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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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2
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Dong J, Prieto Garcia L, Huang Y, Tang W, Lundahl A, Elebring M, Ahlström C, Vildhede A, Sjögren E, Någård M. Understanding Statin-Roxadustat Drug-Drug-Disease Interaction Using Physiologically-Based Pharmacokinetic Modeling. Clin Pharmacol Ther 2023; 114:825-835. [PMID: 37376792 DOI: 10.1002/cpt.2980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023]
Abstract
A different drug-drug interaction (DDI) scenario may exist in patients with chronic kidney disease (CKD) compared with healthy volunteers (HVs), depending on the interplay between drug-drug and disease (drug-drug-disease interaction (DDDI)). Physiologically-based pharmacokinetic (PBPK) modeling, in lieu of a clinical trial, is a promising tool for evaluating these complex DDDIs in patients. However, the prediction confidence of PBPK modeling in the severe CKD population is still low when nonrenal pathways are involved. More mechanistic virtual disease population and robust validation cases are needed. To this end, we aimed to: (i) understand the implications of severe CKD on statins (atorvastatin, simvastatin, and rosuvastatin) pharmacokinetics (PK) and DDI; and (ii) predict untested clinical scenarios of statin-roxadustat DDI risks in patients to guide suitable dose regimens. A novel virtual severe CKD population was developed incorporating the disease effect on both renal and nonrenal pathways. Drug and disease PBPK models underwent a four-way validation. The verified PBPK models successfully predicted the altered PKs in patients for substrates and inhibitors and recovered the observed statin-rifampicin DDIs in patients and the statin-roxadustat DDIs in HVs within 1.25- and 2-fold error. Further sensitivity analysis revealed that the severe CKD effect on statins PK is mainly mediated by hepatic BCRP for rosuvastatin and OATP1B1/3 for atorvastatin. The magnitude of statin-roxadustat DDI in patients with severe CKD was predicted to be similar to that in HVs. PBPK-guided suitable dose regimens were identified to minimize the risk of side effects or therapeutic failure of statins when co-administered with roxadustat.
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Affiliation(s)
- Jin Dong
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gaithersburg, Maryland, USA
| | - Luna Prieto Garcia
- Drug Metabolism and Pharmacokinetics, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals, R&D, AstraZeneca, Gothenburg, Sweden
- Department of Pharmaceutical Biosciences, Translational Drug Discovery and Development, Uppsala University, Uppsala, Sweden
| | - Yingbo Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Weifeng Tang
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gaithersburg, Maryland, USA
| | - Anna Lundahl
- Drug Metabolism and Pharmacokinetics, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals, R&D, AstraZeneca, Gothenburg, Sweden
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Marie Elebring
- Drug Metabolism and Pharmacokinetics, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals, R&D, AstraZeneca, Gothenburg, Sweden
| | - Christine Ahlström
- Drug Metabolism and Pharmacokinetics, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals, R&D, AstraZeneca, Gothenburg, Sweden
| | - Anna Vildhede
- Drug Metabolism and Pharmacokinetics, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals, R&D, AstraZeneca, Gothenburg, Sweden
| | - Erik Sjögren
- Department of Pharmaceutical Biosciences, Translational Drug Discovery and Development, Uppsala University, Uppsala, Sweden
| | - Mats Någård
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Gaithersburg, Maryland, USA
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3
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Vijaywargi G, Kollipara S, Ahmed T, Chachad S. Predicting transporter mediated drug-drug interactions via static and dynamic physiologically based pharmacokinetic modeling: A comprehensive insight on where we are now and the way forward. Biopharm Drug Dispos 2022. [PMID: 36413625 DOI: 10.1002/bdd.2339] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/07/2022] [Accepted: 11/04/2022] [Indexed: 11/23/2022]
Abstract
The greater utilization and acceptance of physiologically-based pharmacokinetic (PBPK) modeling to evaluate the potential metabolic drug-drug interactions is evident by the plethora of literature, guidance's, and regulatory dossiers available in the literature. In contrast, it is not widely used to predict transporter-mediated DDI (tDDI). This is attributed to the unavailability of accurate transporter tissue expression levels, the absence of accurate in vitro to in vivo extrapolations (IVIVE), enzyme-transporter interplay, and a lack of specific probe substrates. Additionally, poor understanding of the inhibition/induction mechanisms coupled with the inability to determine unbound concentrations at the interaction site made tDDI assessment challenging. Despite these challenges, continuous improvements in IVIVE approaches enabled accurate tDDI predictions. Furthermore, the necessity of extrapolating tDDI's to special (pediatrics, pregnant, geriatrics) and diseased (renal, hepatic impaired) populations is gaining impetus and is encouraged by regulatory authorities. This review aims to visit the current state-of-the-art and summarizes contemporary knowledge on tDDI predictions. The current understanding and ability of static and dynamic PBPK models to predict tDDI are portrayed in detail. Peer-reviewed transporter abundance data in special and diseased populations from recent publications were compiled, enabling direct input into modeling tools for accurate tDDI predictions. A compilation of regulatory guidance's for tDDI's assessment and success stories from regulatory submissions are presented. Future perspectives and challenges of predicting tDDI in terms of in vitro system considerations, endogenous biomarkers, the use of empirical scaling factors, enzyme-transporter interplay, and acceptance criteria for model validation to meet the regulatory expectations were discussed.
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Affiliation(s)
- Gautam Vijaywargi
- Biopharmaceutics Group, Global Clinical Management, Dr. Reddy's Laboratories Ltd., Integrated Product Development Organization (IPDO), Hyderabad, Telangana, India
| | - Sivacharan Kollipara
- Biopharmaceutics Group, Global Clinical Management, Dr. Reddy's Laboratories Ltd., Integrated Product Development Organization (IPDO), Hyderabad, Telangana, India
| | - Tausif Ahmed
- Biopharmaceutics Group, Global Clinical Management, Dr. Reddy's Laboratories Ltd., Integrated Product Development Organization (IPDO), Hyderabad, Telangana, India
| | - Siddharth Chachad
- Biopharmaceutics Group, Global Clinical Management, Dr. Reddy's Laboratories Ltd., Integrated Product Development Organization (IPDO), Hyderabad, Telangana, India
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4
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Dubinsky S, Malik P, Hajducek DM, Edginton A. Determining the Effects of Chronic Kidney Disease on Organic Anion Transporter1/3 Activity Through Physiologically Based Pharmacokinetic Modeling. Clin Pharmacokinet 2022; 61:997-1012. [PMID: 35508593 DOI: 10.1007/s40262-022-01121-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/06/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND AND OBJECTIVE The renal excretion of drugs via organic anion transporters 1 and 3 (OAT1/3) is significantly decreased in patients with renal impairment. This study uses physiologically based pharmacokinetic models to quantify the reduction in OAT1/3-mediated secretion of drugs throughout varying stages of chronic kidney disease. METHODS Physiologically based pharmacokinetic models were constructed for four OAT1/3 substrates in healthy individuals: acyclovir, meropenem, furosemide, and ciprofloxacin. Observed data from drug-drug interaction studies with probenecid, a potent OAT1/3 inhibitor, were used to parameterize the contribution of OAT1/3 to the renal elimination of each drug. The models were then translated to patients with chronic kidney disease by accounting for changes in glomerular filtration rate, kidney volume, renal blood flow, plasma protein binding, and hematocrit. Additionally, a relationship was derived between the estimated glomerular filtration rate and the reduction in OAT1/3-mediated secretion of drugs based on the renal extraction ratios of ƿ-aminohippuric acid in patients with varying degrees of renal impairment. The relationship was evaluated in silico by evaluating the predictive performance of each final model in describing the pharmacokinetics (PK) of drugs across stages of chronic kidney disease. RESULTS OAT1/3-mediated renal excretion of drugs was found to be decreased by 27-49%, 50-68%, and 70-96% in stage 3, stage 4, and stage 5 of chronic kidney disease, respectively. In support of the parameterization, physiologically based pharmacokinetic models of four OAT1/3 substrates were able to adequately characterize the PK in patients with different degrees of renal impairment. Total exposure after intravenous administration was predicted within a 1.5-fold error and 85% of the observed data points fell within a 1.5-fold prediction error. The models modestly under-predicted plasma concentrations in patients with end-stage renal disease undergoing intermittent hemodialysis. However, results should be interpreted with caution because of the limited number of molecules analyzed and the sparse sampling in observed chronic kidney disease pharmacokinetic studies. CONCLUSIONS A quantitative understanding of the reduction in OAT1/3-mediated excretion of drugs in differing stages of renal impairment will contribute to better predictive accuracy for physiologically based pharmacokinetic models in drug development, assisting with clinical trial planning and potentially sparing this population from unnecessary toxic exposures.
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Affiliation(s)
- Samuel Dubinsky
- School of Pharmacy, University of Waterloo, Waterloo, ON, Canada
| | - Paul Malik
- School of Pharmacy, University of Waterloo, Waterloo, ON, Canada
| | | | - Andrea Edginton
- School of Pharmacy, University of Waterloo, Waterloo, ON, Canada.
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5
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Tan SPF, Scotcher D, Rostami-Hodjegan A, Galetin A. Effect of Chronic Kidney Disease on the Renal Secretion via Organic Anion Transporters 1/3: Implications for Physiologically-Based Pharmacokinetic Modeling and Dose Adjustment. Clin Pharmacol Ther 2022; 112:643-652. [PMID: 35569107 PMCID: PMC9540491 DOI: 10.1002/cpt.2642] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/07/2022] [Indexed: 12/14/2022]
Abstract
There is growing evidence that active tubular secretory clearance (CLs) may not decline proportionally with the glomerular filtration rate (GFR) in chronic kidney disease (CKD), leading to the overestimation of renal clearance (CLr) when using solely GFR to approximate disease effect on renal elimination. The clinical pharmacokinetic data of 33 renally secreted OAT1/3 substrates were collated to investigate the impact of mild, moderate, and severe CKD on CLr, tubular secretion and protein binding (fu,p). The fu,p of the collated substrates ranged from 0.0026 to 1.0 in healthy populations; observed CKD‐related increase in the fu,p (up to 2.7‐fold) of 8 highly bound substrates (fu,p ≤ 0.2) was accounted for in the analysis. Use of prediction equation based on disease‐related changes in albumin resulted in underprediction of the CKD‐related increase in fu,p of highly bound substrates, highlighting the necessity to measure protein binding in severe CKD. The critical analysis of clinical data for 33 OAT1/3 probes established that decrease in OAT1/3 activity proportional to the changes in GFR was insufficient to recapitulate effects of severe CKD on unbound tubular secretion clearance. OAT1/3‐mediated CLs was estimated to decline by an additional 50% relative to the GFR decline in severe CKD, whereas change in active secretion in mild and moderate CKD was proportional to GFR. Consideration of this additional 50% decline in OAT1/3‐mediated CLs is recommended for physiologically‐based pharmacokinetic models and dose adjustment of OAT1/3 substrates in severe CKD, especially for substrates with high contribution of the active secretion to CLr.
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Affiliation(s)
- Shawn Pei Feng Tan
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester, UK
| | - Daniel Scotcher
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester, UK
| | - Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester, UK.,Certara UK (Simcyp Division), Sheffield, UK
| | - Aleksandra Galetin
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester, UK
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6
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Chu X, Prasad B, Neuhoff S, Yoshida K, Leeder JS, Mukherjee D, Taskar K, Varma MVS, Zhang X, Yang X, Galetin A. Clinical Implications of Altered Drug Transporter Abundance/Function and PBPK Modeling in Specific Populations: An ITC Perspective. Clin Pharmacol Ther 2022; 112:501-526. [PMID: 35561140 DOI: 10.1002/cpt.2643] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 05/09/2022] [Indexed: 12/13/2022]
Abstract
The role of membrane transporters on pharmacokinetics (PKs), drug-drug interactions (DDIs), pharmacodynamics (PDs), and toxicity of drugs has been broadly recognized. However, our knowledge of modulation of transporter expression and/or function in the diseased patient population or specific populations, such as pediatrics or pregnancy, is still emerging. This white paper highlights recent advances in studying the changes in transporter expression and activity in various diseases (i.e., renal and hepatic impairment and cancer) and some specific populations (i.e., pediatrics and pregnancy) with the focus on clinical implications. Proposed alterations in transporter abundance and/or activity in diseased and specific populations are based on (i) quantitative transporter proteomic data and relative abundance in specific populations vs. healthy adults, (ii) clinical PKs, and emerging transporter biomarker and/or pharmacogenomic data, and (iii) physiologically-based pharmacokinetic modeling and simulation. The potential for altered PK, PD, and toxicity in these populations needs to be considered for drugs and their active metabolites in which transporter-mediated uptake/efflux is a major contributor to their absorption, distribution, and elimination pathways and/or associated DDI risk. In addition to best practices, this white paper discusses current challenges and knowledge gaps to study and quantitatively predict the effects of modulation in transporter activity in these populations, together with the perspectives from the International Transporter Consortium (ITC) on future directions.
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Affiliation(s)
- Xiaoyan Chu
- Department of ADME and Discovery Toxicology, Merck & Co., Inc., Kenilworth, New Jersey, USA
| | - Bhagwat Prasad
- Department of Pharmaceutical Sciences, Washington State University, Spokane, Washington, USA
| | | | - Kenta Yoshida
- Clinical Pharmacology, Genentech Research and Early Development, South San Francisco, California, USA
| | - James Steven Leeder
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Kansas City, Kansas City, Missouri, USA
| | - Dwaipayan Mukherjee
- Clinical Pharmacology & Pharmacometrics, Research & Development, AbbVie, Inc., North Chicago, Illinois, USA
| | | | - Manthena V S Varma
- Pharmacokinetics, Dynamics and Metabolism, Medicine Design, Worldwide R&D, Pfizer Inc, Groton, Connecticut, USA
| | - Xinyuan Zhang
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Xinning Yang
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Aleksandra Galetin
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, The University of Manchester, Manchester, UK
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7
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Butrovich MA, Tang W, Boulton DW, Nolin TD, Sharma P. Use of Physiologically Based Pharmacokinetic Modeling to Evaluate the Impact of Chronic Kidney Disease on CYP3A4-Mediated Metabolism of Saxagliptin. J Clin Pharmacol 2022; 62:1018-1029. [PMID: 35247279 PMCID: PMC9545133 DOI: 10.1002/jcph.2043] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 02/25/2022] [Indexed: 11/25/2022]
Abstract
We characterized the impact of chronic kidney disease (CKD) on the cytochrome P450 (CYP) 3A4–mediated metabolism of saxagliptin to its metabolite, 5‐hydroxysaxagliptin, using a physiologically based pharmacokinetic (PBPK) model. A PBPK model of saxagliptin and its CYP3A4 metabolite, 5‐hydroxysaxagliptin, was constructed and validated for oral doses ranging from 5 to 100 mg. The observed ratios of area under the plasma concentration–time curve (AUC) and maximum plasma concentration (Cmax) between healthy subjects and subjects with CKD were compared with those predicted using PBPK model simulations. Simulations were performed with virtual CKD populations having decreased CYP3A4 activity (ie, 64%‐75% of the healthy subjects’ CYP3A4 abundance) and preserved CYP3A4 activity (ie, 100% of the healthy subjects’ CYP3A4 abundance). We found that simulations using decreased CYP3A4 activity generally overpredicted the ratios of saxagliptin AUC and Cmax in CKD compared with those using preserved CYP3A4 activity. Similarly, simulations using decreased CYP3A4 activity underpredicted the ratio of 5‐hydroxysaxagliptin AUC in moderate and severe CKD compared with simulations using preserved CYP3A4 activity. These findings suggest that decreased CYP3A4 activity in CKD underpredicts saxagliptin clearance compared with that observed clinically. Preserving CYP3A4 activity in CKD more closely estimates saxagliptin clearance and 5‐hydroxysaxagliptin exposure changes observed in vivo. Our findings suggest that there is no clinically meaningful impact of CKD on the metabolism of saxagliptin by CYP3A4. Since saxagliptin is not a highly sensitive substrate and validated probe for CYP3A4, this work represents a case study of a CYP3A4 substrate‐metabolite pair and is not a generalization for all CYP3A4 substrates.
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Affiliation(s)
- Morgan A. Butrovich
- Department of Pharmacy and TherapeuticsUniversity of Pittsburgh School of PharmacyPittsburghPennsylvaniaUSA
| | - Weifeng Tang
- Clinical Pharmacology and Quantitative PharmacologyClinical Pharmacology and Safety Sciences, R&D, AstraZenecaGaithersburgMarylandUSA
| | - David W. Boulton
- Clinical Pharmacology and Quantitative PharmacologyClinical Pharmacology and Safety Sciences, R&D, AstraZenecaGaithersburgMarylandUSA
| | - Thomas D. Nolin
- Department of Pharmacy and TherapeuticsUniversity of Pittsburgh School of PharmacyPittsburghPennsylvaniaUSA
| | - Pradeep Sharma
- Clinical Pharmacology and Quantitative PharmacologyClinical Pharmacology and Safety Sciences, R&D, AstraZenecaCambridgeUK
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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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Malik PRV, Yeung CHT, Ismaeil S, Advani U, Djie S, Edginton AN. A Physiological Approach to Pharmacokinetics in Chronic Kidney Disease. J Clin Pharmacol 2021; 60 Suppl 1:S52-S62. [PMID: 33205424 DOI: 10.1002/jcph.1713] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 07/20/2020] [Indexed: 12/27/2022]
Abstract
The conventional approach to approximating the pharmacokinetics of drugs in patients with chronic kidney disease (CKD) only accounts for changes in the estimated glomerular filtration rate. However, CKD is a systemic and multifaceted disease that alters many body systems. Therefore, the objective of this exercise was to develop and evaluate a whole-body mechanistic approach to predicting pharmacokinetics in patients with CKD. Physiologically based pharmacokinetic models were developed in PK-Sim v8.0 (www.open-systems-pharmacology.org) to mechanistically represent the disposition of 7 compounds in healthy human adults. The 7 compounds selected were eliminated by glomerular filtration and active tubular secretion by the organic cation transport system to varying degrees. After a literature search, the healthy adult models were adapted to patients with CKD by numerically accounting for changes in glomerular filtration rate, kidney volume, renal perfusion, hematocrit, plasma protein concentrations, and gastrointestinal transit. Literature-informed interindividual variability was applied to the physiological parameters to facilitate a population approach. Model performance in CKD was evaluated against pharmacokinetic data from 8 clinical trials in the literature. Overall, integration of the CKD parameterization enabled exposure predictions that were within 1.5-fold error across all compounds and patients with varying stages of renal impairment. Notable improvement was observed over the conventional approach to scaling exposure, which failed in all but 1 scenario in patients with advanced CKD. Further research is required to qualify its use for first-in-CKD dose selection and clinical trial planning for a wider selection of renally eliminated compounds, including those subject to anion transport.
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Affiliation(s)
- Paul R V Malik
- School of Pharmacy, University of Waterloo, Kitchener, Ontario, Canada
| | - Cindy H T Yeung
- School of Pharmacy, University of Waterloo, Kitchener, Ontario, Canada
| | - Shams Ismaeil
- School of Pharmacy, University of Waterloo, Kitchener, Ontario, Canada
| | - Urooj Advani
- School of Pharmacy, University of Waterloo, Kitchener, Ontario, Canada
| | - Sebastian Djie
- School of Pharmacy, University of Waterloo, Kitchener, Ontario, Canada
| | - Andrea N Edginton
- School of Pharmacy, University of Waterloo, Kitchener, Ontario, Canada
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10
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Cui C, Li X, Liang H, Hou Z, Tu S, Dong Z, Yao X, Zhang M, Zhang X, Li H, Zuo X, Liu D. Physiologically based pharmacokinetic model of renally cleared antibacterial drugs in Chinese renal impairment patients. Biopharm Drug Dispos 2021; 42:24-34. [PMID: 33340419 PMCID: PMC7898311 DOI: 10.1002/bdd.2258] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 11/13/2020] [Accepted: 12/01/2020] [Indexed: 01/10/2023]
Abstract
To preliminarily develop physiologically based population models for Chinese renal impairment patients and to evaluate the prediction performance of new population models by renally cleared antibacterial drugs. First, demographic data and physiological parameters of Chinese renal impairment patients were collected, and then the coefficients of the relative demographic and physiological equation were recalibrated to construct the new population models. Second, drug‐independent parameters of ceftazidime, cefodizime, vancomycin, and cefuroxime were collected and verified by Chinese healthy volunteers, Caucasian healthy volunteers, and Caucasian renal impairment population models built in Simcyp. Finally, the newly developed population models were applied to predict the plasma concentration of four antibacterial drugs in Chinese renal impairment patients. The new physiologically based pharmacokinetic (PBPK) population models can predict the main pharmacokinetic parameters, including area under the plasma concentration–time curve extrapolated to infinity (AUCinf), renal clearance (CLr), and peak concentration (Cmax), of ceftazidime, cefodizime, vancomycin, and cefuroxime following intravenous administrations with less than twofold error in mild, moderate, and severe Chinese renal impairment patients. The accuracy and precision of the predictions were improved compared with the Chinese healthy volunteers and Caucasian renal impairment population models. The PBPK population models were preliminarily developed and the first‐step validation results of four antibacterial drugs following intravenous administration showed acceptable accuracy and precision. The population models still need more systematic validation by using more drugs and scenarios in future studies to support their applications on dosage recommendation for Chinese renal impairment patients.
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Affiliation(s)
- Cheng Cui
- Drug Clinical Trial Center, Peking University Third Hospital, Beijing, People's Republic of China.,Institute of Medical Innovation, Peking University Third Hospital, Beijing, People's Republic of China
| | - Xiaobei Li
- Drug Clinical Trial Center, Peking University Third Hospital, Beijing, People's Republic of China.,School of Pharmaceutical Sciences, Peking University, Beijing, People's Republic of China
| | - Hao Liang
- Drug Clinical Trial Center, Peking University Third Hospital, Beijing, People's Republic of China.,Institute of Medical Innovation, Peking University Third Hospital, Beijing, People's Republic of China
| | - Zhe Hou
- Drug Clinical Trial Center, Peking University Third Hospital, Beijing, People's Republic of China.,Institute of Medical Innovation, Peking University Third Hospital, Beijing, People's Republic of China
| | - Siqi Tu
- Drug Clinical Trial Center, Peking University Third Hospital, Beijing, People's Republic of China.,School of Pharmaceutical Sciences, Peking University, Beijing, People's Republic of China
| | - Zhongqi Dong
- Janssen China R&D Center, Shanghai, People's Republic of China
| | - Xueting Yao
- Drug Clinical Trial Center, Peking University Third Hospital, Beijing, People's Republic of China.,Institute of Medical Innovation, Peking University Third Hospital, Beijing, People's Republic of China
| | - Miao Zhang
- Drug Clinical Trial Center, Peking University Third Hospital, Beijing, People's Republic of China.,Institute of Medical Innovation, Peking University Third Hospital, Beijing, People's Republic of China
| | - Xuan Zhang
- School of Pharmaceutical Sciences, Peking University, Beijing, People's Republic of China
| | - Haiyan Li
- Drug Clinical Trial Center, Peking University Third Hospital, Beijing, People's Republic of China.,Institute of Medical Innovation, Peking University Third Hospital, Beijing, People's Republic of China.,Department of Cardiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Xiaocong Zuo
- Center of Clinical Pharmacology, Third Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Dongyang Liu
- Drug Clinical Trial Center, Peking University Third Hospital, Beijing, People's Republic of China.,Institute of Medical Innovation, Peking University Third Hospital, Beijing, People's Republic of China
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11
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Shah K, Fischetti B, Cha A, Taft DR. Using PBPK Modeling to Predict Drug Exposure and Support Dosage Adjustments in Patients With Renal Impairment: An Example with Lamivudine. Curr Drug Discov Technol 2020; 17:387-396. [PMID: 30767745 DOI: 10.2174/1570163816666190214164916] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 01/01/2019] [Accepted: 01/30/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND Lamivudine is a nucleoside reverse transcriptase inhibitor used to treat HIV and hepatitis B. It is primarily cleared by the kidney with renal secretion mediated by OCT2 and MATE. OBJECTIVE To use PBPK modeling to assess the impact of renal impairment on lamivudine pharmacokinetics using the Simcyp® Simulator. METHODS The model incorporated the Simcyp® Mechanistic Kidney Model option to predict renal disposition. The model was initially verified using the Simcyp® Healthy Volunteer population. Two discrete patient populations were then created for moderate (GFR 10-40 mL/min) and severe (GFR < 10 mL/min) renal failure (RF), and model simulations were compared to published data. The developed model was then utilized in a clinical study evaluating the clinical experience and plasma exposure of lamivudine when administered at higher than recommended doses to HIV-infected patients with varying degrees of renal impairment. RESULTS Predicted systemic exposure metrics (Cmax, AUC) compared favorably to published clinical data for each population, with the following fold errors (FE, ratio of predicted and observed data) for Cmax/AUC: Healthy Volunteers 1.04/1.04, Moderate RF 1.03/0.78, Severe RF 0.89/0.79. The model captured lamivudine plasma concentrations measured pre- and post-dose (0.5-1.5hr) in study participants (n = 34). Model simulations demonstrated comparable systemic profiles across patient cohorts, supporting the proposed dosage adjustment scheme. CONCLUSION This study illustrates how PBPK modeling can help verify dosing guidelines for patients with varying levels of renal impairment. This approach may also be useful for predicting potential changes in exposure during renal insufficiency for compounds undergoing clinical development.
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Affiliation(s)
- Kushal Shah
- Samuel J. and Joan B. Williamson Institute for Pharmacometrics, Division of Pharmaceutical Sciences, Arnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn 11201, New York, United States
| | - Briann Fischetti
- Division of Pharmacy Practice, Arnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn 11201, New York, United States
- Department of Pharmacy, The Brooklyn Hospital Center, Brooklyn 11201, New York, USA
| | - Agnes Cha
- Division of Pharmacy Practice, Arnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn 11201, New York, United States
- Department of Pharmacy, The Brooklyn Hospital Center, Brooklyn 11201, New York, USA
| | - David R Taft
- Samuel J. and Joan B. Williamson Institute for Pharmacometrics, Division of Pharmaceutical Sciences, Arnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn 11201, New York, United States
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12
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Zhou L, Tong X, Sharma P, Xu H, Al‐Huniti N, Zhou D. Physiologically based pharmacokinetic modelling to predict exposure differences in healthy volunteers and subjects with renal impairment: Ceftazidime case study. Basic Clin Pharmacol Toxicol 2019; 125:100-107. [DOI: 10.1111/bcpt.13209] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Accepted: 02/01/2019] [Indexed: 12/14/2022]
Affiliation(s)
- Li Zhou
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit AstraZeneca Boston Massachusetts
| | - Xiao Tong
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit AstraZeneca Boston Massachusetts
| | - Pradeep Sharma
- Mechanistic Safety and ADME Sciences, Drug Safety and Metabolism, IMED Biotech Unit AstraZeneca Cambridge UK
| | - Hongmei Xu
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit AstraZeneca Boston Massachusetts
| | - Nidal Al‐Huniti
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit AstraZeneca Boston Massachusetts
| | - Diansong Zhou
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit AstraZeneca Boston Massachusetts
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13
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Simulation-Based Analysis of the Impact of Renal Impairment on the Pharmacokinetics of Highly Metabolized Compounds. Pharmaceutics 2019; 11:pharmaceutics11030105. [PMID: 30832339 PMCID: PMC6471170 DOI: 10.3390/pharmaceutics11030105] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 02/25/2019] [Accepted: 02/27/2019] [Indexed: 12/17/2022] Open
Abstract
Renal impairment (RI) is a highly prevalent disease which can alter the pharmacokinetics (PK) of xenobiotics, including those that are predominately metabolized. The expression and activity of drug metabolizing enzymes (DMEs) and protein binding of compounds has been demonstrated to be affected in RI. A simulation based approach allows for the characterization of the impact of changes in these factors on the PK of compounds which are highly metabolized and allows for improved prediction of PK in RI. Simulations with physiologically based pharmacokinetic (PBPK) modeling was utilized to define the impact of these factors in PK in RI for a model substrate, nifedipine. Changes in fraction unbound and DME expression/activity had profound effects on PK in RI. Increasing fraction unbound and DME expression resulted in a reduction in exposure of nifedipine, while the reduction of DME activity resulted in an increase in exposure. In vitro and preclinical data were utilized to inform simulations for nifedipine, sildenafil and zidovudine. Increasing fraction unbound and changes in the expression/activity of DMEs led to improved predictions of PK. Further characterization of the impact of RI on these factors is warranted in order to better inform a priori predictions of PK in RI.
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14
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Mao Q, Lai Y, Wang J. Drug Transporters in Xenobiotic Disposition and Pharmacokinetic Prediction. Drug Metab Dispos 2018; 46:561-566. [PMID: 29636376 DOI: 10.1124/dmd.118.081356] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Accepted: 03/14/2018] [Indexed: 12/18/2022] Open
Abstract
Drug transporters are widely expressed in organs and tissue barriers throughout human and animal bodies. Studies over the last two decades have identified various ATP-binding cassette and solute carrier transporters that play critical roles in the absorption, distribution, metabolism, and elimination of drugs and xenobiotics. This special section contains more than 20 original manuscripts and reviews that cover the most recent advances in the areas of drug transporter research, including the basic biology and function of transporters, expression of drug transporters in organ and tissue barriers, the mechanisms underlying regulation of transporter expression, transporter-mediated drug disposition in animal models, and the development and utilization of new technologies in drug transporter study, as well as pharmacokinetic modeling and simulation to assess transporter involvement in drug disposition and drug-drug interactions. We believe that the topics covered in this special section will advance our understanding of the roles of transporters in drug disposition, efficacy, and safety.
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Affiliation(s)
- Qingcheng Mao
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington (Q.M., J.W.), and Gilead Sciences, Inc., Foster City, California (Y.L.)
| | - Yurong Lai
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington (Q.M., J.W.), and Gilead Sciences, Inc., Foster City, California (Y.L.)
| | - Joanne Wang
- Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington (Q.M., J.W.), and Gilead Sciences, Inc., Foster City, California (Y.L.)
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