Integrating High-Dimensional Transcriptomics and Image Analysis Tools into Early Safety Screening: Proof of Concept for a New Early Drug Development Strategy

A new method for predicting the acute toxicity of carbamate pesticides based on the perspective of binding information with carrier protein

Toxicity is one of the most important factors limiting the success of new drug development. In this paper, we built a fast and convenient new method (Carrier protein binding information-toxicity relationship, CPBITR) for predicting drug acute toxicity based on the perspective of binding information with carrier protein. First, we studied the binding information between carbamate pesticides and human serum albumin (HSA) through various spectroscopic methods and molecular docking. Then a total of 16 models were established to clarify the relationship between binding information with HSA and drug toxicity. The results showed that the binding information was related to toxicity. Finally we obtained the effective toxicity prediction model for carbamate pesticides. And the "Platform for Predicting Drug Toxicity Based on the Information of Binding with Carrier Protein" was established with the Back-propagation neural network model. We proposed and proved that it was feasible to predict drug toxicity from this new perspective: binding with carrier protein. According to this new perspective, toxicity prediction model of other drugs can also be established. This new method has the advantages of convenience and fast, and can be used to screen out low-toxic drugs quickly in the early stage. It is helpful for drug research and development.

Idebenone is a cytoprotective insulin sensitizer whose mechanism is Shc inhibition

When insulin binds insulin receptor, IRS1 signaling is stimulated to trigger the maximal insulin response. p52Shc protein competes directly with IRS1, thus damping and diverting maximal insulin response. Genetic reduction of p52Shc minimizes competition with IRS1, and improves insulin signaling and glucose control in mice, and improves pathophysiological consequences of hyperglycemia. Given the multiple benefits of Shc reduction in vivo, we investigated whether any of 1680 drugs used in humans may function as Shc inhibitors, and thus potentially serve as novel anti-diabetics. Of the 1680, 30 insulin sensitizers were identified by screening in vitro, and of these 30 we demonstrated that 7 bound Shc protein. Of the 7 drugs, idebenone dose-dependently bound Shc protein in the 50-100 nM range, and induced insulin sensitivity and cytoprotection in this same 100 nM range that clinically dosed idebenone reaches in human plasma. By contrast we observe mitochondrial effects of idebenone in the 5,000 nM range that are not reached in human dosing. Multiple assays of target engagement demonstrate that idebenone physically interacts with Shc protein. Idebenone sensitizes mice to insulin in two different mouse models of prediabetes. Genetic depletion of idebenone's target eliminates idebenone's ability to insulin-sensitize in vivo. Thus, idebenone is the first-in-class member of a novel category of insulin-sensitizing and cytoprotective agents, the Shc inhibitors. Idebenone is an approved drug and could be considered for other indications such as type 2 diabetes and fatty liver disease, in which insulin resistance occurs.

Modulating transcription factor activity: Interfering with protein-protein interaction networks

Biophysical parameters that govern transcription factors activity are binding locations across the genome, dwelling time at these regulatory elements and specific protein-protein interactions. Most molecular strategies used to develop small compounds that block transcription factors activity have been based on biochemistry and cell biology methods that that do not take into consideration these key biophysical features. Here, we review the advance in the field of transcription factor biology and describe how their interactome and transcriptional regulation on a genome wide scale have been deciphered. We suggest that this new knowledge has the potential to be used to implement innovative research drug discovery program.

Ab initio chemical safety assessment: A workflow based on exposure considerations and non-animal methods

We describe and illustrate a workflow for chemical safety assessment that completely avoids animal testing. The workflow, which was developed within the SEURAT-1 initiative, is designed to be applicable to cosmetic ingredients as well as to other types of chemicals, e.g. active ingredients in plant protection products, biocides or pharmaceuticals. The aim of this work was to develop a workflow to assess chemical safety without relying on any animal testing, but instead constructing a hypothesis based on existing data, in silico modelling, biokinetic considerations and then by targeted non-animal testing. For illustrative purposes, we consider a hypothetical new ingredient x as a new component in a body lotion formulation. The workflow is divided into tiers in which points of departure are established through in vitro testing and in silico prediction, as the basis for estimating a safe external dose in a repeated use scenario. The workflow includes a series of possible exit (decision) points, with increasing levels of confidence, based on the sequential application of the Threshold of Toxicological (TTC) approach, read-across, followed by an "ab initio" assessment, in which chemical safety is determined entirely by new in vitro testing and in vitro to in vivo extrapolation by means of mathematical modelling. We believe that this workflow could be applied as a tool to inform targeted and toxicologically relevant in vitro testing, where necessary, and to gain confidence in safety decision making without the need for animal testing.

Transcriptional Characterization of Compounds: Lessons Learned from the Public LINCS Data

The NIH-funded LINCS program has been initiated to generate a library of integrated, network-based, cellular signatures (LINCS). A novel high-throughput gene-expression profiling assay known as L1000 was the main technology used to generate more than a million transcriptional profiles. The profiles are based on the treatment of 14 cell lines with one of many perturbation agents of interest at a single concentration for 6 and 24 hours duration. In this study, we focus on the chemical compound treatments within the LINCS data set. The experimental variables available include number of replicates, cell lines, and time points. Our study reveals that compound characterization based on three cell lines at two time points results in more genes being affected than six cell lines at a single time point. Based on the available LINCS data, we conclude that the most optimal experimental design to characterize a large set of compounds is to test them in duplicate in three different cell lines. Our conclusions are constrained by the fact that the compounds were profiled at a single, relative high concentration, and the longer time point is likely to result in phenotypic rather than mechanistic effects being recorded.