Predicting Kinase Selectivity Profiles Using Free-Wilson QSAR Analysis

Sciabola, Simone; Stanton, Robert V.; Wittkopp, Sarah; Wildman, Scott A.; Moshinsky, Deborah; Potluri, Shobha; Xi, Hualin Simon

doi:10.1021/ci800138n

Cited by 36 publications

(30 citation statements)

References 47 publications

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“…In addition to looking at the kinase structure, the available structure-activity data can be used to look for kinases that are inhibited by structurally similar compounds [102,125]. The distances between key features in the kinases and in possible inhibitors can be compared [138].…”

Section: Predicting Specificity and Selectivitymentioning

confidence: 99%

Measuring and interpreting the selectivity of protein kinase inhibitors

2009

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Protein kinase inhibitors are a well-established class of clinically useful drugs, particularly for the treatment of cancer. Achieving inhibitor selectivity for particular protein kinases often remains a significant challenge in the development of new small molecules as drugs or as tools for chemical biology research. This review summarises the methodologies available for measuring kinase inhibitor selectivity, both in vitro and in cells. The interpretation of kinase inhibitor selectivity data is discussed, particularly with reference to the structural biology of the protein targets. Measurement and prediction of kinase inhibitor selectivity will be important for the development of new multi-targeted kinase inhibitors.

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Section: Predicting Specificity and Selectivitymentioning

confidence: 99%

Measuring and interpreting the selectivity of protein kinase inhibitors

2009

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“…Recently, we have used selectivity data generated here to develop quantitative SAR (QSAR) models to predict the selectivity profiles of 2 series. 32 In summary, we have developed and used a panel of biochemical kinase assays for selectivity screening of compounds. Profiles generated from the panel currently have valuable utility throughout the drug discovery process.…”

Section: Figmentioning

confidence: 99%

High-Throughput Biochemical Kinase Selectivity Assays: Panel Development and Screening Applications

et al. 2008

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Kinases represent attractive targets for drug discovery. Eight small-molecule kinase inhibitors are currently marketed in the area of oncology, and numerous others are in clinical trials. Characterization of the selectivity profiles of these compounds is important to target appropriate patient populations and to reduce the potential of toxicity due to off-target effects. The authors describe the development, validation, and utilization of a biochemical kinase assay panel for the selectivity profiling of inhibitors. The panel was developed as 29 radiometric Flashplate assays, and then an initial 13 were transitioned to a nonradiometric Caliper mobility shift assay format. Generation of high-quality data from the panel is detailed along with a comparison of the assay formats. Both assay technologies were found to be suitable for panel screening, but mobility shift assays yielded higher data quality. The selectivity data generated here should be useful in computational modeling and help facilitate, in conjunction with sequence and structural information, the rational design of inhibitors with well-defined selectivity profiles.

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“…Such profiling means that scientists are faced with increasing amounts of data that need to be distilled into human sense. It would be powerful to have a good single selectivity value for quantitatively steering the drug discovery process, for measuring progress of series within a program, for computational drug design [10-12], and for establishing when a compound is sufficiently selective. However, in contrast to, for instance, lipophilicity and potency, where values such as logP or binding constant (K d ) are guiding, quantitative measures for selectivity are still under debate.…”

Section: Introductionmentioning

confidence: 99%

A theoretical entropy score as a single value to express inhibitor selectivity

Uitdehaag

Zaman

2011

BMC Bioinformatics

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BackgroundDesigning maximally selective ligands that act on individual targets is the dominant paradigm in drug discovery. Poor selectivity can underlie toxicity and side effects in the clinic, and for this reason compound selectivity is increasingly monitored from very early on in the drug discovery process. To make sense of large amounts of profiling data, and to determine when a compound is sufficiently selective, there is a need for a proper quantitative measure of selectivity.ResultsHere we propose a new theoretical entropy score that can be calculated from a set of IC50 data. In contrast to previous measures such as the 'selectivity score', Gini score, or partition index, the entropy score is non-arbitary, fully exploits IC50 data, and is not dependent on a reference enzyme. In addition, the entropy score gives the most robust values with data from different sources, because it is less sensitive to errors. We apply the new score to kinase and nuclear receptor profiling data, and to high-throughput screening data. In addition, through analyzing profiles of clinical compounds, we show quantitatively that a more selective kinase inhibitor is not necessarily more drug-like.ConclusionsFor quantifying selectivity from panel profiling, a theoretical entropy score is the best method. It is valuable for studying the molecular mechanisms of selectivity, and to steer compound progression in drug discovery programs.

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Predicting Kinase Selectivity Profiles Using Free-Wilson QSAR Analysis

Cited by 36 publications

References 47 publications

Measuring and interpreting the selectivity of protein kinase inhibitors

Measuring and interpreting the selectivity of protein kinase inhibitors

High-Throughput Biochemical Kinase Selectivity Assays: Panel Development and Screening Applications

A theoretical entropy score as a single value to express inhibitor selectivity

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