Identification of a Kinase Profile that Predicts Chromosome Damage Induced by Small Molecule Kinase Inhibitors

Kinases are heavily pursued pharmaceutical targets because of their mechanistic role in many diseases. Small molecule kinase inhibitors (SMKIs) are a compound class that includes marketed drugs and compounds in various stages of drug development. While effective, many SMKIs have been associated with toxicity including chromosomal damage. Screening for kinase-mediated toxicity as early as possible is crucial, as is a better understanding of how off-target kinase inhibition may give rise to chromosomal damage. To that end, we employed a competitive binding assay and an analytical method to predict the toxicity of SMKIs. Specifically, we developed a model based on the binding affinity of SMKIs to a panel of kinases to predict whether a compound tests positive for chromosome damage. As training data, we used the binding affinity of 113 SMKIs against a representative subset of all kinases (290 kinases), yielding a 113×290 data matrix. Additionally, these 113 SMKIs were tested for genotoxicity in an in vitro micronucleus test (MNT). Among a variety of models from our analytical toolbox, we selected using cross-validation a combination of feature selection and pattern recognition techniques: Kolmogorov-Smirnov/T-test hybrid as a univariate filter, followed by Random Forests for feature selection and Support Vector Machines (SVM) for pattern recognition. Feature selection identified 21 kinases predictive of MNT. Using the corresponding binding affinities, the SVM could accurately predict MNT results with 85% accuracy (68% sensitivity, 91% specificity). This indicates that kinase inhibition profiles are predictive of SMKI genotoxicity. While in vitro testing is required for regulatory review, our analysis identified a fast and cost-efficient method for screening out compounds earlier in drug development. Equally important, by identifying a panel of kinases predictive of genotoxicity, we provide medicinal chemists a set of kinases to avoid when designing compounds, thereby providing a basis for rational drug design away from genotoxicity

Functional Improvement of Regulatory T Cells From Rheumatoid Arthritis Subjects Induced by Capsular Polysaccharide Glucuronoxylomannogalactan

Objective: Regulatory T cells (Treg) play a critical role in the prevention of autoimmunity, and the suppressive activity of these cells is impaired in rheumatoid arthritis (RA). The aim of the present study was to investigate function and properties of Treg of RA patients in response to purified polysaccharide glucuronoxylomannogalactan (GXMGal). Methods: Flow cytometry and western blot analysis were used to investigate the frequency, function and properties of Treg cells. Results: GXMGal was able to: i) induce strong increase of FOXP3 on CD4+ T cells without affecting the number of CD4+CD25+FOXP3+ Treg cells with parallel increase in the percentage of non-conventional CD4+CD25-FOXP3+ Treg cells; ii) increase intracellular levels of TGF-beta1 in CD4+CD25-FOXP3+ Treg cells and of IL-10 in both CD4+CD25+FOXP3+ and CD4+CD25-FOXP3+ Treg cells; iii) enhance the suppressive activity of CD4+CD25+FOXP3+ and CD4+CD25-FOXP3+ Treg cells in terms of inhibition of effector T cell activity and increased secretion of IL-10; iv) decrease Th1 response as demonstrated by inhibition of T-bet activation and down-regulation of IFN-gamma and IL-12p70 production; v) decrease Th17 differentiation by down-regulating pSTAT3 activation and IL-17A, IL-23, IL-21, IL-22 and IL-6 production. Conclusion: These data show that GXMGal improves Treg functions and increases the number and function of CD4+CD25-FOXP3+ Treg cells of RA patients. It is suggested that GXMGal may be potentially useful for restoring impaired Treg functions in autoimmune disorders and for developing Treg cell-based strategies for the treatment of these diseases

Mathematical Modeling and Simulation of Ventricular Activation Sequences: Implications for Cardiac Resynchronization Therapy

Next to clinical and experimental research, mathematical modeling plays a crucial role in medicine. Biomedical research takes place on many different levels, from molecules to the whole organism. Due to the complexity of biological systems, the interactions between components are often difficult or impossible to understand without the help of mathematical models. Mathematical models of cardiac electrophysiology have made a tremendous progress since the first numerical ECG simulations in the 1960s. This paper briefly reviews the development of this field and discusses some example cases where models have helped us forward, emphasizing applications that are relevant for the study of heart failure and cardiac resynchronization therapy

Comparative genomics of the major parasitic worms

Parasitic nematodes (roundworms) and platyhelminths (flatworms) cause debilitating chronic infections of humans and animals, decimate crop production and are a major impediment to socioeconomic development. Here we report a broad comparative study of 81 genomes of parasitic and non-parasitic worms. We have identified gene family births and hundreds of expanded gene families at key nodes in the phylogeny that are relevant to parasitism. Examples include gene families that modulate host immune responses, enable parasite migration though host tissues or allow the parasite to feed. We reveal extensive lineage-specific differences in core metabolism and protein families historically targeted for drug development. From an in silico screen, we have identified and prioritized new potential drug targets and compounds for testing. This comparative genomics resource provides a much-needed boost for the research community to understand and combat parasitic worms