Multivariate calibration of energy-dispersive X-ray diffraction data for predicting the composition of pharmaceutical tablets in packaging

Crews, Chiaki; Kenny, Peter S.; O’Flynn, Daniel; Speller, Robert

doi:10.1016/j.jpba.2017.12.036

Cited by 9 publications

(9 citation statements)

References 23 publications

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“…Maurin et al showed that X‐ray powder diffraction is a fast and reliable method to discriminate the counterfeit and/or imitations of Viagra tablets from original ones by comparing the diffraction patterns . Crews et al developed an advanced energy‐dispersive X‐ray diffraction (XRD) to accurately quantify the ingredients of tablets within the packaging, suggesting its potential for noninvasive screening of counterfeit drugs …”

Section: Analytical Technologies For the Detection Of Pharmaceutical mentioning

confidence: 99%

Materials and Technologies to Combat Counterfeiting of Pharmaceuticals: Current and Future Problem Tackling

et al. 2020

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The globalization of drug trade leads to the expansion of pharmaceutical counterfeiting. The immense threat of low quality drugs to millions of patients is considered to be an under‐addressed global health challenge. Analytical authentication technologies are the most effective methods to identify active pharmaceutical ingredients and impurities. However, most of these analytical testing techniques are expensive and need skilled personnel. To combat counterfeiting of drugs, the package of an increasing number of drugs is being protected through advanced package labeling technologies. Though, package labeling is only effective if the drugs are not repackaged. Therefore “in‐drug labeling,” instead of “drug package labeling,” may become powerful tools to protect drugs. This review aims to overview how advanced micro‐ and nanomaterials might become interesting markers for the labeling of tablets and capsules. Clearly, how well such identifiers can be integrated into “solid drugs” without compromising drug safety and efficacy remains a challenge. Also, incorporation of tags has so far only been reported for the protection of solid drug dosage forms. No doubts that in‐drug labeling technologies for “liquid drugs,” like injectables which contain expensive peptides, monoclonal antibodies, vaccines, dermal fillers, could help to protect them from counterfeiting as well.

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Section: Analytical Technologies For the Detection Of Pharmaceutical mentioning

confidence: 99%

Materials and Technologies to Combat Counterfeiting of Pharmaceuticals: Current and Future Problem Tackling

et al. 2020

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“…Energy dispersive X‐ray diffraction (EDXRD) can meet this requirement and measures the coherent scattering fields, which characterise the samples comprising crystalline and polycrystalline materials. Polycrystalline materials are of particular interest in screening contexts, where investigations have shown the potential of EDXRD in identifying the composition of solid‐dosage pharmaceutical formulations, powder‐form illicit drugs and plastic explosive materials 1–4 . Furthermore, the technology has shown potential in diagnostic medicine to discriminate between healthy and cancerous tissue in breast cancer screening 5 …”

Section: Introductionmentioning

confidence: 99%

“…Polycrystalline materials are of particular interest in screening contexts, where investigations have shown the potential of EDXRD in identifying the composition of soliddosage pharmaceutical formulations, powder-form illicit drugs and plastic explosive materials. [1][2][3][4] Furthermore, the technology has shown potential in diagnostic medicine to discriminate between healthy and cancerous tissue in breast cancer screening. 5 Traditional application of X-ray diffraction in a laboratory setting, either with a synchrotron radiation source or a conventional mono-energetic angular dispersive X-ray diffraction (ADXRD) system, enables high-resolution sample profiles to be recorded.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome the collinearity and overlap within profiles, well-established multivariate calibration methods using orthogonal latent variables, such as principal components regression (PCR) and partial least squares regression (PLSR), have been used in EDXRD studies to quantify target compounds within mixtures. 1,3,8 A disadvantage of this approach is that the latent variables resolved cannot be easily interpreted since they do not represent a pure instrumental response of a single compound.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, ternary mixtures of synthetic samples comprising common pharmaceutical ingredients pressed into tablets have been analysed to resolve their concentrations and the pure instrumental responses in both unpackaged and packaged scenarios. We build on the study by Crews et al, in which the bilinearity of the data set was demonstrated and unpackaged and packaged tablets were modelled separately using PLSR 1 . In the present study, unpackaged and packaged formulations are modelled together in a multiset structure to resolve concentrations for all samples for a given compound as well as a pure instrumental response for each compound.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Determination of ingredients in packaged pharmaceutical tablets by energy dispersive X‐ray diffraction and maximum likelihood principal component analysis multivariate curve resolution‐alternating least squares with correlation constraint

Kenny

Crews

Fearn

et al. 2021

Journal of Chemometrics

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Energy dispersive X‐ray diffraction (EDXRD) and maximum likelihood principal component analysis multivariate curve resolution‐alternating least squares (MLPCA‐MCR‐ALS) with correlation constraint were used to quantify the composition of packaged pharmaceutical formulations. Recorded EDXRD profiles from unpackaged and packaged samples of ternary mixtures were modelled together in order to recover the concentrations as well as the pure profiles of the constituent compounds. MLPCA was used as a data pretreatment step to MCR‐ALS, accounting for the high noise and nonconstant variance observed in the EDXRD profiles and was shown to improve the resolution accuracy of MCR‐ALS for the data set. Local correlation constraints were applied in the MCR‐ALS procedure in order to model unpackaged and packaged samples simultaneously while accounting for the matrix effect of the packaging materials. The composition of the formulations was estimated with root‐mean‐square error of prediction for each component, including paracetamol, being approximately 2.5 %w/w for unpackaged and packaged samples. Paracetamol concentration was resolved simultaneously for the unpackaged and packaged samples to a greater degree of accuracy than achieved by partial least squares regression (PLSR) when modelling the contexts separately. By modelling the effects of the packaging and incorporating accurate reference information of unpackaged samples into the resolution of packaged samples, the potential of EDXRD and MLPCA‐MCR‐ALS for the identification and quantification of packaged solid‐dosage medicine in nondestructive screening and counterfeit medicine detection has been raised.

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Fischer-Tropsch synthesis by reduced graphene oxide nanosheets supported cobalt catalysts: Role of support and metal nanoparticle size on catalyst activity and products selectivity

Torshizi

Pour

Mohammadi

et al. 2020

Front. Chem. Sci. Eng.

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Multivariate calibration of energy-dispersive X-ray diffraction data for predicting the composition of pharmaceutical tablets in packaging

Cited by 9 publications

References 23 publications

Materials and Technologies to Combat Counterfeiting of Pharmaceuticals: Current and Future Problem Tackling

Materials and Technologies to Combat Counterfeiting of Pharmaceuticals: Current and Future Problem Tackling

Determination of ingredients in packaged pharmaceutical tablets by energy dispersive X‐ray diffraction and maximum likelihood principal component analysis multivariate curve resolution‐alternating least squares with correlation constraint

Fischer-Tropsch synthesis by reduced graphene oxide nanosheets supported cobalt catalysts: Role of support and metal nanoparticle size on catalyst activity and products selectivity

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