BSCI-13 DEVELOPMENT OF NOVEL ANTI-BRAIN METASTASIS INHIBITORS
Bibliographic record
Abstract
Abstract The current standard of care (surgery and radiation) for brain metastases (BM) is inadequate as BM have a 90% mortality rate within one year of diagnosis, posing a large unmet clinical need. The Singh lab has generated a large in-house biobank of patient-derived BM cell lines that are established from BM patient tumor samples. We use these BM cell lines to generate murine orthotopic xenograft models of BM and interrogate the biological processes that lead to BM. These models have successfully recapitulated all the stages of their respective BM cascades and additionally captured a “pre-metastatic” population of BM cells that have just seeded the brains of mice before forming mature, clinically detectable tumors. Pre-metastatic cell populations are impossible to detect in human patients but represent a therapeutic window wherein metastasizing cells can be targeted and eradicated before establishing clinically detectable and difficult to treat brain tumors. RNA sequencing of pre-metastatic BM cells revealed a unique deregulated transcriptomic profile that is specific to pre-metastatic cells despite the tumor of origin. Connectivity Map analysis was applied to the gene expression signatures of pre-metastatic BM cells to identity a compound (Drug A) which selectively inhibits BM cell proliferation but is not blood-brain barrier (BBB) penetrant and has not been previously considered in the context of brain metastasis. We synthesized a BBB-penetrant analogue of Drug A and found, using our patient-derived xenograft (PDX) models, that it increased survival benefit relative to both placebo and Drug A. Beginning with this promising scaffold, we will conduct structure-activity hypothesis-driven medicinal chemistry campaigns to optimize this scaffold for brain permeation while maintaining selective anti-BM activity. Development of novel small molecules that target pre-metastatic BM cells could slow or prevent the formation of BM and dramatically improve the prognosis of at-risk cancer patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".