CPT: Pharmacometrics & Systems Pharmacology最新文献

{"title":"EMA perspective on the value of model-informed drug development for labeling recommendations regarding medicine use during pregnancy and breastfeeding","authors":"Efthymios Manolis, Flora Tshinanu Musuamba, Corinne S. de Vries, Pieter J. Colin, Martin B. Oleksiewicz","doi":"10.1002/psp4.13214","DOIUrl":"10.1002/psp4.13214","url":null,"abstract":"<p>At the time of marketing authorization, pregnancy/breastfeeding labeling typically relies mainly on preclinical data. While post-authorization studies are sometimes requested to collect safety data in pregnant and breastfeeding individuals, usually, routine pharmacovigilance (signal detection and post-authorization safety update reports) is relied upon for generating information in this population once the products are on the market.<span><sup>1, 2</sup></span> The overall consequence is possible under-prescription of medicines in these individuals and missing or ambiguous pregnancy-specific dosing recommendations in the SmPC (Summary of Product Characteristics).<span><sup>3, 4</sup></span> Hence, before, and long after real-world evidence is available, the use of medicinal products tends to be discouraged during pregnancy and breastfeeding.</p><p>This has been recognized as an unhelpful situation by regulators around the world,<span><sup>5</sup></span> leading within the European Medicines Agency (EMA) to the development and implementation of a strategy<span><sup>6</sup></span> to enhance the SmPC information on the benefits and risks of medicines in pregnancy and breastfeeding. Central to reaching this objective is to improve the related data collection (breadth and informativeness) during the product lifecycle. Important milestones include the agreement achieved by the International Conference of Harmonization (ICH) to draft a guideline for responsibly including, or permitting to remain, pregnant and breastfeeding individuals in clinical trials,<span><sup>7</sup></span> and the reopening of the Committee for Human Medicinal products (CHMP) guideline on labeling in pregnancy and breastfeeding.<span><sup>8</sup></span></p><p>We anticipate that regulatory developments such as those mentioned above, coupled with the significant development and innovation in nonclinical drug development methodologies and MIDD over the last decades, will shift the current labeling paradigms, to improve accessibility and safe use of medicines during pregnancy and breastfeeding. Several regulatory initiatives are underway, and these will be publicized on a dedicated webpage on the EMA public website in summer 2024.</p><p>MIDD comprises the strategic use of computational modeling and simulation approaches that integrate data, prior information, and knowledge, including drug, nonclinical, clinical, and disease characteristics, to generate evidence. When adequately implemented, modeling and simulation is considered a powerful tool for characterizing the efficacy and safety of drugs in subgroups underrepresented in clinical studies such as pregnant and breastfeeding participants, who also deserve timely access to safe and effective medicines.</p><p>From a physiology and pharmacology point of view, pregnant and breastfeeding individuals represent complex and dynamic systems.<span><sup>9</sup></span> MIDD approaches, including population pharmacokinetics/pharmacodynamics","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 11","pages":"1820-1823"},"PeriodicalIF":3.1,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13214","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141970754","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":"Automated Poisson regression exposure–response analysis for binary outcomes with PoissonERM","authors":"Yuchen Wang,&nbsp;Luke Fostvedt,&nbsp;Jessica Wojciechowski,&nbsp;Donald Irby,&nbsp;Timothy Nicholas","doi":"10.1002/psp4.13207","DOIUrl":"10.1002/psp4.13207","url":null,"abstract":"<p>\u0000 <i>PoissonERM</i> is an R package used to conduct exposure–response (ER) analysis on binary outcomes for establishing the relationship between exposure and the occurrence of adverse events (AE). While Poisson regression could be implemented with <i>glm(), PoissonERM</i> provides a simple way to semi-automate the entire analysis and generate an abbreviated report as an R markdown (Rmd) file that includes the essential analysis details with brief conclusions. <i>PoissonERM</i> processes the provided data set using the information from the user's control script and generates summary tables/figures for the exposure metrics, covariates, and event counts of each endpoint (each type of AE). After checking the incidence rate of each AE, the correlation, and missing values in each covariate, an exposure–response model is developed for each endpoint based on the provided specifications. <i>PoissonERM</i> has the flexibility to incorporate and compare multiple scale transformations in its modeling. The best exposure metric is selected based on a univariate model's <i>p</i>-value or deviance (<span></span><math>\u0000 \u0000 <semantics>\u0000 \u0000 <mrow>\u0000 \u0000 <mi>Δ</mi>\u0000 \u0000 <mi>D</mi>\u0000 </mrow>\u0000 </semantics>\u0000 </math>) as specified. If a covariate search is specified in the control script, the final model is developed using backward elimination. <i>PoissonERM</i> identifies and avoids highly correlated covariates in the final model development of each endpoint. Predicting event incidence rates using external (simulated) exposure metric data is an additional functionality in <i>PoissonERM</i>, which is useful to understand the event occurrence associated with certain dose regimens. The summary outputs of the cleaned data, model developments, and predictions are saved in the working folder and can be compiled into a HTML report using Rmd.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 10","pages":"1615-1629"},"PeriodicalIF":3.1,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11494912/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141859262","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":"Population pharmacokinetics of total and unbound valemetostat and platelet dynamics in healthy volunteers and patients with non-Hodgkin lymphoma","authors":"Masato Fukae,&nbsp;Kyle Baron,&nbsp;Masaya Tachibana,&nbsp;John Mondick,&nbsp;Takako Shimizu","doi":"10.1002/psp4.13201","DOIUrl":"10.1002/psp4.13201","url":null,"abstract":"<p>Valemetostat is an EZH2/1 inhibitor that has been approved in Japan for the treatment of patients with relapsed/refractory adult T-cell leukemia/lymphoma, based mainly on results from a single-arm phase II trial. It is currently under investigation worldwide for the treatment of other non-Hodgkin lymphomas (NHLs), including peripheral T-cell lymphoma, and for solid tumors. Semi-mechanistic population pharmacokinetic modeling of total and unbound valemetostat and an analysis of the platelet time course during treatment with valemetostat were conducted using data from five clinical trials (two in patients with NHL and three in healthy volunteers). Pharmacokinetic data, including 3162 total/1871 unbound valemetostat observations from 102 patients and 72 healthy volunteers, were described by a three-compartment model with sequential zero-/first-order absorption and saturable binding in the central compartment. Alpha-1-acid glycoprotein (AAG) was the most influential covariate for total valemetostat exposure, yet had little impact on unbound exposure, meaning no dose adjustment was warranted based on AAG levels. The longitudinal platelet data from 101 patients (2313 observations) were adequately described by a modified Friberg model with two proliferation compartments, which characterized unique spontaneous recovery of platelet counts without dose modifications. A model-based simulation quantitatively assessed the proposed dose-adjustment guidance in case of platelet count decreased by comparing the probability of treatment discontinuation due to platelet count decreased with or without the dose adjustment. In summary, the models described observed total and unbound valemetostat concentrations and a unique time course of platelets during treatment, which can justify the clinical dose and provide dose-adjustment guidance.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 10","pages":"1641-1654"},"PeriodicalIF":3.1,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11494923/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141792133","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: Model-based estimates of tumor growth inhibition metrics are time-independent: A reply to Mistry","authors":"","doi":"10.1002/psp4.13209","DOIUrl":"10.1002/psp4.13209","url":null,"abstract":"<p>Claret, L., Han, K. and Bruno, R. (2017), Model-based estimates of tumor growth inhibition metrics are time-independent: A reply to Mistry. CPT Pharmacometrics Syst. Pharmacol., 6: 225–225. https://doi.org/10.1002/psp4.12163</p><p>The second author of this paper, K. Han, should be listed as Kelong Han.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 8","pages":"1422"},"PeriodicalIF":3.1,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13209","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141747623","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":"Population pharmacokinetic modeling of sufentanil in adult Korean patients undergoing cardiopulmonary bypass surgery","authors":"Vipada Khaowroongrueng,&nbsp;Kuk Hui Son,&nbsp;Sang-Min Lee,&nbsp;JiYeon Lee,&nbsp;Chun-Gon Park,&nbsp;Seok In Lee,&nbsp;Dongseong Shin,&nbsp;Kwang-Hee Shin","doi":"10.1002/psp4.13205","DOIUrl":"10.1002/psp4.13205","url":null,"abstract":"<p>Sufentanil is frequently used as an anesthetic agent in cardiac surgery owing to its cardiovascular safety and favorable pharmacokinetics. However, the pharmacokinetics profiles of sufentanil in patients undergoing cardiopulmonary bypass (CPB) surgery remain less understood, which is crucial for achieving the desired level of anesthesia and mitigating surgical complications. Therefore, this study aimed to develop a population pharmacokinetic model of sufentanil in patients undergoing CPB surgery and elucidate the clinical factors affecting its pharmacokinetic profile. Adult patients who underwent cardiac surgery with CPB and were administered sufentanil for anesthesia were enrolled. Arterial blood samples were collected to quantify plasma concentrations of sufentanil and clinical laboratory parameters, including inflammatory cytokines. A population pharmacokinetic model was established using nonlinear mixed-effects modeling. Simulations were performed using the pharmacokinetic parameters of the final model. Overall, 20 patients were included in the final analysis. Sufentanil pharmacokinetics were modeled using a two-compartment model, accounting for CPB effects. Sufentanil clearance increased 2.80-fold during CPB and warming phases, while the central compartment volume increased 2.74-fold during CPB. CPB was a significant covariate affecting drug clearance and distribution volume. No other significant covariates were identified despite increased levels of the inflammatory cytokines, including IL-6, IL-8, and TNF-α during CPB. The simulation indicated a 30 μg loading dose and 40 μg/h maintenance infusion for target-controlled infusion. Additionally, a bolus dose of 60 μg was added at CPB initiation to adjust for exposure changes during this phase. Considering the target sufentanil concentrations, a uniform dosing regimen was acceptable for effective analgesia.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 10","pages":"1682-1692"},"PeriodicalIF":3.1,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11494824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141747625","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 mechanistic PK/PD model of AZD0171 (anti-LIF) to support Phase II dose selection","authors":"Azar Shahraz,&nbsp;Mark Penney,&nbsp;Juliana Candido,&nbsp;Grace Opoku-Ansah,&nbsp;Melanie Neubauer,&nbsp;Jim Eyles,&nbsp;Oluwaseun Ojo,&nbsp;Nelson Liu,&nbsp;Nadia M. Luheshi,&nbsp;Alex Phipps,&nbsp;Karthick Vishwanathan","doi":"10.1002/psp4.13204","DOIUrl":"10.1002/psp4.13204","url":null,"abstract":"<p>AZD0171 (INN: Falbikitug) is being developed as a humanized monoclonal antibody (mAb), immunoglobulin G subclass 1 (IgG1), which binds specifically to the immunosuppressive human cytokine leukemia inhibitory factor (LIF) and inhibits downstream signaling by blocking recruitment of glycoprotein 130 (gp130) to the LIF receptor (LIFR) subunit (gp190) and the phosphorylation of signal transducer and activator of transcription 3 (STAT3) and is intended to treat adult participants with advanced solid tumors. LIF is a pleiotropic cytokine (and a member of the IL-6 family of cytokines) involved in many physiological and pathological processes and is highly expressed in a subset of solid tumors, including non-small cell lung cancer (NSCLC), colon, ovarian, prostate, and pancreatic cancer. The aim of this work was to develop a mechanistic PK/PD model to investigate the effect of AZD0171 on tumor LIF levels, predict the level of downstream signaling complex (LIF:LIFR:gp130) inhibition, and examine the dose–response relationship to support dose selection for a Phase II clinical study. Modeling results show that tumor LIF is inhibited in a dose-dependent manner with &gt;90% inhibition for 95% of patients at the Phase II clinical dose of 1500 mg Q2W.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 10","pages":"1670-1681"},"PeriodicalIF":3.1,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11494920/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141747622","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: Simulations to predict clinical trial outcome of bevacizumab plus chemotherapy vs. chemotherapy alone in patients with first-line gastric cancer and elevated plasma VEGF-A","authors":"","doi":"10.1002/psp4.13210","DOIUrl":"10.1002/psp4.13210","url":null,"abstract":"<p>Han, K., Claret, L., Piao, Y., Hegde, P., Joshi, A., Powell, J., Jin, J. and Bruno, R. (2016), Simulations to predict clinical trial outcome of bevacizumab plus chemotherapy vs. chemotherapy alone in patients with first-line gastric cancer and elevated plasma VEGF-A. <i>CPT Pharmacometrics Syst. Pharmacol</i>., 5: 352–358. https://doi.org/10.1002/psp4.12064</p><p>The first author of this paper, K. Han, should be listed as Kelong Han.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 8","pages":"1423"},"PeriodicalIF":3.1,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13210","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141747624","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":"Exposure–response modeling for nausea incidence for cotadutide using a Markov modeling approach","authors":"Hongtao Yu,&nbsp;Sebastian Ueckert,&nbsp;Lina Zhou,&nbsp;Jenny Cheng,&nbsp;Darren Robertson,&nbsp;Lars Hansen,&nbsp;Armando Flor,&nbsp;Victoria Parker,&nbsp;Bengt Hamrén,&nbsp;Anis A. Khan","doi":"10.1002/psp4.13194","DOIUrl":"10.1002/psp4.13194","url":null,"abstract":"<p>Cotadutide is a dual glucagon-like peptide-1 (GLP-1)/glucagon receptor agonist. Gastrointestinal adverse effects are known to be associated with GLP-1 receptor agonism and can be mitigated through tolerance development via a gradual up-titration. This analysis aimed to characterize the relationship between exposure and nausea incidence and to optimize titration schemes. The model was developed with pooled data from cotadutide-administrated studies. Three different modeling approaches, proportional odds (PO), discrete-time Markov, and two-stage discrete-time Markov models, were employed to characterize the exposure–nausea relationship. The severity of nausea was modeled as different states (non-nausea, mild, and moderate/severe). The most appropriate model was selected to perform the covariate analysis, and the final covariate model was used to simulate the nausea event rates for various titration scenarios. The two Markov models demonstrated comparable performance and were better than the PO model. The covariate analysis was conducted with the standard Markov model for operational simplification and identified disease indications (NASH, obesity) and sex as covariates on Markov parameters. The simulations indicated that the biweekly titration with twofold dose escalation is superior to other titration schemes with a relatively low predicted nausea event rate at 600 μg (25%) and a shorter titration interval (8 weeks) to reach the therapeutic dose. The model can be utilized to optimize starting dose and titration schemes for other therapeutics in clinical trials to achieve an optimal risk–benefit balance and reach the therapeutic dose with minimal titration steps.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 9","pages":"1582-1594"},"PeriodicalIF":3.1,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13194","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141751290","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 modeling approach for dosing amiodarone in children on ECMO","authors":"Venkata K. Yellepeddi,&nbsp;John Porter Hunt,&nbsp;Danielle J. Green,&nbsp;Autumn McKnite,&nbsp;Aviva Whelan,&nbsp;Kevin Watt","doi":"10.1002/psp4.13199","DOIUrl":"10.1002/psp4.13199","url":null,"abstract":"<p>Extracorporeal membrane oxygenation (ECMO) is a cardiopulmonary bypass device commonly used to treat cardiac arrest in children. The American Heart Association guidelines for cardiopulmonary resuscitation (CPR) and emergency cardiovascular care recommend using amiodarone as a first-line agent to treat ventricular arrhythmias in children with cardiac arrest. However, there are no dosing recommendations for amiodarone to treat ventricular arrhythmias in pediatric patients on ECMO. Amiodarone has a high propensity for adsorption to the ECMO components due to its physicochemical properties leading to altered pharmacokinetics (PK) in ECMO patients. The change in amiodarone PK due to interaction with ECMO components may result in a difference in optimal dosing in patients on ECMO when compared with non-ECMO patients. To address this clinical knowledge gap, a physiologically-based pharmacokinetic model of amiodarone was developed in adults and scaled to children, followed by the addition of an ECMO compartment. The pediatric model included ontogeny functions of cytochrome P450 (CYP450) enzyme maturation across various age groups. The ECMO compartment was parameterized using the adsorption data of amiodarone obtained from ex vivo studies. Model predictions captured observed concentrations of amiodarone in pediatric patients with ECMO well with an average fold error between 0.5 and 2. Model simulations support an amiodarone intravenous (i.v) bolus dose of 22 mg/kg (neonates), 13 mg/kg (infants), 8 mg/kg (children), and 6 mg/kg (adolescents). This PBPK modeling approach can be applied to explore the dosing of other drugs used in children on ECMO.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 9","pages":"1542-1553"},"PeriodicalIF":3.1,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13199","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141733734","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 guide to developing population files for physiologically-based pharmacokinetic modeling in the Simcyp Simulator","authors":"Liam Curry,&nbsp;Sarah Alrubia,&nbsp;Frederic Y. Bois,&nbsp;Ruth Clayton,&nbsp;Eman El-Khateeb,&nbsp;Trevor N. Johnson,&nbsp;Muhammad Faisal,&nbsp;Sibylle Neuhoff,&nbsp;Kris Wragg,&nbsp;Amin Rostami-Hodjegan","doi":"10.1002/psp4.13202","DOIUrl":"10.1002/psp4.13202","url":null,"abstract":"<p>The Simcyp Simulator is a software platform widely used in the pharmaceutical industry to conduct stochastic physiologically-based pharmacokinetic (PBPK) modeling. This approach has the advantage of combining routinely generated in vitro data on drugs and drug products with knowledge of biology and physiology parameters to predict a priori potential pharmacokinetic changes in absorption, distribution, metabolism, and excretion for populations of interest. Combining such information with pharmacodynamic knowledge of drugs enables planning for potential dosage adjustment when clinical studies are feasible. Although the conduct of dedicated clinical studies in some patient groups (e.g., with hepatic or renal diseases) is part of the regulatory path for drug development, clinical studies for all permutations of covariates potentially affecting pharmacokinetics are impossible to perform. The role of PBPK in filling the latter gap is becoming more appreciated. This tutorial describes the different input parameters required for the creation of a virtual population giving robust predictions of likely changes in pharmacokinetics. It also highlights the considerations needed to qualify the models for such contexts of use. Two case studies showing the step-by-step development and application of population files for obese or morbidly obese patients and individuals with Crohn's disease are provided as the backbone of our tutorial to give some hands-on and real-world examples.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"13 9","pages":"1429-1447"},"PeriodicalIF":3.1,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.13202","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141731094","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}