IMI – Oral biopharmaceutics tools project – Evaluation of bottom-up PBPK prediction success part 2: An introduction to the simulation exercise and overview of results

Margolskee, Alison; Darwich, Adam S.; Pépin, Xavier; Aarons, Leon; Galetin, Aleksandra; Rostami‐Hodjegan, Amin; Carlert, Sara; Hammarberg, Maria; Hilgendorf, Constanze; Johansson, Pernilla; Karlsson, Εva; Murphy, Donal B.; Tannergren, Christer; Thörn, Helena; Yasin, M.al-muzzammil; Mazuir, Florent; Nicolas, Olivier; Ramusovic, Sergej; Xu, Christine; Pathak, Shriram M.; Korjamo, Timo; Laru, Johanna; Malkki, J.; Pappinen, Sari; Tuunainen, Johanna; Dressman, Jennifer B.; Hansmann, Simone; Kostewicz, Edmund; He, Handan; Heimbach, Tycho; Wu, Fan; Hoft, Carolin; Laplanche, Loı̈c; Pang, Yan; Bolger, Michael B.; Huehn, Eva; Lukáčová, Viera; Mullin, James M.; Szeto, Ke X.; Costales, Chester; Lin, Jian; McAllister, Mark; Modi, Sweta; Rotter, Charles J.; Varma, Manthena V.; Wong, Mei; Mitra, Amitava; Bevernage, Jan; Biewenga, Jeike; Peer, Achiel Van; Lloyd, Richard C.; Shardlow, Carole; Langguth, Peter; Mishenzon, Irina; Nguyễn, Mai Ánh; Brown, Jonathan; Lennernäs, Hans; Abrahamsson, Bertil

doi:10.1016/j.ejps.2016.10.036

Cited by 59 publications

(40 citation statements)

References 35 publications

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“…PBPK modeling and simulation approaches have gained popularity in recent years, particularly for biopharmaceutical analysis of oral drug absorption, predicting the impact of drug-drug interactions, selecting an optimal dose and clinical trial design for pediatric applications, and for characterizing the impact of organ impairment. [3][4][5][6] PBPK models typically consist of 3 distinct components: (1) a drug-specific component characterizing the physicochemical properties of the drug, (2) a system-specific component characterizing the functioning of the underlying biological system, in this case the gastrointestinal (GI) tract, and (3) a trial design component characterizing the impact of intrinsic (eg, demographics) and extrinsic factors (eg, drug-drug interactions) on the drug's pharmacokinetics (PK). As a result, PBPK models provide a platform for evaluating the combined impact of multiple factors, such as different formulations or differences in gastric pH, on a drug's PK and thus for in silico BE testing.…”

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confidence: 99%

Physiologically Based Pharmacokinetic Modeling to Evaluate Formulation Factors Influencing Bioequivalence of Metoprolol Extended‐Release Products

Basu

Yang

Fang

et al. 2019

The Journal of Clinical Pharma

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The University of Florida Center for Pharmacometrics and Systems Pharmacology and the Food and Drug Administration Office of Generic Drugs have collaborated on a research project to develop a mechanism‐ and risk‐based strategy that systematically investigates postmarketing reports of therapeutic inequivalence following the switch between brand and generic drug products. In this study we developed and qualified a physiologically based pharmacokinetic model to systematically investigate the influence of drug‐ and formulation‐related properties on the oral absorption and bioequivalence of modified‐release products using metoprolol as an example. Our findings show that the properties of the release‐controlling polymer are the critical attributes for in vitro dissolution, in vivo absorption, and systemic exposure (ie, pharmacokinetics) and, thus, the bioequivalence of metoprolol extended‐release products rather than the properties of the drug itself. Differences in dissolution rate can result in significant differences in maximum plasma concentration but not in area under the concentration‐time curve.

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confidence: 99%

Physiologically Based Pharmacokinetic Modeling to Evaluate Formulation Factors Influencing Bioequivalence of Metoprolol Extended‐Release Products

Basu

Yang

Fang

et al. 2019

The Journal of Clinical Pharma

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“…Relevant information could be very useful in physiologically based pharmacokinetic (PBPK) modeling. Based on a series of recent publications from the OrBiTo project (39)(40)(41), prospective PBPK modeling of plasma profiles, after oral administration, using a bottom-up approaches is limited by the reliability of estimation of clearance and volume of distribution, quality of intestinal permeability data, quality of solubility data, and characterization of intestinal metabolism or transporter involvement. The development of methodologies for implementing BioGIT data into PBPK models and methodologies for the evaluation of processes taking place in the middle/lower small intestine using the BioGIT system as a basis would greatly improve PBPK modeling approaches and facilitate the development of orally administered drug products.…”

Section: Discussionmentioning

confidence: 99%

The BioGIT System: a Valuable In Vitro Tool to Assess the Impact of Dose and Formulation on Early Exposure to Low Solubility Drugs After Oral Administration

Kourentas

Vertzoni

Barmpatsalou

et al. 2018

AAPS J

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The purpose of this study was to evaluate the usefulness of the in vitro biorelevant gastrointestinal transfer (BioGIT) system in assessing the impact of dose and formulation on early exposure by comparing in vitro data with previously collected human plasma data of low solubility active pharmaceutical ingredients. Eight model active pharmaceutical ingredients were tested; Lu 35-138C (salt of weak base in a HP-beta-CD solution, three doses), fenofibrate (solid dispersion, tablet, two doses), AZD2207 EQ (salt of weak base, capsule, three doses), posaconazole (Noxafil® suspension, two doses), SB705498 (weak base, tablets vs. capsules), cyclosporine A (Sandimmun® vs. Sandimmun® Neoral), nifedipine (Adalat® capsule vs. Macorel® tablet), and itraconazole (Sporanox® capsule vs. Sporanox® solution). AUC values were calculated from the apparent concentration versus time data in the duodenal compartment of the BioGIT system. Differences in AUC values were evaluated versus differences in AUC and in AUC values calculated from previously collected plasma data in healthy adults. Ratios of mean AUC, mean AUC, and mean AUC values were estimated using the lowest dose or the formulation with the lower AUC value as denominator. The BioGIT system qualitatively identified the impact of dose and of formulation on early exposure in all cases. Log-transformed mean BioGIT AUC ratios correlated significantly with log-transformed mean plasma AUC ratios. Based on this correlation, BioGIT AUC ratios between 0.3 and 10 directly reflect corresponding plasma AUC ratios. BioGIT system is a valuable tool for the assessment of the impact of dose and formulation on early exposure to low solubility drugs.

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“…For example, it can be challenging to parse the multiple mechanisms leading to incomplete bioavailability, and PBPK modelling has been a valuable tool for moving the scientific basis for this forward. If the input parameters are variable or uncertain, caution is required when interpreting results [88]. Physiologically based pharmacokinetic modelling allows the assessment of the implications of limitations in model inputs.…”

Section: Discussionmentioning

confidence: 99%

Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies

et al. 2019

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Physiologically based pharmacokinetic modelling is well established in the pharmaceutical industry and is accepted by regulatory agencies for the prediction of drug-drug interactions. However, physiologically based pharmacokinetic modelling is valuable to address a much wider range of pharmaceutical applications, and new regulatory impact is expected as its full power is leveraged. As one example, physiologically based pharmacokinetic modelling is already routinely used during drug discovery for in-vitro to in-vivo translation and pharmacokinetic modelling in preclinical species, and this leads to the application of verified models for first-inhuman pharmacokinetic predictions. A consistent cross-industry strategy in this application area would increase confidence in the approach and facilitate further learning. With this in mind, this article aims to enhance a previously published first-inhuman physiologically based pharmacokinetic model-building strategy. Based on the experience of scientists from multiple companies participating in the GastroPlus™ User Group Steering Committee, new Absorption, Distribution, Metabolism and Excretion knowledge is integrated and decision trees proposed for each essential component of a first-inhuman prediction. We have reviewed many relevant scientific publications to identify new findings and highlight gaps that need to be addressed. Finally, four industry case studies for more challenging compounds illustrate and highlight key components of the strategy.

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IMI – Oral biopharmaceutics tools project – Evaluation of bottom-up PBPK prediction success part 2: An introduction to the simulation exercise and overview of results

Cited by 59 publications

References 35 publications

Physiologically Based Pharmacokinetic Modeling to Evaluate Formulation Factors Influencing Bioequivalence of Metoprolol Extended‐Release Products

Physiologically Based Pharmacokinetic Modeling to Evaluate Formulation Factors Influencing Bioequivalence of Metoprolol Extended‐Release Products

The BioGIT System: a Valuable In Vitro Tool to Assess the Impact of Dose and Formulation on Early Exposure to Low Solubility Drugs After Oral Administration

Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies

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