Traditional drug discovery faces a severe efficacy crisis. Repurposing of registered drugs provides an alternative with lower costs and faster drug development timelines. However, the data necessary for the identification of disease modules, i.e. pathways and sub-networks describing the mechanisms of complex diseases which contain potential drug targets, are scattered across independent databases. Moreover, existing studies are limited to predictions for specific diseases or non-translational algorithmic approaches. There is an unmet need for adaptable tools allowing biomedical researchers to employ network-based drug repurposing approaches for their individual use cases. We close this gap with NeDRex, an integrative and interactive platform for network-based drug repurposing and disease module discovery. NeDRex integrates ten different data sources covering genes, drugs, drug targets, disease annotations, and their relationships. NeDRex allows for constructing heterogeneous biological networks, mining them for disease modules, prioritizing drugs targeting disease mechanisms, and statistical validation. We demonstrate the utility of NeDRex in five specific use-cases.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

A long-term objective of network medicine is to replace our current, mainly phenotype-based disease definitions by subtypes of health conditions corresponding to distinct pathomechanisms. For this, molecular and health data are modeled as networks and are mined for pathomechanisms. However, many such studies rely on large-scale disease association data where diseases are annotated using the very phenotype-based disease definitions the network medicine field aims to overcome. This raises the question to which extent the biases mechanistically inadequate disease annotations introduce in disease association data distort the results of studies which use such data for pathomechanism mining. We address this question using global- and local-scale analyses of networks constructed from disease association data of various types. Our results indicate that large-scale disease association data should be used with care for pathomechanism mining and that analyses of such data should be accompanied by close-up analyses of molecular data for well-characterized patient cohorts.

Traditional drug discovery faces a severe efficacy crisis. Repurposing of registered drugs provides an alternative with lower costs, reduced risk, and faster clinical application. The underlying mechanisms of complex diseases are best described by disease modules. These modules represent disease-relevant pathways and contain potential drug targets which can be identified in silico with network-based methods. The data necessary for the identification of disease modules and network-based drug repurposing are scattered across independent databases, moreover, existing studies have been limited to predictions for specific diseases or non-translational algorithmic approaches. Hence, there is an unmet need for adaptable tools allowing biomedical researchers to employ network-based drug repurposing approaches for their specific use cases. We close this gap with NeDRex ¹ , an integrative and interactive platform for network-based drug repurposing (available at https://nedrex.net). NeDRex integrates different data sources covering genes, proteins, drugs, drug targets, disease annotations, and their relationships, resulting in a network with 350,142 nodes and 14,127,004 edges. NeDRex allows for constructing heterogeneous biological networks and mining them. It provides users with a variety of network-based methods (available via NeDRexApp and the web application NeDRex-Web https://web.nedrex.net/) to derive disease modules associated with diseases under study, prioritize drugs targeting disease mechanisms, and statistical validation. Benefiting from the expert-in-the-loop paradigm, researchers from biomedical sciences can leverage their domain knowledge at different points of the workflow. The approach used in NeDRex is also adapted for COVID-19 drug repurposing and available via the web tool CoVex ² ( https://exbio.wzw.tum.de/covex/).

Circulating tumor DNA (ctDNA) is a biomarker that could potentially improve the survival rate of ovarian cancer (OC), e.g., by monitoring treatment response and early relapse detection. However, an optimal method for ctDNA analysis in OC remains to be established. We developed a method for tumor-informed single-nucleotide variant detection of ctDNA in OC using whole-genome sequencing. Tumor and plasma samples obtained at the time of diagnosis from 10 patients with OC were included. The tested method involved applying basic filters with different cut-offs of read depth, allelic depth, and variant allele frequency of tumor and normal DNA. In addition, we applied a new filtering approach using plasma samples from the other included OC patients (the plasma pool) for specific removal of artefacts. The basic filters with varying cut-offs showed minor improvement in signal-to-noise ratio (S2N). However, the addition of the plasma pool filter resulted in a considerable ctDNA signal improvement, indicated by both S2N and z-score. This study demonstrates a promising method for ctDNA detection in OC patients using a tumor-informed approach for whole-genome sequencing. Despite the limited number of patients involved, the results suggest a significant potential of the method for ctDNA signal detection in patients with OC.

Heterogeneous biological networks are an efficient way to represent interaction systems of biomedical entities such as disease modules or drug-protein interactomes. Online resources for multi-omics analyses and other biomedical tools have to either develop a suitable network representation for their results or to omit this feature. This results in a large variety of custom solutions of different quality for network visualization and network-enhanced drug repurposing prediction. We developed Drugst.One, a customizable plug-and-play solution for biomedical web-application developers in need of a feature-rich network explorer closing the gap between gene sets or modules and drugs. Drugst.One integrates multiple databases to complement visualized data, and network algorithms for drug target identification and drug repurposing analyses. Given lists of genes, proteins or small networks, Drugst.One instantly enriches the input with biomedical information such as protein-,drug-protein interactions, and disease associations. Network algorithms enable users to conduct follow-up analyses by connecting scattered proteins, by identifying additional drug targets as well as by ranking drugs regarding their repurposing potential. The lightweight and fully customizable plug-in can be integrated into any web application with just a few lines of code that developers can generate even automatically using the Drugst.One web code generator. Its components and functions are adjustable to fit the host web pages' purpose and style. “Drugst.One” buttons allow the integration into any existing network analysis web tool without the need to integrate the plugin into the website. Drugst.One is available at https://drugst.one/ and as a python package at https://pypi.org/project/drugstone/.