Abstract SY20-02: Discovering tool compounds and chemistry leads by highly accurate kinase virtual screening.
Authors: Martin Eric , Mukherjee Prasenjit , Tian Li , Sullivan David 
Published: August 2014

Abstract
Experimental high throughput screening (HTS) has provided most drug leads for over 20 years. However, a typical HTS of a large corporate archive takes 6-9 months and can cost nearly $1,000,000. Thus, it has long been an ambition of computational chemists to develop fast, inexpensive “virtual-screens” on computers. Unfortunately, conventional docking and QSAR lack the predictive power to serve as an alternative. We have developed 4 novel virtual screening (VS) methodologies specifically for kinases: Profile-QSAR, Kinase-Kernel, AutoShim and Surrogate AutoShim. These computational methods combine a modest amount of IC50 data for a new kinase with a vast kinase knowledgebase from all previous Novartis kinase projects, giving unparalleled IC50 predictions rivaling the accuracy of experimental HTS.
Profile-QSAR, is a 2D substructure-based meta-QSAR. It requires about 500 experimental IC50s for a new kinase, used to train a new model using predicted activities from historical kinase Quantitative Structure-Activity Relationships (QSARs) from 150 kinases. Every prediction is informed by over 3.3 million historical IC50s from over 325,000 compounds, resulting in unprecedented accuracy and generality. Profile-QSAR also predicts cellular activity, selectivity among kinases, and entire kinase profiles for the 150 kinases with sufficient training data.
However, there are nearly 400 additional kinases of potential pharmaceutical interest. Kinase-Kernel models interpolate between each new kinase's nearest neighbors from among the 150 with trained Profile-QSAR models, based on active-site sequence similarity. Thus, activity for the remaining kinases with no training data can also be predicted.
Profile-QSAR and Kinase Kernel models, based only on the 2D substructures of the ligands, have unmatched accuracy. However an orthogonal docking method, which instead uses 3D interactions with the protein active site, could find additional active compounds. Unfortunately, docking suffers 3 key limitations: it is slow, requires a protein structure for the target, and cannot predict reliable IC50s. Our AutoShim method trains highly accurate customized scoring functions for each new protein target by adjusting the weights of pharmacophore interaction“shims," added to the binding site, to reproduce 500 training IC50s. AutoShim has excellent accuracy, and is not restricted to kinases, but is still slow and requires a protein structure.
However, for kinases, Surrogate AutoShim uses a “Universal Kinase Surrogate Receptor” ensemble of 8 diverse kinase crystal structures as a proxy for the actual kinase of interest. Four million internal and commercial compounds have been pre-docked into this surrogate receptor, and the docking scores and pharmacophore interactions were extracted and stored for those billions of docked poses. Surrogate AutoShim models for new kinases can be trained, and accurate IC50’s of these 4 million compounds predicted, in just hours rather than weeks, without further docking. Combined, Profile QSAR, Kinae-Kernel and Surrogate AutoShim identify most of the actives in a compound collection. Activity has been predicted for these 4 million internal and commercial compounds across all 500+ human kinases and numerous cell lines, so an initial VS for any kinase is now just a table lookup. To do this experimentally would take many years and cost at least a billion dollars!
Method2D Profile-QSAR2D Kinase-Kernel3D (Plain) AutoShim3D Surrogate AutoshimBiochemical (IC50)?+++++Selectivity?++−−−Cellular EC50?++−−−Structure free?++−++Fast?++−++w/o IC50 Training data?−++−−Non-family target?−−++−
Iterative virtual screens have been successfully applied to about 50 recent Novartis kinase and protease projects, with external R2=0.35-0.7, enrichments of 20x-50x, and hit rates of 25% to 80% even for completely novel compounds. Applications have covered a wide range of project stages: finding tool compounds for target validation ahead of HTS, virtual screening where protein is too scarce for full HTS, “catch-up” screens for compounds added to the archive since an earlier HTS, rescuing HTS false-negatives, and triaging experimental HTS hits for cellular potency or specific selectivities, selecting compounds from commercial vendors, and enhancing the archive based on kinase profile diversity.
The first application was a “catch-up” VS for PDK1, a mature project looking for new medicinal chemistry starting points. 500,000 compounds had been added to the archive since the original experimental HTS. 342 compounds were chosen from the virtual screen, of which 20% were active, for a 30-fold enrichment. Although significant enrichment was only achieved for compounds with roughly 70% similarity to the known actives, the results were encouraging enough to add more kinase activity data to the underlying knowledgebase.
The next application was for LRRK2, which had too little protein for a full HTS. A medium-throughput screen of 24,000 cpds yielded 1,678 IC50s to train Profile-QSAR and surrogate AutoShim models. The VS results were much better, with 100% hit-rates for compounds with only 50% similarity to known actives and 17% to 20% hit rates even for compounds with virtually no similarity to the known actives.
A recent catch-up bRaf VS of 600,000 new compounds added to the archive since the HTS included predictive models for cellular target modulation and proliferation assays. It gave 100% hit rates down to 50% similarity to known actives, and 60%-80% hit rates even for compounds with virtually no similarity to the known actives.
Examining the model coefficients for the bRaf cell proliferation assay suggested that PDGFR beta activity enhanced the effect of bRaf inhibition. Subsequent publications described a dual bRaf/PDGFRb inhibitor in xenograft models, and clinical escapes from a bRaf drug from upregulating PDGFRb, confirming this hypothesis and supporting the biochemical realism of these models.
We increasingly use kinase VS to find tool compounds to chemically validate biological target hypotheses. Isoform selective tool compounds have been successfully identified for ERRB4 and other kinases.
A striking recent project needed a tool compound for validation, but any TK, AGC or PI3K kinase off-target activity might interfere with the mode of action. A focused set of 100,000 compounds were experimentally screened, and activity for dozens of potential anti-targets were predicted with Profile-QSAR. 900 compounds were screened, and the most promising 20 were tested in a 60-kinase panel. The best of these was rapidly optimized using structure-based design to a very selective 50 pM inhibitor. It modulated the target in cells at 1 nM, but cell proliferation was not inhibited. Therefore, the project was stopped before a scheduled HTS, fragment-based screen, and additional medicinal chemistry efforts, allowing a million or more dollars worth of resources to be redirected to more promising targets.
A more gratifying recent example needed chemical leads very quickly to file patents ahead of a collaborator's pending publications. Kinase VS rapidly identified 4 lead series with good potency, selectivity, physical properties and in vitro PK. These included type 1, type 2, and allosteric binding modes, and a fragment sized lead which led to a covalent inhibitor. Optimized analogues produced successful target validation, providing the team with ample chemical matter within the constrained timeline without an experimental HTS.
Surrogate AutoShim and Profile-QSAR have now also been adapted to the serine and cysteine protease families, as well as HSP90, PDE3, PDE4, ACC and other adenosine nucleotide binding proteins. Similar work on GPCR families is underway.
Citation Format: Eric Martin, Prasenjit Mukherjee, Li Tian, David Sullivan. Discovering tool compounds and chemistry leads by highly accurate kinase virtual screening. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr SY20-02. doi:10.1158/1538-7445.AM2013-SY20-02