Predicting Interindividual Variability of Metabolic Drug–Drug Interactions: Identifying the Causes and Accounting for Them Using a Systems Approach

Rostami‐Hodjegan, Amin

doi:10.1002/9780470538951.ch18

Enzyme Inhibition in Drug Discovery and Development 2009

DOI: 10.1002/9780470538951.ch18

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Predicting Interindividual Variability of Metabolic Drug–Drug Interactions: Identifying the Causes and Accounting for Them Using a Systems Approach

Amin Rostami‐Hodjegan

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Cited by 2 publications

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“…In addition to the mathematical models, described above, the physiologically based drug interaction model, Simcyp Population-Based ADME Simulator (Simcyp Ltd., Sheffield, UK), has recently incorporated a model to predict clinical induction from in vitro induction parameters (Rostami-Hodjegan, 2009). This model accounts for normalization of the E max value by the maximal positive control response, as well as concurrent P450 inhibition and inactivation.…”

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confidence: 99%

In Vitro and in Vivo Induction of Cytochrome P450: A Survey of the Current Practices and Recommendations: A Pharmaceutical Research and Manufacturers of America Perspective

Chu¹,

Einolf²,

Evers³

et al. 2009

Drug Metabolism and Disposition

153

122

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ABSTRACT:Cytochrome P450 (P450) induction is one of the factors that can affect the pharmacokinetics of a drug molecule upon multiple dosing, and it can result in pharmacokinetic drug-drug interactions with coadministered drugs causing potential therapeutic failures. In recent years, various in vitro assays have been developed and used routinely to assess the potential for drug-drug interactions due to P450 induction. There is a desire from the pharmaceutical industry and regulatory agencies to harmonize assay methodologies, data interpretation, and the design of clinical drug-drug interaction studies. In this article, a team of 10 scientists from nine Pharmaceutical Research and Manufacturers of America (PhRMA) member companies conducted an anonymous survey among PhRMA companies to query current practices with regards to the conduct of in vitro induction assays, data interpretation, and clinical induction study practices. The results of the survey are presented in this article, along with reviews of current methodologies of in vitro assays and in vivo studies, including modeling efforts in this area. A consensus recommendation regarding common practices for the conduct of P450 induction studies is included.The convergence of recent advances in molecular biology and genomics, higher throughput chemical synthesis, and automated highthroughput in vitro enzyme and cell-based assays to assess biological activity has led to shorter lead identification and optimization times in drug research. However, these breakthroughs have not yet translated into success in drug development. Surveys suggest that in the last several years, the number of New Drug Applications submitted to regulatory agencies has declined and that the poor success rate during drug development is to some extent due to late-stage failures. It is important that novel and innovative approaches are used for assessing the risks and benefits of new molecular entities (NMEs) early in the research and development life cycle to minimize late-stage attrition.Based on a survey conducted in 2004, the major factors for compound attrition during clinical development are lack of efficacy, toxicity, and safety, and suboptimal pharmacokinetics and/or bioavailability, with the remaining failures due to financial and/or commercial reasons (Kola and Landis, 2004). In that survey, one notable observation was the reduction of compound attrition due to pharmacokinetic and/or bioavailability issues from pre-1991 to the period be- ABBREVIATIONS: NME, new molecular entity; P450, cytochrome P450; DDI, drug-drug interaction; RIF, rifampicin; PhRMA, Pharmaceutical Research and Manufacturers of America; AhR, aryl hydrocarbon receptor; CAR, constitutive androstane receptor; PXR, pregnane X receptor; UGT, uridine diphosphate glucuronosyl transferase; FXR, farnesyl X receptor; PPAR, peroxisome proliferator-activated receptor; VDR, vitamin D receptor; Nrf2, nuclear factor erythroid 2-related factor 2; LBD, ligand binding domain; hPXR, human PXR; SR12813, tetra-ethyl 2-(3,5-di-tertbutyl...

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mentioning

confidence: 99%

In Vitro and in Vivo Induction of Cytochrome P450: A Survey of the Current Practices and Recommendations: A Pharmaceutical Research and Manufacturers of America Perspective

Chu¹,

Einolf²,

Evers³

et al. 2009

Drug Metabolism and Disposition

153

122

View full text Add to dashboard Cite

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“…For predictions of intestinal availability (F G ), the Q Gut model has often been applied. 16,17 Similarly, F A was analyzed using static models by considering effective permeability that was modified by P-gp efflux. [18][19][20] If the Q Gut model is extended by considering active efflux, it may help understand the whole absorption process (i.e., F A Â F G ) by virtue of its simplicity.…”

mentioning

confidence: 99%

Determinants of Intestinal Availability for P-glycoprotein Substrate Drugs Estimated by Extensive Simulation With Mathematical Absorption Models

Ando

Hatakeyama

Sato

et al. 2017

Journal of Pharmaceutical Sciences

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In this study, intestinal drug-drug interactions (DDIs) for substrate drugs of P-glycoprotein were simulated extensively using the extended Q model and translocation model to explore the determinants of DDI. The results of analyses using both models suggested that permeability and active efflux clearance were the major factors that influenced the fraction absorbed (F). The results of simulation for 100 virtual drugs in which parameters were generated considering the actual values of commercially available drugs suggested that the ratio of the pH-corrected passive permeability to the intrinsic efflux clearance (P/CL) relative to that of digoxin would be a useful and quantitative index of P-glycoprotein (P-gp)-mediated DDI risk at lower doses. At higher doses, such as 100 mg, the risk of P-gp-mediated DDI would be significantly reduced because of saturation of P-gp efflux. The simulation suggested that although drugs with lower permeability were more susceptible, even drugs with higher permeability than metoprolol, a representative highly permeable drug, such as BCS class 1 and 2, may experience DDIs owing to P-gp inhibition. Overall, this study demonstrated the usefulness of mathematical intestinal models when only limited observational data are available.

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Predicting Interindividual Variability of Metabolic Drug–Drug Interactions: Identifying the Causes and Accounting for Them Using a Systems Approach

Cited by 2 publications

References 61 publications

In Vitro and in Vivo Induction of Cytochrome P450: A Survey of the Current Practices and Recommendations: A Pharmaceutical Research and Manufacturers of America Perspective

In Vitro and in Vivo Induction of Cytochrome P450: A Survey of the Current Practices and Recommendations: A Pharmaceutical Research and Manufacturers of America Perspective

Determinants of Intestinal Availability for P-glycoprotein Substrate Drugs Estimated by Extensive Simulation With Mathematical Absorption Models

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