Physiologically Based Pharmacokinetic Modeling for Substitutability Analysis of Venlafaxine Hydrochloride Extended-Release Formulations Using Different Release Mechanisms: Osmotic Pump Versus Openable Matrix

Lin, Ho-Pi; Sun, Dajun; Zhang, Xinyuan; Wen, Hu

doi:10.1016/j.xphs.2016.06.015

Cited by 9 publications

(7 citation statements)

References 30 publications

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“…A verified PBPK absorption model simulated the PK profiles after a single oral dose of ER venlafaxine hydrochloride tablets based on either an osmotic pump or an openable matrix design. On the basis of the predicted PK profiles, 90% CIs of peak plasma concentration and AUCt of the venlafaxine openable matrix tablets were within the BE acceptance limits (i.e., 80–125%) when the lag time varied from 0 to 4 hours using drug‐release profiles under most dissolution conditions, indicating limited BE risk for a delayed onset of drug release from the approved generic venlafaxine hydrochloride ER tablets with an openable matrix design …”

Section: Regulatory Impacts Of Qmm On Drug Product Review Guidance Dmentioning

confidence: 85%

“…For nifedipine, a clinical study has been conducted to evaluate PPI effects on PK by in vivo studies . The research helps the FDA assess mechanism release change from osmotic pump to matrix‐based release for their impact on PPI interactions …”

Section: Future Perspectives Of Qmm On Generic Drug Development and R...mentioning

confidence: 99%

“…On the basis of the predicted PK profiles, 90% CIs of peak plasma concentration and AUCt of the venlafaxine openable matrix tablets were within the BE acceptance limits (i.e., 80-125%) when the lag time varied from 0 to 4 hours using drug-release profiles under most dissolution conditions, indicating limited BE risk for a delayed onset of drug release from the approved generic venlafaxine hydrochloride ER tablets with an openable matrix design. 44 MIE for ANDA approval QCP modeling results, once sufficiently verified, can serve as MIE for drug approval. The approval of the NDA for brimonidine topical gel relied on a primary efficacy end point that was a composite success rate at different timepoints.…”

Section: Model-based Pk Metrics' Determination For Be Assessmentmentioning

confidence: 99%

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Generating Model Integrated Evidence for Generic Drug Development and Assessment

Zhao

Kim

Zhang

et al. 2019

Clin Pharma and Therapeutics

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Quantitative methods and modeling (QMM) covers a broad spectrum of tool sets, of which physiologically based models and quantitative clinical pharmacology are most critical for generic drugs. QMM has been increasingly applied by the US Food and Drug Administration (FDA) to facilitating generic drug development and review, and has played a critical role in the modernization of bioequivalence (BE) assessment, especially for locally acting drug products, complex products of other types, and modified-release solid oral dosage forms. QMM has aided the development of novel BE methods, in vitro-only BE approaches, and risk-based evaluations. The future of QMM is model integrated evidence or virtual BE studies that can potentially provide pivotal information for generic drug approval. In summary, QMM is indispensable in modernizing generic drug development, BE assessment, and regulatory decision makings. Regulatory examples demonstrate how QMM can be used in modernizing generic drug development, addressing challenges in BE assessment, and supporting regulatory decision making.New drug development and approval depends on sufficient in vitro and in vivo evidence to support the regulatory assessment of drug product efficacy and safety. A generic drug is approved on the basis of sufficient demonstration of sameness to the corresponding brand drug. For both new and generic drugs, quantitative methods and modeling (QMM) can accelerate product development and regulatory assessment.For both new and generic drug development and approval, a mathematical model can be thought of as a knowledge management system that integrates all scientific understanding and existing data about a drug product regarding its formulation, in vitro/in vivo release, pharmacokinetics (PK), pharmacodynamics (PD), and clinical responses. The discipline of QMM both builds these models and uses them to aid both regulatory and business decisions. QMM in new drug developmentFor new drug development, as shown in Figure 1, the data axis includes information and datasets collected through the full Research and Development course for the active pharmaceutical ingredient (API), formulation, in vitro release, the targeted in vivo release profile, animal and human drug PK, PD response(s), and clinical responses in terms of both efficacy and safety end points. Models describing the relationships between the datasets are captured in the model axis. The use of these models can accelerate decisions and optimize the collection of data.Model-informed drug development (MIDD) under the Prescription Drug User Fee Amendments of 2017 (PDUFA VI) is an initiative to use these models to decrease development uncertainty, cost, time, attritions, and failure rates. It aims to inform drug development and regulatory decision makings by using population PK, dose/exposure-response relationships, and biological and statistical models derived from preclinical and clinical data sources. An MIDD pilot program was launched with goals to provide an opportunity for drug developers and the FDA t...

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Section: Regulatory Impacts Of Qmm On Drug Product Review Guidance Dmentioning

confidence: 85%

Section: Future Perspectives Of Qmm On Generic Drug Development and R...mentioning

confidence: 99%

Section: Model-based Pk Metrics' Determination For Be Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

Generating Model Integrated Evidence for Generic Drug Development and Assessment

Zhao

Kim

Zhang

et al. 2019

Clin Pharma and Therapeutics

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“…There were several limitations in our study. First, the best-fit models for intravenous VEN and ODV were three-compartment model and two-compartment model, respectively, according to pharmacokinetics after IV infusions of VEN and ODV ( Lin et al, 2016 ). But in the current study, a one-compartment model was used to describe the pharmacokinetics of VEN and ODV.…”

Section: Discussionmentioning

confidence: 99%

Joint population pharmacokinetic modeling of venlafaxine and O-desmethyl venlafaxine in healthy volunteers and patients to evaluate the impact of morbidity and concomitant medication

Wang

Huang

et al. 2022

Front. Pharmacol.

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Introduction: Venlafaxine (VEN) is a widely used dual selective serotonin/noradrenaline reuptake inhibitor indicated for depression and anxiety. It undergoes first-pass metabolism to its active metabolite, O-desmethyl venlafaxine (ODV). The aim of the present study was to develop a joint population pharmacokinetic (PPK) model to characterize their pharmacokinetic characters simultaneously.Methods: Plasma concentrations with demographic and clinical data were derived from a bioequivalence study in 24 healthy subjects and a naturalistic TDM setting containing 127 psychiatric patients. A parent-metabolite PPK modeling was performed with NONMEM software using a non-linear mixed effect modeling approach. Goodness of fit plots and normalized prediction distribution error method were used for model validation.Results and conclusion: Concentrations of VEN and ODV were well described with a one-compartment model incorporating first-pass metabolism. The first-pass metabolism was modeled as a first-order conversion. The morbid state and concomitant amisulpride were identified as two significant covariates affecting the clearance of VEN and ODV, which may account for some of the variations in exposure. This model may contribute to the precision medication in clinical practice and may inspire other drugs with pre-system metabolism.

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“…This is because a mechanistic understanding of the complex interplay of polymers, drugs, gastrointestinal (GI) components (e.g., buffer species and bile salts) and hydrodynamics (e.g., motility and shear forces) as well as food effects (e.g., viscosity, fat, protein and fibre) is required, which is not an easy task. Thus, the current approaches to simulating the in vivo dissolution of drug particles from MR formulations are (a) to use the in vitro compendial dissolution profile as a release profile and allow mechanistic models (e.g., Advanced Dissolution Absorption Metabolism (ADAM) in Simcyp ® or Advanced Compartmental Absorption and Transit (ACAT TM ) in Gastroplus ® ) to handle the dissolution of the released drug particles [ 15 , 16 ]; (b) to directly import the dissolution profile into the PBBM (although this is not recommended as it removes inter-subject variability from the simulations); and (c) to fit the in vitro dissolution profile, mainly derived from USP apparatuses, using a Weibull function [ 17 , 18 ]. Thus, the most important input to the PBBMs for MR formulations is still the in vitro dissolution profile.…”

Section: Introductionmentioning

confidence: 99%

Use of In Vitro Dynamic Colon Model (DCM) to Inform a Physiologically Based Biopharmaceutic Model (PBBM) to Predict the In Vivo Performance of a Modified-Release Formulation of Theophylline

Stamatopoulos¹,

O’Farrell

Simmons

et al. 2023

Pharmaceutics

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A physiologically based biopharmaceutic model (PBBM) of a modified-release formulation of theophylline (Uniphyllin Continus® 200 mg tablet) was developed and implemented to predict the pharmacokinetic (PK) data of healthy male volunteers by integrating dissolution profiles measured in a biorelevant in vitro model: the Dynamic Colon Model (DCM). The superiority of the DCM over the United States Pharmacopeia (USP) Apparatus II (USP II) was demonstrated by the superior predictions for the 200 mg tablet (average absolute fold error (AAFE): 1.1–1.3 (DCM) vs. 1.3–1.5 (USP II). The best predictions were obtained using the three motility patterns (antegrade and retrograde propagating waves, baseline) in the DCM, which produced similar PK profiles. However, extensive erosion of the tablet occurred at all agitation speeds used in USP II (25, 50 and 100 rpm), resulting in an increased drug release rate in vitro and overpredicted PK data. The PK data of the Uniphyllin Continus® 400 mg tablet could not be predicted with the same accuracy using dissolution profiles from the DCM, which might be explained by differences in upper gastrointestinal (GI) tract residence times between the 200 and 400 mg tablets. Thus, it is recommended that the DCM be used for dosage forms in which the main release phenomena take place in the distal GI tract. However, the DCM again showed a better performance based on the overall AAFE compared to the USP II. Regional dissolution profiles within the DCM cannot currently be integrated into Simcyp®, which might limit the predictivity of the DCM. Thus, further compartmentalization of the colon within PBBM platforms is required to account for observed intra-regional differences in drug distribution.

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Physiologically Based Pharmacokinetic Modeling for Substitutability Analysis of Venlafaxine Hydrochloride Extended-Release Formulations Using Different Release Mechanisms: Osmotic Pump Versus Openable Matrix

Cited by 9 publications

References 30 publications

Generating Model Integrated Evidence for Generic Drug Development and Assessment

Generating Model Integrated Evidence for Generic Drug Development and Assessment

Joint population pharmacokinetic modeling of venlafaxine and O-desmethyl venlafaxine in healthy volunteers and patients to evaluate the impact of morbidity and concomitant medication

Use of In Vitro Dynamic Colon Model (DCM) to Inform a Physiologically Based Biopharmaceutic Model (PBBM) to Predict the In Vivo Performance of a Modified-Release Formulation of Theophylline

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