Journal of Pharmacokinetics and Pharmacodynamics最新文献_第4页

Virtual clinical trials via a QSP immuno-oncology model to simulate the response to a conditionally activated PD-L1 targeting antibody in NSCLC. 通过 QSP 免疫肿瘤学模型进行虚拟临床试验，模拟 NSCLC 对条件激活的 PD-L1 靶向抗体的反应。

IF 2.2 4区医学

Journal of Pharmacokinetics and Pharmacodynamics Pub Date : 2024-12-01 Epub Date: 2024-06-10 DOI: 10.1007/s10928-024-09928-5

Alberto Ippolito, Hanwen Wang, Yu Zhang, Vahideh Vakil, Aleksander S Popel

{"title":"Virtual clinical trials via a QSP immuno-oncology model to simulate the response to a conditionally activated PD-L1 targeting antibody in NSCLC.","authors":"Alberto Ippolito, Hanwen Wang, Yu Zhang, Vahideh Vakil, Aleksander S Popel","doi":"10.1007/s10928-024-09928-5","DOIUrl":"10.1007/s10928-024-09928-5","url":null,"abstract":"Recently, immunotherapies for antitumoral response have adopted conditionally activated molecules with the objective of reducing systemic toxicity. Amongst these are conditionally activated antibodies, such as PROBODY® activatable therapeutics (Pb-Tx), engineered to be proteolytically activated by proteases found locally in the tumor microenvironment (TME). These PROBODY® therapeutics molecules have shown potential as PD-L1 checkpoint inhibitors in several cancer types, including both effectiveness and locality of action of the molecule as shown by several clinical trials and imaging studies. Here, we perform an exploratory study using our recently published quantitative systems pharmacology model, previously validated for triple-negative breast cancer (TNBC), to computationally predict the effectiveness and targeting specificity of a PROBODY® therapeutics drug compared to the non-modified antibody. We begin with the analysis of anti-PD-L1 immunotherapy in non-small cell lung cancer (NSCLC). As a first contribution, we have improved previous virtual patient selection methods using the omics data provided by the iAtlas database portal compared to methods previously published in literature. Furthermore, our results suggest that masking an antibody maintains its efficacy while improving the localization of active therapeutic in the TME. Additionally, we generalize the model by evaluating the dependence of the response to the tumor mutational burden, independently of cancer type, as well as to other key biomarkers, such as CD8/Treg Tcell and M1/M2 macrophage ratio. While our results are obtained from simulations on NSCLC, our findings are generalizable to other cancer types and suggest that an effective and highly selective conditionally activated PROBODY® therapeutics molecule is a feasible option.","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"747-757"},"PeriodicalIF":2.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579200/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141300881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

pyDarwin machine learning algorithms application and comparison in nonlinear mixed-effect model selection and optimization. pyDarwin 机器学习算法在非线性混合效应模型选择和优化中的应用和比较。

IF 2.2 4区医学

Journal of Pharmacokinetics and Pharmacodynamics Pub Date : 2024-12-01 Epub Date: 2024-06-28 DOI: 10.1007/s10928-024-09932-9

Xinnong Li, Mark Sale, Keith Nieforth, James Craig, Fenggong Wang, David Solit, Kairui Feng, Meng Hu, Robert Bies, Liang Zhao

{"title":"pyDarwin machine learning algorithms application and comparison in nonlinear mixed-effect model selection and optimization.","authors":"Xinnong Li, Mark Sale, Keith Nieforth, James Craig, Fenggong Wang, David Solit, Kairui Feng, Meng Hu, Robert Bies, Liang Zhao","doi":"10.1007/s10928-024-09932-9","DOIUrl":"10.1007/s10928-024-09932-9","url":null,"abstract":"Forward addition/backward elimination (FABE) has been the standard for population pharmacokinetic model selection (PPK) since NONMEM® was introduced. We investigated five machine learning (ML) algorithms (Genetic algorithm [GA], Gaussian process [GP], random forest [RF], gradient boosted random tree [GBRT], and particle swarm optimization [PSO]) as alternatives to FABE. These algorithms were applied to PPK model selection with a focus on comparing the efficiency and robustness of each of them. All machine learning algorithms included the combination of ML algorithms with a local downhill search. The local downhill search consisted of systematically changing one or two \"features\" at a time (a one-bit or a two-bit local search), alternating with the ML methods. An exhaustive search (all possible combinations of model features, N = 1,572,864 models) was the gold standard for robustness, and the number of models examined leading prior to identification of the final model was the metric for efficiency.All algorithms identified the optimal model when combined with the two-bit local downhill search. GA, RF, GBRT, and GP identified the optimal model with only a one-bit local search. PSO required the two-bit local downhill search. In our analysis, GP was the most efficient algorithm as measured by the number of models examined prior to finding the optimal (495 models), and PSO exhibited the least efficiency, requiring 1710 unique models before finding the best solution. Additionally, GP was also the algorithm that needed the longest elapsed time of 2975.6 min, in comparison with GA, which only required 321.8 min.","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"785-796"},"PeriodicalIF":2.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579193/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141468850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of monoclonal antibody disposition predictions using different physiologically based pharmacokinetic modelling platforms. 使用不同基于生理的药代动力学建模平台的单克隆抗体倾向预测的比较。

IF 2.2 4区医学

Journal of Pharmacokinetics and Pharmacodynamics Pub Date : 2024-12-01 Epub Date: 2023-11-12 DOI: 10.1007/s10928-023-09894-4

Pieter-Jan De Sutter, Elke Gasthuys, An Vermeulen

{"title":"Comparison of monoclonal antibody disposition predictions using different physiologically based pharmacokinetic modelling platforms.","authors":"Pieter-Jan De Sutter, Elke Gasthuys, An Vermeulen","doi":"10.1007/s10928-023-09894-4","DOIUrl":"10.1007/s10928-023-09894-4","url":null,"abstract":"Physiologically based pharmacokinetic (PBPK) models can be used to leverage physiological and in vitro data to predict monoclonal antibody (mAb) concentrations in serum and tissues. However, it is currently not known how consistent predictions of mAb disposition are across PBPK modelling platforms. In this work PBPK simulations of IgG, adalimumab and infliximab were compared between three platforms (Simcyp, PK-Sim, and GastroPlus). Accuracy of predicted serum and tissue concentrations was assessed using observed data collected from the literature. Physiological and mAb related input parameters were also compared and sensitivity analyses were carried out to evaluate model behavior when input values were altered. Differences in serum kinetics of IgG between platforms were minimal for a dose of 1 mg/kg, but became more noticeable at higher dosages (> 100 mg/kg) and when reference (healthy) physiological input values were altered. Predicted serum concentrations of both adalimumab and infliximab were comparable across platforms, but were noticeably higher than observed values. Tissue concentrations differed remarkably between the platforms, both for total- and interstitial fluid (ISF) concentrations. The accuracy of total tissue concentrations was within a three-fold of observed values for all tissues, except for brain tissue concentrations, which were overpredicted. Predictions of tissue ISF concentrations were less accurate and were best captured by GastroPlus. Overall, these simulations show that the different PBPK platforms generally predict similar mAb serum concentrations, but variable tissue concentrations. Caution is therefore warranted when PBPK models are used to simulate effect site tissue concentrations of mAbs without data to verify the predictions.","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"639-651"},"PeriodicalIF":2.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89718725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

General quasi-equilibrium multivalent binding model to study diverse and complex drug-receptor interactions of biologics. 通用准平衡多价结合模型，用于研究生物制剂的各种复杂的药物-受体相互作用。

IF 2.2 4区医学

Journal of Pharmacokinetics and Pharmacodynamics Pub Date : 2024-12-01 Epub Date: 2024-08-17 DOI: 10.1007/s10928-024-09936-5

Chee M Ng, Robert J Bauer

{"title":"General quasi-equilibrium multivalent binding model to study diverse and complex drug-receptor interactions of biologics.","authors":"Chee M Ng, Robert J Bauer","doi":"10.1007/s10928-024-09936-5","DOIUrl":"10.1007/s10928-024-09936-5","url":null,"abstract":"Pharmacokinetics and pharmacodynamics of many biologics are influenced by their complex binding to biological receptors. Biologics consist of diverse groups of molecules with different binding kinetics to its receptors including IgG with simple one-to-one drug receptor bindings, bispecific antibody (BsAb) that binds to two different receptors, and antibodies that can bind to six or more identical receptors. As the binding process is typically much faster than elimination (or internalization) and distribution processes, quasi-equilibrium (QE) binding models are commonly used to describe drug-receptor binding kinetics of biologics. However, no general QE modeling framework is available to describe complex binding kinetics for diverse classes of biologics. In this paper, we describe novel approaches of using differential algebraic equations (DAE) to solve three QE multivalent drug-receptor binding (QEMB) models. The first example describes the binding kinetics of three-body equilibria of BsAb that binds to 2 different receptors for trimer formation. The second example models an engineered IgG variant (Multabody) that can bind to 24 identical target receptors. The third example describes an IgG with modified neonatal Fc receptor (FcRn) binding affinity that competes for the same FcRn receptor as endogenous IgG. The model parameter estimates were obtained by fitting the model to all data simultaneously. The models allowed us to study potential roles of cooperative binding on bell-shaped drug exposure-response relationships of BsAb, and concentration-depended distribution of different drug-receptor complexes for Multabody. This DAE-based QEMB model platform can serve as an important tool to better understand complex binding kinetics of diverse classes of biologics.","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"841-857"},"PeriodicalIF":2.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141995945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generation of realistic virtual adult populations using a model-based copula approach. 利用基于模型的共轭方法生成逼真的虚拟成人种群。

IF 2.2 4区医学

Journal of Pharmacokinetics and Pharmacodynamics Pub Date : 2024-12-01 Epub Date: 2024-06-06 DOI: 10.1007/s10928-024-09929-4

Yuchen Guo, Tingjie Guo, Catherijne A J Knibbe, Laura B Zwep, J G Coen van Hasselt

{"title":"Generation of realistic virtual adult populations using a model-based copula approach.","authors":"Yuchen Guo, Tingjie Guo, Catherijne A J Knibbe, Laura B Zwep, J G Coen van Hasselt","doi":"10.1007/s10928-024-09929-4","DOIUrl":"10.1007/s10928-024-09929-4","url":null,"abstract":"Incorporating realistic sets of patient-associated covariates, i.e., virtual populations, in pharmacometric simulation workflows is essential to obtain realistic model predictions. Current covariate simulation strategies often omit or simplify dependency structures between covariates. Copula models are multivariate distribution functions suitable to capture dependency structures between covariates with improved performance compared to standard approaches. We aimed to develop and evaluate a copula model for generation of adult virtual populations for 12 patient-associated covariates commonly used in pharmacometric simulations, using the publicly available NHANES database, including sex, race-ethnicity, body weight, albumin, and several biochemical variables related to organ function. A multivariate (vine) copula was constructed from bivariate relationships in a stepwise fashion. Covariate distributions were well captured for the overall and subgroup populations. Based on the developed copula model, a web application was developed. The developed copula model and associated web application can be used to generate realistic adult virtual populations, ultimately to support model-based clinical trial design or dose optimization strategies.","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"735-746"},"PeriodicalIF":2.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579194/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141283910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Likelihood comparisons in bounded outcome score analysis must be internally consistent. 有界结果得分分析中的可能性比较必须具有内部一致性。

IF 2.2 4区医学

Journal of Pharmacokinetics and Pharmacodynamics Pub Date : 2024-12-01 Epub Date: 2024-07-05 DOI: 10.1007/s10928-024-09933-8

Chuanpu Hu

引用次数: 0

A quantitative systems pharmacology model of plasma kallikrein-kinin system dysregulation in hereditary angioedema. 遗传性血管性水肿中血浆激肽-激肽系统失调的定量系统药理学模型。

IF 2.2 4区医学

Journal of Pharmacokinetics and Pharmacodynamics Pub Date : 2024-12-01 Epub Date: 2024-05-11 DOI: 10.1007/s10928-024-09919-6

Dan Sexton, Hoa Q Nguyen, Salomé Juethner, Haobin Luo, Zhiwei Zhang, Paul Jasper, Andy Z X Zhu

{"title":"A quantitative systems pharmacology model of plasma kallikrein-kinin system dysregulation in hereditary angioedema.","authors":"Dan Sexton, Hoa Q Nguyen, Salomé Juethner, Haobin Luo, Zhiwei Zhang, Paul Jasper, Andy Z X Zhu","doi":"10.1007/s10928-024-09919-6","DOIUrl":"10.1007/s10928-024-09919-6","url":null,"abstract":"Hereditary angioedema (HAE) due to C1-inhibitor deficiency is a rare, debilitating, genetic disorder characterized by recurrent, unpredictable, attacks of edema. The clinical symptoms of HAE arise from excess bradykinin generation due to dysregulation of the plasma kallikrein-kinin system (KKS). A quantitative systems pharmacology (QSP) model that mechanistically describes the KKS and its role in HAE pathophysiology was developed based on HAE attacks being triggered by autoactivation of factor XII (FXII) to activated FXII (FXIIa), resulting in kallikrein production from prekallikrein. A base pharmacodynamic model was constructed and parameterized from literature data and ex vivo assays measuring inhibition of kallikrein activity in plasma of HAE patients or healthy volunteers who received lanadelumab. HAE attacks were simulated using a virtual patient population, with attacks recorded when systemic bradykinin levels exceeded 20 pM. The model was validated by comparing the simulations to observations from lanadelumab and plasma-derived C1-inhibitor clinical trials. The model was then applied to analyze the impact of nonadherence to a daily oral preventive therapy; simulations showed a correlation between the number of missed doses per month and reduced drug effectiveness. The impact of reducing lanadelumab dosing frequency from 300 mg every 2 weeks (Q2W) to every 4 weeks (Q4W) was also examined and showed that while attack rates with Q4W dosing were substantially reduced, the extent of reduction was greater with Q2W dosing. Overall, the QSP model showed good agreement with clinical data and could be used for hypothesis testing and outcome predictions.","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"721-734"},"PeriodicalIF":2.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140908115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Population pharmacokinetic analyses of pozelimab in patients with CD55-deficient protein-losing enteropathy (CHAPLE disease). 波珠单抗在 CD55 缺乏性蛋白失代偿性肠病（CHAPLE 病）患者中的群体药代动力学分析。

IF 2.2 4区医学

Journal of Pharmacokinetics and Pharmacodynamics Pub Date : 2024-12-01 Epub Date: 2024-09-30 DOI: 10.1007/s10928-024-09941-8

Kuan-Ju Lin, Jeanne Mendell, John D Davis, Lutz O Harnisch

{"title":"Population pharmacokinetic analyses of pozelimab in patients with CD55-deficient protein-losing enteropathy (CHAPLE disease).","authors":"Kuan-Ju Lin, Jeanne Mendell, John D Davis, Lutz O Harnisch","doi":"10.1007/s10928-024-09941-8","DOIUrl":"10.1007/s10928-024-09941-8","url":null,"abstract":"Pozelimab, a monoclonal antibody directed against C5, is the first and only treatment for adult and pediatric patients (≥ 1 year) with CD55-deficient protein-losing enteropathy (CHAPLE) disease. A target-mediated drug disposition (TMDD) population pharmacokinetic (PopPK) model was developed using pooled data from four phase 1-3 studies to characterize the pharmacokinetics (PK) of total pozelimab and total C5, and to simulate free pozelimab and free C5 to support the dose regimen in patients with CHAPLE disease. A TMDD PopPK model was developed using total pozelimab and total C5 concentration-time data from 106 participants (82 healthy volunteers; 24 patients with paroxysmal nocturnal hemoglobinuria [PNH]). This model was refined and updated to include PK data from 10 patients with CHAPLE disease from a phase 2/3 study. Stochastic simulations predicted concentration-time profiles for total pozelimab, free pozelimab, and free C5, to obtain pozelimab exposure metrics for patients with CHAPLE disease. A two-compartment TMDD model with two binding sites based on the quasi-equilibrium approximation adequately described the concentration-time profiles of total pozelimab and total C5. Body weight was identified as the most important source of pozelimab PK variability; therefore, the dose was adjusted based on body weight for the predominantly pediatric patients with CHAPLE disease. A robust TMDD PopPK model was developed to describe the PK of total pozelimab and total C5 following pozelimab administration. Reliable predictions for individual exposures of total pozelimab and free C5 were possible and supported the 10 mg/kg weight-based dose regimen in patients with CHAPLE disease.","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"905-917"},"PeriodicalIF":2.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142348912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative models for synthetic data generation: application to pharmacokinetic/pharmacodynamic data. 合成数据生成模型：应用于药代动力学/药效学数据。

IF 2.2 4区医学

Journal of Pharmacokinetics and Pharmacodynamics Pub Date : 2024-12-01 Epub Date: 2024-08-27 DOI: 10.1007/s10928-024-09935-6

Yulun Jiang, Alberto García-Durán, Idris Bachali Losada, Pascal Girard, Nadia Terranova

{"title":"Generative models for synthetic data generation: application to pharmacokinetic/pharmacodynamic data.","authors":"Yulun Jiang, Alberto García-Durán, Idris Bachali Losada, Pascal Girard, Nadia Terranova","doi":"10.1007/s10928-024-09935-6","DOIUrl":"10.1007/s10928-024-09935-6","url":null,"abstract":"The generation of synthetic patient data that reflect the statistical properties of real data plays a fundamental role in today's world because of its potential to (i) be enable proprietary data access for statistical and research purposes and (ii) increase available data (e.g., in low-density regions-i.e., for patients with under-represented characteristics). Generative methods employ a family of solutions for generating synthetic data. The objective of this research is to benchmark numerous state-of-the-art deep-learning generative methods across different scenarios and clinical datasets comprising patient covariates and several pharmacokinetic/pharmacodynamic endpoints. We did this by implementing various probabilistic models aimed at generating synthetic data, such as the Multi-layer Perceptron Conditioning Generative Adversarial Neural Network (MLP cGAN), Time-series Generative Adversarial Networks (TimeGAN), and a more traditional approach like Probabilistic Autoregressive (PAR). We evaluated their performance by calculating discriminative and predictive scores. Furthermore, we conducted comparisons between the distributions of real and synthetic data using Kolmogorov-Smirnov and Chi-square statistical tests, focusing respectively on covariate and output variables of the models. Lastly, we employed pharmacometrics-related metric to enhance interpretation of our results specific to our investigated scenarios. Results indicate that multi-layer perceptron-based conditional generative adversarial networks (MLP cGAN) exhibit the best overall performance for most of the considered metrics. This work highlights the opportunities to employ synthetic data generation in the field of clinical pharmacology for augmentation and sharing of proprietary data across institutions.","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"877-885"},"PeriodicalIF":2.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142080627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visual predictive check of longitudinal models and dropout. 纵向模型和辍学的可视化预测检查。

IF 2.2 4区医学

Journal of Pharmacokinetics and Pharmacodynamics Pub Date : 2024-12-01 Epub Date: 2024-08-18 DOI: 10.1007/s10928-024-09937-4

Chuanpu Hu, Anna G Kondic, Amit Roy

{"title":"Visual predictive check of longitudinal models and dropout.","authors":"Chuanpu Hu, Anna G Kondic, Amit Roy","doi":"10.1007/s10928-024-09937-4","DOIUrl":"10.1007/s10928-024-09937-4","url":null,"abstract":"Visual predictive checks (VPC) are commonly used to evaluate pharmacometrics models. However their performance may be hampered if patients with worse outcomes drop out earlier, as often occurs in clinical trials, especially in oncology. While methods accounting for dropouts have appeared in literature, they vary in assumptions, flexibility, and performance, and the differences between them are not widely understood. This manuscript aims to elucidate which methods can be used to handle VPC with dropout and when, along with a more informative VPC approach using confidence intervals. Additionally, we propose constructing the confidence interval based on the observed data instead of the simulated data. The theoretical framework for incorporating dropout in VPCs is developed and applied to propose two approaches: full and conditional. The full approach is implemented using a parametric time-to-event model, while the conditional approach is implemented using both parametric and Cox proportional-hazard (CPH) models. The practical performances of these approaches are illustrated with an application to the tumor growth dynamics (TGD) modeling of data from two cancer clinical trials of nivolumab and docetaxel, where patients were followed until disease progression. The dataset consisted of 3504 tumor size measurements from 855 subjects, which were described by a TGD model. The dropout of subjects was described by a Weibull or CPH model. Simulated datasets were also used to further illustrate the properties of the VPC methods. The results showed that the more familiar full approach might not provide meaningful improvement for TGD model evaluation over the naive approach of not adjusting for dropout, and could be outperformed by the conditional approach using either the Weibull model or the Cox proportional hazard model. Overall, including confidence intervals in VPC should improve interpretation, the conditional approach was shown to be more generally applicable when dropout occurs, and the nonparametric approach could provide additional robustness.","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":" ","pages":"859-875"},"PeriodicalIF":2.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141995946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0