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Hunt JP, Dubinsky S, McKnite AM, Cheung KWK, van Groen BD, Giacomini KM, de Wildt SN, Edginton AN, Watt KM. Maximum likelihood estimation of renal transporter ontogeny profiles for pediatric PBPK modeling. CPT Pharmacometrics Syst Pharmacol 2024; 13:576-588. [PMID: 38156758 PMCID: PMC11015082 DOI: 10.1002/psp4.13102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 12/01/2023] [Accepted: 12/14/2023] [Indexed: 01/03/2024] Open
Abstract
Optimal treatment of infants with many renally cleared drugs must account for maturational differences in renal transporter (RT) activity. Pediatric physiologically-based pharmacokinetic (PBPK) models may incorporate RT activity, but this requires ontogeny profiles for RT activity in children, especially neonates, to predict drug disposition. Therefore, RT expression measurements from human kidney postmortem cortical tissue samples were normalized to represent a fraction of mature RT activity. Using these data, maximum likelihood estimated the distributions of RT activity across the pediatric age spectrum, including preterm and term neonates. PBPK models of four RT substrates (acyclovir, ciprofloxacin, furosemide, and meropenem) were evaluated with and without ontogeny profiles using average fold error (AFE), absolute average fold error (AAFE), and proportion of observations within the 5-95% prediction interval. Novel maximum likelihood profiles estimated ontogeny distributions for the following RT: OAT1, OAT3, OCT2, P-gp, URAT1, BCRP, MATE1, MRP2, MRP4, and MATE-2 K. Profiles for OAT3, P-gp, and MATE1 improved infant furosemide and neonate meropenem PBPK model AFE from 0.08 to 0.70 and 0.53 to 1.34 and model AAFE from 12.08 to 1.44 and 2.09 to 1.36, respectively, and improved the percent of data within the 5-95% prediction interval from 48% to 98% for neonatal ciprofloxacin simulations, respectively. Even after accounting for other critical population-specific maturational differences, novel RT ontogeny profiles substantially improved neonatal PBPK model performance, providing validated estimates of maturational differences in RT activity for optimal dosing in children.
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Affiliation(s)
| | | | | | | | - Bianca D. van Groen
- Roche Pharma and Early Development (pRED), Roche Innovation Center BaselBaselSwitzerland
| | | | - Saskia N. de Wildt
- Erasmus MCRotterdamThe Netherlands
- Radboud UniversityNijmegenThe Netherlands
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Wu J, Xiao Z, Wang M, Wu W, Ma X, Liang X, Zheng L, Ding S, Luo J, Cao Y, Hong Z, Chen J, Zhao Q, Ding D. The impact of kidney function on plasma neurofilament light and phospho-tau 181 in a community-based cohort: the Shanghai Aging Study. Alzheimers Res Ther 2024; 16:32. [PMID: 38347655 PMCID: PMC10860286 DOI: 10.1186/s13195-024-01401-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 01/26/2024] [Indexed: 02/15/2024]
Abstract
BACKGROUND The blood-based biomarkers are approaching the clinical practice of Alzheimer's disease (AD). Chronic kidney disease (CKD) has a potential confounding effect on peripheral protein levels. It is essential to characterize the impact of renal function on AD markers. METHODS Plasma phospho-tau181 (P-tau181), and neurofilament light (NfL) were assayed via the Simoa HD-X platform in 1189 dementia-free participants from the Shanghai Aging Study (SAS). The estimated glomerular filter rate (eGFR) was calculated. The association between renal function and blood NfL, P-tau181 was analyzed. An analysis of interactions between various demographic and comorbid factors and eGFR was conducted. RESULTS The eGFR levels were negatively associated with plasma concentrations of NfL and P-tau181 (B = - 0.19, 95% CI - 0.224 to - 0.156, P < 0.001; B = - 0.009, 95% CI - 0.013 to -0.005, P < 0.001, respectively). After adjusting for demographic characteristics and comorbid diseases, eGFR remained significantly correlated with plasma NfL (B = - 0.010, 95% CI - 0.133 to - 0.068, P < 0.001), but not with P-tau181 (B = - 0.003, 95% CI - 0.007 to 0.001, P = 0.194). A significant interaction between age and eGFR was found for plasma NfL (Pinteraction < 0.001). In participants ≥ 70 years and with eGFR < 60 ml/min/1.73 m2, the correlation between eGFR and plasma NfL was significantly remarkable (B = - 0.790, 95% CI - 1.026 to - 0,554, P < 0.001). CONCLUSIONS Considering renal function and age is crucial when interpreting AD biomarkers in the general aging population.
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Affiliation(s)
- Jie Wu
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhenxu Xiao
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Mengjing Wang
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- Departemnt of Nephrology, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Wanqing Wu
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaoxi Ma
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaoniu Liang
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Li Zheng
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Saineng Ding
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Jianfeng Luo
- Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China
| | - Yang Cao
- Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, 70182, Örebro, Sweden
| | - Zhen Hong
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Jing Chen
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- Departemnt of Nephrology, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Qianhua Zhao
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
- National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
| | - Ding Ding
- Institute of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
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3
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Granda ML, Huang W, Yeung CK, Isoherranen N, Kestenbaum B. Predicting complex kidney drug handling using a physiologically-based pharmacokinetic model informed by biomarker-estimated secretory clearance and blood flow. Clin Transl Sci 2024; 17:e13678. [PMID: 37921258 PMCID: PMC10766039 DOI: 10.1111/cts.13678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 10/04/2023] [Accepted: 10/23/2023] [Indexed: 11/04/2023] Open
Abstract
Kidney function-adjusted drug dosing is currently based solely on the estimated glomerular filtration rate (GFR), however, kidney drug handling is accomplished by a combination of filtration, tubular secretion, and re-absorption. Mechanistic physiologically-based pharmacokinetic (PBPK) models recapitulate anatomic compartments to predict elimination from estimated perfusion, filtration, secretion, and re-absorption, but clinical applications are limited by a lack of empiric individual-level measurements of these functions. We adapted and validated a PBPK model to predict drug clearance from individual biomarker-based estimates of kidney perfusion and secretory clearance. We estimated organic anion transporter-mediated secretion via kynurenic acid clearance and kidney blood flow (KBF) via isovalerylglycine clearance in human participants, incorporating these measurements with GFR into the model to predict kidney drug clearance. We compared measured and model-predicted clearances of administered tenofovir and oseltamivir, which are cleared by both filtration and secretion. There were 27 outpatients (age 55 ± 15 years, mean iohexol-GFR [iGFR] 76 ± 31 mL/min/1.73 m2 ) in this drug clearance study. The mean observed and mechanistic model-predicted tenofovir clearances were 169 ± 102 mL/min and 163 ± 80 mL/min, respectively; estimated mean error of the mechanistic model was 37.1 mL/min (95% confidence interval [CI]: 24-52.9), compared to a mean error of 41.8 mL/min (95% CI: 25-61.6) from regression model. The mean observed and model-predicted oseltamivir carboxylate clearances were 183 ± 104 mL/min and 179 ± 89 mL/min, respectively; estimated mean error of the mechanistic model was 42.9 mL/min (95% CI: 29.7-56.4), versus error of 48.1 mL/min (95% CI: 31.2-67.3) from the regression model. Individualized estimates of tubular secretion and KBF improved the accuracy of PBPK model-predicted tenofovir and oseltamivir kidney clearances, suggesting the potential for biomarker-informed measures of kidney function to refine personalized drug dosing.
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Affiliation(s)
- Michael L. Granda
- Division of Nephrology, Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
- Kidney Research InstituteSeattleWashingtonUSA
| | - Weize Huang
- Clinical PharmacologyGenentech Inc.South San FranciscoCaliforniaUSA
- Department of Pharmaceutics, School of PharmacyUniversity of WashingtonSeattleWashingtonUSA
| | - Catherine K. Yeung
- Kidney Research InstituteSeattleWashingtonUSA
- Department of Pharmacy, School of PharmacyUniversity of WashingtonSeattleWashingtonUSA
| | - Nina Isoherranen
- Department of Pharmaceutics, School of PharmacyUniversity of WashingtonSeattleWashingtonUSA
| | - Bryan Kestenbaum
- Division of Nephrology, Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
- Kidney Research InstituteSeattleWashingtonUSA
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Chang SY, Huang W, Chapron A, Quiñones AJL, Wang J, Isoherranen N, Shen DD, Kelly EJ, Himmelfarb J, Yeung CK. Incorporating Uremic Solute-mediated Inhibition of OAT1/3 Improves PBPK Prediction of Tenofovir Renal and Systemic Disposition in Patients with Severe Kidney Disease. Pharm Res 2023; 40:2597-2606. [PMID: 37704895 DOI: 10.1007/s11095-023-03594-x] [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: 05/30/2023] [Accepted: 08/23/2023] [Indexed: 09/15/2023]
Abstract
BACKGROUND Dose modification of renally secreted drugs in patients with chronic kidney disease (CKD) has relied on serum creatinine concentration as a biomarker to estimate glomerular filtration (GFR) under the assumption that filtration and secretion decline in parallel. A discrepancy between actual renal clearance and predicted renal clearance based on GFR alone is observed in severe CKD patients with tenofovir, a compound secreted by renal OAT1/3. Uremic solutes that inhibit OAT1/3 may play a role in this divergence. METHODS To examine the impact of transporter inhibition by uremic solutes on tenofovir renal clearance, we determined the inhibitory potential of uremic solutes hippuric acid, indoxyl sulfate, and p-cresol sulfate. The inhibition parameters (IC50) were incorporated into a previously validated mechanistic kidney model; simulated renal clearance and plasma PK profile were compared to data from clinical studies. RESULTS Without the incorporation of uremic solute inhibition, the PBPK model failed to capture the observed data with an absolute average fold error (AAFE) > 2. However, when the inhibition of renal uptake transporters and uptake transporters in the slow distribution tissues were included, the AAFE value was within the pre-defined twofold model acceptance criterion, demonstrating successful model extrapolation to CKD patients. CONCLUSION A PBPK model that incorporates inhibition by uremic solutes has potential to better predict renal clearance and systemic disposition of secreted drugs in patients with CKD. Ongoing research is warranted to determine if the model can be expanded to include other OAT1/3 substrate drugs and to evaluate how these findings can be translated to clinical guidance for drug selection and dose optimization in patients with CKD.
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Affiliation(s)
- Shih-Yu Chang
- Department of Pharmacy, School of Pharmacy, University of Washington, 1959 NE Pacific St. H375, Box 357630, Seattle, WA, 98195, USA
- Janssen Research and Development, Raritan, NJ, USA
| | - Weize Huang
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, WA, 98195, USA
- Genentech Inc, South San Francisco, CA, USA
| | - Alenka Chapron
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, WA, 98195, USA
| | - Antonio J López Quiñones
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, WA, 98195, USA
- Revolution Medicines, San Francisco, CA, USA
| | - Joanne Wang
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, WA, 98195, USA
| | - Nina Isoherranen
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, WA, 98195, USA
| | - Danny D Shen
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, WA, 98195, USA
| | - Edward J Kelly
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, WA, 98195, USA
- Division of Nephrology, Department of Medicine, Kidney Research Institute, University of Washington, Seattle, WA, 98195, USA
| | - Jonathan Himmelfarb
- Division of Nephrology, Department of Medicine, Kidney Research Institute, University of Washington, Seattle, WA, 98195, USA
| | - Catherine K Yeung
- Department of Pharmacy, School of Pharmacy, University of Washington, 1959 NE Pacific St. H375, Box 357630, Seattle, WA, 98195, USA.
- Division of Nephrology, Department of Medicine, Kidney Research Institute, University of Washington, Seattle, WA, 98195, USA.
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5
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Li X, Jusko WJ. Utility of Minimal Physiologically Based Pharmacokinetic Models for Assessing Fractional Distribution, Oral Absorption, and Series-Compartment Models of Hepatic Clearance. Drug Metab Dispos 2023; 51:1403-1418. [PMID: 37460222 PMCID: PMC10506700 DOI: 10.1124/dmd.123.001403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 07/13/2023] [Indexed: 09/16/2023] Open
Abstract
Minimal physiologically based pharmacokinetic (mPBPK) models are physiologically relevant, require less information than full PBPK models, and offer flexibility in pharmacokinetics (PK). The well-stirred hepatic model (WSM) is commonly used in PBPK, whereas the more plausible dispersion model (DM) poses computational complexities. The series-compartment model (SCM) mimics the DM but is easier to operate. This work implements the SCM and mPBPK models for assessing fractional tissue distribution, oral absorption, and hepatic clearance using literature-reported blood and liver concentration-time data in rats for compounds mainly cleared by the liver. Further handled were various complexities, including nonlinear hepatic binding and metabolism, differing absorption kinetics, and sites of administration. The SCM containing one to five (n) liver subcompartments yields similar fittings and provides comparable estimates for hepatic extraction ratio (ER), prehepatic availability (Fg ), and first-order absorption rate constants (ka ). However, they produce decreased intrinsic clearances (CLint ) and liver-to-plasma partition coefficients (Kph ) with increasing n as expected. Model simulations demonstrated changes in intravenous and oral PK profiles with alterations in Kph and ka and with hepatic metabolic zonation. The permeability (PAMPA P) of the various compounds well explained the fitted fractional distribution (fd ) parameters. The SCM and mPBPK models offer advantages in distinguishing systemic, extrahepatic, and hepatic clearances. The SCM allows for incorporation of liver zonation and is useful in assessing changes in internal concentration gradients potentially masked by similar blood PK profiles. Improved assessment of intraorgan drug concentrations may offer insights into active moieties driving metabolism, biliary excretion, pharmacodynamics, and hepatic toxicity. SIGNIFICANCE STATEMENT: The minimal physiologically based pharmacokinetic model and the series-compartment model are useful in assessing oral absorption and hepatic clearance. They add flexibility in accounting for various drug- or system-specific complexities, including fractional distribution, nonlinear binding and saturable hepatic metabolism, and hepatic zonation. These models can offer improved insights into the intraorgan concentrations that reflect physiologically active moieties often driving disposition, pharmacodynamics, and toxicity.
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Affiliation(s)
- Xiaonan Li
- Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York
| | - William J Jusko
- Department of Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York
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Deepika D, Kumar V. The Role of "Physiologically Based Pharmacokinetic Model (PBPK)" New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3473. [PMID: 36834167 PMCID: PMC9966583 DOI: 10.3390/ijerph20043473] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration-time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.
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Affiliation(s)
- Deepika Deepika
- Environmental Engineering Laboratory, Departament d’Enginyeria Quimica, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalonia, Spain
- Pere Virgili Health Research Institute (IISPV), Hospital Universitari Sant Joan de Reus, Universitat Rovira i Virgili, 43204 Reus, Catalonia, Spain
| | - Vikas Kumar
- Environmental Engineering Laboratory, Departament d’Enginyeria Quimica, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalonia, Spain
- Pere Virgili Health Research Institute (IISPV), Hospital Universitari Sant Joan de Reus, Universitat Rovira i Virgili, 43204 Reus, Catalonia, Spain
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Yang Y, Zhang X. Integration of Engineered Delivery with the Pharmacokinetics of Medical Candidates via Physiology-Based Pharmacokinetics. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2486:57-69. [PMID: 35437718 DOI: 10.1007/978-1-0716-2265-0_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic computational model that can be used to predict a drug product's ADME (absorption, distribution, metabolism, and excretion) and pharmacokinetics (PK). In recent years, PBPK modeling and simulation has been used increasingly to address many biopharmaceutics and clinical pharmacology questions, such as the effect of formulations, intrinsic factors (age, organ dysfunction, etc.), and extrinsic factors (comedications, food) on the PK of an investigational drug product. In this chapter, we will briefly introduce various PBPK models for ADME prediction and general procedures for PBPK modeling and simulations. The readers are encouraged to read updated literature on new applications of PBPK modeling and simulation which is still an emerging area in pharmaceutical development.
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Affiliation(s)
- Yuching Yang
- Division of Pharmacometrics, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Xinyuan Zhang
- Division of Pharmacometrics, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA.
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8
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Scotcher D, Galetin A. PBPK Simulation-Based Evaluation of Ganciclovir Crystalluria Risk Factors: Effect of Renal Impairment, Old Age, and Low Fluid Intake. AAPS J 2021; 24:13. [PMID: 34907479 PMCID: PMC8816528 DOI: 10.1208/s12248-021-00654-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 10/02/2021] [Indexed: 11/30/2022] Open
Abstract
Dosing guidance is often lacking for chronic kidney disease (CKD) due to exclusion of such patients from pivotal clinical trials. Physiologically based pharmacokinetic (PBPK) modelling supports model-informed dosing when clinical data are lacking, but application of these approaches to patients with impaired renal function is not yet at full maturity. In the current study, a ganciclovir PBPK model was developed for patients with normal renal function and extended to CKD population. CKD-related changes in tubular secretion were explored in the mechanistic kidney model and implemented either as proportional or non-proportional decline relative to GFR. Crystalluria risk was evaluated in different clinical settings (old age, severe CKD and low fluid intake) by simulating ganciclovir medullary collecting duct (MCD) concentrations. The ganciclovir PBPK model captured observed changes in systemic pharmacokinetic endpoints in mild-to-severe CKD; these trends were evident irrespective of assumed pathophysiological mechanism of altered active tubular secretion in the model. Minimal difference in simulated ganciclovir MCD concentrations was noted between young adult and geriatric populations with normal renal function and urine flow (1 mL/min), with lower concentrations predicted for severe CKD patients. High crystalluria risk was identified at reduced urine flow (0.1 mL/min) as simulated ganciclovir MCD concentrations exceeded its solubility (2.6–6 mg/mL), irrespective of underlying renal function. The analysis highlighted the importance of appropriate distribution of virtual subjects’ systems data in CKD populations. The ganciclovir PBPK model illustrates the ability of this translational tool to explore individual and combined effects of age, urine flow, and renal impairment on local drug renal exposure.
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Affiliation(s)
- Daniel Scotcher
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT, UK
| | - Aleksandra Galetin
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT, UK.
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9
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Imaoka T, Huang W, Shum S, Hailey DW, Chang SY, Chapron A, Yeung CK, Himmelfarb J, Isoherranen N, Kelly EJ. Bridging the gap between in silico and in vivo by modeling opioid disposition in a kidney proximal tubule microphysiological system. Sci Rep 2021; 11:21356. [PMID: 34725352 PMCID: PMC8560754 DOI: 10.1038/s41598-021-00338-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 09/27/2021] [Indexed: 11/22/2022] Open
Abstract
Opioid overdose, dependence, and addiction are a major public health crisis. Patients with chronic kidney disease (CKD) are at high risk of opioid overdose, therefore novel methods that provide accurate prediction of renal clearance (CLr) and systemic disposition of opioids in CKD patients can facilitate the optimization of therapeutic regimens. The present study aimed to predict renal clearance and systemic disposition of morphine and its active metabolite morphine-6-glucuronide (M6G) in CKD patients using a vascularized human proximal tubule microphysiological system (VPT-MPS) coupled with a parent-metabolite full body physiologically-based pharmacokinetic (PBPK) model. The VPT-MPS, populated with a human umbilical vein endothelial cell (HUVEC) channel and an adjacent human primary proximal tubular epithelial cells (PTEC) channel, successfully demonstrated secretory transport of morphine and M6G from the HUVEC channel into the PTEC channel. The in vitro data generated by VPT-MPS were incorporated into a mechanistic kidney model and parent-metabolite full body PBPK model to predict CLr and systemic disposition of morphine and M6G, resulting in successful prediction of CLr and the plasma concentration–time profiles in both healthy subjects and CKD patients. A microphysiological system together with mathematical modeling successfully predicted renal clearance and systemic disposition of opioids in CKD patients and healthy subjects.
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Affiliation(s)
- Tomoki Imaoka
- Department of Pharmaceutics, School of Pharmacy, University of Washington, HSB Room H272, 1959 NE Pacific Street, Seattle, WA, 98195, USA
| | - Weize Huang
- Department of Pharmaceutics, School of Pharmacy, University of Washington, HSB Room H272, 1959 NE Pacific Street, Seattle, WA, 98195, USA
| | - Sara Shum
- Department of Pharmaceutics, School of Pharmacy, University of Washington, HSB Room H272, 1959 NE Pacific Street, Seattle, WA, 98195, USA
| | - Dale W Hailey
- Lynn and Mike Garvey Imaging Core, Institute for Stem Cell and Regenerative Medicine, Seattle, WA, 98109, USA.,Department of Laboratory Medicine and Pathology, School of Medicine, University of Washington, Seattle, WA, 98195, USA
| | - Shih-Yu Chang
- Department of Pharmacy, School of Pharmacy, University of Washington, Seattle, WA, 98195, USA
| | - Alenka Chapron
- Department of Pharmaceutics, School of Pharmacy, University of Washington, HSB Room H272, 1959 NE Pacific Street, Seattle, WA, 98195, USA
| | - Catherine K Yeung
- Department of Pharmacy, School of Pharmacy, University of Washington, Seattle, WA, 98195, USA.,Division of Nephrology, Department of Medicine, Kidney Research Institute, University of Washington, 1959 NE Pacific Street, HSB Room H272, Seattle, WA, 98195, USA
| | - Jonathan Himmelfarb
- Division of Nephrology, Department of Medicine, Kidney Research Institute, University of Washington, 1959 NE Pacific Street, HSB Room H272, Seattle, WA, 98195, USA
| | - Nina Isoherranen
- Department of Pharmaceutics, School of Pharmacy, University of Washington, HSB Room H272, 1959 NE Pacific Street, Seattle, WA, 98195, USA
| | - Edward J Kelly
- Department of Pharmaceutics, School of Pharmacy, University of Washington, HSB Room H272, 1959 NE Pacific Street, Seattle, WA, 98195, USA. .,Division of Nephrology, Department of Medicine, Kidney Research Institute, University of Washington, 1959 NE Pacific Street, HSB Room H272, Seattle, WA, 98195, USA.
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10
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Wang H, Brown PC, Chow EC, Ewart L, Ferguson SS, Fitzpatrick S, Freedman BS, Guo GL, Hedrich W, Heyward S, Hickman J, Isoherranen N, Li AP, Liu Q, Mumenthaler SM, Polli J, Proctor WR, Ribeiro A, Wang J, Wange RL, Huang S. 3D cell culture models: Drug pharmacokinetics, safety assessment, and regulatory consideration. Clin Transl Sci 2021; 14:1659-1680. [PMID: 33982436 PMCID: PMC8504835 DOI: 10.1111/cts.13066] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 04/15/2021] [Accepted: 04/16/2021] [Indexed: 12/12/2022] Open
Abstract
Nonclinical testing has served as a foundation for evaluating potential risks and effectiveness of investigational new drugs in humans. However, the current two-dimensional (2D) in vitro cell culture systems cannot accurately depict and simulate the rich environment and complex processes observed in vivo, whereas animal studies present significant drawbacks with inherited species-specific differences and low throughput for increased demands. To improve the nonclinical prediction of drug safety and efficacy, researchers continue to develop novel models to evaluate and promote the use of improved cell- and organ-based assays for more accurate representation of human susceptibility to drug response. Among others, the three-dimensional (3D) cell culture models present physiologically relevant cellular microenvironment and offer great promise for assessing drug disposition and pharmacokinetics (PKs) that influence drug safety and efficacy from an early stage of drug development. Currently, there are numerous different types of 3D culture systems, from simple spheroids to more complicated organoids and organs-on-chips, and from single-cell type static 3D models to cell co-culture 3D models equipped with microfluidic flow control as well as hybrid 3D systems that combine 2D culture with biomedical microelectromechanical systems. This article reviews the current application and challenges of 3D culture systems in drug PKs, safety, and efficacy assessment, and provides a focused discussion and regulatory perspectives on the liver-, intestine-, kidney-, and neuron-based 3D cellular models.
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Affiliation(s)
- Hongbing Wang
- Department of Pharmaceutical SciencesUniversity of Maryland School of PharmacyBaltimoreMarylandUSA
| | - Paul C. Brown
- Center for Drug Evaluation and ResearchUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
| | - Edwin C.Y. Chow
- Office of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
| | | | - Stephen S. Ferguson
- Division of the National Toxicology ProgramNational Institute of Environmental Health SciencesResearch Triangle ParkNorth CarolinaUSA
| | - Suzanne Fitzpatrick
- Office of the Center DirectorCenter for Food Safety and Applied NutritionUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
| | - Benjamin S. Freedman
- Division of NephrologyDepartment of PathologyKidney Research Institute, and Institute for Stem Cell and Regenerative MedicineUniversity of WashingtonSeattleWashingtonUSA
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Grace L. Guo
- Department of Pharmacology and ToxicologyErnest Mario School of PharmacyRutgers UniversityPiscatawayNew JerseyUSA
| | - William Hedrich
- Pharmaceutical Candidate Optimization, Metabolism and PharmacokineticsBristol‐Myers Squibb CompanyPrincetonNew JerseyUSA
| | | | - James Hickman
- NanoScience Technology CenterUniversity of Central FloridaOrlandoFloridaUSA
| | - Nina Isoherranen
- Department of PharmaceuticsSchool of PharmacyUniversity of WashingtonSeattleWashingtonUSA
| | - Albert P. Li
- In Vitro ADMET LaboratoriesColumbiaMarylandUSA
- In Vitro ADMET LaboratoriesMaldenMassachusettsUSA
| | - Qi Liu
- Office of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
| | - Shannon M. Mumenthaler
- Lawrence J. Ellison Institute for Transformative MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - James Polli
- Department of Pharmaceutical SciencesUniversity of Maryland School of PharmacyBaltimoreMarylandUSA
| | - William R. Proctor
- Predictive Toxicology, Safety AssessmentGenentech, IncSouth San FranciscoCaliforniaUSA
| | - Alexandre Ribeiro
- Office of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
| | - Jian‐Ying Wang
- Department of SurgeryCell Biology GroupUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Ronald L. Wange
- Center for Drug Evaluation and ResearchUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
| | - Shiew‐Mei Huang
- Office of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
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11
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Gaohua L, Miao X, Dou L. Crosstalk of physiological pH and chemical pKa under the umbrella of physiologically based pharmacokinetic modeling of drug absorption, distribution, metabolism, excretion, and toxicity. Expert Opin Drug Metab Toxicol 2021; 17:1103-1124. [PMID: 34253134 DOI: 10.1080/17425255.2021.1951223] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Introduction: Physiological pH and chemical pKa are two sides of the same coin in defining the ionization of a drug in the human body. The Henderson-Hasselbalch equation and pH-partition hypothesis form the theoretical base to define the impact of pH-pKa crosstalk on drug ionization and thence its absorption, distribution, metabolism, excretion, and toxicity (ADMET).Areas covered: Human physiological pH is not constant, but a diverse, dynamic state regulated by various biological mechanisms, while the chemical pKa is generally a constant defining the acidic dissociation of the drug at various environmental pH. Works on pH-pKa crosstalk are scattered in the literature, despite its significant contributions to drug pharmacokinetics, pharmacodynamics, safety, and toxicity. In particular, its impacts on drug ADMET have not been effectively linked to the physiologically based pharmacokinetic (PBPK) modeling and simulation, a powerful tool increasingly used in model-informed drug development (MIDD).Expert opinion: Lacking a full consideration of the interactions of physiological pH and chemical pKa in a PBPK model limits scientists' capability in mechanistically describing the drug ADMET. This mini-review compiled literature knowledge on pH-pKa crosstalk and its impacts on drug ADMET, from the viewpoint of PBPK modeling, to pave the way to a systematic incorporation of pH-pKa crosstalk into PBPK modeling and simulation.
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Affiliation(s)
- Lu Gaohua
- Research & Early Development, Princeton, New Jersey, USA
| | - Xiusheng Miao
- Drug Metabolism and Pharmacokinetics, GlaxoSmithKline, Collegeville, Pennsylvania, USA
| | - Liu Dou
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Guangzhou, Guangdong, China
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12
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Incorporating renal excretion via the OCT2 transporter in physiologically based kinetic modelling to predict in vivo kinetics of mepiquat in rat. Toxicol Lett 2021; 343:34-43. [PMID: 33639197 DOI: 10.1016/j.toxlet.2021.02.013] [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: 12/11/2020] [Revised: 02/19/2021] [Accepted: 02/22/2021] [Indexed: 01/14/2023]
Abstract
The present study aimed at incorporating active renal excretion via the organic cation transporter 2 (OCT2) into a generic rat physiologically based kinetic (PBK) model using an in vitro human renal proximal tubular epithelial cell line (SA7K) and mepiquat chloride (MQ) as the model compound. The Vmax (10.5 pmol/min/mg protein) and Km (20.6 μM) of OCT2 transport of MQ were determined by concentration-dependent uptake in SA7K cells using doxepin as inhibitor. PBK model predictions incorporating these values in the PBK model were 6.7-8.4-fold different from the reported in vivo data on the blood concentration of MQ in rat. Applying an overall scaling factor that also corrects for potential differences in OCT2 activity in the SA7K cells and in vivo kidney cortex and species differences resulted in adequate predictions for in vivo kinetics of MQ in rat (2.3-3.2-fold). The results indicate that using SA7K cells to define PBK parameters for active renal OCT2 mediated excretion with adequate scaling enables incorporation of renal excretion via the OCT2 transporter in PBK modelling to predict in vivo kinetics of mepiquat in rat. This study demonstrates a proof-of-principle on how to include active renal excretion into generic PBK models.
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13
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Lie W, Cheong EJY, Goh EML, Moy HY, Cannaert A, Stove CP, Chan ECY. Diagnosing intake and rationalizing toxicities associated with 5F-MDMB-PINACA and 4F-MDMB-BINACA abuse. Arch Toxicol 2020; 95:489-508. [PMID: 33236189 DOI: 10.1007/s00204-020-02948-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Accepted: 11/05/2020] [Indexed: 11/25/2022]
Abstract
5F-MDMB-PINACA and 4F-MDMB-BINACA are synthetic cannabinoids (SCs) that elicit cannabinoid psychoactive effects. Defining pharmacokinetic-pharmacodynamic (PK-PD) relationships governing SCs and their metabolites are paramount to investigating their in vivo toxicological outcomes. However, the disposition kinetics and cannabinoid receptor (CB) activities of the primary metabolites of SCs are largely unknown. Additionally, reasons underlying the selection of ester hydrolysis metabolites (EHMs) as urinary biomarkers are often unclear. Here, metabolic reaction phenotyping was performed to identify key metabolizing enzymes of the parent SCs. Hepatic clearances of parent SCs and their EHMs were estimated from microsomal metabolic stability studies. Renal clearances were simulated using a mechanistic kidney model incorporating in vitro permeability and organic anionic transporter 3 (OAT3)-mediated uptake data. Overall clearances were considered in tandem with estimated volumes of distribution for in vivo biological half-lives (t1/2) predictions. Interactions of the compounds with CB1 and CB2 were investigated using a G-protein coupled receptor activation assay. We demonstrated that similar enzymatic isoforms were implicated in the metabolism of 5F-MDMB-PINACA and 4F-MDMB-BINACA. Our in vivo t1/2 determinations verified the rapid elimination of parent SCs and suggest prolonged circulation of their EHMs. The pronounced attenuation of the potencies and efficacies of the metabolites against CB1 and CB2 further suggests how toxic manifestations of SC abuse are likely precipitated by augmented exposure to parent SCs. Notably, basolateral OAT3-mediated uptake of the EHMs substantiates their higher urinary abundance. These novel insights underscore the importance of mechanistic, quantitative and systematic characterization of PK-PD relationships in rationalizing the toxicities of SCs.
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Affiliation(s)
- Wen Lie
- Department of Pharmacy, Faculty of Science, National University of Singapore, 18 Science Drive 4, Singapore, 117543, Singapore
| | - Eleanor Jing Yi Cheong
- Department of Pharmacy, Faculty of Science, National University of Singapore, 18 Science Drive 4, Singapore, 117543, Singapore
| | - Evelyn Mei Ling Goh
- Analytical Toxicology Laboratory, Applied Sciences Group, Health Sciences Authority, 11 Outram Road, Singapore, 169078, Singapore
| | - Hooi Yan Moy
- Analytical Toxicology Laboratory, Applied Sciences Group, Health Sciences Authority, 11 Outram Road, Singapore, 169078, Singapore
| | - Annelies Cannaert
- Laboratory of Toxicology, Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ottergemsesteenweg 460, 9000, Ghent, Belgium
| | - Christophe P Stove
- Laboratory of Toxicology, Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, Ottergemsesteenweg 460, 9000, Ghent, Belgium
| | - Eric Chun Yong Chan
- Department of Pharmacy, Faculty of Science, National University of Singapore, 18 Science Drive 4, Singapore, 117543, Singapore.
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14
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Franchetti Y, Nolin TD. Dose Optimization in Kidney Disease: Opportunities for PBPK Modeling and Simulation. J Clin Pharmacol 2020; 60 Suppl 1:S36-S51. [PMID: 33205428 DOI: 10.1002/jcph.1741] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 08/18/2020] [Indexed: 12/19/2022]
Abstract
Kidney disease affects pharmacokinetic (PK) profiles of not only renally cleared drugs but also nonrenally cleared drugs. The impact of kidney disease on drug disposition has not been fully elucidated, but describing the extent of such impact is essential for conducting dose optimization in kidney disease. Accurate evaluation of kidney function has been a clinical interest for dose optimization, and more scientists pay attention and conduct research for clarifying the role of drug transporters, metabolic enzymes, and their interplay in drug disposition as kidney disease progresses. Physiologically based pharmacokinetic (PBPK) modeling and simulation can provide valuable insights for dose optimization in kidney disease. It is a powerful tool to integrate discrete knowledge from preclinical and clinical research and mechanistically investigate system- and drug-dependent factors that may contribute to the changes in PK profiles. PBPK-based prediction of drug exposures may be used a priori to adjust dosing regimens and thereby minimize the likelihood of drug-related toxicity. With real-time clinical studies, parameter estimation may be performed with PBPK approaches that can facilitate identification of sources of interindividual variability. PBPK modeling may also facilitate biomarker research that aids dose optimization in kidney disease. U.S. Food and Drug Administration guidances related to conduction of PK studies in kidney impairment and PBPK documentation provide the foundation for facilitating model-based dose-finding research in kidney disease.
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Affiliation(s)
- Yoko Franchetti
- Department of Pharmaceutical Sciences, Center for Clinical Pharmaceutical Sciences, University of Pittsburgh School of Pharmacy, Pittsburgh, Pennsylvania, USA
| | - Thomas D Nolin
- Department of Pharmacy and Therapeutics, Center for Clinical Pharmaceutical Sciences, University of Pittsburgh School of Pharmacy, Pittsburgh, Pennsylvania, USA
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15
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Huang W, Isoherranen N. Novel Mechanistic PBPK Model to Predict Renal Clearance in Varying Stages of CKD by Incorporating Tubular Adaptation and Dynamic Passive Reabsorption. CPT Pharmacometrics Syst Pharmacol 2020; 9:571-583. [PMID: 32977369 PMCID: PMC7577018 DOI: 10.1002/psp4.12553] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 07/22/2020] [Indexed: 11/13/2022] Open
Abstract
Chronic kidney disease (CKD) has significant effects on renal clearance (CLr ) of drugs. Physiologically-based pharmacokinetic (PBPK) models have been used to predict CKD effects on transporter-mediated renal active secretion and CLr for hydrophilic nonpermeable compounds. However, no studies have shown systematic PBPK modeling of renal passive reabsorption or CLr for hydrophobic permeable drugs in CKD. The goal of this study was to expand our previously developed and verified mechanistic kidney model to develop a universal model to predict changes in CLr in CKD for permeable and nonpermeable drugs that accounts for the dramatic nonlinear effect of CKD on renal passive reabsorption of permeable drugs. The developed model incorporates physiologically-based tubular changes of reduced water reabsorption/increased tubular flow rate per remaining functional nephron in CKD. The final adaptive kidney model successfully (absolute fold error (AFE) all < 2) predicted renal passive reabsorption and CLr for 20 permeable and nonpermeable test compounds across the stages of CKD. In contrast, use of proportional glomerular filtration rate reduction approach without addressing tubular adaptation processes in CKD to predict CLr generated unacceptable CLr predictions (AFE = 2.61-7.35) for permeable compounds in severe CKD. Finally, the adaptive kidney model accurately predicted CLr of para-amino-hippuric acid and memantine, two secreted compounds, in CKD, suggesting successful integration of active secretion into the model, along with passive reabsorption. In conclusion, the developed adaptive kidney model enables mechanistic predictions of in vivo CLr through CKD progression without any empirical scaling factors and can be used for CLr predictions prior to assessment of drug disposition in renal impairment.
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Affiliation(s)
- Weize Huang
- Department of PharmaceuticsSchool of PharmacyUniversity of WashingtonSeattleWashingtonUSA
| | - Nina Isoherranen
- Department of PharmaceuticsSchool of PharmacyUniversity of WashingtonSeattleWashingtonUSA
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16
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Miller NA, Reddy MB, Heikkinen AT, Lukacova V, Parrott N. Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies. Clin Pharmacokinet 2020; 58:727-746. [PMID: 30729397 DOI: 10.1007/s40262-019-00741-9] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Physiologically based pharmacokinetic modelling is well established in the pharmaceutical industry and is accepted by regulatory agencies for the prediction of drug-drug interactions. However, physiologically based pharmacokinetic modelling is valuable to address a much wider range of pharmaceutical applications, and new regulatory impact is expected as its full power is leveraged. As one example, physiologically based pharmacokinetic modelling is already routinely used during drug discovery for in-vitro to in-vivo translation and pharmacokinetic modelling in preclinical species, and this leads to the application of verified models for first-in-human pharmacokinetic predictions. A consistent cross-industry strategy in this application area would increase confidence in the approach and facilitate further learning. With this in mind, this article aims to enhance a previously published first-in-human physiologically based pharmacokinetic model-building strategy. Based on the experience of scientists from multiple companies participating in the GastroPlus™ User Group Steering Committee, new Absorption, Distribution, Metabolism and Excretion knowledge is integrated and decision trees proposed for each essential component of a first-in-human prediction. We have reviewed many relevant scientific publications to identify new findings and highlight gaps that need to be addressed. Finally, four industry case studies for more challenging compounds illustrate and highlight key components of the strategy.
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Affiliation(s)
- Neil A Miller
- Systems Modeling and Translational Biology, GlaxoSmithKline R&D, Ware, Hertfordshire, UK.
| | - Micaela B Reddy
- Department of Clinical Pharmacology, Array BioPharma, Boulder, CO, USA
| | | | | | - Neil Parrott
- Pharmaceutical Sciences, Roche Pharma Research and Early Development, Roche Innovation Centre Basel, Basel, Switzerland
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17
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Scotcher D, Arya V, Yang X, Zhao P, Zhang L, Huang S, Rostami‐Hodjegan A, Galetin A. A Novel Physiologically Based Model of Creatinine Renal Disposition to Integrate Current Knowledge of Systems Parameters and Clinical Observations. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2020; 9:310-321. [PMID: 32441889 PMCID: PMC7306622 DOI: 10.1002/psp4.12509] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 02/16/2020] [Indexed: 01/11/2023]
Abstract
Creatinine is the most common clinical biomarker of renal function. As a substrate for renal transporters, its secretion is susceptible to inhibition by drugs, resulting in transient increase in serum creatinine and false impression of damage to kidney. Novel physiologically based models for creatinine were developed here and (dis)qualified in a stepwise manner until consistency with clinical data. Data from a matrix of studies were integrated, including systems data (common to all models), proteomics-informed in vitro-in vivo extrapolation of all relevant transporter clearances, exogenous administration of creatinine (to estimate endogenous synthesis rate), and inhibition of different renal transporters (11 perpetrator drugs considered for qualification during creatinine model development and verification on independent data sets). The proteomics-informed bottom-up approach resulted in the underprediction of creatinine renal secretion. Subsequently, creatinine-trimethoprim clinical data were used to inform key model parameters in a reverse translation manner, highlighting best practices and challenges for middle-out optimization of mechanistic models.
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Affiliation(s)
- Daniel Scotcher
- Centre for Applied Pharmacokinetic ResearchUniversity of ManchesterManchesterUK
| | - Vikram Arya
- Office of Clinical PharmacologyOffice of Translational SciencesCentre for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
| | - Xinning Yang
- Office of Clinical PharmacologyOffice of Translational SciencesCentre for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
| | - Ping Zhao
- Office of Clinical PharmacologyOffice of Translational SciencesCentre for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
- Present address:
Bill & Melinda Gates FoundationSeattleWashingtonUSA
| | - Lei Zhang
- Office of Research and StandardsOffice of Generic DrugsCentre for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
| | - Shiew‐Mei Huang
- Office of Clinical PharmacologyOffice of Translational SciencesCentre for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
| | - Amin Rostami‐Hodjegan
- Centre for Applied Pharmacokinetic ResearchUniversity of ManchesterManchesterUK
- CertaraSheffieldUK
| | - Aleksandra Galetin
- Centre for Applied Pharmacokinetic ResearchUniversity of ManchesterManchesterUK
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18
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Shum S, Imaoka T, Huang W, Chang SY, Yeung C, Himmelfarb J, Kelly E, Isoherranen N. Predicting renal clearance of morphine and morphine‐6‐glucuronide using the “organ‐on‐a‐chip” technology and physiologically‐based pharmacokinetic modeling. FASEB J 2020. [DOI: 10.1096/fasebj.2020.34.s1.04121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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19
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Huang W, Czuba LC, Isoherranen N. Mechanistic PBPK Modeling of Urine pH Effect on Renal and Systemic Disposition of Methamphetamine and Amphetamine. J Pharmacol Exp Ther 2020; 373:488-501. [PMID: 32198137 DOI: 10.1124/jpet.120.264994] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Accepted: 03/18/2020] [Indexed: 02/03/2023] Open
Abstract
The effect of urine pH on renal excretion and systemic disposition has been observed for many drugs and metabolites. When urine pH is altered, tubular ionization, passive reabsorption, renal clearance, and systemic exposure of drugs and metabolites may all change dramatically, raising clinically significant concerns. Surprisingly, the urine pH effect on drug disposition is not routinely explored in humans, and regulatory agencies have neither developed guidance on this issue nor required industry to conduct pertinent human trials. In this study, we hypothesized that physiologically based pharmacokinetic (PBPK) modeling could be used as a cost-effective method to examine potential urine pH effect on drug and metabolite disposition. Our previously developed and verified mechanistic kidney model was integrated with a full-body PBPK model to simulate renal clearance and area under the plasma concentration-time curve (AUC) with varying urine pH statuses using methamphetamine and amphetamine as model compounds. We first developed and verified drug models for methamphetamine and amphetamine under normal urine pH condition [absolute average fold error (AAFE) < 1.25 at study level]. Then, acidic and alkaline urine scenarios were simulated. Our simulation results show that the renal excretion and plasma concentration-time profiles for methamphetamine and amphetamine could be recapitulated under different urine pH (AAFE < 2 at individual level). The methamphetamine-amphetamine parent-metabolite full-body PBPK model also successfully simulated amphetamine plasma concentration-time profiles (AAFE < 1.25 at study level) and amphetamine/methamphetamine urinary concentration ratios (AAFE < 2 at individual level) after dosing methamphetamine. This demonstrates that our mechanistic PBPK model can predict urine pH effect on systemic and urinary disposition of drugs and metabolites. SIGNIFICANCE STATEMENT: Our study shows that integrating mechanistic kidney model with full-body physiologically based pharmacokinetic model can predict the magnitude of alteration in renal excretion and area under the plasma concentration-time curve (AUC) of drugs and metabolites when urine pH is changed. This provides a cost-effective method to evaluate the likelihood of renal and systemic disposition changes due to varying urine pH. This is important because multiple drugs and diseases can alter urine pH, leading to quantitatively and clinically significant changes in drug and metabolite disposition that may require adjustment of therapy.
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Affiliation(s)
- Weize Huang
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington
| | - Lindsay C Czuba
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington
| | - Nina Isoherranen
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington
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20
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Li Z, Litchfield J, Tess DA, Carlo AA, Eng H, Keefer C, Maurer TS. A Physiologically Based in Silico Tool to Assess the Risk of Drug-Related Crystalluria. J Med Chem 2020; 63:6489-6498. [PMID: 32130005 DOI: 10.1021/acs.jmedchem.9b01995] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Drug precipitation in the nephrons of the kidney can cause drug-induced crystal nephropathy (DICN). To aid mitigation of this risk in early drug discovery, we developed a physiologically based in silico model to predict DICN in rats, dogs, and humans. At a minimum, the likelihood of DICN is determined by the level of systemic exposure to the molecule, the molecule's physicochemical properties and the unique physiology of the kidney. Accordingly, the proposed model accounts for these properties in order to predict drug exposure relative to solubility along the nephron. Key physiological parameters of the kidney were codified in a manner consistent with previous reports. Quantitative structure-activity relationship models and in vitro assays were used to estimate drug-specific physicochemical inputs to the model. The proposed model was calibrated against urinary excretion data for 42 drugs, and the utility for DICN prediction is demonstrated through application to 20 additional drugs.
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Affiliation(s)
- Zhenhong Li
- Pfizer Worldwide Research, Development and Medical, Medicine Design, Cambridge, Massachusetts 02139, United States
| | - John Litchfield
- Pfizer Worldwide Research, Development and Medical, Medicine Design, Cambridge, Massachusetts 02139, United States
| | - David A Tess
- Pfizer Worldwide Research, Development and Medical, Medicine Design, Cambridge, Massachusetts 02139, United States
| | - Anthony A Carlo
- Pfizer Worldwide Research, Development and Medical, Medicine Design, Groton, Connecticut 06340, United States
| | - Heather Eng
- Pfizer Worldwide Research, Development and Medical, Medicine Design, Groton, Connecticut 06340, United States
| | - Christopher Keefer
- Pfizer Worldwide Research, Development and Medical, Medicine Design, Groton, Connecticut 06340, United States
| | - Tristan S Maurer
- Pfizer Worldwide Research, Development and Medical, Medicine Design, Cambridge, Massachusetts 02139, United States
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21
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Sakolish C, Chen Z, Dalaijamts C, Mitra K, Liu Y, Fulton T, Wade TL, Kelly EJ, Rusyn I, Chiu WA. Predicting tubular reabsorption with a human kidney proximal tubule tissue-on-a-chip and physiologically-based modeling. Toxicol In Vitro 2020; 63:104752. [PMID: 31857146 PMCID: PMC7053805 DOI: 10.1016/j.tiv.2019.104752] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 12/14/2019] [Accepted: 12/16/2019] [Indexed: 12/22/2022]
Abstract
Kidney is a major route of xenobiotic excretion, but the accuracy of preclinical data for predicting in vivo clearance is limited by species differences and non-physiologic 2D culture conditions. Microphysiological systems can potentially increase predictive accuracy due to their more realistic 3D environment and incorporation of dynamic flow. We used a renal proximal tubule microphysiological device to predict renal reabsorption of five compounds: creatinine (negative control), perfluorooctanoic acid (positive control), cisplatin, gentamicin, and cadmium. We perfused compound-containing media to determine renal uptake/reabsorption, adjusted for non-specific binding. A physiologically-based parallel tube model was used to model reabsorption kinetics and make predictions of overall in vivo renal clearance. For all compounds tested, the kidney tubule chip combined with physiologically-based modeling reproduces qualitatively and quantitatively in vivo tubular reabsorption and clearance. However, because the in vitro device lacks filtration and tubular secretion components, additional information on protein binding and the importance of secretory transport is needed in order to make accurate predictions. These and other limitations, such as the presence of non-physiological compounds such as antibiotics and bovine serum albumin in media and the need to better characterize degree of expression of important transporters, highlight some of the challenges with using microphysiological devices to predict in vivo pharmacokinetics.
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Affiliation(s)
- Courtney Sakolish
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA.
| | - Zunwei Chen
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA.
| | - Chimeddulam Dalaijamts
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA.
| | - Kusumica Mitra
- Geochemical and Environmental Research Group, Texas A&M University, College Station, TX 77845, USA.
| | - Yina Liu
- Geochemical and Environmental Research Group, Texas A&M University, College Station, TX 77845, USA.
| | - Tracy Fulton
- Geochemical and Environmental Research Group, Texas A&M University, College Station, TX 77845, USA
| | - Terry L Wade
- Geochemical and Environmental Research Group, Texas A&M University, College Station, TX 77845, USA.
| | - Edward J Kelly
- Department of Pharmaceutics, University of Washington, and Division of Nephrology, University of Washington Kidney Research Institute, Seattle, WA 98195, USA; Division of Nephrology, University of Washington Kidney Research Institute, Seattle, WA 98195, USA.
| | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA.
| | - Weihsueh A Chiu
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA.
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Moxon TE, Li H, Lee MY, Piechota P, Nicol B, Pickles J, Pendlington R, Sorrell I, Baltazar MT. Application of physiologically based kinetic (PBK) modelling in the next generation risk assessment of dermally applied consumer products. Toxicol In Vitro 2020; 63:104746. [DOI: 10.1016/j.tiv.2019.104746] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 12/04/2019] [Accepted: 12/09/2019] [Indexed: 02/06/2023]
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Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor. Sci Rep 2019; 9:18782. [PMID: 31827176 PMCID: PMC6906481 DOI: 10.1038/s41598-019-55325-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 11/25/2019] [Indexed: 01/07/2023] Open
Abstract
Prediction of pharmacokinetic profiles of new chemical entities is essential in drug development to minimize the risks of potential withdrawals. The excretion of unchanged compounds by the kidney constitutes a major route in drug elimination and plays an important role in pharmacokinetics. Herein, we created in silico prediction models of the fraction of drug excreted unchanged in the urine (fe) and renal clearance (CLr), with datasets of 411 and 401 compounds using freely available software; notably, all models require chemical structure information alone. The binary classification model for fe demonstrated a balanced accuracy of 0.74. The two-step prediction system for CLr was generated using a combination of the classification model to predict excretion-type compounds and regression models to predict the CLr value for each excretion type. The accuracies of the regression models increased upon adding a descriptor, which was the observed and predicted fraction unbound in plasma (fu,p); 78.6% of the samples in the higher range of renal clearance fell within 2-fold error with predicted fu,p value. Our prediction system for renal excretion is freely available to the public and can be used as a practical tool for prioritization and optimization of compound synthesis in the early stage of drug discovery.
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Huang W, Isoherranen N. Sampling Site Has a Critical Impact on Physiologically Based Pharmacokinetic Modeling. J Pharmacol Exp Ther 2019; 372:30-45. [PMID: 31604807 DOI: 10.1124/jpet.119.262154] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Accepted: 10/09/2019] [Indexed: 12/11/2022] Open
Abstract
It has been shown that arterial (central) and venous (peripheral) plasma drug concentrations can be very different. While pharmacokinetic studies typically measure drug concentrations from the peripheral vein such as the arm vein, physiologically based pharmacokinetic (PBPK) models generally output simulated concentrations from the central venous compartment that physiologically represents the right atrium, a merge of the superior and inferior vena cava. In this study, a physiologically based peripheral forearm sampling site model was developed and verified using nicotine, ketamine, lidocaine, and fentanyl as model drugs. This verified model allows output of simulated peripheral venous concentrations that can be meaningfully compared with observed pharmacokinetic data from the arm vein. The generalized effect of PBPK model sampling site on simulation output was investigated. Drugs and metabolites with large volumes of distribution showed considerable concentration discrepancy between the simulated central venous compartment and the peripheral arm vein after intravenous or oral administration, resulting in significant differences in values for C max and time taken to reach C max (t max ) In addition, the simulated central venous metabolite profile showed an unexpected profile that was not observed in the peripheral arm vein. Using fentanyl as a model compound, we show that using the wrong sampling site in PBPK models can lead to biased model evaluation and subsequent erroneous model parameter optimization. Such an error in model parameters along with the discrepant sampling site could dramatically mislead the pharmacokinetic prediction in unstudied clinical scenarios, affecting the assessment of drug safety and efficacy. Overall, this study shows that PBPK model publications should specify the model sampling sites and match them with those employed in clinical studies. SIGNIFICANCE STATEMENT: Our study shows that sampling from the central venous compartment (right atrium) during physiologically based pharmacokinetic model development gives rise to biased model evaluation and erroneous model parameterization when observed data are collected from the peripheral arm vein. This can lead to a clinically significant error in predictions of plasma concentration-time profiles in unstudied scenarios. To address this error, we developed and verified a novel peripheral sampling site model to simulate arm vein drug concentrations that can be applied to different drug dosing scenarios.
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Affiliation(s)
- Weize Huang
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington
| | - Nina Isoherranen
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington
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Cheong EJY, Teo DWX, Chua DXY, Chan ECY. Systematic Development and Verification of a Physiologically Based Pharmacokinetic Model of Rivaroxaban. Drug Metab Dispos 2019; 47:1291-1306. [DOI: 10.1124/dmd.119.086918] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2019] [Accepted: 08/28/2019] [Indexed: 12/22/2022] Open
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Isoherranen N, Madabushi R, Huang S. Emerging Role of Organ-on-a-Chip Technologies in Quantitative Clinical Pharmacology Evaluation. Clin Transl Sci 2019; 12:113-121. [PMID: 30740886 PMCID: PMC6440571 DOI: 10.1111/cts.12627] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 01/26/2019] [Indexed: 12/28/2022] Open
Abstract
The recently enacted Prescription Drug User Fee Act (PDUFA) VI includes in its performance goals "enhancing regulatory science and expediting drug development." The key elements in "enhancing regulatory decision tools to support drug development and review" include "advancing model-informed drug development (MIDD)." This paper describes (i) the US Food and Drug Administration (FDA) Office of Clinical Pharmacology's continuing efforts in developing quantitative clinical pharmacology models (disease, drug, and clinical trial models) to advance MIDD, (ii) how emerging novel tools, such as organ-on-a-chip technologies or microphysiological systems, can provide new insights into physiology and disease mechanisms, biomarker identification and evaluation, and elucidation of mechanisms of adverse drug reactions, and (iii) how the single organ or linked organ microphysiological systems can provide critical system parameters for improved physiologically-based pharmacokinetic and pharmacodynamic evaluations. Continuous public-private partnerships are critical to advance this field and in the application of these new technologies in drug development and regulatory review.
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Affiliation(s)
- Nina Isoherranen
- Office of Clinical Pharmacology (OCP)Office of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
- Department of PharmaceuticsSchool of PharmacyUniversity of WashingtonSeattleWashingtonUSA
| | - Rajanikanth Madabushi
- Office of Clinical Pharmacology (OCP)Office of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
| | - Shiew‐Mei Huang
- Office of Clinical Pharmacology (OCP)Office of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug Administration (FDA)Silver SpringMarylandUSA
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Li Z, Fisher C, Gardner I, Ghosh A, Litchfield J, Maurer TS. Modeling Exposure to Understand and Predict Kidney Injury. Semin Nephrol 2019; 39:176-189. [DOI: 10.1016/j.semnephrol.2018.12.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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28
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Matsuzaki T, Scotcher D, Darwich AS, Galetin A, Rostami-Hodjegan A. Towards Further Verification of Physiologically-Based Kidney Models: Predictability of the Effects of Urine-Flow and Urine-pH on Renal Clearance. J Pharmacol Exp Ther 2018; 368:157-168. [DOI: 10.1124/jpet.118.251413] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Accepted: 11/05/2018] [Indexed: 01/05/2023] Open
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