European fingerprint study on omeprazole drug substances using a multi analytical approach and chemometrics as a tool for the discrimination of manufacturing sources

Rebiere, Hervé; Grange, Y.; Deconinck, Éric; Courselle, P.; Acevska, Jelena; Brezovska, Katerina; Maurin, Jan K.; Rundlöf, Torgny; Portela, M.J.; Olsen, L. Stengelshøj; Offerlé, C.; Bertrand, Michel

doi:10.1016/j.jpba.2021.114444

Cited by 8 publications

(10 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The obtained LVs have been extensively used as relevant features for DF applications [ 118 , 120 , 121 , 122 ]; for overview purposes [ 104 ] and outlier identification [ 72 ].…”

Section: Data Fusionmentioning

confidence: 99%

“…The currently available pharmaceutical DF based applications are limited, suggesting its slow integration into this field. DF has been successfully applied for classification purposes, here including excipient qualification studies based on physical characteristics (XRPD and particle size distribution data) [ 150 ], the identification of counterfeit products [ 96 , 120 ], and the detection of product quality deviations [ 104 ].…”

Section: Integrating Df Into Patmentioning

confidence: 99%

“… [ 104 ] Identification of counterfeit pharmaceutical packaging LIBS; ATR-FTIR MLDF kNN; LDA PCA DF-individual model comparison MLDF > individual model e.d. [ 96 ] Omeprazole fingerprinting to detect counterfeit products HPLC-UV; GC-MS; NIR; NMR; XRPD LLDF; MLDF PCA; HCA PCA DF-individual model comparison DF > individual model (f. fused data) / [ 120 ] Advanced qualification of pharmaceutical excipients XRPD; PSD data LLDF; MLDF MB-PLS - Predict LV’s of one method using data originating from other sources DF > individual model e.d. [ 150 ] PROCESS CONTROL Automotive Establish good and stable operating conditions for an autobody assembly process Seal gap; margin and flushness measurements LLDF PCA - /; complementary univariate sources Enhanced process control / [ 194 ] …”

Section: Table A1mentioning

confidence: 99%

See 2 more Smart Citations

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

et al. 2022

View full text Add to dashboard Cite

The release of the FDA’s guidance on Process Analytical Technology has motivated and supported the pharmaceutical industry to deliver consistent quality medicine by acquiring a deeper understanding of the product performance and process interplay. The technical opportunities to reach this high-level control have considerably evolved since 2004 due to the development of advanced analytical sensors and chemometric tools. However, their transfer to the highly regulated pharmaceutical sector has been limited. To this respect, data fusion strategies have been extensively applied in different sectors, such as food or chemical, to provide a more robust performance of the analytical platforms. This survey evaluates the challenges and opportunities of implementing data fusion within the PAT concept by identifying transfer opportunities from other sectors. Special attention is given to the data types available from pharmaceutical manufacturing and their compatibility with data fusion strategies. Furthermore, the integration into Pharma 4.0 is discussed.

show abstract

“…The obtained LVs have been extensively used as relevant features for DF applications [ 118 , 120 , 121 , 122 ]; for overview purposes [ 104 ] and outlier identification [ 72 ].…”

Section: Data Fusionmentioning

confidence: 99%

Section: Integrating Df Into Patmentioning

confidence: 99%

Section: Table A1mentioning

confidence: 99%

See 1 more Smart Citation

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In recent years, studies of fingerprinting analysis to discover possible falsifications of medicinal products or APIs have been performed (Anzanello et al, 2014;Custers et al, 2014;Acevska et al, 2015;Custers et al, 2016a;Raimondo et al, 2020;Deconinck et al, 2022). The European Official Medicines Control Laboratories (OMCLs) network performed fingerprinting studies on APIs of different manufacturers/origins to develop a tool to detect falsification of the origin of active ingredients (Raimondo et al, 2020;Rebiere et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Investigating metformin-active substances from different manufacturing sources by NIR, NMR, high-resolution LC-MS, and chemometric analysis for the prospective classification of legal medicines

Raimondo

Aureli²,

D’Ettorre³

et al. 2023

Front. Anal. Sci.

View full text Add to dashboard Cite

Introduction: The characterisation of active substances is an essential tool to ensure the traceability and authenticity of legal medicines. Metformin is a well-established biguanide derivative recommended in oral formulations as a first-line treatment for type 2 diabetes. With its increasing demand, metformin is likely to be an attractive target for falsification and substandard production, thus posing health risks to consumers. Methods that are able to identify even small differences in active pharmaceutical ingredients (APIs) are deemed necessary. The detection of fraudulent practices in APIs is not straightforward, and a single technique that can provide sufficient information to unambiguously address this issue is still not available.Methods: This study investigated an integrated analytical platform based on NIR, 1H-NMR, 13C-NMR, and high-resolution LC-MS combined with chemometrics to profile 32 metformin hydrochloride samples originating from several global authorised manufacturers. The study's aim was to explore differences in the chemical characteristics of metformin hydrochloride APIs to identify or predict a possible classification for each manufacturer in view of prospective authenticity studies. Different pre-processing methods were applied; bucket tables for 1H- and 13C-NMR were obtained, while mass spectrometry data were processed in targeted and untargeted modes. Datasets were individually analysed and merged by a multivariate unsupervised method and performing principal component analysis (PCA). Results and Discussion: The results evidenced differences in cluster behaviour, depending on manufacturers. Each technique has shown a specific clustering tendency, highlighting how different analytical approaches are able to characterise metformin APIs. Some manufacturers’ samples, however, showed similar behaviour independently of the techniques. NIR and 1H-NMR were confirmed as the more predictive techniques if taken individually; 1H-NMR, in particular, achieved good separation between the samples of the two most representative manufacturers. For LC-MS, the targeted approach resulted in a separation in groups clearer than that of the untargeted approach. Nevertheless, the untargeted LC-MS approaches presented in this paper could be a possible alternative to obtaining different information for drug substances, with several different and complex synthetic pathways leading to several unknown impurities. Further grouping of manufacturers emerged by data fusion, highlighting its potential in the traceability of metformin.

show abstract

“…5 In such a context, the use of chemometric analysis of APIs and medicinal products intended for characterisation of physicalchemical properties and impurity prole represents a valid tool for tracing the source and authenticity of APIs as well as nished products. [6][7][8][9][10][11][12][13] The nature and quantity of residual solvents, 14,15 proles of impurities, 6,16 heavy metal residues, isotopic ngerprinting [17][18][19][20][21][22] and polymorphic forms are features closely related to the synthetic route and manufacturing. The combination of analytical methods and chemometric analysis could provide deep knowledge of the characteristics of an API.…”

Section: Introductionmentioning

confidence: 99%

A NIR, ¹H-NMR, LC-MS and chemometrics pilot study on the origin of carvedilol drug substances: a tool for discovering falsified active pharmaceutical ingredients

et al. 2022

View full text Add to dashboard Cite

show abstract

European fingerprint study on omeprazole drug substances using a multi analytical approach and chemometrics as a tool for the discrimination of manufacturing sources

Cited by 8 publications

References 18 publications

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

Investigating metformin-active substances from different manufacturing sources by NIR, NMR, high-resolution LC-MS, and chemometric analysis for the prospective classification of legal medicines

A NIR, ¹H-NMR, LC-MS and chemometrics pilot study on the origin of carvedilol drug substances: a tool for discovering falsified active pharmaceutical ingredients

Contact Info

Product

Resources

About

European fingerprint study on omeprazole drug substances using a multi analytical approach and chemometrics as a tool for the discrimination of manufacturing sources

Cited by 8 publications

References 18 publications

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

Challenges and Opportunities of Implementing Data Fusion in Process Analytical Technology—A Review

Investigating metformin-active substances from different manufacturing sources by NIR, NMR, high-resolution LC-MS, and chemometric analysis for the prospective classification of legal medicines

A NIR, 1H-NMR, LC-MS and chemometrics pilot study on the origin of carvedilol drug substances: a tool for discovering falsified active pharmaceutical ingredients

Contact Info

Product

Resources

About

A NIR, ¹H-NMR, LC-MS and chemometrics pilot study on the origin of carvedilol drug substances: a tool for discovering falsified active pharmaceutical ingredients