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J Pharm Sci. 2014 Nov;103(11):3377-3397. doi: 10.1002/jps.24162. Epub 2014 Sep 24.

The biopharmaceutics risk assessment roadmap for optimizing clinical drug product performance.

Author information

1
Office of New Drug Quality Assessment, US Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, Maryland. Electronic address: arzu.selen@fda.hhs.gov.
2
Quantitative Clinical Pharmacology, AstraZeneca, Macclesfield, UK.
3
Faculty of Pharmaceutical Sciences, University of Copenhagen, Copenhagen, Denmark.
4
Bristol-Myers Squibb, New Brunswick, New Jersey.
5
Physiomics PLC, Oxford, UK.
6
Merck & Company, West Point, Pennsylvania.
7
US FDA/CVM, Rockville, Maryland.
8
Department of Pharmacy, University of Uppsala, Uppsala, Sweden.
9
Child Development Center, University of California, Irvine, California.
10
iRND3, Mountain View, California.
11
School of Pharmacy, University of Maryland, Baltimore, Maryland.
12
College of Pharmacy and Health Sciences, St. John's University, Queens, New York.
13
Pfizer Inc., Groton, Connecticut.
14
Institute of Pharmaceutical Technology Biocenter, Johann Wolfgang Goethe University, Frankfurt, Germany.

Abstract

The biopharmaceutics risk assessment roadmap (BioRAM) optimizes drug product development and performance by using therapy-driven target drug delivery profiles as a framework to achieve the desired therapeutic outcome. Hence, clinical relevance is directly built into early formulation development. Biopharmaceutics tools are used to identify and address potential challenges to optimize the drug product for patient benefit. For illustration, BioRAM is applied to four relatively common therapy-driven drug delivery scenarios: rapid therapeutic onset, multiphasic delivery, delayed therapeutic onset, and maintenance of target exposure. BioRAM considers the therapeutic target with the drug substance characteristics and enables collection of critical knowledge for development of a dosage form that can perform consistently for meeting the patient's needs. Accordingly, the key factors are identified and in vitro, in vivo, and in silico modeling and simulation techniques are used to elucidate the optimal drug delivery rate and pattern. BioRAM enables (1) feasibility assessment for the dosage form, (2) development and conduct of appropriate "learning and confirming" studies, (3) transparency in decision-making, (4) assurance of drug product quality during lifecycle management, and (5) development of robust linkages between the desired clinical outcome and the necessary product quality attributes for inclusion in the quality target product profile.

KEYWORDS:

Quality by Design (QbD); bioavailability; biopharmaceutics classification system (BCS); clinical trial simulations; controlled delivery; in silico modeling; in vitro models; in vitro/in vivo correlations (IVIVC); oral drug delivery; pharmacodynamics; pharmacokinetics

PMID:
25256402
DOI:
10.1002/jps.24162
[Indexed for MEDLINE]

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