Drug therapy and drug discovery are in a conceptual crisis. Hardly any new drug principles are discovered. Existing drugs have a catastrophic number needed to treat. Hardly any therapy targets a disease mechanism, because it is not known. Instead symptoms, biomarkers and risk factors are treated. Moreover we currently systemise medicine according to 19th and 20th century disease terms, which are mainly organ and symptom-based but not mechanistic. Network medicine utilizes common genetic origins, markers and co-morbidities to uncover mechanistic links between diseases. These links can be summarized in the diseasome, a comprehensive network of disease–disease relationships and clusters. The diseasome has been influential during the past decade, although most of its links are not followed up experimentally. We propose a new disease taxonomy based on mechanism and abolishing organ- and symptom-based disease definitions. Terms as hypertension, heart failure, arrhythmia will in future be considered mere disease phenotypes, most likely comprised of several endotypes and linked to several co-morbities. Several such mechanistic clusters of disease phenotypes have been identified. One links to cyclic GMP and reactive oxygen species sources and targets. When examine the disease associations in a non-hypothesis based manner in order to identify possibly previously unrecognized clinical indications. Surprisingly, we find that sGC, the cardiovascular target of nitroglycerin, is closest linked to neurological disorders, an application that has so far not been explored clinically. Indeed, when investigating the neurological indication of this cluster with the highest unmet medical need, ischemic stroke, pre-clinically we find that sGC activity is virtually absent post-stroke. Conversely, a heme-free form of sGC, apo-sGC, was now the predominant isoform suggesting it may be a mechanism-based target in stroke. Indeed, this repurposing hypothesis could be validated experimentally in vivo as specific activators of apo-sGC were directly neuroprotective, reduced infarct size and increased survival. Thus, common mechanism clusters of the diseasome allow direct drug repurposing across previously unrelated disease phenotypes redefining them in a mechanism-based manner. Our example of repurposing apo-sGC activators for ischemic stroke should be urgently validated clinically as a possible first-in-class neuroprotective therapy and serves as a proof-of-concept for redefining disease, identifying new therapies. The REPO-TRIAL H2020 programme will develop an innovative in-silico based approach to improve the efficacy and precision of drug repurposing trials. We have chosen drug repurposing as it has the shortest time for clinical validation and translation. Validation of all putatively de novo discovered drug repositionings within the time-frame of this programme would be unrealistic. To improve efficacy and precision, and to adopt our computer simulation parameters and models, we choose a systems medicine based in-silico approach that identifies mechanistically related disease phenotypes and, as a result, a virtual patient cohort. We then validate this in-silico drug repurposing via high precision clinical trials in patients with cerebrocardiovascular phenotypes stratified using an exclusive mechanistic biomarker panel. We thus innovate two biomedical product classes, drugs and diagnostics. With this we will establish generally applicable in silico trials for other mechanistically related or defined disease phenotypes, for which size, duration, and risks will be reduced and precision increased. This generates rapid patient benefit, reduces drug development costs as well as risks, and enhances industrial competitiveness. Scientifically, we will contribute to reducing the uncertainty and vagueness of many of our current disease definitions that describe a symptom or apparent phenotype in an organ rather than defining diseases mechanistically as disturbance of self-regulation equilibria of biomolecular processes. Finally, we will reduce animal experimentation and animal numbers in general by applying a preclinical randomised confirmatory trial (pRCTs) concept and preclinical systematic reviews and meta-analyses facilitated by our open access pre-clinicaltrials.org platform, a pendant to clinicaltrials.gov.
With a double degree in Medicine and Pharmacy Harald Schmidt has a passion for innovative drug discovery and therapy. As an ERC Advanced Investigator, Europe's most prestigious research award, he performs high risk/high potential benefit research in areas of major medical need. His multi-national leadership experience in Academia and Industry has led to excellent scientific achievements (Hirsch-index 77) with high socio-economic impact such as patents and biotech spin-offs. He is a reputed drug expert, successful entrepreneur, dedicated teacher and team leader.
Jan Baumbach studied Applied Computer Science in the Natural Sciences at Bielefeld University in Germany. His research career started at Rothamsted Research in Harpenden (UK) where he worked on computational methods for the integration of molecular biology data. He returned to the Center for Biotechnology in Bielefeld for his PhD studies on the reconstruction of bacterial transcriptional regulatory networks. He developed CoryneRegNet, the reference database and analysis platform for corynebacterial gene regulations. Afterwards, at the University of California at Berkeley, he worked in the Algorithms group of Richard Karp on protein homology detection. In Berkeley, he also developed Transitivity Clustering, a novel clustering framework for large-scale biomedical data sets. Since March 2010, Jan was head of the Computational Systems Biology group at the Max Planck Institute for Informatics and the Cluster of Excellence for Multimodal Computing and Interaction at Saarland University in Saarbrücken, Germany. In October 2012, he moved to the University of Southern Denmark as head of the Computational Biology group. His current research concentrates on the combined analysis of biological networks together with OMICS data, the modeling of genetic expression pathways as well as biomarker discovery and computational methods for personalized medicine. He was study program coordinator of Computational BioMedicine program from 2015 to 2017. In January 2018 he moved to the Technical University of Munich as chair of the Experimental Bioinformatics.
On major obstacle in current medicine and drug development is inherent in the way we define and approach diseases. Here, we will discuss the diagnostic and prognostic value of (multi-)omics panels in general. We will have a closer look at breast cancer subtyping and treatment outcome, as case example, using gene expression panels - and we will discuss the current "best practice" in the light of critical statistical considerations. Afterwards, we will introduce computational approaches for network-based medicine aka. systems medicine. We will discuss novel developments in graph-based machine learning using examples ranging from Huntington's disease mechanisms via lung cancer drug target discovery back to where we started, i.e. breast cancer subtyping and treatment optimization - but now from a systems medicine point of view. We conclude that systems medicine and modern artificial intelligence open new avenues to shape future medicine.
Cells are not created equal. The Human Cell Atlas (HCA) project aims to build atlas of all human cell types and cell states with their molecular signatures. It is a fundamental step toward the comprehensive understanding of the human body, the super-complex system composed of tens of trillions of cells that are all developed from a single cell. Single-cell sequencing especially single-cell RNA-sequencing (scRNA-seq) is the key technology for obtaining the molecular signatures of a large amount of single cells at the whole transcriptome scale. Other single-cell omics technologies including single-cell ATAC-seq, single-cell methylation sequencing, single-cell Hi-C as well as in-situ technologies like single-cell spatial transcriptomics are also under rapid development. Such single-cell technologies convert each cell to a mathematical vector in the high-dimensional spaces of the gene expression and other omics features. Therefore, computational analyses become the key component of all HCA studies. This talk will give an overview on some major bioinformatics challenges in this field, and present examples of our on-going work on new methods for differential expression analysis and dimension reduction.
Prof. Xuegong Zhang earned his BS degree in Industrial Automation in 1989 and Ph.D. degree in Pattern Recognition and Intelligent Systems in 1994, both from Tsinghua University. He joined the faculty of Tsinghua University in 1994, where he is now a Professor of Pattern Recognition and Bioinformatics in the Department of Automation, and an Adjunct Professor of the School of Life Sciences and also the School of Medicine. Dr. Zhang worked at Harvard School of Public Health as a visiting scientist on computational biology in 2001-2002, and had been a visiting scholar in the MCB program at USC in 2007. He is the Director of the Bioinformatics Division, Beijing Research Institute for Information Science and Technology (BNRist), and the Acting Director of the Tsinghua University Center for Systems and Synthetic Biology. His research interests include machine learning, biological data mining especially for single-cell sequencing data and metagenomic sequencing data, and the analyses of multi-modal big healthcare data.