacokinetic (PK) data from critically ill patients served for external validation. Posterior predictions were benchmarked against population and individual (MAP) outputs from Pmetrics 3.09 and population median profiles from 1000 Monte Carlo simulations per patient. Predictive performance was assessed using relative median prediction error (rMPE) and relative median absolute prediction error (rMAPE), for the combined cohort overall and stratified by CRRT status. Sparse sampling scenarios (one, two, and three observations per patient) were also evaluated.</p><p><strong>Results: </strong>Eighteen patients were evaluated (non-CRRT n = 13; CRRT n = 5). In Pmetrics, rMPE ranged from -7.7% to 9.4% and rMAPE from 18.5% to 25.5% across renal replacement strata and prediction types. Simulation-based population predictions yielded greater error, with rMPE/rMAPE of -25%/34% in CRRT patients and -21%/30% in non-CRRT patients. Across Bayesian Dosing Simulator (BDS) sampling scenarios, prediction error remained within prespecified limits (rMPE ±20%, rMAPE ≤ 30%), with F20 ranging from 80% to 94% and F30 from 87% to 94%.</p><p><strong>Conclusion: </strong>A validated nonparametric meropenem model was successfully implemented within an open-source Bayesian framework yielding predictive accuracy comparable to the reference model with robust performance under sparse sampling, supporting its feasibility for individualized meropenem dosing. Prospective evaluation of its clinical safety and effectiveness is needed.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":"46 5","pages":"e70148"},"PeriodicalIF":3.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147778373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cohort Study to Determine the Impact of CYP3A5 Genotype on Tacrolimus Dosing Requirements and Trough Concentrations in Heart Transplant Recipients.","authors":"Yanting Wu, Michael T Eadon, Laurie Schenkelberg, Roopa A Rao, Todd C Skaar, Emma M Tillman, Tyler Shugg","doi":"10.1002/phar.70116","DOIUrl":"10.1002/phar.70116","url":null,"abstract":"<p><strong>Background: </strong>Tacrolimus is primarily metabolized by Cytochrome P450 (CYP)3A4/5. The Clinical Pharmacogenetics Implementation Consortium recommends increasing the initial dose 1.5- to 2-fold in CYP3A5 expressers to enhance transplant outcomes. Our objective was to investigate the impact of CYP3A5 expresser status on tacrolimus dosing requirements and attainment of target trough concentrations in heart transplant recipients.</p><p><strong>Methods: </strong>We performed a retrospective cohort analysis of tacrolimus dose, concentration, demographics, CYP3A4/5 genotype, concomitant medications, and biochemical data in heart transplant recipients from December 2020 to August 2023. The primary outcome was the time to first therapeutic trough concentration, compared by CYP3A5 expression status. Secondary outcomes included the tacrolimus dose at target trough and dose-adjusted tacrolimus trough concentration (C₀/D). Stepwise multiple regression was performed to account for potential covariates. Moreover, clinical outcomes were assessed at 1-year post-transplantation and compared based on CYP3A5 expression status.</p><p><strong>Results: </strong>Among 33 patients, CYP3A5 expressers (27.3%) required longer to achieve therapeutic trough concentrations (median [Q1, Q3]: expressers: 14 [9.5, 16] days vs. nonexpressers 7.5 [6.0, 11] days; p = 0.0073) and required nearly double the tacrolimus dose to reach target concentrations (10 [5.5, 13] mg/day for expressers vs. 5 [3.3, 5.9] mg/day for nonexpressers; p = 0.0019). Conversely, the C₀/D was nearly 2-fold higher in nonexpressers 2.0 [1.6, 3.4] ng/(mL*mg) than expressers (1.1 [0.83, 1.7] ng/(mL*mg); p = 0.0015). Stepwise regression identified route of administration (sublingual vs. oral) at therapeutic trough and initial dose as covariates for all outcomes. All clinical outcomes showed no significant differences based on CYP3A5 expression status, with the exception that poor metabolizers demonstrated higher serum creatinine elevation at 1-week post-transplantation (p = 0.047).</p><p><strong>Conclusion: </strong>Our findings highlight the impact of CYP3A5 expresser status on the time needed and dosing requirements to attain tacrolimus therapeutic concentrations in heart transplant recipients, suggesting CYP3A5-guided dosing strategies may improve rapid attainment of therapeutic tacrolimus concentrations.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":"46 3","pages":"e70116"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12884575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146143160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pharmacokinetic and Pharmacodynamic Principles and Concepts Remain Relevant in the Era of Artificial Intelligence and Machine Learning.","authors":"Keith A Rodvold","doi":"10.1002/phar.70097","DOIUrl":"10.1002/phar.70097","url":null,"abstract":"","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70097"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145952771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing the Power of Artificial Intelligence to Enhance Drug Therapy Research.","authors":"William L Baker, Alexandre Chan","doi":"10.1002/phar.70105","DOIUrl":"10.1002/phar.70105","url":null,"abstract":"","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70105"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145966520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI in ID Pharmacotherapy-We Should Not Be Afraid to Put the Car Before the Horse.","authors":"Marc H Scheetz","doi":"10.1002/phar.70103","DOIUrl":"10.1002/phar.70103","url":null,"abstract":"","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70103"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145952720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence Large Language Model-Influenced Bias on Trainees and Patients in Pharmacotherapeutic Decision Making.","authors":"Andrew Chantha Hean, Youngil Chang","doi":"10.1002/phar.70100","DOIUrl":"10.1002/phar.70100","url":null,"abstract":"","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70100"},"PeriodicalIF":3.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145952722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of Artificial Intelligence (AI)-Powered Self-Care Recommendations for Management of Minor Ailments: A Comparative Analysis.","authors":"Sham ZainAlAbdin, Saba Kendakji, Fatema Alrahbi, Kholoud Alazeezi, Maha Jawas, Shamma Alshamsi, Thuraya Almessabi, Nazar Zaki, Salahdein Aburuz","doi":"10.1002/phar.70089","DOIUrl":"10.1002/phar.70089","url":null,"abstract":"<p><strong>Introduction: </strong>Self-care and self-medication are increasingly viewed as helpful approaches to managing minor ailments; however, patients are often not confident in making informed choices. Pharmacists have traditionally assisted patients in this domain, but the emergence of digital health technologies has transformed the way individuals seek information towards the use of artificial intelligence (AI) tools. ChatGPT-4o mini, Gemini, and Copilot are recently growing popular for health-related guidance. Despite the accessibility and ease of use that these AI tools offer, their accuracy, patient-centeredness, and reliability in supporting self-care remain insufficiently evaluated.</p><p><strong>Aims and objectives: </strong>The primary objective of this study is to evaluate and compare the performance of ChatGPT-4o mini, Gemini, and Copilot in the context of patient self-care by assessing the accuracy, patient-centeredness, and comprehensiveness of their responses against standard recommendations.</p><p><strong>Materials and methods: </strong>Ninety-one case scenarios representing the most common minor ailments were introduced to the three AI models to generate responses that were subsequently assessed and compared with established standard recommendations by three of the study investigators. Evaluation of the responses was conducted on their accuracy, patient-centeredness, comprehensiveness, and similarity. An inter-reliability test was also carried out to confirm the consistency between the three evaluators' assessments.</p><p><strong>Results: </strong>The study findings indicate that ChatGPT-4o mini significantly exceeded Gemini and Copilot in terms of accuracy and presented as mean ± SD (ChatGPT-4o mini: 4.4 ± 0.6, Gemini: 4.1 ± 0.8, Copilot: 3.7 ± 0.7, p < 0.001), patient-centeredness (ChatGPT-4o mini: 4.7 ± 0.6, Gemini: 4.3 ± 1.0, Copilot: 4.2 ± 0.8, p < 0.001), and comprehensiveness (ChatGPT-4o mini: 4.6 ± 0.7, Gemini: 4.2 ± 0.8, Copilot: 3.4 ± 0.7; p < 0.001) among 91 minor ailment case scenarios. Gemini and Copilot showed moderate and low performance, respectively, particularly in complex cases, in contrast to ChatGPT-4o mini. Inter-rater reliability was excellent (Cronbach's alpha ≥ 0.9), confirming assessment consistency. Cosine similarity analysis indicated high overlap between AI and standard recommendations.</p><p><strong>Conclusion: </strong>This study shows that AI tools are reliable and precise instruments for self-care of mild diseases. These findings highlight ChatGPT-4o mini's superior reliability and patient-centeredness for self-medication guidance, while underscoring the need for human oversight. However, there is a small chance of variation and errors in the AI-generated responses, which may prohibit complete dependence on AI for self-care recommendations.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70089"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145669336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning-Based Prediction of Prolonged Duration of Mechanical Ventilation Using Medication Data.","authors":"Brian Murray, Bokai Zhao, Zhetao Chen, Susan E Smith, Yanlei Kong, Ye Shen, Sheng Li, Xianyan Chen, Andrea Sikora","doi":"10.1002/phar.70090","DOIUrl":"10.1002/phar.70090","url":null,"abstract":"<p><strong>Introduction: </strong>Prediction algorithms for prolonged mechanical ventilation (PMV) in the intensive care unit (ICU) have rarely incorporated detailed medication data, despite medications being important causal contributors to patient outcomes. The purpose of this study was to develop and validate PMV prediction models to assess the contribution of medication-related variables alongside established physiologic predictors.</p><p><strong>Methods: </strong>In this retrospective cohort study, models were developed using data from a random sample of 318 adults admitted to ICUs within the University of North Carolina (UNC) health system who received mechanical ventilation for ≥ 24 h from October 2015 to October 2020. Validation was performed in two datasets: a temporally distinct cohort from UNC from June 2021 to June 2023, and a cohort from Oregon Health Sciences University from June 2020 to June 2023. Logistic regression and supervised, classification-based machine learning (ML) models [XGBoost, Random Forest, Support Vector Machine (SVM)] were trained on 30 demographic, clinical, laboratory, and medication-related variables. The primary outcome was area under the receiver operating characteristic (AUROC) of developed prediction models for the occurrence of PMV.</p><p><strong>Results: </strong>The base logistic regression model with medication regimen complexity and severity of illness data added was the best-performing regression model, achieving an AUROC of 0.75. Random Forest and SVM ML models achieved AUROCs of 0.78. Model discrimination decreased modestly in external validation. Explainability analyses of ML models expectedly included severity of illness scores and respiratory indices among the most important features, but also consistently included the medication regimen complexity-intensive care unit (MRC-ICU) score and other medication metrics. Incorporation of medication data yielded modest improvements in overall discrimination and negative predictive value.</p><p><strong>Conclusions: </strong>Medication-related variables contributed incremental value to PMV prediction. ML methods provided marginal improvements over regression models. These findings highlight the potential value of medication data in prediction modeling for patient outcomes but emphasize the need to contextualize the value of complex models over simpler alternatives.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70090"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145912636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"C-Reactive Protein and Neutrophil-To-Lymphocyte Ratio: Can They Be Used Interchangeably in Tracking Clozapine-Related Inflammation?","authors":"Nicoline Bihelek, Chad A Bousman, William G Honer, Reza Rafizadeh","doi":"10.1002/phar.70094","DOIUrl":"10.1002/phar.70094","url":null,"abstract":"<p><strong>Background: </strong>Clozapine initiation often triggers inflammatory responses that can alter metabolism via Cytochrome P450 1A2 (CYP1A2) suppression. Although C-reactive protein (CRP) is the recommended marker, it may be unavailable in community settings. Neutrophil-to-lymphocyte ratio (NLR), routinely measured, could serve as a surrogate, though its value in detecting clozapine-related inflammation and metabolic changes remains unclear.</p><p><strong>Aims: </strong>This study aimed to assess the relationship between CRP and NLR in individuals treated with clozapine, evaluate whether NLR can act as a proxy for elevated CRP (> 5 mg/L), and determine whether NLR, like CRP, explains variability in clozapine metabolism (concentration to dose (C/D) ratios) after adjusting for covariates.</p><p><strong>Methods: </strong>We performed a retrospective cohort study of clozapine-treated inpatients at the British Columbia Psychosis Program (2012-2021). Patients with clozapine levels and matched complete blood counts (CBCs) (±7 days) were included, with CRP added when available. Multivariate mixed models assessed associations between CRP, NLR, and clozapine C/D ratios, while receiver operating characteristic (ROC) analyses evaluated NLR as a proxy for elevated CRP.</p><p><strong>Results: </strong>Among 150 patients, 760 clozapine serum/CBC pairs and 212 CRP measurements met eligibility criteria. NLR was modestly associated with CRP (estimate = 0.027, p < 0.001). ROC analysis indicated that NLR had limited predictive utility, with an area under the curve (AUC) of 0.640 for detecting CRP > 5 mg/L. Subsequent analyses for higher CRP thresholds (> 10 and > 20 mg/L) produced comparable NLR AUC values of 0.621 and 0.669, respectively. Neutrophil count alone demonstrated marginally better performance but remained similarly limited in predictive value. In multivariate models, CRP but not NLR, was independently associated with clozapine C/D ratios.</p><p><strong>Conclusion: </strong>Our findings indicate that although NLR and other hematological indices are easily accessible and may provide some indication of inflammation, they cannot substitute for CRP in guiding clozapine titration decisions. Where CRP is unavailable, NLR > 3 may be cautiously informative, though CRP remains the preferred marker for early detection and dose adjustment to optimize tolerability, adherence, and safety during clozapine initiation.</p>","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70094"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12862047/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145834617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to \"Pharmacokinetics of Ceftolozane/Tazobactam in Patients With Partial-and Full-Thickness Skin Burns\".","authors":"","doi":"10.1002/phar.70091","DOIUrl":"10.1002/phar.70091","url":null,"abstract":"","PeriodicalId":20013,"journal":{"name":"Pharmacotherapy","volume":" ","pages":"e70091"},"PeriodicalIF":3.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145805129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}