Multiscale interactome analysis coupled with off-target drug predictions reveals drug repurposing candidates for human coronavirus disease

The COVID-19 pandemic has highlighted the urgent need for the identification of new antiviral drug therapies for a variety of diseases. COVID-19 is caused by infection with the human coronavirus SARS-CoV-2, while other related human coronaviruses cause diseases ranging from severe respiratory infections to the common cold. We developed a computational approach to identify new antiviral drug targets and repurpose clinically-relevant drug compounds for the treatment of a range of human coronavirus diseases. Our approach is based on graph convolutional networks (GCN) and involves multiscale host-virus interactome analysis coupled to off-target drug predictions. Cell-based experimental assessment reveals several clinically-relevant drug repurposing candidates predicted by the in silico analyses to have antiviral activity against human coronavirus infection. In particular, we identify the MET inhibitor capmatinib as having potent and broad antiviral activity against several coronaviruses in a MET-independent manner, as well as novel roles for host cell proteins such as IRAK1/4 in supporting human coronavirus infection, which can inform further drug discovery studies.We gratefully acknowledge funding that supported this research support from the Ryerson University Faculty of Science (CNA), as well as funding support in the form of a CIFAR Catalyst Grant (JPJ and CNA), an NSERC Alliance Grant (CNA) and the Ryerson COVID-19 SRC Response Fund award (CNA). BW is partly supported by CIFAR AI Chairs Program. This work was also supported by a Mitacs award (BW), the European Union’s Horizon 2020 research and innovation program under a Marie Sklodowska-Curie grant (ER), by the CIFAR Azrieli Global Scholar program (JPJ), by the Ontario Early Researcher Awards program (JPJ and CNA), and by the Canada Research Chairs program (JPJ). We also thank Dr. James Rini (University of Toronto) for the kind gift of the 9.8E12 antibody used to detect the 229E Spike protein, and Dr. Scott Gray-Owen (University of Toronto) for the kind gift of the NL63 human coronavirus.Peer reviewe

PANC Study (Pancreatitis: A National Cohort Study): national cohort study examining the first 30 days from presentation of acute pancreatitis in the UK

Abstract Background Acute pancreatitis is a common, yet complex, emergency surgical presentation. Multiple guidelines exist and management can vary significantly. The aim of this first UK, multicentre, prospective cohort study was to assess the variation in management of acute pancreatitis to guide resource planning and optimize treatment. Methods All patients aged greater than or equal to 18 years presenting with acute pancreatitis, as per the Atlanta criteria, from March to April 2021 were eligible for inclusion and followed up for 30 days. Anonymized data were uploaded to a secure electronic database in line with local governance approvals. Results A total of 113 hospitals contributed data on 2580 patients, with an equal sex distribution and a mean age of 57 years. The aetiology was gallstones in 50.6 per cent, with idiopathic the next most common (22.4 per cent). In addition to the 7.6 per cent with a diagnosis of chronic pancreatitis, 20.1 per cent of patients had a previous episode of acute pancreatitis. One in 20 patients were classed as having severe pancreatitis, as per the Atlanta criteria. The overall mortality rate was 2.3 per cent at 30 days, but rose to one in three in the severe group. Predictors of death included male sex, increased age, and frailty; previous acute pancreatitis and gallstones as aetiologies were protective. Smoking status and body mass index did not affect death. Conclusion Most patients presenting with acute pancreatitis have a mild, self-limiting disease. Rates of patients with idiopathic pancreatitis are high. Recurrent attacks of pancreatitis are common, but are likely to have reduced risk of death on subsequent admissions. </jats:sec

An Exploration into the Molecular Recognition of Signal Transducer and Activator of Transcription 3 Protein Using Rationally Designed Small Molecule Binders

Signal transducer and activator of transcription 3 (STAT3) is a cancer-driving proto-oncoprotein that represents a novel target for the development of chemotherapeutics. In this study, the functional requirements to furnish a potent STAT3 inhibitor was investigated. First, a series of peptidomimetic inhibitors were rationally designed from lead parent peptides. Prepared peptidomimetics overcame the limitations normally associated with peptide agents and displayed improved activity in biophysical evaluations. Notably, lead peptidomimetic agents possessed micromolar cellular activity which was unobserved in both parent peptides. Peptidomimetic design relied on computational methods that were also employed in the design of purine based STAT3 inhibitory molecules. Docking studies with lead STAT3-SH2 domain inhibitory molecules identified key structural and chemical information required for the construction of a pharmacophore model. 2,6,9-heterotrisubstituted purines adequately fulfilled the pharmacophore model and a library of novel purine-based STAT3 inhibitory molecules was prepared utilizing Mitsunobu chemistry. Several agents from this new library displayed high affinity for the STAT3 protein and effectively disrupted the STAT3:STAT3-DNA complex. Furthermore, these agents displayed cancer-cell specific toxicity through a STAT3 dependant mechanism. While purine agents elicited cellular effects, the dose required for cellular efficacy was much higher than those observed for in vitro STAT3 dimer disruption. The diminished cellular activity could be attributed to the apparent poor cell permeability of the first generation purine library; thus, a second library of purine molecules was constructed to improve cell penetration. Unfortunately, iii 2nd generation purine inhibitors failed to disrupt phosphorylated STAT3 activity and suffered from poor cell permeability. However, a lead sulfamate agent was discovered that showed potent activity against multiple myeloma cancer cells. Investigations revealed potential kinase inhibitory activity as the source of the sulfamate purine’s biological effect. Explorations into the development of a potent STAT3 SH2 domain binder, including the creation of salicylic purine and constrained pyrimidine molecules, are ongoing. Finally, progress towards the creation of a macrocyclic purine combinatorial library has been pursued and is reported herein.Ph

Design, Synthesis And In Vitro Characterization Of Novel Hybrid Peptidomimetic Inhibitors Of Stat3 Protein

Aberrant activation of oncogenic signal transducer and activator of transcription 3 (STAT3) protein signaling pathways has been extensively implicated in human cancers. Given STAT3\u27s prominent dysregulatory role in malignant transformation and tumorigenesis, there has been a significant effort to discover STAT3-specific inhibitors as chemical probes for defining the aberrant STAT3-mediated molecular events that support the malignant phenotype. To identify novel, STAT3-selective inhibitors suitable for interrogating STAT3 signaling in tumor cells, we explored the design of hybrid molecules by conjugating a known STAT3 inhibitory peptidomimetic, ISS610 to the high-affinity STAT3-binding peptide motif derived from the ILR/gp-130. Several hybrid molecules were examined in in vitro biophysical and biochemical studies for inhibitory potency against STAT3. Lead inhibitor 14aa was shown to strongly bind to STAT3 (K D = 900 nM), disrupt STAT3:phosphopeptide complexes (K i = 5 μM) and suppress STAT3 activity in in vitro DNA binding activity/electrophoretic mobility shift assay (EMSA). Moreover, lead STAT3 inhibitor 14aa induced a time-dependent inhibition of constitutive STAT3 activation in v-Src transformed mouse fibroblasts (NIH3T3/v-Src), with 80% suppression of constitutively-active STAT3 at 6 h following treatment of NIH3T3/v-Src. However, STAT3 activity recovered at 24 h after treatment of cells, suggesting potential degradation of the compound. Results further showed a suppression of aberrant STAT3 activity in NIH3T3/v-Src by the treatment with compound 14aa-OH, which is the non-pTyr version of compound 14aa. The effect of compounds 14aa and 14aa-OH are accompanied by a moderate loss of cell viability. © 2011 Elsevier Ltd. All rights reserved

Assessing Methods and Obstacles in Chemical Space Exploration

Benchmarking the performance of generative methods for drug design is complex and multifaceted. In this report, we propose a separation of concerns for de novo drug design, categorizing the task into three main categories: generation, discrimination, and exploration. We demonstrate that changes to any of these three concerns impacts benchmark performance for drug design tasks. In this report we present Deriver, an open-source Python package that acts as a modular framework for molecule generation, with a focus on integrating multiple generative methods. Using Deriver, we demonstrate that changing parameters related to each of these three concerns impacts chemical space traversal significantly, and that the freedom to independently adjust each is critical to real-world applications having conflicting priorities. We find that combining multiple generative methods can improve optimization of molecular properties, and lower the chance of becoming trapped in local minima. Additionally, filtering molecules for drug-likeness (based on physicochemical properties and SMARTS pattern matching) before they are scored can hinder exploration, but can improve the quality of the final molecules. Finally, we demonstrate that any given task has an exploration algorithm best suited to it, though in practice linear probabilistic sampling generally results in the best outcomes, when compared to Monte Carlo sampling or greedy sampling. We intend that Deriver, which is being made freely available, will be helpful to others interested in collaboratively improving existing methods in de novo drug design centered around inheritance of molecular structure, modularity, extensibility, and separation of concerns

PolypharmDB, a Deep Learning-Based Resource, Quickly Identifies Repurposed Drug Candidates for COVID-19

There is an immediate need to discover treatments for COVID-19, the pandemic caused by the SARS-CoV-2 virus. Standard small molecule drug discovery workflows that start with library screens are an impractical path forward given the timelines to discover, develop, and test clinically. To accelerate the time to patient testing, here we explored the therapeutic potential of small molecule drugs that have been tested to some degree in a clinical environment, including approved medications, as possible therapeutic interventions for COVID-19. Motivating our process is a concept termed polypharmacology, i.e. off-target interactions that may hold therapeutic potential. In this work, we used Ligand Design, our deep learning drug design platform, to interrogate the polypharmacological profiles of an internal collection of small molecule drugs with federal approval or going through clinical trials, with the goal of identifying molecules predicted to modulate targets relevant for COVID-19 treatment. Resulting from our efforts is PolypharmDB, a resource of drugs and their predicted binding of protein targets across the human proteome. Mining PolypharmDB yielded molecules predicted to interact with human and viral drug targets for COVID-19, including host proteins linked to viral entry and proliferation and key viral proteins associated with the virus life-cycle. Further, we assembled a collection of prioritized approved drugs for two specific host-targets, TMPRSS2 and cathepsin B, whose joint inhibition was recently shown to block SARS-CoV-2 virus entry into host cells. Overall, we demonstrate that our approach facilitates rapid response, identifying 30 prioritized candidates for testing for their possible use as anti-COVID drugs. Using the PolypharmDB resource, it is possible to identify repurposed drug candidates for newly discovered targets within a single work day. We are making a complete list of the molecules we identified available at no cost to partners with the ability to test them for efficacy, in vitro and/or clinically.</div

Giving Drugs a Second Chance: Overcoming Regulatory and Financial Hurdles in Repurposing Approved Drugs As Cancer Therapeutics

The repositioning or “repurposing” of existing therapies for alternative disease indications is an attractive approach that can save significant investments of time and money during drug development. For cancer indications, the primary goal of repurposed therapies is on efficacy, with less restriction on safety due to the immediate need to treat this patient population. This report provides a high-level overview of how drug developers pursuing repurposed assets have previously navigated funding efforts, regulatory affairs, and intellectual property laws to commercialize these “new” medicines in oncology. This article provides insight into funding programs (e.g., government grants and philanthropic organizations) that academic and corporate initiatives can leverage to repurpose drugs for cancer. In addition, we highlight previous examples where secondary uses of existing, Food and Drug Administration- or European Medicines Agency-approved therapies have been predicted in silico and successfully validated in vitro and/or in vivo (i.e., animal models and human clinical trials) for certain oncology indications. Finally, we describe the strategies that the pharmaceutical industry has previously employed to navigate regulatory considerations and successfully commercialize their drug products. These factors must be carefully considered when repurposing existing drugs for cancer to best benefit patients and drug developers alike