Abstract 14: Computer-aided discovery of small-molecule inhibitors targeting neural transcription factor BRN2 in neuroendocrine prostate tumors
Bibliographic record
Abstract
Abstract Therapies targeting tumor-fueling androgens have been mainstay treatments of advanced prostate cancer for almost 5 decades. While the androgen receptor (AR) inhibitor enzalutamide (ENZ) prolongs survival of castration-resistant prostate cancer patients (CRPC), ENZ-resistant (ENZR) tumors rapidly recur. Our laboratory has identified that ENZ resistance can occur both in the presence and absence of continued classical AR activity. Lack of AR activity is clinically relevant, as it has been estimated that up to 25% of men who die from advanced CRPC have non-AR driven disease and lethal AR inactive neuroendocrine and anaplastic phenotypes of CRPC emerge after hormone therapy. Recently our group identified the neural transcription factor BRN2 as a major clinically relevant driver of neuroendocrine prostate cancer (NEPC). Hence, using the integrated power of computational drug discovery platform and biologic testing, we identified first-in-field inhibitors for BRN2. First, a homology model of BRN2 protein was generated and used as an input to perform large-scale virtual screening and virtual hits were tested in a series of experiments. Cpd 18-14, a lead BRN2 inhibitor, exhibited profound inhibition of BRN2 in transcriptional assay and binding to the BRN2 in cellular and in vitro assays. Furthermore, Cpd18-14 reduced the expression of neuroendocrine genes SOX2 and NCAM1. Importantly, Cpd18-14 displayed antiproliferative activity specifically in patient-derived BRN2hi NCI-H660 and in house-developed 42DENZRcells while displaying no effect on BRN2low 16DCRPC and LNCaP cells. Further, the most stable binding orientation of Cpd18-14 was determined using molecular dynamics simulations and was used to carry out structure-based lead optimization. As a result, we developed several synthetic derivatives with significant improvement in antiproliferative activity and metabolic stability. Finally, Cpd18-94 reduced the growth of NCI-H660 and 42DENZR NEPC tumors significantly in xenograft models. We anticipate that the developed drug prototypes will lay a foundation for the development of small-molecule therapies capable of combating highly aggressive and lethal form of neuroendocrine tumors. Citation Format: Ravi Munuganti, Daksh Thaper, Adeleke Aguda, Soojin Kim, Olena Sivak, Amina Zoubeidi. Computer-aided discovery of small-molecule inhibitors targeting neural transcription factor BRN2 in neuroendocrine prostate tumors [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 14.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".