ArticleThe AAPS journal2017
Characterising Drug Release from Immediate-Release Formulations of a Poorly Soluble Compound, Basmisanil, Through Absorption Modelling and Dissolution Testing.
Article in The AAPS journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The trial behind it
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Who cites it
13 citing papers in PubMed.
- PKPD and cardiac single cell modeling of a DDI study with a CYP3A4 substrate and itraconazole to quantify the effects on QT interval duration.Journal of pharmacokinetics and pharmacodynamics · 2020Trial
- Discriminative Dissolution Method Development Through an aQbD Approach.AAPS PharmSciTech · 2023Article
- Streamlining Food Effect Assessment - Are Repeated Food Effect Studies Needed? An IQ Analysis.The AAPS journal · 2023Article
- Developing a Formulation Strategy Coupled with PBPK Modeling and Simulation for the Weakly Basic Drug Albendazole.Pharmaceutics · 2023Article
- Pediatric Mini-Tablets: Predicting the Hidden Risk of Fill Errors.Pharmaceutics · 2023Article
- Physiologically Based Biopharmaceutics Modeling of Food Effect for Basmisanil: A Retrospective Case Study of the Utility for Formulation Bridging.Pharmaceutics · 2023Article
- The Use of Physiologically Based Pharmacokinetic Analyses-in Biopharmaceutics Applications -Regulatory and Industry Perspectives.Pharmaceutical research · 2022Review
- Development and Validation of a Discriminatory Dissolution Model for an Immediately Release Dosage Form by DOE and Statistical Approaches.AAPS PharmSciTech · 2021Article
- Basmisanil, a highly selective GABAScientific reports · 2021Article
- Computational Approaches in Preclinical Studies on Drug Discovery and Development.Frontiers in chemistry · 2020Review
- Dissolution and Translational Modeling Strategies Toward Establishing an In Vitro-In Vivo Link-a Workshop Summary Report.The AAPS journal · 2019Article
- PBPK Absorption Modeling: Establishing the In Vitro-In Vivo Link-Industry Perspective.The AAPS journal · 2019Article
- In Vivo Predictive Dissolution and Simulation Workshop Report: Facilitating the Development of Oral Drug Formulation and the Prediction of Oral Bioperformance.The AAPS journal · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study aimed to characterise the mechanism of release and absorption of Basmisanil, a biopharmaceutics classification system (BCS) class 2 compound, from immediate-release formulations via mechanistic absorption modelling, dissolution testing, and Raman imaging. An oral absorption model was developed in GastroPlus® and verified with single-dose pharmacokinetic data in humans. The properties and drug release behaviour of different oral Basmisanil formulations were characterised via biorelevant dissolution and Raman imaging studies. Finally, an in vitro-in vivo correlation (IVIVC) model was developed using conventional and mechanistic deconvolution methods for comparison. The GastroPlus model accurately simulated oral Basmisanil exposure from tablets and granules formulations containing micronized drug. Absorption of oral doses below 200 mg was mostly dissolution rate-limited and thus particularly sensitive to formulation properties. Indeed, reduced exposure was observed for a 120-mg film-coated tablet and the slower dissolution rate measured in biorelevant media was attributed to differences in drug load. This hypothesis was confirmed when Raman imaging showed that the percolation threshold was exceeded in this formulation. This biorelevant dissolution method clearly differentiated between the formulations and was used to develop a robust IVIVC model. The study demonstrates the applicability and impact of mechanistic absorption modelling and biopharmaceutical in vitro tools for rational drug development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.