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Abstract B25: “TargetDBR”—A DNA repair drug and target discovery collaboration: Exploiting synthetic lethal, high content, and functional cellular reporter assays to accelerate DNA repair targeted drug discovery

2017· article· en· W2604404138 on OpenAlexaboutno aff
Jonathan J. Hollick, Laura Abriola, Françoise Bono, Denise C. Hegan, Pamela Klingbeil, Yanfeng Liu, Ranjini K. Sundaram, Yulia V. Surovtseva, Mark Whittaker, Ranjit S. Bindra, Peter M. Glazer

Bibliographic record

VenueMolecular Cancer Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsSynthetic lethalityDNA damageDNA repairHomologous recombinationClonogenic assayDrug discoveryDNA Damage RepairGenome instabilityBiologyCancer researchRAD51High-content screeningDNAComputational biologyCell cultureCellGeneticsBioinformatics

Abstract

fetched live from OpenAlex

Abstract Most cancer therapies involve a component of treatment that inflicts DNA damage in tumor cells, such as double-strand breaks (DSBs), which are considered the most serious threat to genomic integrity. Inhibition of DSB repair sensitizes cells to these therapies. Mutations have been reported in nearly every DNA repair pathway and these pathways often exhibit redundancy. Inhibition of functional repair factors can induce synthetic lethality in repair-deficient tumors even in the absence of exogenous DNA damage and whilst sparing healthy tissue. We demonstrate a compound and target discovery platform comprising the integration of isogenic cell line screening, high content repair foci assays and relative DSB repair pathway reporting cells, along with chemical biology and medicinal chemistry. Two diverse drug-like compound library screens have been conducted. Screening for synthetic lethality with deficiency in FANCD2 and BRCA2 led to a hit series broadly active against a range of homologous recombination (HR) repair deficiencies, which is now undergoing medicinal chemistry optimization and target deconvolution studies. Outcome of these initial chemoproteomic target pulldown experiments will be presented. A second cellular screen, (50k compounds) for inhibition of BRCA1 and Rad51 foci formation following radiomimetic drug induced DNA damage, has led to identification of compounds that inhibit damage response after ionizing radiation and selectively inhibit HR repair. Focusing on therapeutic targeting of proliferating tumor cells, our research platform has enabled novel hit compound identification, mechanistic profiling, hit-to-lead optimization chemistry and proof of concept efficacy in in vitro tumor models. Effects on tumor cell clonogenic survival and sensitization towards chemotherapeutics and ionizing radiation will be presented, along with comparisons to known DNA repair inhibitors that demonstrate these compounds' differentiated mechanisms of action. TargetDBR has created a technology platform focused on DNA repair inhibitor discovery and has identified differentiated hits and lead series with potential for development and opportunity to meet the need for new druggable targets in oncology. Citation Format: Jonathan J. Hollick, Laura Abriola, Francoise Bono, Denise Hegan, Pamela Klingbeil, Yanfeng Liu, Ranjini Sundaram, Yulia V. Surovtseva, Mark Whittaker, Ranjit S. Bindra, Peter M. Glazer. “TargetDBR”—A DNA repair drug and target discovery collaboration: Exploiting synthetic lethal, high content, and functional cellular reporter assays to accelerate DNA repair targeted drug discovery [abstract]. In: Proceedings of the AACR Special Conference on DNA Repair: Tumor Development and Therapeutic Response; 2016 Nov 2-5; Montreal, QC, Canada. Philadelphia (PA): AACR; Mol Cancer Res 2017;15(4_Suppl):Abstract nr B25.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.045
GPT teacher head0.313
Teacher spread0.268 · 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 teacher head, not a consensus.

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
Published2017
Admission routes1
Has abstractyes

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