Literature DB >> 25256402

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

Arzu Selen1, Paul A Dickinson2, Anette Müllertz3, John R Crison4, Hitesh B Mistry5, Maria T Cruañes6, Marilyn N Martinez7, Hans Lennernäs8, Tim L Wigal9, David C Swinney10, James E Polli11, Abu T M Serajuddin12, Jack A Cook13, Jennifer B Dressman14.   

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.
© 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

Entities:  

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

Mesh:

Substances:

Year:  2014        PMID: 25256402     DOI: 10.1002/jps.24162

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.534


  11 in total

1.  Application of Absorption Modeling in Rational Design of Drug Product Under Quality-by-Design Paradigm.

Authors:  Filippos Kesisoglou; Amitava Mitra
Journal:  AAPS J       Date:  2015-05-22       Impact factor: 4.009

2.  The BioGIT System: a Valuable In Vitro Tool to Assess the Impact of Dose and Formulation on Early Exposure to Low Solubility Drugs After Oral Administration.

Authors:  Alexandros Kourentas; Maria Vertzoni; Vicky Barmpatsalou; Patrick Augustijns; Stefania Beato; James Butler; Rene Holm; Neils Ouwerkerk; Joerg Rosenberg; Tomokazu Tajiri; Christer Tannergren; Mira Symillides; Christos Reppas
Journal:  AAPS J       Date:  2018-05-24       Impact factor: 4.009

3.  Approaches for Establishing Clinically Relevant Dissolution Specifications for Immediate Release Solid Oral Dosage Forms.

Authors:  Andre Hermans; Andreas M Abend; Filippos Kesisoglou; Talia Flanagan; Michael J Cohen; Dorys A Diaz; Y Mao; Limin Zhang; Gregory K Webster; Yiqing Lin; David A Hahn; Carrie A Coutant; Haiyan Grady
Journal:  AAPS J       Date:  2017-08-22       Impact factor: 4.009

Review 4.  Solid Dispersion Formulations by FDM 3D Printing-A Review.

Authors:  Garba M Khalid; Nashiru Billa
Journal:  Pharmaceutics       Date:  2022-03-23       Impact factor: 6.525

5.  Characterization of Solid Dispersion of Itraconazole Prepared by Solubilization in Concentrated Aqueous Solutions of Weak Organic Acids and Drying.

Authors:  Tapan Parikh; Harpreet K Sandhu; Tanaji T Talele; Abu T M Serajuddin
Journal:  Pharm Res       Date:  2016-03-07       Impact factor: 4.200

6.  Time-dependent classification accuracy curve under marker-dependent sampling.

Authors:  Zhaoyin Zhu; Xiaofei Wang; Paramita Saha-Chaudhuri; Andrzej S Kosinski; Stephen L George
Journal:  Biom J       Date:  2016-04-27       Impact factor: 2.207

7.  Dissolution and Translational Modeling Strategies Enabling Patient-Centric Drug Product Development: the M-CERSI Workshop Summary Report.

Authors:  Andreas Abend; Tycho Heimbach; Michael Cohen; Filippos Kesisoglou; Xavier Pepin; Sandra Suarez-Sharp
Journal:  AAPS J       Date:  2018-04-09       Impact factor: 4.009

Review 8.  Cell-specific biomarkers and targeted biopharmaceuticals for breast cancer treatment.

Authors:  Mei Liu; Zhiyang Li; Jingjing Yang; Yanyun Jiang; Zhongsi Chen; Zeeshan Ali; Nongyue He; Zhifei Wang
Journal:  Cell Prolif       Date:  2016-06-16       Impact factor: 6.831

9.  Developing Clinically Relevant Dissolution Specifications for Oral Drug Products-Industrial and Regulatory Perspectives.

Authors:  Mark McAllister; Talia Flanagan; Karin Boon; Xavier Pepin; Christophe Tistaert; Masoud Jamei; Andreas Abend; Evangelos Kotzagiorgis; Claire Mackie
Journal:  Pharmaceutics       Date:  2019-12-23       Impact factor: 6.321

Review 10.  The Future of Pharmaceutical Manufacturing Sciences.

Authors:  Jukka Rantanen; Johannes Khinast
Journal:  J Pharm Sci       Date:  2015-08-17       Impact factor: 3.534

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