Evidence map›Paper›PMID 30790200›Full record

ReviewThe AAPS journal2019

First-Principles and Empirical Approaches to Predicting In Vitro Dissolution for Pharmaceutical Formulation and Process Development and for Product Release Testing.

Nikolay Zaborenko, Zhenqi Shi, Claudia C Corredor, Brandye M Smith-Goettler, Limin Zhang, Andre Hermans, Colleen M Neu, Md Anik Alam, Michael J Cohen, Xujin Lu and 2 more

Abstract readReview
In one paragraph

Review in The AAPS journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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  13. Portable and automated analyzer for rapid and high precisionJournal of pharmaceutical analysis · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Nikolay ZaborenkoSmall Molecule Design and Development, Eli Lilly and Company, Lilly Technology Center North, B302, Drop 3210, Indianapolis, Indiana, 46285, USA.
Zhenqi ShiSmall Molecule Design and Development, Eli Lilly and Company, Lilly Technology Center North, B302, Drop 3210, Indianapolis, Indiana, 46285, USA. shi_zhenqi@lilly.com.
Claudia C CorredorDrug Product Science and Technology, Bristol-Myers Squibb, New Brunswick, New Jersey, 08903, USA.
Brandye M Smith-GoettlerMerck & Co., Inc., Kenilworth, New Jersey, 07033, USA.
Limin ZhangDrug Product Science and Technology, Bristol-Myers Squibb, New Brunswick, New Jersey, 08903, USA.
Andre HermansMerck & Co., Inc., Kenilworth, New Jersey, 07033, USA.
Colleen M NeuMerck & Co., Inc., Kenilworth, New Jersey, 07033, USA.
Md Anik AlamAnalytical Research and Development, Pfizer Inc., Groton, Connecticut, 06340, USA.
Michael J CohenGlobal Chemistry and Manufacturing Controls, Pfizer Inc., Groton, Connecticut, 06340, USA.
Xujin LuDrug Product Science and Technology, Bristol-Myers Squibb, New Brunswick, New Jersey, 08903, USA.
Leah XiongMerck & Co., Inc., Kenilworth, New Jersey, 07033, USA.
Brian M ZacourDrug Product Science and Technology, Bristol-Myers Squibb, New Brunswick, New Jersey, 08903, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This manuscript represents the perspective of the Dissolution Working Group of the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) and of two focus groups of the American Association of Pharmaceutical Scientists (AAPS): Process Analytical Technology (PAT) and In Vitro Release and Dissolution Testing (IVRDT). The intent of this manuscript is to show recent progress in the field of in vitro predictive dissolution modeling and to provide recommended general approaches to developing in vitro predictive dissolution models for both early- and late-stage formulation/process development and batch release. Different modeling approaches should be used at different stages of drug development based on product and process understanding available at those stages. Two industry case studies of current approaches used for modeling tablet dissolution are presented. These include examples of predictive model use for product development within the space explored during formulation and process optimization, as well as of dissolution models as surrogate tests in a regulatory filing. A review of an industry example of developing a dissolution model for real-time release testing (RTRt) and of academic case studies of enabling dissolution RTRt by near-infrared spectroscopy (NIRS) is also provided. These demonstrate multiple approaches for developing data-rich empirical models in the context of science- and risk-based process development to predict in vitro dissolution. Recommendations of modeling best practices are made, focused primarily on immediate-release (IR) oral delivery products for new drug applications. A general roadmap is presented for implementation of dissolution modeling for enhanced product understanding, robust control strategy, batch release testing, and flexibility toward post-approval changes.

Indexed as

Drug LiberationModels, BiologicalAdministration, OralCapsulesChemistry, PharmaceuticalDrug DevelopmentTabletsCapsulesTabletsdrug dissolutionempirical modelingfirst principlesin vitro dissolutionmodeling and simulation (M&S)

Identifiers

PMID30790200
PMCPMC6394641

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.