Crystal Engineering of Active Pharmaceutical Ingredients with Low Aqueous Solubility and Bioavailability
Bibliographic record
Abstract
Approximately 75% of new molecular entities approved by the Food and Drug Administration (FDA) for use in the pharmaceutical industry are found to have poor aqueous solubility. This undesirable attribute leads to consequences such as higher doses required to reach therapeutic levels, greater vulnerability to food effects, lesser fraction absorbed in the small intestine and damage to the environment due to increased quantity of excretion. The addition of an excipient (i.e. a FDA approved inactive ingredient) to the molecular structure of an active pharmaceutical ingredient (API) through intermolecular bonding is of growing interest because the properties of the API can be tuned without further clinical testing. Crystal engineering utilizes the knowledge of intermolecular interactions to design new solids with improved properties (e.g. solubility, stability, bioavailability, dissolution rates). In this thesis, these techniques are applied to increase the solubility of three APIs with low solubility: esomeprazole magnesium, curcumin and rufinamide. Through an intense screening process, novel solid states were discovered including a water/butanol solvate of esomeprazole magnesium and a co-amorphous mixture comprised of curcumin and folic acid dihydrate. The co-amorphous mixture was found to have increased dissolution rate compared to curcumin and can be repositioned as a prenatal drug. Characterization of these products include powder and single crystal X-ray diffraction, differential scanning calorimetry, thermogravimetric analysis, Fourier Transform infrared spectroscopy, solution nuclear magnetic resonance spectroscopy and dynamic vapour sorption. Screening of rufinamide did not lead to the discovery of any new forms, but the refined molecular structure of the metastable form is reported.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".