Prediction of Dissolution Profiles From Process Parameters, Formulation, and Spectroscopic Measurements

Zhao, Yuxiang; Li, Wenlong; Shi, Zhenqi; Drennen, James K.; Anderson, Carl A.

doi:10.1016/j.xphs.2019.01.023

Cited by 25 publications

(13 citation statements)

References 15 publications

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“…In the pharmaceutical world, ML has mostly been used to predict and optimise drug release [209][210][211][212][213][214][215][216][217][218][219][220]. Medicines' drug dissolution profiles are a fundamental characterisation technique in pharmaceutics [221].…”

Section: Machine Learning In the Pre-printing Stagementioning

confidence: 99%

Harnessing artificial intelligence for the next generation of 3D printed medicines

Elbadawi

McCoubrey

Gavins

et al. 2021

Advanced Drug Delivery Reviews

102

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Machine Learning In the Pre-printing Stagementioning

confidence: 99%

Harnessing artificial intelligence for the next generation of 3D printed medicines

Elbadawi

McCoubrey

Gavins

et al. 2021

Advanced Drug Delivery Reviews

102

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“…The use of spectral ranges [143] and pre-processing [144] also help to enhance model performance [145] and the method parameters such as those commonly reported for chromatographic methods [146] are also reported: specificity, accuracy, linearity and precision. For process NIR applications, some of the areas where NIR brings considerable utility is in moisture content, particle sizing, form identification, density, and blend uniformity [137].…”

Section: Nir In Food and Pharmaceutical Raw Materialsmentioning

confidence: 99%

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Bwambok

Siraj

Macchi

et al. 2020

Sensors

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Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.

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“…Harmonizing with QbD models, NIR spectral techniques have been utilized for optimization of various attributes of the pharmaceutical industry ( Haneef and Beg, 2021 , Taleuzzaman et al, 2021 ). Some of these attributes are process related e.g., fluidized bed granulation and tablet coating ( Liu et al, 2017 ), drying ( Pauli et al, 2018 ), monitoring of blending ( Harting and Kleinebudde, 2019 , Nagy et al, 2018 , Riolo et al, 2018 ), other attributes are related to quality control testing of the product e.g., content uniformity testing ( Arruabarrena et al, 2014 , Nagy et al, 2017 ), dissolution testing ( Galata et al, 2021 , Galata et al, 2019 , Ojala et al, 2020 , Zhao et al, 2019 ), particle size determination ( Bittner et al, 2011 , Pauli et al, 2019 ), and detecting polymorphs and counterfeit drugs ( Dégardin et al, 2016 , Terra and Poppi, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

“…FDA recently recommended spectral techniques and mathematical models as a potential alternative to the convenient methods of dissolution testing ( FDA, 2019 ). Researchers have been extensively deploying multi-variate models for prediction of dissolution using various mathematical algorithms such as Principal Component Regression ( Otsuka et al, 2007 ), Partial Least Square ( Galata et al, 2019 , Zhao et al, 2019 ), and Artificial Neural Networks ( Galata et al, 2019 ). In parallel, various methods were also utilized for optimization of calibration and modeling, such as mathematical preprocessing of spectral data ( Martens and Stark, 1991 ), and wavelength selection methods ( Deng et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

“…Researchers approached different methodologies for prediction of dissolution profile of tablets, for instance, Freitas et al designed formulations with varied excipient contents ( Freitas et al, 2005 ), Galata et al designed formulations with varied compression forces ( Galata et al, 2019 ), while Zhao et al designed formulations with varied combination of formulation and process related variables ( Zhao et al, 2019 ). Generally, there are substantial variables to be considered for predicting reliable results of content uniformity and dissolution testing of solid dosage forms.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Industrial application of QbD and NIR chemometric models in quality improvement of immediate release tablets

Ramadan

Basalious

Abdallah

2021

Saudi Pharmaceutical Journal

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Quality by Design (QbD) and chemometric models are different sides of the same coin. While QbD models utilize experimentally designed settings for optimization of some quality attributes, these settings can also be utilized for chemometric prediction of the same attributes. We aimed to synchronize optimization of comparative dissolution results of carvedilol immediate release tablets with chemometric prediction of dissolution profile and content uniformity of the product. As an industrial application, selection of variables for optimization was done by performing risk assessment utilizing the archived product records at the pharmaceutical site. Experimental tablets were produced with 20 different settings with the variables being contents of sucrose, sodium starch glycolate, lactose monohydrate, and avicel Ph 101. Contents of the excipients were modelled with F1 dissimilarity factor and F2 similarity factor in HCL, acetate, and USP dissolution media to determine the design space. We initiatively utilized Partial Least Square based Structural Equation Modelling (PLS-SEM) to explore how the excipients and their NIR records explained dissolution of the product. Finally, the optimized formula was utilized with varied content of carvedilol for chemometric prediction of the content uniformity.

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Prediction of Dissolution Profiles From Process Parameters, Formulation, and Spectroscopic Measurements

Cited by 25 publications

References 15 publications

Harnessing artificial intelligence for the next generation of 3D printed medicines

Harnessing artificial intelligence for the next generation of 3D printed medicines

QCM Sensor Arrays, Electroanalytical Techniques and NIR Spectroscopy Coupled to Multivariate Analysis for Quality Assessment of Food Products, Raw Materials, Ingredients and Foodborne Pathogen Detection: Challenges and Breakthroughs

Industrial application of QbD and NIR chemometric models in quality improvement of immediate release tablets

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