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Abstract A142: Molecular modeling approach to the rational design of promiscuous quinazoline-based EGFR inhibitors

2009· article· en· W2322879380 on OpenAlexaff
Williams Christopher, Larroque Anne-Laure, Ying Huang, Todorova Margarita, Sylvie Barchéchath, Sylvia Lauwagie, Gina Belinsky, Vincent Philippe, Nahid Golabi, Qiyu Qiu, Zakaria Rachid, Bertrand J. Jean‐Claude

Bibliographic record

VenueMolecular Cancer Therapeutics · 2009
Typearticle
Languageen
FieldChemistry
TopicQuinazolinone synthesis and applications
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsCancer researchEpidermal growth factor receptorTyrosine kinaseQuinazolineKinaseBiologyErlotinibPTENProto-oncogene tyrosine-protein kinase SrcReceptor tyrosine kinaseBiochemistryAfatinibDocking (animal)ChemistryPharmacologySignal transductionCell biologyPI3K/AKT/mTOR pathwayReceptorMedicineStereochemistry

Abstract

fetched live from OpenAlex

Abstract Solid tumors at the advanced stages are often characterized by the overexpression of tyrosine kinase receptors that stimulate growth through the MAP kinase pathway and activate antiapoptotic signaling through the PI3K kinase pathway. One such receptor is the epidermal growth factor receptor (EGFR) that is overexpressed in many tumors including brain, lung, breast, ovarian and prostate carcinomas. EGFR can synergize with other tyrosine kinases such as Src to promote invasion and metastasis. However, despite the implication of EGFR in multiple processes, inhibitors of its tyrosine kinase activity such as ZD1839 or erlotinib showed moderate antiproliferative activity against certain tumor types in the clinic. It has been reported that EGFR expressing cells that harbor dysfunctional PTEN, are moderately sensitive to EGFR inhibitors. Given the complexity of cell response the latter class of inhibitors, we thought it of interest to investigate agents that are not only directed at EGFR but also to divergent targets such as Src or DNA, with the purpose of producing single compounds with greater potency than their single inhibitor counterpart. These molecules termed “combi-molecules” were designed to remain small enough for their quinazoline moiety to be able to bind to the ATP site of EGFR and to block a divergent target such as Src or DNA. Using molecular modeling, a structure-based drug design program was used to identify a linker that could be placed between the quinazoline moiety required for binding in the ATP site and the appendage directed at the divergent target. A solvent exposed ASP residue near the opening of the EGFR ATP binding pocket was used to optimize interaction with ionizable linkers. This interaction was found to be tolerant of bulky substituents and was exploited to append other pharmacophores to the combi-molecules. The results showed that in the category of mixed EGFR-DNA targeting molecules, the EGFR inhibitory potency of EGFR was in the low micromolar to nM range and the compounds also retained significant DNA damaging potential. In the category of EGFR-Src targeting molecules, we identified SB163 that contained a quinazoline moiety, the ionizable spacer and an analogue of PP2. SB163 showed significant antiproliferative and antimetastatic property in a Boyden Chamber assay and its activity was superior to that of a combination of known Src inhibitor PP2 + ZD1839, a clinical inhibitor of EGFR. Furthermore, sub-cellular distribution studies, using a fluorescent probe containing the optimized ionizable spacer showed that their biodistribution is unique with preferential localization in the perinuclear region. The results in toto suggests that in the design of promiscuous molecules, an ionizable basic arm linked to the aminoquinazoline moiety preserves EGFR inhibitory potency and allow freedom to append other moiety directed at the cross- target. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):A142.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.280
Teacher spread0.240 · 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 designSimulation or modeling
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".

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Citations0
Published2009
Admission routes1
Has abstractyes

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