Daniel Muthas

Evaluation of the use of imaging parameters for the detection of compound-induced hepatotoxicity in 384-well cultures of HepG2 cells and cryopreserved primary human hepatocytes.

Drug-induced liver injury (DILI) is a major cause of failed drug development, withdrawal and restricted usage. Therefore screening assays which aid selection of candidate drugs with reduced propensity to cause DILI are required. We have investigated the toxicity of 144 drugs, 108 of which caused DILI, using assays identified in the literature as having some predictivity for hepatotoxicity. The validated assays utilised either HepG2 cells, HepG2 cells in the presence of rat S9 fraction or isolated human hepatocytes. All parameters were quantified by multiplexed and automated high content fluorescence microscopy, at appropriate time points after compound administration (4, 24 or 48h). The individual endpoint which identified drugs that caused DILI with greatest precision was maximal fold induction in CM-H2DFFDA staining in hepatocytes after 24h (41% sensitivity, 86% specificity). However, hierarchical clustering analysis of all endpoints provided the most sensitive identification of drugs which caused DILI (58% sensitivity, 75% specificity). We conclude that multi-parametric high content cell toxicity assays can enable in vitro detection of drugs that have high propensity to cause DILI in vivo but that many DILI compounds exhibit few in vitro signals when evaluated using these assays.

A critical assessment of modeling safety-related drug attrition

This paper challenges the general desire to find simple rules and guidelines to reduce attrition due to toxicity and clinical safety. We present an analysis of 150 AstraZeneca development compounds and evaluate some of the published guidelines and their ability to identify compounds with safety liabilities. Interestingly, none of the current guidelines were able to discriminate compounds that successfully reached Phase II. The analysis was extended to recently approved drugs (2009–2011) and we found that a large portion did not comply and would never have reached patients if such guidelines had been applied at an early stage.

Application of Data Mining and Visualization Techniques for the Prediction of Drug-Induced Nausea in Man

The therapeutic value of many drugs can be limited by gastrointestinal (GI) adverse effects such as nausea and vomiting. Nausea is a subjective human sensation, hence little is known about preclinical biomarkers that may accurately and effectively predict its presence in man. The aim of this analysis was to use informatics and data-mining tools to identify plausible preclinical GI effects that may be associated with nausea and that could be of potential use in its prediction. A total of 86 marketed drugs were used in this analysis, and the main outcome was a confirmation that nausogenic and non-nausogenic drugs can be clearly separated based on their preclinical GI observations. Specifically, combinations of common preclinical GI effects (vomiting, diarrhea, and salivary hypersecretion) proved to be strong predictors. The model was subsequently validated with a subset of 20 blinded proprietary small molecules and successfully predicted clinical outcome in 90% of cases. This investigation demonstrated the feasibility of data-mining approaches to facilitate discovery of novel, plausible associations that can be used to understand drug-induced adverse effects.

Neutrophils in ulcerative colitis: a review of selected biomarkers and their potential therapeutic implications

Abstract Objectives: This review article describes the role of neutrophils in mucosal injury and the resulting crypt abscesses characteristic of ulcerative colitis. We also review selected biomarkers for monitoring neutrophil presence and activity in the mucosa as well as their potential as therapeutic targets. Material: We have collated and selectively reviewed data on the most prominent well-established and emerging neutrophil-related biomarkers and potential therapeutic targets (calprotectin, lactoferrin, CXCR1, CXCR2, MMP-9, NGAL, elafin, HNE, pANCAs, MPO, CD16, CD177, CD64, HNPs, SLPI and PTX3) in ulcerative colitis. Results: Systemic and intestinal neutrophil activity increases substantially in active ulcerative colitis, driving tissue damage and extra-intestinal manifestations. Calprotectin is a robust neutrophil and disease biomarker, and a few neutrophil-related targets are being clinically explored as therapeutic targets. Conclusion: We propose that targeting neutrophils and their inflammatory mediators per se is an opportunity that should be explored to identify new effective medical therapies. The overall clinical goal for neutrophil-targeted therapy will be to modulate, but not completely silence, neutrophil activity, thereby abolishing the destructive inflammation with associated acute and chronic tissue damage without compromising host-defense.

Exploiting Pharmacological Similarity to Identify Safety Concerns – Listen to What the Data Tells You

Whilst most new drugs are designed to act on a single target or a small number of targets, many do show broad pharmacological activity. In some cases this can be beneficial and necessary for efficacy and in others it can be detrimental, leading to increased safety liability. To probe off‐target pharmacology most drug discovery programs include screening against a broad panel of targets that represent known troublesome pharmacology. Hits against any one of these targets can then be subjected to a risk assessment for potential safety problems in preclinical or clinical studies. In addition, the secondary pharmacology profile can also be thought of as an alternative description of the compound and as such can be used as a method for assessing ‘similarity’. Consequently, inspection of the in vivo findings of pharmacological neighbors can give important insights into potential safety liabilities that are neither identified by pure chemical similarity searches nor by risk assessment on individual targets. Here we show that the pharmacological profile contains additional information as compared to chemical similarity, and also demonstrate how this can be used in the hazard assessment done during drug discovery and development.

Unexplored therapeutic opportunities in the human genome

A large proportion of biomedical research and the development of therapeutics is focused on a small fraction of the human genome. In a strategic effort to map the knowledge gaps around proteins encoded by the human genome and to promote the exploration of currently understudied, but potentially druggable, proteins, the US National Institutes of Health launched the Illuminating the Druggable Genome (IDG) initiative in 2014. In this article, we discuss how the systematic collection and processing of a wide array of genomic, proteomic, chemical and disease-related resource data by the IDG Knowledge Management Center have enabled the development of evidence-based criteria for tracking the target development level (TDL) of human proteins, which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome. We then present spotlights on the TDL categories as well as key drug target classes, including G protein-coupled receptors, protein kinases and ion channels, which illustrate the nature of the unexplored opportunities for biomedical research and therapeutic development.

Chapter 5:Information, Informatics and Modeling in Predictive Toxicology

The varied nature and enormous volume of safety-related data makes assimilation, standardization and ultimate utilization a tremendous challenge. Many of the current data sources that can influence safety-related decisions in drug discovery and development derive from other areas of toxicology or are not directly focused on drug safety. These data sources are nevertheless extremely useful if used in the right context and if the user is aware of the limitations. Modeling of safety data is also possible under certain conditions and can be extremely useful in categorizing and prioritizing compounds for synthesis or for testing. This chapter outlines both the various sources of data that can be relevant to drug safety but also some of the modeling methods, pitfalls and areas of greatest interest and impact.