Biopharmaceutic IVIVE—Mechanistic Modeling of Single- and Two-Phase In Vitro Experiments to Obtain Drug-Specific Parameters for Incorporation Into PBPK Models

Pathak, Shriram M.; Schaefer, Kerstin J.; Jamei, Masoud; Turner, David B.

doi:10.1016/j.xphs.2018.11.034

Cited by 37 publications

(24 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the naproxen case example, starting from in vitro solubility and dissolution data, an approach of stepwise sequential estimation/confirmation of biopharmaceutical parameters was followed, (Pathak et al, 2019) before applying them to the PBPK model. In vitro dissolution profiles in conventional and biorelevant media were translated to different in vivo dissolution scenarios by implementing an in vitro-in vivo-extrapolation (IVIVE) strategy.…”

Section: Discussionmentioning

confidence: 99%

Establishing virtual bioequivalence and clinically relevant specifications using in vitro biorelevant dissolution testing and physiologically-based population pharmacokinetic modeling. case example: Naproxen

Loisios-Konstantinidis

Cristofoletti

Fotaki

et al. 2020

European Journal of Pharmaceutical Sciences

Self Cite

View full text Add to dashboard Cite

Background: Physiologically-based population pharmacokinetic modeling (popPBPK) coupled with in vitro biopharmaceutics tools such as biorelevant dissolution testing can serve as a powerful tool to establish virtual bioequivalence and set clinically relevant specifications. One of several applications of popPBPK modeling is in the emerging field of virtual bioequivalence (VBE), where it can be used to streamline drug development by implementing model-informed formulation design and to inform regulatory decision-making e.g., with respect to evaluating the possibility of extending BCS-based biowaivers beyond BCS Class I and III compounds in certain cases. Methods: In this study, Naproxen, a BCS class II weak acid was chosen as the model compound. In vitro biorelevant solubility and dissolution experiments were performed and the resulting data were used as an input to the PBPK model, following a stepwise workflow for the confirmation of the biopharmaceutical parameters. The naproxen PBPK model was developed by implementing a middleout approach and verified against clinical data obtained from the literature. Once confidence in the performance of the model was achieved, several in vivo dissolution scenarios, based on model-based analysis of the in vitro data, were used to simulate clinical trials in healthy adults. Inter-occasion variability (IOV) was also added to critical physiological parameters and mechanistically propagated through the simulations. The various trials were simulated on a "worst/best case" dissolution scenario and average bioequivalence was assessed according to Cmax, AUC and tmax. Results: VBE results demonstrated that naproxen products with in vitro dissolution reaching 85% dissolved within 90 minutes would lie comfortably within the bioequivalence limits for Cmax and AUC. Based on the establishment of VBE, a dissolution "safe space" was designed and a clinically relevant specification for naproxen products was proposed. The interplay between formulation-related and drug-specific PK parameters (e.g., t1/2) to predict the in vivo performance was also investigated. Conclusion: Over a wide range of values, the in vitro dissolution rate is not critical for the clinical performance of naproxen products and therefore naproxen could be eligible for BCS-based biowaivers 3 based on in vitro dissolution under intestinal conditions. This approach may also be applicable to other poorly soluble acidic compounds with long half-lives, providing an opportunity to streamline drug development and regulatory decision-making without putting the patient at a risk.

show abstract

Section: Discussionmentioning

confidence: 99%

Establishing virtual bioequivalence and clinically relevant specifications using in vitro biorelevant dissolution testing and physiologically-based population pharmacokinetic modeling. case example: Naproxen

Loisios-Konstantinidis

Cristofoletti

Fotaki

et al. 2020

European Journal of Pharmaceutical Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…The second type consists of drug-related parameters, such as the plasma ratio, organ/blood partition coefficient, and permeability (Nestorov, 2003 ). Recently, PBPK models have been widely constructed to predict drug-related parameters (Chow et al, 2016 ; Pathak et al, 2019 ; Song et al, 2020 ). For example, Chow et al used a physiologically based model to predict drug solubility and effective permeability (Chow et al, 2016 ) to examine the potential impact of excipients on oral drug absorption.…”

Section: In Silico Approachesmentioning

confidence: 99%

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Zhou

et al. 2020

Front. Chem.

210

View full text Add to dashboard Cite

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

show abstract

“…PBPK modelling was carried out using the ADAM (Advanced Dissolution, Absorption, and Metabolism) model, which is available as part of the Simcyp PBPK simulator (Version 18, Release 2). Modelling was carried out in a stepwise fashion using an in vitro-in vivo extrapolation (IVIV_E) of dissolution and solubility, as outlined previously by Pathak et al [25,30] and Hens et al [35] Aqueous and bile micelle-mediated solubility data were estimated using the SIVA (Simcyp In Vitro Analysis) toolkit, using solubility data from Section 2.2.1 and other literature sources [18,25,30]. Initially, the intrinsic solubility and solubility factor were calculated using the experimental pH solubility profile in SIVA.…”

Section: Pbpk Modellingmentioning

confidence: 99%

On the Usefulness of Two Small-Scale In Vitro Setups in the Evaluation of Luminal Precipitation of Lipophilic Weak Bases in Early Formulation Development

et al. 2020

View full text Add to dashboard Cite

A small-scale biphasic dissolution setup and a small-scale dissolution-permeation (D-P) setup were evaluated for their usefulness in simulating the luminal precipitation of three lipophilic weak bases-dipyridamole, ketoconazole and itraconazole. The transition from the gastric to intestinal environment was incorporated into both experimental procedures. Emulsification during the biphasic dissolution experiments had a minimal impact on the data, when appropriate risk mitigation steps were incorporated. Precipitation parameters estimated from the in vitro data were inputted into the Simcyp ® physiologically based pharmacokinetic (PBPK) modelling software and simulated human plasma profiles were compared with previously published pharmacokinetic data. Average C max and AUC values estimated using experimentally derived precipitation parameters from the biphasic experiments deviated from corresponding published actual values less than values estimated using the default simulator parameters for precipitation. The slow rate of transport through the biomimetic membrane in the D-P setup limited its usefulness in forecasting the rates of in vivo precipitation used in the modelling of average plasma profiles.

show abstract

Biopharmaceutic IVIVE—Mechanistic Modeling of Single- and Two-Phase In Vitro Experiments to Obtain Drug-Specific Parameters for Incorporation Into PBPK Models

Cited by 37 publications

References 44 publications

Establishing virtual bioequivalence and clinically relevant specifications using in vitro biorelevant dissolution testing and physiologically-based population pharmacokinetic modeling. case example: Naproxen

Establishing virtual bioequivalence and clinically relevant specifications using in vitro biorelevant dissolution testing and physiologically-based population pharmacokinetic modeling. case example: Naproxen

Computational Approaches in Preclinical Studies on Drug Discovery and Development

On the Usefulness of Two Small-Scale In Vitro Setups in the Evaluation of Luminal Precipitation of Lipophilic Weak Bases in Early Formulation Development

Contact Info

Product

Resources

About