Prediction of Intestinal First-Pass Drug Metabolism

Yang, Jiansong; Jamei, Masoud; Yeo, Karen Rowland; Tucker, Geoffrey T.; Rostami‐Hodjegan, Amin

doi:10.2174/138920007782109733

Cited by 333 publications

(364 citation statements)

References 56 publications

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“…The Advance Dissolution Absorption and Metabolism (ADAM) model was used if the compound had characteristics suggesting solubility rate-limited absorption and information was available on the type of formulation, pH-solubility profile, dissolution and other pharmaceutical factors such as the supersaturation ratio and precipitation rate. Unless information was available suggesting otherwise, parameters determining gut metabolism such as f uGut (unbound fraction within the enterocyte) and Q gut (nominal flow from gut model [17]), were assumed to be 1 (for f uGut ) and predicted within Simcyp W (for Q gut ).…”

Section: Prediction Of Absorptionmentioning

confidence: 99%

Application of IVIVE and PBPK modeling in prospective prediction of clinical pharmacokinetics: strategy and approach during the drug discovery phase with four case studies

Chen¹,

Jin²,

Mukadam³

et al. 2012

Biopharm & Drug Disp

102

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Prospective simulations of human pharmacokinetic (PK) parameters and plasma concentration-time curves using in vitro in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models are becoming a vital part of the drug discovery and development process. This paper presents a strategy to deliver prospective simulations in support of clinical candidate nomination. A three stage approach of input parameter evaluation, model selection and multiple scenario simulation is utilized to predict the key components influencing human PK; absorption, distribution and clearance. The Simcyp W simulator is used to illustrate the approach and four compounds are presented as case studies. In general, the prospective predictions captured the observed clinical data well. Predicted C max was within 2-fold of observed data for all compounds and AUC was within 2-fold for all compounds following a single dose and three out of four compounds following multiple doses. Similarly, t max was within 2-fold of observed data for all compounds. However, C last was less accurately captured with two of the four compounds predicting C last within 2-fold of observed data following a single dose. The trend in results was towards overestimation of AUC and C last , this was particularly apparent for compound 2 for which clearance was likely underestimated via IVIVE. The prospective approach to simulating human PK using IVIVE and PBPK modeling outlined here attempts to utilize all available in silico, in vitro and in vivo preclinical data in order to determine the most appropriate assumptions to use in prospective predictions of absorption, distribution and clearance to aid clinical candidate nomination.

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Section: Prediction Of Absorptionmentioning

confidence: 99%

Application of IVIVE and PBPK modeling in prospective prediction of clinical pharmacokinetics: strategy and approach during the drug discovery phase with four case studies

Chen¹,

Jin²,

Mukadam³

et al. 2012

Biopharm & Drug Disp

102

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“…An F >85% indicates high permeability. If F is <85%, then F a can be estimated using the observed clinical data and in vitro results 14, 15. However, in vitro estimates of permeability used in such estimations are limited by the variability between studies, which in turn reflects on the quantitative prediction of F a and represents a great challenge for in vitro ‐ in vivo extrapolations.…”

mentioning

confidence: 99%

Novel Application of the Two‐Period Microtracer Approach to Determine Absolute Oral Bioavailability and Fraction Absorbed of Ertugliflozin

Raje

Callegari

Sahasrabudhe

et al. 2018

Clinical Translational Sci

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Ertugliflozin, a sodium glucose cotransporter‐2 inhibitor, is approved in the United States for treatment of type 2 diabetes mellitus. A novel two‐period study design with 14C microtracer dosing in each period was used to determine absolute oral bioavailability (F) and fraction absorbed (Fa) of ertugliflozin. Eight healthy adult men received 100‐μg i.v. 14C‐ertugliflozin (400 nCi) dose 1 h after a 15‐mg oral unlabeled ertugliflozin dose (period 1), followed by 100 μg 14C‐ertugliflozin orally along with 15 mg oral unlabeled ertugliflozin (period 2). Unlabeled ertugliflozin plasma concentrations were determined using high‐performance liquid‐chromatography tandem mass spectrometry (HPLC‐MS/MS). 14C‐ertugliflozin plasma concentrations were determined using HPLC‐accelerator mass spectrometry (AMS) and 14C urine concentrations were determined using AMS. F ((area under the curve (AUC)p.o./14C‐AUCi.v.)*(14C‐Dosei.v./Dosep.o.)) and Fa ((14C_Total_Urinep.o./14C_Total_Urinei.v.)* (14C‐Dosei.v./14C‐Dosep.o.)) were estimated. Estimates of F and Fa were 105% and 111%, respectively. Oral absorption of ertugliflozin was complete under fasted conditions and F was ∼100%. Ertugliflozin was well tolerated.

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“…As cytochrome P450 3A (CYP3A) comprises a large percentage of intestinal CYP enzymes [1], CYP3A substrates are most affected by gut metabolism. However, at the discovery stage, where metabolism of an investigational drug is not yet fully characterized and in the absence of clinical data, F g has to be predicted using pragmatic approaches such as the 'Q gut ' model [2]. The Q gut model uses the 'well-stirred' liver model of hepatic drug clearance to describe F g but the flow term Q gut is a hybrid of both permeability through the enterocyte membrane and villous blood flow [3].…”

mentioning

confidence: 99%

“…The Q gut model uses the 'well-stirred' liver model of hepatic drug clearance to describe F g but the flow term Q gut is a hybrid of both permeability through the enterocyte membrane and villous blood flow [3]. (2) where fu gut is the fraction of drug unbound in the enterocyte, and CLu int,gut is the net intrinsic metabolic clearance in the gut based on the unbound drug concentration. While fu gut cannot be measured experimentally, it can be an important parameter in quantifying the first-pass effect in the gut [2].…”

mentioning

confidence: 99%

“…(2) where fu gut is the fraction of drug unbound in the enterocyte, and CLu int,gut is the net intrinsic metabolic clearance in the gut based on the unbound drug concentration. While fu gut cannot be measured experimentally, it can be an important parameter in quantifying the first-pass effect in the gut [2]. A case example is presented in which a physiologically based pharmacokinetic (PBPK) approach has been used to model the absorption of a lipophilic BCS (biopharmaceutical classification system) Class II investigational compound predominantly metabolized by CYP3A4 and not shown to be a P-gp substrate (in vitro studies), when administered as a nanosuspension formulation.…”

mentioning

confidence: 99%

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From preclinical to human – prediction of oral absorption and drug–drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach in an industrial setting: a workflow by using case example

Sinha

Snoeys

Osselaer

et al. 2012

Biopharm & Drug Disp

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Purpose: A case example is presented in which the physiologically based modeling approach has been used to model the absorption of a lipophilic BCS Class II compound predominantly metabolized by CYP3A4, and to assess the interplay of absorption related parameters with the drug-drug interaction (DDI) potential. Methods: The PBPK model was built in the rat using Gastroplus W to study the absorption characteristics of the compound. Subsequently relevant model parameters were used to predict the non-linear human PK observed during first-in-human study after optimizing the absorption model for colonic absorption, bile micelle solubilization and unbound fraction in gut enterocytes (fu gut ) using SIMCYP W simulator. The model fitted absorption parameters were then used to assess the drug-drug interaction (DDI) potential of the test compound when administered along with multiple doses of a potent CYP 3A4 inhibitor, ketoconazole. The impact of fu gut in the extent of DDI was assessed using parameter sensitivity analysis. Results and Conclusions: After optimizing the preclinical model and taking into consideration bile micelle solubilization and colonic absorption, the non-linear pharmacokinetics of the test compound was satisfactorily predicted in man. Sensitivity analysis performed with the absorption parameter fu gut indicated that it could be an important parameter in predicting oral absorption. In addition, DDI simulations using SIMCYP W suggest that C max and AUC ratios may also be sensitive to the fu gut input in the model. Since fu gut cannot be measured experimentally, sensitivity analysis may help in assessing the importance of fu gut in human PK and DDI prediction using SIMCYP W .

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Prediction of Intestinal First-Pass Drug Metabolism

Cited by 333 publications

References 56 publications

Application of IVIVE and PBPK modeling in prospective prediction of clinical pharmacokinetics: strategy and approach during the drug discovery phase with four case studies

Application of IVIVE and PBPK modeling in prospective prediction of clinical pharmacokinetics: strategy and approach during the drug discovery phase with four case studies

Novel Application of the Two‐Period Microtracer Approach to Determine Absolute Oral Bioavailability and Fraction Absorbed of Ertugliflozin

From preclinical to human – prediction of oral absorption and drug–drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach in an industrial setting: a workflow by using case example

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