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ArticlePharmaceutical research2025

Dissolution Profiles Comparison Using Various Model Independent Statistical Approaches: Can We Increase Chance of Similarity?

Rajkumar Boddu, Karthik Parsa, Priyansh Pandya, Sivacharan Kollipara

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Pharmaceutical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
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2citing papers in PubMed
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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

2 citing papers in PubMed.

  1. Article
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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

4 authors.

Rajkumar BodduBiopharmaceutics Group, Global Clinical Management, Dr. Reddy's Laboratories Ltd., Integrated Product Development Organization (IPDO), BachupallyMedchal Malkajgiri District, Hyderabad, 500 090, Telangana, India.ORCID http://orcid.org/0000-0002-8666-3643
Karthik ParsaDigital and Process Excellence, Dr. Reddy's Laboratories Ltd., Integrated Product Development Organization (IPDO), BachupallyMedchal Malkajgiri District, Hyderabad, 500 090, Telangana, India.
Priyansh PandyaDigital and Process Excellence, Dr. Reddy's Laboratories Ltd., Integrated Product Development Organization (IPDO), BachupallyMedchal Malkajgiri District, Hyderabad, 500 090, Telangana, India.ORCID http://orcid.org/0009-0000-3366-5322
Sivacharan KolliparaBiopharmaceutics Group, Global Clinical Management, Dr. Reddy's Laboratories Ltd., Integrated Product Development Organization (IPDO), BachupallyMedchal Malkajgiri District, Hyderabad, 500 090, Telangana, India. sivacharankollipara@drreddys.com.ORCID http://orcid.org/0000-0002-7332-9021

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeIn vitro dissolution testing is a critical quality attribute for solid dosage forms. Apart from similarity factor (f2), other alternatives namely model independent and dependent methods are suggested by regulatory agencies. Current manuscript attempts to compare various model independent approaches on dissolution similarity.

methodsDissolution data with various degrees of variability (10-20%, 40-50%, 70-80%) are compared using similarity factor f2 (estimated, expected, bias corrected with percentile & BCa intervals) and novel approaches such as EDNE, SE, T2EQ, and MSD. Further, a flow chart is proposed to assist selection of suitable methodology.

resultsThe expected f2 was stringent as compared to other f2 types and the Bca confidence intervals approach increased chance of acceptance as compared to conventional f2 bootstrap. Further, EDNE results synchronized with f2 analysis. Outcome from SE, T2EQ approaches depends on value of equivalence margin. MSD approach was most stringent as compared to others. Finally, a decision tree has been proposed to facilitate the selection of appropriate methodology for similarity analysis with consideration of regulatory perspectives.

conclusionsOverall, various model independent approaches are compared for dissolution similarity analysis. This comprehensive guidance will assist formulation and biopharmaceutics scientists to enhance the success rate of similarity while ensuring regulatory compliance and thus helps to achieve drug product with consistent performance.

Indexed as

Drug LiberationModels, StatisticalChemistry, PharmaceuticalDecision TreesPharmaceutical PreparationsSolubilityPharmaceutical PreparationsBias corrected and acceleratedDissolution similarityEDNEExpected f2MSDSET2EQ

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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.