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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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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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Holt K, Nagar S, Korzekwa K. Methods to Predict Volume of Distribution. CURRENT PHARMACOLOGY REPORTS 2019; 5:391-399. [PMID: 34168949 PMCID: PMC8221585 DOI: 10.1007/s40495-019-00186-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
PURPOSE OF REVIEW Prior to human studies, knowledge of drug disposition in the body is useful to inform decisions on drug safety and efficacy, first in human dosing, and dosing regimen design. It is therefore of interest to develop predictive models for primary pharmacokinetic parameters, clearance, and volume of distribution. The volume of distribution of a drug is determined by the physiological properties of the body and physiochemical properties of the drug, and is used to determine secondary parameters, including the half-life. The purpose of this review is to provide an overview of current methods for the prediction of volume of distribution of drugs, discuss a comparison between the methods, and identify deficiencies in current predictive methods for future improvement. RECENT FINDINGS Several volumes of distribution prediction methods are discussed, including preclinical extrapolation, physiological methods, tissue composition-based models to predict tissue:plasma partition coefficients, and quantitative structure-activity relationships. Key factors that impact the prediction of volume of distribution, such as permeability, transport, and accuracy of experimental inputs, are discussed. A comparison of current methods indicates that in general, all methods predict drug volume of distribution with an absolute average fold error of 2-fold. Currently, the use of composition-based PBPK models is preferred to models requiring in vivo input. SUMMARY Composition-based models perfusion-limited PBPK models are commonly used at present for prediction of tissue:plasma partition coefficients and volume of distribution, respectively. A better mechanistic understanding of important drug distribution processes will result in improvements in all modeling approaches.
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Affiliation(s)
- Kimberly Holt
- Department of Pharmaceutical Sciences, Temple University School of Pharmacy, 3307 N. Broad Street, Philadelphia, PA 19140, USA
| | - Swati Nagar
- Department of Pharmaceutical Sciences, Temple University School of Pharmacy, 3307 N. Broad Street, Philadelphia, PA 19140, USA
| | - Ken Korzekwa
- Department of Pharmaceutical Sciences, Temple University School of Pharmacy, 3307 N. Broad Street, Philadelphia, PA 19140, USA
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Pharmacokinetics, Tissue Distribution, Plasma Protein Binding Studies of 10-Dehydroxyl-12-Demethoxy-Conophylline, a Novel Anti-Tumor Candidate, in Rats. Molecules 2019; 24:molecules24020283. [PMID: 30646543 PMCID: PMC6359039 DOI: 10.3390/molecules24020283] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 01/09/2019] [Accepted: 01/10/2019] [Indexed: 01/02/2023] Open
Abstract
10-Dehydroxyl-12-demethoxy-conophylline is a natural anticancer candidate. The motivation of this study was to explore the pharmacokinetic profiles, tissue distribution, and plasma protein binding of 10-dehydroxyl-12-demethoxy-conophylline in Sprague Dawley rats. A rapid, sensitive, and specific ultra-performance liquid chromatography (UPLC) system with a fluorescence (FLR) detection method was developed for the determination of 10-dehydroxyl-12-demethoxy-conophylline in different rat biological samples. After intravenous (i.v.) dosing of 10-dehydroxyl-12-demethoxy-conophylline at different levels (4, 8, and 12 mg/kg), the half-life t1/2α of intravenous administration was about 7 min and the t1/2β was about 68 min. The AUC0→∞ increased in a dose-proportional manner from 68.478 μg/L·min for 4 mg/kg to 305.616 mg/L·min for 12 mg/kg. After intragastrical (i.g.) dosing of 20 mg/kg, plasma levels of 10-dehydroxyl-12-demethoxy-conophylline peaked at about 90 min. 10-dehydroxyl-12-demethoxy-conophyllinea absolute oral bioavailability was only 15.79%. The pharmacokinetics process of the drug was fit to a two-room model. Following a single i.v. dose (8 mg/kg), 10-dehydroxyl-12-demethoxy-conophylline was detected in all examined tissues with the highest in kidney, liver, and lung. Equilibrium dialysis was used to evaluate plasma protein binding of 10-dehydroxyl-12-demethoxy-conophylline at three concentrations (1.00, 2.50, and 5.00 µg/mL). Results indicated a very high protein binding degree (over 80%), reducing substantially the free fraction of the compound.
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Nigade PB, Gundu J, Sreedhara Pai K, Nemmani KVS. Prediction of Tissue-to-Plasma Ratios of Basic Compounds in Mice. Eur J Drug Metab Pharmacokinet 2018; 42:835-847. [PMID: 28194579 DOI: 10.1007/s13318-017-0402-5] [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] [Indexed: 11/29/2022]
Abstract
BACKGROUND Majority of reported studies so far developed correlation regression equations using the rat muscle-to-plasma drug concentration ratio (Kp-muscle) to predict tissue-to-plasma drug concentration ratios (Kp-tissues). Use of regression equations derived from rat Kp-muscle may not be ideal to predict the mice tissue-Kps as there are species differences. OBJECTIVES (i) To develop the linear regression equations using mouse tissue-Kps; (ii) to assess the correlation between organ blood flow and/or organ weight with tissue-Kps and (iii) compare the observed tissue-Kps from mice with corresponding predicted tissue-Kps using Richter's rat-Kp specific equations. METHOD Disposition of 12 small molecules were investigated extensively in mouse plasma and tissues after a single oral dose administration. Linear correlation was assessed for each of the tissue with rest of the other tissues, separately for weak and strong bases. RESULT Newly developed regression equations using mice tissue-Kps, predicted 79% data points within twofold. As observed correlation r 2 range was 0.75-0.98 between Kp-muscle and Kp-brain, -spleen, -skin, -liver, -lung, suggesting superior correlation between the tissue-Kps. Order of tissue-Kps, showed that tissue concentrations were directly proportional to the organ blood flow and inversely to the organ weight. Further, the observed tissue-Kps from mice were compared with corresponding predicted tissue-Kps using Richter's rat-Kp specific equations. Overall, 46, 54 and 63% data points were under predicted (<0.5-fold) for liver, spleen and lung, respectively. Whereas 63 and 75% data points were over predicted (>twofold) for skin and brain, respectively. These findings suggest that cross species extrapolation predictability is poor. CONCLUSION All these findings together suggest that mouse specific regression equations developed under controlled experimental conditions could be most appropriate for predicting mouse tissue-Kps for compounds with wide range of volume of distribution.
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Affiliation(s)
- Prashant B Nigade
- Department of Drug Metabolism and Pharmacokinetics, Lupin Limited (Research Park), Pune, India. .,DMPK, Novel Drug Discovery and Development Department, Lupin Limited (Research Park), 46A/47A, Village Nande, Taluka Mulshi, Pune, 412 115, India.
| | - Jayasagar Gundu
- Department of Drug Metabolism and Pharmacokinetics, Lupin Limited (Research Park), Pune, India
| | - K Sreedhara Pai
- Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal University, Manipal, India
| | - Kumar V S Nemmani
- Department of Pharmacology, Lupin Limited (Research Park), Pune, India
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Nigade PB, Gundu J, Pai KS, Nemmani KVS, Talwar R. Prediction of volume of distribution in preclinical species and humans: application of simplified physiologically based algorithms. Xenobiotica 2018; 49:528-539. [DOI: 10.1080/00498254.2018.1474399] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Prashant B. Nigade
- Department of Drug Metabolism and Pharmacokinetics, Lupin Limited (Research Park), Pune, India
| | - Jayasagar Gundu
- Department of Drug Metabolism and Pharmacokinetics, Lupin Limited (Research Park), Pune, India
| | - K. Sreedhara Pai
- Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | | | - Rashmi Talwar
- Department of Pharmacology, Lupin Limited (Research Park), Pune, India
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Nigade PB, Gundu J, Pai KS, Nemmani KVS. Prediction of Tumor-to-Plasma Ratios of Basic Compounds in Subcutaneous Xenograft Mouse Models. Eur J Drug Metab Pharmacokinet 2017; 43:331-346. [PMID: 29250739 DOI: 10.1007/s13318-017-0454-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
BACKGROUND Predicting target site drug concentrations is of key importance for rank ordering compounds before proceeding to chronic pharmacodynamic models. We propose generic tumor-specific correlation-based regression equations to predict tumor-to-plasma ratios (tumor-Kps) in slow- and fast-growing xenograft mouse models. METHODS Disposition of 14 basic small molecules was investigated extensively in mouse plasma, tissues and tumors after a single oral dose administration. Linear correlation was assessed and compared between tumor-Kp and normal tissue-to-plasma ratio (tissue-Kps) separately for each tumor xenograft. The developed regression equations were validated by leave-one-out cross-validation (LOOCV) method. RESULT Both slow- and fast-growing tumor-Kps showed good correlation (r 2 ≥ 0.7) with majority of the normal tissue-Kps. Substantial difference was observed in the slopes of developed equations between two xenografts, which was in line with observed difference in tumor distribution. The linear correlations between tumor-Kp and skin- or spleen-Kp were within the acceptable statistical criteria (LOOCV) across xenografts and the class of compounds evaluated. Since > 70% of tumor-Kps from the test data sets were predicted within a factor of twofold for both slow- and fast-growing xenograft mouse models, the results validate the applicability of the developed equations across xenografts. CONCLUSION Tumor-specific correlation-based regression equations were developed and their applicability was adequately validated across xenografts. These equations could be successfully translated to predict tumor concentrations in order to preclude experimental tumor-Kp determination.
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Affiliation(s)
- Prashant B Nigade
- Department of Drug Metabolism and Pharmacokinetics, Lupin Limited (Research Park), 46A/47A, Village Nande, Taluka Mulshi, Pune, 412 115, India.
| | - Jayasagar Gundu
- Department of Drug Metabolism and Pharmacokinetics, Lupin Limited (Research Park), 46A/47A, Village Nande, Taluka Mulshi, Pune, 412 115, India
| | - K Sreedhara Pai
- Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal University, Manipal, India
| | - Kumar V S Nemmani
- Department of Pharmacology, Lupin Limited (Research Park), Pune, India
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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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9
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Tylutki Z, Polak S. Plasma vs heart tissue concentration in humans - literature data analysis of drugs distribution. Biopharm Drug Dispos 2015; 36:337-351. [PMID: 25765563 DOI: 10.1002/bdd.1944] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2014] [Revised: 01/05/2015] [Accepted: 03/04/2015] [Indexed: 12/12/2022]
Abstract
Little is known about the uptake of drugs into the human heart, although it is of great importance nowadays, when science desires to predict tissue level behavior rather than to measure it. Although the drug concentration in cardiac tissue seems a better predictor for physiological and electrophysiological changes than its level in plasma, knowledge of this value is very limited. Tissue to plasma partition coefficients (Kp) come to rescue since they characterize the distribution of a drug among tissues as being one of the input parameters in physiologically based pharmacokinetic (PBPK) models. The article reviews cardiac surgery and forensic medical studies to provide a reference for drug concentrations in human cardiac tissue. Firstly, the focus is on whether a drug penetrates into heart tissue at a therapeutic level; the provided values refer to antibiotics, antifungals and anticancer drugs. Drugs that directly affect cardiomyocyte electrophysiology are another group of interest. Measured levels of amiodarone, digoxin, perhexiline and verapamil in different sites in human cardiac tissue where the compounds might meet ion channels, gives an insight into how these more lipophilic drugs penetrate the heart. Much data are derived from postmortem studies and they provide insight to the cardiac distribution of more than 200 drugs. The analysis depicts potential problems in defining the active concentration location, what may indirectly suggest multiple mechanisms involved in the drug distribution within the heart. Copyright © 2015 John Wiley & Sons, Ltd.
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Affiliation(s)
- Zofia Tylutki
- Unit of Pharmacoepidemiology and Pharmacoeconomics, Department of Social Pharmacy, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9 Str, , 30-688, Cracow, Poland
| | - Sebastian Polak
- Unit of Pharmacoepidemiology and Pharmacoeconomics, Department of Social Pharmacy, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9 Str, , 30-688, Cracow, Poland
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Poulin P, Chen YH, Ding X, Gould SE, Hop CE, Messick K, Oeh J, Liederer BM. Prediction of Drug Distribution in Subcutaneous Xenografts of Human Tumor Cell Lines and Healthy Tissues in Mouse: Application of the Tissue Composition-Based Model to Antineoplastic Drugs. J Pharm Sci 2015; 104:1508-21. [DOI: 10.1002/jps.24336] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2014] [Revised: 12/05/2014] [Accepted: 12/12/2014] [Indexed: 12/20/2022]
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Freitas AA, Limbu K, Ghafourian T. Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients. J Cheminform 2015; 7:6. [PMID: 25767566 PMCID: PMC4356883 DOI: 10.1186/s13321-015-0054-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2014] [Accepted: 01/27/2015] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. RESULTS Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. CONCLUSIONS Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.
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Affiliation(s)
- Alex A Freitas
- />School of Computing, University of Kent, Canterbury, CT2 7NF UK
| | - Kriti Limbu
- />Medway School of Pharmacy, Universities of Kent and Greenwich, Chatham, Kent, ME4 4TB UK
| | - Taravat Ghafourian
- />Medway School of Pharmacy, Universities of Kent and Greenwich, Chatham, Kent, ME4 4TB UK
- />Drug Applied Research Centre and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
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Charifson PS, Walters WP. Acidic and Basic Drugs in Medicinal Chemistry: A Perspective. J Med Chem 2014; 57:9701-17. [DOI: 10.1021/jm501000a] [Citation(s) in RCA: 131] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Paul S. Charifson
- Vertex Pharmaceuticals Incorporated, 50 Northern Avenue Boston, Massachusetts 02210, United States
| | - W. Patrick Walters
- Vertex Pharmaceuticals Incorporated, 50 Northern Avenue Boston, Massachusetts 02210, United States
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Ball K, Bouzom F, Scherrmann JM, Walther B, Declèves X. A Physiologically Based Modeling Strategy during Preclinical CNS Drug Development. Mol Pharm 2014; 11:836-48. [DOI: 10.1021/mp400533q] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Kathryn Ball
- Centre de Pharmacocinétique et Métabolisme, Groupe de Recherche Servier, Orléans, France
| | - François Bouzom
- Centre de Pharmacocinétique et Métabolisme, Groupe de Recherche Servier, Orléans, France
| | - Jean-Michel Scherrmann
- Neuropsychopharmacologie
des addictions (CNRS UMR 8206), Faculté de Pharmacie, Université Paris Descartes, Paris, France
- INSERM U705, Neuropsychopharmacologie des addictions, Paris, France
| | - Bernard Walther
- Centre de Pharmacocinétique et Métabolisme, Groupe de Recherche Servier, Orléans, France
| | - Xavier Declèves
- Neuropsychopharmacologie
des addictions (CNRS UMR 8206), Faculté de Pharmacie, Université Paris Descartes, Paris, France
- INSERM U705, Neuropsychopharmacologie des addictions, Paris, France
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Extrapolating In Vitro Results to Predict Human Toxicity. METHODS IN PHARMACOLOGY AND TOXICOLOGY 2014. [DOI: 10.1007/978-1-4939-0521-8_24] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Abstract
The chemical structure of any drug determines its pharmacokinetics and pharmacodynamics. Detailed understanding of relationships between the drug chemical structure and individual disposition pathways (i.e., distribution and elimination) is required for efficient use of existing drugs and effective development of new drugs. Different approaches have been developed for this purpose, ranging from statistics-based quantitative structure-property (or structure-pharmacokinetic) relationships (QSPR) analysis to physiologically based pharmacokinetic (PBPK) models. This review critically analyzes currently available approaches for analysis and prediction of drug disposition on the basis of chemical structure. Models that can be used to predict different aspects of disposition are presented, including: (a) value of the individual pharmacokinetic parameter (e.g., clearance or volume of distribution), (b) efficiency of the specific disposition pathway (e.g., biliary drug excretion or cytochrome P450 3A4 metabolism), (c) accumulation in a specific organ or tissue (e.g., permeability of the placenta or accumulation in the brain), and (d) the whole-body disposition in the individual patients. Examples of presented pharmacological agents include "classical" low-molecular-weight compounds, biopharmaceuticals, and drugs encapsulated in specialized drug-delivery systems. The clinical efficiency of agents from all these groups can be suboptimal, because of inefficient permeability of the drug to the site of action and/or excessive accumulation in other organs and tissues. Therefore, robust and reliable approaches for chemical structure-based prediction of drug disposition are required to overcome these limitations. PBPK models are increasingly being used for prediction of drug disposition. These models can reflect the complex interplay of factors that determine drug disposition in a mechanistically correct fashion and can be combined with other approaches, for example QSPR-based prediction of drug permeability and metabolism, pharmacogenomic data and tools, pharmacokinetic-pharmacodynamic modeling approaches, etc. Moreover, the PBPK models enable detailed analysis of clinically relevant scenarios, for example the effect of the specific conditions on the time course of the analyzed drug in the individual organs and tissues, including the site of action. It is expected that further development of such combined approaches will increase their precision, enhance the effectiveness of drugs, and lead to individualized drug therapy for different patient populations (geriatric, pediatric, specific diseases, etc.).
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Yun YE, Cotton CA, Edginton AN. Development of a decision tree to classify the most accurate tissue-specific tissue to plasma partition coefficient algorithm for a given compound. J Pharmacokinet Pharmacodyn 2013; 41:1-14. [PMID: 24258064 DOI: 10.1007/s10928-013-9342-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2013] [Accepted: 11/07/2013] [Indexed: 01/11/2023]
Abstract
Physiologically based pharmacokinetic (PBPK) modeling is a tool used in drug discovery and human health risk assessment. PBPK models are mathematical representations of the anatomy, physiology and biochemistry of an organism and are used to predict a drug's pharmacokinetics in various situations. Tissue to plasma partition coefficients (Kp), key PBPK model parameters, define the steady-state concentration differential between tissue and plasma and are used to predict the volume of distribution. The experimental determination of these parameters once limited the development of PBPK models; however, in silico prediction methods were introduced to overcome this issue. The developed algorithms vary in input parameters and prediction accuracy, and none are considered standard, warranting further research. In this study, a novel decision-tree-based Kp prediction method was developed using six previously published algorithms. The aim of the developed classifier was to identify the most accurate tissue-specific Kp prediction algorithm for a new drug. A dataset consisting of 122 drugs was used to train the classifier and identify the most accurate Kp prediction algorithm for a certain physicochemical space. Three versions of tissue-specific classifiers were developed and were dependent on the necessary inputs. The use of the classifier resulted in a better prediction accuracy than that of any single Kp prediction algorithm for all tissues, the current mode of use in PBPK model building. Because built-in estimation equations for those input parameters are not necessarily available, this Kp prediction tool will provide Kp prediction when only limited input parameters are available. The presented innovative method will improve tissue distribution prediction accuracy, thus enhancing the confidence in PBPK modeling outputs.
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Affiliation(s)
- Yejin Esther Yun
- School of Pharmacy, University of Waterloo, 200 University Ave W, Waterloo, ON, Canada
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Poulin P, Dambach DM, Hartley DH, Ford K, Theil FP, Harstad E, Halladay J, Choo E, Boggs J, Liederer BM, Dean B, Diaz D. An Algorithm for Evaluating Potential Tissue Drug Distribution in Toxicology Studies from Readily Available Pharmacokinetic Parameters. J Pharm Sci 2013; 102:3816-29. [DOI: 10.1002/jps.23670] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2013] [Revised: 06/21/2013] [Accepted: 06/27/2013] [Indexed: 01/10/2023]
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Poulin P, Haddad S. Hepatocyte Composition-Based Model as a Mechanistic Tool for Predicting the Cell Suspension: Aqueous Phase Partition Coefficient of Drugs in In Vitro Metabolic Studies. J Pharm Sci 2013; 102:2806-18. [DOI: 10.1002/jps.23602] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2013] [Revised: 04/23/2013] [Accepted: 04/24/2013] [Indexed: 12/21/2022]
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Poulin P, Hop CE, Salphati L, Liederer BM. Correlation of Tissue-Plasma Partition Coefficients Between Normal Tissues and Subcutaneous Xenografts of Human Tumor Cell Lines in Mouse as a Prediction Tool of Drug Penetration in Tumors. J Pharm Sci 2013; 102:1355-69. [DOI: 10.1002/jps.23452] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2012] [Revised: 12/10/2012] [Accepted: 01/03/2013] [Indexed: 12/20/2022]
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Jones HM, Mayawala K, Poulin P. Dose selection based on physiologically based pharmacokinetic (PBPK) approaches. AAPS JOURNAL 2012; 15:377-87. [PMID: 23269526 DOI: 10.1208/s12248-012-9446-2] [Citation(s) in RCA: 72] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2012] [Accepted: 11/28/2012] [Indexed: 12/13/2022]
Abstract
Physiologically based pharmacokinetic (PBPK) models are built using differential equations to describe the physiology/anatomy of different biological systems. Readily available in vitro and in vivo preclinical data can be incorporated into these models to not only estimate pharmacokinetic (PK) parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. They provide a mechanistic framework to understand and extrapolate PK and dose across in vitro and in vivo systems and across different species, populations and disease states. Using small molecule and large molecule examples from the literature and our own company, we have shown how PBPK techniques can be utilised for human PK and dose prediction. Such approaches have the potential to increase efficiency, reduce the need for animal studies, replace clinical trials and increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however some limitations need to be addressed to realise its application and utility more broadly.
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Affiliation(s)
- Hannah M Jones
- Systems Modelling and Simulation Group, Department of Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide R&D, 35 Cambridgepark Drive, Cambridge, MA 02140, USA.
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Poulin P, Haddad S. Advancing Prediction of Tissue Distribution and Volume of Distribution of Highly Lipophilic Compounds from a Simplified Tissue-Composition-Based Model as a Mechanistic Animal Alternative Method. J Pharm Sci 2012; 101:2250-61. [DOI: 10.1002/jps.23090] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2011] [Revised: 01/26/2012] [Accepted: 02/02/2012] [Indexed: 12/30/2022]
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Zou P, Zheng N, Yang Y, Yu LX, Sun D. Prediction of volume of distribution at steady state in humans: comparison of different approaches. Expert Opin Drug Metab Toxicol 2012; 8:855-72. [DOI: 10.1517/17425255.2012.682569] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Stepensky D. The Øie-Tozer model of drug distribution and its suitability for drugs with different pharmacokinetic behavior. Expert Opin Drug Metab Toxicol 2012; 7:1233-43. [PMID: 21919805 DOI: 10.1517/17425255.2011.613823] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
INTRODUCTION Drug distribution is a major pharmacokinetic process that affects the time course of drug concentrations in tissues, biological fluids and the resulting pharmacological activities. Drug distribution may follow different pathways and patterns, and is governed by the drug's physicochemical properties and the body's physiology. The classical Øie-Tozer model is frequently used for predicting volume of drug distribution and for pharmacokinetic calculations. AREAS COVERED In this review, the suitability of the Øie-Tozer model for drugs that exhibit different distribution patterns is critically analyzed and illustrated. The method used is a pharmacokinetic modeling and simulation approach. It is demonstrated that the major limitation of the Øie-Tozer model stems from its focus on the total drug concentrations and not on the active (unbound) concentrations. Moreover, the Øie-Tozer model may be inappropriate for drugs with nonlinear or complex pharmacokinetic behavior, such as biopharmaceuticals, drug conjugates or for drugs incorporated into drug delivery systems. Distribution mechanisms and alternative distribution models for these drugs are discussed. EXPERT OPINION The Øie-Tozer model can serve for predicting unbound volume of drug distribution for 'classical' small molecular mass drugs with linear pharmacokinetics. However, more detailed mechanism-based distribution models should be used in preclinical and clinical settings for drugs that exhibit more complex pharmacokinetic behavior.
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Affiliation(s)
- David Stepensky
- Ben-Gurion University of the Negev, Department of Pharmacology and School of Pharmacy, P.O. Box 653, Beer-Sheva 84105, Israel.
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Poulin P, Kenny JR, Hop CECA, Haddad S. In vitro-in vivo extrapolation of clearance: modeling hepatic metabolic clearance of highly bound drugs and comparative assessment with existing calculation methods. J Pharm Sci 2011; 101:838-51. [PMID: 22009717 DOI: 10.1002/jps.22792] [Citation(s) in RCA: 78] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2011] [Revised: 09/28/2011] [Accepted: 09/29/2011] [Indexed: 01/10/2023]
Abstract
In vitro-in vivo extrapolation (IVIVE) is an important method for estimating the hepatic metabolic clearance (CL) of drugs. This study highlights a problematic area observed when using microsomal data to predict in vivo CL of drugs that are highly bound to plasma proteins, and further explores mechanisms for human CL predictions by associating additional processes to IVIVE disconnect. Therefore, this study attempts to develop a novel IVIVE calculation method, which consists of adjusting the binding terms in a well-stirred liver model. A comparative assessment between the IVIVE method proposed here and previously published methods of Obach (1999. Drug Metab Dispos 27:1350-1359) and Berezhkovskiy (2010. J Pharm Sci 100:1167-1783) was also performed. The assessment was confined by the availability of measured in vitro and in vivo data in humans for 25 drugs highly bound to plasma proteins, for which it can be assumed that metabolism is the major route of elimination. Here, we argue that a difference in drug ionization and binding proteins such as albumin (AL) and alpha-1-acid glycoprotein (AAG) in plasma and liver also needs to be considered in IVIVE based on mechanistic studies. Therefore, converting unbound fraction in plasma to liver essentially increased the predicted CL values, which resulted in much more accurate estimates of in vivo CL as compared with the other IVIVE methods tested. The impact on CL estimate was more apparent for drugs binding to AL than to AAG. This is a mechanistic rational for explaining a considerable proportion of the divergence between previously estimated and observed CL values. Human CL was predicted within 1.5-fold, twofold, and threefold of the observed CL for 84%, 96%, and 100% of the compounds, respectively. Overall, this study demonstrates a significant improvement in the mechanism-based prediction of metabolic CL for these 25 highly bound drugs from in vitro data determined with microsomes, which should facilitate the application of physiologically based pharmacokinetic (PBPK) models in drug discovery and development.
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Affiliation(s)
- Patrick Poulin
- Consultant, 4009 Sylvia Daoust, Québec City, Québec G1X 0A6, Canada.
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Poulin P, Jones RD, Jones HM, Gibson CR, Rowland M, Chien JY, Ring BJ, Adkison KK, Ku MS, He H, Vuppugalla R, Marathe P, Fischer V, Dutta S, Sinha VK, Björnsson T, Lavé T, Yates JW. PHRMA CPCDC initiative on predictive models of human pharmacokinetics, part 5: Prediction of plasma concentration–time profiles in human by using the physiologically‐based pharmacokinetic modeling approach. J Pharm Sci 2011; 100:4127-57. [DOI: 10.1002/jps.22550] [Citation(s) in RCA: 132] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2010] [Revised: 02/01/2011] [Accepted: 03/04/2011] [Indexed: 11/09/2022]
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Jones RD, Jones HM, Rowland M, Gibson CR, Yates JW, Chien JY, Ring BJ, Adkison KK, Ku MS, He H, Vuppugalla R, Marathe P, Fischer V, Dutta S, Sinha VK, Björnsson T, Lavé T, Poulin P. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 2: Comparative assessment of prediction methods of human volume of distribution. J Pharm Sci 2011; 100:4074-89. [DOI: 10.1002/jps.22553] [Citation(s) in RCA: 85] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2010] [Revised: 02/01/2011] [Accepted: 02/28/2011] [Indexed: 01/08/2023]
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Poulin P, Haddad S. Microsome composition-based model as a mechanistic tool to predict nonspecific binding of drugs in liver microsomes. J Pharm Sci 2011; 100:4501-17. [DOI: 10.1002/jps.22619] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2011] [Revised: 04/21/2011] [Accepted: 04/22/2011] [Indexed: 01/03/2023]
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Edginton AN, Joshi G. Have physiologically-based pharmacokinetic models delivered? Expert Opin Drug Metab Toxicol 2011; 7:929-34. [DOI: 10.1517/17425255.2011.585968] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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Small H, Gardner I, Jones HM, Davis J, Rowland M. Measurement of binding of basic drugs to acidic phospholipids using surface plasmon resonance and incorporation of the data into mechanistic tissue composition equations to predict steady-state volume of distribution. Drug Metab Dispos 2011; 39:1789-93. [PMID: 21764943 DOI: 10.1124/dmd.111.040253] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Acidic phospholipid binding plays an important role in determining the tissue distribution of basic drugs. This article describes the use of surface plasmon resonance to measure binding affinity (K(D)) of three basic drugs to phosphatidylserine, a major tissue acidic phospholipid. The data are incorporated into mechanistic tissue composition equations to allow prediction of the steady-state volume of distribution (V(ss)). The prediction accuracy of V(ss) using this approach is compared with the original methodology described by Rodgers et al. (J Pharm Sci 94:1259-1276), in which the binding to acidic phospholipids is calculated from the blood/plasma concentration ratio (BPR). The compounds used in this study [amlodipine, propranolol, and 3-dimethylaminomethyl-4-(4-methylsulfanyl-phenoxy)-benzenesulfonamide (UK-390957)] showed higher affinity binding to phosphatidylserine than to phosphatidylcholine. When the binding affinity to phosphatidylserine was incorporated into mechanistic tissue composition equations, the V(ss) was more accurately predicted for all three compounds by using the surface plasmon resonance measurement than by using the BPR to estimate acidic phospholipid binding affinity. The difference was particularly marked for UK-390957, a sulfonamide that has a high BPR due to binding to carbonic anhydrase. The novel approach described in this article allows the binding affinity of drugs to an acidic phospholipid (phosphatidylserine) to be measured directly and demonstrates the utility of the binding data in the prediction of V(ss).
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Affiliation(s)
- Helen Small
- Pfizer Global Research & Development, Sandwich, Kent, United Kingdom
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Vuppugalla R, Marathe P, He H, Jones RDO, Yates JWT, Jones HM, Gibson CR, Chien JY, Ring BJ, Adkison KK, Ku MS, Fischer V, Dutta S, Sinha VK, Björnsson T, Lavé T, Poulin P. PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 4: prediction of plasma concentration-time profiles in human from in vivo preclinical data by using the Wajima approach. J Pharm Sci 2011; 100:4111-26. [PMID: 21480234 DOI: 10.1002/jps.22551] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2010] [Revised: 02/01/2011] [Accepted: 03/04/2011] [Indexed: 11/12/2022]
Abstract
The objective of this study was to evaluate the performance of the Wajima allometry (Css -MRT) approach published in the literature, which is used to predict the human plasma concentration-time profiles from a scaling of preclinical species data. A diverse and blinded dataset of 108 compounds from PhRMA member companies was used in this evaluation. The human intravenous (i.v.) and oral (p.o.) pharmacokinetics (PK) data were available for 18 and 107 drugs, respectively. Three different scenarios were adopted for prediction of human PK profiles. In the first scenario, human clearance (CL) and steady-state volume of distribution (Vss ) were predicted by unbound fraction corrected intercept method (FCIM) and Øie-Tozer (OT) approaches, respectively. Quantitative structure activity relationship (QSAR)-based approaches (TSrat-dog ) based on compound descriptors together with rat and dog data were utilized in the second scenario. Finally, in the third scenario, CL and Vss were predicted using the FCIM and Jansson approaches, respectively. For the prediction of oral pharmacokinetics, the human bioavailability and absorption rate constant were assumed as the average of preclinical species. Various statistical techniques were used for assessing the accuracy of the simulation scenarios. The human CL and Vss were predicted within a threefold error range for about 75% of the i.v. drugs. However, the accuracy in predicting key p.o. PK parameters appeared to be lower with only 58% of simulations falling within threefold of observed parameters. The overall ability of the Css -MRT approach to predict the curve shape of the profile was in general poor and ranged between low to medium level of confidence for most of the predictions based on the selected criteria.
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Affiliation(s)
- Ragini Vuppugalla
- Metabolism and Pharmacokinetics, Bristol-Myer's Squibb Company, Princeton, New Jersey 08543
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