Kinome-wide Activity Modeling from Diverse Public High-Quality Data Sets

Schürer, Stephan C.; Muskal, Steven M.

doi:10.1021/ci300403k

Cited by 59 publications

(49 citation statements)

References 31 publications

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“…This can be critical for drug development programs in which the best hits from phenotypic screening have chemistries not suitable for hit-to-lead development. Kinase activity predictors (43) can eventually be used in lieu of kinase profiling panels to accelerate the in silico identification of such compounds and also the repurposing of approved drugs. Additionally, the modular nature of this approach means that it is not limited to neurite outgrowth and axon regeneration studies; rather, it should be applicable to any drug discovery campaign in which a phenotypic assay is used to screen highly annotated chemical libraries.…”

Section: Discussionmentioning

confidence: 99%

Rational Polypharmacology: Systematically Identifying and Engaging Multiple Drug Targets To Promote Axon Growth

Al-Ali¹,

Lee²,

Danzi³

et al. 2015

ACS Chem. Biol.

115

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Mammalian Central Nervous System (CNS) neurons regrow their axons poorly following injury, resulting in irreversible functional losses. Identifying therapeutics that encourage CNS axon repair has been difficult, in part because multiple etiologies underlie this regenerative failure. This suggests a particular need for drugs that engage multiple molecular targets. Although multi-target drugs are generally more effective than highly selective alternatives, we lack systematic methods for discovering such drugs. Target-based screening is an efficient technique for identifying potent modulators of individual targets. In contrast, phenotypic screening can identify drugs with multiple targets; however, these targets remain unknown. To address this gap, we combined the two drug discovery approaches using machine learning and information theory. We screened compounds in a phenotypic assay with primary CNS neurons and also in a panel of kinase enzyme assays. We used learning algorithms to relate the compounds’ kinase inhibition profiles to their influence on neurite outgrowth. This allowed us to identify kinases that may serve as targets for promoting neurite outgrowth, as well as others whose targeting should be avoided. We found that compounds that inhibit multiple targets (polypharmacology) promote robust neurite outgrowth in vitro. One compound with exemplary polypharmacology, was found to promote axon growth in a rodent spinal cord injury model. A more general applicability of our approach is suggested by its ability to deconvolve known targets for a breast cancer cell line, as well as targets recently shown to mediate drug resistance.

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Section: Discussionmentioning

confidence: 99%

Rational Polypharmacology: Systematically Identifying and Engaging Multiple Drug Targets To Promote Axon Growth

Al-Ali¹,

Lee²,

Danzi³

et al. 2015

ACS Chem. Biol.

115

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“…Structures and data were processed as previously described (47). Briefly, structures were standardized using the same Pipeline Pilot in-house protocol as above.…”

Section: Methodsmentioning

confidence: 99%

Chemical Interrogation of the Neuronal Kinome Using a Primary Cell-Based Screening Assay

et al. 2013

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A fundamental impediment to functional recovery from spinal cord injury (SCI) and traumatic brain injury is the lack of sufficient axonal regeneration in the adult central nervous system. There is thus a need to develop agents that can stimulate axon growth to re-establish severed connections. Given the critical role played by protein kinases in regulating axon growth and the potential for pharmacological intervention, small molecule protein kinase inhibitors present a promising therapeutic strategy. Here, we report a robust cell-based phenotypic assay, utilizing primary rat hippocampal neurons, for identifying small molecule kinase inhibitors that promote neurite growth. The assay is highly reliable and suitable for medium throughput screening, as indicated by its Z′-factor of 0.73. A focused structurally diverse library of protein kinase inhibitors was screened, revealing several compound groups with the ability to strongly and consistently promote neurite growth. The best performing bioassay hit robustly and consistently promoted axon growth in a postnatal cortical slice culture assay. This study can serve as a jumping-off point for structure activity relationship (SAR) and other drug discovery approaches towards the development of drugs for treating SCI and related neurological pathologies.

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“…Examples of such applications are the global mapping of pharmacological space by Paolini and co-workers, [3] the Similarity Ensemble Approach (SEA), [4] the Bayesian models for adverse drug reactions by Bender and coworkers, [5] the models used for polypharmacological optimization by Hopkins et al, [6] and the kinome-wide activity modeling studies by Schuerer and Muskal. [7] These methods can be used to predict off-target effects based on heterogeneous public activity data and chemical similarity analysis. Usually, public off-target toxicity models like human Ether-à-go-go-Related Gene (hERG) [8] and cytochrome P450 (CYP) models [9], [10] are based and validated on mixed public IC 50 data, since there is not enough public data available that originates from one single assay.…”

Section: Introductionmentioning

confidence: 99%

Comparability of Mixed IC50 Data – A Statistical Analysis

et al. 2013

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The biochemical half maximal inhibitory concentration (IC50) is the most commonly used metric for on-target activity in lead optimization. It is used to guide lead optimization, build large-scale chemogenomics analysis, off-target activity and toxicity models based on public data. However, the use of public biochemical IC50 data is problematic, because they are assay specific and comparable only under certain conditions. For large scale analysis it is not feasible to check each data entry manually and it is very tempting to mix all available IC50 values from public database even if assay information is not reported. As previously reported for Ki database analysis, we first analyzed the types of errors, the redundancy and the variability that can be found in ChEMBL IC50 database. For assessing the variability of IC50 data independently measured in two different labs at least ten IC50 data for identical protein-ligand systems against the same target were searched in ChEMBL. As a not sufficient number of cases of this type are available, the variability of IC50 data was assessed by comparing all pairs of independent IC50 measurements on identical protein-ligand systems. The standard deviation of IC50 data is only 25% larger than the standard deviation of Ki data, suggesting that mixing IC50 data from different assays, even not knowing assay conditions details, only adds a moderate amount of noise to the overall data. The standard deviation of public ChEMBL IC50 data, as expected, resulted greater than the standard deviation of in-house intra-laboratory/inter-day IC50 data. Augmenting mixed public IC50 data by public Ki data does not deteriorate the quality of the mixed IC50 data, if the Ki is corrected by an offset. For a broad dataset such as ChEMBL database a Ki- IC50 conversion factor of 2 was found to be the most reasonable.

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Kinome-wide Activity Modeling from Diverse Public High-Quality Data Sets

Cited by 59 publications

References 31 publications

Rational Polypharmacology: Systematically Identifying and Engaging Multiple Drug Targets To Promote Axon Growth

Rational Polypharmacology: Systematically Identifying and Engaging Multiple Drug Targets To Promote Axon Growth

Chemical Interrogation of the Neuronal Kinome Using a Primary Cell-Based Screening Assay

Comparability of Mixed IC50 Data – A Statistical Analysis

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