Quantitative Estimation of Avicel Polymer and its Impact on Spray Pattern, Plume Geometry and Droplet Size in Nasal Spray Products
Bibliographic record
Abstract
Throughout the world, the regulatory agencies like US FDA, MHRA, Health Canada recommend that during the development of generic products like Nasal sprays, Ophthalmic solutions, the inactive ingredients in the generic product formulation must be qualitatively (Q1) and quantitatively (Q2) similar to the reference listed drug product. This requires a careful deformulation/reverse engineering study of the reference listed drug product or innovator product for all the listed inactive ingredients. Avicel is one of the most important ingredients primarily used in various Nasal spray and ophthalmic suspension products as a suspending agent. It is a mixture of microcrystalline cellulose and carboxy methyl cellulose. The aim of this study is to develop a simple yet robust analytical method for the accurate quantitative estimation of this polymer for generic product development to regulatory markets. We have not found any reported method for the accurate quantitation of this polymer from any of the pharmaceutical dosage forms. Hence, this article will certainly accelerate the generic development of different pharmaceutical dosage forms for regulatory markets. Due to its non chromophoric nature, Avicel polymer is derivatized to a readily quantifiable compound by using diphenylamine reagent. The complex is measured at wavelength 635 nm without any interference. The polymer can be successfully quantified at concentration of 0.26 μg/ml and above from various pharmaceutical products such as tablets, nasal spray suspensions, ophthalmic products etc. This paper gives a simple yet accurate and precise method for the quantitative estimation of Avicel by derivatized spectrophotometry from various pharmaceutical products.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".