Physiologically based pharmacokinetic (PBPK) modelling tools: how to fit with our needs?

Bouzom, François; Ball, Kathryn; Perdaems, Nathalie; Walther, Bernard

doi:10.1002/bdd.1767

Cited by 82 publications

(53 citation statements)

References 55 publications

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“…The need of scaling factors for transport parameters obtained in vitro is well recognized in the prediction of drug absorption, distribution, and elimination via transporters (Bouzom et al, 2012;Tsamandouras et al, 2013;Hsu et al, 2014). In some cases, the scaling factor may represent the differences in transporter protein abundance between in vitro and in vivo, but it may also reflect the dissimilarities in activity per milligram of protein owing to several factors, including membrane environment or differences in posttranslational modifications (Straumann et al, 2006;Croset et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Prediction of Renal Transporter Mediated Drug-Drug Interactions for Pemetrexed Using Physiologically Based Pharmacokinetic Modeling

et al. 2014

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Pemetrexed, an anionic anticancer drug with a narrow therapeutic index, is eliminated mainly by active renal tubular secretion. The in vitro to in vivo extrapolation approach used in this work was developed to predict possible drug-drug interactions (DDIs) that may occur after coadministration of pemetrexed and nonsteroidal anti-inflammatory drugs (NSAIDs), and it included in vitro assays, risk assessment models, and physiologically based pharmacokinetic (PBPK) models. The pemetrexed transport and its inhibition parameters by several NSAIDs were quantified using HEK-PEAK cells expressing organic anion transporter (OAT) 3 or OAT4. The NSAIDs were ranked according to their DDI index, calculated as the ratio of their maximum unbound concentration in plasma over the concentration inhibiting 50% (IC 50 ) of active pemetrexed transport. A PBPK model for ibuprofen, the NSAID with the highest DDI index, was built incorporating active renal secretion in Simcyp Simulator. The bottom-up model for pemetrexed underpredicted the clearance by 2-fold. The model we built using a scaling factor of 5.3 for the maximal uptake rate (V max ) of OAT3, which estimated using plasma concentration profiles from patients given a 10-minute infusion of 500 mg/m 2 of pemetrexed supplemented with folic acid and vitamin B 12 , recovered the clinical data adequately. The observed/predicted increases in C max and the area under the plasma-concentration time curve (AUC 0-inf ) of pemetrexed when ibuprofen was coadministered were 1.1 and 1.0, respectively. The coadministration of all other NSAIDs was predicted to have no significant impact on the AUC 0-inf based on their DDI indexes. The PBPK model reasonably reproduced pemetrexed concentration time profiles in cancer patients and its interaction with ibuprofen.

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Section: Discussionmentioning

confidence: 99%

Prediction of Renal Transporter Mediated Drug-Drug Interactions for Pemetrexed Using Physiologically Based Pharmacokinetic Modeling

et al. 2014

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“…Commercial providers of PBPK modeling software invariably provide such services as part of their offering. Regardless of the source of the models, an educational effort by the developers to inform the users is of paramount importance and should be considered in developing any policies focusing on expanding the utilization of PBPK-IVIVE as discussed in this issue by Bouzum et al [4].…”

Section: Experience With Methodology As Opposed To Experience With Immentioning

confidence: 98%

Physiologically based pharmacokinetic (PBPK) modeling: It is here to stay!

Rostami‐Hodjegan

Tamai

Pang

2012

Biopharm & Drug Disp

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The current issue of Biopharmaceutics and Drug Disposition is dedicated to the theme of 'physiologically based pharmacokinetic (PBPK) modeling' and its applications in drug developmental and regulatory assessment. The editors deemed it necessary to provide an opening commentary to highlight many of important aspects related to the resurgence of PBPK and to offer suggestions for further improvement in the propagation of this discipline to other areas of modeling. Two guest editors, Drs Shiew-Mei Huang and Bill Smith, who have considerable experience in regulatory and drug development science, have provided their personal views in separate commentaries. These perspectives focus on further requirements to progress the field (including demonstration of validity and added value, educational needs, quality of modeling, best practices and so on). We are grateful to all authors and guest editors who contributed to the special issue and we hope that the readers, particularly those working in industrial settings, find the special issue of interest and practical use for implementing policies on the expanded use of PBPK. Past and Present Status of PBPK in Drug DevelopmentPhysiologically based pharmacokinetic (PBPK) modeling is an extremely useful approach with the ability to predict the temporal profiles of xenobiotics and their metabolites both in animals and humans exposed to one or several environmental chemicals or drugs. PBPK modeling is not a new subject and there have been numerous publications describing the methodology and its applications. However, not until recently and with few exceptions, these efforts have been largely academic, and in many cases related to environmental toxicology rather than drug development. Many reviews have provided reference to the PBPK methodology. However, the range of applications of PBPK modeling as a tool in drug development and policies in embracing this type of modeling as a link between the preclinical and clinical studies has not been adequately addressed in the past. This issue has assembled a collection of reports to focus on the applications and they cover various practices in industry, with relevance to study design and regulatory assessment.Recent analysis of FDA submissions carried out by Zhao et al. has indicated a sharp increase in the number of applications of PBPK in drug development for both NDA and IND cases [1]. Some have considered the exploitation of PBPK modeling in drug development to be the result of a greater connectivity between classical PBPK and in vitro-in vivo extrapolation (IVIVE) techniques in recent years [2] while others are encouraged by the availability of user-friendly software and increased computing power, providing the ease to foster a much greater enthusiasm towards the more mechanistic models preferred for the regulatory framework to usages of PBPK modeling more frequently than before [3].

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“…110 treatments and 25 biomarkers) for which drug labelling information-warnings, dosage, usage, etc.-can be influenced by patient pharmacogenomics (http:// www.fda.gov/Drugs/ScienceResearch/ResearchAreas/Pharma cogenetics/ucm083378.htm). Of these, more than 50% are due to just three markers: CYP2D6 (40 treatments), G6PD (glucose 6-phosphate dehydrogenase) (20) and CYP2C19 (16). All these biomarkers are related to drug metabolism, as are three other biomarkers affecting relatively large numbers of treatments, namely CYP2C9 (4), TPMT (thiopurine methyltransferase) (4) and UGT1A1 (5).…”

Section: Personalized Medicine In Current Clinical Practicementioning

confidence: 99%

“…expression of drug-metabolizing enzymes) drug PK in characterized subpopulations and individuals can be simulated [17,18]. The performance of PBPK modelling in predicting PK has been recently assessed [19] and available methods, including software for performing PBPK modelling, reviewed [20]. Linking predictions of time-dependent drug concentrations at molecular targets with empirical or mechanistic PD models (to create PBPK/ PD models) permits the prediction of therapeutic and/or toxic responses to drugs as driven by their PK [21].…”

Section: Non-universal Therapy In Drug Discovery and Developmentmentioning

confidence: 99%

A physiome interoperability roadmap for personalized drug development

et al. 2016

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One contribution of 12 to a theme issue 'The Human Physiome: a necessary key to the creative destruction of medicine'. The goal of developing therapies and dosage regimes for characterized subgroups of the general population can be facilitated by the use of simulation models able to incorporate information about inter-individual variability in drug disposition (pharmacokinetics), toxicity and response effect (pharmacodynamics). Such observed variability can have multiple causes at various scales, ranging from gross anatomical differences to differences in genome sequence. Relevant data for many of these aspects, particularly related to molecular assays (known as '-omics'), are available in online resources, but identification and assignment to appropriate model variables and parameters is a significant bottleneck in the model development process. Through its efforts to standardize annotation with consequent increase in data usability, the human physiome project has a vital role in improving productivity in model development and, thus, the development of personalized therapy regimes. Here, we review the current status of personalized medicine in clinical practice, outline some of the challenges that must be overcome in order to expand its applicability, and discuss the relevance of personalized medicine to the more widespread challenges being faced in drug discovery and development. We then review some of (i) the key data resources available for use in model development and (ii) the potential areas where advances made within the physiome modelling community could contribute to physiologically based pharmacokinetic and physiologically based pharmacokinetic/pharmacodynamic modelling in support of personalized drug development. We conclude by proposing a roadmap to further guide the physiome community in its on-going efforts to improve data usability, and integration with modelling efforts in the support of personalized medicine development.

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Physiologically based pharmacokinetic (PBPK) modelling tools: how to fit with our needs?

Cited by 82 publications

References 55 publications

Prediction of Renal Transporter Mediated Drug-Drug Interactions for Pemetrexed Using Physiologically Based Pharmacokinetic Modeling

Prediction of Renal Transporter Mediated Drug-Drug Interactions for Pemetrexed Using Physiologically Based Pharmacokinetic Modeling

Physiologically based pharmacokinetic (PBPK) modeling: It is here to stay!

A physiome interoperability roadmap for personalized drug development

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