The Microphysiology Systems Database for Analyzing and Modeling Compound Interactions with Human and Animal Organ Models
Bibliographic record
Abstract
Microfluidic human organ models, microphysiology systems (MPS), are currently being developed as predictive models of drug safety and efficacy in humans. To design and validate MPS as predictive of human safety liabilities requires safety data for a reference set of compounds, combined with in vitro data from the human organ models. To address this need, we have developed an internet database, the MPS database (MPS-Db), as a powerful platform for experimental design, data management, and analysis, and to combine experimental data with reference data, to enable computational modeling. The present study demonstrates the capability of the MPS-Db in early safety testing using a human liver MPS to relate the effects of tolcapone and entacapone in the in vitro model to human in vivo effects. These two compounds were chosen to be evaluated as a representative pair of marketed drugs because they are structurally similar, have the same target, and were found safe or had an acceptable risk in preclinical and clinical trials, yet tolcapone induced unacceptable levels of hepatotoxicity while entacapone was found to be safe. Results demonstrate the utility of the MPS-Db as an essential resource for relating in vitro organ model data to the multiple biochemical, preclinical, and clinical data sources on in vivo drug effects.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".