Selectivity Data: Assessment, Predictions, Concordance, and Implications

Gao, Canzhu; Cahya, Suntara; Nicolaou, Christos A.; Wang, Jibo; Watson, Ian; Cummins, David J.; Iversen, Philip W.; Vieth, Michał

doi:10.1021/jm400798j

Cited by 11 publications

(17 citation statements)

References 26 publications

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“…Several measures are available to evaluate the similarities of kinase drug targets [15, 46, 79–82], including sequence, structure, and inhibitor properties. For drug discovery purposes, experimental binding data regarding cross-reactivity of inhibitors may be the most relevant, although this data may be generated in different ways, with diversity arising from choice of binding or activity assays, conditions, target protein form, etc.…”

Section: Resultsmentioning

confidence: 99%

Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR

Narayanan¹,

Gruber²,

Engh³

2017

J Cheminform

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Drug design of protein kinase inhibitors is now greatly enabled by thousands of publicly available X-ray structures, extensive ligand binding data, and optimized scaffolds coming off patent. The extensive data begin to enable design against a spectrum of targets (polypharmacology); however, the data also reveal heterogeneities of structure, subtleties of chemical interactions, and apparent inconsistencies between diverse data types. As a result, incorporation of all relevant data requires expert choices to combine computational and informatics methods, along with human insight. Here we consider polypharmacological targeting of protein kinases ALK, MET, and EGFR (and its drug resistant mutant T790M) in non small cell lung cancer as an example. Both EGFR and ALK represent sources of primary oncogenic lesions, while drug resistance arises from MET amplification and EGFR mutation. A drug which inhibits these targets will expand relevant patient populations and forestall drug resistance. Crizotinib co-targets ALK and MET. Analysis of the crystal structures reveals few shared interaction types, highlighting proton-arene and key CH–O hydrogen bonding interactions. These are not typically encoded into molecular mechanics force fields. Cheminformatics analyses of binding data show EGFR to be dissimilar to ALK and MET, but its structure shows how it may be co-targeted with the addition of a covalent trap. This suggests a strategy for the design of a focussed chemical library based on a pan-kinome scaffold. Tests of model compounds show these to be compatible with the goal of ALK, MET, and EGFR polypharmacology.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0229-8) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR

Narayanan¹,

Gruber²,

Engh³

2017

J Cheminform

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“…Kinome-wide profiling data allow the creation and evaluation of computational models not only for activity but also selectivity predictions. Thibault Varin presented an application of ligand-based models in screening[7] campaigns at Lilly, and the discovery and initial optimization of selective RIO2 kinase inhibitors. Using chemical similarity, he selected from a set of virtual, robot-capable reactions a set of 8 compounds.…”

Section: Discussionmentioning

confidence: 99%

“…9)[7][13]. These were applied to predicting polypharmacology, modes-of-action for phenotypic screens, toxicity profiling, and selecting commercial compounds with diverse selectivity profiles for chemical archive enhancement.…”

Section: Discussionmentioning

confidence: 99%

Perspective on computational and structural aspects of kinase discovery from IPK2014

Martin

Knapp

Engh

et al. 2015

Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics

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Recent advances in understanding the activity and selectivity of kinase inhibitors and their relationships to protein structure are presented. Conformational selection in kinases is studied from empirical, data-driven and simulation approaches. Ligand binding and its affinity are, in many cases, determined by the predetermined active and inactive conformation of kinases. Binding affinity and selectivity predictions highlight the current state of the art and advances in computational chemistry as it applies to kinase inhibitor discovery. Kinome wide inhibitor profiling and cell panel profiling lead to a better understanding of selectivity and allow for target validation and patient tailoring hypotheses.

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“…Although these authors proposed an efficient algorithm for excluding records with errors of annotation, they did not explicitly investigate ways to compile data sets from databases of biologically active compounds specifically for the creation of predictive QSAR models. 1,4 Gao et al 5 reported a study of two sets of kinase inhibitors tested in two different assays. In contrast to our approach, the authors focused on the calculation of the concordance of kinase selectivity data for overlapping sets of compounds generated from four independent profiling sources but not on the analysis of the database contents for application to QSAR modeling.…”

Section: Comparison With Other Approaches From Literaturementioning

confidence: 99%

“…Several methods have been suggested to reduce this inconsistency in publicly available bioactivity databases. 1,4,5 Typically, these approaches are based on selecting only compounds investigated by a single team of authors to reduce the impact of different experimental conditions on the assay result.…”

Section: Introductionmentioning

confidence: 99%

QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors

Tarasova

Urusova

Филимонов

et al. 2015

J. Chem. Inf. Model.

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Large-scale databases are important sources of training sets for various QSAR modeling approaches. Generally, these databases contain information extracted from different sources. This variety of sources can produce inconsistency in the data, defined as sometimes widely diverging activity results for the same compound against the same target. Because such inconsistency can reduce the accuracy of predictive models built from these data, we are addressing the question of how best to use data from publicly and commercially accessible databases to create accurate and predictive QSAR models. We investigate the suitability of commercially and publicly available databases to QSAR modeling of antiviral activity (HIV-1 reverse transcriptase (RT) inhibition). We present several methods for the creation of modeling (i.e., training and test) sets from two, either commercially or freely available, databases: Thomson Reuters Integrity and ChEMBL. We found that the typical predictivities of QSAR models obtained using these different modeling set compilation methods differ significantly from each other. The best results were obtained using training sets compiled for compounds tested using only one method and material (i.e., a specific type of biological assay). Compound sets aggregated by target only typically yielded poorly predictive models. We discuss the possibility of "mix-and-matching" assay data across aggregating databases such as ChEMBL and Integrity and their current severe limitations for this purpose. One of them is the general lack of complete and semantic/computer-parsable descriptions of assay methodology carried by these databases that would allow one to determine mix-and-matchability of result sets at the assay level.

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Selectivity Data: Assessment, Predictions, Concordance, and Implications

Cited by 11 publications

References 26 publications

Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR

Data driven polypharmacological drug design for lung cancer: analyses for targeting ALK, MET, and EGFR

Perspective on computational and structural aspects of kinase discovery from IPK2014

QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors

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