Understanding the in vivo performance of enteric coated tablets using an in vitro-in silico-in vivo approach: Case example diclofenac

Kambayashi, Atsushi; Blume, Henning; Dressman, Jennifer B.

doi:10.1016/j.ejpb.2013.09.009

Cited by 51 publications

(33 citation statements)

References 33 publications

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“…Advancements in computer science and physiologically based mathematical models have led to the expansion of the potential applications of PBPK modeling. For example, more complex absorption models such as advanced dissolution, absorption, and metabolism (ADAM) models (Jamei et al, 2009b) and advanced compartmental absorption and transit (ACAT) models (Agoram et al, 2001) have been developed that enable the use of PBPK modeling for the simulation of food effects (Shono et al, 2009;Turner et al, 2012;Heimbach et al, 2013;Xia et al, 2013b;Patel et al, 2014;Zhang et al, 2014), the impact of drug properties on absorption kinetics (Kambayashi et al, 2013;Parrott et al, 2014), and intestinal interactions (Fenneteau et al, 2010). The development of sophisticated models that allow for the simulation of multiple inhibitors or inducers, relevant metabolites, and multiple mechanisms of interaction have permitted the prediction of complex DDIs involving enzymes, transporters, and multiple interaction mechanisms Reki c et al, 2011;Varma et al, 2012Varma et al, , 2013Dhuria et al, 2013;Gertz et al, 2013Gertz et al, , 2014Guo et al, 2013;Kudo et al, 2013;Siccardi et al, 2013;Wang et al, 2013a;Sager et al, 2014;Chen et al, 2015;Shi et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…For some absorption models, model performance was determine to be high if error was ,25%, medium if error was 25%-50%, low if error was 50%-100%, and inaccurate if error was greater than 2-fold (Sjögren et al, 2013). Importantly, many of the absorption models systematically evaluated model performance in terms of the plasma concentration-time curves rather than specific PK parameters using a similarity factor (f 1 or f 2 ) to calculate the percent difference between the simulated and measured plasma concentrations at each measured time point (Shono et al, 2009;Wagner et al, 2012;Fei et al, 2013;Kambayashi et al, 2013;Wang et al, 2013a). In addition, many absorption models were evaluated using statistical criteria such as linear regression between observed and predicted parameters or concentrations and method of residuals (Shono et al, 2009;Turner et al, 2012;Kambayashi et al, 2013).…”

Section: Data Sets Used In Model Verification Included: (A) Single Domentioning

confidence: 99%

“…Importantly, many of the absorption models systematically evaluated model performance in terms of the plasma concentration-time curves rather than specific PK parameters using a similarity factor (f 1 or f 2 ) to calculate the percent difference between the simulated and measured plasma concentrations at each measured time point (Shono et al, 2009;Wagner et al, 2012;Fei et al, 2013;Kambayashi et al, 2013;Wang et al, 2013a). In addition, many absorption models were evaluated using statistical criteria such as linear regression between observed and predicted parameters or concentrations and method of residuals (Shono et al, 2009;Turner et al, 2012;Kambayashi et al, 2013). In some studies evaluating PBPK models of biologics (Kletting et al, 2010;Cao et al, 2013) model performances and discrimination between different models was achieved using statistical criteria that account for the added degrees of freedom in the model.…”

Section: Data Sets Used In Model Verification Included: (A) Single Domentioning

confidence: 99%

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Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

et al. 2015

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Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms "PBPK" and "physiologically based pharmacokinetic model" to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.

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Section: Introductionmentioning

confidence: 99%

Section: Data Sets Used In Model Verification Included: (A) Single Domentioning

confidence: 99%

Section: Data Sets Used In Model Verification Included: (A) Single Domentioning

confidence: 99%

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Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

et al. 2015

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“…Among the most critical factors are the patient's sex (male vs. female) and age. Major breakthroughs will result not only from our ability to forecast such physiological implications during the early stages of the development of a drug (Kambayashi et al 2013) but also how these would interact beyond the local site of action. The consequences are twofold: to streamline the drug development process by increasing the likelihood of success and reducing time to market through optimal design of formulations; and to enable the development of patient-specific formulations increasing the likelihood of treatment success targeting specific patient sub-populations (Dickschen et al 2014).…”

Section: Qsp: Towards a Framework For Contextmentioning

confidence: 99%

Quantitative Systems Pharmacology: A Framework for Context

Androulakis

2016

Curr Pharmacol Rep

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Quantitative Systems Pharmacology (QSP) is receiving increased attention. As the momentum builds and the expectations grow it is important to (re)assess and formalize the basic concepts and approaches. In this short review, I argue that QSP, in addition to enabling the rational integration of data and development of complex models, maybe more importantly, provides the foundations for developing an integrated framework for the assessment of drugs and their impact on disease within a broader context expanding the envelope to account in great detail for physiology, environment and prior history. I articulate some of the critical enablers, major obstacles and exciting opportunities manifesting themselves along the way. Charting such overarching themes will enable practitioners to identify major and defining factors as the field progressively moves towards personalized and precision health care delivery.

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“…often leads to a greater dispersion of the in vivo dissolution profile as compared to that occurring in the more distal portions of the GI tract (33,34). This time-associated relationship in the magnitude of the variance can exist even in situations when the in vivo metric has been generated using physiologically based pharmacokinetic (PBPK) models that have accommodated gastric emptying time into the in vivo predictions of product performance (35). Furthermore, in terms of the implementation of the F2 metric, it is recognized that there may be a greater magnitude of variability during the early vs later time points.…”

Section: Potential Applications and Considerations For Its Novel Implmentioning

confidence: 99%

Evaluating In Vivo-In Vitro Correlation Using a Bayesian Approach

Qiu

Martinez

Tiwari

2016

AAPS J

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Abstract. A Bayesian approach with frequentist validity has been developed to support inferences derived from a BLevel A^in vivo-in vitro correlation (IVIVC). Irrespective of whether the in vivo data reflect in vivo dissolution or absorption, the IVIVC is typically assessed using a linear regression model. Confidence intervals are generally used to describe the uncertainty around the model. While the confidence intervals can describe population-level variability, it does not address the individual-level variability. Thus, there remains an inability to define a range of individual-level drug concentration-time profiles across a population based upon the BLevel A^predictions. This individual-level prediction is distinct from what can be accomplished by a traditional linear regression approach where the focus of the statistical assessment is at a marginal rather than an individual level. The objective of this study is to develop a hierarchical Bayesian method for evaluation of IVIVC, incorporating both the individual-and population-level variability, and to use this method to derive Bayesian tolerance intervals with matching priors that have frequentist validity in evaluating an IVIVC. In so doing, we can now generate population profiles that incorporate not only variability in subject pharmacokinetics but also the variability in the in vivo product performance.

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Understanding the in vivo performance of enteric coated tablets using an in vitro-in silico-in vivo approach: Case example diclofenac

Cited by 51 publications

References 33 publications

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

Quantitative Systems Pharmacology: A Framework for Context

Evaluating In Vivo-In Vitro Correlation Using a Bayesian Approach

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