Characterization of the Pharmacokinetic Properties and In Vivo Efficacy of a Candidate Bone Marrow Niche Altering Compound
Bibliographic record
Abstract
Acute myeloid leukemia (AML) is an aggressive cancer of the blood and bone marrow (BM) which affects the myeloid lineage of the hematopoietic hierarchy and causes the accumulation of immature and non-functional myeloid progenitor cells. While chemotherapy is generally effective at achieving remission in AML patients, long-term survival remains low due to the high rates of treatment related mortality among elderly patients as well as the high incidence of relapse, necessitating the development of novel therapeutics. Compounds which can influence mesenchymal stem cell (MSC) differentiation in the BM niche are of particular interest due to the role these cells play in HSC regulation as well as their disruption during AML disease progression. Our lab has utilized a phenotypic screen to discover compounds which could induce adipogenesis in BM-MSCs. Predicting which of these hit compounds are likely to be effective inducers of adipogenesis within the BM niche could streamline the drug discovery process by eliminating the need to examine the in vivo efficacy for compounds which are likely to be ineffective. Here, we aimed to evaluate if the in vivo efficacy of an MSC modulating compound could be predicted in vitro by assessing the compound’s bioavailability using a novel pharmacokinetic model and comparing the compound’s concentration in circulation to the minimum effective concentration observed in an in vitro phenotypic screen.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".