CPT: Pharmacometrics & Systems Pharmacology最新文献

{"title":"Physiologically-based pharmacokinetic modeling predicts the drug interaction potential of GLS4 in co-administered with ritonavir","authors":"Zexu Sun,&nbsp;Nan Zhao,&nbsp;Ran Xie,&nbsp;Bo Jia,&nbsp;Junyu Xu,&nbsp;Lin Luo,&nbsp;Yulei Zhuang,&nbsp;Yuyu Peng,&nbsp;Xinchang Liu,&nbsp;Yingjun Zhang,&nbsp;Xia Zhao,&nbsp;Zhaoqian Liu,&nbsp;Yimin Cui","doi":"10.1002/psp4.13184","DOIUrl":"10.1002/psp4.13184","url":null,"abstract":"<p>GLS4 is a first-in-class hepatitis B virus (HBV) capsid assembly modulator (class I) that is co-administered with ritonavir to maintain the anticipated concentration required for the effective antiviral activity of GLS4. In this study, the first physiologically-based pharmacokinetic (PBPK) model for GLS4/ritonavir was successfully developed. The predictive performance of the PBPK model was verified using data from 39 clinical studies, including single-dose, multiple-dose, food effects, and drug–drug interactions (DDI). The PBPK model accurately described the PK profiles of GLS4 and ritonavir, with predicted values closely aligning with observed data. Based on the verified GLS4/ritonavir model, it prospectively predicts the effect of hepatic impairment (HI) and DDI on its pharmacokinetics (PK). Notably, CYP3A4 inducers significantly influenced GLS4 exposure when co-administered with ritonavir; co-administered GLS4 and ritonavir significantly influenced the exposure of CYP3A4 substrates. Additionally, with the severity of HI increased, there was a corresponding increase in the exposure to GLS4 when co-administered with ritonavir. The GLS4/ritonavir PBPK model can potentially be used as an alternative to clinical studies or guide the design of clinical trial protocols.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 9","pages":"1503-1512"},"PeriodicalIF":3.1,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13184","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731095","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":"Hepatic OATP1B zonal distribution: Implications for rifampicin-mediated drug–drug interactions explored within a PBPK framework","authors":"Mattie Hartauer,&nbsp;William A. Murphy,&nbsp;Kim L. R. Brouwer,&nbsp;Roz Southall,&nbsp;Sibylle Neuhoff","doi":"10.1002/psp4.13188","DOIUrl":"10.1002/psp4.13188","url":null,"abstract":"<p>OATP1B facilitates the uptake of xenobiotics into hepatocytes and is a prominent target for drug–drug interactions (DDIs). Reduced systemic exposure of OATP1B substrates has been reported following multiple-dose rifampicin; one explanation for this observation is OATP1B induction. Non-uniform hepatic distribution of OATP1B may impact local rifampicin tissue concentrations and rifampicin-mediated protein induction, which may affect the accuracy of transporter- and/or metabolizing enzyme-mediated DDI predictions. We incorporated quantitative zonal OATP1B distribution data from immunofluorescence imaging into a PBPK modeling framework to explore rifampicin interactions with OATP1B and CYP substrates. PBPK models were developed for rifampicin, two OATP1B substrates, pravastatin and repaglinide (also metabolized by CYP2C8/CYP3A4), and the CYP3A probe, midazolam. Simulated hepatic uptake of pravastatin and repaglinide increased from the periportal to the pericentral region (approximately 2.1-fold), consistent with OATP1B distribution data. Simulated rifampicin unbound intracellular concentrations increased in the pericentral region (1.64-fold) compared to simulations with uniformly distributed OATP1B. The absolute average fold error of the rifampicin PBPK model for predicting substrate maximal concentration (<i>C</i>_max) and area under the plasma concentration–time curve (AUC) ratios was 1.41 and 1.54, respectively (nine studies). In conclusion, hepatic OATP1B distribution has a considerable impact on simulated zonal substrate uptake clearance values and simulated intracellular perpetrator concentrations, which regulate transporter and metabolic DDIs. Additionally, accounting for rifampicin-mediated OATP1B induction in parallel with inhibition improved model predictions. This study provides novel insight into the effect of hepatic OATP1B distribution on site-specific DDI predictions and the impact of accounting for zonal transporter distributions within PBPK models.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 9","pages":"1513-1527"},"PeriodicalIF":3.1,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13188","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141426566","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":"Simulating realistic patient profiles from pharmacokinetic models by a machine learning postprocessing correction of residual variability","authors":"Christos Kaikousidis,&nbsp;Robert R. Bies,&nbsp;Aristides Dokoumetzidis","doi":"10.1002/psp4.13182","DOIUrl":"10.1002/psp4.13182","url":null,"abstract":"<p>We address the problem of model misspecification in population pharmacokinetics (PopPK), by modeling residual unexplained variability (RUV) by machine learning (ML) methods in a postprocessing step after conventional model building. The practical purpose of the method is the generation of realistic virtual patient profiles and the quantification of the extent of model misspecification, by introducing an appropriate metric, to be used as an additional diagnostic of model quality. The proposed methodology consists of the following steps: After developing a PopPK model, the individual residual errors <i>IRES = DV–IPRED</i>, are computed, where DV are the observations and IPRED the individual predictions and are modeled by ML to obtain <i>IRES</i>_<i>ML</i>. Correction of the IPREDs can then be carried out as <i>DV</i>_<i>ML</i> <i>= IPRED + IRES</i>_<i>ML</i>. The methodology was tested in a PK study of ropinirole, for which a PopPK model was developed while a second deliberately misspecified model was also considered. Various supervised ML algorithms were tested, among which Random Forest gave the best results. The ML model was able to correct individual predictions as inspected in diagnostic plots and most importantly it simulated realistic profiles that resembled the real data and canceled out the artifacts introduced by the elevated RUV, even in the case of the heavily misspecified model. Furthermore, a metric to quantify the extent of model misspecification was introduced based on the <i>R</i>² between IRES and IRES_ML, following the rationale that the greater the extent of variability explained by the ML model, the higher the degree of model misspecification present in the original model.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 9","pages":"1476-1487"},"PeriodicalIF":3.1,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13182","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141320673","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":"Is PBPK useful to inform product label on managing clinically significant drug interactions mediated by cytokine release syndrome?","authors":"Xinyuan Zhang,&nbsp;Ping Zhao","doi":"10.1002/psp4.13185","DOIUrl":"10.1002/psp4.13185","url":null,"abstract":"&lt;p&gt;Evaluating drug interactions caused by cytokine release syndrome (CRS) with PBPK (Physiologically Based Pharmacokinetic) modeling has been reported in some bispecific antibody regulatory submissions for 10 years. However, the published regulatory reviews and sponsors' analyses seem to disagree on the roles of PBPK modeling in regulatory decision-making. In this editorial, we reviewed and provided our opinions on the FDA's current practice and sponsors' position in evaluating CRS-mediated drug interactions. We discussed what has been done and what is lacking in the current PBPK approach assessing the CRS-mediated drug interactions and proposed areas to bridge the gaps. And finally, we call to actions to improve the current practice toward a patient-centric clinical pharmacology approach with more quantitative assessment and management of CRS-mediated drug interactions.&lt;/p&gt;&lt;p&gt;The manuscript by Willemin et al.&lt;span&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt; described the use of a PBPK approach to evaluate the effect of elevated IL-6 following the treatment of teclistamab on the PK of CYP enzyme (1A2, 2C9, 2C19, 3A4, 3A5) substrates. This marks the 4th PBPK publication by CPT-PSP of the effect of CRS as a result of biologics-treatment on co-medications that are CYP substrates, after blinatumomab,&lt;span&gt;&lt;sup&gt;2&lt;/sup&gt;&lt;/span&gt; mosunetuzumab,&lt;span&gt;&lt;sup&gt;3&lt;/sup&gt;&lt;/span&gt; and glofitamab.&lt;span&gt;&lt;sup&gt;4&lt;/sup&gt;&lt;/span&gt; The scientific community and drug developers are using the PBPK modeling tool to study the effect of CRS on the PK and safety of co-administered CYP substrate drugs. However, there seems to be a gap between the peer-reviewed papers&lt;span&gt;&lt;sup&gt;1-4&lt;/sup&gt;&lt;/span&gt; and the regulatory evaluations&lt;span&gt;&lt;sup&gt;5-8&lt;/sup&gt;&lt;/span&gt; in terms of concluding the impact of PBPK predictions. In this editorial, we examine the gap and share our opinions on the value, expectation, and future of PBPK modeling in this specific area with the aim of increasing awareness, calling for enhanced predictive performance, and ultimately, achieving patient-centric clinical pharmacology.&lt;/p&gt;&lt;p&gt;Cytokine release syndrome is characterized by the rapid release of pro-inflammatory cytokines and immune cell activation. T cell-engaging bispecific antibodies can cause transient release of cytokines that may potentially suppress CYP450 enzymes. Utilizing the PBPK modeling approach to evaluate the CRS-mediated drug interactions in a regulatory submission can be traced back to the first FDA-approved T-cell-engaging bispecific antibody, blinatumomab, in 2014.&lt;span&gt;&lt;sup&gt;5&lt;/sup&gt;&lt;/span&gt; Over the past 10 years, a few additional T-cell-engaging bispecific antibodies were approved by FDA (mosunetuzumab, tebentafusp, teclistamab, epcoritamab, glofitamab, and talquetamab). We examined the FDA's biologics license application assessment packages, USPIs (United States Prescribing Information), and relevant PBPK publications to see how drug interactions mediated by CRS were evaluated and reported to healthcare professionals.&lt;/p&gt;&lt;p&gt;Amon","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 7","pages":"1083-1087"},"PeriodicalIF":3.1,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13185","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141310294","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":"Quantification of the effect of GLP-1R agonists on body weight using in vitro efficacy information: An extension of the Hall body composition model","authors":"Rolien Bosch,&nbsp;Eric J. G. Sijbrands,&nbsp;Nelleke Snelder","doi":"10.1002/psp4.13183","DOIUrl":"10.1002/psp4.13183","url":null,"abstract":"<p>Obesity has become a major public health concern worldwide. Pharmacological interventions with the glucagon-like peptide-1 receptor agonists (GLP-1RAs) have shown promising results in facilitating weight loss and improving metabolic outcomes in individuals with obesity. Quantifying drug effects of GLP-1RAs on energy intake (EI) and body weight (BW) using a QSP modeling approach can further increase the mechanistic understanding of these effects, and support obesity drug development. An extensive literature-based dataset was created, including data from several diet, liraglutide and semaglutide studies and their effects on BW and related parameters. The Hall body composition model was used to quantify and predict effects on EI. The model was extended with (1) a lifestyle change/placebo effect on EI, (2) a weight loss effect on activity for the studies that included weight management support, and (3) a GLP-1R agonistic effect using in vitro potency efficacy information. The estimated reduction in EI of clinically relevant dosages of semaglutide (2.4 mg) and liraglutide (3.0 mg) was 34.5% and 13.0%, respectively. The model adequately described the resulting change in BW over time. At 20 weeks the change in BW was estimated to be −17% for 2.4 mg semaglutide and −8% for 3 mg liraglutide, respectively. External validation showed the model was able to predict the effect of semaglutide on BW in the STEP 1 study. The GLP-1RA body composition model can be used to quantify and predict the effect of novel GLP-1R agonists on BW and changes in underlying processes using early in vitro efficacy information.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 9","pages":"1488-1502"},"PeriodicalIF":3.1,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13183","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141310295","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":"SAAM II: A general mathematical modeling rapid prototyping environment","authors":"Simone Perazzolo","doi":"10.1002/psp4.13181","DOIUrl":"10.1002/psp4.13181","url":null,"abstract":"<p>Simulation Analysis and Modeling II (SAAM II) is a graphical modeling software used in life sciences for compartmental model analysis, particularly, but not exclusively, appreciated in pharmacokinetics (PK) and pharmacodynamics (PD), metabolism, and tracer modeling. Its intuitive “circles and arrows” visuals allow users to easily build, solve, and fit compartmental models without the need for coding. It is suitable for rapid prototyping of models for complex kinetic analysis or PK/PD problems, and in educating students and non-modelers. Although it is straightforward in design, SAAM II incorporates sophisticated algorithms programmed in C to address ordinary differential equations, deal with complex systems via forcing functions, conduct multivariable regression featuring the Bayesian maximum a posteriori, perform identifiability and sensitivity analyses, and offer reporting functionalities, all within a single package. After 26 years from the last SAAM II tutorial paper, we demonstrate here SAAM II's updated applicability to current life sciences challenges. We review its features and present four contemporary case studies, including examples in target-mediated PK/PD, CAR-T-cell therapy, viral dynamics, and transmission models in epidemiology. Through such examples, we demonstrate that SAAM II provides a suitable interface for rapid model selection and prototyping. By enabling the fast creation of detailed mathematical models, SAAM II addresses a unique requirement within the mathematical modeling community.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 7","pages":"1088-1102"},"PeriodicalIF":3.1,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13181","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141305611","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 bootstrapping method to optimize go/no-go decisions from single-arm, signal-finding studies in oncology","authors":"Raunak Dutta,&nbsp;Aparna Mohan,&nbsp;Jacqueline Buros-Novik,&nbsp;Gregory Goldmacher,&nbsp;Omobolaji O. Akala,&nbsp;Brian Topp","doi":"10.1002/psp4.13161","DOIUrl":"10.1002/psp4.13161","url":null,"abstract":"<p>Phase Ib trials are common in oncology development but often are not powered for statistical significance. Go/no-go decisions are largely driven by observed trends in response data. We applied a bootstrapping method to systematically compare tumor dynamic end points to historical control data to identify drugs with clinically meaningful efficacy. A proprietary mathematical model calibrated to phase Ib anti–PD-1 therapy trial data (KEYNOTE-001) was used to simulate thousands of phase Ib trials (<i>n</i> = 30) with a combination of anti–PD-1 therapy and four novel agents with varying efficacy. A redacted bootstrapping method compared these results to a simulated phase III control arm (<i>N</i> = 511) while adjusting for differences in trial duration and cohort size to determine the probability that the novel agent provides clinically meaningful efficacy. Receiver operating characteristic (ROC) analysis showed strong ability to separate drugs with modest (area under ROC [AUROC] = 83%), moderate (AUROC = 96%), and considerable efficacy (AUROC = 99%) from placebo in early-phase trials (<i>n</i> = 30). The method was shown to effectively move drugs with a range of efficacy through an in silico pipeline with an overall success rate of 93% and false-positive rate of 7.5% from phase I to phase III. This model allows for effective comparisons of tumor dynamics from early clinical trials with more mature historical control data and provides a framework to predict drug efficacy in early-phase trials. We suggest this method should be employed to improve decision making in early oncology trials.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 8","pages":"1317-1326"},"PeriodicalIF":3.1,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141305610","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 “Physiologically-based pharmacokinetic pharmacodynamic parent-metabolite model of edoxaban to predict drug–drug-disease interactions: M4 contribution”","authors":"","doi":"10.1002/psp4.13187","DOIUrl":"10.1002/psp4.13187","url":null,"abstract":"<p>Xu R, Liu W, Ge W, He H, Jiang Q. <i>CPT Pharmacometrics Syst Pharmacol</i>. 2023;12(8):1093-1106.</p><p>In the title page, the author affiliation “Wenyuan Liu^1,3 and Weihong Ge^1,3” was incorrect. This should be changed to “Wenyuan Liu³ and Weihong Ge^3.”</p><p>We apologize for this error.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 7","pages":"1279"},"PeriodicalIF":3.1,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13187","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141283269","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":"Joint modeling of monocyte HLA-DR expression trajectories predicts 28-day mortality in severe SARS-CoV-2 patients","authors":"Gaelle Baudemont,&nbsp;Coralie Tardivon,&nbsp;Guillaume Monneret,&nbsp;Martin Cour,&nbsp;Thomas Rimmelé,&nbsp;Lorna Garnier,&nbsp;Hodane Yonis,&nbsp;Jean-Christophe Richard,&nbsp;Remy Coudereau,&nbsp;Morgane Gossez,&nbsp;Florent Wallet,&nbsp;Marie-Charlotte Delignette,&nbsp;Frederic Dailler,&nbsp;Marielle Buisson,&nbsp;Anne-Claire Lukaszewicz,&nbsp;Laurent Argaud,&nbsp;Cédric Laouenan,&nbsp;Julie Bertrand,&nbsp;Fabienne Venet,&nbsp;for the RICO study group","doi":"10.1002/psp4.13145","DOIUrl":"10.1002/psp4.13145","url":null,"abstract":"<p>The recent SarsCov2 pandemic has disrupted healthcare system notably impacting intensive care units (ICU). In severe cases, the immune system is dysregulated, associating signs of hyperinflammation and immunosuppression. In the present work, we investigated, using a joint modeling approach, whether the trajectories of cellular immunological parameters were associated with survival of COVID-19 ICU patients. This study is based on the REA-IMMUNO-COVID cohort including 538 COVID-19 patients admitted to ICU between March 2020 and May 2022. Measurements of monocyte HLA-DR expression (mHLA-DR), counts of neutrophils, of total lymphocytes, and of CD4+ and CD8+ subsets were performed five times during the first month after ICU admission. Univariate joint models combining survival at day 28 (D28), hospital discharge and longitudinal analysis of those biomarkers’ kinetics with mixed-effects models were performed prior to the building of a multivariate joint model. We showed that a higher mHLA-DR value was associated with a lower risk of death. Predicted mHLA-DR nadir cutoff value that maximized the Youden index was 5414 Ab/C and led to an AUC = 0.70 confidence interval (95%CI) = [0.65; 0.75] regarding association with D28 mortality while dynamic predictions using mHLA-DR kinetics until D7, D12 and D20 showed AUCs of 0.82 [0.77; 0.87], 0.81 [0.75; 0.87] and 0.84 [0.75; 0.93]. Therefore, the final joint model provided adequate discrimination performances at D28 after collection of biomarker samples until D7, which improved as more samples were collected. After severe COVID-19, decreased mHLA-DR expression is associated with a greater risk of death at D28 independently of usual clinical confounders.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 7","pages":"1130-1143"},"PeriodicalIF":3.1,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13145","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141261511","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 modeling of pantoprazole to evaluate the role of CYP2C19 genetic variation and obesity in the pediatric population","authors":"Elizabeth J. Thompson,&nbsp;Angela Jeong,&nbsp;Victória E. Helfer,&nbsp;Valentina Shakhnovich,&nbsp;Andrea Edginton,&nbsp;Stephen J. Balevic,&nbsp;Laura P. James,&nbsp;David N. Collier,&nbsp;Ravinder Anand,&nbsp;Daniel Gonzalez,&nbsp;the Best Pharmaceuticals for Children Act – Pediatric Trials Network Steering Committee","doi":"10.1002/psp4.13167","DOIUrl":"10.1002/psp4.13167","url":null,"abstract":"<p>Pantoprazole is a proton pump inhibitor indicated for the treatment of gastroesophageal reflux disease, a condition that disproportionately affects children with obesity. Appropriately dosing pantoprazole in children with obesity requires understanding the body size metric that best guides dosing, but pharmacokinetic (PK) trials using traditional techniques are limited by the need for larger sample sizes and frequent blood sampling. Physiologically-based PK (PBPK) models are an attractive alternative that can account for physiologic-, genetic-, and drug-specific changes without the need for extensive clinical trial data. In this study, we explored the effect of obesity on pantoprazole PK and evaluated label-suggested dosing in this population. An adult PBPK model for pantoprazole was developed using data from the literature and accounting for genetic variation in <i>CYP2C19</i>. The adult PBPK model was scaled to children without obesity using age-associated changes in anatomical and physiological parameters. Lastly, the pediatric PBPK model was expanded to children with obesity. Three pantoprazole dosing strategies were evaluated: 1 mg/kg total body weight, 1.2 mg/kg lean body weight, and US Food and Drug Administration-recommended weight-tiered dosing. Simulated concentration–time profiles from our model were compared with data from a prospective cohort study (PAN01; NCT02186652). Weight-tiered dosing resulted in the most (&gt;90%) children with pantoprazole exposures in the reference range, regardless of obesity status or CYP2C19 phenotype, confirming results from previously published population PK models. PBPK models may allow for the efficient study of physiologic and developmental effects of obesity on PK in special populations where clinical trial data may be limited.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 8","pages":"1394-1408"},"PeriodicalIF":3.1,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13167","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141261515","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}