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Duvnjak Z, Schaedeli Stark F, Cosson V, Retout S, Schindler E, Abrantes JA. Simulation-based evaluation of the Pharmpy Automatic Model Development tool for population pharmacokinetic modeling in early clinical drug development. CPT Pharmacometrics Syst Pharmacol 2024. [PMID: 39155545 DOI: 10.1002/psp4.13213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 07/05/2024] [Accepted: 07/14/2024] [Indexed: 08/20/2024] Open
Abstract
The Pharmpy Automatic Model Development (AMD) tool automates the building of population pharmacokinetic (popPK) models by utilizing a systematic stepwise process. In this study, the performance of the AMD tool was assessed using simulated datasets. Ten true models mimicking classical popPK models were created. From each true model, dataset replicates were simulated assuming a typical phase I study design-single and multiple ascending doses with/without dichotomous food effect, with rich PK sampling. For every dataset replicate, the AMD tool automatically built an AMD model utilizing NONMEM for parameter estimation. The AMD models were compared to the true and reference models (true model fitted to simulated datasets) based on their model components, predicted population and individual secondary PK parameters (SP) (AUC0-24, cmax, ctrough), and model quality metrics (e.g., model convergence, parameter relative standard errors (RSEs), Bayesian Information Criterion (BIC)). The models selected by the AMD tool closely resembled the true models, particularly in terms of distribution and elimination, although differences were observed in absorption and inter-individual variability components. Bias associated with the derived SP was low. In general, discrepancies between AMD and true SP were also observed for reference models and therefore were attributed to the inherent stochasticity in simulations. In summary, the AMD tool was found to be a valuable asset in automating repetitive modeling tasks, yielding reliable PK models in the scenarios assessed. This tool has the potential to save time during early clinical drug development that can be invested in more complex modeling activities within model-informed drug development.
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Affiliation(s)
- Zrinka Duvnjak
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universität Berlin, Berlin, Germany
- Graduate Research Training Program PharMetrX, Berlin/Potsdam, Germany
| | - Franziska Schaedeli Stark
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Valérie Cosson
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Sylvie Retout
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - Emilie Schindler
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
| | - João A Abrantes
- Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
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2
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Kaikousidis C, Bies RR, Dokoumetzidis A. Simulating realistic patient profiles from pharmacokinetic models by a machine learning postprocessing correction of residual variability. CPT Pharmacometrics Syst Pharmacol 2024. [PMID: 38877660 DOI: 10.1002/psp4.13182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 05/14/2024] [Accepted: 05/21/2024] [Indexed: 06/16/2024] Open
Abstract
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 IRES = DV-IPRED, are computed, where DV are the observations and IPRED the individual predictions and are modeled by ML to obtain IRESML. Correction of the IPREDs can then be carried out as DVML = IPRED + IRESML. 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 R2 between IRES and IRESML, 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.
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Affiliation(s)
- Christos Kaikousidis
- Department of Pharmacy, National and Kapodistrian University of Athens, Athens, Greece
| | - Robert R Bies
- Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York, USA
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3
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Wellhagen GJ, Yassen A, Garmann D, Bröker A, Solms A, Zhang Y, Kjellsson MC, Karlsson MO. Evaluation of covariate effects in item response theory models. CPT Pharmacometrics Syst Pharmacol 2024; 13:812-822. [PMID: 38436514 PMCID: PMC11098156 DOI: 10.1002/psp4.13120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 01/26/2024] [Accepted: 02/12/2024] [Indexed: 03/05/2024] Open
Abstract
Item response theory (IRT) models are usually the best way to analyze composite or rating scale data. Standard methods to evaluate covariate or treatment effects in IRT models do not allow to identify item-specific effects. Finding subgroups of patients who respond differently to certain items could be very important when designing inclusion or exclusion criteria for clinical trials, and aid in understanding different treatment responses in varying disease manifestations. We present a new method to investigate item-specific effects in IRT models, which is based on inspection of residuals. The method was investigated in a simulation exercise with a model for the Epworth Sleepiness Scale. We also provide a detailed discussion as a guidance on how to build a robust covariate IRT model.
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Takahashi T, Jaber MM, Smith AR, Jacobson PA, Fisher J, Kirstein MN. Predictive Value of C-Reactive Protein and Albumin for Temporal Within-Individual Pharmacokinetic Variability of Voriconazole in Pediatric Hematopoietic Cell Transplant Patients. J Clin Pharmacol 2021; 62:855-862. [PMID: 34970774 DOI: 10.1002/jcph.2024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 12/28/2021] [Indexed: 11/10/2022]
Abstract
Voriconazole is a widely used antifungal agent in immunocompromised patients, but its utility is limited by its variable exposure and narrow therapeutic index. Population pharmacokinetic (PK) models have been used to characterize voriconazole PK and derive individualized dosing regimens. However, determinants of temporal within-patient variability of voriconazole PK were not well-established. We aimed to characterize temporal variability of voriconazole PK within individuals and identify predictive clinical factors. This study was conducted as a part of a single-institution, phase I study of intravenous voriconazole in children undergoing HCT (NCT02227797). We analyzed voriconazole PK study data collected at week 1 and again at week 2 after the start of voriconazole therapy in 59 pediatric HCT patients (age <21 years). Population PK analysis using nonlinear mixed effect modeling was performed to analyze temporal within-individual variability of voriconazole PK by incorporating a between-occasion variability term in the model. A two-compartment linear elimination model incorporating body weight and CYP2C19 phenotype described the data. Ratio of individual voriconazole clearance between weeks 1 to 2 ranged from 0.11 to 3.3 (-9.1 to +3.3-fold change). Incorporation of covariate effects by serum C-reactive protein (CRP) and albumin levels decreased between-occasion variability of clearance (coefficient of variation: from 59.5% to 41.2%) and improved the model fit (p<0.05). As significant covariates on voriconazole PK, CRP and albumin concentrations may potentially serve as useful biomarkers as part of therapeutic drug monitoring. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Takuto Takahashi
- Division of Hematology and Oncology, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA.,Division of Blood and Marrow Transplant, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA.,Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USA
| | - Mutaz M Jaber
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USA
| | - Angela R Smith
- Division of Blood and Marrow Transplant, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA.,Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA
| | - Pamala A Jacobson
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USA
| | - James Fisher
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USA
| | - Mark N Kirstein
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USA.,Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA
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Arshad U, Chasseloup E, Nordgren R, Karlsson MO. Development of visual predictive checks accounting for multimodal parameter distributions in mixture models. J Pharmacokinet Pharmacodyn 2019; 46:241-250. [PMID: 30968312 PMCID: PMC6560505 DOI: 10.1007/s10928-019-09632-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 03/29/2019] [Indexed: 01/18/2023]
Abstract
The assumption of interindividual variability being unimodally distributed in nonlinear mixed effects models does not hold when the population under study displays multimodal parameter distributions. Mixture models allow the identification of parameters characteristic to a subpopulation by describing these multimodalities. Visual predictive check (VPC) is a standard simulation based diagnostic tool, but not yet adapted to account for multimodal parameter distributions. Mixture model analysis provides the probability for an individual to belong to a subpopulation (IPmix) and the most likely subpopulation for an individual to belong to (MIXEST). Using simulated data examples, two implementation strategies were followed to split the data into subpopulations for the development of mixture model specific VPCs. The first strategy splits the observed and simulated data according to the MIXEST assignment. A shortcoming of the MIXEST-based allocation strategy was a biased allocation towards the dominating subpopulation. This shortcoming was avoided by splitting observed and simulated data according to the IPmix assignment. For illustration purpose, the approaches were also applied to an irinotecan mixture model demonstrating 36% lower clearance of irinotecan metabolite (SN-38) in individuals with UGT1A1 homo/heterozygote versus wild-type genotype. VPCs with segregated subpopulations were helpful in identifying model misspecifications which were not evident with standard VPCs. The new tool provides an enhanced power of evaluation of mixture models.
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Affiliation(s)
- Usman Arshad
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
- Faculty of Medicine and University Hospital Cologne, Center for Pharmacology, Department I of Pharmacology, University of Cologne, Gleueler Str 24, 50931, Cologne, Germany.
| | - Estelle Chasseloup
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Rikard Nordgren
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Mats O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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Ibrahim MMA, Nordgren R, Kjellsson MC, Karlsson MO. Variability Attribution for Automated Model Building. AAPS JOURNAL 2019; 21:37. [PMID: 30850918 PMCID: PMC6505507 DOI: 10.1208/s12248-019-0310-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 02/19/2019] [Indexed: 11/30/2022]
Abstract
We investigated the possible advantages of using linearization to evaluate models of residual unexplained variability (RUV) for automated model building in a similar fashion to the recently developed method “residual modeling.” Residual modeling, although fast and easy to automate, cannot identify the impact of implementing the needed RUV model on the imprecision of the rest of model parameters. We used six RUV models to be tested with 12 real data examples. Each example was first linearized; then, we assessed the agreement in improvement of fit between the base model and its extended models for linearization and conventional analysis, in comparison to residual modeling performance. Afterward, we compared the estimates of parameters’ variabilities and their uncertainties obtained by linearization to conventional analysis. Linearization accurately identified and quantified the nature and magnitude of RUV model misspecification similar to residual modeling. In addition, linearization identified the direction of change and quantified the magnitude of this change in variability parameters and their uncertainties. This method is implemented in the software package PsN for automated model building/evaluation with continuous data.
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Affiliation(s)
- Moustafa M A Ibrahim
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.,Department of Pharmacy Practice, Helwan University, Cairo, Egypt
| | - Rikard Nordgren
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Maria C Kjellsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Mats O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
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7
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Ibrahim MMA, Ueckert S, Freiberga S, Kjellsson MC, Karlsson MO. Model-Based Conditional Weighted Residuals Analysis for Structural Model Assessment. AAPS JOURNAL 2019; 21:34. [PMID: 30815754 PMCID: PMC6394649 DOI: 10.1208/s12248-019-0305-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Accepted: 01/30/2019] [Indexed: 11/30/2022]
Abstract
Nonlinear mixed effects models are widely used to describe longitudinal data to improve the efficiency of drug development process or increase the understanding of the studied disease. In such settings, the appropriateness of the modeling assumptions is critical in order to draw correct conclusions and must be carefully assessed for any substantial violations. Here, we propose a new method for structure model assessment, based on assessment of bias in conditional weighted residuals (CWRES). We illustrate this method by assessing prediction bias in two integrated models for glucose homeostasis, the integrated glucose-insulin (IGI) model, and the integrated minimal model (IMM). One dataset was simulated from each model then analyzed with the two models. CWRES outputted from each model fitting were modeled to capture systematic trends in CWRES as well as the magnitude of structural model misspecifications in terms of difference in objective function values (ΔOFVBias). The estimates of CWRES bias were used to calculate the corresponding bias in conditional predictions by the inversion of first-order conditional estimation method’s covariance equation. Time, glucose, and insulin concentration predictions were the investigated independent variables. The new method identified correctly the bias in glucose sub-model of the integrated minimal model (IMM), when this bias occurred, and calculated the absolute and proportional magnitude of the resulting bias. CWRES bias versus the independent variables agreed well with the true trends of misspecification. This method is fast easily automated diagnostic tool for model development/evaluation process, and it is already implemented as part of the Perl-speaks-NONMEM software.
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Affiliation(s)
- Moustafa M A Ibrahim
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.,Department of Pharmacy Practice, Helwan University, Cairo, Egypt
| | - Sebastian Ueckert
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Svetlana Freiberga
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Maria C Kjellsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Mats O Karlsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
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Ibarra M, Dalla Costa T, Schaiquevich P, Cristofoletti R, Hernández González I, Fajardo-Robledo NS, Aragón Novoa M, Pecchio M, Cortinez I, Trocóniz IF, Romero-Tejeda EM. Iberoamerican Pharmacometrics Network Congress 2018 Report: Fostering Modeling and Simulation Approaches for Drug Development and Regulatory and Clinical Applications in Latin America. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2019; 8:197-200. [PMID: 30681295 PMCID: PMC6482274 DOI: 10.1002/psp4.12387] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 01/14/2019] [Accepted: 01/15/2019] [Indexed: 11/11/2022]
Affiliation(s)
- Manuel Ibarra
- Pharmaceutical Sciences Department, Faculty of Chemistry, Bioavailability and Bioequivalence Centre for Medicine Evaluation, Universidad de la República, Montevideo, Uruguay
| | - Teresa Dalla Costa
- Pharmacokinetics and PK/PD Modeling Laboratory, Faculty of Pharmacy, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Paula Schaiquevich
- National Scientific and Technical Research Council, Buenos Aires, Argentina.,Unit of Clinical Pharmacokinetics, Hospital de Pediatria JP Garrahan, Buenos Aires, Argentina
| | - Rodrigo Cristofoletti
- Division of Therapeutic Equivalence, Brazilian Health Surveillance Agency, Brasilia, Brazil.,Center for Pharmacometrics and Systems Pharmacology, Department of Pharmaceutics, College of Pharmacy, University of Florida, Orlando, Florida, USA
| | | | - Nicte S Fajardo-Robledo
- Pharmacobiology Department, University Center of Exact Sciences and Engineering, University of Guadalajara, Guadalajara, Mexico
| | | | - Marisín Pecchio
- Instituto de Investigaciones Científicas y Servicios de Alta Tecnología,, Panamá, República de Panamá
| | - Ignacio Cortinez
- Department of Anaesthesiology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Iñaki F Trocóniz
- Pharmacometrics and Systems Pharmacology, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Elba M Romero-Tejeda
- Pharmacobiology Department, University Center of Exact Sciences and Engineering, University of Guadalajara, Guadalajara, Mexico
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