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Abstract LB_C11: Small-molecule bifunctional inhibitors of PARP1/2 and HDAC enzymes

2023· article· en· W4389227464 on OpenAlexaff
Sarah Truong, Louise Ramos, Beibei Zhai, Jay Joshi, Fariba Ghaidi, Mona Marzban, Hans Adomat, Charles Chen, John Langlands, Dennis Brown, Jeffrey Bacha, Colin C. Collins, Poul Sorensen, Shen Wang, Mads Daugaard

Bibliographic record

VenueMolecular Cancer Therapeutics · 2023
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOlaparibPARP1PARP inhibitorHistone deacetylasePharmacologyCancer researchVorinostatDNA repairPoly ADP ribose polymeraseSynthetic lethalityBiologyPolymeraseHistoneEnzymeBiochemistryDNA

Abstract

fetched live from OpenAlex

Abstract Background: Poly(ADP-ribose) polymerase (PARP) plays a major role in DNA repair and PARP inhibitors (PARPi) have shown promise for the treatment of cancers with DNA damage repair defects, such as BRCA mutations. PARPi are currently approved for treatment of BRCA-mutated cancers and as a maintenance therapy for patients who have responded to platinum-based chemotherapy. To improve efficacy, overcome resistance, and expand the usage of PARPi to non-BRCA mutated disease, combination therapies with PARPi have emerged as an area of interest. Histone deacetylases (HDACs) play a major role in DNA repair and inhibition of HDACs has been shown to reduce tumor growth. PARPi combined with HDACi has shown enhanced efficacy in pre-clinical studies in various cancers, and a clinical trial of olaparib and vorinostat against metastatic breast cancer is ongoing. However, combination therapies can be limited clinically due to overlapping toxicities and differing pharmacokinetics. Here, we report the efficacy of a novel class of bifunctional small-molecule compounds (kt-3000) with both PARP1 and PARP2 inhibitory and HDAC inhibitory activities. Methods: PARP1 and PARP2 activity was measured using Trevigen Universal Colorimetric PARP Assay kit and BPS Bioscience PARP2 Colorimetric Assay kit respectively. HDAC activity was measured using HeLa nuclear extracts and a fluorogenic peptide-based biochemical assay. In vitro metabolism was determined by HPLC after incubation with human liver microsomes for up to 45 mins. Pharmacokinetic profiling was examined in mice up to 6 h after oral/intraperitoneal (IP) dosing. Results: kt-3000 compounds showed potent inhibition of PARP1 and PARP2 activity with IC50 values comparable to olaparib (low nM) and inhibition of HDACs with IC50 values in the low µM range. Metabolic and pharmacokinetic profiling suggest drug-like qualities in the class. Conclusion: kt-3000 molecules demonstrate potent inhibition of PARP1/2 and HDAC activities, with suitable pharmacokinetic properties for oral delivery. Further refinement and development of these bifunctional single-molecule inhibitors may offer a novel therapeutic opportunity for treatment of cancers with and without DNA damage repair defects. Citation Format: Sarah Truong, Louise Ramos, Beibei Zhai, Jay Joshi, Fariba Ghaidi, Mona Marzban, Hans Adomat, Charles Chen, John Langlands, Dennis Brown, Jeffrey Bacha, Colin Collins, Poul Sorensen, Wang Shen, Mads Daugaard. Small-molecule bifunctional inhibitors of PARP1/2 and HDAC enzymes [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr LB_C11.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.048
GPT teacher head0.320
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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