Application of in Silico Tools in Clinical Practice using Ketoconazole as a Model Drug
Bibliographic record
Abstract
Hypochlorhydria is a condition where the production of hydrochloric acid in the stomach is decreased. As a result, the intragastric pH is elevated. This condition can be due to a series of causes, such as disease (gastric mucosal infection caused by Helicobacter pylori and is prominent in AIDS patients), ethnicity, age and also the use of antisecretory agents. This may significantly impact the absorption of other drugs that have pH-dependent solubility, such as ketoconazole, a weak base. Within this context, the purpose of this study was to demonstrate how GastroPlusTM - a physiological based software program- can be used to predict clinical pharmacokinetics of ketoconazole in a normal physiological state vs. elevated gastric pH. A simple physiologically based pharmacokinetic model was built and validated to explore the impact that different physiologic conditions in the stomach (hypochlorhydria, drug administered with water and Coca Cola®) had on ketoconazole's bioavailability. The developed model was able to accurately predict the impact of increased pH and beverage co-administration on dissolution and absorption of the drug, and confirmed that complete gastric dissolution is essential. Particle size only mattered in hypochlorhydric conditions due to the incomplete gastric dissolution, as its absorption would depend on intestinal dissolution, which corroborates with previous studies. Therefore, in silico approaches are a potential tool to assess a pharmaceutical product's performance and efficacy under different physiological and pathophysiological states supporting the assessment of different dosing strategies in clinical practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".