Dataset Information

Fast, Spectroscopy-Based Prediction of In Vitro Dissolution Profile of Extended Release Tablets Using Artificial Neural Networks.

ABSTRACT: The pharmaceutical industry has never seen such a vast development in process analytical methods as in the last decade. The application of near-infrared (NIR) and Raman spectroscopy in monitoring production lines has also become widespread. This work aims to utilize the large amount of information collected by these methods by building an artificial neural network (ANN) model that can predict the dissolution profile of the scanned tablets. An extended release formulation containing drotaverine (DR) as a model drug was developed and tablets were produced with 37 different settings, with the variables being the DR content, the hydroxypropyl methylcellulose (HPMC) content and compression force. NIR and Raman spectra of the tablets were recorded in both the transmission and reflection method. The spectra were used to build a partial least squares prediction model for the DR and HPMC content. The ANN model used these predicted values, along with the measured compression force, as input data. It was found that models based on both NIR and Raman spectra were capable of predicting the dissolution profile of the test tablets within the acceptance limit of the f₂ difference factor. The performance of these ANN models was compared to PLS models using the same data as input, and the prediction of the ANN models was found to be more accurate. The proposed method accomplishes the prediction of the dissolution profile of extended release tablets using either NIR or Raman spectra.

SUBMITTER: Galata DL

PROVIDER: S-EPMC6723897 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Fast, Spectroscopy-Based Prediction of In Vitro Dissolution Profile of Extended Release Tablets Using Artificial Neural Networks.

Galata Dorián László DL Farkas Attila A Könyves Zsófia Z Mészáros Lilla Alexandra LA Szabó Edina E Csontos István I Pálos Andrea A Marosi György G Nagy Zsombor Kristóf ZK Nagy Brigitta B

Pharmaceutics 20190809 8

The pharmaceutical industry has never seen such a vast development in process analytical methods as in the last decade. The application of near-infrared (NIR) and Raman spectroscopy in monitoring production lines has also become widespread. This work aims to utilize the large amount of information collected by these methods by building an artificial neural network (ANN) model that can predict the dissolution profile of the scanned tablets. An extended release formulation containing drotaverine ( ...[more]

PMID: 31405029

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Project description:PurposeThe aim of this study was to establish the suitability of terahertz (THz) transmission measurements to accurately measure and predict the critical quality attributes of disintegration time and the amount of active pharmaceutical ingredient (API) dissolved after 15, 20 and 25 min for commercial tablets processed at production scale.MethodsSamples of 18 batches of biconvex tablets from a production-scale design of experiments study into exploring the design space of a commercial tablet manufacturing process were used. The tablet production involved the process steps of high-shear wet granulation, fluid-bed drying and subsequent compaction. The 18 batches were produced using a 4 factor split plot design to study the effects of process changes on the disintegration time. Non-destructive and contactless terahertz transmission measurements of the whole tablets without prior sample preparation were performed to measure the effective refractive index and absorption coefficient of 6 tablets per batch.ResultsThe disintegration time (R 2?=?0.86) and API dissolved after 15 min (R 2?=?0.96) linearly correlates with the effective refractive index, n eff, measured at terahertz frequencies. In contrast, no such correlation could be established from conventional hardness measurements. The magnitude of n eff represents the optical density of the sample and thus it reflects both changes in tablet porosity as well as granule density. For the absorption coefficient, ? eff, we observed a better correlation with dissolution after 20 min (R 2?=?0.96) and a weaker correlation with disintegration (R 2?=?0.83) compared to n eff.ConclusionThe measurements of n eff and ? eff provide promising predictors for the disintegration and dissolution time of tablets. The high penetration power of terahertz radiation makes it possible to sample a significant volume proportion of a tablet without any prior sample preparation. Together with the short measurement time (seconds), the potential to measure content uniformity and the fact that the method requires no chemometric models this technology shows clear promise to be established as a process analyser to non-destructively predict critical quality attributes of tablets.

Project description:Baclofen, a racemic GABA-B (GABAB) receptor agonist, is commonly used for the management of multiple sclerosis-related spasticity but is associated with frequent dosing and poor tolerability. Arbaclofen, the active R-enantiomer of baclofen, exhibits 100- to 1000-fold greater specificity for the GABAB receptor compared with the S-enantiomer and ∼5-fold greater potency compared with racemic baclofen. Arbaclofen extended-release tablets have a dosing interval of 12 hours and have shown a favourable safety and efficacy profile in early-phase clinical development. The current Phase 3 study was designed to evaluate the efficacy and safety of arbaclofen extended-release tablets in patients with multiple sclerosis-related spasticity. In this multicentre, double-blind, placebo-controlled study, adults with multiple sclerosis-related spasticity were randomized to arbaclofen extended-release 40 mg/day, arbaclofen extended-release 80 mg/day or placebo for 12 weeks. The co-primary end-points were the change from baseline to Week 12 in the Total Numeric-transformed Modified Ashworth Scale in the Most Affected Limb score and the Clinical Global Impression of Change score. A hierarchical testing procedure was used to evaluate the co-primary end-points; analyses for the 80 mg/day group were considered inferential only if the arbaclofen extended-release 40 mg/day and placebo groups demonstrated a statistically significant difference (P ≤ 0.05) for both end-points. Five hundred thirty-six patients were included in the study. At Week 12, the least squares mean change from baseline in Total Numeric-transformed Modified Ashworth Scale in the Most Affected Limb score was -1.67 (95% confidence interval: -1.97 to -1.36) and -1.28 (95% confidence interval: -1.57 to -0.99) in the arbaclofen extended-release 40 mg/day and placebo groups, respectively (least squares mean difference: -0.39; P < 0.048). Improvements were seen in the mean Clinical Global Impression of Change scores for both the arbaclofen extended-release 40 mg/day and placebo groups; however, no statistically significant difference was observed between them (least squares mean difference: -0.10; P = 0.43). Most adverse events were of mild-moderate severity. Arbaclofen extended-release 40 mg/day for 12 weeks significantly reduced multiple sclerosis-related spasticity compared with placebo and was safe and well tolerated over the 12-week treatment period. Although arbaclofen extended-release 40 mg/day improved Clinical Global Impression of Change scores, a significant difference from placebo was not observed.

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Dataset Information

Fast, Spectroscopy-Based Prediction of In Vitro Dissolution Profile of Extended Release Tablets Using Artificial Neural Networks.

Publications

Fast, Spectroscopy-Based Prediction of In Vitro Dissolution Profile of Extended Release Tablets Using Artificial Neural Networks.

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