ReviewThe AAPS journal2023
Biopharmaceutics Risk Assessment-Connecting Critical Bioavailability Attributes with In Vitro, In Vivo Properties and Physiologically Based Biopharmaceutics Modeling to Enable Generic Regulatory Submissions.
Review in The AAPS journal, 2023. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Model-Integrated Bioequivalence (MIBE) in Generic Drug Research: Can We Ease the Bioequivalence Burden?Pharmaceutics · 2026Review
- Exploring Additional Strength Biowaiver Perspectives in the ICH M13B Framework for Immediate Release Solid Oral Dosage Forms: Opportunities & Challenges.Therapeutic innovation & regulatory science · 2026Review
- Leveraging Model Integrated Bioequivalence (MIBE) to Address Regulatory Concerns and Waive Repeat Bioequivalence Studies-A Case Study with Clopidogrel.Journal of clinical pharmacology · 2026Article
- Application of Physiologically Based Biopharmaceutics Modeling (PBBM) to Establish Clinically Relevant Dissolution Specifications for a Prolonged Release Tablet Formulation of Verapamil, a BCS Class I Drug.AAPS PharmSciTech · 2025Article
- Assessing Impact of Food on Oral Drug Bioequivalence Supporting ICH M13 A with the Advancements of Physiologically Based Pharmacokinetic Modeling.Pharmaceutical research · 2025Article
- Formulation Strategy of BCS-II Drugs by Coupling Mechanistic In-Vitro and Nonclinical In-Vivo Data with PBPK: Fundamentals of Absorption-Dissolution to Parameterization of Modelling and Simulation.AAPS PharmSciTech · 2025Review
- Physiologically Based Biopharmaceutics Model of Apixaban for Biopharmaceutics Risk Assessment.Pharmaceutics · 2025Article
- A critical review on approaches to generate and validate virtual population for physiologically based pharmacokinetic models: Methodologies, case studies and way forward.European journal of clinical pharmacology · 2024Review
- A Semi-Mechanistic Physiologically Based Biopharmaceutics Model to Describe Complex and Saturable Absorption of Metformin: Justification of Dissolution Specifications for Extended Release Formulation.AAPS PharmSciTech · 2024Article
- Advancing Virtual Bioequivalence for Orally Administered Drug Products: Methodology, Real-World Applications and Future Outlook.Pharmaceuticals (Basel, Switzerland) · 2024Review
- Playing Hide-and-Seek with Tyrosine Kinase Inhibitors: Can We Overcome Administration Challenges?The AAPS journal · 2024Review
- Conventional vs Mechanistic IVIVC: A Comparative Study in Establishing Dissolution Safe Space for Extended Release Formulations.AAPS PharmSciTech · 2024Article
- Dissolution Profiles Comparison Using Conventional and Bias Corrected and Accelerated f2 Bootstrap Approaches with Different Software's: Impact of Variability, Sample Size and Number of Bootstraps.AAPS PharmSciTech · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quality risk assessment following ICH Q9 principles is an important activity to ensure optimal clinical efficacy and safety of a drug product. Typically, risk assessment is focused on product performance wherein critical material attributes, formulation variables, and process parameters are evaluated from a manufacturing perspective. Extending ICH Q9 principles to biopharmaceutics risk assessment to identify factors that can impact in vivo performance is an upcoming area. This is evident by recent regulatory trends wherein a new term critical bioavailability attributes (CBA) has been coined to identify such factors. Although significant work has been performed for biopharmaceutics risk assessment for new molecules, there is a need for harmonized biopharmaceutics risk assessment workflow for generic submissions. In this manuscript, we attempted to provide a framework for performing biopharmaceutics risk assessment for generic regulatory submissions. A detailed workflow for performing biopharmaceutics risk assessment includes identification of initial CBA (iCBA), their confirmatory evaluation followed by definition of the control strategy. Tools for biopharmaceutics risk assessment, i.e., bio-discriminatory dissolution method and physiologically based biopharmaceutics modeling (PBBM) were discussed from a practical perspective. Furthermore, a case study for CBA evaluation using PBBM modeling for an extended-release product for regulatory submission has been described using the proposed workflow. Finally, future directions of integrating CBA evaluation, biopharmaceutics risk assessment to the FDA Knowledge Aided Structured Assessment (KASA) initiative, the necessity of risk assessment templates, and knowledge sharing between industry and academia are discussed. Overall, the work described in this manuscript can facilitate and provide guidance for biopharmaceutics risk assessment for generic submissions.
Indexed as
Identifiers
37498474What OpenQuestion holds
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.