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Yin DE, Palin AC, Lombo TB, Mahon RN, Poon B, Wu DY, Atala A, Brooks KM, Chen S, Coyne CB, D’Souza MP, Fackler OT, Furler O’Brien RL, Garcia-de-Alba C, Jean-Philippe P, Karn J, Majji S, Muotri AR, Ozulumba T, Sakatis MZ, Schlesinger LS, Singh A, Spiegel HM, Struble E, Sung K, Tagle DA, Thacker VV, Tidball AM, Varthakavi V, Vunjak-Novakovic G, Wagar LE, Yeung CK, Ndhlovu LC, Ott M. 3D human tissue models and microphysiological systems for HIV and related comorbidities. Trends Biotechnol 2024; 42:526-543. [PMID: 38071144 PMCID: PMC11065605 DOI: 10.1016/j.tibtech.2023.10.008] [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: 07/03/2023] [Revised: 10/22/2023] [Accepted: 10/24/2023] [Indexed: 03/03/2024]
Abstract
Three-dimensional (3D) human tissue models/microphysiological systems (e.g., organs-on-chips, organoids, and tissue explants) model HIV and related comorbidities and have potential to address critical questions, including characterization of viral reservoirs, insufficient innate and adaptive immune responses, biomarker discovery and evaluation, medical complexity with comorbidities (e.g., tuberculosis and SARS-CoV-2), and protection and transmission during pregnancy and birth. Composed of multiple primary or stem cell-derived cell types organized in a dedicated 3D space, these systems hold unique promise for better reproducing human physiology, advancing therapeutic development, and bridging the human-animal model translational gap. Here, we discuss the promises and achievements with 3D human tissue models in HIV and comorbidity research, along with remaining barriers with respect to cell biology, virology, immunology, and regulatory issues.
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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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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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Zamir A, Alqahtani F, Rasool MF. Chronic kidney disease and physiologically based pharmacokinetic modeling: a critical review of existing models. Expert Opin Drug Metab Toxicol 2024; 20:95-105. [PMID: 38270999 DOI: 10.1080/17425255.2024.2311154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/24/2024] [Indexed: 01/27/2024]
Abstract
INTRODUCTION Physiologically based pharmacokinetic (PBPK) modeling is a paradigm shift in this era for determining the exposure of drugs in pediatrics, geriatrics, and patients with chronic diseases where clinical trials are difficult to conduct. AREAS COVERED This review has collated data regarding published PBPK models on chronic kidney disease (CKD), including the drug and system-specific input model parameters and model evaluation criteria. Four databases were used from 13th June 2023 to 10th July 2023 for identifying the relevant studies that met the inclusion/exclusion criteria. Alterations in plasma protein (albumin/alpha-1 acid glycoprotein), gastric emptying time, hematocrit, small intestinal transit time, the abundance of cytochrome (CYP) 450 enzymes, glomerular filtration rate, and physicochemical parameters for different drugs were explicitly elaborated from earlier reported studies. Moreover, model evaluation depicted that models in CKD for most of the included drugs were within the allowed two-fold error range. EXPERT OPINION This review will provide insights for researchers on applying PBPK models in managing patients with different levels of CKD to prevent undesirable side effects and increase the effectiveness of drug therapy.
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Affiliation(s)
- Ammara Zamir
- Department of Pharmacy Practice, Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
| | - Faleh Alqahtani
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud Universi-ty, Riyadh, Saudi Arabia
| | - Muhammad Fawad Rasool
- Department of Pharmacy Practice, Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
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Van Neste M, Bogaerts A, Nauwelaerts N, Macente J, Smits A, Annaert P, Allegaert K. Challenges Related to Acquisition of Physiological Data for Physiologically Based Pharmacokinetic (PBPK) Models in Postpartum, Lactating Women and Breastfed Infants-A Contribution from the ConcePTION Project. Pharmaceutics 2023; 15:2618. [PMID: 38004596 PMCID: PMC10674226 DOI: 10.3390/pharmaceutics15112618] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/21/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
Physiologically based pharmacokinetic (PBPK) modelling is a bottom-up approach to predict pharmacokinetics in specific populations based on population-specific and medicine-specific data. Using an illustrative approach, this review aims to highlight the challenges of incorporating physiological data to develop postpartum, lactating women and breastfed infant PBPK models. For instance, most women retain pregnancy weight during the postpartum period, especially after excessive gestational weight gain, while breastfeeding might be associated with lower postpartum weight retention and long-term weight control. Based on a structured search, an equation for human milk intake reported the maximum intake of 153 mL/kg/day in exclusively breastfed infants at 20 days, which correlates with a high risk for medicine reactions at 2-4 weeks in breastfed infants. Furthermore, the changing composition of human milk and its enzymatic activities could affect pharmacokinetics in breastfed infants. Growth in breastfed infants is slower and gastric emptying faster than in formula-fed infants, while a slower maturation of specific metabolizing enzymes in breastfed infants has been described. The currently available PBPK models for these populations lack structured systematic acquisition of population-specific data. Future directions include systematic searches to fully identify physiological data. Following data integration as mathematical equations, this holds the promise to improve postpartum, lactation and infant PBPK models.
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Affiliation(s)
- Martje Van Neste
- Clinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, 3000 Leuven, Belgium;
- L-C&Y, KU Leuven Child & Youth Institute, 3000 Leuven, Belgium; (A.B.); (A.S.)
| | - Annick Bogaerts
- L-C&Y, KU Leuven Child & Youth Institute, 3000 Leuven, Belgium; (A.B.); (A.S.)
- Department of Development and Regeneration, KU Leuven, 3000 Leuven, Belgium
- Faculty of Health, University of Plymouth, Devon PL4 8AA, UK
| | - Nina Nauwelaerts
- Drug Delivery and Disposition, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, 3000 Leuven, Belgium; (N.N.); (J.M.); (P.A.)
| | - Julia Macente
- Drug Delivery and Disposition, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, 3000 Leuven, Belgium; (N.N.); (J.M.); (P.A.)
| | - Anne Smits
- L-C&Y, KU Leuven Child & Youth Institute, 3000 Leuven, Belgium; (A.B.); (A.S.)
- Department of Development and Regeneration, KU Leuven, 3000 Leuven, Belgium
- Neonatal Intensive Care Unit, University Hospitals Leuven, 3000 Leuven, Belgium
| | - Pieter Annaert
- Drug Delivery and Disposition, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, 3000 Leuven, Belgium; (N.N.); (J.M.); (P.A.)
- BioNotus GCV, 2845 Niel, Belgium
| | - Karel Allegaert
- Clinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, 3000 Leuven, Belgium;
- L-C&Y, KU Leuven Child & Youth Institute, 3000 Leuven, Belgium; (A.B.); (A.S.)
- Department of Development and Regeneration, KU Leuven, 3000 Leuven, Belgium
- Department of Hospital Pharmacy, Erasmus University Medical Center, 3000 CA Rotterdam, The Netherlands
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Komura H, Watanabe R, Mizuguchi K. The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery. Pharmaceutics 2023; 15:2619. [PMID: 38004597 PMCID: PMC10675155 DOI: 10.3390/pharmaceutics15112619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/05/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evolving model-building technologies from a simplified relationship between ADME endpoints and physicochemical properties to machine learning, including support vector machines, random forests, and convolution neural networks. Recently, in the field of in silico ADME research, there has been a shift toward evaluating the in vivo parameters or plasma concentrations of NCEs instead of using predictive results to guide chemical structure design. Another research hotspot is the establishment of a computational prediction platform to strengthen academic drug discovery. Bioinformatics projects have produced a series of in silico ADME models using free software and open-access databases. In this review, we introduce prediction models for various ADME parameters and discuss the currently available academic drug discovery platforms.
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Affiliation(s)
- Hiroshi Komura
- University Research Administration Center, Osaka Metropolitan University, 1-2-7 Asahimachi, Abeno-ku, Osaka 545-0051, Osaka, Japan
| | - Reiko Watanabe
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita 565-0871, Osaka, Japan; (R.W.); (K.M.)
- Artificial Intelligence Centre for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health, and Nutrition (NIBIOHN), 3-17 Senrioka-shinmachi, Settu 566-0002, Osaka, Japan
| | - Kenji Mizuguchi
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita 565-0871, Osaka, Japan; (R.W.); (K.M.)
- Artificial Intelligence Centre for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health, and Nutrition (NIBIOHN), 3-17 Senrioka-shinmachi, Settu 566-0002, Osaka, Japan
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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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Deng G, Yang F, Sun N, Liang D, Cen A, Zhang C, Ni S. Physiologically based pharmacokinetic-pharmacodynamic evaluation of meropenem in CKD and hemodialysis individuals. Front Pharmacol 2023; 14:1126714. [PMID: 36959849 PMCID: PMC10027930 DOI: 10.3389/fphar.2023.1126714] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 02/21/2023] [Indexed: 03/09/2023] Open
Abstract
Objective: Chronic kidney disease (CKD) has significant effects on renal clearance of drugs. The application of antibiotics in CKD patients to achieve the desired therapeutic effect is challenging. This study aims to determine meropenem plasma exposure in the CKD population and further investigate optimal dosing regimens. Methods: A healthy adult PBPK model was established using the meropenem's physicochemical parameters, pharmacokinetic parameters, and available clinical data, and it was scaled to the populations with CKD and dialysis. The differences between the predicted concentration, Cmax, and AUClast predicted and observed model values were assessed by mean relative deviations (MRD) and geometric mean fold errors (GMFE) values and plotting the goodness of fit plot to evaluate the model's performance. Finally, dose recommendations for CKD and hemodialysis populations were performed by Monte Carlo simulations. Results: The PBPK models of meropenem in healthy, CKD, and hemodialysis populations were successfully established. The MRD values of the predicted concentration and the GMFE values of Cmax and AUClast were within 0.5-2.0-fold of the observed data. The simulation results of the PBPK model showed the increase in meropenem exposure with declining kidney function in CKD populations. The dosing regimen of meropenem needs to be further adjusted according to the renal function of CKD patients. In patients receiving hemodialysis, since meropenem declined more rapidly during the on-dialysis session than the off-dialysis session, pharmacodynamic evaluations were performed for two periods separately, and respective optimal dosing regimens were determined. Conclusion: The established PBPK model successfully predicted meropenem pharmacokinetics in patients with CKD and hemodialysis and could further be used to optimize dosing recommendations, providing a reference for personalized clinical medication.
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Affiliation(s)
- Guoliang Deng
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
- Department of Pharmacy, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Fan Yang
- Department of Hepatobiliary Surgery, Guangzhou Eighth People’s Hospital, Guangzhou, Guangdong, China
| | - Ning Sun
- Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Danhong Liang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
- Department of Pharmacy, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Anfen Cen
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
- Department of Pharmacy, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
| | - Chen Zhang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
- Department of Pharmacy, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
- *Correspondence: Chen Zhang, ; Suiqin Ni,
| | - Suiqin Ni
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
- Department of Pharmacy, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
- *Correspondence: Chen Zhang, ; Suiqin Ni,
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Li X, Chen C, Ding N, Zhang T, Zheng P, Yang M. Physiologically based pharmacokinetic modelling and simulation to predict the plasma concentration profile of schaftoside after oral administration of total flavonoids of Desmodium styracifolium. Front Pharmacol 2022; 13:1073535. [PMID: 36588682 PMCID: PMC9794590 DOI: 10.3389/fphar.2022.1073535] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 11/30/2022] [Indexed: 01/03/2023] Open
Abstract
Introduction: The total flavonoids of Desmodium styracifolium (TFDS) are the flavonoid extracts purified from Desmodii Styracifolii Herba. The capsule of TFDS was approved for the treatment of urolithiasis by NMPA in 2022. Schaftoside is the representative compound of TFDS that possesses antilithic and antioxidant effects. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model of schaftoside to simulate its plasma concentration profile in rat and human after oral administration of the total flavonoids of Desmodium styracifolium. Methods: The physiologically based pharmacokinetic model of schaftoside was firstly developed and verified by the pharmacokinetic data in rats following intravenous injection and oral administration of the total flavonoids of Desmodium styracifolium. Then the PBPK model was extrapolated to human with PK-Sim® software. In order to assess the accuracy of the extrapolation, a preliminary multiple-dose clinical study was performed in four healthy volunteers aged 18-45 years old. The predictive performance of PBPK model was mainly evaluated by visual predictive checks and fold error of Cmax and AUC0-t of schaftoside (the ratio of predicted to observed). Finally, the adult PBPK model was scaled to several subpopulations including elderly and renally impaired patients. Results: Schaftoside underwent poor metabolism in rat and human liver microsomes in vitro, and in vivo it was extensively excreted into urine and bile as an unchanged form. By utilizing literature and experimental data, the PBPK model of schaftoside was well established in rat and human. The predicted plasma concentration profiles of schaftoside were consistent with the corresponding observed data, and the fold error values were within the 2-fold acceptance criterion. No significant pharmacokinetic differences were observed after extrapolation from adult (18-40 years old) to elderly populations (71-80 years) in PK-Sim®. However, the plasma concentration of schaftoside was predicted to be much higher in renally impaired patients. The maximum steady-state plasma concentrations in patients with chronic kidney disease stage 3, 4 and 5 were 3.41, 12.32 and 23.77 times higher, respectively, than those in healthy people. Conclusion: The established PBPK model of schaftoside provided useful insight for dose selection of the total flavonoids of Desmodium styracifolium in different populations. This study provided a feasible way for the assessment of efficacy and safety of herbal medicines.
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Affiliation(s)
- Xue Li
- Phase I Clinical Research Lab, LongHua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chao Chen
- Phase I Clinical Research Lab, LongHua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Nan Ding
- Phase I Clinical Research Lab, LongHua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Tianjiao Zhang
- Phase I Clinical Research Lab, LongHua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Peiyong Zheng
- Clinical Research Center, LongHua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China,*Correspondence: Peiyong Zheng, ; Ming Yang,
| | - Ming Yang
- Phase I Clinical Research Lab, LongHua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China,Clinical Research Center, LongHua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China,*Correspondence: Peiyong Zheng, ; Ming Yang,
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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: 0] [Impact Index Per Article: 0] [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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11
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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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12
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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: 7] [Impact Index Per Article: 2.3] [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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13
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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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