Blood-based identification of non-responders to anti-TNF therapy in rheumatoid arthritis.

BACKGROUND: Faced with an increasing number of choices for biologic therapies, rheumatologists have a critical need for better tools to inform rheumatoid arthritis (RA) disease management. The ability to identify patients who are unlikely to respond to first-line biologic anti-TNF therapies prior to their treatment would allow these patients to seek alternative therapies, providing faster relief and avoiding complications of disease. METHODS: We identified a gene expression classifier to predict, pre-treatment, which RA patients are unlikely to respond to the anti-TNF infliximab. The classifier was trained and independently evaluated using four published whole blood gene expression data sets, in which RA patients (n = 116 = 44 + 15 + 30 + 27) were treated with infliximab, and their response assessed 14-16 months post treatment according to the European League Against Rheumatism (EULAR) response criteria. For each patient, prior knowledge was used to group gene expression measurements into disease-relevant biological signaling mechanisms that were used as the input features for regularized logistic regression. RESULTS: The classifier produced a substantial enrichment of non-responders (59 %, given by the cross validated test precision) compared to the full population (27 % non-responders), while identifying nearly a third of non-responders. Given this classifier performance, treatment of predicted non-responders with alternative biologics would decrease their chance of non-response by between a third and a half, substantially improving their odds of effective treatment and stemming further disease progression. The classifier consisted of 18 signaling mechanisms, which together indicated that higher inflammatory signaling mediated by TNF and other cytokines was present pre-treatment in the blood of patients who responded to infliximab treatment. In contrast, non-responders were classified by relatively higher levels of specific metabolic activities in the blood prior to treatment. CONCLUSIONS: We were able to successfully produce a classifier to identify a population of RA patients significantly enriched in anti-TNF non-responders across four different patient cohorts. Additional prospective studies are needed to validate and refine the classifier for clinical use.

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.

Company Profile: Selventa, Inc.

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 Selventa's 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.

Early patient stratification and predictive biomarkers in drug discovery and development: a case study of ulcerative colitis anti-TNF therapy.

The current drug discovery paradigm is long, costly, and prone to failure. For projects in early development, lack of efficacy in Phase II is a major contributor to the overall failure rate. Efficacy failures often occur from one of two major reasons: either the investigational agent did not achieve the required pharmacology or the mechanism targeted by the investigational agent did not significantly contribute to the disease in the tested patient population. The latter scenario can arise due to insufficient study power stemming from patient heterogeneity. If the subset of disease patients driven by the mechanism that is likely to respond to the drug can be identified and selected before enrollment begins, efficacy and response rates should improve. This will not only augment drug approval percentages, but will also minimize the number of patients at risk of side effects in the face of a suboptimal response to treatment. Here we describe a systems biology approach using molecular profiling data from patients at baseline for the development of predictive biomarker content to identify potential responders to a molecular targeted therapy before the drug is tested in humans. A case study is presented where a classifier to predict response to a TNF targeted therapy for ulcerative colitis is developed a priori and verified against a test set of patients where clinical outcomes are known. This approach will promote the tandem development of drugs with predictive response, patient selection biomarkers.

Elevated GM-CSF and IL-1beta levels compromise the ability of p38 MAPK inhibitors to modulate TNFalpha 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.

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.

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.

BACKGROUND: The 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. RESULTS: Phylogeny, 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. CONCLUSION: We 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.

Applying computational modeling to drug discovery and development.

Computational models of cells, tissues and organisms are necessary for increased understanding of biological systems. In particular, modeling approaches will be crucial for moving biology from a descriptive to a predictive science. Pharmaceutical companies identify molecular interventions that they predict will lead to therapies at the organism level, suggesting that computational biology can play a key role in the pharmaceutical industry. We discuss pharmaceutically-relevant computational modeling approaches currently used as predictive tools. Specific examples demonstrate how companies can employ these computational models to improve the efficiency of transforming targets into therapies.

Identification of endoglin as a functional marker that defines long-term repopulating hematopoietic stem cells.

We describe a strategy to obtain highly enriched long-term repopulating (LTR) hematopoietic stem cells (HSCs) from bone marrow side-population (SP) cells by using a transgenic reporter gene driven by a stem cell enhancer. To analyze the gene-expression profile of the rare HSC population, we developed an amplification protocol termed "constant-ratio PCR," in which sample and control cDNAs are amplified in the same PCR. This protocol allowed us to identify genes differentially expressed in the enriched LTR-HSC population by oligonucleotide microarray analysis using as little as 1 ng of total RNA. Endoglin, an ancillary transforming growth factor beta receptor, was differentially expressed by the enriched HSCs. Importantly, endoglin-positive cells, which account for 20% of total SP cells, contain all the LTR-HSC activity within bone marrow SP. Our results demonstrate that endoglin, which plays important roles in angiogenesis and hematopoiesis, is a functional marker that defines LTR HSCs. Our overall strategy may be applicable for the identification of markers for other tissue-specific stem cells.