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Demonstration of Advanced Data Mining Tools for Optimization of Pellets Employing Modified Extrusion-pelletization Technique

2020· article· en· W3114442524 on OpenAlexaff
Hardik Rana, Mansi Dholakia, Mukesh Gohel, Abdelwahab Omri, Vaishali Thakkar, Tejal Gandhi

Bibliographic record

VenueCurrent Drug Therapy · 2020
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug Solubulity and Delivery Systems
Canadian institutionsLaurentian University
Fundersnot available
KeywordsPelletsExtrusionMaterials scienceDissolutionPelletizingChromatographyFactorial experimentChemical engineeringChemistryComposite materialMathematicsEngineering

Abstract

fetched live from OpenAlex

Background: The multi particulate drug delivery system is preferred due to its numerous advantages but the batch to batch consistency and to achieve desired physical properties are the major challenges in the formulation of such dosage form. Objective: The objective of the present study was to explore the concept of quality by design for the development of galantamine HBr controlled release pellets using a modified palletization technique. Methods: Compritol 888 and Ethocel were chosen as hydrophobic release retardants, while Avicel was chosen as pelletization aid. A compatibility study was conducted between the drug and excipients. Drug loaded extrudes were prepared by using a mixture of isopropyl alcohol, and dichloromethane. Before converting the wet extrudes into pellets, pregelatinized starch was sprinkled on them to improve the physical properties of the pellets. The pellets were characterized for size, shape, and flow. The critical evaluation parameter was the drug dissolution pattern in distilled water. The dissolution data were treated with advanced data mining techniques. The in-vivo profile was predicted employing pharmacokinetic parameters of the drug and in-vitro drug release data of optimized batch pellets. Results: The failure mode and effect analysis revealed that the amount of Compritol 888 ATO and Ethocel were the most critical formulation parameters. The results of FTIR and DSC revealed compatibility between the drug and the excipients. The spherical pellets exhibited good flow. The drug dissolution studies of the batches, prepared according to the central composite design, revealed modified drug release. Multiple regression analysis and analysis of variance were performed to identify statistically significant factors. Contour plots demonstrated the impact of the amount of Compritol 888 and ethyl cellulose. The Design-Expert software was used to identify optimized formulation. The predicted in-vivo plasma concentration-time profile revealed the modified drug release up to 12h. Conclusions: Compritol and Ethocel were able to retard the drug release up to 12 hrs in distilled water. The innovative finding of this study is the use of a dry binder (pregelatinized starch) to improve the characteristics of pellets. Other dry binders are expected to show a similar effect. The newer processing technique can be of use in the industry.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.750
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.380
GPT teacher head0.463
Teacher spread0.083 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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