Drug repositioning for psychiatric and neurological disorders through a network medicine approach

Abstract

Psychiatric and neurological disorders (PNDs) affect millions worldwide and only a few drugs achieve complete therapeutic success in the treatment of these disorders. Due to the high cost of developing novel drugs, drug repositioning represents a promising alternative method of treatment. In this manuscript, we used a network medicine approach to investigate the molecular characteristics of PNDs and identify novel drug candidates for repositioning. Using IBM Watson for Drug Discovery, a powerful machine learning text-mining application, we built knowledge networks containing connections between PNDs and genes or drugs mentioned in the scientific literature published in the past 50 years. This approach revealed several drugs that target key PND-related genes, which have never been used to treat these disorders to date. We validate our framework by detecting drugs that have been undergoing clinical trial for treating some of the PNDs, but have no published results in their support. Our data provides comprehensive insights into the molecular pathology of PNDs and offers promising drug repositioning candidates for follow-up trials.

The evolution of knowledge on genes associated with human diseases

“…When two diseases share a genetic mechanism, they can also present common clinical or epidemiological characteristics, despite having distinct etiological backgrounds (Barabási et al, 2011). These similarities can inform researchers of potential treatment options (Lüscher Dias et al, 2020). Here, we showed that diseases from inflammatory, psychiatric, and infectious etiologies significantly share genes with each other.…”

“…Recently, machine learning approaches have been used to analyze and integrate a variety of biological and medical data (Littmann et al, 2020;Zitnik et al, 2019). These include methods that integrate electronic health records (Rajkomar et al, 2018), capture latent knowledge from the material science literature (Tshitoyan et al, 2019), and discover potential novel drugs to treat psychiatric and neurological disorders using cognitive computing and network medicine analysis of the medical literature (Lüscher Dias et al, 2020).…”

“…Network medicine (Barabási et al, 2011), a contemporary approach to studying relationships between genes and diseases, has also been made possible because of the large amounts of data on genes and diseases available online. Moreover, knowledge networks, that is, complex graphs that connect concepts according to the established knowledge, can be analyzed under the network medicine framework to produce novel insights from medical knowledge (Bai et al, 2016;Lüscher Dias et al, 2020).…”

The evolution of knowledge on genes associated with human diseases

“…Recently, machine learning text-mining bibliometric approaches have been used to analyze and integrate a variety of biological, medical, and environmental science data (Littmann et al, 2020;Tan et al, 2021;Zitnik et al, 2019). These include methods that integrate electronic health records (Rajkomar et al, 2018), capture latent knowledge from the material science literature (Tshitoyan et al, 2019), investigate the evolution of research in environmental sciences (Tan et al, 2021), and discover potential novel drugs to treat psychiatric and neurological disorders using cognitive computing and network medicine analysis of the medical literature (Lu ¨scher Dias et al, 2020).…”

Fenfluramine as antiseizure medication for epilepsy

“…In parallel, in vitro models of high predictability and databases of results from transcriptomic analysis promise to increase the effectiveness and accuracy of this method. 2 Epilepsy is now well recognized as a symptom of many underlying causes, more accurately now referred to as the epilepsies. Recognizing the cause, often the result of a genetic mutation, has resulted in increasing recognition for targeting drug repositioning within the central nervous system, while endocrine and metabolic disorders represent relatively common sources of further repositioned therapeutic agents.…”