Analysis of Kinase Inhibitor Selectivity using a Thermodynamics-Based Partition Index

Cheng, Alan C.; Eksterowicz, John; Geuns‐Meyer, Stephanie; Sun, Yaxiong

doi:10.1021/jm100301x

Cited by 46 publications

(66 citation statements)

References 22 publications

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“…Overall kinase selectivity is quantified by the kinase partition index (KPI) which meters selectivity relative to on-target potency (1 is perfectly selective, 0 is absolutely nonselective; refs. 42,43). The drugs have a wide range of kinome selectivity profiles.…”

Section: Biochemical Interaction Analysesmentioning

confidence: 99%

Spectrum and Degree of CDK Drug Interactions Predicts Clinical Performance

Chen

Lee

et al. 2016

Molecular Cancer Therapeutics

325

302

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Therapeutically targeting aberrant intracellular kinase signaling is attractive from a biological perspective but drug development is often hindered by toxicities and inadequate efficacy. Predicting drug behaviors using cellular and animal models is confounded by redundant kinase activities, a lack of unique substrates, and cell-specific signaling networks. Cyclin-dependent kinase (CDK) drugs exemplify this phenomenon because they are reported to target common processes yet have distinct clinical activities. Tumor cell studies of ATP-competitive CDK drugs (dinaciclib, AG-024322, abemaciclib, palbociclib, ribociclib) indicate similar pharmacology while analyses in untransformed cells illuminates significant differences. To resolve this apparent disconnect, drug behaviors are described at the molecular level. Nonkinase binding studies and kinome interaction analysis (recombinant and endogenous kinases) reveal that proteins outside of the CDK family appear to have little role in dinaciclib/palbociclib/ribociclib pharmacology, may contribute for abemaciclib, and confounds AG-024322 analysis. CDK2 and CDK6 cocrystal structures with the drugs identify the molecular interactions responsible for potency and kinase selectivity. Efficient drug binding to the unique hinge architecture of CDKs enables selectivity toward most of the human kinome. Selectivity between CDK family members is achieved through interactions with nonconserved elements of the ATPbinding pocket. Integrating clinical drug exposures into the analysis predicts that both palbociclib and ribociclib are CDK4/6 inhibitors, abemaciclib inhibits CDK4/6/9, and dinaciclib is a broad-spectrum CDK inhibitor (CDK2/3/4/6/9). Understanding the molecular components of potency and selectivity also facilitates rational design of future generations of kinase-directed drugs.

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Section: Biochemical Interaction Analysesmentioning

confidence: 99%

Spectrum and Degree of CDK Drug Interactions Predicts Clinical Performance

Chen

Lee

et al. 2016

Molecular Cancer Therapeutics

325

302

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“…21−23 While performing an exhaustive selectivity screen against all possible interaction partners is still intractable, it is possible to construct selectivity screening panels that can be used to gain insights and find more selective compounds. 21 …”

Section: Introductionmentioning

confidence: 99%

Rational Approaches to Improving Selectivity in Drug Design

Huggins

Sherman

Tidor³

2012

J. Med. Chem.

279

265

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“…The area A' is larger than area A, and therefore the coefficient is larger. (c) The partition coefficient [16] is a ratio of association constants. The numbers 1, 2, 3... refer to kinases in the profiling panel.…”

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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Analysis of Kinase Inhibitor Selectivity using a Thermodynamics-Based Partition Index

Cited by 46 publications

References 22 publications

Spectrum and Degree of CDK Drug Interactions Predicts Clinical Performance

Spectrum and Degree of CDK Drug Interactions Predicts Clinical Performance

Rational Approaches to Improving Selectivity in Drug Design

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

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