EmergenTec (Austria)

Aims/hypothesisThe sodium–glucose cotransporter 2 (SGLT2) inhibitor canagliflozin slows progression of kidney function decline in type 2 diabetes. The aim of this study was to assess the effect of the SGLT2 inhibitor canagliflozin on biomarkers for progression of diabetic kidney disease (DKD).MethodsA canagliflozin mechanism of action (MoA) network model was constructed based on an in vitro transcriptomics experiment in human proximal tubular cells and molecular features linked to SGLT2 inhibitors from scientific literature. This model was mapped onto an established DKD network model that describes molecular processes associated with DKD. Overlapping areas in both networks were subsequently used to select candidate biomarkers that change with canagliflozin therapy. These biomarkers were measured in 296 stored plasma samples from a previously reported 2 year clinical trial comparing canagliflozin with glimepiride.ResultsForty-four proteins present in the canagliflozin MoA molecular model overlapped with proteins in the DKD network model. These proteins were considered candidates for monitoring impact of canagliflozin on DKD pathophysiology. For ten of these proteins, scientific evidence was available suggesting that they are involved in DKD progression. Of these, compared with glimepiride, canagliflozin 300 mg/day decreased plasma levels of TNF receptor 1 (TNFR1; 9.2%; p < 0.001), IL-6 (26.6%; p = 0.010), matrix metalloproteinase 7 (MMP7; 24.9%; p = 0.011) and fibronectin 1 (FN1; 14.9%; p = 0.055) during 2 years of follow-up.Conclusions/interpretationThe observed reduction in TNFR1, IL-6, MMP7 and FN1 suggests that canagliflozin contributes to reversing molecular processes related to inflammation, extracellular matrix turnover and fibrosis.Trial registration ClinicalTrials.gov NCT00968812

Productivity in drug R&D continues seeing significant attrition in clinical stage testing. Approval of new molecular entities proceeds with slow pace specifically when it comes to chronic, age-related diseases, calling for new conceptual approaches, methodological implementation and organizational adoption in drug development. Detailed phenotyping of disease presentation together with comprehensive representation of drug mechanism of action is considered as a path forward, and a big data spectrum has become available covering behavioral, clinical and molecular characteristics, the latter combining reductionist and explorative strategies. On this basis integrative analytics in the realm of Systems Biology has emerged, essentially aiming at traversing associations into causal relationships for bridging molecular disease specifics and clinical phenotype surrogates and finally explaining drug response and outcome. From a conceptual perspective bottom-up modeling approaches are available, with dynamical hierarchies as formalism capable of describing clinical findings as emergent properties of an underlying molecular process network comprehensively resembling disease pathology. In such representation biomarker candidates serve as proxy of a molecular process set, at the interface of a corresponding representation of drug mechanism of action allowing patient stratification and prediction of drug response. In practical implementation network analytics on a protein coding gene level has provided a number of example cases for matching disease presentation and drug molecular effect, and workflows combining computational hypothesis generation and experimental evaluation have become available for systematically optimizing biomarker candidate selection. With biomarker-based enrichment strategies in adaptive clinical trials, implementation routes for tackling development attrition are provided. Predictive biomarkers add precision in drug development and as companion diagnostics in clinical practice.

Medications approved for diabetes-associated renal and cardiovascular morbidities and candidate drugs currently in development are subject to substantial variability in drug response. Heterogeneity on a molecular phenotype level is not apparent at clinical presentation, which means that inter-individual differences in drug effect at the molecular level are masked. These findings identify the need for optimising patient phenotyping via use of molecular biomarkers for a personalised therapy approach. Molecular diversity may, on the one hand, result from the effect of genetic polymorphisms on drug transport, metabolism and effective target modulation. Equally relevant, differences may be due to molecular pathologies. The presence of distinct molecular phenotypes is suggested by classifiers aimed at modelling progressive disease. Such functions for prognosis incorporate a complex set of clinical variables or a multitude of molecular markers reflecting a diverse set of molecular disease mechanisms. This information on disease pathology and the mechanism of action of the drug needs to be systematically integrated with data on molecular biomarkers to develop an experimental tool for personalising medicine. The large amount of molecular data available for characterising diabetes-associated morbidities allows for elucidation of molecular process model representations of disease pathologies. Selecting biomarker candidates on such grounds and, in turn identifying their association with progressive disease allows for the identification of molecular processes associated with disease progression. The molecular effect of a drug can also be modelled at a molecular process level, and the integration of disease pathology and drug effect molecular models reveals candidate biomarkers for assessing drug response. Such tools serve as enrichment strategies aimed at adding precision to drug development and use.

Muscle invasive bladder carcinoma is a complex, multifactorial disease caused by disruptions and alterations of several molecular pathways that result in heterogeneous phenotypes and variable disease outcome. Combining this disparate knowledge may offer insights for deciphering relevant molecular processes regarding targeted therapeutic approaches guided by molecular signatures allowing improved phenotype profiling. The aim of the study is to characterize muscle invasive bladder carcinoma on a molecular level by incorporating scientific literature screening and signatures from omics profiling. Public domain omics signatures together with molecular features associated with muscle invasive bladder cancer were derived from literature mining to provide 286 unique protein-coding genes. These were integrated in a protein-interaction network to obtain a molecular functional map of the phenotype. This feature map educated on three novel disease-associated pathways with plausible involvement in bladder cancer, namely Regulation of actin cytoskeleton, Neurotrophin signalling pathway and Endocytosis. Systematic integration approaches allow to study the molecular context of individual features reported as associated with a clinical phenotype and could potentially help to improve the molecular mechanistic description of the disorder.

The discovery of novel classes of non-coding RNAs (ncRNAs) has revolutionized medicine. Long thought to be a mere cellular housekeeper, surprising functions have recently been uncovered. MicroRNAs (miRNAs), are a representative of the class of short ncRNAs, play a fundamental role in the control of DNA and protein biosynthesis and activity as well as pathology. Currently, miRNAs are being investigated as diagnostic and prognostic markers and potential therapeutic targets in kidney transplantation for such indolent processes as ischaemia-reperfusion injury, humoral rejection or viral infections. It is realistic to believe that monitoring of renal allograft recipients in the future will include genome-wide miRNA profiling of biological fluids. Based on these individual profiles, an informed decision on therapeutic consequences will be possible. A first success with a specific suppression of miRNAs by antisense oligonucleotides was achieved in experimental studies of reperfusion injury and humoral rejection. Proof of this concept in men comes from studies in such indolent viral infections as Ebola and hepatitis C, where anti-miR therapy led to sustained viral clearance. In this review, we summarize the basis of the recent ncRNA revolution and its implication for kidney transplantation.

The field of biomarker research has experienced a major boost in recent years, and the number of publications on biomarker studies evaluating given, but also proposing novel biomarker candidates is increasing rapidly for numerous clinically relevant disease areas. However, individual markers often lack sensitivity and specificity in the clinical context, resting essentially on the intra-individual phenotype variability hampering sensitivity, or on assessing more general processes downstream of the causative molecular events characterizing a disease term, in consequence impairing disease specificity. The trend to circumvent these shortcomings goes towards utilizing multimarker panels, thus combining the strength of individual markers to further enhance performance regarding both sensitivity and specificity. A way of identifying the optimal composition of individual markers in a panel approach is to pick each marker as representative for a specific pathophysiological (mechanistic) process relevant for the disease under investigation, hence resulting in a multimarker panel for covering the set of pathophysiological processes underlying the frequently multifactorial composition of a clinical phenotype.Here we outline a procedure of identifying such sets of disease-specific pathophysiological processes (units) delineated on the basis of disease-associated molecular feature lists derived from literature mining as well as aggregated, publicly available Omics profiling experiments. With such molecular units in hand, providing an improved reflection of a specific clinical phenotype, biomarker candidates can then be assigned to or novel candidates are to be selected from these units, subsequently resulting in a multimarker panel promising improved accuracy in disease diagnosis as well as prognosis.

Increasing evidence accumulates on the central involvement of microRNAs (miRNAs) in disease pathophysiology. We identified distinctly deregulated miRNAs in human renal allograft biopsies from patients undergoing acute cellular rejection, antibody-mediated rejection (ABMR), and delayed graft function (DGF). Sixty-five posttransplantation kidney biopsy samples covering 41 cases with acute rejection (15 vascular rejection, 15 interstitial rejection, and 11 ABMR), 14 DGF cases, and 10 protocol biopsies serving as controls were analyzed using the Affymetrix GeneChip miRNA Array. Differentially regulated miRNAs were identified by Student's t test and Bonferroni correction. Target genes for the set of miRNAs were retrieved from miRTarBase (experimentally verified targets) as well as by applying the target prediction routines DIANAmT, miRanda, and Targetscan. Patients with acute cellular rejection, ABMR, and DGF discriminate from the control group (protocol biopsies) in unsupervised clustering of miRNA profiles, clearly identifying deregulated miRNAs in rejection and DGF. Angiogenesis, apoptosis, and transforming growth factor-β signaling were identified as relevant pathways in ischemic response following an integrative analysis of miRNA targets and mRNA expression profiles. Inflammation by chemokine and cytokine signaling, T-cell activation, and B-cell activation were identified as relevant in acute rejection accordingly. These data suggest that distinct miRNA signatures playing a role in specific biological pathways discriminate acute cellular and humoral rejection and DGF. This finding serves as valuable tool for a rational selection of diagnostic, prognostic, and potentially therapeutic molecular targets of posttransplantation events.