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Wu Y, Sinclair G, Avanasi R, Pecquet A. Physiologically based kinetic (PBK) modeling of propiconazole using a machine learning-enhanced read-across approach for interspecies extrapolation. ENVIRONMENT INTERNATIONAL 2024; 189:108804. [PMID: 38857551 DOI: 10.1016/j.envint.2024.108804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 06/05/2024] [Accepted: 06/06/2024] [Indexed: 06/12/2024]
Abstract
A significant challenge in the traditional human health risk assessment of agrochemicals is the uncertainty in quantifying the interspecies differences between animal models and humans. To work toward a more accurate and animal-free risk determination, new approaches such as physiologically based kinetic (PBK) modeling have been used to perform dosimetry extrapolation from animals to humans. However, the regulatory use and acceptance of PBK modeling is limited for chemicals that lack in vivo animal pharmacokinetic (PK) data, given the inability to evaluate models. To address these challenges, this study developed PBK models in the absence of in vivo PK data for the fungicide propiconazole, an activator of constitutive androstane receptor (CAR)/pregnane X receptor (PXR). A fit-for-purpose read-across approach was integrated with hierarchical clustering - an unsupervised machine learning algorithm, to bridge the knowledge gap. The integration allowed the incorporation of a broad spectrum of attributes for analog consideration, and enabled the analog selection in a simple, reproducible, and objective manner. The applicability was evaluated and demonstrated using penconazole (source) and three pseudo-unknown target chemicals (epoxiconazole, tebuconazole and triadimefon). Applying this machine learning-enhanced read-across approach, difenoconazole was selected as the most appropriate analog for propiconazole. A mouse PBK model was developed and evaluated for difenoconazole (source), with the mode of action of CAR/PXR activation incorporated to simulate the in vivo autoinduction of metabolism. The difenoconazole mouse model then served as a template for constructing the propiconazole mouse model. A parallelogram approach was subsequently applied to develop the propiconazole rat and human models, enabling a quantitative assessment of interspecies differences in dosimetry. This integrated approach represents a substantial advancement toward refining risk assessment of propiconazole within the framework of animal alternative safety assessment strategies.
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Affiliation(s)
- Yaoxing Wu
- Product Safety, Syngenta Crop Protection LLC, Greensboro NC 27409, USA.
| | - Gabriel Sinclair
- Product Safety, Syngenta Crop Protection LLC, Greensboro NC 27409, USA
| | | | - Alison Pecquet
- Product Safety, Syngenta Crop Protection LLC, Greensboro NC 27409, USA
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2
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Mi K, Sun L, Zhang L, Tang A, Tian X, Hou Y, Sun L, Huang L. A physiologically based pharmacokinetic/pharmacodynamic model to determine dosage regimens and withdrawal intervals of aditoprim against Streptococcus suis. Front Pharmacol 2024; 15:1378034. [PMID: 38694922 PMCID: PMC11061430 DOI: 10.3389/fphar.2024.1378034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 03/26/2024] [Indexed: 05/04/2024] Open
Abstract
Introduction: Streptococcus suis (S. suis) is a zoonotic pathogen threatening public health. Aditoprim (ADP), a novel veterinary medicine, exhibits an antibacterial effect against S. suis. In this study, a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model was used to determine the dosage regimens of ADP against S. suis and withdrawal intervals. Methods: The PBPK model of ADP injection can predict drug concentrations in plasma, liver, kidney, muscle, and fat. A semi-mechanistic pharmacodynamic (PD) model, including susceptible subpopulation and resistant subpopulation, is successfully developed by a nonlinear mixed-effect model to evaluate antibacterial effects. An integrated PBPK/PD model is conducted to predict the time-course of bacterial count change and resistance development under different ADP dosages. Results: ADP injection, administrated at 20 mg/kg with 12 intervals for 3 consecutive days, can exert an excellent antibacterial effect while avoiding resistance emergence. The withdrawal interval at the recommended dosage regimen is determined as 18 days to ensure food safety. Discussion: This study suggests that the PBPK/PD model can be applied as an effective tool for the antibacterial effect and safety evaluation of novel veterinary drugs.
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Affiliation(s)
- Kun Mi
- MOA Laboratory for Risk Assessment of Quality and Safety of Livestock and Poultry Products, Huazhong Agricultural University, Wuhan, China
- National Reference Laboratory of Veterinary Drug Residues (HZAU) and MOA Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan, China
| | - Lei Sun
- MOA Laboratory for Risk Assessment of Quality and Safety of Livestock and Poultry Products, Huazhong Agricultural University, Wuhan, China
- National Reference Laboratory of Veterinary Drug Residues (HZAU) and MOA Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan, China
| | - Lan Zhang
- MOA Laboratory for Risk Assessment of Quality and Safety of Livestock and Poultry Products, Huazhong Agricultural University, Wuhan, China
- Department of Veterinary Medicine Science, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Aoran Tang
- National Reference Laboratory of Veterinary Drug Residues (HZAU) and MOA Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan, China
- Department of Veterinary Medicine Science, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Xiaoyuan Tian
- MOA Laboratory for Risk Assessment of Quality and Safety of Livestock and Poultry Products, Huazhong Agricultural University, Wuhan, China
- Department of Veterinary Medicine Science, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Yixuan Hou
- National Reference Laboratory of Veterinary Drug Residues (HZAU) and MOA Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan, China
- Department of Veterinary Medicine Science, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Lingling Sun
- Department of Veterinary Medicine Science, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Lingli Huang
- MOA Laboratory for Risk Assessment of Quality and Safety of Livestock and Poultry Products, Huazhong Agricultural University, Wuhan, China
- National Reference Laboratory of Veterinary Drug Residues (HZAU) and MOA Key Laboratory for Detection of Veterinary Drug Residues, Huazhong Agricultural University, Wuhan, China
- Department of Veterinary Medicine Science, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
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Fragki S, Piersma AH, Westerhout J, Kienhuis A, Kramer NI, Zeilmaker MJ. Applicability of generic PBK modelling in chemical hazard assessment: A case study with IndusChemFate. Regul Toxicol Pharmacol 2022; 136:105267. [DOI: 10.1016/j.yrtph.2022.105267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 08/20/2022] [Accepted: 09/26/2022] [Indexed: 11/09/2022]
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Sweeney LM. Case study on the impact of the source of metabolism parameters in next generation physiologically based pharmacokinetic models: Implications for occupational exposures to trimethylbenzenes. Regul Toxicol Pharmacol 2022; 134:105238. [PMID: 35931234 DOI: 10.1016/j.yrtph.2022.105238] [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: 06/02/2022] [Revised: 07/14/2022] [Accepted: 07/19/2022] [Indexed: 10/16/2022]
Abstract
Physiologically based pharmacokinetic (PBPK) models are a means of making important linkages between exposure assessment and in vitro toxicity. A key constraint on rapid application of PBPK models in risk assessment is traditional reliance on substance-specific in vivo toxicokinetic data to evaluate model quality. Bounding conditions, in silico, in vitro, and chemical read-across approaches have been proposed as alternative sources for metabolic clearance estimates. A case study to test consistency of predictive ability across these approaches was conducted using trimethylbenzenes (TMB) as prototype chemicals. Substantial concordance was found among TMB isomers with respect to accuracy (or inaccuracy) of approaches to estimating metabolism; for example, the bounding conditions never reproduced the human in vivo toxicokinetic data within two-fold. Using only approaches that gave acceptable prediction of in vivo toxicokinetics for the source compound (1,2,4-TMB) substantially narrowed the range of plausible internal doses for a given external dose for occupational, emergency response, and environmental/community health risk assessment scenarios for TMB isomers. Thus, risk assessments developed using the target compound models with a constrained subset of metabolism estimates (determined for source chemical models) can be used with greater confidence that internal dosimetry will be estimated with accuracy sufficient for the purpose at hand.
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Affiliation(s)
- Lisa M Sweeney
- UES, Inc, 4401 Dayton Xenia Road, Dayton, OH, 45432, USA(contractor assigned to the U.S. Air Force Research Laboratory 711th Human Performance Wing, Wright Patterson AFB, OH USA).
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Chang X, Tan YM, Allen DG, Bell S, Brown PC, Browning L, Ceger P, Gearhart J, Hakkinen PJ, Kabadi SV, Kleinstreuer NC, Lumen A, Matheson J, Paini A, Pangburn HA, Petersen EJ, Reinke EN, Ribeiro AJS, Sipes N, Sweeney LM, Wambaugh JF, Wange R, Wetmore BA, Mumtaz M. IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making. TOXICS 2022; 10:232. [PMID: 35622645 PMCID: PMC9143724 DOI: 10.3390/toxics10050232] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/24/2022] [Indexed: 02/04/2023]
Abstract
During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.
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Affiliation(s)
- Xiaoqing Chang
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Yu-Mei Tan
- U.S. Environmental Protection Agency, Office of Pesticide Programs, 109 T.W. Alexander Drive, Durham, NC 27709, USA;
| | - David G. Allen
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Shannon Bell
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Paul C. Brown
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20903, USA; (P.C.B.); (A.J.S.R.); (R.W.)
| | - Lauren Browning
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Patricia Ceger
- Inotiv-RTP, 601 Keystone Park Drive, Suite 200, Morrisville, NC 27560, USA; (X.C.); (D.G.A.); (S.B.); (L.B.); (P.C.)
| | - Jeffery Gearhart
- The Henry M. Jackson Foundation, Air Force Research Laboratory, 711 Human Performance Wing, Wright-Patterson Air Force Base, OH 45433, USA;
| | - Pertti J. Hakkinen
- National Library of Medicine, National Center for Biotechnology Information, 8600 Rockville Pike, Bethesda, MD 20894, USA;
| | - Shruti V. Kabadi
- U.S. Food and Drug Administration, Center for Food Safety and Applied Nutrition, Office of Food Additive Safety, 5001 Campus Drive, HFS-275, College Park, MD 20740, USA;
| | - Nicole C. Kleinstreuer
- National Institute of Environmental Health Sciences, National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, P.O. Box 12233, Research Triangle Park, NC 27709, USA;
| | - Annie Lumen
- U.S. Food and Drug Administration, National Center for Toxicological Research, 3900 NCTR Road, Jefferson, AR 72079, USA;
| | - Joanna Matheson
- U.S. Consumer Product Safety Commission, Division of Toxicology and Risk Assessment, 5 Research Place, Rockville, MD 20850, USA;
| | - Alicia Paini
- European Commission, Joint Research Centre (JRC), 21027 Ispra, Italy;
| | - Heather A. Pangburn
- Air Force Research Laboratory, 711 Human Performance Wing, 2729 R Street, Area B, Building 837, Wright-Patterson Air Force Base, OH 45433, USA;
| | - Elijah J. Petersen
- U.S. Department of Commerce, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA;
| | - Emily N. Reinke
- U.S. Army Public Health Center, 8252 Blackhawk Rd., Aberdeen Proving Ground, MD 21010, USA;
| | - Alexandre J. S. Ribeiro
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20903, USA; (P.C.B.); (A.J.S.R.); (R.W.)
| | - Nisha Sipes
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA; (N.S.); (J.F.W.); (B.A.W.)
| | - Lisa M. Sweeney
- UES, Inc., 4401 Dayton-Xenia Road, Beavercreek, OH 45432, Assigned to Air Force Research Laboratory, 711 Human Performance Wing, Wright-Patterson Air Force Base, OH 45433, USA;
| | - John F. Wambaugh
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA; (N.S.); (J.F.W.); (B.A.W.)
| | - Ronald Wange
- U.S. Food and Drug Administration, Center for Drug Evaluation and Research, 10903 New Hampshire Avenue, Silver Spring, MD 20903, USA; (P.C.B.); (A.J.S.R.); (R.W.)
| | - Barbara A. Wetmore
- U.S. Environmental Protection Agency, Center for Computational Toxicology and Exposure, 109 TW Alexander Dr., Research Triangle Park, NC 27711, USA; (N.S.); (J.F.W.); (B.A.W.)
| | - Moiz Mumtaz
- Agency for Toxic Substances and Disease Registry, Office of the Associate Director for Science, 1600 Clifton Road, S102-2, Atlanta, GA 30333, USA
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In Silico Prediction of Pharmacokinetic Profile for Human Oral Drug Candidates Which Lack Clinical Pharmacokinetic Experiment Data. Eur J Drug Metab Pharmacokinet 2022; 47:403-417. [PMID: 35171461 DOI: 10.1007/s13318-022-00758-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/25/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUNDS AND OBJECTIVES In silico methods which can generate high-quality physiologically based pharmacokinetic (PBPK) models for arbitrary drug candidates are greatly needed to select developable drug candidates that escape drug attrition because of the poor pharmacokinetic profile. The purpose of this study is to develop a novel protocol to preliminarily predict the concentration profile of a target drug based on the PBPK model of a structurally similar template drug by combining two software platforms for PBPK modeling, the SimCYP simulator and ADMET Predictor. METHODS The method was evaluated by utilizing 13 drug pairs from 18 drugs in the built-in database of the SimCYP software. All drug pairs have Tanimoto scores (TS) no less than 0.5. As each drug in a drug pair can serve as both target and template, 26 sets were studied in this work. Three versions (V1, V2 and V3) of models for the target drug were constructed by replacing the corresponding parameters of the template drug step by step with those predicted by ADMET Predictor for the target drug. V1 represents the replacement of molecular weight (MW), V2 includes the replacement of parameter MW, fraction unbound in plasma (fu), blood-to-plasma partition ratio (B/P), logarithm of the octanol-buffer partition coefficient (log Po:w) and acid dissociation constant (pKa). In V3, all above-mentioned parameters as well as human jejunum effective permeability (Peff), Vd and cytochrome P450 (CYP) metabolism parameters (Km, Vmax or CLint) are modified. Normalized root mean square error (NRMSE) was used for the evaluation of the model performance. RESULTS We found that the performance of the three versions of the models depends on structural similarity of the drug pairs. For Group I drug pairs (TS ≤ 0.7), V2 and V3 performed better than V1 in terms of NRMSE; for Group II drug pairs (0.7 < TS ≤ 0.9), 8 out of 10 V3 models had NRMSE < 0.2, the cutoff we applied to judge whether the simulated concentration-time (C-T) curve was satisfactory or not. V3 outperformed the V1 and V2 versions. For the two drug pairs belonging to Group III (TS > 0.9), V2 outperformed V1 and V3, suggesting more unnecessary replacement can lower the performance of PBPK models. We also investigated how the prediction accuracy of ADMET Predictor as well as its collaboration with SimCYP influences the quality of PBPK models constructed using SimCYP. CONCLUSION In conclusion, we generated practical guidance on applying two mainstream software packages, ADMET Predictor and SimCYP, to construct PBPK models for drugs or drug candidates that lack ADME parameters in model construction.
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7
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Thompson CV, Firman JW, Goldsmith MR, Grulke CM, Tan YM, Paini A, Penson PE, Sayre RR, Webb S, Madden JC. A Systematic Review of Published Physiologically-based Kinetic Models and an Assessment of their Chemical Space Coverage. Altern Lab Anim 2021; 49:197-208. [PMID: 34836462 DOI: 10.1177/02611929211060264] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Across multiple sectors, including food, cosmetics and pharmaceutical industries, there is a need to predict the potential effects of xenobiotics. These effects are determined by the intrinsic ability of the substance, or its derivatives, to interact with the biological system, and its concentration-time profile at the target site. Physiologically-based kinetic (PBK) models can predict organ-level concentration-time profiles, however, the models are time and resource intensive to generate de novo. Read-across is an approach used to reduce or replace animal testing, wherein information from a data-rich chemical is used to make predictions for a data-poor chemical. The recent increase in published PBK models presents the opportunity to use a read-across approach for PBK modelling, that is, to use PBK model information from one chemical to inform the development or evaluation of a PBK model for a similar chemical. Essential to this process, is identifying the chemicals for which a PBK model already exists. Herein, the results of a systematic review of existing PBK models, compliant with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) format, are presented. Model information, including species, sex, life-stage, route of administration, software platform used and the availability of model equations, was captured for 7541 PBK models. Chemical information (identifiers and physico-chemical properties) has also been recorded for 1150 unique chemicals associated with these models. This PBK model data set has been made readily accessible, as a Microsoft Excel® spreadsheet, providing a valuable resource for those developing, using or evaluating PBK models in industry, academia and the regulatory sectors.
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Affiliation(s)
- Courtney V Thompson
- School of Pharmacy and Biomolecular Sciences, 4589Liverpool John Moores University, Liverpool, UK
| | - James W Firman
- School of Pharmacy and Biomolecular Sciences, 4589Liverpool John Moores University, Liverpool, UK
| | - Michael R Goldsmith
- Office of Research and Development, Center for Computational Toxicology and Exposure, Chemical Characterization and Exposure Division, 427887US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Christopher M Grulke
- Office of Research and Development, Center for Computational Toxicology and Exposure, Chemical Characterization and Exposure Division, 427887US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Yu-Mei Tan
- Office of Pesticide Programs, Health Effects Division, 138030US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Alicia Paini
- 99013European Commission Joint Research Centre (JRC), Ispra, Italy
| | - Peter E Penson
- School of Pharmacy and Biomolecular Sciences, 4589Liverpool John Moores University, Liverpool, UK
| | - Risa R Sayre
- Office of Research and Development, Center for Computational Toxicology and Exposure, Chemical Characterization and Exposure Division, 427887US Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Steven Webb
- Syngenta, Product Safety, Early Stage Research, 101825Jealott's Hill International Research Centre, Bracknell, UK
| | - Judith C Madden
- School of Pharmacy and Biomolecular Sciences, 4589Liverpool John Moores University, Liverpool, UK
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Armitage JM, Hughes L, Sangion A, Arnot JA. Development and intercomparison of single and multicompartment physiologically-based toxicokinetic models: Implications for model selection and tiered modeling frameworks. ENVIRONMENT INTERNATIONAL 2021; 154:106557. [PMID: 33892222 DOI: 10.1016/j.envint.2021.106557] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 02/05/2021] [Accepted: 04/02/2021] [Indexed: 05/21/2023]
Abstract
This study describes the development and intercomparison of generic physiologically-based toxicokinetic (PBTK) models for humans comprised of internally consistent one-compartment (1Co-) and multi-compartment (MCo-) implementations (G-PBTK). The G-PBTK models were parameterized for an adult male (70 kg) using common physiological parameters and in vitro biotransformation rate estimates and subsequently evaluated using independent concentration versus time data (n = 6) and total elimination half-lives (n = 15) for diverse organic chemicals. The model performance is acceptable considering the inherent uncertainty in the biotransformation rate data and the absence of model calibration. The G-PBTK model was then applied using hypothetical neutral organics, acidic ionizable organics and basic ionizable organics (IOCs) to identify combinations of partitioning properties and biotransformation rates leading to substantial discrepancies between 1Co- and MCo-PBTK calculations for whole body concentrations and half-lives. The 1Co- and MCo-PBTK model calculations for key toxicokinetic parameters are broadly consistent unless biotransformation is rapid (e.g., half-life less than five days). When half-lives are relatively short, discrepancies are greatest for the neutral organics and least for the acidic IOCs which follows from the estimated volumes of distribution (e.g., VDSS = 9.6-15.4 L/kg vs 0.3-1.6 L/kg for the neutral and acidic compounds respectively) and the related approach to internal chemical equilibrium. The model intercomparisons demonstrate that 1Co-PBTK models can be applied with confidence to many exposure scenarios, particularly those focused on chronic or repeat exposures and for prioritization and screening-level decision contexts. However, MCo-PBTK models may be necessary in certain contexts, particularly for intermittent, short-term and highly variable exposures. A key recommendation to guide model selection and the development of tiered PBTK modeling frameworks that emerges from this study is the need to harmonize models with respect to parameterization and process descriptions to the greatest extent possible when proceeding from the application of simpler to more complex modeling tools as part of chemical assessment activities.
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Affiliation(s)
- James M Armitage
- AES Armitage Environmental Sciences, Inc., Ottawa, Ontario K1L 8C3, Canada; Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario M1C 1A4, Canada.
| | - Lauren Hughes
- ARC Arnot Research and Consulting, Toronto, Ontario M4M 1W4, Canada
| | - Alessandro Sangion
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario M1C 1A4, Canada; ARC Arnot Research and Consulting, Toronto, Ontario M4M 1W4, Canada
| | - Jon A Arnot
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario M1C 1A4, Canada; ARC Arnot Research and Consulting, Toronto, Ontario M4M 1W4, Canada; Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario M5S 1A8, Canada
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AlSaieedi A, Salhi A, Tifratene F, Raies AB, Hungler A, Uludag M, Van Neste C, Bajic VB, Gojobori T, Essack M. DES-Tcell is a knowledgebase for exploring immunology-related literature. Sci Rep 2021; 11:14344. [PMID: 34253812 PMCID: PMC8275784 DOI: 10.1038/s41598-021-93809-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 06/24/2021] [Indexed: 12/02/2022] Open
Abstract
T-cells are a subtype of white blood cells circulating throughout the body, searching for infected and abnormal cells. They have multifaceted functions that include scanning for and directly killing cells infected with intracellular pathogens, eradicating abnormal cells, orchestrating immune response by activating and helping other immune cells, memorizing encountered pathogens, and providing long-lasting protection upon recurrent infections. However, T-cells are also involved in immune responses that result in organ transplant rejection, autoimmune diseases, and some allergic diseases. To support T-cell research, we developed the DES-Tcell knowledgebase (KB). This KB incorporates text- and data-mined information that can expedite retrieval and exploration of T-cell relevant information from the large volume of published T-cell-related research. This KB enables exploration of data through concepts from 15 topic-specific dictionaries, including immunology-related genes, mutations, pathogens, and pathways. We developed three case studies using DES-Tcell, one of which validates effective retrieval of known associations by DES-Tcell. The second and third case studies focuses on concepts that are common to Grave’s disease (GD) and Hashimoto’s thyroiditis (HT). Several reports have shown that up to 20% of GD patients treated with antithyroid medication develop HT, thus suggesting a possible conversion or shift from GD to HT disease. DES-Tcell found miR-4442 links to both GD and HT, and that miR-4442 possibly targets the autoimmune disease risk factor CD6, which provides potential new knowledge derived through the use of DES-Tcell. According to our understanding, DES-Tcell is the first KB dedicated to exploring T-cell-relevant information via literature-mining, data-mining, and topic-specific dictionaries.
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Affiliation(s)
- Ahdab AlSaieedi
- Department of Medical Laboratory Technology (MLT), Faculty of Applied Medical Sciences (FAMS), King Abdulaziz University (KAU), Jeddah, 21589-80324, Saudi Arabia
| | - Adil Salhi
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Faroug Tifratene
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Arwa Bin Raies
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Arnaud Hungler
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Mahmut Uludag
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Christophe Van Neste
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Vladimir B Bajic
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Takashi Gojobori
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Magbubah Essack
- Computer, Electrical, and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
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Breen M, Ring CL, Kreutz A, Goldsmith MR, Wambaugh JF. High-throughput PBTK models for in vitro to in vivo extrapolation. Expert Opin Drug Metab Toxicol 2021; 17:903-921. [PMID: 34056988 DOI: 10.1080/17425255.2021.1935867] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
INTRODUCTION Toxicity data are unavailable for many thousands of chemicals in commerce and the environment. Therefore, risk assessors need to rapidly screen these chemicals for potential risk to public health. High-throughput screening (HTS) for in vitro bioactivity, when used with high-throughput toxicokinetic (HTTK) data and models, allows characterization of these thousands of chemicals. AREAS COVERED This review covers generic physiologically based toxicokinetic (PBTK) models and high-throughput PBTK modeling for in vitro-in vivo extrapolation (IVIVE) of HTS data. We focus on 'httk', a public, open-source set of computational modeling tools and in vitro toxicokinetic (TK) data. EXPERT OPINION HTTK benefits chemical risk assessors with its ability to support rapid chemical screening/prioritization, perform IVIVE, and provide provisional TK modeling for large numbers of chemicals using only limited chemical-specific data. Although generic TK model design can increase prediction uncertainty, these models provide offsetting benefits by increasing model implementation accuracy. Also, public distribution of the models and data enhances reproducibility. For the httk package, the modular and open-source design can enable the tool to be used and continuously improved by a broad user community in support of the critical need for high-throughput chemical prioritization and rapid dose estimation to facilitate rapid hazard assessments.
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Affiliation(s)
- Miyuki Breen
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Caroline L Ring
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Anna Kreutz
- Oak Ridge Institute for Science and Education (ORISE) fellow at the Center for Computational Toxicology and Exposure, Office of Research and Development, Research Triangle Park, NC, USA
| | - Michael-Rock Goldsmith
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - John F Wambaugh
- Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
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11
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Madia F, Pillo G, Worth A, Corvi R, Prieto P. Integration of data across toxicity endpoints for improved safety assessment of chemicals: the example of carcinogenicity assessment. Arch Toxicol 2021; 95:1971-1993. [PMID: 33830278 PMCID: PMC8166685 DOI: 10.1007/s00204-021-03035-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 03/18/2021] [Indexed: 12/13/2022]
Abstract
In view of the need to enhance the assessment of consumer products called for in the EU Chemicals Strategy for Sustainability, we developed a methodology for evaluating hazard by combining information across different systemic toxicity endpoints and integrating the information with new approach methodologies. This integrates mechanistic information with a view to avoiding redundant in vivo studies, minimising reliance on apical endpoint tests and ultimately devising efficient testing strategies. Here, we present the application of our methodology to carcinogenicity assessment, mapping the available information from toxicity test methods across endpoints to the key characteristics of carcinogens. Test methods are deconstructed to allow the information they provide to be organised in a systematic way, enabling the description of the toxicity mechanisms leading to the adverse outcome. This integrated approach provides a flexible and resource-efficient means of fully exploiting test methods for which test guidelines are available to fulfil regulatory requirements for systemic toxicity assessment as well as identifying where new methods can be integrated.
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Affiliation(s)
- Federica Madia
- European Commission, Joint Research Centre (JRC), Via E. Fermi 2749, 21027, Ispra, VA, Italy.
| | - Gelsomina Pillo
- European Commission, Joint Research Centre (JRC), Via E. Fermi 2749, 21027, Ispra, VA, Italy
| | - Andrew Worth
- European Commission, Joint Research Centre (JRC), Via E. Fermi 2749, 21027, Ispra, VA, Italy
| | - Raffaella Corvi
- European Commission, Joint Research Centre (JRC), Via E. Fermi 2749, 21027, Ispra, VA, Italy
| | - Pilar Prieto
- European Commission, Joint Research Centre (JRC), Via E. Fermi 2749, 21027, Ispra, VA, Italy
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12
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Integration of evidence to evaluate the potential for neurobehavioral effects following exposure to USFDA-approved food colors. Food Chem Toxicol 2021; 151:112097. [DOI: 10.1016/j.fct.2021.112097] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 02/23/2021] [Accepted: 02/25/2021] [Indexed: 01/02/2023]
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13
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Assessment of the predictive capacity of a physiologically based kinetic model using a read-across approach. ACTA ACUST UNITED AC 2021; 18:100159. [PMID: 34027243 PMCID: PMC8130669 DOI: 10.1016/j.comtox.2021.100159] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 02/02/2021] [Accepted: 02/03/2021] [Indexed: 11/26/2022]
Abstract
Potential regulatory application of PBK modelling information to assist read-across. Presents workflow to read across PBK model information from data-rich to data-poor chemicals. Describes appropriate analogue selection based on a set of specific criteria. Uses estragole and safrole as source chemicals for a target chemical - methyleugenol. Example of PBK model validation where in vivo kinetic data are lacking.
With current progress in science, there is growing interest in developing and applying Physiologically Based Kinetic (PBK) models in chemical risk assessment, as knowledge of internal exposure to chemicals is critical to understanding potential effects in vivo. In particular, a new generation of PBK models is being developed in which the model parameters are derived from in silico and in vitro methods. To increase the acceptance and use of these “Next Generation PBK models”, there is a need to demonstrate their validity. However, this is challenging in the case of data-poor chemicals that are lacking in kinetic data and for which predictive capacity cannot, therefore, be assessed. The aim of this work is to lay down the fundamental steps in using a read across framework to inform modellers and risk assessors on how to develop, or evaluate, PBK models for chemicals without in vivo kinetic data. The application of a PBK model that takes into account the absorption, distribution, metabolism and excretion characteristics of the chemical reduces the uncertainties in the biokinetics and biotransformation of the chemical of interest. A strategic flow-charting application, proposed herein, allows users to identify the minimum information to perform a read-across from a data-rich chemical to its data-poor analogue(s). The workflow analysis is illustrated by means of a real case study using the alkenylbenzene class of chemicals, showing the reliability and potential of this approach. It was demonstrated that a consistent quantitative relationship between model simulations could be achieved using models for estragole and safrole (source chemicals) when applied to methyleugenol (target chemical). When the PBK model code for the source chemicals was adapted to utilise input values relevant to the target chemical, simulation was consistent between the models. The resulting PBK model for methyleugenol was further evaluated by comparing the results to an existing, published model for methyleugenol, providing further evidence that the approach was successful. This can be considered as a “read-across” approach, enabling a valid PBK model to be derived to aid the assessment of a data poor chemical.
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Cox EJ, Tian DD, Clarke JD, Rettie AE, Unadkat JD, Thummel KE, McCune JS, Paine MF. Modeling Pharmacokinetic Natural Product-Drug Interactions for Decision-Making: A NaPDI Center Recommended Approach. Pharmacol Rev 2021; 73:847-859. [PMID: 33712517 PMCID: PMC7956993 DOI: 10.1124/pharmrev.120.000106] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The popularity of botanical and other purported medicinal natural products (NPs) continues to grow, especially among patients with chronic illnesses and patients managed on complex prescription drug regimens. With few exceptions, the risk of a given NP to precipitate a clinically significant pharmacokinetic NP-drug interaction (NPDI) remains understudied or unknown. Application of static or dynamic mathematical models to predict and/or simulate NPDIs can provide critical information about the potential clinical significance of these complex interactions. However, methods used to conduct such predictions or simulations are highly variable. Additionally, published reports using mathematical models to interrogate NPDIs are not always sufficiently detailed to ensure reproducibility. Consequently, guidelines are needed to inform the conduct and reporting of these modeling efforts. This recommended approach from the Center of Excellence for Natural Product Drug Interaction Research describes a systematic method for using mathematical models to interpret the interaction risk of NPs as precipitants of potential clinically significant pharmacokinetic NPDIs. A framework for developing and applying pharmacokinetic NPDI models is presented with the aim of promoting accuracy, reproducibility, and generalizability in the literature. SIGNIFICANCE STATEMENT: Many natural products (NPs) contain phytoconstituents that can increase or decrease systemic or tissue exposure to, and potentially the efficacy of, a pharmaceutical drug; however, no regulatory agency guidelines exist to assist in predicting the risk of these complex interactions. This recommended approach from a multi-institutional consortium designated by National Institutes of Health as the Center of Excellence for Natural Product Drug Interaction Research provides a framework for modeling pharmacokinetic NP-drug interactions.
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Affiliation(s)
- Emily J Cox
- Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (J.D.C., A.E.R., J.D.U., K.E.T., J.S.M., M.F.P.); Department of Pharmaceutical Sciences, Washington State University, Spokane, Washington (E.J.C., D.-D.T., J.D.C., M.F.P.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (J.D.U., K.E.T.), University of Washington, Seattle, Washington; and Department of Population Sciences, City of Hope, Duarte, California (J.S.M.)
| | - Dan-Dan Tian
- Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (J.D.C., A.E.R., J.D.U., K.E.T., J.S.M., M.F.P.); Department of Pharmaceutical Sciences, Washington State University, Spokane, Washington (E.J.C., D.-D.T., J.D.C., M.F.P.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (J.D.U., K.E.T.), University of Washington, Seattle, Washington; and Department of Population Sciences, City of Hope, Duarte, California (J.S.M.)
| | - John D Clarke
- Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (J.D.C., A.E.R., J.D.U., K.E.T., J.S.M., M.F.P.); Department of Pharmaceutical Sciences, Washington State University, Spokane, Washington (E.J.C., D.-D.T., J.D.C., M.F.P.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (J.D.U., K.E.T.), University of Washington, Seattle, Washington; and Department of Population Sciences, City of Hope, Duarte, California (J.S.M.)
| | - Allan E Rettie
- Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (J.D.C., A.E.R., J.D.U., K.E.T., J.S.M., M.F.P.); Department of Pharmaceutical Sciences, Washington State University, Spokane, Washington (E.J.C., D.-D.T., J.D.C., M.F.P.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (J.D.U., K.E.T.), University of Washington, Seattle, Washington; and Department of Population Sciences, City of Hope, Duarte, California (J.S.M.)
| | - Jashvant D Unadkat
- Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (J.D.C., A.E.R., J.D.U., K.E.T., J.S.M., M.F.P.); Department of Pharmaceutical Sciences, Washington State University, Spokane, Washington (E.J.C., D.-D.T., J.D.C., M.F.P.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (J.D.U., K.E.T.), University of Washington, Seattle, Washington; and Department of Population Sciences, City of Hope, Duarte, California (J.S.M.)
| | - Kenneth E Thummel
- Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (J.D.C., A.E.R., J.D.U., K.E.T., J.S.M., M.F.P.); Department of Pharmaceutical Sciences, Washington State University, Spokane, Washington (E.J.C., D.-D.T., J.D.C., M.F.P.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (J.D.U., K.E.T.), University of Washington, Seattle, Washington; and Department of Population Sciences, City of Hope, Duarte, California (J.S.M.)
| | - Jeannine S McCune
- Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (J.D.C., A.E.R., J.D.U., K.E.T., J.S.M., M.F.P.); Department of Pharmaceutical Sciences, Washington State University, Spokane, Washington (E.J.C., D.-D.T., J.D.C., M.F.P.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (J.D.U., K.E.T.), University of Washington, Seattle, Washington; and Department of Population Sciences, City of Hope, Duarte, California (J.S.M.)
| | - Mary F Paine
- Center of Excellence for Natural Product Drug Interaction Research, Spokane, Washington (J.D.C., A.E.R., J.D.U., K.E.T., J.S.M., M.F.P.); Department of Pharmaceutical Sciences, Washington State University, Spokane, Washington (E.J.C., D.-D.T., J.D.C., M.F.P.); Departments of Medicinal Chemistry (A.E.R.) and Pharmaceutics (J.D.U., K.E.T.), University of Washington, Seattle, Washington; and Department of Population Sciences, City of Hope, Duarte, California (J.S.M.)
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Ball N, Madden J, Paini A, Mathea M, Palmer AD, Sperber S, Hartung T, van Ravenzwaay B. Key read across framework components and biology based improvements. MUTATION RESEARCH-GENETIC TOXICOLOGY AND ENVIRONMENTAL MUTAGENESIS 2020; 853:503172. [DOI: 10.1016/j.mrgentox.2020.503172] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 03/09/2020] [Accepted: 03/11/2020] [Indexed: 12/18/2022]
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Madden JC, Pawar G, Cronin MT, Webb S, Tan YM, Paini A. In silico resources to assist in the development and evaluation of physiologically-based kinetic models. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.comtox.2019.03.001] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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17
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Verscheijden LFM, Koenderink JB, de Wildt SN, Russel FGM. Development of a physiologically-based pharmacokinetic pediatric brain model for prediction of cerebrospinal fluid drug concentrations and the influence of meningitis. PLoS Comput Biol 2019; 15:e1007117. [PMID: 31194730 PMCID: PMC6592555 DOI: 10.1371/journal.pcbi.1007117] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 06/25/2019] [Accepted: 05/21/2019] [Indexed: 01/28/2023] Open
Abstract
Different pediatric physiologically-based pharmacokinetic (PBPK) models have been described incorporating developmental changes that influence plasma drug concentrations. Drug disposition into cerebrospinal fluid (CSF) is also subject to age-related variation and can be further influenced by brain diseases affecting blood-brain barrier integrity, like meningitis. Here, we developed a generic pediatric brain PBPK model to predict CSF concentrations of drugs that undergo passive transfer, including age-appropriate parameters. The model was validated for the analgesics paracetamol, ibuprofen, flurbiprofen and naproxen, and for a pediatric meningitis population by empirical optimization of the blood-brain barrier penetration of the antibiotic meropenem. Plasma and CSF drug concentrations derived from the literature were used to perform visual predictive checks and to calculate ratios between simulated and observed area under the concentration curves (AUCs) in order to evaluate model performance. Model-simulated concentrations were comparable to observed data over a broad age range (3 months–15 years postnatal age) for all drugs investigated. The ratios between observed and simulated AUCs (AUCo/AUCp) were within 2-fold difference both in plasma (range 0.92–1.09) and in CSF (range 0.64–1.23) indicating acceptable model performance. The model was also able to describe disease-mediated changes in neonates and young children (<3m postnatal age) related to meningitis and sepsis (range AUCo/AUCp plasma: 1.64–1.66, range AUCo/AUCp CSF: 1.43–1.73). Our model provides a new computational tool to predict CSF drug concentrations in children with and without meningitis and can be used as a template model for other compounds that passively enter the CNS. Developmental processes in children affect pharmacokinetics and should ideally be taken into account when establishing drug dosing regimens. One way to incorporate developmental differences is by making use of physiologically-based pharmacokinetic (PBPK) models in which kinetic equations are used to describe drug disposition processes and developmental biology. With these equations the absorption of drugs into the model, the flow of drugs between different compartments (representing major organs/tissues), and excretion from the model are predicted. PBPK models can also be used to describe drug concentrations in different target tissues, which often correlate better with the clinical effects. Here, we developed a generic pediatric PBPK model of drug disposition in the cerebrospinal fluid (CSF), that was able to describe clinically measured drug concentrations of several drugs in neonates and children. The model could be useful in predicting CSF concentrations of other drugs in pediatric populations where clinical data is often sparse or absent and by this means guide first-in-child dose recommendations.
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Affiliation(s)
- Laurens F. M. Verscheijden
- Department of Pharmacology and Toxicology, Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Nijmegen, The Netherlands
| | - Jan B. Koenderink
- Department of Pharmacology and Toxicology, Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Nijmegen, The Netherlands
| | - Saskia N. de Wildt
- Department of Pharmacology and Toxicology, Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Nijmegen, The Netherlands
- Intensive Care and Department of Pediatric Surgery, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands
| | - Frans G. M. Russel
- Department of Pharmacology and Toxicology, Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Nijmegen, The Netherlands
- * E-mail:
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18
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Desalegn A, Bopp S, Asturiol D, Lamon L, Worth A, Paini A. Role of Physiologically Based Kinetic modelling in addressing environmental chemical mixtures - A review. ACTA ACUST UNITED AC 2019; 10:158-168. [PMID: 31218267 PMCID: PMC6559215 DOI: 10.1016/j.comtox.2018.09.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 06/24/2018] [Accepted: 09/26/2018] [Indexed: 11/21/2022]
Abstract
The availability and applicability of Physiologically Based Kinetic (PBK) models for mixtures is reviewed. PBK models can support risk assessment of mixtures by incorporating the toxicokinetic processes. Quantitative structure-activity relationship (QSAR) models can be used to fill data gaps in PBK modelling. PBK models for mixtures can be improved by including various types of interactions.
The role of Physiologically Based Kinetic (PBK) modelling in assessing mixture toxicology has been growing for the last three decades. It has been widely used to investigate and address interactions in mixtures. This review describes the current state-of-the-art of PBK models for chemical mixtures and to evaluate the applications of PBK modelling for mixtures with emphasis on their role in chemical risk assessment. A total of 35 mixture PBK models were included after searching web resources (Scopus, PubMed, Web of Science, and Google Scholar), screening for duplicates, and excluding articles based on eligibility criteria. Binary mixtures and volatile organic compounds accounted for two-thirds of the chemical mixtures identified. The most common exposure route and modelled system were found to be inhalation and rats respectively. Twenty two (22) models were for binary mixtures, 5 for ternary mixtures, 3 for quaternary mixtures, and 5 for complex mixtures. Both bottom-up and top-down PBK modelling approaches are described. Whereas bottom-up approaches are based on a series of binary interactions, top-down approaches are based on the lumping of mixture components. Competitive inhibition is the most common type of interaction among the various types of mixtures, and usually becomes a concern at concentrations higher than environmental exposure levels. It leads to reduced biotransformation that either means a decrease in the amount of toxic metabolite formation or an increase in toxic parent chemical accumulation. The consequence is either lower or higher toxicity compared to that estimated for the mixture based on the additivity principle. Therefore, PBK modelling can play a central role in predicting interactions in chemical mixture risk assessment.
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Affiliation(s)
| | | | | | | | | | - Alicia Paini
- Corresponding author at: European Commission, Joint Research Centre, Via E. Fermi 2749, 21027 Ispra, VA, Italy.
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19
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Ellison CA, Blackburn KL, Carmichael PL, Clewell HJ, Cronin MTD, Desprez B, Escher SE, Ferguson SS, Grégoire S, Hewitt NJ, Hollnagel HM, Klaric M, Patel A, Salhi S, Schepky A, Schmitt BG, Wambaugh JF, Worth A. Challenges in working towards an internal threshold of toxicological concern (iTTC) for use in the safety assessment of cosmetics: Discussions from the Cosmetics Europe iTTC Working Group workshop. Regul Toxicol Pharmacol 2019; 103:63-72. [PMID: 30653989 PMCID: PMC6644721 DOI: 10.1016/j.yrtph.2019.01.016] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 01/02/2019] [Accepted: 01/07/2019] [Indexed: 11/22/2022]
Abstract
The Threshold of Toxicological Concern (TTC) is an important risk assessment tool which establishes acceptable low-level exposure values to be applied to chemicals with limited toxicological data. One of the logical next steps in the continued evolution of TTC is to develop this concept further so that it is representative of internal exposures (TTC based on plasma concentration). An internal TTC (iTTC) would provide threshold values that could be utilized in exposure-based safety assessments. As part of a Cosmetics Europe (CosEu) research program, CosEu has initiated a project that is working towards the development of iTTCs that can be used for the human safety assessment. Knowing that the development of an iTTC is an ambitious and broad-spanning topic, CosEu organized a Working Group comprised a balance of multiple stakeholders (cosmetics and chemical industries, the EPA and JRC and academia) with relevant experience and expertise and workshop to critically evaluate the requirements to establish an iTTC. Outcomes from the workshop included an evaluation on the current state of the science for iTTC, the overall iTTC strategy, selection of chemical databases, capture and curation of chemical information, ADME and repeat dose data, expected challenges, as well as next steps and ongoing work.
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Affiliation(s)
- Corie A Ellison
- The Procter & Gamble Company, Cincinnati, OH, United States.
| | | | - Paul L Carmichael
- Unilever Safety and Environmental Assurance Center, Bedfordshire, UK
| | | | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, England, UK
| | | | - Sylvia E Escher
- Fraunhofer Institute for Toxicology and Experimental Medicine, Hannover, Germany
| | - Steve S Ferguson
- National Institute of Environmental Health Sciences, North Carolina, United States
| | | | | | | | | | - Atish Patel
- Research Institute for Fragrance Materials, New Jersey, United States
| | | | | | | | - John F Wambaugh
- United States Environmental Protection Agency, National Center for Computational Toxicology, North Carolina, United States
| | - Andrew Worth
- European Commission, Joint Research Centre, Ispra, Italy
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20
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Shebley M, Einolf HJ. Practical Assessment of Clinical Drug-Drug Interactions in Drug Development Using Physiologically Based Pharmacokinetics Modeling. Clin Pharmacol Ther 2019; 105:1326-1328. [PMID: 30893473 DOI: 10.1002/cpt.1394] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 02/03/2019] [Indexed: 01/05/2023]
Affiliation(s)
- Mohamad Shebley
- Clinical Pharmacology and Pharmacometrics, AbbVie, Inc., North Chicago, Illinois, USA
| | - Heidi J Einolf
- Novartis Pharmacokinetic Sciences, East Hanover, New Jersey, USA
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Tan YM, Worley RR, Leonard JA, Fisher JW. Challenges Associated With Applying Physiologically Based Pharmacokinetic Modeling for Public Health Decision-Making. Toxicol Sci 2019; 162:341-348. [PMID: 29385573 DOI: 10.1093/toxsci/kfy010] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
The development and application of physiologically based pharmacokinetic (PBPK) models in chemical toxicology have grown steadily since their emergence in the 1980s. However, critical evaluation of PBPK models to support public health decision-making across federal agencies has thus far occurred for only a few environmental chemicals. In order to encourage decision-makers to embrace the critical role of PBPK modeling in risk assessment, several important challenges require immediate attention from the modeling community. The objective of this contemporary review is to highlight 3 of these challenges, including: (1) difficulties in recruiting peer reviewers with appropriate modeling expertise and experience; (2) lack of confidence in PBPK models for which no tissue/plasma concentration data exist for model evaluation; and (3) lack of transferability across modeling platforms. Several recommendations for addressing these 3 issues are provided to initiate dialog among members of the PBPK modeling community, as these issues must be overcome for the field of PBPK modeling to advance and for PBPK models to be more routinely applied in support of public health decision-making.
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Affiliation(s)
- Yu-Mei Tan
- National Exposure Research Laboratory, United States Environmental Protection Agency, Research Triangle Park, North Carolina 27709
| | - Rachel R Worley
- Agency for Toxic Substances and Disease Registry, Atlanta, Georgia 30341
| | - Jeremy A Leonard
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 37830
| | - Jeffrey W Fisher
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arizona 72079
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Paini A, Leonard J, Joossens E, Bessems J, Desalegn A, Dorne J, Gosling J, Heringa M, Klaric M, Kliment T, Kramer N, Loizou G, Louisse J, Lumen A, Madden J, Patterson E, Proença S, Punt A, Setzer R, Suciu N, Troutman J, Yoon M, Worth A, Tan Y. Next generation physiologically based kinetic (NG-PBK) models in support of regulatory decision making. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2019; 9:61-72. [PMID: 31008414 PMCID: PMC6472623 DOI: 10.1016/j.comtox.2018.11.002] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 11/02/2018] [Accepted: 11/08/2018] [Indexed: 02/06/2023]
Abstract
The fields of toxicology and chemical risk assessment seek to reduce, and eventually replace, the use of animals for the prediction of toxicity in humans. In this context, physiologically based kinetic (PBK) modelling based on in vitro and in silico kinetic data has the potential to a play significant role in reducing animal testing, by providing a methodology capable of incorporating in vitro human data to facilitate the development of in vitro to in vivo extrapolation of hazard information. In the present article, we discuss the challenges in: 1) applying PBK modelling to support regulatory decision making under the toxicology and risk-assessment paradigm shift towards animal replacement; 2) constructing PBK models without in vivo animal kinetic data, while relying solely on in vitro or in silico methods for model parameterization; and 3) assessing the validity and credibility of PBK models built largely using non-animal data. The strengths, uncertainties, and limitations of PBK models developed using in vitro or in silico data are discussed in an effort to establish a higher degree of confidence in the application of such models in a regulatory context. The article summarises the outcome of an expert workshop hosted by the European Commission Joint Research Centre (EC-JRC) - European Union Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM), on "Physiologically-Based Kinetic modelling in risk assessment - reaching a whole new level in regulatory decision-making" held in Ispra, Italy, in November 2016, along with results from an international survey conducted in 2017 and recently reported activities occurring within the PBK modelling field. The discussions presented herein highlight the potential applications of next generation (NG)-PBK modelling, based on new data streams.
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Affiliation(s)
- A. Paini
- European Commission Joint Research Centre, Ispra, Italy
| | - J.A. Leonard
- Oak Ridge Institute for Science and Education, 100 ORAU Way, Oak Ridge, TN 37830, USA
| | - E. Joossens
- European Commission Joint Research Centre, Ispra, Italy
| | - J.G.M. Bessems
- European Commission Joint Research Centre, Ispra, Italy
- Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - A. Desalegn
- European Commission Joint Research Centre, Ispra, Italy
| | - J.L. Dorne
- European Food Safety Authority, 1a, Via Carlo Magno, 1A, 43126 Parma PR, Italy
| | - J.P. Gosling
- School of Mathematics, University of Leeds, Leeds, UK
| | - M.B. Heringa
- RIVM - The National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | | | - T. Kliment
- European Commission Joint Research Centre, Ispra, Italy
| | - N.I. Kramer
- Institute for Risk Assessment Sciences, Utrecht University, P.O. Box 80177, 3508TD Utrecht, The Netherlands
| | - G. Loizou
- Health and Safety Executive, Buxton, UK
| | - J. Louisse
- Division of Toxicology, Wageningen University, Tuinlaan 5, 6703 HE Wageningen, The Netherlands
- RIKILT Wageningen University and Research, Akkermaalsbos 2, 6708 WB Wageningen, The Netherlands
| | - A. Lumen
- Division of Biochemical Toxicology, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA
| | - J.C. Madden
- School of Pharmacy and Bimolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
| | - E.A. Patterson
- School of Engineering, University of Liverpool, Liverpool L69 3GH, UK
| | - S. Proença
- European Commission Joint Research Centre, Ispra, Italy
- Institute for Risk Assessment Sciences, Utrecht University, P.O. Box 80177, 3508TD Utrecht, The Netherlands
| | - A. Punt
- RIKILT Wageningen University and Research, Akkermaalsbos 2, 6708 WB Wageningen, The Netherlands
| | - R.W. Setzer
- U.S. Environmental Protection Agency, National Exposure Research Laboratory, 109 TW Alexander Drive, Research Triangle Park, NC 27709, USA
| | - N. Suciu
- DiSTAS, Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - J. Troutman
- Central Product Safety, The Procter & Gamble Company, Cincinnati, OH, USA
| | - M. Yoon
- ScitoVation, 6 Davis Drive, PO Box 110566, Research Triangle Park, NC 27709, USA
- ToxStrategies, Research Triangle Park Office, 1249 Kildaire Farm Road 134, Cary, NC 27511, USA
| | - A. Worth
- European Commission Joint Research Centre, Ispra, Italy
| | - Y.M. Tan
- School of Engineering, University of Liverpool, Liverpool L69 3GH, UK
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23
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Chebekoue SF, Krishnan K. A framework for application of quantitative property-property relationships (QPPRs) in physiologically based pharmacokinetic (PBPK) models for high-throughput prediction of internal dose of inhaled organic chemicals. CHEMOSPHERE 2019; 215:634-646. [PMID: 30347358 DOI: 10.1016/j.chemosphere.2018.10.041] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Revised: 10/03/2018] [Accepted: 10/06/2018] [Indexed: 06/08/2023]
Abstract
New generation of toxicological tests and assessment strategies require validated toxicokinetic data or models that are lacking for most chemicals. This study aimed at developing a quantitative property-property relationship (QPPR)-based human physiologically based pharmacokinetic (PBPK) modeling framework for high-throughput predictions of inhalation toxicokinetics of organic chemicals. A PBPK model was parameterized with QPPR-derived values for hepatic clearance (CLh) and partition coefficients (P) [blood:air (Pba) and tissue:air (Pta) and tissue:blood (Ptb)]. The model was initially applied to an evaluation dataset of 40 organic chemicals in the applicability domain, and then to an expanded dataset of 249 organic chemicals from diverse chemical classes. 'Batch' analyses were performed for rapid assessments of hundreds of chemicals. The simulations of inhalation toxicokinetics following an 8-h exposure to 1 ppm of each chemical were successful. The mean ratios of their predicted-to-experimental values were within a factor of 1.36-2.36 for Ptb and 1.18 for CLh, for 80% of the chemicals in the evaluation dataset. The predicted 24-h area under the venous blood concentration-time curve (AUC24) values were within the predicted envelopes obtained while using experimental values of Pba and considering either no or maximal hepatic extraction. The reliability analysis (based on combined sensitivity and uncertainty analyses) indicated that AUC24 predictions for 55% of the expanded dataset were moderately to highly reliable, with 46% exhibiting highly reliable values. Overall, the modeling framework suggests that molecular structure and chemical properties can together be effectively used to obtain first-cut estimates of the toxicokinetics of data-poor organic chemicals for screening and prioritization purposes.
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Affiliation(s)
- Sandrine F Chebekoue
- École de Santé Publique de l'Université de Montréal (ESPUM), Montréal, Québec, Canada.
| | - Kannan Krishnan
- École de Santé Publique de l'Université de Montréal (ESPUM), Montréal, Québec, Canada; Institut de recherche Robert-Sauvé en santé et en sécurité du travail (IRSST), Montréal, Québec, Canada.
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24
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Finding synergies for 3Rs – Toxicokinetics and read-across: Report from an EPAA partners' Forum. Regul Toxicol Pharmacol 2018; 99:5-21. [DOI: 10.1016/j.yrtph.2018.08.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 07/17/2018] [Accepted: 08/16/2018] [Indexed: 01/11/2023]
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25
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Ellison CA. Structural and functional pharmacokinetic analogs for physiologically based pharmacokinetic (PBPK) model evaluation. Regul Toxicol Pharmacol 2018; 99:61-77. [DOI: 10.1016/j.yrtph.2018.09.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 09/05/2018] [Accepted: 09/06/2018] [Indexed: 10/28/2022]
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26
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Madden J, Ebbrell D, Cronin M. Identifying potential analogues for read-across in Physiologically-Based Kinetic (PBK) modelling. Toxicol Lett 2018. [DOI: 10.1016/j.toxlet.2018.06.606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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27
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A Semi-Physiologically Based Pharmacokinetic Model Describing the Altered Metabolism of Midazolam Due to Inflammation in Mice. Pharm Res 2018; 35:162. [PMID: 29931580 DOI: 10.1007/s11095-018-2447-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 06/15/2018] [Indexed: 12/11/2022]
Abstract
PURPOSE To investigate influence of inflammation on metabolism and pharmacokinetics (PK) of midazolam (MDZ) and construct a semi-physiologically based pharmacokinetic (PBPK) model to predict PK in mice with inflammatory disease. METHODS Glucose-6-phosphate isomerase (GPI)-mediated inflammation was used as a preclinical model of arthritis in DBA/1 mice. CYP3A substrate MDZ was selected to study changes in metabolism and PK during the inflammation. The semi-PBPK model was constructed using mouse physiological parameters, liver microsome metabolism, and healthy animal PK data. In addition, serum cytokine, and liver-CYP (cytochrome P450 enzymes) mRNA levels were examined. RESULTS The in vitro metabolite formation rate was suppressed in liver microsomes prepared from the GPI-treated mice as compared to the healthy mice. Further, clearance of MDZ was reduced during inflammation as compared to the healthy group. Finally, the semi-PBPK model was used to predict PK of MDZ after GPI-mediated inflammation. IL-6 and TNF-α levels were elevated and liver-cyp3a11 mRNA was reduced after GPI treatment. CONCLUSION The semi-PBPK model successfully predicted PK parameters of MDZ in the disease state. The model may be applied to predict PK of other drugs under disease conditions using healthy animal PK and liver microsomal data as inputs.
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28
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Castellví Q, Sánchez-Velázquez P, Moll X, Berjano E, Andaluz A, Burdío F, Bijnens B, Ivorra A. Modeling liver electrical conductivity during hypertonic injection. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2018; 34:e2904. [PMID: 28557354 DOI: 10.1002/cnm.2904] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Revised: 05/10/2017] [Accepted: 05/20/2017] [Indexed: 06/07/2023]
Abstract
Metastases in the liver frequently grow as scattered tumor nodules that neither can be removed by surgical resection nor focally ablated. Previously, we have proposed a novel technique based on irreversible electroporation that may be able to simultaneously treat all nodules in the liver while sparing healthy tissue. The proposed technique requires increasing the electrical conductivity of healthy liver by injecting a hypersaline solution through the portal vein. Aiming to assess the capability of increasing the global conductivity of the liver by means of hypersaline fluids, here, it is presented a mathematical model that estimates the NaCl distribution within the liver and the resulting conductivity change. The model fuses well-established compartmental pharmacokinetic models of the organ with saline injection models used for resuscitation treatments, and it considers changes in sinusoidal blood viscosity because of the hypertonicity of the solution. Here, it is also described a pilot experimental study in pigs in which different volumes of NaCl 20% (from 100 to 200 mL) were injected through the portal vein at different flow rates (from 53 to 171 mL/minute). The in vivo conductivity results fit those obtained by the model, both quantitatively and qualitatively, being able to predict the maximum conductivity with a 14.6% average relative error. The maximum conductivity value was 0.44 second/m, which corresponds to increasing 4 times the mean basal conductivity (0.11 second/m). The results suggest that the presented model is well suited for predicting on liver conductivity changes during hypertonic saline injection.
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Affiliation(s)
- Quim Castellví
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, 08018, Spain
| | | | - Xavier Moll
- Departament de Medicina i Cirurgia Animals, Universitat Autònoma de Barcelona, Barcelona, 08193, Spain
| | - Enrique Berjano
- BioMIT, Department of Electronic Engineering, Universitat Politècnica de València, Valencia, 46022, Spain
| | - Anna Andaluz
- Departament de Medicina i Cirurgia Animals, Universitat Autònoma de Barcelona, Barcelona, 08193, Spain
| | - Fernando Burdío
- General Surgery Department, Hospital del Mar, Barcelona, 08003, Spain
| | - Bart Bijnens
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, 08018, Spain
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, 08010, Spain
| | - Antoni Ivorra
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, 08018, Spain
- Serra Húnter Fellow, Universitat Pompeu Fabra, Barcelona, 08018, Spain
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29
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Watson DE, Hunziker R, Wikswo JP. Fitting tissue chips and microphysiological systems into the grand scheme of medicine, biology, pharmacology, and toxicology. Exp Biol Med (Maywood) 2017; 242:1559-1572. [PMID: 29065799 DOI: 10.1177/1535370217732765] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Microphysiological systems (MPS), which include engineered organoids (EOs), single organ/tissue chips (TCs), and multiple organs interconnected to create miniature in vitro models of human physiological systems, are rapidly becoming effective tools for drug development and the mechanistic understanding of tissue physiology and pathophysiology. The second MPS thematic issue of Experimental Biology and Medicine comprises 15 articles by scientists and engineers from the National Institutes of Health, the IQ Consortium, the Food and Drug Administration, and Environmental Protection Agency, an MPS company, and academia. Topics include the progress, challenges, and future of organs-on-chips, dissemination of TCs into Pharma, children's health protection, liver zonation, liver chips and their coupling to interconnected systems, gastrointestinal MPS, maturation of immature cardiomyocytes in a heart-on-a-chip, coculture of multiple cell types in a human skin construct, use of synthetic hydrogels to create EOs that form neural tissue models, the blood-brain barrier-on-a-chip, MPS models of coupled female reproductive organs, coupling MPS devices to create a body-on-a-chip, and the use of a microformulator to recapitulate endocrine circadian rhythms. While MPS hardware has been relatively stable since the last MPS thematic issue, there have been significant advances in cell sourcing, with increased reliance on human-induced pluripotent stem cells, and in characterization of the genetic and functional cell state in MPS bioreactors. There is growing appreciation of the need to minimize perfusate-to-cell-volume ratios and respect physiological scaling of coupled TCs. Questions asked by drug developers are followed by an analysis of the potential value, costs, and needs of Pharma. Of highest value and lowest switching costs may be the development of MPS disease models to aid in the discovery of disease mechanisms; novel compounds including probes, leads, and clinical candidates; and mechanism of action of drug candidates. Impact statement Microphysiological systems (MPS), which include engineered organoids and both individual and coupled organs-on-chips and tissue chips, are a rapidly growing topic of research that addresses the known limitations of conventional cellular monoculture on flat plastic - a well-perfected set of techniques that produces reliable, statistically significant results that may not adequately represent human biology and disease. As reviewed in this article and the others in this thematic issue, MPS research has made notable progress in the past three years in both cell sourcing and characterization. As the field matures, currently identified challenges are being addressed, and new ones are being recognized. Building upon investments by the Defense Advanced Research Projects Agency, National Institutes of Health, Food and Drug Administration, Defense Threat Reduction Agency, and Environmental Protection Agency of more than $200 million since 2012 and sizable corporate spending, academic and commercial players in the MPS community are demonstrating their ability to meet the translational challenges required to apply MPS technologies to accelerate drug development and advance toxicology.
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Affiliation(s)
| | - Rosemarie Hunziker
- 2 National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD 20892, USA
| | - John P Wikswo
- 3 Departments of Biomedical Engineering, Molecular Physiology & Biophysics, and Physics & Astronomy, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN 37235-1807, USA
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30
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Paini A, Leonard JA, Kliment T, Tan YM, Worth A. Investigating the state of physiologically based kinetic modelling practices and challenges associated with gaining regulatory acceptance of model applications. Regul Toxicol Pharmacol 2017; 90:104-115. [PMID: 28866268 PMCID: PMC5656087 DOI: 10.1016/j.yrtph.2017.08.019] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 08/22/2017] [Accepted: 08/29/2017] [Indexed: 01/14/2023]
Abstract
Physiologically based kinetic (PBK) models are used widely throughout a number of working sectors, including academia and industry, to provide insight into the dosimetry related to observed adverse health effects in humans and other species. Use of these models has increased over the last several decades, especially in conjunction with emerging alternative methods to animal testing, such as in vitro studies and data-driven in silico quantitative-structure-activity-relationship (QSAR) predictions. Experimental information derived from these new approach methods can be used as input for model parameters and allows for increased confidence in models for chemicals that did not have in vivo data for model calibration. Despite significant advancements in good modelling practice (GMP) for model development and evaluation, there remains some reluctance among regulatory agencies to use such models during the risk assessment process. Here, the results of a survey disseminated to the modelling community are presented in order to inform the frequency of use and applications of PBK models in science and regulatory submission. Additionally, the survey was designed to identify a network of investigators involved in PBK modelling and knowledgeable of GMP so that they might be contacted in the future for peer review of PBK models, especially in regards to vetting the models to such a degree as to gain a greater acceptance for regulatory purposes. Physiologically Based kinetic (PBK) models are used widely in academia, industry, and government. Good modelling practice (GMP) for model development and evaluation continues to expand. Further guidance for establishing GMP is called for. There remains some reluctance among regulatory agencies to use PBK models. The next generation of PBK models could be developed using only data from in vitro and in silico methods.
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Affiliation(s)
- Alicia Paini
- European Commission, Joint Research Centre, Directorate Health, Consumers and Reference Materials, Via E Fermi 2749, 21027 Ispra, Italy.
| | - Jeremy A Leonard
- Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831, USA
| | | | - Yu-Mei Tan
- U.S. Environmental Protection Agency, National Research Laboratory, Research Triangle Park, NC 27709, USA
| | - Andrew Worth
- European Commission, Joint Research Centre, Directorate Health, Consumers and Reference Materials, Via E Fermi 2749, 21027 Ispra, Italy
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31
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Sala Benito JV, Paini A, Richarz AN, Meinl T, Berthold MR, Cronin MTD, Worth AP. Automated workflows for modelling chemical fate, kinetics and toxicity. Toxicol In Vitro 2017; 45:249-257. [PMID: 28323105 PMCID: PMC5745146 DOI: 10.1016/j.tiv.2017.03.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Revised: 03/10/2017] [Accepted: 03/16/2017] [Indexed: 01/15/2023]
Abstract
Automation is universal in today's society, from operating equipment such as machinery, in factory processes, to self-parking automobile systems. While these examples show the efficiency and effectiveness of automated mechanical processes, automated procedures that support the chemical risk assessment process are still in their infancy. Future human safety assessments will rely increasingly on the use of automated models, such as physiologically based kinetic (PBK) and dynamic models and the virtual cell based assay (VCBA). These biologically-based models will be coupled with chemistry-based prediction models that also automate the generation of key input parameters such as physicochemical properties. The development of automated software tools is an important step in harmonising and expediting the chemical safety assessment process. In this study, we illustrate how the KNIME Analytics Platform can be used to provide a user-friendly graphical interface for these biokinetic models, such as PBK models and VCBA, which simulates the fate of chemicals in vivo within the body and in vitro test systems respectively. The VCBA is a mathematical model that simulates in vitro fate of chemicals and the corresponding cellular effect. The VCBA has been implemented in an open access web-based KNIME platform for ease of use. KNIME Analytics Platform can be used to provide a user-friendly graphical interface for biokinetic models.
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Affiliation(s)
- J V Sala Benito
- Chemical Safety and Alternative Methods Unit, EURL ECVAM, Directorate F - Health, Consumers and Reference Materials, Joint Research Centre, European Commission, Ispra, Italy
| | - Alicia Paini
- Chemical Safety and Alternative Methods Unit, EURL ECVAM, Directorate F - Health, Consumers and Reference Materials, Joint Research Centre, European Commission, Ispra, Italy.
| | - Andrea-Nicole Richarz
- Liverpool John Moores University, School of Pharmacy and Biomolecular Sciences, Byrom Street, Liverpool L3 3AF, UK
| | | | - Michael R Berthold
- Universität Konstanz, Fachbereich Informatik und Informationswissenschaft, Box 712, 78457 Konstanz, Germany
| | - Mark T D Cronin
- Liverpool John Moores University, School of Pharmacy and Biomolecular Sciences, Byrom Street, Liverpool L3 3AF, UK
| | - Andrew P Worth
- Chemical Safety and Alternative Methods Unit, EURL ECVAM, Directorate F - Health, Consumers and Reference Materials, Joint Research Centre, European Commission, Ispra, Italy
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