David de Graaf

Cellular Imaging Predictions of Clinical Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is the most common adverse event causing drug nonapprovals and drug withdrawals. Using drugs as test agents and measuring a panel of cellular phenotypes that are directly linked to key mechanisms of hepatotoxicity, we have developed an in vitro testing strategy that is predictive of many clinical outcomes of DILI. Mitochondrial damage, oxidative stress, and intracellular glutathione, all measured by high content cellular imaging in primary human hepatocyte cultures, are the three most important features contributing to the hepatotoxicity prediction. When applied to over 300 drugs and chemicals including many that caused rare and idiosyncratic liver toxicity in humans, our testing strategy has a true-positive rate of 50-60% and an exceptionally low false-positive rate of 0-5%. These in vitro predictions can augment the performance of the combined traditional preclinical animal tests by identifying idiosyncratic human hepatotoxicants such as nimesulide, telithromycin, nefazodone, troglitazone, tetracycline, sulindac, zileuton, labetalol, diclofenac, chlorzoxazone, dantrolene, and many others. Our findings provide insight to key DILI mechanisms, and suggest a new approach in hepatotoxicity testing of pharmaceuticals.

A network-based approach to quantifying the impact of biologically active substances

Fe at u re s P E R S P E C T IV E Society increasingly demands close scrutiny of the potential health risks of long-term exposure to biologically active substances, such as therapeutic drugs or environmental toxins. Such risks are typically assessed a posteriori through clinical epidemiology studies. However, disease might take decades to manifest, at a point where changes in therapeutic regime, life style or exposure would not prevent disease onset. Moreover, disease risk as assessed correlatively in epidemiological studies is not intended to elucidate the mechanisms that link perturbations in molecular signaling to disease and, thus, provides fewer options for intervention. Here, we propose that network-based approaches to pharmacology are a valuable way to not only quantify biological network perturbations caused by active substances, but also identify mechanisms and biomarkers modulated in response to exposure and related to disease onset. We also discuss progress towards a generalizable approach for a mechanistic biological impact assessment. Novel computational methods that derive the quantitative biological impact [defined as a biological impact factor (BIF)] from underlying system-wide data using defined causal biological (i.e. molecular) network models as the substrate for data analysis are currently under

Multiple effects of acetaminophen and p38 inhibitors: Towards pathway toxicology

The majority of drug‐related toxicities are idiosyncratic, with little pathophysiological insight and mechanistic understanding. Pathway toxicology is an emerging field of toxicology in the post‐genomic era that studies the molecular interactions between toxicants and biological pathways as a way to bridge this knowledge gap. Using two case studies – acetaminophen and p38 MAPK inhibitors – this review illustrates how a pathway‐based perspective has advanced our understanding of compound and target‐based toxicities. The advancement of pathway toxicology will be dependent on integrated applications of techniques from basic sciences and a fundamental understanding of the interdependence of multiple biological pathways in living organisms.

A protein interaction atlas for the nuclear receptors: properties and quality of a hub-based dimerisation network

BackgroundThe nuclear receptors are a large family of eukaryotic transcription factors that constitute major pharmacological targets. They exert their combinatorial control through homotypic heterodimerisation. Elucidation of this dimerisation network is vital in order to understand the complex dynamics and potential cross-talk involved.ResultsPhylogeny, protein-protein interactions, protein-DNA interactions and gene expression data have been integrated to provide a comprehensive and up-to-date description of the topology and properties of the nuclear receptor interaction network in humans. We discriminate between DNA-binding and non-DNA-binding dimers, and provide a comprehensive interaction map, that identifies potential cross-talk between the various pathways of nuclear receptors.ConclusionWe infer that the topology of this network is hub-based, and much more connected than previously thought. The hub-based topology of the network and the wide tissue expression pattern of NRs create a highly competitive environment for the common heterodimerising partners. Furthermore, a significant number of negative feedback loops is present, with the hub protein SHP [NR0B2] playing a major role. We also compare the evolution, topology and properties of the nuclear receptor network with the hub-based dimerisation network of the bHLH transcription factors in order to identify both unique themes and ubiquitous properties in gene regulation. In terms of methodology, we conclude that such a comprehensive picture can only be assembled by semi-automated text-mining, manual curation and integration of data from various sources.

Systems diagnostics: anticipating the next generation of diagnostic tests based on mechanistic insight into disease.

Societal demand for faster and more accurate assignment of treatments is based in both patient care needs and in health economics. From a patient care standpoint, there needs to be a transformation from the empiric method of therapeutic decision making to avoid unwanted side effects from inefficacious treatments. For health economics, the delay in effective therapy and expenditures for ineffective therapies add to the burden of care. To accomplish this transformation, we need to modify our current method of classifying disease from a phenotypic description to one that incorporates the different molecular drivers that created the observed phenotype. To do so, a deeper, systems-based understanding of these disease drivers is required, which will yield a new generation of diagnostic tests, or systems diagnostics.

Elevated GM-CSF and IL-1β levels compromise the ability of p38 MAPK inhibitors to modulate TNFα levels in the human monocytic/macrophage U937 cell line

Rheumatoid arthritis (RA) is a complex, multicellular disease involving a delicate balance between both pro- and anti-inflammatory cytokines which ultimately determines the disease phenotype. The simultaneous presence of multiple signaling molecules, and more specifically their relative levels, potentially influences the efficacy of directed therapies. Using the human U937 monocytic cell line, we generated a self-consistent dataset measuring 50 cytokines and 23 phosphoproteins in the presence of 6 small molecule inhibitors under 15 stimulatory conditions throughout a 24 hour time course. From this dataset, we are able to explore phosphoprotein and cytokine relationships, as well as evaluate the significance of cellular context on the ability of small molecule inhibitors to block inflammatory processes. We show that the ability of a p38 inhibitor to attenuate TNFalpha production is influenced by local levels of GM-CSF and IL-1beta, two cytokines known to be elevated in the joints of RA patients. Within the cell, compensatory mechanisms between signaling pathways are apparent, as selective p38 MAPK inhibition results in the increased phosphorylation of other MAPKs (ERK and JNK) and their downstream substrates (CREB, c-Jun, and ATF-2). Further, we demonstrate that TNFalpha-neutralizing antibodies have secondary effects on cytokine production, impacting more than just TNFalpha alone. p38 MAPK inhibition using a small molecule inhibitor also blocks production of anti-inflammatory cytokines including IL-10, IL-1ra and IL-2ra. Collectively, the impact of cell context on TNFalpha production and unintended blockade of anti-inflammatory cytokines may compromise the efficacy of p38 inhibitors in a clinical setting. The effort described in this work evaluates the effect of inhibitors on multiple endpoints (both intra- and extracellular), under a range of biologically relevant conditions, thus providing a unique means for differentiation of compounds and potential opportunity for improved pharmacological manipulation of disease endpoints in RA.

A decade of Systems Biology: where are we and where are we going to?

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Chapter 25 Systems Biology and Kinase Signaling

see front matter 2013 Published by Elsevier Ltd. http://dx.doi.org/10.1016/j.drudis.2013. integrated into many research organizations. Its emergence is largely the result of the limitations of functional genomics which is mainly focused on linking simple phenotypes to single genes. This reductionist, but powerful approach, has been the major contributor to our current understanding of the function of individual genes and proteins, and will continue to be in the decades to come as our understanding of gene and protein function is still far from exhaustive. However, functional genomics has not been able to yield the understanding of the dynamic behavior of complex biological systems that is so crucial to understand life, and more specifically disease and therapeutic intervention, because it can provide only limited insights into complex multi-gene diseases and biological processes. Systems Biology is a highly multidisciplinary approach to decoding life [1] that combines computational with experimental methods to elucidate biological mechanisms and their dynamic behavior. It considers the biological systems as a whole [1] and aims at the detailed understanding of the mechanisms of action of active substances in experimental model systems in vitro and in vivo, including in humans. Systems Biology will thus create the knowledge of the dynamic interactions between the many diverse molecular and cellular entities of a complex biological system and how the perturbation of these interactions, through active substances, alterations of the entities (e.g., through coding mutations) or modulation of their abundance (e.g., through non-coding mutations) leads to adverse reactions and disease. As universal laws have done in physics, Systems Biology will thus elucidate the fundamental rules, and ultimately the laws that describe and explain the emergent properties of biological systems; in other words, life itself. Like rules and laws in physics, Biological Networks may well be the description of complex systems that will enable our understanding of therapeutic interventions and how they influence disease progression. The application of Systems Biology, therefore, leads to new approaches in Biomedical Research including pharmacology and toxicology [2], diagnostics [3], as well as drug development [4] and paves the way for the development of a more precise and personalized approach to medicine, both from a therapeutic and a prevention perspective [5]. To reach these ambitious goals, the first objective of Systems Biology is to describe the relevant Biological Networks and how their perturbations lead to disease. To this end, both experimental

Company Profile: Selventa, Inc.

Publisher Summary Systems Biology enables a better understanding of the complexity of cell signaling pathways and offers potential insight into targets for disease intervention. It has a solid foundation in both experimental cell biology and computational methods. It can explain the effect of feedback loops on signaling pathways and can be extended to modeling entire systems such as in the case of the ErbB receptors. The first level of complexity in the understanding of kinase signaling is to correctly assign the pathway topology of connections in the pathway. In most cases, researchers build kinase signaling pathway models based on historically well-defined canonical pathways. Computational methods have been applied to determine the connections in systems that are not well-defined by canonical pathways. This is either done by semi-automated and/or curated literature causal modeling or by statistical methods based on large-scale data from expression or proteomic studies. Many methods, including clustering, Bayesian analysis and principal component analysis have been used to find relationships and “fingerprints” in gene expression data.

Abstract 703: Verification of a novel approach to identify disease mechanisms in breast cancer patient subsets and application of this approach to indicating and characterizing tamoxifen resistance

Selventa, Inc. (MA, USA) is a biomarker discovery company that enables personalized healthcare. Originally founded as Genstruct, Inc., Selventa has undergone significant evolution from a technology-based service provider to an active partner in the development of diagnostic tests, functioning as a molecular dashboard of disease activity using a unique platform. As part of that evolution, approximately 2 years ago the company was rebranded as Selventa to reflect its new identity and mission. The contributions to biomedical research by Selventa are based on in silico, reverse-engineering methods to determine biological causality. That is, given a set of in vitro or in vivo biological observations, which biological mechanisms can explain the measured results? Facilitated by a large and carefully curated knowledge base, these in silico methods generated new insights into the mechanisms driving a disease. As Selventas methods would enable biomarker discovery and be directly applicable to generating novel diagnostics, the scientists at Selventa have focused on the development of predictive biomarkers of response in autoimmune and oncologic diseases. Selventa is presently building a portfolio of independent, as well as partnered, biomarker projects with the intention to create diagnostic tests that predict response to therapy.