Bourdakou, M. | bioRxiv Science

bioRxiv2023

DReAmocracy: A Method to Capitalize on Prior Drug Discovery Efforts to Highlight Candidate Drugs for Repurposing

In the area of drug research, several computational drug repurposing studies have highlighted candidate repurposed drugs, as well as drugs from clinical trial studies in different phases. To our knowledge, the aggregation of the proposed lists of drugs by previous studies has not been extensively exploited towards the generation of a dynamic reference matrix regarding the disease-related frequencies of drug features such as the modes of action, the initial indications and the targeted pathways. To fill this knowledge gap, we performed a weight-modulated majority voting of the modes of action, initial indications and targeted pathways of the drugs in a well-known repository, namely the Drug Repurposing Hub. Our method, DReAmocracy, exploits this pile of information and creates frequency tables and finally a disease suitability score for each drug. As a testbed, we applied this method to a group of neurodegenerative diseases (Alzheimers, Parkinsons, Huntingtons and Multiple Sclerosis). A super-reference table with drug suitability scores has been created for all four neurodegenerative diseases and can be queried for any drug candidate against them. Top-scored drugs for Alzheimers disease include agomelatine and mirtazapine, for Parkinsons disease apomorphine and pramipexole, for Huntingtons fluphezine and perphezine, and for Multiple Sclerosis zonisamide and disopyramide. Overall, DReAmocracy is a methodology that leverages the existing experimental and/or computational knowledge (1) to reveal trends in selected tracks of drug discovery research that includes modes of action, targeted pathways and initial indications for the investigated drugs and (2) to score new candidate drugs for repurposing against a selected disease.

bioinformatics↗

bioRxiv2020

PathWalks: Identifying pathway communities using a disease-related map of integrated information

Understanding disease underlying biological mechanisms and respective interactions remains an elusive, time consuming and costly task. The realization of computational methodologies that can propose pathway/mechanism communities and reveal respective relationships can be of great value as it can help expedite the process of identifying how perturbations in a single pathway can affect other pathways. Random walks is a stochastic approach that can be used for both efficient discovery of strong connections and identification of communities formed in networks. The approach has grown in popularity as it efficiently exposes key network components and reveals strong interactions among genes, proteins, metabolites, pathways and drugs. Using random walks in biology, we need to overcome two key challenges: 1) construct disease-specific biological networks by integrating information from available data sources as they become available, and 2) provide guidance to the walker so as it can follow plausible trajectories that comply with inherent biological constraints. In this work, we present a methodology called PathWalks, where a random walker crosses a pathway-to-pathway network under the guidance of a disease-related map. The latter is a gene network that we construct by integrating multi-source information regarding a specific disease. The most frequent trajectories highlight communities of pathways that are expected to be strongly related to the disease under study. We present maps for Alzheimers Disease and Idiopathic Pulmonary Fibrosis and we use them as case-studies for identifying pathway communities through the application of PathWalks. In the case of Alzheimers Disease, the most visited pathways are the "Alzheimers disease" and the "Calcium signaling" pathways which have indeed the strongest association with Alzheimers Disease. Interestingly however, in the top-20 visited pathways we identify the "Kaposi sarcoma-associated herpesvirus infection" (HHV-8) and the "Human papillomavirus infection" (HPV) pathways suggesting that viruses may be involved in the development and progression of Alzheimers. Similarly, most of the highlighted pathways in Idiopathic Pulmonary Fibrosis are backed by the bibliography. We establish that "MAPK signaling" and "Cytokine-cytokine receptor interaction" pathways are the most visited. However, the "NOD receptor signaling" pathway is also in the top-40 edges. In Idiopathic Pulmonary Fibrosis samples, increased NOD receptor signaling has been associated with augmented concentrations of certain strains of Streptococcus. Additional experimental evidence is required however to further explore and ascertain the above indications.

bioinformatics↗