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Enregistrement W2279288073 · doi:10.1158/1535-7163.targ-09-cn08-01

Abstract CN08-01: 3D-QSAR based design of novel inhibitors of topoisomerase II

2009· article· en· W2279288073 sur OpenAlexaffabout
Brian B. Hasinoff

Notice bibliographique

RevueMolecular Cancer Therapeutics · 2009
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueCancer therapeutics and mechanisms
Établissements canadiensUniversity of Manitoba
Organismes subventionnairesnon disponible
Mots-clésQuantitative structure–activity relationshipChemistrySteric effectsStereochemistryLigand (biochemistry)TopoisomeraseComputational biologyComputational chemistryDNABiologyBiochemistry

Résumé

récupéré en direct d'OpenAlex

Abstract Anticancer drugs that target topoisomerase II are among the most effective and widely used drugs in the anticancer therapeutic armamentarium. Quantitative structure activity relationship (QSAR) analysis of a series of structurally related analogs can be used to design more active analogs. Classical QSAR analysis relies on correlations with “flat” 2D-QSAR parameters to achieve this goal. Examples of 2D parameters might include log Poct, HOMO/LUMO values, numbers of H-bond donors/acceptors, or rotatable bonds. The design of more active analogs may either be ligand-based or structure-based, where the 3D structure of the binding site of the target molecule is known. 3D-QSAR methodology. Three-dimensional quantitative structure-activity analysis (3D-QSAR) is an advance over classical 2D-QSAR methods in that the 3D structures of the ligands are explicitly used to develop a predictive model. It is the interaction of the 3D structure of the drug with its protein or DNA target that determines its degree of binding and hence its activity. A strength of the 3D-QSAR method is that it can be used even when no X-ray structure of the target is available. In fact 3D-QSAR can be used to define the optimum shape of a virtual binding site. 3D-QSAR employs comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) in order to determine the ligand binding-site interaction factors important for activity. In the more sophisticated CoMSIA analysis steric, electrostatic, hydrophobic and H-bond donor and acceptor field energy contributions are all evaluated for their contributions to the activity. CoMFA, however, utilizes only the former two fields. The field energies are calculated by placing the molecules in a 3D lattice of a regularly spaced grid of probe atoms. Bisphenol inhibitors of topoisomerase IIα. As the result of a screen for novel inhibitors of topoisomerase II we identified a new lead bisphenol compound that had low micromolar topoisomerase II and K562 cell growth inhibitory activity (1). Most of the compounds displayed only low-fold resistance to a K562 subline with reduced levels of topoisomerase IIα suggesting that they acted as catalytic inhibitors rather than as topoisomerase II poisons. A 3D-QSAR analysis on 23 analogs was used to identify the ring and bridge substituents that were important for activity and to guide the synthesis of new analogs. H-bond acceptors on the meta position of the phenyl ring and H-bond donors on both the para and meta positions favored inhibition of topoisomerase IIα. Purine inhibitors of topoisomerase IIα. Similarly, a 3D-QSAR model was also developed for a series of 24 substituted purine analogs that had low to sub-micromolar activity for the inhibition of the ATPase activity of topoisomerase IIα (2). None of the compounds increased levels of the topoisomerase IIα-covalent complex, which suggested that they acted as catalytic inhibitors rather than topoisomerase II poisons. 3D-QSAR analysis showed that inhibition of topoisomerase IIα was most strongly correlated with the hydrophobic field generated by a phenyl group bonded to an sulfur atom. Combining 3D-QSAR and structure-based design of DNA intercalating and bisintercalating topoisomerase II targeted anthrapyrazole compounds. Finally, 3D-QSAR methods and structure-based design were combined in designing DNA intercalating and bisintercalating anthrapyrazole analogs of losoxantrone and piroxantrone. In these studies anthrapyrazole analogs were docked into an X-ray structure of DNA and their GOLD docking scores were correlated with DNA binding and inhibition of topoisomerase IIα decatenation catalytic activity (3). Conformations obtained from the results of docking into the DNA gave superior correlations compared to structures that were energy minimized. This result indicated that the docked poses were closer to the biologically active conformations. The 3D-QSAR analysis of K562 cell cytotoxicity, DNA binding and topoisomerase IIα inhibition showed that H-bond donor interactions and electrostatic interactions with the protonated side chains made the largest contribution to the total field. In an extension of this work a series of bisanthrapyrazoles containing ester and amide linkers of varying lengths were synthesized that were designed to be bisintercalators that could span 4 base pairs and thus bind more strongly to DNA (4,5). Several of the bisintercalators bound to DNA as strongly as doxorubicin and displayed low micromolar cell growth and topoisomerase IIα inhibition. Support: CIHR and a Canada Research Chair in Drug Development Citation Information: Mol Cancer Ther 2009;8(12 Suppl):CN08-01.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,014

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,029
Tête enseignante GPT0,282
Écart entre enseignants0,253 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2009
Routes d'admission2
Résumé présentoui

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