Abstract 3883: Cryo-EM-guided enhancement of target selectivity of a novel p97 inhibitor for treating multiple myeloma and acute myeloid leukemia
Bibliographic record
Abstract
Abstract The AAA ATPase p97, also known as valosin-containing protein (VCP), serves to regulate protein homeostasis by facilitating the translocation of ubiquitinated proteins from membranes or chromatin to the proteasome for degradation. Certain cancers, including multiple myeloma (MM) and acute myeloid leukemia (AML) are known for the intracellular overexpression of proteins, and are thus susceptible to the inhibition of p97 and resulting proteotoxic stress. A known p97 inhibitor, CB-5083 entered human phase 1 clinical trials in 2015, but trials were terminated due to side effects that were later traced to strong interaction of the compound with the phosphodiesterase PDE6. To address this problem, we devised a subtractive approach that leveraged high resolution structures of CB-5083 bound to PDE6 and p97. Using this information, we designed a p97 inhibitor that no longer displayed significant off-target binding but retained strong on-target binding. The optimization process started when we identified the 7-azaindole core from an in silico screen as a suitable hit pharmacophore and confirmed the binding mode of several variants of this scaffold in p97 by cryo-EM. Through a structure-guided medicinal chemistry program, we identified GND-135, a potent, on-target binder through the synthesis of less than 100 compounds. The design was supported with about 50 cryo-EM structures throughout the optimization process. Compared to CB-5083, our compound GND-135 displays greatly reduced PDE6 activity (< 1% inhibition at 10 μM versus 89% inhibition for CB-5083) and more potent p97 ATPase inhibition (3 nM IC50 versus 12 nM for CB-5083). We also evaluated the pharmacokinetic profile of GND-135 in mice; the half-life and clearance of 6.6 h and 15.9 mL/min/kg respectively were deemed suitable for daily administration. Based on the initial profiling, we carried out an efficacy study in a cell derived xenograft mouse model of AML using a U937 cell line with subcutaneous implantation. In this model, where GND-135 was administered IP (40 mg/kg QD) and CB-5083 was administered orally (40 mg/kg QD) the compounds showed statistically comparable efficacy, which was differentiated from untreated control. Thus, GND-135 is a novel p97 inhibitor lead compound with on-target selectivity powered by cryo-EM driven rational drug design. Citation Format: Jason Crawford, Ravi Munuganti, Charles Leung, Kriti Singh, Ellen Gates, Xing Zhu, Marcel Bally, Nancy Dos Santos, Maryam Sharifiaghdam, Zeynab Nosrati, Peter Axerio-Cilies, Alison Berezuk, Spencer Cholak, Sriram Subramaniam. Cryo-EM-guided enhancement of target selectivity of a novel p97 inhibitor for treating multiple myeloma and acute myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3883.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".