CPT: Pharmacometrics & Systems Pharmacology最新文献

{"title":"Transporter-Drive Interstitial Tissue Exposure and Pharmacodynamic Response of Meropenem in Sepsis: A Mechanistic PBPK Modeling Study","authors":"Laura Ben Olivo,&nbsp;Jessica Luisa Silva de Lemos,&nbsp;Vinicius Jardim Rodrigues,&nbsp;Bibiana Verlindo de Araújo","doi":"10.1002/psp4.70335","DOIUrl":"10.1002/psp4.70335","url":null,"abstract":"<p>A whole-body mechanistic PBPK model for meropenem (MPN) was developed in PK-Sim and validated using published plasma and tissue concentration–time data in healthy volunteers and critically ill patients. Renal elimination was implemented as glomerular filtration plus active tubular secretion (basolateral uptake via OAT3 with apical efflux), and non-renal clearance via DHP-mediated hydrolysis. The model was scaled to sepsis or septic shock by incorporating disease-specific physiological changes and optimizing OAT3 activity and tissue permeability to reproduce observed variability. Simulations in a virtual septic population assessed unbound interstitial concentrations in clinically relevant tissues under ILAS-recommended typical and maximum dosing MPN regimens. Antibacterial effect against <i>Escherichia coli</i> and <i>Klebsiella pneumoniae</i> was evaluated using a published PK/PD model driven by simulated unbound tissue concentrations. The PBPK model reproduced observed plasma profiles in healthy volunteers and captured plasma and subcutaneous interstitial exposure in sepsis. Simulations showed clear dissociation between plasma and interstitial exposure and marked tissue-specific heterogeneity. Predicted clearance increased in sepsis (augmented renal clearance) and decreased in septic shock (impaired renal function and secretion). Although maximum dosing increased plasma and tissue exposure, PD simulations indicated effects were already near the plateau with standard dosing, yielding minimal additional antibacterial benefit from routine dose escalation. Transporter-informed PBPK/PD modeling explains dynamic, severity-dependent changes in meropenem clearance and tissue exposure in sepsis, highlights limitations of plasma-only assessment, and supports individualized, mechanism-informed optimization rather than universal dose scaling.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13534823/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148873180","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 Generalization of the Ternary Binding Model to Membrane-Confined Systems With Finite Copy Number","authors":"Hamid Bellout,&nbsp;Angela Li,&nbsp;Dean Bottino,&nbsp;Konstantin Piatkov","doi":"10.1002/psp4.70323","DOIUrl":"https://doi.org/10.1002/psp4.70323","url":null,"abstract":"&lt;p&gt;The standard Douglass ternary binding model (TBM) for three-body equilibria assumes a well-mixed, three-dimensional solution. When applied to bispecific T-cell engagers (BiTEs), however, the productive trimeric complex forms not in bulk solution but within a nanoscale membrane synapse with finite receptor copy numbers. We present a generalization of the TBM to membrane-confined systems that replaces the macroscopic bulk volume with a coarse-grained reactive contact volume defined by synapse geometry and microvillus topology, and extends the deterministic equilibrium to a stochastic description via the chemical master equation. The framework preserves the original algebra while restoring its representational capacity for the regime in which therapeutic activity occurs. A key finding is that conventional bulk mapping places the system in the affinity-limited regime, where antigen density is mathematically inert and the TBM predicts identical dose–response regardless of target expression. Membrane confinement shifts effective antigen concentration by six orders of magnitude—from &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;∼&lt;/mo&gt;\u0000 &lt;msup&gt;\u0000 &lt;mn&gt;10&lt;/mn&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;−&lt;/mo&gt;\u0000 &lt;mn&gt;3&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ sim {10}^{-3} $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; nM to &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;∼&lt;/mo&gt;\u0000 &lt;msup&gt;\u0000 &lt;mn&gt;10&lt;/mn&gt;\u0000 &lt;mn&gt;3&lt;/mn&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ sim {10}^3 $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; nM—restoring antigen density as a governing variable for trimer formation. Using blinatumomab (anti-CD19 BiTE) as a case study, we introduce the &lt;i&gt;absolute formation dose&lt;/i&gt; &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;d&lt;/mi&gt;\u0000 &lt;mfenced&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;N&lt;/mi&gt;\u0000 &lt;mo&gt;*&lt;/mo&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mfenced&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ dleft({N}^{ast}right) $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;: the drug concentration required to produce a fixed number of ternary complexes sufficient for T-cell activation. This metric replaces the conventional &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;T&lt;/mi&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;mn&gt;50&lt;/mn&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ T{F}_{50} $$&lt;/annotation&gt;\u0000 ","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70323","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849323","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":"The 95% CDIRAs, a Credible Interval Based Method to Capture Uncertainty in Population Modeling: Pharmacokinetics Versus Sum of Exponentials Case Study","authors":"Linda Wanika,&nbsp;Ine Skottheim Rusten,&nbsp;James Kermode,&nbsp;Michael J. Chappell","doi":"10.1002/psp4.70326","DOIUrl":"10.1002/psp4.70326","url":null,"abstract":"<p>Determining the credibility of population PK/PD models is a challenge, particularly when performing uncertainty quantification (UQ) for parameter estimates, which is often represented through relative standard error (RSE) values. It is important to note that RSE values are primarily based on the variance of the parameter distribution and is therefore less sensitive to the overall shape and quantiles of a distribution which also reflects the uncertainty of a parameter. This can lead to unreliable uncertainty results and misleading interpretations. Robust UQ is important for model parameter estimation, as the results from in silico modeling are often used to inform subsequent steps in drug development. To capture the overall parameter uncertainty obtained from parametric approaches, the 95% credible interval ratios (95% CDIRAs) are introduced. The 95% CDIRAs only require the distribution for the individual parameter and consider the shape and quantiles of the distribution in addition to the variance. To showcase the 95% CDIRAs, an exemplar case study comprising simulated plasma concentration data was analyzed to assess whether a one compartment absorption population PK model or a reparameterized sum of exponentials (SOE) model can provide a more credible fit to plasma concentration data. Both metrics identified the population PK model as a more credible fit compared to the reparameterized SOE model. However, on occasions the precision classifications of the RSE values were higher than the adopted 95% CDIRAs precision classifications, which further indicates that uncertainty assessment based on variance alone does not necessarily encompass the full level of uncertainty for a parameter estimate.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70326","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148825808","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 Physiologically Based Pharmacokinetic Modeling to Optimize Assessment of Age-Related Fluoxetine Accumulation in the Elderly","authors":"Yoo Jin Jang,&nbsp;Dong-Gyu Heo,&nbsp;Eunjin Hong","doi":"10.1002/psp4.70319","DOIUrl":"https://doi.org/10.1002/psp4.70319","url":null,"abstract":"<p>This study quantified age-related differences in fluoxetine pharmacokinetics and evaluated the influence of CYP2D6 phenotype using physiologically based pharmacokinetic (PBPK) modeling integrated with clinical therapeutic drug monitoring data. A PBPK model for fluoxetine and its active metabolite norfluoxetine was implemented in the Simcyp Simulator and verified using published pharmacokinetic studies. Simulations of repeated fluoxetine administration (20 mg once daily) were performed in younger adults (18–65 years) and elderly individuals (65–98 years). Model predictions were compared with therapeutic drug monitoring data from Korean 47 patients (18–88 years) receiving fluoxetine for at least 5 weeks. Simulations demonstrated delayed steady-state attainment and reduced clearance in elderly individuals, resulting in approximately two-fold higher fluoxetine exposure after prolonged dosing and 1.5-fold higher total active moiety exposure than in younger adults. Clinical observations supported these findings, with significantly higher dose-normalized trough concentrations in elderly patients. Although CYP2D6 phenotype affected parent-drug exposure, total active moiety exposure varied only modestly across phenotypes, suggesting that CYP2D6 phenotyping may not be necessary when titrating fluoxetine doses in elderly patients. These findings demonstrate that aging may enhance fluoxetine accumulation during chronic therapy, supporting cautious dose titration and extended monitoring in the elderly population.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70319","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148785225","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":"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}