Network medicine and systems pharmacology approaches to predicting adverse drug effects

Identifying and understanding the relationships between drug intake and adverse effects that can occur due to inadvertent molecular interactions between drugs and targets is a difficult task, especially considering the numerous variables that can influence the onset of such events. The ability to predict these side effects in advance would help physicians develop strategies to avoid or counteract them. In this article, we review the main computational methods for predicting side effects caused by drug molecules, highlighting their performance, limitations and application cases. Furthermore, we provide an overall view of resources, such as databases and tools, useful for building side effect prediction analyses

Prioritizing repurposable drugs for Alzheimer’s disease using network-based analysis with concurrent assessment of Long QT syndrome risk

Alzheimer's disease affects 6.9 million Americans aged 65 and older, a number expected to double by 2060. Eight FDA-approved drugs target Alzheimer's, but no cure is available, and most treatments are symptomatic. Drug repurposing, the use of FDA-approved drugs for new indications, is a promising strategy to address this lack of effective therapies. However, despite prior safety approval, repurposable drugs may still trigger unexpected side-effects in new contexts. This study introduces a network-based approach to minimize side-effect risk in drug repositioning, focusing on QT interval prolongation, a cardiac side-effect observed in Alzheimer's patients treated with acetylcholinesterase inhibitors. The method integrates Mode-of-Action and Random Walk with Restart analyses to identify repositioning candidates while assessing QT-related risk. This strategy identified promising compounds including acamprosate, tolcapone, sitagliptin, and diazoxide, with potential to mitigate disease pathology. Gene set enrichment analysis was used to computationally assess the compounds' ability to reverse disease-related gene expression signatures

Identification of Potential Repurposable Drugs in Alzheimer’s Disease Exploiting a Bioinformatics Analysis

Alzheimer’s disease (AD) is a neurologic disorder causing brain atrophy and the death of brain cells. It is a progressive condition marked by cognitive and behavioral impairment that significantly interferes with daily activities. AD symptoms develop gradually over many years and eventually become more severe, and no cure has been found yet to arrest this process. The present study is directed towards suggesting putative novel solutions and paradigms for fighting AD pathogenesis by exploiting new insights from network medicine and drug repurposing strategies. To identify new drug–AD associations, we exploited SAveRUNNER, a recently developed network-based algorithm for drug repurposing, which quantifies the vicinity of disease-associated genes to drug targets in the human interactome. We complemented the analysis with an in silico validation of the candidate compounds through a gene set enrichment analysis, aiming to determine if the modulation of the gene expression induced by the predicted drugs could be counteracted by the modulation elicited by the disease. We identified some interesting compounds belonging to the beta-blocker family, originally approved for treating hypertension, such as betaxolol, bisoprolol, and metoprolol, whose connection with a lower risk to develop Alzheimer’s disease has already been observed. Moreover, our algorithm predicted multi-kinase inhibitors such as regorafenib, whose beneficial effects were recently investigated for neuroinflammation and AD pathology, and mTOR inhibitors such as sirolimus, whose modulation has been associated with AD

A single-cell transcriptomic analysis of Neuroblastomas revealed a selective cGAS-STING pathway suppression in malignant cells

Neuroblastoma is a disease of disordered development accounting for 15% of childhood cancer deaths. The "cold"immunophenotype frequently occurring of these tumors is likely to contribute to its aggressiveness and refractoriness to treatments, including immune checkpoint blockade, observed in high risk neuroblastoma. The mechanisms that contribute to the cold immunostate have not been elucidated, yet. However, recent studies have reported the involvement of MYCN amplification in reducing the immune infiltrate in high-risk Neuroblastoma tumors. Its action mainly concerns suppressed interferon responses and pro-inflammatory pathways. An important pathway deputed to the activation of type I interferon responses and pro-inflammatory cytokines release is the cyclic GMP-AMP synthase (cGAS) - stimulator of interferon genes (STING) cytosolic DNA sensing pathway. Recent preclinical studies suggest a promising therapeutic effect of cGAS-STING pathway reactivation in Neuroblastomas. However, very little is known about the regulation of the cGAS-STING pathway in Neuroblastoma malignant cells, and its relationship with the MYCN amplification state. To this end, we performed a single-cell transcriptomic analysis of primary human Neuroblastoma cells compared with their normal fetal and embryonic progenitors. The results of the high-resolution analysis of cGAS-STING pathway showed that Neuroblastomas malignant cells have a much lower expression of this pathway compared to normal progenitors and other cell phenotypes populating the tumor microenvironment, possibly acquiring an evolutionary advantage. Moreover, the cGAS-STING pathway anticorrelated with MYCN expression suggesting a putative regulatory mechanism of the STING pathwa