CPT: Pharmacometrics & Systems Pharmacology最新文献

{"title":"Introduction to Single-Cell Physiologically-Based Pharmacokinetic (scPBPK) Models","authors":"Anshul Saini,&nbsp;James M. Gallo","doi":"10.1002/psp4.70320","DOIUrl":"https://doi.org/10.1002/psp4.70320","url":null,"abstract":"<p>The current investigation introduces single-cell physiologically based pharmacokinetic (scPBPK) models to gain insight into drug disposition at the cellular scale. The transition from standard PBPK (sPBPK) models to scPBPK models required depiction of expression-dependent (ED) processes, such as drug metabolism or membrane transport. ED processes utilize weighting functions—a defined or data-driven distribution—that yield heterogeneity in individual cell kinetics. Two scPBPK model examples are provided, one involving a drug (AZD1775) subject to three ED blood–brain barrier transport processes, and another drug (midazolam) with a single ED process of metabolism by hepatocytes. For both examples, the weighting function for each ED process was defined by a negative binomial distribution that is often used in scRNAseq analytics. The AZD1775 model simulations indicated a large degree of single-cell drug concentration heterogeneity, whereas those for midazolam did not due to high membrane transport relative to metabolism. scPBPK models offer a means to probe cellular pharmacokinetics compatible with modern omic technologies and may be extended to pharmacodynamic models.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70320","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784592","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":"Transitioning from Transcriptomics to Proteomics: Enhancing Mechanistic Accuracy in PBPK Modeling via Absolute Protein Abundances","authors":"Chen Ning,&nbsp;Alessandra Pugliano,&nbsp;Maximilian Winter,&nbsp;Keliang Wu,&nbsp;Pieter Annaert","doi":"10.1002/psp4.70316","DOIUrl":"https://doi.org/10.1002/psp4.70316","url":null,"abstract":"<p>Reliable physiologically based pharmacokinetic (PBPK) modeling depends on tissue-specific expression profiles that reflect protein activities governing drug disposition. In PK-Sim, existing expression databases rely on transcriptomics data. However, mRNA levels often exhibit limited correlation with protein abundance, frequently necessitating empirical expression modification to align bottom-up simulations with clinical observations. To address this limitation, we developed ProteinDB as a proteomics-based expression database for PK-Sim, using proteomics data primarily from PaxDb v6.0. Raw proteomics data were mapped to gene identifiers and standardized into absolute concentrations (μmol/L tissue) before integration into PK-Sim. Cross-platform comparisons were performed for hepatic protein abundance across PBPK platforms, while cross-omics comparisons were made of relative tissue distributions with transcriptomics-based PK-Sim databases. The performance of ProteinDB in PBPK modeling was evaluated using the probe substrates midazolam, digoxin, rifampicin, and tizanidine, with associated drug–drug interactions. Cross-platform comparisons showed strong agreement for most hepatic enzymes and transporters, while revealing divergences for proteins with greater inter-individual variability, lower abundance, or limited evidence base. Cross-omics analyses demonstrated tissue-dependent discrepancies between transcript- and protein-based expression patterns, with higher consistency observed for kidney and small intestine, particularly with the RT-PCR and Bgee databases. For PBPK modeling, ProteinDB showed consistently comparable or superior predictive performance for systemic exposure and other clinical endpoints compared with transcriptomics-based baseline and empirically modified library profiles. By providing a direct physiological basis for system parameterization, ProteinDB offers a robust alternative to current transcriptomics PK-Sim databases and reduces the reliance on empirical expression modification, thus improving the reliability of prospective PBPK modeling.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70316","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148783908","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":"Reliable Apparent Permeability Prediction Through Benchmarking, Stability, Uncertainty Quantification, and Explainable Machine Learning","authors":"Rajdeep Mondal,&nbsp;Rajith K. R. Rajoli,&nbsp;Andrew Owen,&nbsp;Soumitra Samanta","doi":"10.1002/psp4.70305","DOIUrl":"10.1002/psp4.70305","url":null,"abstract":"&lt;p&gt;Apparent permeability (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;mi&gt;app&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {P}_{app} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;) of a drug molecule serves as a principal indicator of drug absorption for pharmacokinetic modeling, and accurate prediction of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;mi&gt;app&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {P}_{app} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; values using machine learning would avoid time-consuming and expensive In Vitro experiments. In this study, four different feature representations were compared for predicting &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;log&lt;/mi&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;mi&gt;app&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ log {P}_{app} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; values, with each representation serving as input to three subsequent regression models. The best predictive model achieved an &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;R&lt;/mi&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {R}^2 $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; value of 0.68 and RMSE of 0.42 on the test data. Furthermore, the SHAP values corresponding to individual molecular descriptors were analyzed to interpret the influence of these descriptors on the model prediction. Some key properties like octanol–water partition coefficient and number of basic atoms were found to have a strong influence on the &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;mi&gt;app&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {P}_{app} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; value. Additionally, some specific molecular substructures were identified that contribute either positively or negatively to the model output, thereby inferring effects on drug permeability. Finally, the uncertainty in the predictions was quantified through a distribution-free and model-agnostic method, &lt;i&gt;jackknife+&lt;/i&gt;, used for the estimation of confidence intervals. At the optimal experimental setting, the confidence intervals derived using the overall best performing model covered 96.12% and 86.73% of the original &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 ","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148720290","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":"Relevance-Based Score for MIPD Model Selection: Systematic Review and External Evaluation of popPK Tacrolimus Models in Adult Transplant Recipients","authors":"Samuel Baroudi,&nbsp;Bénédicte Franck,&nbsp;Jean-Baptiste Woillard,&nbsp;Ntobe-Bunkete Béni,&nbsp;Emmanuelle Comets,&nbsp;Florian Lemaitre","doi":"10.1002/psp4.70315","DOIUrl":"10.1002/psp4.70315","url":null,"abstract":"<p>Selecting the most relevant models for model-informed precision dosing (MIPD) remains challenging, particularly when numerous population pharmacokinetic (popPK) models are available, making external evaluation of all candidates time- and resource-consuming. We aimed to develop and assess an adaptable relevance-based scoring methodology, illustrated with tacrolimus in adult transplant recipients. Candidate criteria were derived from published guidelines and external evaluations. A 23-item relevance score was developed, covering training dataset, study design, evaluation methodology, covariates, and parameter precision. For tacrolimus, a systematic literature review identified 28 models which were scored for relevance and externally evaluated using an independent dataset from Rennes University Hospital (86 patients). Predictive performances were assessed at individual and population levels using bias and imprecision metrics. Predicted and observed AUCs were compared against therapeutic thresholds. Models were then ranked using an external evaluation score (maximum 14 points) based on predefined acceptance limits for bias and imprecision. Correlations between relevance-based and external scores and ranks were assessed using Pearson and Spearman correlations. The mean relevance score was 22.3/56 (range: 6–38). Higher scores were associated with larger and richer datasets, prospective or multicentric designs, inclusion of clinically relevant covariates, and precise parameter estimates. Individual predictions showed acceptable bias and imprecision, whereas population predictions were consistently inaccurate. AUC concordance (56%–94%) was mainly limited by overestimation. Highest external score was 6/14. Correlation between relevance-based and external scores was moderate (Pearson = 0.56; Spearman = 0.61). This work provides a structured basis for popPK model selection and identified tacrolimus models suitable for MIPD applications.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70315","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148720313","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":"Context Engineering for AI-Assisted Pharmacometrics: A Practical Tutorial","authors":"Ari Pritchard-Bell,&nbsp;Chih-Wei Lin,&nbsp;William Holmes,&nbsp;Sameer Doshi","doi":"10.1002/psp4.70317","DOIUrl":"10.1002/psp4.70317","url":null,"abstract":"<p>Large language models can execute pharmacometric workflows, but they make consequential domain-specific errors when task instructions lack adequate details. This tutorial teaches pharmacometricians how to define self-contained tasks that embed domain-specific rules, verification criteria, and worked examples into each step of a pharmacometric workflow. These individual tasks are then organized into a structured <i>task library</i> where each task runs in a fresh LLM instance (with clean context), with information passed between tasks through shared workspace files. The tutorial covers context engineering, controlling what information reaches the LLM at each decision point, along with verification layers and methods to iteratively refine the task library. We demonstrate the approach on a synthetic population PK/PD scenario and provide the task library and implementation guide in the Supplementary Material.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13442926/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148677511","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":"Beyond Traditional Covariates: An Interpretable Machine Learning Workflow for Improved Hybrid Pharmacometric Modeling","authors":"Freek J. A. Relouw,&nbsp;Jort J. M. T. Lokers,&nbsp;Tim Preijers,&nbsp;Sebastiaan D. T. Sassen,&nbsp;Natal A. W. van Riel,&nbsp;Birgit C. P. Koch","doi":"10.1002/psp4.70313","DOIUrl":"10.1002/psp4.70313","url":null,"abstract":"<p>Model-informed precision dosing is often constrained by the limited generalizability of traditional population pharmacokinetic models, especially in critically ill patients. A hybrid machine learning-population pharmacokinetic framework is proposed to improve a priori pharmacokinetic predictions by integrating real-world clinical data. This approach was applied to vancomycin trough concentration prediction. Two widely used two-compartment population pharmacokinetic models provided individual pharmacokinetic parameter estimates. Maximum a posteriori Bayesian estimation was used to adjust population parameters for individual patients based on drug administration records, therapeutic drug monitoring values, and patient-specific covariates from the MIMIC-IV database. The resulting clearance and central volume of distribution estimates served as training targets for XGBoost and symbolic regression models. Machine learning-predicted parameters were reinserted into the original pharmacokinetic equations to generate a priori vancomycin trough concentrations without reliance on therapeutic drug monitoring input. The hybrid models demonstrated improved prediction accuracy over traditional population pharmacokinetic covariate models and reduced vancomycin trough concentration prediction error by up to ~20%. XGBoost generally provided the highest predictive performance, while symbolic regression produced interpretable mathematical expressions revealing associations between non-traditional clinical predictors and pharmacokinetic parameters, highlighting a trade-off between accuracy and interpretability. This framework illustrates the potential of combining machine learning with population pharmacokinetic modeling to refine pharmacokinetic parameter estimation and support more precise, individualized dosing. The workflow is adaptable to other drugs and patient populations, offering a generalizable methodological strategy to identify non-traditional predictors and enhance existing pharmacometric model performance in real-world clinical settings.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70313","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148668569","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":"Bayesian Workflow for Minimal PBPK Models: Case Study of Dapagliflozin","authors":"Anna Mikhailova,&nbsp;Kirill Peskov,&nbsp;Gabriel Helmlinger,&nbsp;Victor Sokolov","doi":"10.1002/psp4.70307","DOIUrl":"10.1002/psp4.70307","url":null,"abstract":"<p>Numerous clinical trials (CTs) and population pharmacokinetic (PK) models have been published on dapagliflozin, an approved SGLT2 inhibitor used to treat type 2 diabetes, heart failure, and chronic kidney disease. This study proposes a Bayesian workflow for the development of minimal physiologically-based PK models (mPBPK) that integrates all available PK information, to support uncertainty quantification and informed drug development. First, a systematic collection of published dapagliflozin PK data and models was performed. A mPBPK model was then developed, and posterior parameter distributions were obtained based on data from parallel-design CTs. Three Bayesian modeling packages: <i>NIMBLE</i> (v1.1.0), <i>MCSim</i> (v6.2.0), and <i>Torsten</i> (v0.89.0), and three alternative prior specifications were evaluated. Model validation was performed using PK data from crossover CTs and urinary recovery data, followed by sensitivity analysis. 18 studies reporting PK data and 10 reporting PK models were identified. Posterior distributions were comparable across programs: 95% credible intervals overlapped. <i>Torsten</i> showed superior sampling efficiency, as compared to <i>NIMBLE</i> and <i>MCSim</i> (5.7 and 10.0 times higher in tails and central posterior regions, respectively); however, the average effective sample size per hour was comparable for <i>Torsten</i> and <i>MCSim</i>. The predicted urinary recovery was 2.2%–4.4% (mean 3.2%), while the observed values were in the 0.8%–4.0% range (mean 2.0%). Glomerular filtration rate and fraction unbound were the main contributors to inter-trial variability in urinary recovery, while volume of distribution and clearance had the highest influence on maximum concentration and area-under-the-concentration curve, respectively. The proposed Bayesian workflow is flexible and transferable to other mechanistic model types.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13429805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148663575","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 Systematic Review of Published Physiologically Based Pharmacokinetic Models for Drug Excretion Into Human Breastmilk: Knowledge Gaps and Opportunities to Optimize Reporting and Modeling Practices.","authors":"Joshua Kiptoo, Davies Otieno, Kenta Yoshida, Neel Deferm, Aida Nakayiwa Kawuma, Francis Williams Ojara, Adeniyi Olagunju, Jackson K Mukonzo, Catriona Waitt","doi":"10.1002/psp4.70321","DOIUrl":"10.1002/psp4.70321","url":null,"abstract":"<p><p>Physiologically based pharmacokinetic (PBPK) modeling is increasingly used to predict infant exposure to medications via breast milk. Existing guidance on reporting of PBPK modeling analyses is unstructured and nonbinding to researchers. We evaluated reporting practices and modeling approaches in published PBPK modeling studies. A systematic search was conducted across three online databases. Studies published through March 2025 that leveraged PBPK to evaluate milk drug excretion and infant exposure were included. We developed a 22-item structured data abstraction tool-Lactation PBPK Model Reporting Checklist (LAC-PBPK). Study findings were reported using descriptive and narrative analyses. Twenty-six published studies modeling 54 medications met the inclusion criteria. Using the data abstraction tool, only 4 (18%) of reporting domains were reported in all studies. These include the drug modeled, choice of mammary distribution model, context of PBPK modeling (model purpose), and the modeling platform. Most studies (88.4%) implemented whole-body PBPK model structure, with one-half (53%) assuming perfusion-rate-limited milk excretion. Assumptions reflect modeling limitations, primarily due to inadequate reporting in clinical lactation studies and knowledge gaps in infant and mammary physiology. PBPK models can support mechanistic assessment of drug excretion into breast milk, but confidence in model outputs depends on intended use, data availability, and transparent reporting. This highlights the need for a prioritized, fit-for-purpose lactation PBPK reporting framework. From a regulatory perspective, transparent reporting and cautious selection of approaches for estimating infant milk intake, model performance evaluation, physiological extrapolation, and parameter uncertainty analyses are essential to ensure reproducibility and confidence in PBPK-informed infant exposure assessment.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":"e70321"},"PeriodicalIF":2.8,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13453128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148697079","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 Systems Pharmacology Model of Aging Identifies Optimal Combination Therapies With Secondary Benefits on Weight Loss and Metabolic Health.","authors":"Igor Goryanin, Bob Damms, Irina Goryanin","doi":"10.1002/psp4.70322","DOIUrl":"10.1002/psp4.70322","url":null,"abstract":"<p><p>Aging is a systems-level process linking metabolic dysfunction, inflammation, impaired repair, frailty, and multimorbidity, whereas existing pharmacological strategies usually optimize disease-specific endpoints such as weight loss or HbA1c rather than aging-related trajectories. We developed an SBML-compliant quantitative systems pharmacology (QSP) model in which aging is represented as a dynamic, pharmacologically modifiable endpoint. The model integrates four coupled layers: metabolic/pharmacodynamic responses to GLP-1 receptor agonism, SGLT2 inhibition, metformin and rapamycin; adverse-event dynamics; aging states including damage accumulation, repair capacity, frailty and biological age gap; and biomarker outputs including GDF15, cystatin C, leptin, adiponectin and estimated glucose disposal rate. The semaglutide submodel was calibrated against published STEP trial endpoints, and Bayesian hierarchical meta-analysis, global sensitivity analysis, practical identifiability analysis and internal consistency checks were used to assess model behavior. The calibrated model reproduced semaglutide-associated weight loss, HbA1c reduction and transient nausea within pre-specified error benchmarks. Bayesian meta-analysis confirmed strong metabolic effects for semaglutide, moderate glycaemic effects for SGLT2 inhibitors and metformin, and a near-zero HbA1c effect for rapamycin. Sensitivity analysis revealed largely orthogonal metabolic and aging parameter spaces. Combination simulations identified two mechanistically distinct optima: GLP-1 receptor agonist plus SGLT2 inhibitor plus metformin for metabolic improvement, and GLP-1 receptor agonist plus SGLT2 inhibitor plus rapamycin for aging-related benefit. Metabolic optimisation and aging optimisation are therefore mechanistically distinct objectives that do not converge on the same drug combination. These predictions are hypothesis-generating and require external validation against independent longitudinal datasets and clinical safety evaluation before translation to treatment recommendations.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":"e70322"},"PeriodicalIF":2.8,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13454951/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148700461","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":"Modeling Treatment Response in Tuberculosis Early Bactericidal Activity Trials.","authors":"Mairi C W McClean, Simon E Koele, Julia Dreisbach, Susanne Mirold-Mei, Fred Njeleka, Daniel Mapamba, Bariki Mtafya, Patrick P J Phillips, Veronique R De Jager, Rodney Dawson, Kim Narunsky, Andreas H Diacon, Elin M Svensson, Norbert Heinrich, Francesco Paolo Casale, Michael Hoelscher","doi":"10.1002/psp4.70311","DOIUrl":"10.1002/psp4.70311","url":null,"abstract":"<p><p>Culture-based monitoring of bacterial load is slow and susceptible to missing data, contributing to the length and cost of TB clinical trials. Non-culture-based alternatives, like the Tuberculosis Molecular Load Bacterial Assay (TB-MBLA), could represent a solution. Our objectives were to evaluate TB-MBLA as a biomarker in early bactericidal activity (EBA) studies and explore whether combining biomarkers with joint modeling could provide insight into underlying biological processes. We generated TB-MBLA (LifeArc) data from sputum samples from all 78 patients from the PanACEA BTZ-043 Phase Ib/IIa trial and derived a summary measure of the joint distribution of the three TB-MBLA, colony forming units (CFU), and time-to-positivity (TTP) biomarkers as the first principal component derived from a probabilistic principal component analysis (pPCA). With TB-MBLA marker and the principal component 1 (PC1) values, we reevaluated the original stage IIa dose-response and stages Ib/IIa pharmacokinetics-pharmacodynamics (PK-PD) exposure-response analyses, applying linear and non-linear mixed models, respectively. For TB-MBLA, we could not detect an exposure-response effect in the PK-PD analysis, in contrast with CFU and TTP. When combining biomarkers, we observed a significant but less pronounced E_max exposure-response between days 0-3 compared with CFU and TTP alone. We also successfully applied pPCA as a modeling framework and show evidence that combining CFU and TTP in a joint latent component can improve detection of treatment effects compared with either biomarker alone. In this study, we present novel EBA data for the first-in-class antimycobacterial compound BTZ-043 and contextualize the value of emerging bacteriological markers within the EBA trial framework. Trial Registration: ClinicalTrials.gov identifier: NCT04044001.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 8","pages":"e70311"},"PeriodicalIF":2.8,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148688529","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}