Translational Systems Pharmacology-Based Predictive Assessment of Drug-Induced Cardiomyopathy

Drug-induced cardiomyopathy contributes to drug attrition. We compared two pipelines of predictive modeling: (1) applying elastic net (EN) to differentially expressed genes (DEGs) of drugs; (2) applying integer linear programming (ILP) to construct each drug’s signaling pathway starting from its targets to downstream proteins, to transcription factors, and to its DEGs in human cardiomyocytes, and then subjecting the genes/proteins in the drugs’ signaling networks to EN regression. We classified 31 drugs with availability of DEGs into 13 toxic and 18 nontoxic drugs based on a clinical cardiomyopathy incidence cutoff of 0.1%. The ILP-augmented modeling increased prediction accuracy from 79% to 88% (sensitivity: 88%; specificity: 89%) under leave-one-out cross validation. The ILP-constructed signaling networks of drugs were better predictors than DEGs. Per literature, the microRNAs that reportedly regulate expression of our six top predictors are of diagnostic value for natural heart failure or doxorubicin-induced cardiomyopathy. This translational predictive modeling might uncover potential biomarkers

Similar glycaemic control and less hypoglycaemia during active titration after insulin initiation with glargine 300 units/mL and degludec 100 units/mL: A subanalysis of the BRIGHT study

Aim: To further investigate glycaemic control and hypoglycaemia in BRIGHT, focusing on the titration period. Materials and Methods: BRIGHT was a multicentre, open-label, randomized, active-controlled, two-arm, parallel-group, 24-week study in insulin-naïve patients with uncontrolled type 2 diabetes initiated on glargine 300 U/mL (Gla-300) (N = 466) or degludec (IDeg-100) (N = 463). Predefined efficacy and safety outcomes were investigated during the initial 12-week titration period. In addition, patients’ characteristics and clinical outcomes were assessed descriptively, stratified by confirmed (≤3.9 mmol/L) hypoglycaemia incidence during the initial titration period. Results: At week 12, HbA1c was comparable between Gla-300 (7.32%) and IDeg-100 (7.23%), with similar least squares (LS) mean reductions from baseline (−1.37% and − 1.39%, respectively; LS mean difference of 0.02; 95% confidence interval: −0.08 to 0.12). Patients who experienced hypoglycaemia during the initial titration period had numerically greater HbA1c reductions by week 12 than patients who did not (−1.46% vs. −1.28%), and higher incidence of anytime (24 hours; 73.3% vs. 35.7%) and nocturnal (00:00–06:00 hours; 30.0% vs. 11.9%) hypoglycaemia between weeks 13–24. Conclusions: The use of Gla-300 resulted in similar glycaemic control as IDeg-100 during the initial 12-week titration period of the BRIGHT study, when less anytime (24 hours) hypoglycaemia with Gla-300 versus IDeg-100 has been reported. Experiencing hypoglycaemia shortly after initiating Gla-300 or IDeg-100 may be associated with hypoglycaemia incidence in the longer term, potentially impacting glycaemic management

Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury

Identification of signaling pathways that are functional in a specific biological context is a major challenge in systems biology, and could be instrumental to the study of complex diseases and various aspects of drug discovery. Recent approaches have attempted to combine gene expression data with prior knowledge of protein connectivity in the form of a PPI network, and employ computational methods to identify subsets of the protein–protein-interaction (PPI) network that are functional, based on the data at hand. However, the use of undirected networks limits the mechanistic insight that can be drawn, since it does not allow for following mechanistically signal transduction from one node to the next. To address this important issue, we used a directed, signaling network as a scaffold to represent protein connectivity, and implemented an Integer Linear Programming (ILP) formulation to model the rules of signal transduction from one node to the next in the network. We then optimized the structure of the network to best fit the gene expression data at hand. We illustrated the utility of ILP modeling with a case study of drug induced lung injury. We identified the modes of action of 200 lung toxic drugs based on their gene expression profiles and, subsequently, merged the drug specific pathways to construct a signaling network that captured the mechanisms underlying Drug Induced Lung Disease (DILD). We further demonstrated the predictive power and biological relevance of the DILD network by applying it to identify drugs with relevant pharmacological mechanisms for treating lung injury.Institute for Collaborative Biotechnologies (Grant W911NF-09-0001

The Effect of Hole Transport Material Pore Filling on Photovoltaic Performance in Solid-State Dye-Sensitized Solar Cells

A detailed investigation of the effect of hole transport material (HTM) pore filling on the photovoltaic performance of solid-state dye-sensitized solar cells (ss-DSCs) and the specific mechanisms involved is reported. It is demonstrated that the efficiency and photovoltaic characteristics of ss-DSCs improve with the pore filling fraction (PFF) of the HTM, 2,2’,7,7’-tetrakis-( N, N-di-p-methoxyphenylamine)9,9’-spirobifluorene(spiro-OMeTAD). The mechanisms through which the improvement of photovoltaic characteristics takes place were studied with transient absorption spectroscopy and transient photovoltage/photocurrent measurements. It is shown that as the spiro- OMeTAD PFF is increased from 26% to 65%, there is a higher hole injection efficiency from dye cations to spiro-OMeTAD because more dye molecules are covered with spiro-OMeTAD, an order-of-magnitude slower recombination rate because holes can diffuse further away from the dye/HTM interface, and a 50% higher ambipolar diffusion coefficient due to an improved percolation network. Device simulations predict that if 100% PFF could be achieved for thicker devices, the efficiency of ss-DSCs using a conventional ruthenium dye would increase by 25% beyond its current value

Corrigendum to "Satellite observations and model simulations of tropospheric NO₂ columns over south-eastern Europe" published in Atmos. Chem. Phys., 9, 6119–6134, 2009

No abstract available

Endpoint Estimates for N-dimensional Hardy Operators and Their Commutators

In this paper, it is proved that the higher dimensional Hardy operator is bounded from Hardy space to Lebesgue space. The endpoint estimate for the commutator generated by Hardy operator and (central) BMO function is also discussed.Comment: 8 page

Identifying Drug Effects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data

Understanding the mechanisms of cell function and drug action is a major endeavor in the pharmaceutical industry. Drug effects are governed by the intrinsic properties of the drug (i.e., selectivity and potency) and the specific signaling transduction network of the host (i.e., normal vs. diseased cells). Here, we describe an unbiased, phosphoproteomicbased approach to identify drug effects by monitoring drug-induced topology alterations. With the proposed method, drug effects are investigated under several conditions on a cell-type specific signaling network. First, starting with a generic pathway made of logical gates, we build a cell-type specific map by constraining it to fit 13 key phopshoprotein signals under 55 experimental cases. Fitting is performed via a formulation as an Integer Linear Program (ILP) and solution by standard ILP solvers; a procedure that drastically outperforms previous fitting schemes. Then, knowing the cell topology, we monitor the same key phopshoprotein signals under the presence of drug and cytokines and we re-optimize the specific map to reveal the drug-induced topology alterations. To prove our case, we make a pathway map for the hepatocytic cell line HepG2 and we evaluate the effects of 4 drugs: 3 selective inhibitors for the Epidermal Growth Factor Receptor (EGFR) and a non selective drug. We confirm effects easily predictable from the drugs’ main target (i.e. EGFR inhibitors blocks the EGFR pathway) but we also uncover unanticipated effects due to either drug promiscuity or the cell’s specific topology. An interesting finding is that the selective EGFR inhibitor Gefitinib is able to inhibit signaling downstream the Interleukin-1alpha (IL-1α) pathway; an effect that cannot be extracted from binding affinity based approaches. Our method represents an unbiased approach to identify drug effects on a small to medium size pathways and is scalable to larger topologies with any type of signaling perturbations (small molecules, 3 RNAi etc). The method is a step towards a better picture of drug effects in pathways, the cornerstone in identifying the mechanisms of drug efficacy and toxicity

Nonparametric Simulation of Signal Transduction Networks with Semi-Synchronized Update

Simulating signal transduction in cellular signaling networks provides predictions of network dynamics by quantifying the changes in concentration and activity-level of the individual proteins. Since numerical values of kinetic parameters might be difficult to obtain, it is imperative to develop non-parametric approaches that combine the connectivity of a network with the response of individual proteins to signals which travel through the network. The activity levels of signaling proteins computed through existing non-parametric modeling tools do not show significant correlations with the observed values in experimental results. In this work we developed a non-parametric computational framework to describe the profile of the evolving process and the time course of the proportion of active form of molecules in the signal transduction networks. The model is also capable of incorporating perturbations. The model was validated on four signaling networks showing that it can effectively uncover the activity levels and trends of response during signal transduction process