Justification of disintegration testing beyond current FDA criteria using in vitro and in silico models
Bibliographic record
Abstract
Lukas Uebbing,1,2,* Lukas Klumpp,1,3,* Gregory K Webster,4 Raimar Löbenberg1 1Faculty of Pharmacy and Pharmaceutical Sciences, Katz Group-Rexall Centre for Pharmacy and Health Research, University of Alberta, Edmonton, Canada; 2Institute of Pharmacy and Biochemistry, Johannes Gutenberg University, Mainz, 3Institute of Pharmaceutical Technology, Goethe University Frankfurt, Frankfurt, Germany; 4Global Research and Development, AbbVie Inc., North Chicago, IL, USA *These authors contributed equally to this work Abstract: Drug product performance testing is an important part of quality-by-design approaches, but this process often lacks the underlying mechanistic understanding of the complex interactions between the disintegration and dissolution processes involved. Whereas a recent draft guideline by the US Food and Drug Administration (FDA) has allowed the replacement of dissolution testing with disintegration testing, the mentioned criteria are not globally accepted. This study provides scientific justification for using disintegration testing rather than dissolution testing as a quality control method for certain immediate release (IR) formulations. A mechanistic approach, which is beyond the current FDA criteria, is presented. Dissolution testing via United States Pharmacopeial Convention Apparatus II at various paddle speeds was performed for immediate and extended release formulations of metronidazole. Dissolution profile fitting via DDSolver and dissolution profile predictions via DDDPlus™ were performed. The results showed that Fickian diffusion and drug particle properties (DPP) were responsible for the dissolution of the IR tablets, and that formulation factors (eg, coning) impacted dissolution only at lower rotation speeds. Dissolution was completely formulation controlled if extended release tablets were tested and DPP were not important. To demonstrate that disintegration is the most important dosage form attribute when dissolution is DPP controlled, disintegration, intrinsic dissolution and dissolution testing were performed in conventional and disintegration impacting media (DIM). Tablet disintegration was affected by DIM and model fitting to the Korsmeyer–Peppas equation showed a growing effect of the formulation in DIM. DDDPlus was able to predict tablet dissolution and the intrinsic dissolution profiles in conventional media and DIM. The study showed that disintegration has to occur before DPP-dependent dissolution can happen. The study suggests that disintegration can be used as performance test of rapidly disintegrating tablets beyond the FDA criteria. The scientific criteria and justification is that dissolution has to be DPP dependent, originated from active pharmaceutical ingredient characteristics and formulations factors have to be negligible. Keywords: API, dissolution, disintegration, DDDPlus, quality-by-design, product specification, model fitting
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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 | 0.025 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".