Effects of a Therapeutic Lifestyle Modification Program on Inflammatory Chemokines and Insulin Resistance in Subjects With Metabolic Syndrome

Background. Although therapeutic lifestyle modification (TLM) effectively improves the values of diagnostic biomarkers of metabolic syndrome, less is known about its effects on inflammatory chemokines and insulin resistance (IR) in patients with this syndrome. Objectives. To examine the effects of a short-term TLM program on inflammatory chemokines (monocyte chemoattractant protein-1 [MCP-1], retinol binding protein-4 [RBP-4]) and IR in subjects with metabolic syndrome. Method. Twenty-nine women (aged 66.5 ± 9.5 years) with metabolic syndrome were randomly assigned to the TLM intervention group (n = 16) or control group (n = 13). The TLM intervention group was provided with 4 weeks of health screening, education, exercise, diet, and counseling. Participants in the control group were instructed to maintain their usual lifestyle behavior. Outcome variables measured included MCP-1, RBP-4, fasting glucose, fasting insulin, and homeostasis model assessment (HOMA). An intention-to-treat strategy was not followed, and the final number of subjects in the analysis was 22 (14 in the TLM group and 8 in the control group). Results. After a 4-week TLM program, MCP-1, fasting insulin, and HOMA were significantly decreased in the TLM group compared to those in the control group (all p &lt; .05). Conclusions. We conclude that a short-term TLM program could be effective for improving inflammatory state and IR, which are significant preceding biomarkers for cardiovascular complications in subjects with metabolic syndrome. </jats:p

Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets (‘polypharmacology’). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.</jats:p

Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU-based disease: the Multi-Targeting Drug DREAM Challenge

AbstractA continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets (‘polypharmacology’). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.Author SummaryMany modern drugs are developed with the goal of modulating a single cellular pathway or target. However, many drugs are, in fact, ‘dirty;’ they target multiple cellular pathways or targets. This phenomenon is known as multi-targeting or polypharmacology. While some strive to develop ‘cleaner’ therapeutics that eliminate secondary targets, recent work has shown that multi-targeting therapeutics have key advantages for a variety of diseases. However, while multi-targeting drugs that affect a precisely-defined profile of targets may be more effective, it is difficult to computationally predict which molecules have desirable target profiles. Here, we report the results of a competitive crowdsourcing project (the Multi-Targeting Drug DREAM Challenge), where we challenged participants to predict chemicals that have desired target profiles for cancer and neurodegenerative disease.</jats:sec

Crowdsourced mapping of unexplored target space of kinase inhibitors

AbstractDespite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound–kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.</jats:p