A Review of Inverse Gas Chromatography and its Development as a Tool to Characterize Anisotropic Surface Properties of Pharmaceutical Solids
Bibliographic record
Abstract
Surface properties can profoundly impact the bulk and interfacial behavior of pharmaceutical solids, and also their manufacturability, processability in drug product processes, dissolution kinetics and mechanism in drug delivery. Variation in the inter- and intra-molecular interactions gives rise to anisotropic surface properties of crystalline solids which display direction-dependent characteristics relative to the orientation of the crystal unit structure. Despite its establishment since the 1950s, inverse gas chromatography (IGC) is still an evolving technology in the field of pharmaceutical R&D. In this review, the principles behind IGC as a physicochemical technique to measure the surface properties of solids are presented. The introduction is followed by an overview of its utility in pharmaceutical R&D, spanning a variety of applications including batch-to-batch variability, solid-solid transitions, physical stability, interfacial behavior in powder processing, and more. For anisotropic materials, IGC has been utilized to characterize the heterogeneity of materials using adsorption and energy distribution functions. Recent development and applications of IGC at finite concentration (IGC-FC) to determine the surface heterogeneity distribution of solids are presented. This methodology overcomes a number of limitations associated with traditional experiments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".