Characterization of In Vitro Glucuronidation Clearance of a Range of Drugs in Human Kidney Microsomes: Comparison with Liver and Intestinal Glucuronidation and Impact of Albumin

Gill, Katherine L.; Houston, J. Brian; Galetin, Aleksandra

doi:10.1124/dmd.111.043984

Cited by 102 publications

(120 citation statements)

References 44 publications

(102 reference statements)

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“…It is evident from Table I that human kidney subcellular fractions (microsomes, S9) and recombinant enzyme expression systems are the most commonly applied sources of kidney drug-metabolising enzymes in in vitro assays (7,(14)(15)(16). Subcellular fractions require supplementation with appropriate cofactors lost during the preparation procedure and these are highlighted in Table I.…”

Section: Use Of In Vitro Systems To Understand Renal Drug Eliminationmentioning

confidence: 99%

“…Analogous to human liver microsomes, investigation of renal glucuronidation requires inclusion of alamethicin. This pore-forming peptide disrupts microsomal membranes to overcome reaction latency associated with UGTs due to localisation of the enzyme active site facing the lumen of the endoplasmic reticulum (7,17). Furthermore, the addition of albumin has been implemented in the renal glucuronidation in vitro assays (7) in order to account for the inhibitory effect of fatty acids released during microsomal incubations on UGTs.…”

Section: Use Of In Vitro Systems To Understand Renal Drug Eliminationmentioning

confidence: 99%

“…Furthermore, the addition of albumin has been implemented in the renal glucuronidation in vitro assays (7) in order to account for the inhibitory effect of fatty acids released during microsomal incubations on UGTs. Although the 'albumin' effect is enzyme specific, its inclusion in the in vitro assay improved prediction accuracy of renal glucuronidation clearance when using microsomal data (7). Identification of the individual enzymes contributing to the overall renal metabolic clearance can be done through reaction phenotyping and use of selective chemical inhibitors.…”

Section: Use Of In Vitro Systems To Understand Renal Drug Eliminationmentioning

confidence: 99%

“…Kidney weight may also be used as an IVIVE scaling factor for in vitro data generated in kidney slice assays (Table III). Data on the weight and volume of human kidney, including potential covariation with factors such as age, gender, ethnicity, body weight and/or body height, have been reported and collated previously (7,(63)(64)(65). Inter-study variability may be low when using the same method to measure kidney size, whereas there appears to be systematic differences between the different methodological approaches (66,67).…”

Section: Kidney Weight Volume and Blood Flowmentioning

confidence: 99%

“…For example, microsomal metabolism data, reported as activity or intrinsic clearance (CL int ) per milligram of microsomal protein, are scaled by the amount of microsomal protein per gram of the organ of interest, e.g. microsomal protein per gram kidney (MPPGK) in the case of kidney (7). Expression of a specific protein is accounted for by the relative expression factor (REF), which represents the ratio of the abundance of a particular protein (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Key to Opening Kidney for In Vitro–In Vivo Extrapolation Entrance in Health and Disease: Part I: In Vitro Systems and Physiological Data

Scotcher

Jones²,

Posada

et al. 2016

AAPS J

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ABSTRACT.The programme for the 2015 AAPS Annual Meeting and Exhibition (Orlando, FL; 25-29 October 2015) included a sunrise session presenting an overview of the state-of-the-art tools for in vitro-in vivo extrapolation (IVIVE) and mechanistic prediction of renal drug disposition. These concepts are based on approaches developed for prediction of hepatic clearance, with consideration of scaling factors physiologically relevant to kidney and the unique and complex structural organisation of this organ. Physiologically relevant kidney models require a number of parameters for mechanistic description of processes, supported by quantitative information on renal physiology (system parameters) and in vitro/in silico drug-related data. This review expands upon the themes raised during the session and highlights the importance of high quality in vitro drug data generated in appropriate experimental setup and robust system-related information for successful IVIVE of renal drug disposition. The different in vitro systems available for studying renal drug metabolism and transport are summarised and recent developments involving state-of-the-art technologies highlighted. Current gaps and uncertainties associated with system parameters related to human kidney for the development of physiologically based pharmacokinetic (PBPK) model and quantitative prediction of renal drug disposition, excretion, and/or metabolism are identified.

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Section: Use Of In Vitro Systems To Understand Renal Drug Eliminationmentioning

confidence: 99%

Section: Use Of In Vitro Systems To Understand Renal Drug Eliminationmentioning

confidence: 99%

Section: Use Of In Vitro Systems To Understand Renal Drug Eliminationmentioning

confidence: 99%

Section: Kidney Weight Volume and Blood Flowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Key to Opening Kidney for In Vitro–In Vivo Extrapolation Entrance in Health and Disease: Part I: In Vitro Systems and Physiological Data

Scotcher

Jones²,

Posada

et al. 2016

AAPS J

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A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein‐bound drugs and the impact of errors in plasma protein binding

Nagar

Korzekwa

2016

Biopharm & Drug Disp

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Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data was often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding, and blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for terminal elimination half-life (t1/2, 100% of drugs), peak plasma concentration (Cmax, 100%), area under the plasma concentration-time curve (AUC0–t, 95.4%), clearance (CLh, 95.4%), mean retention time (MRT, 95.4%), and steady state volume (Vss, 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model.

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Unequivocal evidence supporting the segregated flow intestinal model that discriminates intestine versus liver first‐pass removal with PBPK modeling

Pang

Yang

Noh

2017

Biopharm & Drug Disp

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Merits of the segregated flow model (SFM), highlighting the intestine as inert serosa and active enterocyte regions, with a smaller fractional (f < 0.3) intestinal flow (Q ) perfusing the enterocyte region, are described. Less drug in the circulation reaches the enterocytes due to the lower flow (f Q ) in comparison with drug administered into the gut lumen, fostering the idea of route-dependent intestinal removal. The SFM has been found superior to the traditional model (TM), which views the serosa and enterocytes totally as a well-mixed tissue perfused by 100% of the intestinal flow, Q . The SFM model is able to explain the lower extents of intestinal metabolism of enalapril, morphine and midazolam with i.v. vs. p.o. dosing. For morphine, the urine/bile ratio of the metabolite, morphine glucuronide MGurineMGbile for p.o. was 2.6× that of i.v. This was due to the higher proportion of intestinally formed morphine glucuronide, appearing more in urine than in bile due to its low permeability and greater extent of intestinal formation with p.o. administration. By contrast, the TM predicted the same MGurineMGbile for p.o. vs. i.v. The TM predicted that the contributions of the intestine:liver to first-pass removal were 46%:54% for both p.o. and i.v. The SFM predicted same 46%:54% (intestine:liver) for p.o., but 9%:91% for i.v. By contrast, the kinetics of codeine, the precursor of morphine, was described equally well by the SFM- and TM-PBPK models, a trend suggesting that intestinal metabolism of codeine is negligible. Fits to these PBPK models further provide insightful information towards metabolite formation: available fractions and the fractions of hepatic and total clearances that form the metabolite in question. The SFM-PBPK model is useful to identify not only the presence of intestinal metabolism but the contributions of the intestine and liver for metabolite formation. Copyright © 2016 John Wiley & Sons, Ltd.

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Characterization of In Vitro Glucuronidation Clearance of a Range of Drugs in Human Kidney Microsomes: Comparison with Liver and Intestinal Glucuronidation and Impact of Albumin

Cited by 102 publications

References 44 publications

Key to Opening Kidney for In Vitro–In Vivo Extrapolation Entrance in Health and Disease: Part I: In Vitro Systems and Physiological Data

Key to Opening Kidney for In Vitro–In Vivo Extrapolation Entrance in Health and Disease: Part I: In Vitro Systems and Physiological Data

A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein‐bound drugs and the impact of errors in plasma protein binding

Unequivocal evidence supporting the segregated flow intestinal model that discriminates intestine versus liver first‐pass removal with PBPK modeling

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