1
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Lautz LS, Dorne JLCM, Punt A. Application of partition coefficient methods to predict tissue:plasma affinities in common farm animals: Influence of ionisation state. Toxicol Lett 2024; 398:140-149. [PMID: 38925423 DOI: 10.1016/j.toxlet.2024.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 05/17/2024] [Accepted: 06/21/2024] [Indexed: 06/28/2024]
Abstract
Tissue affinities are conventionally determined from in vivo steady-state tissue and plasma or plasma-water chemical concentration data. In silico approaches were initially developed for preclinical species but standardly applied and tested in human physiologically-based kinetic (PBK) models. Recently, generic PBK models for farm animals have been made available and require partition coefficients as input parameters. In the current investigation, data for species-specific tissue compositions have been collected, and prediction of chemical distribution in various tissues of livestock species for cattle, chicken, sheep and swine have been performed. Overall, tissue composition was very similar across the four farm animal species. However, small differences were observed in moisture, fat and protein content in the various organs within each species. Such differences could be attributed to factors such as variations in age, breed, and weight of the animals and general conditions of the animal itself. With regards to the predictions of tissue:plasma partition coefficients, 80 %, 71 %, 77 % of the model predictions were within a factor 10 using the methods of Berezhkovskiy (2004), Rodgers and Rowland (2006) and Schmitt (2008). The method of Berezhkovskiy (2004) was often providing the most reliable predictions except for swine, where the method of Schmitt (2008) performed best. In addition, investigation of the impact of chemical classes on prediction performance, all methods had very similar reliability. Notwithstanding, no clear pattern regarding specific chemicals or tissues could be detected for the values predicted outside a 10-fold change in certain chemicals or specific tissues. This manuscript concludes with the need for future research, particularly focusing on lipophilicity and species differences in protein binding.
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Affiliation(s)
- L S Lautz
- Wageningen Food Safety Research, Akkermaalsbos 2, Wageningen, WB 6708, the Netherlands.
| | - J-L C M Dorne
- European Food Safety Authority, Via Carlo Magno 1A, Parma 43126, Italy
| | - A Punt
- Wageningen Food Safety Research, Akkermaalsbos 2, Wageningen, WB 6708, the Netherlands
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2
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Poulin P. First-in-Human Predictions of Hepatic Clearance for Drugs With the Well-Stirred Model: Comparative Assessment Between Models of Fraction Unbound Based Either on the Free Drug Hypothesis, Albumin-Facilitated Hepatic Uptake or Dynamic Binding Kinetics. J Pharm Sci 2024:S0022-3549(24)00193-X. [PMID: 38796154 DOI: 10.1016/j.xphs.2024.05.021] [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: 02/27/2024] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 05/28/2024]
Abstract
The well-stirred model (WSM) is commonly used to predict the hepatic clearance in vivo (CLH) of drugs. The necessary intrinsic clearance of the unbound drug (CLint-in vitro-unbound) is generated in the in vitro assays in the presence of microsomes or hepatocytes but in the absence of plasma proteins. The value of CLint-in vitro-unbound can be extrapolated with the fraction unbound determined in vitro in plasma (fup) only if the fraction unbound in vivo in liver is the same. However, this approach resulted to a systematic underprediction bias of CLH. With the goal of reducing this bias, two new models of fraction unbound were published in this journal. These models estimate the binding kinetics of the rates of association and dissociation of the drug-protein complex and propose that more dissociation in the liver compared to plasma will increase the fraction unbound available for the metabolism. Consequently, these two models generated higher values of fraction unbound, implying a lower underprediction bias of CLH with the WSM. The first model was developed by Poulin et al. and is referring to the value of fup that is adjusted (fu-adjusted) to quantify the effect of a full dissociation of the drug-protein complex at the hepatocyte membrane in accordance with the theory of the albumin-facilitated hepatic uptake. A second model was developed by Yan et al. who presented a dynamic fraction unbound (fu-dynamic) measuring the real dissociation kinetics of the drug-protein complex with a new in vitro assay in the presence and absence of a recombinant liver enzyme in plasma. Therefore, the objective of this study was to make the first comparative assessment between these two models. The results indicate that, in general, the WSM combined with the values of fu-adjusted was the most accurate approach for predicting CLH. The WSM combined with the values of fu-dynamic has underperformed particularly with the acidic and neutral drugs binding to the albumin and presenting a low metabolic turnover in vitro. Therefore, the new in vitro assay for fu-dynamic gave an underprediction bias of CLH for these drug properties. However, the values of fu-adjusted are significantly higher than those values of fu-dynamic, and, this resulted to no underprediction bias, which is reinforcing the theory of the ALB-facilitated hepatic uptake. For the other neutral and acidic drugs, the models of fu-dynamic and fu-adjusted are in closer agreement. Finally, for the basic drugs, the models of fu-adjusted and fu-dynamic as well as a third model only considering a pH gradient effect on fup are almost accurately equivalent.
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Affiliation(s)
- Patrick Poulin
- Consultant Patrick Poulin Inc., Québec City, Québec, Canada; School of Public Health, Université de Montréal, Montréal, Québec, Canada.
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3
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Zhang S, Wang Z, Chen J, Luo X, Mai B. Multimodal Model to Predict Tissue-to-Blood Partition Coefficients of Chemicals in Mammals and Fish. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:1944-1953. [PMID: 38240238 DOI: 10.1021/acs.est.3c08016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
Tissue-to-blood partition coefficients (Ptb) are key parameters for assessing toxicokinetics of xenobiotics in organisms, yet their experimental data were lacking. Experimental methods for measuring Ptb values are inefficient, underscoring the urgent need for prediction models. However, most existing models failed to fully exploit Ptb data from diverse sources, and their applicability domain (AD) was limited. The current study developed a multimodal model capable of processing and integrating textual (categorical features) and numerical information (molecular descriptors/fingerprints) to simultaneously predict Ptb values across various species, tissues, blood matrices, and measurement methods. Artificial neural network algorithms with embedding layers were used for the multimodal modeling. The corresponding unimodal models were developed for comparison. Results showed that the multimodal model outperformed unimodal models. To enhance the reliability of the model, a method considering categorical features, weighted molecular similarity density, and weighted inconsistency in molecular activities of structure-activity landscapes was used to characterize the AD. The model constrained by the AD exhibited better prediction accuracy for the validation set, with the determination coefficient, root mean-square error, and mean absolute error being 0.843, 0.276, and 0.213 log units, respectively. The multimodal model coupled with the AD characterization can serve as an efficient tool for internal exposure assessment of chemicals.
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Affiliation(s)
- Shuying Zhang
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Resources Utilization and Protection, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
| | - Zhongyu Wang
- Solid Waste and Chemicals Management Center, Ministry of Ecology and Environment of the People's Republic of China, Beijing 100029, China
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Xiaojun Luo
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Resources Utilization and Protection, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
| | - Bixian Mai
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Resources Utilization and Protection, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
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4
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Zhang S, Luo X, Mai B. Multi-task machine learning models for simultaneous prediction of tissue-to-blood partition coefficients of chemicals in mammals. ENVIRONMENTAL RESEARCH 2024; 241:117603. [PMID: 37939805 DOI: 10.1016/j.envres.2023.117603] [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: 09/01/2023] [Revised: 10/25/2023] [Accepted: 11/04/2023] [Indexed: 11/10/2023]
Abstract
Tissue-to-blood partition coefficients (Ptb) are crucial for assessing the distribution of chemicals in organisms. Given the lack of experimental data and laborious nature of experimental methods, there is an urgent need to develop efficient predictive models. With the help of machine learning algorithms, i,e., random forest (RF), and artificial neural network (ANN), this study developed multi-task (MT) models that can simultaneously predict Ptb values for various mammalian tissues, including liver, muscle, brain, lung, and adipose. Single-task (ST) models using partial least squares regression, RF, and ANN algorithms for each endpoint were established for comparison. Overall, the performances of MT models were superior to those of ST models. The MT model using ANN algorithms showed the highest prediction accuracy with determination coefficients ranging from 0.704 to 0.886, root mean square errors between 0.223 and 0.410, and mean absolute errors ranging from 0.178 to 0.285 log units. Results showed that lipophilicity and polarizability of molecules significantly influence their partition behavior in organisms. Applicability domains (ADs) of the models were characterized by weighted molecular similarity density, and weighted inconsistency in molecular activities of structure-activity landscapes. When constrained by ADs, the models displayed enhanced predictive accuracy, making them valuable tools for the risk assessment and management of chemicals.
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Affiliation(s)
- Shuying Zhang
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Resources Utilization and Protection, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China
| | - Xiaojun Luo
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Resources Utilization and Protection, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China.
| | - Bixian Mai
- State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Resources Utilization and Protection, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China
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5
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Poulin P, Nicolas JM, Bouzom F. A New Version of the Tissue Composition-Based Model for Improving the Mechanism-Based Prediction of Volume of Distribution at Steady-State for Neutral Drugs. J Pharm Sci 2024; 113:118-130. [PMID: 37634869 DOI: 10.1016/j.xphs.2023.08.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/18/2023] [Accepted: 08/19/2023] [Indexed: 08/29/2023]
Abstract
In-vitro models are available in the literature for predicting the volume of distribution at steady-state (Vdss) of drugs. The mechanistic model refers to the tissue composition-based model (TCM), which includes important factors that govern Vdss such as drug physiochemistry and physiological data. The recognized TCM published by Rodgers and Rowland (TCM-RR) and a subsequent adjustment made by Simulations Plus Inc. (TCM-SP) have been shown to be generally less accurate with neutral compared to ionized drugs. Therefore, improving these models for neutral drugs becomes necessary. The objective of this study was to propose a new TCM for improving the prediction of Vdss for neutral drugs. The new TCM included two modifications of the published models (i) accentuate the effect of the blood-to-plasma ratio (BPR) that should cover permeated molecules across the biomembranes, which is lacking in these models for neutral compounds, and (ii) use a different approach to estimate the binding in tissues. The new TCM was validated with a large dataset of 202 commercial and proprietary compounds including preclinical and clinical data. All scenario datasets were predicted more accurately with the TCM-New, whereas all statistical parameters indicate that the TCM-New showed significant improvements in terms of accuracy over the TCM-RR and TCM-SP. Predictions of Vdss were frequently more accurate for the TCM-new with 83% within twofold error versus only 50% for the TCM-RR. And more than 95% of the predictions were within threefold error and patient interindividual differences can be predicted with the TCM-New, greatly exceeding the accuracy of the published models. Overall, the new TCM incorporating BPR significantly improved the Vdss predictions in animals and humans for neutral drugs, and, hence, has the potential to better support the drug discovery and facilitate the first-in-human predictions.
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Affiliation(s)
- Patrick Poulin
- Consultant Patrick Poulin Inc., Québec City, Québec, Canada; School of Public Health, Université de Montréal, Montréal, Québec, Canada.
| | | | - François Bouzom
- DMPK, Development Science, UCB Pharma, Braine I'Alleud, Belgium; Current: Simulations Plus, Inc., 42505 10th Street West, Lancaster, CA 93534, USA
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6
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Zhang X, Li Z. Co-PBK: a computational biomonitoring tool for assessing chronic internal exposure to chemicals and metabolites. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2023; 25:2167-2180. [PMID: 37982278 DOI: 10.1039/d3em00396e] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
Toxic chemicals are released into the environment through diverse human activities. An increasing number of chronic diseases are associated with ambient pollution, thus posing a threat to people. Given the high consumption of resources for human biomonitoring, this study proposed coupled physiologically-based kinetic (co-PBK) modeling matrices as a biomonitoring tool for simplifying chronic internal exposure estimates of environmental chemicals and their metabolites using naphthalene (NAP) and its metabolites (i.e., 1-OHN and 2-OHN) as simulation examples. According to the simulation of the steady-state mass among various organs/tissues via the co-PBK modeling matrices, fat had the highest potential bioaccumulation of NAP and its metabolites. With respect to body fluids, 1-OHN and 2-OHN tended to bioaccumulate more in the bile than in the urine. According to the sensitivity analysis, the calculated sensitivity factors for the first-order kinetics-based rate constants imply that due to the biotransformation process, target organs/tissues (e.g., liver and kidneys) would be continuously exposed to more NAP metabolites under chronic exposure. Meanwhile, 1-OHN may be more stably transported to the urine than 2-OHN for further human biomonitoring during long-term internal exposure. According to the case study of simulating population chronic exposure to NAP in Shenzhen, the co-PBK modeling estimated the population exposure to NAP with an intake rate of 8.77 × 10-2 mg d-1 and the aggregated urinary concentration of NAP metabolites of 2.60 μg L-1. Furthermore, the accuracy of the urinary levels between the real-world data and the values simulated by the co-PBK modeling was assessed and the root-mean-square error of c1-OHN,urine was found to be lower than that of c2-OHN,urine.
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Affiliation(s)
- Xiaoyu Zhang
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong 518107, China.
| | - Zijian Li
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong 518107, China.
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7
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Berkane W, El Aroussi B, Bouchard M, Marchand G, Haddad S. Determination of blood:air, urine:air and plasma:air partition coefficients of selected microbial volatile organic compounds. CHEMOSPHERE 2023; 343:140305. [PMID: 37769913 DOI: 10.1016/j.chemosphere.2023.140305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 09/15/2023] [Accepted: 09/25/2023] [Indexed: 10/02/2023]
Abstract
Partition coefficients (PCs) are essential parameters for understanding the toxicokinetics of chemicals in the human body since they are used in the description of different processes of absorption, distribution, and excretion in physiologically based pharmacokinetic (PBPK) models used in chemical exposure and risk assessment. The goal of this study was to determine urine:air, blood:air and plasma:air partition coefficients (PCs) of microbial volatile organic compounds (mVOCs) previously selected as having high potential as biomarkers of indoor mold exposure. To achieve this goal, the vial-equilibration technique was used, and quantification was performed using headspace gas chromatography tandem mass spectrometry (HS-GC-MS/MS) analysis. Matrix:air PCs of 19 different mVOCs have been successfully determined and their values ranged between 14 and 3586 for urine:air, 78 and 4721 for blood:air and 64 and 5604 for plasma:air PCs. Water:air PCs were also determined, and their values varied between 16 and 2210, showing a good correlation with urine:air PCs for 17 compounds of the selected mVOCs (R2 = 0.97, slope close to unity) indicating that water:air PCs below 103 may be a good surrogate for urine:air PCs. All studied mVOCs have high blood:air PCs (greater than 78) indicating strong pulmonary uptake. Due to their high blood:urine PCs, some mVOCs may be more easily measured in blood than in urine. This work is an important preliminary step toward the use of mVOCs as potential biomarkers of indoor mold exposure. The data obtained in this study will help to determine the most appropriate matrix to use in this biomonitoring approach and will eventually facilitate the development of PBPK models for these chemicals.
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Affiliation(s)
- Wissam Berkane
- Department of Environmental and Occupational Health (DSEST), School of Public Health, Université de Montréal, Montréal, Québec, Canada; Centre de recherche en santé publique (CReSP) de l'Université de Montréal, Montréal, Québec, Canada
| | - Badr El Aroussi
- Department of Environmental and Occupational Health (DSEST), School of Public Health, Université de Montréal, Montréal, Québec, Canada; Centre de recherche en santé publique (CReSP) de l'Université de Montréal, Montréal, Québec, Canada
| | - Michèle Bouchard
- Department of Environmental and Occupational Health (DSEST), School of Public Health, Université de Montréal, Montréal, Québec, Canada; Centre de recherche en santé publique (CReSP) de l'Université de Montréal, Montréal, Québec, Canada
| | - Geneviève Marchand
- Department of Environmental and Occupational Health (DSEST), School of Public Health, Université de Montréal, Montréal, Québec, Canada; Institut de recherche Robert-Sauvé en santé et en sécurité du travail (IRSST), Montréal, Québec, Canada
| | - Sami Haddad
- Department of Environmental and Occupational Health (DSEST), School of Public Health, Université de Montréal, Montréal, Québec, Canada; Centre de recherche en santé publique (CReSP) de l'Université de Montréal, Montréal, Québec, Canada.
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8
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Phénix J, Côté J, Dieme D, Recinto SJ, Oestereich F, Efrem S, Haddad S, Bouchard M, Munter LM. CETP inhibitor evacetrapib enters mouse brain tissue. Front Pharmacol 2023; 14:1171937. [PMID: 37533630 PMCID: PMC10390775 DOI: 10.3389/fphar.2023.1171937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 06/26/2023] [Indexed: 08/04/2023] Open
Abstract
High levels of plasma cholesterol, especially high levels of low-density lipoprotein cholesterol (LDL-C), have been associated with an increased risk of Alzheimer's disease. The cholesteryl ester transfer protein (CETP) in plasma distributes cholesteryl esters between lipoproteins and increases LDL-C in plasma. Epidemiologically, decreased CETP activity has been associated with sustained cognitive performance during aging, longevity, and a lower risk of Alzheimer's disease. Thus, pharmacological CETP inhibitors could be repurposed for the treatment of Alzheimer's disease as they are safe and effective at lowering CETP activity and LDL-C. Although CETP is mostly expressed by the liver and secreted into the bloodstream, it is also expressed by astrocytes in the brain. Therefore, it is important to determine whether CETP inhibitors can enter the brain. Here, we describe the pharmacokinetic parameters of the CETP inhibitor evacetrapib in the plasma, liver, and brain tissues of CETP transgenic mice. We show that evacetrapib crosses the blood-brain barrier and is detectable in brain tissue 0.5 h after a 40 mg/kg i.v. injection in a non-linear function. We conclude that evacetrapib may prove to be a good candidate to treat CETP-mediated cholesterol dysregulation in Alzheimer's disease.
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Affiliation(s)
- Jasmine Phénix
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
- Cell Information Systems Group, Montreal, QC, Canada
| | - Jonathan Côté
- Department of Environmental and Occupational Health, School of Public Health, Université de Montréal, Montreal, QC, Canada
- Public Health Research Center (CReSP), Université de Montréal, Montreal, QC, Canada
| | - Denis Dieme
- Department of Environmental and Occupational Health, School of Public Health, Université de Montréal, Montreal, QC, Canada
- Public Health Research Center (CReSP), Université de Montréal, Montreal, QC, Canada
| | - Sherilyn J. Recinto
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
- Cell Information Systems Group, Montreal, QC, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Felix Oestereich
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
- Cell Information Systems Group, Montreal, QC, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Sasen Efrem
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
- Cell Information Systems Group, Montreal, QC, Canada
| | - Sami Haddad
- Department of Environmental and Occupational Health, School of Public Health, Université de Montréal, Montreal, QC, Canada
- Public Health Research Center (CReSP), Université de Montréal, Montreal, QC, Canada
| | - Michèle Bouchard
- Department of Environmental and Occupational Health, School of Public Health, Université de Montréal, Montreal, QC, Canada
- Public Health Research Center (CReSP), Université de Montréal, Montreal, QC, Canada
| | - Lisa Marie Munter
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
- Cell Information Systems Group, Montreal, QC, Canada
- Centre de Recherche en Biologie Structurale (CRBS), Montreal, QC, Canada
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9
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Schulz JA, Stresser DM, Kalvass JC. Plasma Protein-Mediated Uptake and Contradictions to the Free Drug Hypothesis: A Critical Review. Drug Metab Rev 2023:1-34. [PMID: 36971325 DOI: 10.1080/03602532.2023.2195133] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
According to the free drug hypothesis (FDH), only free, unbound drug is available to interact with biological targets. This hypothesis is the fundamental principle that continues to explain the vast majority of all pharmacokinetic and pharmacodynamic processes. Under the FDH, the free drug concentration at the target site is considered the driver of pharmacodynamic activity and pharmacokinetic processes. However, deviations from the FDH are observed in hepatic uptake and clearance predictions, where observed unbound intrinsic hepatic clearance (CLint,u) is larger than expected. Such deviations are commonly observed when plasma proteins are present and form the basis of the so-called plasma protein-mediated uptake effect (PMUE). This review will discuss the basis of plasma protein binding as it pertains to hepatic clearance based on the FDH, as well as several hypotheses that may explain the underlying mechanisms of PMUE. Notably, some, but not all, potential mechanisms remained aligned with the FDH. Finally, we will outline possible experimental strategies to elucidate PMUE mechanisms. Understanding the mechanisms of PMUE and its potential contribution to clearance underprediction is vital to improving the drug development process.
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10
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Yau E, Gertz M, Ogungbenro K, Aarons L, Olivares-Morales A. A "middle-out approach" for the prediction of human drug disposition from preclinical data using simplified physiologically based pharmacokinetic (PBPK) models. CPT Pharmacometrics Syst Pharmacol 2023; 12:346-359. [PMID: 36647756 PMCID: PMC10014056 DOI: 10.1002/psp4.12915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 11/03/2022] [Accepted: 12/08/2022] [Indexed: 01/18/2023] Open
Abstract
Simplified physiologically based pharmacokinetic (PBPK) models using estimated tissue-to-unbound plasma partition coefficients (Kpus) were previously investigated by fitting them to in vivo pharmacokinetic (PK) data. After optimization with preclinical data, the performance of these models for extrapolation of distribution kinetics to human were evaluated to determine the best approach for the prediction of human drug disposition and volume of distribution (Vss) using PBPK modeling. Three lipophilic bases were tested (diazepam, midazolam, and basmisanil) for which intravenous PK data were available in rat, monkey, and human. The models with Kpu scalars using k-means clustering were generally the best for fitting data in the preclinical species and gave plausible Kpu values. Extrapolations of plasma concentrations for diazepam and midazolam using these models and parameters obtained were consistent with the observed clinical data. For diazepam and midazolam, the human predictions of Vss after optimization in rats and monkeys were better compared with the Vss estimated from the traditional PBPK modeling approach (varying from 1.1 to 3.1 vs. 3.7-fold error). For basmisanil, the sparse preclinical data available could have affected the model performance for fitting and the subsequent extrapolation to human. Overall, this work provides a rational strategy to predict human drug distribution using preclinical PK data within the PBPK modeling strategy.
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Affiliation(s)
- Estelle Yau
- Centre for Applied Pharmacokinetic Research, The University of Manchester, Manchester, UK.,Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Michael Gertz
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
| | - Kayode Ogungbenro
- Centre for Applied Pharmacokinetic Research, The University of Manchester, Manchester, UK
| | - Leon Aarons
- Centre for Applied Pharmacokinetic Research, The University of Manchester, Manchester, UK
| | - Andrés Olivares-Morales
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland
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11
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Dawson DE, Lau C, Pradeep P, Sayre RR, Judson RS, Tornero-Velez R, Wambaugh JF. A Machine Learning Model to Estimate Toxicokinetic Half-Lives of Per- and Polyfluoro-Alkyl Substances (PFAS) in Multiple Species. TOXICS 2023; 11:98. [PMID: 36850973 PMCID: PMC9962572 DOI: 10.3390/toxics11020098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/09/2023] [Accepted: 01/18/2023] [Indexed: 06/18/2023]
Abstract
Per- and polyfluoroalkyl substances (PFAS) are a diverse group of man-made chemicals that are commonly found in body tissues. The toxicokinetics of most PFAS are currently uncharacterized, but long half-lives (t½) have been observed in some cases. Knowledge of chemical-specific t½ is necessary for exposure reconstruction and extrapolation from toxicological studies. We used an ensemble machine learning method, random forest, to model the existing in vivo measured t½ across four species (human, monkey, rat, mouse) and eleven PFAS. Mechanistically motivated descriptors were examined, including two types of surrogates for renal transporters: (1) physiological descriptors, including kidney geometry, for renal transporter expression and (2) structural similarity of defluorinated PFAS to endogenous chemicals for transporter affinity. We developed a classification model for t½ (Bin 1: <12 h; Bin 2: <1 week; Bin 3: <2 months; Bin 4: >2 months). The model had an accuracy of 86.1% in contrast to 32.2% for a y-randomized null model. A total of 3890 compounds were within domain of the model, and t½ was predicted using the bin medians: 4.9 h, 2.2 days, 33 days, and 3.3 years. For human t½, 56% of PFAS were classified in Bin 4, 7% were classified in Bin 3, and 37% were classified in Bin 2. This model synthesizes the limited available data to allow tentative extrapolation and prioritization.
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Affiliation(s)
- Daniel E. Dawson
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
| | - Christopher Lau
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Public Health and Environmental Assessment, 109 T.W. Alexander Drive, Research Triangle Park, NC 277011, USA
| | - Prachi Pradeep
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
- Oak Ridge Institutes for Science and Education, Oak Ridge, TN 37830, USA
| | - Risa R. Sayre
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
| | - Richard S. Judson
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
| | - Rogelio Tornero-Velez
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
| | - John F. Wambaugh
- U.S. Environmental Protection Agency, Office of Research and Development, Center for Computational Toxicology and Exposure, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
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12
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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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13
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Kapraun DF, Sfeir M, Pearce RG, Davidson-Fritz SE, Lumen A, Dallmann A, Judson RS, Wambaugh JF. Evaluation of a rapid, generic human gestational dose model. Reprod Toxicol 2022; 113:172-188. [PMID: 36122840 PMCID: PMC9761697 DOI: 10.1016/j.reprotox.2022.09.004] [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: 04/10/2022] [Revised: 08/30/2022] [Accepted: 09/14/2022] [Indexed: 10/14/2022]
Abstract
Chemical risk assessment considers potentially susceptible populations including pregnant women and developing fetuses. Humans encounter thousands of chemicals in their environments, few of which have been fully characterized. Toxicokinetic (TK) information is needed to relate chemical exposure to potentially bioactive tissue concentrations. Observational data describing human gestational exposures are unavailable for most chemicals, but physiologically based TK (PBTK) models estimate such exposures. Development of chemical-specific PBTK models requires considerable time and resources. As an alternative, generic PBTK approaches describe a standardized physiology and characterize chemicals with a set of standard physical and TK descriptors - primarily plasma protein binding and hepatic clearance. Here we report and evaluate a generic PBTK model of a human mother and developing fetus. We used a published set of formulas describing the major anatomical and physiological changes that occur during pregnancy to augment the High-Throughput Toxicokinetics (httk) software package. We simulated the ratio of concentrations in maternal and fetal plasma and compared to literature in vivo measurements. We evaluated the model with literature in vivo time-course measurements of maternal plasma concentrations in pregnant and non-pregnant women. Finally, we prioritized chemicals measured in maternal serum based on predicted fetal brain concentrations. This new model can be used for TK simulations of 859 chemicals with existing human-specific in vitro TK data as well as any new chemicals for which such data become available. This gestational model may allow for in vitro to in vivo extrapolation of point of departure doses relevant to reproductive and developmental toxicity.
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Affiliation(s)
- Dustin F Kapraun
- Center for Public Health and Environmental Assessment, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Mark Sfeir
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831, USA
| | - Robert G Pearce
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831, USA
| | - Sarah E Davidson-Fritz
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Annie Lumen
- National Center for Toxicological Research, US Food and Drug Administration, USA
| | - André Dallmann
- Pharmacometrics/Modeling and Simulation, Research and Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany
| | - Richard S Judson
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - John F Wambaugh
- Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA.
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14
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Tabbal S, El Aroussi B, Bouchard M, Marchand G, Haddad S. A new headspace solid-phase microextraction coupled with gas chromatography-tandem mass spectrometry method for the simultaneous quantification of 21 microbial volatile organic compounds in urine and blood. CHEMOSPHERE 2022; 296:133901. [PMID: 35143866 DOI: 10.1016/j.chemosphere.2022.133901] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 02/03/2022] [Accepted: 02/04/2022] [Indexed: 06/14/2023]
Abstract
Mold growth can cause the development of several metabolites including microbial volatile organic compounds (mVOCs). These latter may be considered as potential biomarkers of fungal presence and have been detected in human biological matrices such as urine and blood. Exposure to molds and their metabolites (e.g., mVOCs, mycotoxins) in occupational settings, is responsible for several health effects. Thus, this exposure cannot be neglected and must be evaluated. Herein, a method has been developed to quantify 21 mVOCs in urine and human blood by headspace solid phase micro-extraction (HS-SPME) coupled with gas chromatography-triple quadrupole mass spectrometry (GC-MS/MS). The parameters influencing the extraction process, such as the type of fiber, the incubation and extraction time and temperature and the desorption time, have been optimized to ensure better mVOCs extraction. The developed method showed good linearity over the concentration range of the compounds (R2 ˃ 0.995) for all the mVOCs in all the matrices. The low limits of detection (LOD) ranging from 0.7 to 417 ng/L in urine and from 1 to 507 ng/L in blood, make the developed methods sensitive and effective for biomonitoring of exposure at low levels. Recoveries, at low and high concentrations, were between 87% and 120% in urine and between 83% and 118% in blood. The repeatability and the intermediate precision in terms of coefficients of variation (CV%) was lower than 13% and 8.58% respectively for all compounds in all matrices. These values show satisfactory accuracy and precision of the developed method. Thus, this practical, simple, and sensitive method is well suited for the simultaneous quantification of target mVOCs.
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Affiliation(s)
- Sarah Tabbal
- Department of Environmental and Occupational Health (DSEST), University of Montréal, Montréal, Québec, Canada; Centre de recherche en santé publique (CReSP), Montréal, Québec, Canada
| | - Badr El Aroussi
- Department of Environmental and Occupational Health (DSEST), University of Montréal, Montréal, Québec, Canada; Centre de recherche en santé publique (CReSP), Montréal, Québec, Canada
| | - Michèle Bouchard
- Department of Environmental and Occupational Health (DSEST), University of Montréal, Montréal, Québec, Canada; Centre de recherche en santé publique (CReSP), Montréal, Québec, Canada
| | - Geneviève Marchand
- Institut de recherche Robert-Sauvé en santé et en sécurité du travail (IRSST), Montréal, Québec, Canada
| | - Sami Haddad
- Department of Environmental and Occupational Health (DSEST), University of Montréal, Montréal, Québec, Canada; Centre de recherche en santé publique (CReSP), Montréal, Québec, Canada.
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15
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Wang W, Ouyang D. Opportunities and challenges of physiologically based pharmacokinetic modeling in drug delivery. Drug Discov Today 2022; 27:2100-2120. [PMID: 35452792 DOI: 10.1016/j.drudis.2022.04.015] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 03/03/2022] [Accepted: 04/13/2022] [Indexed: 12/15/2022]
Abstract
Physiologically based pharmacokinetic (PBPK) modeling is an important in silico tool to bridge drug properties and in vivo PK behaviors during drug development. Over the recent decade, the PBPK method has been largely applied to drug delivery systems (DDS), including oral, inhaled, transdermal, ophthalmic, and complex injectable products. The related therapeutic agents have included small-molecule drugs, therapeutic proteins, nucleic acids, and even cells. Simulation results have provided important insights into PK behaviors of new dosage forms, which strongly support drug regulation. In this review, we comprehensively summarize recent progress in PBPK applications in drug delivery, which shows large opportunities for facilitating drug development. In addition, we discuss the challenges of applying this methodology from a practical viewpoint.
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Affiliation(s)
- Wei Wang
- Institute of Chinese Medical Sciences (ICMS), State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macau, China; Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Macau, China
| | - Defang Ouyang
- Institute of Chinese Medical Sciences (ICMS), State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macau, China; Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Macau, China.
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16
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Najjar A, Schepky A, Krueger CT, Dent M, Cable S, Li H, Grégoire S, Roussel L, Noel-Voisin A, Hewitt NJ, Cardamone E. Use of Physiologically-Based Kinetics Modelling to Reliably Predict Internal Concentrations of the UV Filter, Homosalate, After Repeated Oral and Topical Application. Front Pharmacol 2022; 12:802514. [PMID: 35058784 PMCID: PMC8763688 DOI: 10.3389/fphar.2021.802514] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 11/22/2021] [Indexed: 01/09/2023] Open
Abstract
Ethical and legal considerations have led to increased use of non-animal methods to evaluate the safety of chemicals for human use. We describe the development and qualification of a physiologically-based kinetics (PBK) model for the cosmetic UV filter ingredient, homosalate, to support its safety without the need of generating further animal data. The intravenous (IV) rat PBK model, using PK-Sim®, was developed and validated using legacy in vivo data generated prior to the 2013 EU animal-testing ban. Input data included literature or predicted physicochemical and pharmacokinetic properties. The refined IV rat PBK model was subject to sensitivity analysis to identify homosalate-specific sensitive parameters impacting the prediction of Cmax (more sensitive than AUC(0-∞)). These were then considered, together with population modeling, to calculate the confidence interval (CI) 95% Cmax and AUC(0-∞). Final model parameters were established by visual inspection of the simulations and biological plausibility. The IV rat model was extrapolated to oral administration, and used to estimate internal exposures to doses tested in an oral repeated dose toxicity study. Next, a human PBK dermal model was developed using measured human in vitro ADME data and a module to represent the dermal route. Model performance was confirmed by comparing predicted and measured values from a US-FDA clinical trial (Identifier: NCT03582215, https://clinicaltrials.gov/). Final exposure estimations were obtained in a virtual population and considering the in vitro and input parameter uncertainty. This model was then used to estimate the Cmax and AUC(0–24 h) of homosalate according to consumer use in a sunscreen. The developed rat and human PBK models had a good biological basis and reproduced in vivo legacy rat and human clinical kinetics data. They also complied with the most recent WHO and OECD recommendations for assessing the confidence level. In conclusion, we have developed a PBK model which predicted reasonably well the internal exposure of homosalate according to different exposure scenarios with a medium to high level of confidence. In the absence of in vivo data, such human PBK models will be the heart of future completely non-animal risk assessments; therefore, valid approaches will be key in gaining their regulatory acceptance. Clinical Trial Registration: https://clinicaltrials.gov/, identifier, NCT03582215
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Affiliation(s)
| | | | | | - Matthew Dent
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, United Kingdom
| | - Sophie Cable
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, United Kingdom
| | - Hequn Li
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, United Kingdom
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17
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Bois FY, Tebby C, Brochot C. PBPK Modeling to Simulate the Fate of Compounds in Living Organisms. Methods Mol Biol 2022; 2425:29-56. [PMID: 35188627 DOI: 10.1007/978-1-0716-1960-5_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Pharmacokinetics study the fate of xenobiotics in a living organism. Physiologically based pharmacokinetic (PBPK) models provide realistic descriptions of xenobiotics' absorption, distribution, metabolism, and excretion processes. They model the body as a set of homogeneous compartments representing organs, and their parameters refer to anatomical, physiological, biochemical, and physicochemical entities. They offer a quantitative mechanistic framework to understand and simulate the time-course of the concentration of a substance in various organs and body fluids. These models are well suited for performing extrapolations inherent to toxicology and pharmacology (e.g., between species or doses) and for integrating data obtained from various sources (e.g., in vitro or in vivo experiments, structure-activity models). In this chapter, we describe the practical development and basic use of a PBPK model from model building to model simulations, through implementation with an easily accessible free software.
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Affiliation(s)
| | - Cleo Tebby
- INERIS, Unit of Experimental Toxicology and Modelling, Verneuil en Halatte, France
| | - Céline Brochot
- INERIS, Unit of Experimental Toxicology and Modelling, Verneuil en Halatte, France
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18
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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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19
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Black SR, Nichols JW, Fay KA, Matten SR, Lynn SG. Evaluation and comparison of in vitro intrinsic clearance rates measured using cryopreserved hepatocytes from humans, rats, and rainbow trout. Toxicology 2021; 457:152819. [PMID: 33984406 DOI: 10.1016/j.tox.2021.152819] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/17/2021] [Accepted: 05/05/2021] [Indexed: 11/16/2022]
Abstract
In vitro and in silico methods that can reduce the need for animal testing are being used with increasing frequency to assess chemical risks to human health and the environment. The rate of hepatic biotransformation is an important species-specific parameter for determining bioaccumulation potential and extrapolating in vitro bioactivity to in vivo effects. One approach to estimating hepatic biotransformation is to employ in vitro systems derived from liver tissue to measure chemical (substrate) depletion over time which can then be translated to a rate of intrinsic clearance (CLint). In the present study, cryopreserved hepatocytes from humans, rats, and rainbow trout were used to measure CLint values for 54 industrial and pesticidal chemicals at starting test concentrations of 0.1 and 1 μM. A data evaluation framework that emphasizes the behavior of Heat-Treated Controls (HTC) was developed to identify datasets suitable for rate reporting. Measured or estimated ("greater than" or "less than") CLint values were determined for 124 of 226 (55 %) species-chemical-substrate concentration datasets with acceptable analytical chemistry. A large percentage of tested chemicals exhibited low HTC recovery values, indicating a substantial abiotic loss of test chemical over time. An evaluation of KOW values for individual chemicals suggested that in vitro test performance declined with increasing chemical hydrophobicity, although differences in testing devices for mammals and fish also likely played a role. The current findings emphasize the value of negative controls as part of a rigorous approach to data quality assessment for in vitro substrate depletion studies. Changes in current testing protocols can be expected to result in the collection of higher quality data. However, poorly soluble chemicals are likely to remain a challenge for CLint determination.
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Affiliation(s)
- Sherry R Black
- RTI International, Discovery Sciences, 3040 East Cornwallis Road, Durham, NC 27709 USA.
| | - John W Nichols
- US Environmental Protection Agency, Office of Research and Development, Great Lakes Toxicology and Ecology Division (GLTED), 6201 Congdon Blvd, Duluth, MN 55804 USA.
| | - Kellie A Fay
- US Environmental Protection Agency, Office of Pollution Prevention and Toxics (OPPT), William Jefferson Clinton Building, 1200 Pennsylvania Avenue NW, Washington, DC 20460 USA.
| | - Sharlene R Matten
- US Environmental Protection Agency, Office of Science Coordination and Policy (OSCP), William Jefferson Clinton Building, 1200 Pennsylvania Avenue NW, Washington, DC 20460 USA.
| | - Scott G Lynn
- US Environmental Protection Agency, Office of Science Coordination and Policy (OSCP), William Jefferson Clinton Building, 1200 Pennsylvania Avenue NW, Washington, DC 20460 USA.
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20
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Ammar HO, Ghorab MM, Saleh MS, Ghoneim AM. Olanzapine Mesoporous Nanostructured Lipid Carrier: Optimization, Characterization, In Vivo Assessment, and Physiologically Based Pharmacokinetic Modeling. IEEE Trans Nanobioscience 2021; 20:166-174. [PMID: 33493118 DOI: 10.1109/tnb.2021.3052080] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
A promising approach has been emerging to enhance dissolution of hydrophobicdrugsby encapsulation in mesoporous silica materials. Olanzapine is a practically insoluble antipsychotic drug which is subjected to excessive first pass effect and shows inadequate oral bioavailability. Therefore, mesoporous silica was used to improve bioavailability of olanzapine incorporated in nano-structured lipid carriers (NLCs). These systems were characterized for their particle size, polydispersity index (PDI), zeta potential, entrapment efficiency (EE) and differential scanning calorimetry (DSC) as well asits release profile. The optimized mesoporous NLC system displayed nano-spherical particles (120.56 nm), possessed high entrapment efficiency (88.46%) and the highest percentage of drug released after six hours (75.13%). The biological performance of the optimized system was assessed in comparison with the drug suspension in healthy albino rabbits. The optimized system showed significantly (P < 0.05) prolonged MRT (8.47 h), higher Cmax (22.12± 0.40 ng/ml) and Tmax (2.0 h) values compared to drug suspension. Physiologically based pharmacokinetic (PBPK) model was simulated and verified. All the predicted results were within 0.6 and 1-fold of the reported data. To set a conclusion, in vitro results as well as in vivo pharmacokinetic study and PBPK data showed an enhancement in bioavailability of the optimized NLCs system over the plain drug suspension. These results proved the potentiality of incorporating olanzapine in mesoporous NLC for a significant improvement in oral bioavailability of olanzapine.
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21
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A Novel Experimental and Theoretical Method for Estimating Albumin-Mediated Hepatic Uptake Based on the Albumin Binding Fraction in Plasma and Human PK Prediction Using a Physiologically-Based Pharmacokinetic Approach. J Pharm Sci 2021; 110:2262-2273. [PMID: 33476657 DOI: 10.1016/j.xphs.2021.01.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 01/11/2021] [Accepted: 01/12/2021] [Indexed: 01/15/2023]
Abstract
Recently, protein-facilitated uptake has been suggested to be an important factor in the precise prediction of the pharmacokinetic (PK) profiles of drugs. In our previous study, a physiologically-based pharmacokinetic (PBPK) approach considering the mechanism of albumin-mediated hepatic uptake was developed for predicting human PK profiles. It was assumed that drugs affected by albumin-mediated hepatic uptake would bind only to albumin, which means that there would be over-estimation of the contribution of protein-facilitated uptake for a drug that could bind to multiple proteins. In this study, we developed a method that can evaluate the albumin binding fraction in plasma considering the affinity for other proteins. Based on the albumin binding fraction, the contribution of albumin-mediated hepatic uptake was theoretically estimated, and then the human PK profiles were predicted by our proposed PBPK approach incorporating this mechanism. As a result, the predicted human PK profiles agreed well with the observed ones, and the absolute average fold error of PK parameters was almost within a 1.5-fold error on average. These findings show the importance of considering protein-facilitated uptake and also suggest that our proposed PBPK approach can be useful in scientific discussions with regulatory authorities.
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22
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Systematic Analysis of the Whole-Body Tissue Distribution and Fatty Acid Compositions of Membrane Lipids in CD1 and NMRI Mice and Wistar Rats. Int J Anal Chem 2020. [DOI: 10.1155/2020/8819437] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Understanding the tissue distribution of phospholipids and glycerolipids in animal models enables promoting the pharmacokinetic study of drugs and related PK predictions. The measurement of lipid compositions in animal models, usually mice and rats, without a standardized approach hindered the accuracy of PBPK investigation. In this work, high resolution mass spectrometry was applied to profile the tissue distribution of phospholipids and glycerolipids in 12 organs/tissues of mice and rats. Using this method, not only the amounts of phospholipids and glycerolipids in each organ/tissue but also the fatty acid compositions were acquired. In order to explore the interspecies specificity of lipid distribution in different organs/tissues, three animal species including CD1 mice, NMRI mice, and Wister rats were used in this systematic study. Globally, more organ specificity was observed. It was found that the brain is the organ containing the most abundant phosphatidylserine lipids (PSs) in all three animal models, leading to brain tissues having the most concentrated acidic phospholipids. Diverse fatty acid compositions in each lipid class were clearly revealed. Certain tissues/organs also had a specific selection of unique fatty acid compositions, for example, unreferenced FA(18 : 2) in the brain. It turned out that the access of free fatty acids affects the incorporation of acyl chain in phospholipids and glycerolipids. In the analysis, ether lipids were also profiled with the observation of dominant ePEs in brain tissues. However, little interspecies difference was found for fatty acid constituents and tissues distribution of phospholipids and glycerolipids.
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23
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Utembe W, Clewell H, Sanabria N, Doganis P, Gulumian M. Current Approaches and Techniques in Physiologically Based Pharmacokinetic (PBPK) Modelling of Nanomaterials. NANOMATERIALS 2020; 10:nano10071267. [PMID: 32610468 PMCID: PMC7407857 DOI: 10.3390/nano10071267] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 06/03/2020] [Accepted: 06/13/2020] [Indexed: 02/08/2023]
Abstract
There have been efforts to develop physiologically based pharmacokinetic (PBPK) models for nanomaterials (NMs). Since NMs have quite different kinetic behaviors, the applicability of the approaches and techniques that are utilized in current PBPK models for NMs is warranted. Most PBPK models simulate a size-independent endocytosis from tissues or blood. In the lungs, dosimetry and the air-liquid interface (ALI) models have sometimes been used to estimate NM deposition and translocation into the circulatory system. In the gastrointestinal (GI) tract, kinetics data are needed for mechanistic understanding of NM behavior as well as their absorption through GI mucus and their subsequent hepatobiliary excretion into feces. Following absorption, permeability (Pt) and partition coefficients (PCs) are needed to simulate partitioning from the circulatory system into various organs. Furthermore, mechanistic modelling of organ- and species-specific NM corona formation is in its infancy. More recently, some PBPK models have included the mononuclear phagocyte system (MPS). Most notably, dissolution, a key elimination process for NMs, is only empirically added in some PBPK models. Nevertheless, despite the many challenges still present, there have been great advances in the development and application of PBPK models for hazard assessment and risk assessment of NMs.
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Affiliation(s)
- Wells Utembe
- National Institute for Occupational Health, P.O. Box 4788, Johannesburg 2000, South Africa; (W.U.); (N.S.)
| | - Harvey Clewell
- Ramboll US Corporation, Research Triangle Park, NC 27709, USA;
| | - Natasha Sanabria
- National Institute for Occupational Health, P.O. Box 4788, Johannesburg 2000, South Africa; (W.U.); (N.S.)
| | - Philip Doganis
- School of Chemical Engineering, National Technical University of Athens, Zografou Campus, 15780 Athens, Greece;
| | - Mary Gulumian
- National Institute for Occupational Health, P.O. Box 4788, Johannesburg 2000, South Africa; (W.U.); (N.S.)
- Hematology and Molecular Medicine, University of the Witwatersrand, Johannesburg 2000, South Africa
- Correspondence: ; Tel.: +27-11-712-6428
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24
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Yau E, Olivares-Morales A, Gertz M, Parrott N, Darwich AS, Aarons L, Ogungbenro K. Global Sensitivity Analysis of the Rodgers and Rowland Model for Prediction of Tissue: Plasma Partitioning Coefficients: Assessment of the Key Physiological and Physicochemical Factors That Determine Small-Molecule Tissue Distribution. AAPS JOURNAL 2020; 22:41. [PMID: 32016678 DOI: 10.1208/s12248-020-0418-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 01/07/2020] [Indexed: 12/14/2022]
Abstract
In physiologically based pharmacokinetic (PBPK) modelling, the large number of input parameters, limited amount of available data and the structural model complexity generally hinder simultaneous estimation of uncertain and/or unknown parameters. These parameters are generally subject to estimation. However, the approaches taken for parameter estimation vary widely. Global sensitivity analyses are proposed as a method to systematically determine the most influential parameters that can be subject to estimation. Herein, a global sensitivity analysis was conducted to identify the key drug and physiological parameters influencing drug disposition in PBPK models and to potentially reduce the PBPK model dimensionality. The impact of these parameters was evaluated on the tissue-to-unbound plasma partition coefficients (Kpus) predicted by the Rodgers and Rowland model using Latin hypercube sampling combined to partial rank correlation coefficients (PRCC). For most drug classes, PRCC showed that LogP and fraction unbound in plasma (fup) were generally the most influential parameters for Kpu predictions. For strong bases, blood:plasma partitioning was one of the most influential parameter. Uncertainty in tissue composition parameters had a large impact on Kpu and Vss predictions for all classes. Among tissue composition parameters, changes in Kpu outputs were especially attributed to changes in tissue acidic phospholipid concentrations and extracellular protein tissue:plasma ratio values. In conclusion, this work demonstrates that for parameter estimation involving PBPK models and dimensionality reduction purposes, less influential parameters might be assigned fixed values depending on the parameter space, while influential parameters could be subject to parameters estimation.
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Affiliation(s)
- Estelle Yau
- Centre for Applied Pharmacokinetic Research (CAPKR), The University of Manchester, Manchester, UK.,Roche Pharma and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Andrés Olivares-Morales
- Roche Pharma and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland.
| | - Michael Gertz
- Roche Pharma and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Neil Parrott
- Roche Pharma and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Adam S Darwich
- Centre for Applied Pharmacokinetic Research (CAPKR), The University of Manchester, Manchester, UK.,Logistics and Informatics in Health Care, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), KTH Royal Institute of Technology, Stockholm, Sweden
| | - Leon Aarons
- Roche Pharma and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Kayode Ogungbenro
- Roche Pharma and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070, Basel, Switzerland
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Allen TEH, Goodman JM, Gutsell S, Russell PJ. Quantitative Predictions for Molecular Initiating Events Using Three-Dimensional Quantitative Structure-Activity Relationships. Chem Res Toxicol 2019; 33:324-332. [PMID: 31517476 DOI: 10.1021/acs.chemrestox.9b00136] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The aim of human toxicity risk assessment is to determine a safe dose or exposure to a chemical for humans. This requires an understanding of the exposure of a person to a chemical and how much of the chemical is required to cause an adverse effect. To do this computationally, we need to understand how much of a chemical is required to perturb normal biological function in an adverse outcome pathway (AOP). The molecular initiating event (MIE) is the first step in an adverse outcome pathway and can be considered as a chemical interaction between a chemical toxicant and a biological molecule. Key chemical characteristics can be identified and used to model the chemistry of these MIEs. In this study, we do just this by using chemical substructures to categorize chemicals and 3D quantitative structure-activity relationships (QSARs) based on comparative molecular field analysis (CoMFA) to calculate molecular activity. Models have been constructed across a variety of human biological targets, the glucocorticoid receptor, mu opioid receptor, cyclooxygenase-2 enzyme, human ether-à-go-go related gene channel, and dopamine transporter. These models tend to provide molecular activity estimation well within one log unit and electronic and steric fields that can be visualized to better understand the MIE and biological target of interest. The outputs of these fields can be used to identify key aspects of a chemical's chemistry which can be changed to reduce its ability to activate a given MIE. With this methodology, the quantitative chemical activity can be predicted for a wide variety of MIEs, which can feed into AOP-based chemical risk assessments, and understanding of the chemistry behind the MIE can be gained.
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Affiliation(s)
- Timothy E H Allen
- Centre for Molecular Informatics, Department of Chemistry , University of Cambridge , Lensfield Road , Cambridge CB2 1EW , United Kingdom
| | - Jonathan M Goodman
- Centre for Molecular Informatics, Department of Chemistry , University of Cambridge , Lensfield Road , Cambridge CB2 1EW , United Kingdom
| | - Steve Gutsell
- Unilever Safety and Environmental Assurance Centre , Colworth Science Park , Sharnbrook, Bedfordshire MK44 1LQ , United Kingdom
| | - Paul J Russell
- Unilever Safety and Environmental Assurance Centre , Colworth Science Park , Sharnbrook, Bedfordshire MK44 1LQ , United Kingdom
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Price E, Gesquiere AJ. An in vitro assay and artificial intelligence approach to determine rate constants of nanomaterial-cell interactions. Sci Rep 2019; 9:13943. [PMID: 31558741 PMCID: PMC6763461 DOI: 10.1038/s41598-019-50208-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Accepted: 09/05/2019] [Indexed: 12/11/2022] Open
Abstract
In vitro assays and simulation technologies are powerful methodologies that can inform scientists of nanomaterial (NM) distribution and fate in humans or pre-clinical species. For small molecules, less animal data is often needed because there are a multitude of in vitro screening tools and simulation-based approaches to quantify uptake and deliver data that makes extrapolation to in vivo studies feasible. Small molecule simulations work because these materials often diffuse quickly and partition after reaching equilibrium shortly after dosing, but this cannot be applied to NMs. NMs interact with cells through energy dependent pathways, often taking hours or days to become fully internalized within the cellular environment. In vitro screening tools must capture these phenomena so that cell simulations built on mechanism-based models can deliver relationships between exposure dose and mechanistic biology, that is biology representative of fundamental processes involved in NM transport by cells (e.g. membrane adsorption and subsequent internalization). Here, we developed, validated, and applied the FORECAST method, a combination of a calibrated fluorescence assay (CF) with an artificial intelligence-based cell simulation to quantify rates descriptive of the time-dependent mechanistic biological interactions between NMs and individual cells. This work is expected to provide a means of extrapolation to pre-clinical or human biodistribution with cellular level resolution for NMs starting only from in vitro data.
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Affiliation(s)
- Edward Price
- NanoScience Technology Center, University of Central Florida, Orlando, FL, 32826, USA
- Department of Chemistry, University of Central Florida, Orlando, FL, 32816, USA
| | - Andre J Gesquiere
- NanoScience Technology Center, University of Central Florida, Orlando, FL, 32826, USA.
- Department of Chemistry, University of Central Florida, Orlando, FL, 32816, USA.
- Department of Materials Science and Engineering, University of Central Florida, Orlando, FL, 32816, USA.
- The College of Optics and Photonics (CREOL), University of Central Florida, Orlando, FL, 32816, USA.
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27
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Domínguez-Romero E, Scheringer M. A review of phthalate pharmacokinetics in human and rat: what factors drive phthalate distribution and partitioning? Drug Metab Rev 2019; 51:314-329. [DOI: 10.1080/03602532.2019.1620762] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Elena Domínguez-Romero
- Research Centre for Toxic Compounds in the Environment (RECETOX), Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Martin Scheringer
- Research Centre for Toxic Compounds in the Environment (RECETOX), Faculty of Science, Masaryk University, Brno, Czech Republic
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29
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Volarath P, Zang Y, Kabadi SV. Application of Computational Methods for the Safety Assessment of Food Ingredients. ACTA ACUST UNITED AC 2019. [DOI: 10.1007/978-3-030-16443-0_12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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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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Mortuza T, Muralidhara S, White CA, Cummings BS, Hines C, Bruckner JV. Effect of dose and exposure protocol on the toxicokinetics and first-pass elimination of trichloroethylene and 1,1,1-trichloroethane. Toxicol Appl Pharmacol 2018; 360:185-192. [PMID: 30287391 DOI: 10.1016/j.taap.2018.09.043] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 09/28/2018] [Accepted: 09/29/2018] [Indexed: 11/19/2022]
Abstract
Trichloroethylene (TCE) and 1,1,1-trichloroethane (TRI) are frequent contaminants of drinking water and of groundwater at hazardous waste sites. There is relatively little information on the target organ deposition of TRI, despite its ingestion and common occurrence in humans. An important aim of the study was to delineate and contrast the toxicokinetics (TK) and bioavailability (F) of TRI and its well metabolized congener, TCE. Blood profiles were obtained from male Sprague-Dawley rats given aqueous emulsions of 6 or 48 mg TRI/kg and 10 or 50 mg TCE/kg as an oral bolus (po) or by gastric infusion (gi) over 2 h. TCE exhibited nonlinear TK, with a disproportionate increase in AUC and decrease in clearance and F with increase in dose. TRI exhibited linear TK. F did not vary significantly with TRI dose or dosage regimen. F values were substantially higher for TRI than for the respective TCE groups. TRI was distributed widely to tissues of rats gavaged with 6 mg TRI/kg, with accumulation in fat. This experiment yielded tissue uptake and elimination profiles and in vivo tissue:blood partition coefficients (PCs). Finally, additional rats were given 10 mg/kg of TCE and TRI po, ia and iv, so that first-pass hepatic (FPh) and pulmonary (FPp) elimination could be measured directly. Total and FPh elimination of TCE exceeded that of TRI. TRI, with its higher air:blood PC, exhibited the higher FPp. TCE and TRI, despite several common physical and chemical properties resulting in similar absorption and systemic distribution, displayed dissimilar dosage and dose rate effects on their TK.
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Affiliation(s)
- Tanzir Mortuza
- Department of Pharmaceutical and Biomedical Sciences, College of Pharmacy, University of Georgia, Athens, GA 30602-2354, Georgia
| | - Srinivasa Muralidhara
- Department of Pharmaceutical and Biomedical Sciences, College of Pharmacy, University of Georgia, Athens, GA 30602-2354, Georgia
| | - Catherine A White
- Department of Pharmaceutical and Biomedical Sciences, College of Pharmacy, University of Georgia, Athens, GA 30602-2354, Georgia
| | - Brian S Cummings
- Department of Pharmaceutical and Biomedical Sciences, College of Pharmacy, University of Georgia, Athens, GA 30602-2354, Georgia
| | - Carey Hines
- Department of Pharmaceutical and Biomedical Sciences, College of Pharmacy, University of Georgia, Athens, GA 30602-2354, Georgia
| | - James V Bruckner
- Department of Pharmaceutical and Biomedical Sciences, College of Pharmacy, University of Georgia, Athens, GA 30602-2354, Georgia.
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Nicolas CI, Mansouri K, Phillips KA, Grulke CM, Richard AM, Williams AJ, Rabinowitz J, Isaacs KK, Yau A, Wambaugh JF. Rapid experimental measurements of physicochemical properties to inform models and testing. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 636:901-909. [PMID: 29729507 PMCID: PMC6214190 DOI: 10.1016/j.scitotenv.2018.04.266] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 04/19/2018] [Accepted: 04/20/2018] [Indexed: 04/14/2023]
Abstract
The structures and physicochemical properties of chemicals are important for determining their potential toxicological effects, toxicokinetics, and route(s) of exposure. These data are needed to prioritize the risk for thousands of environmental chemicals, but experimental values are often lacking. In an attempt to efficiently fill data gaps in physicochemical property information, we generated new data for 200 structurally diverse compounds, which were rigorously selected from the USEPA ToxCast chemical library, and whose structures are available within the Distributed Structure-Searchable Toxicity Database (DSSTox). This pilot study evaluated rapid experimental methods to determine five physicochemical properties, including the log of the octanol:water partition coefficient (known as log(Kow) or logP), vapor pressure, water solubility, Henry's law constant, and the acid dissociation constant (pKa). For most compounds, experiments were successful for at least one property; log(Kow) yielded the largest return (176 values). It was determined that 77 ToxPrint structural features were enriched in chemicals with at least one measurement failure, indicating which features may have played a role in rapid method failures. To gauge consistency with traditional measurement methods, the new measurements were compared with previous measurements (where available). Since quantitative structure-activity/property relationship (QSAR/QSPR) models are used to fill gaps in physicochemical property information, 5 suites of QSPRs were evaluated for their predictive ability and chemical coverage or applicability domain of new experimental measurements. The ability to have accurate measurements of these properties will facilitate better exposure predictions in two ways: 1) direct input of these experimental measurements into exposure models; and 2) construction of QSPRs with a wider applicability domain, as their predicted physicochemical values can be used to parameterize exposure models in the absence of experimental data.
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Affiliation(s)
- Chantel I Nicolas
- ScitoVation, LLC 6 Davis Drive, Durham, NC 27703, USA; National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831, USA
| | - Kamel Mansouri
- ScitoVation, LLC 6 Davis Drive, Durham, NC 27703, USA; National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831, USA
| | - Katherine A Phillips
- National Exposure Research Laboratory, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA
| | - Christopher M Grulke
- National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA
| | - Ann M Richard
- National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA
| | - Antony J Williams
- National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA
| | - James Rabinowitz
- National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA
| | - Kristin K Isaacs
- National Exposure Research Laboratory, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA
| | - Alice Yau
- Southwest Research Institute, San Antonio, TX 78238, USA
| | - John F Wambaugh
- National Center for Computational Toxicology, Office of Research and Development, US EPA, Research Triangle Park, NC 27711, USA.
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Poulin P, Collet SH, Atrux-Tallau N, Linget JM, Hennequin L, Wilson CE. Application of the Tissue Composition-Based Model to Minipig for Predicting the Volume of Distribution at Steady State and Dermis-to-Plasma Partition Coefficients of Drugs Used in the Physiologically Based Pharmacokinetics Model in Dermatology. J Pharm Sci 2018; 108:603-619. [PMID: 30222978 DOI: 10.1016/j.xphs.2018.09.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 09/05/2018] [Accepted: 09/06/2018] [Indexed: 11/25/2022]
Abstract
The minipig continues to build a reputation as a viable alternative large animal model to predict humans in dermatology and toxicology studies. Therefore, it is essential to describe and predict the pharmacokinetics in that species to speed up the clinical candidate selection. Essential input parameters in whole-body physiologically based pharmacokinetic models are the tissue-to-plasma partition coefficients and the resulting volume of distribution at steady-state (Vss). Mechanistic in vitro- and in silico-based models used for predicting these parameters of tissue distribution of drugs refer to the tissue composition-based model (TCM). Robust TCMs were initially developed for some preclinical species (e.g., rat and dog) and human; however, there is currently no model available for the minipig. Therefore, the objective of this present study was to develop a TCM for the minipig and to estimate the corresponding tissue composition data. Drug partitioning into the tissues was predominantly governed by lipid and protein binding effects in addition to drug solubilization and pH gradient effects in the aqueous phase on both sides of the biological membranes; however, some more complex tissue distribution processes such as drug binding to the collagen-laminin material in dermis and a restricted drug partitioning into membranes of tissues for compounds that are amphiphilic and contain sulfur atom(s) were also challenged. The model was validated by predicting Vss and the dermis-to-plasma partition coefficients (Kp-dermis) of 68 drugs. The prediction of Kp-dermis was extended to humans for comparison with the minipig. The results indicate that the extended TCM provided generally good agreements with observations in the minipig showing that it is also applicable to this preclinical species. In general, up to 86% and 100% of the predicted Vss values are respectively within 2-fold and 3-fold errors compared to the experimentally determined values, whereas these numbers are 78% and 94% for Kp-dermis when the anticipated outlier compounds are not included. Binding data to dermis are comparable between minipigs and humans. Overall, this study is a first step toward developing a mechanistic TCM for the minipig, with the aim of increasing the use of physiologically based pharmacokinetic models of drugs for that species in addition to rats, dogs, and humans because such models are used in preclinical and clinical transdermal studies.
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Affiliation(s)
- Patrick Poulin
- Consultant Patrick Poulin Inc., Québec City, Québec, Canada; School of Public Health, University of Montréal, Montréal, Québec, Canada.
| | | | | | | | | | - Claire E Wilson
- DMPK - Research, Nestlé Skin Health R & D, Sophia-Antipolis, France
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Strope CL, Mansouri K, Clewell HJ, Rabinowitz JR, Stevens C, Wambaugh JF. High-throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 615:150-160. [PMID: 28964990 PMCID: PMC6055917 DOI: 10.1016/j.scitotenv.2017.09.033] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Revised: 08/30/2017] [Accepted: 09/04/2017] [Indexed: 05/16/2023]
Abstract
Chemical ionization plays an important role in many aspects of pharmacokinetic (PK) processes such as protein binding, tissue partitioning, and apparent volume of distribution at steady state (Vdss). Here, estimates of ionization equilibrium constants (i.e., pKa) were analyzed for 8132 pharmaceuticals and 24,281 other compounds to which humans might be exposed in the environment. Results revealed broad differences in the ionization of pharmaceutical chemicals and chemicals with either near-field (in the home) or far-field sources. The utility of these high-throughput ionization predictions was evaluated via a case-study of predicted PK Vdss for 22 compounds monitored in the blood and serum of the U.S. population by the U.S. Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES). The chemical distribution ratio between water and tissue was estimated using predicted ionization states characterized by pKa. Probability distributions corresponding to ionizable atom types (IATs) were then used to analyze the sensitivity of predicted Vdss on predicted pKa using Monte Carlo methods. 8 of the 22 compounds were predicted to be ionizable. For 5 of the 8 the predictions based upon ionization are significantly different from what would be predicted for a neutral compound. For all but one (foramsulfuron), the probability distribution of predicted Vdss generated by IAT sensitivity analysis spans both the neutral prediction and the prediction using ionization. As new data sets of chemical-specific information on metabolism and excretion for hundreds of chemicals are being made available (e.g., Wetmore et al., 2015), high-throughput methods for calculating Vdss and tissue-specific PK distribution coefficients will allow the rapid construction of PK models to provide context for both biomonitoring data and high-throughput toxicity screening studies such as Tox21 and ToxCast.
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Affiliation(s)
- Cory L Strope
- Risk Assessment Division, Office of Pollution Prevention and Toxics, Office of Chemical Safety and Pollution Prevention, U.S. Environmental Protection Agency, Washington, DC, USA; ORISE Postdoctoral Research Fellow, National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA; The Hamner Institutes for Health Sciences, Research Triangle Park, NC, USA.
| | - Kamel Mansouri
- ORISE Postdoctoral Research Fellow, National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA; ScitoVation, 6 Davis Drive, PO Box 110566, Research Triangle Park, NC, USA
| | - Harvey J Clewell
- ScitoVation, 6 Davis Drive, PO Box 110566, Research Triangle Park, NC, USA
| | - James R Rabinowitz
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Caroline Stevens
- Ecosystems Research Division, National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Athens, GA, USA
| | - John F Wambaugh
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
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Radomyski A, Giubilato E, Suciu NA, Critto A, Ciffroy P. Modelling Bioaccumulation in Aquatic Organisms and in Mammals. MODELLING THE FATE OF CHEMICALS IN THE ENVIRONMENT AND THE HUMAN BODY 2018. [DOI: 10.1007/978-3-319-59502-3_9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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Abstract
Pharmacokinetics is the study of the fate of xenobiotics in a living organism. Physiologically based pharmacokinetic (PBPK) models provide realistic descriptions of xenobiotics' absorption, distribution, metabolism, and excretion processes. They model the body as a set of homogeneous compartments representing organs, and their parameters refer to anatomical, physiological, biochemical, and physicochemical entities. They offer a quantitative mechanistic framework to understand and simulate the time-course of the concentration of a substance in various organs and body fluids. These models are well suited for performing extrapolations inherent to toxicology and pharmacology (e.g., between species or doses) and for integrating data obtained from various sources (e.g., in vitro or in vivo experiments, structure-activity models). In this chapter, we describe the practical development and basic use of a PBPK model from model building to model simulations, through implementation with an easily accessible free software.
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Bell SM, Chang X, Wambaugh JF, Allen DG, Bartels M, Brouwer KLR, Casey WM, Choksi N, Ferguson SS, Fraczkiewicz G, Jarabek AM, Ke A, Lumen A, Lynn SG, Paini A, Price PS, Ring C, Simon TW, Sipes NS, Sprankle CS, Strickland J, Troutman J, Wetmore BA, Kleinstreuer NC. In vitro to in vivo extrapolation for high throughput prioritization and decision making. Toxicol In Vitro 2017; 47:213-227. [PMID: 29203341 DOI: 10.1016/j.tiv.2017.11.016] [Citation(s) in RCA: 146] [Impact Index Per Article: 20.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 11/28/2017] [Accepted: 11/30/2017] [Indexed: 01/10/2023]
Abstract
In vitro chemical safety testing methods offer the potential for efficient and economical tools to provide relevant assessments of human health risk. To realize this potential, methods are needed to relate in vitro effects to in vivo responses, i.e., in vitro to in vivo extrapolation (IVIVE). Currently available IVIVE approaches need to be refined before they can be utilized for regulatory decision-making. To explore the capabilities and limitations of IVIVE within this context, the U.S. Environmental Protection Agency Office of Research and Development and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods co-organized a workshop and webinar series. Here, we integrate content from the webinars and workshop to discuss activities and resources that would promote inclusion of IVIVE in regulatory decision-making. We discuss properties of models that successfully generate predictions of in vivo doses from effective in vitro concentration, including the experimental systems that provide input parameters for these models, areas of success, and areas for improvement to reduce model uncertainty. Finally, we provide case studies on the uses of IVIVE in safety assessments, which highlight the respective differences, information requirements, and outcomes across various approaches when applied for decision-making.
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Affiliation(s)
- Shannon M Bell
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Xiaoqing Chang
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - John F Wambaugh
- U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Research Triangle Park, NC 27709, USA.
| | - David G Allen
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | | | - Kim L R Brouwer
- UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Campus Box 7569, Chapel Hill, NC 27599, USA.
| | - Warren M Casey
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC 27709, USA.
| | - Neepa Choksi
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Stephen S Ferguson
- National Toxicology Program, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC 27709, USA.
| | | | - Annie M Jarabek
- U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Research Triangle Park, NC 27709, USA.
| | - Alice Ke
- Simcyp Limited (a Certara company), John Street, Sheffield, S2 4SU, United Kingdom.
| | - Annie Lumen
- National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA.
| | - Scott G Lynn
- U.S. Environmental Protection Agency, William Jefferson Clinton Building, 1200 Pennsylvania Ave. NW, Washington, DC 20460, USA.
| | - Alicia Paini
- European Commission, Joint Research Centre, Directorate Health, Consumers and Reference Materials, Chemical Safety and Alternative Methods Unit incorporating EURL ECVAM, Via E. Fermi 2749, Ispra, Varese 20127, Italy.
| | - Paul S Price
- U.S. Environmental Protection Agency, 109 T.W. Alexander Dr., Research Triangle Park, NC 27709, USA.
| | - Caroline Ring
- Oak Ridge Institute for Science and Education, P.O. Box 2008, Oak Ridge, TN 37831, USA.
| | - Ted W Simon
- Ted Simon LLC, 4184 Johnston Road, Winston, GA 30187, USA.
| | - Nisha S Sipes
- National Toxicology Program, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC 27709, USA.
| | - Catherine S Sprankle
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - Judy Strickland
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC 27709, USA.
| | - John Troutman
- Central Product Safety, The Procter & Gamble Company, Cincinnati, OH 45202, USA.
| | - Barbara A Wetmore
- ScitoVation LLC, 6 Davis Drive, Research Triangle Park, NC 27709, USA.
| | - Nicole C Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC 27709, USA.
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Pearce RG, Setzer RW, Davis JL, Wambaugh JF. Evaluation and calibration of high-throughput predictions of chemical distribution to tissues. J Pharmacokinet Pharmacodyn 2017; 44:549-565. [PMID: 29032447 PMCID: PMC6186149 DOI: 10.1007/s10928-017-9548-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 09/30/2017] [Indexed: 12/25/2022]
Abstract
Toxicokinetics (TK) provides critical information for integrating chemical toxicity and exposure assessments in order to determine potential chemical risk (i.e., the margin between toxic doses and plausible exposures). For thousands of chemicals that are present in our environment, in vivo TK data are lacking. The publicly available R package "httk" (version 1.8, named for "high throughput TK") draws from a database of in vitro data and physico-chemical properties in order to run physiologically-based TK (PBTK) models for 553 compounds. The PBTK model parameters include tissue:plasma partition coefficients (Kp) which the httk software predicts using the model of Schmitt (Toxicol In Vitro 22 (2):457-467, 2008). In this paper we evaluated and modified httk predictions, and quantified confidence using in vivo literature data. We used 964 rat Kp measured by in vivo experiments for 143 compounds. Initially, predicted Kp were significantly larger than measured Kp for many lipophilic compounds (log10 octanol:water partition coefficient > 3). Hence the approach for predicting Kp was revised to account for possible deficiencies in the in vitro protein binding assay, and the method for predicting membrane affinity was revised. These changes yielded improvements ranging from a factor of 10 to nearly a factor of 10,000 for 83 Kp across 23 compounds with only 3 Kp worsening by more than a factor of 10. The vast majority (92%) of Kp were predicted within a factor of 10 of the measured value (overall root mean squared error of 0.59 on log10-transformed scale). After applying the adjustments, regressions were performed to calibrate and evaluate the predictions for 12 tissues. Predictions for some tissues (e.g., spleen, bone, gut, lung) were observed to be better than predictions for other tissues (e.g., skin, brain, fat), indicating that confidence in the application of in silico tools to predict chemical partitioning varies depending upon the tissues involved. Our calibrated model was then evaluated using a second data set of human in vivo measurements of volume of distribution (Vss) for 498 compounds reviewed by Obach et al. (Drug Metab Dispos 36(7):1385-1405, 2008). We found that calibration of the model improved performance: a regression of the measured values as a function of the predictions has a slope of 1.03, intercept of - 0.04, and R2 of 0.43. Through careful evaluation of predictive methods for chemical partitioning into tissues, we have improved and calibrated these methods and quantified confidence for TK predictions in humans and rats.
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Affiliation(s)
- Robert G Pearce
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA
- Oak Ridge Institute for Science and Education, Oak Ridge, TN, 37831, USA
| | - R Woodrow Setzer
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA
| | - Jimena L Davis
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA
- Syngenta, Research Triangle Park, NC, 27709, USA
| | - John F Wambaugh
- National Center for Computational Toxicology, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, 109 T.W. Alexander Dr, Durham, NC, 27711, USA.
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39
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Modeling of adipose/blood partition coefficient for environmental chemicals. Food Chem Toxicol 2017; 110:274-285. [DOI: 10.1016/j.fct.2017.10.044] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Revised: 10/25/2017] [Accepted: 10/26/2017] [Indexed: 11/20/2022]
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40
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Amaraneni M, Pang J, Bruckner JV, Muralidhara S, Mortuza TB, Gullick D, Hooshfar S, White CA, Cummings BS. Influence of Maturation on In Vivo Tissue to Plasma Partition Coefficients for Cis - and Trans -Permethrin. J Pharm Sci 2017; 106:2144-2151. [DOI: 10.1016/j.xphs.2017.04.024] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2017] [Revised: 04/13/2017] [Accepted: 04/13/2017] [Indexed: 01/14/2023]
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41
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Development of QSARs for parameterizing Physiology Based ToxicoKinetic models. Food Chem Toxicol 2017; 106:114-124. [DOI: 10.1016/j.fct.2017.05.029] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 04/13/2017] [Accepted: 05/14/2017] [Indexed: 11/23/2022]
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European regulatory perspective on pediatric physiologically based pharmacokinetic models. ACTA ACUST UNITED AC 2017. [DOI: 10.4155/ipk-2016-0025] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Physiologically based pharmacokinetic (PBPK) models offer a mechanistic understanding of the disposition of the drug in the body. When well informed and conducted, this may lead to more efficient clinical studies in the vulnerable pediatric population. A review of pediatric submissions to European regulatory authorities has shown a limited number of recent examples. The use of PBPK models to inform pediatric drug development is encouraged. It is, however, important to consider the confidence in the predictions. A qualification of the PBPK platform for the intended purpose should be performed, and a learn-and-confirm approach is recommended. Further research in this area is encouraged to inform important parameters, and to increase availability of pediatric data for model qualification.
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Korzekwa K, Nagar S. On the Nature of Physiologically-Based Pharmacokinetic Models -A Priori or A Posteriori? Mechanistic or Empirical? Pharm Res 2016; 34:529-534. [PMID: 28028770 DOI: 10.1007/s11095-016-2089-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Accepted: 12/19/2016] [Indexed: 10/20/2022]
Abstract
Physiologically-based pharmacokinetic (PBPK) models explicitly incorporate tissue-specific blood flows, partition coefficients, and metabolic processes. Since PBPK models are derived using physiologic parameters and interactions of the compound with tissue components, these models are considered to be "bottom up" as opposed to "top down". Modeling approaches can be characterized as either a posteriori (observational) or a priori (based solely on theory). Furthermore, approaches can be mechanistic (structure and components based on mechanisms) or empirical (based on observations alone). Both "bottom up" and "top down" approaches can incorporate either empirical or mechanistic components. In this perspective, we discuss some of the methods and assumptions of current PBPK modeling approaches. Specifically, we discuss drug partitioning into phospholipids and neutral lipids, use of blood-plasma ratios to estimate basic drug tissue partitioning, and clearance of neutral and acidic drugs. Based on these discussions, we believe that current PBPK models are mechanistic but a posteriori and semi-empirical.
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Affiliation(s)
- Ken Korzekwa
- Department of Pharmaceutical Sciences, Temple University School of Pharmacy, 3307 N Broad Street, Philadelphia, Pennsylvania, 19140, USA.
| | - Swati Nagar
- Department of Pharmaceutical Sciences, Temple University School of Pharmacy, 3307 N Broad Street, Philadelphia, Pennsylvania, 19140, USA
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Chen Y, Zhao K, Liu F, Xie Q, Zhong Z, Miao M, Liu X, Liu L. Prediction of Deoxypodophyllotoxin Disposition in Mouse, Rat, Monkey, and Dog by Physiologically Based Pharmacokinetic Model and the Extrapolation to Human. Front Pharmacol 2016; 7:488. [PMID: 28018224 PMCID: PMC5159431 DOI: 10.3389/fphar.2016.00488] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Accepted: 11/29/2016] [Indexed: 11/13/2022] Open
Abstract
Deoxypodophyllotoxin (DPT) is a potential anti-tumor candidate prior to its clinical phase. The aim of the study was to develop a physiologically based pharmacokinetic (PBPK) model consisting of 13 tissue compartments to predict DPT disposition in mouse, rat, monkey, and dog based on in vitro and in silico inputs. Since large interspecies difference was found in unbound fraction of DPT in plasma, we assumed that Kt:pl,u (unbound tissue-to-plasma concentration ratio) was identical across species. The predictions of our model were then validated by in vivo data of corresponding preclinical species, along with visual predictive checks. Reasonable matches were found between observed and predicted plasma concentrations and pharmacokinetic parameters in all four animal species. The prediction in the related seven tissues of mouse was also desirable. We also attempted to predict human pharmacokinetic profile by both the developed PBPK model and interspecies allometric scaling across mouse, rat and monkey, while dog was excluded from the scaling. The two approaches reached similar results. We hope the study will help in the efficacy and safety assessment of DPT in future clinical studies and provide a reference to the preclinical screening of similar compounds by PBPK model.
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Affiliation(s)
- Yang Chen
- Center of Pharmacokinetics and Metabolism, College of Pharmacy, China Pharmaceutical University Nanjing, China
| | - Kaijing Zhao
- Center of Pharmacokinetics and Metabolism, College of Pharmacy, China Pharmaceutical University Nanjing, China
| | - Fei Liu
- Center of Pharmacokinetics and Metabolism, College of Pharmacy, China Pharmaceutical University Nanjing, China
| | - Qiushi Xie
- Center of Pharmacokinetics and Metabolism, College of Pharmacy, China Pharmaceutical University Nanjing, China
| | - Zeyu Zhong
- Center of Pharmacokinetics and Metabolism, College of Pharmacy, China Pharmaceutical University Nanjing, China
| | - Mingxing Miao
- Center of Pharmacokinetics and Metabolism, College of Pharmacy, China Pharmaceutical University Nanjing, China
| | - Xiaodong Liu
- Center of Pharmacokinetics and Metabolism, College of Pharmacy, China Pharmaceutical University Nanjing, China
| | - Li Liu
- Center of Pharmacokinetics and Metabolism, College of Pharmacy, China Pharmaceutical University Nanjing, China
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Nagar S, Korzekwa K. Drug Distribution. Part 1. Models to Predict Membrane Partitioning. Pharm Res 2016; 34:535-543. [PMID: 27981450 DOI: 10.1007/s11095-016-2085-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Accepted: 12/08/2016] [Indexed: 01/08/2023]
Abstract
PURPOSE Tissue partitioning is an important component of drug distribution and half-life. Protein binding and lipid partitioning together determine drug distribution. METHODS Two structure-based models to predict partitioning into microsomal membranes are presented. An orientation-based model was developed using a membrane template and atom-based relative free energy functions to select drug conformations and orientations for neutral and basic drugs. RESULTS The resulting model predicts the correct membrane positions for nine compounds tested, and predicts the membrane partitioning for n = 67 drugs with an average fold-error of 2.4. Next, a more facile descriptor-based model was developed for acids, neutrals and bases. This model considers the partitioning of neutral and ionized species at equilibrium, and can predict membrane partitioning with an average fold-error of 2.0 (n = 92 drugs). CONCLUSIONS Together these models suggest that drug orientation is important for membrane partitioning and that membrane partitioning can be well predicted from physicochemical properties.
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Affiliation(s)
- Swati Nagar
- Department of Pharmaceutical Sciences, Temple University School of Pharmacy, 3307 N Broad Street, Philadelphia, Pennsylvania, 19140, USA
| | - Ken Korzekwa
- Department of Pharmaceutical Sciences, Temple University School of Pharmacy, 3307 N Broad Street, Philadelphia, Pennsylvania, 19140, USA.
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Drug Distribution Part 2. Predicting Volume of Distribution from Plasma Protein Binding and Membrane Partitioning. Pharm Res 2016; 34:544-551. [PMID: 27966088 DOI: 10.1007/s11095-016-2086-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Accepted: 12/08/2016] [Indexed: 01/03/2023]
Abstract
PURPOSE Volume of distribution is an important pharmacokinetic parameter in the distribution and half-life of a drug. Protein binding and lipid partitioning together determine drug distribution. METHODS Here we present a simple relationship that estimates the volume of distribution with the fraction of drug unbound in both plasma and microsomes. Model equations are based upon a two-compartment system and the experimental fractions unbound in plasma and microsomes represent binding to plasma proteins and cellular lipids, respectively. RESULTS The protein and lipid binding components were parameterized using a dataset containing human in vitro and in vivo parameters for 63 drugs. The resulting equation explains ~84% of the variance in the log of the volume of distribution with an average fold-error of 1.6, with 3 outliers. CONCLUSIONS These results suggest that Vss can be predicted for most drugs from plasma protein binding and microsomal partitioning.
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Saylor K, Zhang C. A simple physiologically based pharmacokinetic model evaluating the effect of anti-nicotine antibodies on nicotine disposition in the brains of rats and humans. Toxicol Appl Pharmacol 2016; 307:150-164. [PMID: 27473014 DOI: 10.1016/j.taap.2016.07.017] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2016] [Revised: 07/22/2016] [Accepted: 07/25/2016] [Indexed: 02/06/2023]
Abstract
Physiologically based pharmacokinetic (PBPK) modeling was applied to investigate the effects of anti-nicotine antibodies on nicotine disposition in the brains of rats and humans. Successful construction of both rat and human models was achieved by fitting model outputs to published nicotine concentration time course data in the blood and in the brain. Key parameters presumed to have the most effect on the ability of these antibodies to prevent nicotine from entering the brain were selected for investigation using the human model. These parameters, which included antibody affinity for nicotine, antibody cross-reactivity with cotinine, and antibody concentration, were broken down into different, clinically-derived in silico treatment levels and fed into the human PBPK model. Model predictions suggested that all three parameters, in addition to smoking status, have a sizable impact on anti-nicotine antibodies' ability to prevent nicotine from entering the brain and that the antibodies elicited by current human vaccines do not have sufficient binding characteristics to reduce brain nicotine concentrations. If the antibody binding characteristics achieved in animal studies can similarly be achieved in human studies, however, nicotine vaccine efficacy in terms of brain nicotine concentration reduction is predicted to meet threshold values for alleviating nicotine dependence.
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Affiliation(s)
- Kyle Saylor
- Department of Biological Systems Engineering, Virginia Tech, Seitz Hall, RM 210, 155 Ag Quad Lane, Blacksburg, VA 24061, USA.
| | - Chenming Zhang
- Department of Biological Systems Engineering, Virginia Tech, Seitz Hall, RM 210, 155 Ag Quad Lane, Blacksburg, VA 24061, USA.
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Fàbrega F, Nadal M, Schuhmacher M, Domingo JL, Kumar V. Influence of the uncertainty in the validation of PBPK models: A case-study for PFOS and PFOA. Regul Toxicol Pharmacol 2016; 77:230-9. [DOI: 10.1016/j.yrtph.2016.03.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Revised: 03/08/2016] [Accepted: 03/12/2016] [Indexed: 10/22/2022]
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49
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Henneberger L, Goss KU, Endo S. Equilibrium Sorption of Structurally Diverse Organic Ions to Bovine Serum Albumin. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2016; 50:5119-5126. [PMID: 27098963 DOI: 10.1021/acs.est.5b06176] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Reliable partitioning data are essential for assessing the bioaccumulation potential and the toxicity of chemicals. In contrast to neutral organic chemicals, the partitioning behavior of ionogenic organic chemicals (IOCs) is still a black box for environmental scientists. Partitioning to serum albumin, the major protein in blood plasma, strongly influences the freely dissolved concentration of many chemicals (including IOCs), which affects their transport and distribution in the body. Because consistent data sets for partitioning of IOCs are rarely available, bovine serum albumin-water partition coefficients (KBSA/w) were measured in this study for 45 anionic and 4 cationic organic chemicals, including various substituted benzoic and naphthoic acids, sulfonates and several pesticides and pharmaceuticals. The results of this study suggest that binding to BSA is substantially influenced by the three-dimensional structure of the chemicals and the position of substitutions on the sorbing molecules. For example, we found a difference of >1.5 log units between isomeric chemicals such as 3,4-dichlorobenzoic acid and 2,6-dichlorobenzoic acid, and 1-naphthoic acid and 2-naphthoic acid. Conventional modeling approaches (e.g., based on octanol-water partition coefficients) poorly predict log KBSA/w of organic ions (R(2) ≤ 0.5), partially because they do not capture the observed steric effects. Hence, alternative modeling strategies will be required for accurate prediction of serum albumin-water partition coefficients of organic ions.
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Affiliation(s)
- Luise Henneberger
- Helmholtz Centre for Environmental Research UFZ, Permoserstrasse 15, D-04318 Leipzig, Germany
| | - Kai-Uwe Goss
- Helmholtz Centre for Environmental Research UFZ, Permoserstrasse 15, D-04318 Leipzig, Germany
- University of Halle-Wittenberg , Institute of Chemistry, Kurt-Mothes-Strasse 2, D-06120 Halle, Germany
| | - Satoshi Endo
- Helmholtz Centre for Environmental Research UFZ, Permoserstrasse 15, D-04318 Leipzig, Germany
- Osaka City University , Urban Research Plaza & Graduate School of Engineering, Sugimoto 3-3-138, Sumiyoshi-ku, 558-8585 Osaka, Japan
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50
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Jónsdóttir SÓ, Reffstrup TK, Petersen A, Nielsen E. Physicologically Based Toxicokinetic Models of Tebuconazole and Application in Human Risk Assessment. Chem Res Toxicol 2016; 29:715-34. [DOI: 10.1021/acs.chemrestox.5b00341] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Svava Ósk Jónsdóttir
- National Food Institute, Technical University of Denmark, Mørkhøj Bygade 19, DK-2860 Søborg, Denmark
| | - Trine Klein Reffstrup
- National Food Institute, Technical University of Denmark, Mørkhøj Bygade 19, DK-2860 Søborg, Denmark
| | - Annette Petersen
- National Food Institute, Technical University of Denmark, Mørkhøj Bygade 19, DK-2860 Søborg, Denmark
| | - Elsa Nielsen
- National Food Institute, Technical University of Denmark, Mørkhøj Bygade 19, DK-2860 Søborg, Denmark
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