Design and synthesis of potent dual inhibitors of JAK2 and HDAC based on fusing the pharmacophores of XL019 and vorinostat.

Specifically blocking more than one oncogenic pathway simultaneously in a cancer cell with a combination of different drugs is the mainstay of the majority of cancer treatments. Being able to do this via two targeted pathways without inducing side effects through a general mechanism, such as chemotherapy, could bring benefit to patients. In this work we describe a new dual inhibitor of the JAK-STAT and HDAC pathways through designing and developing two types of molecule based on the JAK2 selective inhibitor XL019 and the pan-HDAC inhibitor, vorinostat. Both series of compounds had examples with low nanomolar JAK2 and HDAC1/6 inhibition. In some cases good HDAC1 selectivity was achieved while retaining HDAC6 activity. The observed potency is explained through molecular docking studies of all three enzymes. One example, 69c had 16-25 fold selectivity against the three other JAK-family proteins JAK1, JAK3 and TYK2. A number of compounds had sub-micromolar potencies against a panel of 4 solid tumor cell lines and 4 hematological cell lines with the most potent compound, 45h, having a cellular IC50 of 70 nM against the multiple myeloma cell line KMS-12-BM. Evidence of both JAK and HDAC pathway inhibition is presented in Hela cells showing that both pathways are modulated. Evidence of apoptosis with two compounds in 4 sold tumor cell lines is also presented.

Design and Synthesis of Ligand Efficient Dual Inhibitors of Janus Kinase (JAK) and Histone Deacetylase (HDAC) Based on Ruxolitinib and Vorinostat.

Concomitant inhibition of multiple oncogenic pathways is a desirable goal in cancer therapy. To achieve such an outcome with a single molecule would simplify treatment regimes. Herein the core features of ruxolitinib (1), a marketed JAK1/2 inhibitor, have been merged with the HDAC inhibitor vorinostat (2), leading to new molecules that are bispecific targeted JAK/HDAC inhibitors. A preferred pyrazole substituted pyrrolopyrimidine, 24, inhibits JAK1 and HDACs 1, 2, 3, 6, and 10 with IC50 values of less than 20 nM, is <100 nM potent against JAK2 and HDAC11, and is selective for the JAK family against a panel of 97 kinases. Broad cellular antiproliferative potency of 24 is supported by demonstration of JAK-STAT and HDAC pathway blockade in hematological cell lines. Methyl analogue 45 has an even more selective profile. This study provides new leads for assessment of JAK and HDAC pathway dual inhibiton achieved with a single molecule.

Design and Synthesis of Janus Kinase 2 (JAK2) and Histone Deacetlyase (HDAC) Bispecific Inhibitors Based on Pacritinib and Evidence of Dual Pathway Inhibition in Hematological Cell Lines.

Blockage of more than one oncoprotein or pathway is now a standard approach in modern cancer therapy. Multiple inhibition is typically achieved with two or more drugs. Herein, we describe a pharmacophore merging strategy combining the JAK2/FLT3 inhibitor pacritnib with the pan-HDAC inhibitor, vorinostat, to create bispecific single molecules with both JAK and HDAC targeted inhibition. A preferred ether hydroxamate, 51, inhibits JAK2 and HDAC6 with low nanomolar potency, is <100 nM potent against HDACs 2 and 10, submicromolar potent against HDACs 1, 8, and 11, and >50-fold selective for JAK2 in a panel of 97 kinases. Broad cellular antiproliferative potency is supported by demonstration of JAK-STAT and HDAC pathway blockade in several hematological cell lines, inhibition of colony formation in HEL cells, and analysis of apoptosis. This study provides new tool compounds for further exploration of dual JAK-HDAC pathway inhibiton achieved with a single molecule.

Reducing multiclass cancer classification to binary by output coding and SVM.

Multiclass cancer classification based on microarray data is presented. The binary classifiers used combine support vector machines with a generalized output-coding scheme. Different coding strategies, decoding functions and feature selection methods are incorporated and validated on two cancer datasets: GCM and ALL. Using random coding strategy and recursive feature elimination, the testing accuracy achieved is as high as 83% on GCM data with 14 classes. Comparing with other classification methods, our method is superior in classificatory performance.

Dimension reduction-based penalized logistic regression for cancer classification using microarray data.

The use of penalized logistic regression for cancer classification using microarray expression data is presented. Two dimension reduction methods are respectively combined with the penalized logistic regression so that both the classification accuracy and computational speed are enhanced. Two other machine-learning methods, support vector machines and least-squares regression, have been chosen for comparison. It is shown that our methods have achieved at least equal or better results. They also have the advantage that the output probability can be explicitly given and the regression coefficients are easier to interpret. Several other aspects, such as the selection of penalty parameters and components, pertinent to the application of our methods for cancer classification are also discussed.