CPT: Pharmacometrics & Systems Pharmacology最新文献

{"title":"Correction to “Development of an Agent-Based Model to Investigate Durability of Factor IX Activity in Hemophilia B Patients Treated With Etranacogene Dezaparvovec”","authors":"","doi":"10.1002/psp4.70312","DOIUrl":"10.1002/psp4.70312","url":null,"abstract":"<p>\u0000 <span>Li, Y</span>, <span>Nandy, P</span>, <span>Jordie, E</span>, et al., “ <span>Development of an Agent-Based Model to Investigate Durability of Factor IX Activity in Hemophilia B Patients Treated With Etranacogene Dezaparvovec</span>.” <i>CPT: Pharmacometrics and Systems Pharmacology</i> <span>2026</span>; <span>15</span>(<span>7</span>):e70286. https://doi.org/10.1002/psp4.70286.\u0000 </p><p>In the first paragraph of the “3.2 Long-Term Predictions and Drivers of Durability” section, the text “The model-predicted mean FIX activity at Year 5 was 23.5% (95% CI 4.92%, 72.2%), overlapping with the reported range of 31.6% ± 15.7% (mean ± standard deviation) [31].” was incorrect.</p><p>This should have read: “The model-predicted mean FIX activity at Year 5 was 27.7% (95% CI 4.92%, 72.2%), overlapping with the reported range of 31.6% ± 15.7% (mean ± standard deviation) [31].”</p><p>We apologize for this error.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70312","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148599760","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":"DDIapp—A Web-Based Application for Static Drug–Drug Interaction Assessment","authors":"Hirotaka Watase,&nbsp;Sylvie Klieber,&nbsp;Yorgos M. Psarellis,&nbsp;Nikhil Pillai,&nbsp;Panteleimon D. Mavroudis,&nbsp;Saroj Dhakal","doi":"10.1002/psp4.70300","DOIUrl":"10.1002/psp4.70300","url":null,"abstract":"<p>In this work we describe the development of a web-based application for static drug–drug interaction (DDI) risk assessment in accordance with the International Council for Harmonization (ICH) M12 guidance. The app was built using the Shiny for Python framework and it employs a modular mathematical modeling structure that incorporates models for the assessment of enzyme inhibition, enzyme induction, and transporter inhibition at intestinal, hepatic, and renal levels. In addition, a “net effect” model estimates the combined impact of inhibition and induction on victim drug exposure, expressed as the area under the curve ratio (<i>AUCR</i>). Matrix-specific approaches for estimating unbound fractions in microsomes and hepatocytes (<i>f</i>_u,mic and <i>f</i>_u,hep), as indicated in regulatory alignment, are implemented. The application provides a dynamic interface that supports flexible parameter input, real-time evaluation across multiple DDI scenarios, and automated risk categorization using predefined thresholds based on the ICH M12 guidelines, with visual cues to aid risk interpretation. Additional features include integrated equation display for transparency and interpretation, export capabilities to PDF and Excel formats and a glossary with links to guidance documents and resources from major regulatory authorities (FDA, EMA, PMDA and NMPA). This DDIapp is validated against Certara's drug–drug interaction calculator under ICH M12 evaluation conditions. DDIapp offers a user-friendly platform for assessing the risk of pharmacokinetic drug interactions as a perpetrator involving metabolic enzymes and transporters, supporting both scientific research and regulatory decision-making.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13401703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148590905","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":"Physiologically Based Pharmacokinetic Model of Brigatinib in Healthy Volunteers and Patients With Cancer","authors":"Yichao Xu,&nbsp;Xinhua Hu,&nbsp;Pengfei Zhao,&nbsp;Lu Wang,&nbsp;Zourong Ruan,&nbsp;Bo Jiang,&nbsp;Honggang Lou","doi":"10.1002/psp4.70302","DOIUrl":"https://doi.org/10.1002/psp4.70302","url":null,"abstract":"<p>Brigatinib, an oral ALK inhibitor for metastatic NSCLC, lacks dosing guidance for special populations such as the Chinese. This study developed a physiologically based pharmacokinetic (PBPK) model using European data from patients with hepatic/renal impairment and drug–drug interaction (DDI) studies (itraconazole, rifampin). The model was then applied to predict (1) pharmacokinetics (PK) in the Chinese population; (2) PK in Chinese patients with hepatic/renal impairment; and (3) DDI in Chinese patients. Validated against clinical data, the model successfully predicted brigatinib PK alone and with CYP3A4 modulators. Food simulations showed a slight absorption delay without clinically meaningful exposure reduction. In hepatic/renal impairment, the model accurately predicted exposure changes across severity groups (fold error &lt; 2). Extending the European-validated model to Chinese populations, the findings demonstrate reliable predictions of brigatinib PK in Chinese individuals as well as in Chinese patients with hepatic or renal impairment.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70302","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148533910","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":"Using Stochastic Simulation-Estimation and Automated Model Development to Assess Power and Accuracy for Covariate Identification","authors":"Ya-Han Hsu,&nbsp;Bárbara Costa,&nbsp;Nuno Vale,&nbsp;Thomas P. C. Dorlo,&nbsp;Mats O. Karlsson","doi":"10.1002/psp4.70299","DOIUrl":"https://doi.org/10.1002/psp4.70299","url":null,"abstract":"<p>When study designs are evaluated using clinical trial simulations for their ability to identify covariate effects in population pharmacokinetic (PopPK) modeling, it is typically assumed that the true model will be known at the data analysis stage. In this study, this was compared with the more realistic assumption that the PopPK model needs to be built on the data generated by the planned study. Three approaches were compared: (i) stochastic simulation and re-estimation (SSE) with the simulation model, (ii) automated model development (AMD) with exploratory covariate search (AMD-exploratory), and (iii) AMD forcing the covariate effect into the model from the start and reevaluating it in the end (AMD-structural). With a simulated covariate effect (a hypothetical pregnancy effect on clearance), we assessed (i) the type 1 error (T1E) and the power of covariate identification and (ii) covariate parameter accuracy. The T1E rate was controlled in SSE and AMD-exploratory but 20% inflated for AMD-structural. The power of covariate identification in rich, medium, and sparse designs was (i) 99%, 100%, and 79% in SSE, (ii) 74%, 72%, and 41% in AMD-exploratory, and (iii) 92%, 93%, and 80% in AMD-structural. Sparse designs amplified power differences between strategies, with AMD-exploratory often selecting alternative or no covariates. The rRMSE of covariate parameter estimates was lowest in SSE (27%, 22%, and 42%), followed by AMD-exploratory (34%, 26%, and 49%) and then AMD-structural (42%, 47%, and 60%). SSE provides optimistic power estimates as model building is data-driven, while AMD-based approaches incorporate model uncertainty and reflect real-world analysis conditions.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70299","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148533907","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":"Application of Population-Based Meta-Heuristic Algorithms for Robust Initialization in Mechanistic Pharmacometric Modeling","authors":"Dongjin Lee,&nbsp;Chih-Wei Lin","doi":"10.1002/psp4.70301","DOIUrl":"https://doi.org/10.1002/psp4.70301","url":null,"abstract":"<p>Mechanistic PK/PD models represent complex biological systems in which model parameters are in nonlinear, multidimensional parameter spaces and often require robust optimization informed by experimental data, making parameter estimation a critical yet challenging aspect of pharmacometric analysis. Moreover, optimization methods for nonlinear-mixed effects PK/PD modeling are highly sensitive to initial parameter values, and poor initial estimates frequently result in convergence failure or suboptimal fits. To address the challenges, this study introduces a systematic framework that integrates population-based meta-heuristic algorithms to identify optimal or near-optimal initial estimates that facilitate subsequent nonlinear mixed-effects modeling. Nineteen distinct population-based meta-heuristic algorithms, including evolutionary, swarm-based, and bio-inspired methods, were evaluated on two mechanistic ODE-based models: (1) a two-compartment pharmacokinetic model with linear and Michaelis–Menten elimination and (2) the Friberg myelosuppression model. The temporal evolution of model goodness-of-fit over iterations was evaluated for each algorithm, and the effects of population size and iteration number on the algorithm performance were also examined. The results demonstrate population-based optimization algorithms efficiently and autonomously refine parameter estimates over iterations. Jellyfish search optimizer, symbiotic organisms search, and memetic algorithm showed strong performance compared to others with respect to optimization accuracy and computational efficiency across both models. As expected, increasing population size and iteration number enhanced the optimization performance. Overall, population-based meta-heuristic algorithms demonstrate superior capability in navigating nonlinear, high-dimensional parameter spaces, generating robust initial estimates for subsequent modeling workflows, and enhancing convergence stability, computational efficiency, and parameter identifiability.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70301","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148533996","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":"Extrapolating Vincristine-Induced Peripheral Neuropathy From Caucasian to Kenyan Populations: Impact of Type I and Type II Selection Bias","authors":"Maddalena Centanni,&nbsp;Mirjam Esther van de Velde,&nbsp;Alwin D. R. Huitema,&nbsp;Gertjan L. Kaspers,&nbsp;Mats O. Karlsson,&nbsp;Lena E. Friberg","doi":"10.1002/psp4.70303","DOIUrl":"10.1002/psp4.70303","url":null,"abstract":"<p>Vincristine is a cornerstone of pediatric chemotherapy, but its use is limited by vincristine-induced peripheral neuropathy (VIPN). Paradoxically, African children tolerate higher vincristine doses with minimal neurotoxicity, raising questions about the pharmacological mechanisms and model generalizability across populations. In this study, we re-estimated vincristine pharmacokinetic (PK) and PK-pharmacodynamic (PKPD) models using data from both Dutch and Kenyan pediatric cohorts and designed five simulation scenarios to assess the impact of Type I (informative censoring) and Type II (population-specific effect modifier) selection bias on model predictions. A three-compartment PK model with saturable distribution and a proportional-odds PKPD model with Markov elements jointly described vincristine disposition and VIPN risk. Kenyan children showed lower clearance but markedly reduced PD sensitivity, resulting in negligible predicted VIPN even at higher doses. Incorporating informative censoring improved internal validity by capturing the observed dropout dynamics, while mechanistic extrapolation using CYP3A5, ABCB1, and CEP72 genotype distributions increased external validity and aligned model predictions better with empirical outcomes. Clinically, these findings support population-specific vincristine dosing strategies and suggest that reduced neurotoxicity in African children reflects lower neuronal susceptibility rather than reduced systemic (plasma) exposure. Methodologically, this work demonstrates how unrecognized selection bias can distort PKPD extrapolations across populations and highlights the value of integrating mechanistic and genetic information within model-based frameworks to improve the safety and external validity of model-informed dosing strategies beyond the original study populations.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70303","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148520120","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":"A Physiologically Based Pharmacokinetic Model to Predict Potential Drug–Drug Interactions of TPN171, a Novel Phosphodiesterase Type 5 Inhibitor","authors":"Guanghui Tian,&nbsp;Tao Chen,&nbsp;Lei Li,&nbsp;Zhiqiang Wang,&nbsp;Hao Yang,&nbsp;Jiaxiang Juan,&nbsp;Yuanqin Duan,&nbsp;Jianghong Pei,&nbsp;Jingying Jia","doi":"10.1002/psp4.70298","DOIUrl":"10.1002/psp4.70298","url":null,"abstract":"<p>TPN171 is a potent phosphodiesterase type 5 inhibitor used to treat pulmonary arterial hypertension and erectile dysfunction, primarily metabolized by CYP3A4. This study aimed to evaluate its drug–drug interaction (DDI) potential and determine the optimal dosing when co-administered with CYP3A4 modulators. A physiologically based pharmacokinetic (PBPK) model was developed and validated using clinical DDI data for itraconazole (strong CYP3A4 inhibitor) and rifampin (strong CYP3A4 inducer). The model was then applied to predict DDIs with moderate (diltiazem, fluconazole) and mild (fluvoxamine) inhibitors, as well as moderate (efavirenz) and mild (zanubrutinib) inducers. Predicted AUC_0−t ratios aligned with observed data: itraconazole (2.42-fold predicted vs. 2.67- fold observed) and rifampicin (0.14-fold predicted vs. 0.096-fold observed). For other modulators, geometric mean AUC_0−t ratios were 2.40 with diltiazem, 3.27 with fluconazole (400 mg), 2.07 with fluconazole (100 mg), 1.16 with fluvoxamine, 0.53 with efavirenz, and 0.70 with zanubrutinib. These simulations indicate that strong and moderate CYP3A4 inhibitors and inducers significantly alter TPN171 exposure, whereas weak modulators have no clinically meaningful impact. These findings provide a scientific basis for dose recommendations when TPN171 is used with CYP3A4 modulators.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70298","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148497036","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":"Development of a Disease-Specific Virtual Malaria Population for Physiologically-Based Pharmacokinetic Modeling","authors":"Junjie Ding,&nbsp;Qi Pei,&nbsp;Richard M. Hoglund,&nbsp;Joel Tarning","doi":"10.1002/psp4.70294","DOIUrl":"10.1002/psp4.70294","url":null,"abstract":"<p>Malaria remains a major global health challenge, with an urgent need for new therapies to combat the evolving resistance to anti-malarial treatments. Physiologically-based pharmacokinetic (PBPK) modeling offers a promising approach to accurately predict PK, optimize dosing strategies, reduce development time and cost, and de-risk the development of novel anti-malarial compounds. In this study, we developed and validated a virtual malaria population, reflecting the pathophysiological changes associated with acute uncomplicated malaria infections. Key alterations included elevated plasma α1-acid glycoprotein (+118%), reduced plasma albumin (−16.8%), decreased estimated glomerular filtration rate (−10%), reduced hepatic enzyme abundance (−26% to −42%), increased blood flow (+40%), and prolonged gastric emptying time (+~45 min), with parameter magnitudes systematically obtained from the literature. A dynamic function was incorporated to describe the evolution of these biological changes during the acute infection and treatment. The virtual population was used for PK predictions of quinine, dihydroartemisinin, amodiaquine, and desethylamodiaquine, which differ in metabolic pathways and plasma protein binding characteristics. Sensitivity analyses indicated that plasma protein levels had the largest impact on PK exposure, followed by enzyme abundance and blood flow, whereas eGFR contributed minimally. The developed virtual malaria population provides a proof-of-concept translational framework that may improve PK predictions during the acute infection and support the development of novel anti-malarial therapies.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70294","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148469521","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":"Model Ensembling and Machine Learning Approaches to Predict the First Dose of Amoxicillin in Intensive Care","authors":"Mihály Leiwolf,&nbsp;Nicolas Gregoire,&nbsp;Sophie Magréault,&nbsp;Bénédicte Franck,&nbsp;Ombeline Krekounian,&nbsp;Jean-Baptiste Woillard,&nbsp;Vincent Aranzana-Climent","doi":"10.1002/psp4.70296","DOIUrl":"10.1002/psp4.70296","url":null,"abstract":"<p>A priori model-informed precision dosing (MIPD) recommends an appropriate first dose based solely on the patient's covariates enabling faster target attainment without required concentration measurements. Population pharmacokinetic (PopPK) model ensembling and machine learning (ML) approaches were developed and evaluated to predict a first dose of amoxicillin in intensive care. Following a bibliographic review, a virtual patient population was simulated based on cohorts from four published adult amoxicillin PopPK models. Steady-state trough concentrations were simulated using cohort-specific dosing regimens. As reference methods, weighed model ensembling (WME) and classification tree (CT)-informed ensembling were implemented. Two novel ensembling strategies were developed: regression tree (RT)-informed ensembling, using RT to predict the log individual prediction/observation ratio, and factor analysis of mixed data (FAMD), assigning model weights based on patient similarity to original model cohorts. In parallel, four ML algorithms (support vector machine, <i>k</i>-nearest neighbors, random forest, and XGBoost) were trained to predict the dose achieving target concentrations based on covariates and dosing scheme. All approaches were compared with single-model PopPK dosing, standard dosing, a nomogram, and externally validated using clinical data. Most MIPD methods outperformed standard dosing. On simulated data, ensembling (24%–40% correct predictions) and ML (36%–39%) exceeded single-model approaches (17%–32%). ML-based ensembling eliminates the need for model selection and increases target attainment in both simulated and clinical data. However, the generalizability of such results remains hampered by the lack of high-quality clinical datasets to support model training.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 7","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70296","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148435563","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":"A Combined Experimental and Predictive Modeling Study of Diltiazem Release From Natural Polymer Microcapsules in Different Media","authors":"Zehra Üstün,&nbsp;Ümit Kara","doi":"10.1002/psp4.70290","DOIUrl":"10.1002/psp4.70290","url":null,"abstract":"<p>The release behavior of diltiazem-loaded microcapsules prepared from gelatin B and gum arabic was evaluated experimentally and mechanistically in distilled water and simulated saliva. Release amounts were quantified by liquid chromatography over 5–720 min and used directly as input for kinetic and mechanistic modeling. Kinetic analysis was performed using different models, and model performances were compared using the Akaike and Bayesian information criteria (AIC, BIC). The Weibull model provided the best fit in both media (<i>R</i>² = 0.989 for distilled water, <i>R</i>² = 0.994 for simulated saliva). A two-compartment mechanistic model developed in SimBiology/MATLAB yielded release rate constants of 0.0111 min⁻¹ (water) and 0.0026 min⁻¹ (saliva). Agreement between the Weibull mechanistic model and experimental results was <i>R</i>² = 0.989 for distilled water (RMSE = 0.524 mg) and <i>R</i>² = 0.994 for simulated saliva (RMSE = 0.348 mg). Model validation was performed using Visual Predictive Check (VPC) with <i>N</i> = 200 virtual individuals, confirming that all observed data points fell within the 5%–95% percentile prediction interval. The validated model was further extended to a sublingual pH variability scenario (pH 6.2–7.4), demonstrating that saliva pH influences cumulative release through its effect on the unionized fraction of diltiazem.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 7","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70290","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148435409","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}