Evidence map›Paper›PMID 41137134›Full record

ArticleBMC chemistry2025

Green UHPLC approach for the quantitative determination of tiopronin residues in cleaning validation processes.

Jagdish Gohel, Ajay Patel, Rajendra Kotadiya

Abstract read
In one paragraph

Article in BMC chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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

3 authors.

Jagdish GohelRamanbhai Patel College of Pharmacy, Charotar University of Science and Technology, Off Nadiad-Petlad Highway, 388421, Changa, India.ORCID http://orcid.org/0009-0009-6846-6580
Ajay PatelAmneal Pharmaceuticals Private Limited, Ahmedabad, India.
Rajendra KotadiyaDepartment of Quality Assurance, Parul Institute of Pharmacy Research, Parul University, At. Limda, Ta. Waghodiya, Vadodara, Gujarat, India. rajendra.kotadiya39851@paruluniversity.ac.in.ORCID http://orcid.org/0000-0002-5484-874X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study presents the development and validation of a novel reverse-phase ultra-high-performance liquid chromatography (UHPLC) method for quantifying tiopronin residues on manufacturing equipment surfaces as part of cleaning validation. Tiopronin, a synthetic thiol compound with therapeutic applications, requires stringent manufacturing controls to ensure product safety and efficacy. Existing analytical techniques mainly focus on quantifying tiopronin in pharmaceutical formulations or biological matrices. However, no methods specifically address cleaning validation for tiopronin production equipment. Using an Analytical Quality by Design approach, this UHPLC method was optimized and validated by ICH Q2 (R2) guidelines. The technique employs a Waters ACQUITY UPLC H-Class PLUS C-18 column (100 mm × 2.1 mm, 1.7 µm) with a mobile phase of 88:12 (v/v) 0.1% v/v orthophosphoric acid (pH 2.1) and acetonitrile, achieving a tiopronin retention time of 1.3 min. The method demonstrated specificity, precision, accuracy, and linearity over a concentration range of 0.302 to 3.027 µg/mL. The limit of detection and quantification were 0.100 µg/mL and 0.301 µg/mL, respectively. The greenness of the method was assessed using AGREE, GAPI, and RGB 12 tools. The scores obtained were 0.67 for AGREE, 85.0 for BAGI, and 82.1 for RGB 12 suggesting proposed method is environment friendly. This novel method provides an efficient, eco-friendly solution for routine cleaning validation, addressing a significant gap in existing analytical techniques for tiopronin manufacturing.

Indexed as

AQbDCleaning validationGreen scoreResidue quantificationUHPLC

Identifiers

PMID41137134
PMCPMC12553291

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LicenceCC BY-NC-ND
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Registered trials

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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.