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Record W4282979207 · doi:10.1158/1538-7445.am2022-346

Abstract 346: Synergistic antiproliferative activity of novel RAD51 inhibitor JKYN-1 and its mesylate salt with standard-of-care cancer drugs

2022· article· en· W4282979207 on OpenAlexaff
Peter J. Ferguson, Mark Vincent, Yousef Najajreh, Brian H. Shilton, Stephen Ritter, Rima Al‐awar, Richard Marcellus, Mohammed Mohammed, Methvin Isaac, James Koropatnick

Bibliographic record

VenueCancer Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer therapeutics and mechanisms
Canadian institutionsWestern UniversityOntario Institute for Cancer ResearchLondon Health Sciences Centre
Fundersnot available
KeywordsCancer cellCancerCytotoxicityCancer researchChemistryPharmacologyRAD51Cell growthBiologyIn vitroDNA repairDNABiochemistryGenetics

Abstract

fetched live from OpenAlex

Abstract The inherent genetic instability of cancer cells and the dependence of many tumor types on oncogenic drivers contribute selectivity of anticancer agents against tumor cells. That selectivity is limited, and toxicity to normal cells remains a major limitation to the success of chemotherapy. To increase selectivity by exploiting cancer cell genetic instability, we demonstrated that the small molecule IBR2 (an inhibitor of the DNA repair protein RAD51) enhanced cytotoxicity of numerous anticancer drugs including agents that do not directly target DNA (J Pharmacol Expt Ther, 364: 46-54, 2018. doi.org/10.1124/jpet.117.241661). We demonstrated the ability of IBR2 and a derivative [IBR120, (R)-3-(2-(benzylsulfonyl)isoindolin-1-yl)-1h-indole] to synergistically inhibit proliferation of a wider range of cancer cell lines in combination with a broad range of anticancer drugs (Proc. Amer. Assoc. Cancer Res., 60: Abst. 3057, 2019). To improve the activity and potentially increase selectivity for inhibiting RAD51, modifications were made to the structure of IBR120 using a virtual drug-protein docking program, yielding the compound JKYN-1. JKYN-1 inhibits proliferation of cancer cell lines approximately 5 times more strongly than IBR120. Given the potential importance of combining JKYN-1 with targeted anticancer drugs to increase therapeutic index, and the synergy previously observed between IBR120 and agents targeted against specific tumor types, JKYN-1 was tested in combination with targeted agents against a panel of tumor cell lines. Four- to five-day drug exposures were conducted in 96-well plates. Relative cell density determined using vital stains (alamarBlue©, neutral red) was reported as a percent of the fluorescence/absorbance of control cultures. Cell lines were representative of tumors from breast (MCF-7), prostate (DU145, LNCaP), stomach (N87), pancreas (PANC-1, Capan-1, Capan-2) and lung (A549b, H1650). The chemotherapy agents included inhibitors of epidermal growth factor receptor (osimertinib, afatinib), other tyrosine kinases (regorafenib, imatinib), sex steroid receptors (4-OH-tamoxifen, enzalutamide), and microtubule function (docetaxel). To improve solubility, a methylsulfonate salt of JKYN-1 was used for most experiments. JKYN-1-mesylate decreased the concentration of drugs that inhibited proliferation by 50% (IC50) by up to 90%, depending on the drug and cell line, indicating synergy between the agents. There were some combinations in which additivity but no synergy was observed, indicating selectivity for this interaction. Individual combinations will be presented. The ability of JKYN-1 to enhance antiproliferative activity of a wide variety of anticancer agents, and its potential selectivity for cancer cells, make possible the future use of RAD51 inhibitors as systemic therapy potentiators to improve clinical outcomes. Citation Format: Peter J. Ferguson, Mark D. Vincent, Yousef Najajreh, Brian Shilton, Stephen Ritter, Rima Al-awar, Richard Marcellus, Mohammed Mohammed, Methvin Isaac, James Koropatnick. Synergistic antiproliferative activity of novel RAD51 inhibitor JKYN-1 and its mesylate salt with standard-of-care cancer drugs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 346.

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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.044
GPT teacher head0.373
Teacher spread0.329 · 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
Published2022
Admission routes1
Has abstractyes

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