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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Nerurkar PV, Yokoyama J, Ichimura K, Kutscher S, Wong J, Bittenbender HC, Deng Y. Medium Roasting and Brewing Methods Differentially Modulate Global Metabolites, Lipids, Biogenic Amines, Minerals, and Antioxidant Capacity of Hawai'i-Grown Coffee ( Coffea arabica). Metabolites 2023; 13:412. [PMID: 36984852 PMCID: PMC10051321 DOI: 10.3390/metabo13030412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 03/01/2023] [Accepted: 03/06/2023] [Indexed: 03/12/2023] Open
Abstract
In the United States, besides the US territory Puerto Rico, Hawai'i is the only state that grows commercial coffee. In Hawai'i, coffee is the second most valuable agricultural commodity. Health benefits associated with moderate coffee consumption, including its antioxidant capacity, have been correlated to its bioactive components. Post-harvest techniques, coffee variety, degree of roasting, and brewing methods significantly impact the metabolites, lipids, minerals, and/or antioxidant capacity of brewed coffees. The goal of our study was to understand the impact of roasting and brewing methods on metabolites, lipids, biogenic amines, minerals, and antioxidant capacity of two Hawai'i-grown coffee (Coffea arabica) varieties, "Kona Typica" and "Yellow Catuai". Our results indicated that both roasting and coffee variety significantly modulated several metabolites, lipids, and biogenic amines of the coffee brews. Furthermore, regardless of coffee variety, the antioxidant capacity of roasted coffee brews was higher in cold brews. Similarly, total minerals were higher in "Kona Typica" cold brews followed by "Yellow Catuai" cold brews. Hawai'i-grown coffees are considered "specialty coffees" since they are grown in unique volcanic soils and tropical microclimates with unique flavors. Our studies indicate that both Hawai'i-grown coffees contain several health-promoting components. However, future studies are warranted to compare Hawai'i-grown coffees with other popular brand coffees and their health benefits in vivo.
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Affiliation(s)
- Pratibha V. Nerurkar
- Laboratory of Metabolic Disorders and Alternative Medicine, Department of Molecular Biosciences and Bioengineering (MBBE), College of Tropical Agriculture and Human Resources (CTAHR), University of Hawai‘i at Manoa (UHM), Honolulu, HI 96822, USA
| | - Jennifer Yokoyama
- Laboratory of Metabolic Disorders and Alternative Medicine, Department of Molecular Biosciences and Bioengineering (MBBE), College of Tropical Agriculture and Human Resources (CTAHR), University of Hawai‘i at Manoa (UHM), Honolulu, HI 96822, USA
| | - Kramer Ichimura
- Laboratory of Metabolic Disorders and Alternative Medicine, Department of Molecular Biosciences and Bioengineering (MBBE), College of Tropical Agriculture and Human Resources (CTAHR), University of Hawai‘i at Manoa (UHM), Honolulu, HI 96822, USA
| | - Shannon Kutscher
- Laboratory of Metabolic Disorders and Alternative Medicine, Department of Molecular Biosciences and Bioengineering (MBBE), College of Tropical Agriculture and Human Resources (CTAHR), University of Hawai‘i at Manoa (UHM), Honolulu, HI 96822, USA
| | - Jamie Wong
- Laboratory of Metabolic Disorders and Alternative Medicine, Department of Molecular Biosciences and Bioengineering (MBBE), College of Tropical Agriculture and Human Resources (CTAHR), University of Hawai‘i at Manoa (UHM), Honolulu, HI 96822, USA
| | - Harry C. Bittenbender
- Department of Tropical Plant and Soil Sciences (TPSS), CTAHR, UHM, Honolulu, HI 96822, USA
| | - Youping Deng
- Bioinformatics Core, Departmentt of Quantitative Health Sciences, University of Hawai‘i Cancer Center (UHCC), John A. Burns School of Medicine (JABSOM), UHM, Honolulu, HI 96813, USA
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