Evidence map›Paper›PMID 37085637›Full record

ArticleThe AAPS journal2023

Harmonizing Biopredictive Methodologies Through the Product Quality Research Institute (PQRI) Part I: Biopredictive Dissolution of Ibuprofen and Dipyridamole Tablets.

Yasuhiro Tsume, Lee Ashworth, Marival Bermejo, Joan Cheng, Vincent Cicale, Jennifer Dressman, Masahiro Fushimi, Isabel Gonzalez-Alvarez, Yiwang Guo, Corinne Jankovsky and 8 more

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In one paragraph

Article in The AAPS journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors.

Yasuhiro TsumeMerck & Co., Inc., 126 E Lincoln Avenue, Rahway, New Jersey, USA. yasuhiro.tsume@merck.com.
Lee AshworthAstraZeneca, Macclesfield, UK.
Marival BermejoMiguel Hernandez University, Alicante, Spain.
Joan ChengUniversity of Minnesota, Minneapolis, Minneapolis, USA.
Vincent CicaleBristol-Myers Squibb Company, New Brunswick, New Jersey, USA.
Jennifer DressmanFraunhofer Institute for Translational Medicine Pharmacology, Frankfurt, Germany.
Masahiro FushimiSawai Pharmaceutical Co. Ltd., Osaka, Japan.
Isabel Gonzalez-AlvarezMiguel Hernandez University, Alicante, Spain.
Yiwang GuoUniversity of Minnesota, Minneapolis, Minneapolis, USA.
Corinne JankovskyBoehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, Connecticut, USA.
Xujin LuBristol-Myers Squibb Company, New Brunswick, New Jersey, USA.
Kazuki MatsuiSawai Pharmaceutical Co. Ltd., Osaka, Japan.
Sanjaykumar PatelMerck & Co., Inc., 126 E Lincoln Avenue, Rahway, New Jersey, USA.
Natalie SandersonAstraZeneca, Macclesfield, UK.
Changquan Calvin SunUniversity of Minnesota, Minneapolis, Minneapolis, USA.
Naveen K ThakralSchrodinger Inc., New York, New York, USA.
Miki YamaneSawai Pharmaceutical Co. Ltd., Osaka, Japan.
Laurin ZöllerGoethe Universität, Frankfurt, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Assessing in vivo performance to inform formulation selection and development decisions is an important aspect of drug development. Biopredictive dissolution methodologies for oral dosage forms have been developed to understand in vivo performance, assist in formulation development/optimization, and forecast the outcome of bioequivalence studies by combining them with simulation tools to predict plasma profiles in humans. However, unlike compendial dissolution methodologies, the various biopredictive methodologies have not yet been harmonized or standardized. This manuscript presents the initial phases of an effort to develop best practices and move toward standardization of the biopredictive methodologies through the Product Quality Research Institute (PQRI, https://pqri.org ) entitled "The standardization of in vitro predictive dissolution methodologies and in silico bioequivalence study Working Group." This Working Group (WG) is comprised of participants from 10 pharmaceutical companies and academic institutes. The project will be accomplished in a total of five phases including assessing the performance of dissolution protocols designed by the individual WG members, and then building "best practice" protocols based on the initial dissolution profiles. After refining the "best practice" protocols to produce equivalent dissolution profiles, those will be combined with physiologically based biopharmaceutics models (PBBM) to predict plasma profiles. In this manuscript, the first two of the five phases are reported, namely generating biopredictive dissolution profiles for ibuprofen and dipyridamole and using those dissolution profiles with PBBM to match the clinical plasma profiles. Key experimental parameters are identified, and this knowledge will be applied to build the "best practice" protocol in the next phase.

Indexed as

DipyridamoleIbuprofenAcademies and InstitutesAdministration, OralHumansModels, BiologicalSolubilityTabletsDipyridamoleIbuprofenTabletsbiopredictive dissolutionbiorelevantbuffer speciesdissolution profilein vivo predictivemodelsimulation

Identifiers

What OpenQuestion holds

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Registered trials

None linked

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.