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

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

Bibliographic record

VenueMolecular Cancer Therapeutics · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer therapeutics and mechanisms
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsQuantitative structure–activity relationshipChemistrySteric effectsStereochemistryLigand (biochemistry)TopoisomeraseComputational biologyComputational chemistryDNABiologyBiochemistry

Abstract

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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.

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)0.001
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 score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.029
GPT teacher head0.282
Teacher spread0.253 · 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 routes2
Has abstractyes

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