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Sanghavi K, Ribbing J, Rogers JA, Ahmed MA, Karlsson MO, Holford N, Chasseloup E, Ahamadi M, Kowalski KG, Cole S, Kerwash E, Wade JR, Liu C, Wang Y, Trame MN, Zhu H, Wilkins JJ. Covariate modeling in pharmacometrics: General points for consideration. CPT Pharmacometrics Syst Pharmacol 2024; 13:710-728. [PMID: 38566433 PMCID: PMC11098153 DOI: 10.1002/psp4.13115] [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: 08/18/2023] [Revised: 01/15/2024] [Accepted: 02/05/2024] [Indexed: 04/04/2024] Open
Abstract
Modeling the relationships between covariates and pharmacometric model parameters is a central feature of pharmacometric analyses. The information obtained from covariate modeling may be used for dose selection, dose individualization, or the planning of clinical studies in different population subgroups. The pharmacometric literature has amassed a diverse, complex, and evolving collection of methodologies and interpretive guidance related to covariate modeling. With the number and complexity of technologies increasing, a need for an overview of the state of the art has emerged. In this article the International Society of Pharmacometrics (ISoP) Standards and Best Practices Committee presents perspectives on best practices for planning, executing, reporting, and interpreting covariate analyses to guide pharmacometrics decision making in academic, industry, and regulatory settings.
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Affiliation(s)
| | | | | | - Mariam A. Ahmed
- Quantitative Clinical Pharmacology, Takeda PharmaceuticalCambridgeMassachusettsUSA
| | | | - Nick Holford
- Department of Pharmacology & Clinical PharmacologyUniversity of AucklandAucklandNew Zealand
| | | | | | | | - Susan Cole
- Medical and Healthcare product Regulatory Agency (MHRA)LondonUK
| | - Essam Kerwash
- Medical and Healthcare product Regulatory Agency (MHRA)LondonUK
| | | | - Chao Liu
- Applied Innovation Quantitative Solutions, BeiGeneWashingtonDCUSA
| | - Yaning Wang
- Createrna Science and TechnologyClarksburgMarylandUSA
| | - Mirjam N. Trame
- Integrated Drug Development Northeast Regional LeadCertaraMassachusettsUSA
| | - Hao Zhu
- Division of Pharmacometrics, Office of Clinical PharmacologyCenter for Drug Evaluation and Research, Food and Drug AdministrationSilver SpringsMarylandUSA
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Ronchi D, Tosca EM, Bartolucci R, Magni P. Go beyond the limits of genetic algorithm in daily covariate selection practice. J Pharmacokinet Pharmacodyn 2024; 51:109-121. [PMID: 37493851 PMCID: PMC10982092 DOI: 10.1007/s10928-023-09875-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 07/08/2023] [Indexed: 07/27/2023]
Abstract
Covariate identification is an important step in the development of a population pharmacokinetic/pharmacodynamic model. Among the different available approaches, the stepwise covariate model (SCM) is the most used. However, SCM is based on a local search strategy, in which the model-building process iteratively tests the addition or elimination of a single covariate at a time given all the others. This introduces a heuristic to limit the searching space and then the computational complexity, but, at the same time, can lead to a suboptimal solution. The application of genetic algorithms (GAs) for covariate selection has been proposed as a possible solution to overcome these limitations. However, their actual use during model building is limited by the extremely high computational costs and convergence issues, both related to the number of models being tested. In this paper, we proposed a new GA for covariate selection to address these challenges. The GA was first developed on a simulated case study where the heuristics introduced to overcome the limitations affecting currently available GA approaches resulted able to limit the selection of redundant covariates, increase replicability of results and reduce convergence times. Then, we tested the proposed GA on a real-world problem related to remifentanil. It obtained good results both in terms of selected covariates and fitness optimization, outperforming the SCM.
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Affiliation(s)
- D Ronchi
- Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di Pavia, 27100, Pavia, Italy
| | - E M Tosca
- Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di Pavia, 27100, Pavia, Italy
| | - R Bartolucci
- Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di Pavia, 27100, Pavia, Italy
- Clinical Pharmacology & Pharmacometrics, Janssen Research & Development, Beerse, Belgium
| | - P Magni
- Dipartimento di Ingegneria Industriale e dell'Informazione, Università degli Studi di Pavia, 27100, Pavia, Italy.
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Uno M, Nakamaru Y, Yamashita F. Application of machine learning techniques in population pharmacokinetics/pharmacodynamics modeling. Drug Metab Pharmacokinet 2024; 56:101004. [PMID: 38795660 DOI: 10.1016/j.dmpk.2024.101004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 01/22/2024] [Accepted: 02/10/2024] [Indexed: 05/28/2024]
Abstract
Population pharmacokinetics/pharmacodynamics (pop-PK/PD) consolidates pharmacokinetic and pharmacodynamic data from many subjects to understand inter- and intra-individual variability due to patient backgrounds, including disease state and genetics. The typical workflow in pop-PK/PD analysis involves the determination of the structure model, selection of the error model, analysis based on the base model, covariate modeling, and validation of the final model. Machine learning is gaining considerable attention in the medical and various fields because, in contrast to traditional modeling, which often assumes linear or predefined relationships, machine learning modeling learns directly from data and accommodates complex patterns. Machine learning has demonstrated excellent capabilities for prescreening covariates and developing predictive models. This review introduces various applications of machine learning techniques in pop-PK/PD research.
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Affiliation(s)
- Mizuki Uno
- Department of Drug Delivery Research, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshidashimoadachi-cho, Sakyo-ku, Kyoto, 606-8501, Japan
| | - Yuta Nakamaru
- Department of Drug Delivery Research, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshidashimoadachi-cho, Sakyo-ku, Kyoto, 606-8501, Japan
| | - Fumiyoshi Yamashita
- Department of Drug Delivery Research, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshidashimoadachi-cho, Sakyo-ku, Kyoto, 606-8501, Japan.
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Tang M, Lhermie G. Risk factors associated with calf mortality in Western Canadian cow-calf operations. Prev Vet Med 2023; 218:105989. [PMID: 37579720 DOI: 10.1016/j.prevetmed.2023.105989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/27/2023] [Accepted: 07/31/2023] [Indexed: 08/16/2023]
Abstract
This study examined the influence of management practices and herd demographics on calf mortality proportions in Western Canadian cow-calf operations, utilizing data from the second Western Canadian Cow-calf Survey. The survey was conducted between October 23, 2017, and February 28, 2018. The survey, which was open to all cow-calf producers across Western Canada, provided producer-reported data regarding calf death loss and corresponding herd-level factors. A fractional logit model was employed to identify significant factors associated with calf mortality proportions. The findings revealed that shorter breeding seasons (<63 days), calves born within the same season, and regular pregnancy checks for breeding females were negatively associated with calf mortality proportions. Conversely, regular breeding soundness evaluations for breeding bulls, traditional weaning methods, and vaccinating heifers for scours showed positive associations with increased calf mortality proportions. Herd operations where dams were vaccinated against clostridial and bovine respiratory diseases had lower calf mortality proportions. Notably, operations with experienced primary decision-makers holding off-farm jobs had lower predicted calf mortality proportions compared to those managed by full-time cattle producers. Higher predicted calf mortality proportions were observed in operations employing a backgrounding system. The study's limitations included potential biases due to its cross-sectional nature and reliance on producer-reported data, which might lead to an underestimation of calf mortality proportions. Also, the limited sample size and missing data might have affected the statistical significance of some variables. This study contributed to the research on cow-calf operation by using a fractional logit model to analyze the correlation between risk factors and calf mortality proportions, and by identifying novel herd-level risk factors. It provided a basis for future research aimed at developing empirically-based management strategies to optimize calf health outcomes.
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Affiliation(s)
- Minfeng Tang
- Simpson Centre for Food and Agricultural Policy, The School of Public Policy, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada.
| | - Guillaume Lhermie
- Simpson Centre for Food and Agricultural Policy, The School of Public Policy, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
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Guo B, Wu H, Pei L, Zhu X, Zhang D, Wang Y, Luo P. Study on the spatiotemporal dynamic of ground-level ozone concentrations on multiple scales across China during the blue sky protection campaign. ENVIRONMENT INTERNATIONAL 2022; 170:107606. [PMID: 36335896 DOI: 10.1016/j.envint.2022.107606] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 10/25/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
Surface ozone (O3), one of the harmful air pollutants, generated significantly negative effects on human health and plants. Existing O3 datasets with coarse spatiotemporal resolution and limited coverage, and the uncertainties of O3 influential factors seriously restrain related epidemiology and air pollution studies. To tackle above issues, we proposed a novel scheme to estimate daily O3 concentrations on a fine grid scale (1 km × 1 km) from 2018 to 2020 across China based on machine learning methods using hourly observed ground-level pollutant concentrations data, meteorological data, satellite data, and auxiliary data including digital elevation model (DEM), land use data (LUD), normalized difference vegetation index (NDVI), population (POP), and nighttime light images (NTL), and to identify the difference of influential factors of O3 on diverse urbanization and topography conditions. Some findings were achieved. The correlation coefficients (R2) between O3 concentrations and surface net solar radiation (SNSR), boundary layer height (BLH), 2 m temperature (T2M), 10 m v-component (MVW), and NDVI were 0.80, 0.40, 0.35, 0.30, and 0.20, respectively. The random forest (RF) demonstrated the highest validation R2 (0.86) and lowest validation RMSE (13.74 μg/m3) in estimating O3 concentrations, followed by support vector machine (SVM) (R2 = 0.75, RMSE = 18.39 μg/m3), backpropagation neural network (BP) (R2 = 0.74, RMSE = 19.26 μg/m3), and multiple linear regression (MLR) (R2 = 0.52, RMSE = 25.99 μg/m3). Our China High-Resolution O3 Dataset (CHROD) exhibited an acceptable accuracy at different spatial-temporal scales. Additionally, O3 concentrations showed decreasing trend and represented obviously spatiotemporal heterogeneity across China from 2018 to 2020. Overall, O3 was mainly affected by human activities in higher urbanization regions, while O3 was mainly controlled by meteorological factors, vegetation coverage, and elevation in lower urbanization regions. The scheme of this study is useful and valuable in understanding the mechanism of O3 formation and improving the quality of the O3 dataset.
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Affiliation(s)
- Bin Guo
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China.
| | - Haojie Wu
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China
| | - Lin Pei
- School of Exercise and Health Sciences, Xi'an Physical Education University, Xi'an, Shaanxi 710068, China; School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710043, China.
| | - Xiaowei Zhu
- Department of Mechanical and Materials Engineering, Portland State University, Portland, OR 97207, USA.
| | - Dingming Zhang
- College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China
| | - Yan Wang
- School of Geography and Tourism, Shaanxi Normal University, Xi'an, Shaanxi 710119, China
| | - Pingping Luo
- School of Water and Environment, Chang'an University, Xi'an, Shaanxi 710054, China.
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Zwep LB, Duisters KLW, Jansen M, Guo T, Meulman JJ, Upadhyay PJ, van Hasselt JGC. Identification of high-dimensional omics-derived predictors for tumor growth dynamics using machine learning and pharmacometric modeling. CPT Pharmacometrics Syst Pharmacol 2021; 10:350-361. [PMID: 33792207 PMCID: PMC8099445 DOI: 10.1002/psp4.12603] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 01/07/2021] [Accepted: 02/01/2021] [Indexed: 12/26/2022] Open
Abstract
Pharmacometric modeling can capture tumor growth inhibition (TGI) dynamics and variability. These approaches do not usually consider covariates in high-dimensional settings, whereas high-dimensional molecular profiling technologies ("omics") are being increasingly considered for prediction of anticancer drug treatment response. Machine learning (ML) approaches have been applied to identify high-dimensional omics predictors for treatment outcome. Here, we aimed to combine TGI modeling and ML approaches for two distinct aims: omics-based prediction of tumor growth profiles and identification of pathways associated with treatment response and resistance. We propose a two-step approach combining ML using least absolute shrinkage and selection operator (LASSO) regression with pharmacometric modeling. We demonstrate our workflow using a previously published dataset consisting of 4706 tumor growth profiles of patient-derived xenograft (PDX) models treated with a variety of mono- and combination regimens. Pharmacometric TGI models were fit to the tumor growth profiles. The obtained empirical Bayes estimates-derived TGI parameter values were regressed using the LASSO on high-dimensional genomic copy number variation data, which contained over 20,000 variables. The predictive model was able to decrease median prediction error by 4% as compared with a model without any genomic information. A total of 74 pathways were identified as related to treatment response or resistance development by LASSO, of which part was verified by literature. In conclusion, we demonstrate how the combined use of ML and pharmacometric modeling can be used to gain pharmacological understanding in genomic factors driving variation in treatment response.
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Affiliation(s)
- Laura B. Zwep
- Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
- Mathematical InstituteLeiden UniversityLeidenThe Netherlands
| | | | - Martijn Jansen
- Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
| | - Tingjie Guo
- Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
- Department of Intensive Care MedicineAmsterdam UMCVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | | | - Parth J. Upadhyay
- Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
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A longitudinal item response model for Aberrant Behavior Checklist (ABC) data from children with autism. J Pharmacokinet Pharmacodyn 2020; 47:241-253. [DOI: 10.1007/s10928-020-09686-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 04/02/2020] [Indexed: 12/31/2022]
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Wellhagen GJ, Kjellsson MC, Karlsson MO. A Bounded Integer Model for Rating and Composite Scale Data. AAPS J 2019; 21:74. [PMID: 31172350 PMCID: PMC6554249 DOI: 10.1208/s12248-019-0343-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Accepted: 05/24/2019] [Indexed: 01/27/2023] Open
Abstract
Rating and composite scales are commonly used to assess treatment efficacy. The two main strategies for modelling such endpoints are to treat them as a continuous or an ordered categorical variable (CV or OC). Both strategies have disadvantages, including making assumptions that violate the integer nature of the data (CV) and requiring many parameters for scales with many response categories (OC). We present a method, called the bounded integer (BI) model, which utilises the probit function with fixed cut-offs to estimate the probability of a certain score through a latent variable. This method was successfully implemented to describe six data sets from four different therapeutic areas: Parkinson's disease, Alzheimer's disease, schizophrenia, and neuropathic pain. Five scales were investigated, ranging from 11 to 181 categories. The fit (likelihood) was better for the BI model than for corresponding OC or CV models (ΔAIC range 11-1555) in all cases but one (∆AIC - 63), while the number of parameters was the same or lower. Markovian elements were successfully implemented within the method. The performance in external validation, assessed through cross-validation, was also in favour of the new model (ΔOFV range 22-1694) except in one case (∆OFV - 70). A residual for diagnostic purposes is discussed. This study shows that the BI model respects the integer nature of data and is parsimonious in terms of number of estimated parameters.
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Affiliation(s)
- Gustaf J Wellhagen
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden
| | - Maria C Kjellsson
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden
| | - Mats O Karlsson
- Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24, Uppsala, Sweden.
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Population pharmacokinetics of levodopa/carbidopa microtablets in healthy subjects and Parkinson's disease patients. Eur J Clin Pharmacol 2018; 74:1299-1307. [PMID: 29882153 PMCID: PMC6132549 DOI: 10.1007/s00228-018-2497-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 05/29/2018] [Indexed: 11/27/2022]
Abstract
OBJECTIVES Low dose, dispersible, levodopa/carbidopa microtablets with an automatic dose dispenser have been developed to facilitate individualized levodopa treatment. The aim of this study was to characterize the pharmacokinetics (PK) of levodopa and carbidopa after microtablet administration, and evaluate the impact of potential covariates. METHODS The population PK analysis involved data from 18 healthy subjects and 18 Parkinson's disease patients included in two single-dose, open-label levodopa/carbidopa microtablet studies. The analysis was carried out using non-linear mixed effects modeling. Bodyweight was included on all disposition parameters according to allometric scaling. Potential influence of additional covariates was investigated using graphical evaluation and adjusted adaptive least absolute shrinkage and selection operator. RESULTS Dispositions of levodopa and carbidopa were best described by a two- and one-compartment model respectively. Double-peak profiles were described using two parallel absorption compartments. Levodopa apparent clearance was found to decrease with increasing carbidopa dose (15% lower with 75 compared to 50 mg of carbidopa) and disease stage (by 18% for Hoehn and Yahr 1 to 4). Carbidopa apparent clearance was found to decrease with age (28% between the age of 60 and 80 years). An external evaluation showed the model to be able to reasonably well predict levodopa concentrations following multiple-dose microtablet administration in healthy subjects. CONCLUSIONS The presented models adequately described the PK of levodopa and carbidopa, following microtablet administration. The developed model may in the future be combined with a pharmacokinetic-pharmacodynamic target and used for individualized dose selection, utilizing the flexibility offered by the microtablets.
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