Potentially repurposable drugs for schizophrenia identified from its interactome

Schizophrenia Interactome-Derived Repurposable Drugs and Randomized Controlled Trials of Two Candidates

There is a substantial unmet need for effective and patient-acceptable drugs to treat severe mental illnesses such as schizophrenia (SZ). Computational analysis of genomic, transcriptomic, and pharmacologic data generated in the past 2 decades enables repurposing of drugs or compounds with acceptable safety profiles, namely those that are U.S. Food and Drug Administration approved or have reached late stages in clinical trials. We developed a rational approach to achieve this computationally for SZ by studying drugs that target the proteins in its protein interaction network (interactome). This involved contrasting the transcriptomic modulations observed in the disorder and the drug; our analyses resulted in 12 candidate drugs, 9 of which had additional supportive evidence whereby their target networks were enriched for pathways relevant to SZ etiology or for genes that had an association with diseases pathogenically similar to SZ. To translate these computational results to the clinic, these shortlisted drugs must be tested empirically through randomized controlled trials, in which their previous safety approvals obviate the need for time-consuming phase 1 and 2 studies. We selected 2 among the shortlisted candidates based on likely adherence and side-effect profiles. We are testing them through adjunctive randomized controlled trials for patients with SZ or schizoaffective disorder who experienced incomplete resolution of psychotic features with conventional treatment. The integrated computational analysis for identifying and ranking drugs for clinical trials can be iterated as additional data are obtained. Our approach could be expanded to enable disease subtype-specific drug discovery in the future and should also be exploited for other psychiatric disorders.

Multi- and poly-pharmacology of carbonic anhydrase inhibitors

Eight genetically distinct families of the enzyme carbonic anhydrase (CA, EC 4.2.1.1) were described in organisms allover the phylogenetic tree. They catalyze the hydration of CO2 to bicarbonate and protons, and are involved in pH regulation, chemosensing and metabolism. The 15 α-CA isoforms present in humans are pharmacological drug targets known for decades, their inhibitors being used as diuretics, antiglaucoma, antiepileptic or antiobesity drugs, as well as for the management of acute mountain sickness, idiopathic intracranial hypertension and recently, as antitumor theragnostic agents. Other potential applications include the use of CA inhibitors (CAIs) in inflammatory conditions, cerebral ischemia, neuropathic pain, or for Alzheimer's/Parkinson's disease management. CAs from pathogenic bacteria, fungi, protozoans and nematodes started to be considered as drug targets in recent years, with notable advances registered ultimately. CAIs have a complex multipharmacology probably unique to this enzyme, which has been exploited intensely but may lead to other relevant applications in the future, due to the emergence of drug design approaches which afforded highly isoform-selective compounds for most α-CAs known to date. They belong to a multitude of chemical classes (sulfonamides and isosteres, (iso)coumarins and related compounds, mono- and dithiocarbamates, selenols, ninhydrines, boronic acids, benzoxaboroles, etc). The polypharmacology of CAIs will also be discussed since drugs originally discovered for the treatment of non-CA related conditions (topiramate, zonisamide, celecoxib, pazopanib, thiazide and high-ceiling diuretics) show efective inhibition against many CAs, which led to their repurposing for diverse pharmacological applications. Significance Statement Carbonic anhydrase inhibitors have multiple pharmacologic applications as diuretics, antiglaucoma, antiepileptic, antiobesity, anti-acute mountain sickness, anti-idiopathic intracranial hypertension and as antitumor drugs. Their use in inflammatory conditions, cerebral ischemia, neuropathic pain, or neurodegenerations started to be investigated recently. Parasite carbonic anhydrases are also drug targets for antiinfectives with novel mechanisms of action which can by pass drug resistance to commonly used such agents. Drugs discovered for the management of other conditions that effectively inhibit these enzymes exert interesting polypharmacologic effects.

Malignant peritoneal mesothelioma interactome with 417 novel protein-protein interactions

BACKGROUND

Malignant peritoneal mesothelioma (MPeM) is an aggressive cancer affecting the abdominal peritoneal lining and intra-abdominal organs, with a median survival of ~2.5 years.

METHODS

We constructed the protein interactome of 59 MPeM-associated genes with previously known protein-protein interactions (PPIs) as well as novel PPIs predicted using our previously developed HiPPIP computational model and analysed it for transcriptomic and functional associations and for repurposable drugs.

RESULTS

The MPeM interactome had over 400 computationally predicted PPIs and 4700 known PPIs. Transcriptomic evidence validated 75.6% of the genes in the interactome and 65% of the novel interactors. Some genes had tissue-specific expression in extramedullary hematopoietic sites and the expression of some genes could be correlated with unfavourable prognoses in various cancers. 39 out of 152 drugs that target the proteins in the interactome were identified as potentially repurposable for MPeM, with 29 having evidence from prior clinical trials, animal models or cell lines for effectiveness against peritoneal and pleural mesothelioma and primary peritoneal cancer. Functional modules related to chromosomal segregation, transcriptional dysregulation, IL-6 production and hematopoiesis were identified from the interactome. The MPeM interactome overlapped significantly with the malignant pleural mesothelioma interactome, revealing shared molecular pathways.

CONCLUSIONS

Our findings demonstrate the utility of the interactome in uncovering biological associations and in generating clinically translatable results.

Network-based drug repurposing for schizophrenia

Despite recent progress, the challenges in drug discovery for schizophrenia persist. However, computational drug repurposing has gained popularity as it leverages the wealth of expanding biomedical databases. Network analyses provide a comprehensive understanding of transcription factor (TF) regulatory effects through gene regulatory networks, which capture the interactions between TFs and target genes by integrating various lines of evidence. Using the PANDA algorithm, we examined the topological variances in TF-gene regulatory networks between individuals with schizophrenia and healthy controls. This algorithm incorporates binding motifs, protein interactions, and gene co-expression data. To identify these differences, we subtracted the edge weights of the healthy control network from those of the schizophrenia network. The resulting differential network was then analysed using the CLUEreg tool in the GRAND database. This tool employs differential network signatures to identify drugs that potentially target the gene signature associated with the disease. Our analysis utilised a large RNA-seq dataset comprising 532 post-mortem brain samples from the CommonMind project. We constructed co-expression gene regulatory networks for both schizophrenia cases and healthy control subjects, incorporating 15,831 genes and 413 overlapping TFs. Through drug repurposing, we identified 18 promising candidates for repurposing as potential treatments for schizophrenia. The analysis of TF-gene regulatory networks revealed that the TFs in schizophrenia predominantly regulate pathways associated with energy metabolism, immune response, cell adhesion, and thyroid hormone signalling. These pathways represent significant targets for therapeutic intervention. The identified drug repurposing candidates likely act through TF-targeted pathways. These promising candidates, particularly those with preclinical evidence such as rimonabant and kaempferol, warrant further investigation into their potential mechanisms of action and efficacy in alleviating the symptoms of schizophrenia.

Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders

Despite advances in pharmacology and neuroscience, the path to new medications for psychiatric disorders largely remains stagnated. Drug repurposing offers a more efficient pathway compared with de novo drug discovery with lower cost and less risk. Various computational approaches have been applied to mine the vast amount of biomedical data generated over recent decades. Among these methods, network-based drug repurposing stands out as a potent tool for the comprehension of multiple domains of knowledge considering the interactions or associations of various factors. Aligned well with the poly-pharmacology paradigm shift in drug discovery, network-based approaches offer great opportunities to discover repurposing candidates for complex psychiatric disorders. In this review, we present the potential of network-based drug repurposing in psychiatry focusing on the incentives for using network-centric repurposing, major network-based repurposing strategies and data resources, applications in psychiatry and challenges of network-based drug repurposing. This review aims to provide readers with an update on network-based drug repurposing in psychiatry. We expect the repurposing approach to become a pivotal tool in the coming years to battle debilitating psychiatric disorders.

Interactome of SARS-CoV-2 Modulated Host Proteins With Computationally Predicted PPIs: Insights From Translational Systems Biology Studies

Accelerated efforts to identify intervention strategies for the COVID-19 pandemic caused by SARS-CoV-2 need to be supported by deeper investigations into host invasion and response mechanisms. We constructed the neighborhood interactome network of the 332 human proteins targeted by SARS-CoV-2 proteins, augmenting it with 1,941 novel human protein-protein interactions predicted using our High-precision Protein-Protein Interaction Prediction (HiPPIP) model. Novel interactors, and the interactome as a whole, showed significant enrichment for genes differentially expressed in SARS-CoV-2-infected A549 and Calu-3 cells, postmortem lung samples of COVID-19 patients and blood samples of COVID-19 patients with severe clinical outcomes. The PPIs connected host proteins to COVID-19 blood biomarkers, ACE2 (SARS-CoV-2 entry receptor), genes differentiating SARS-CoV-2 infection from other respiratory virus infections, and SARS-CoV-targeted host proteins. Novel PPIs facilitated identification of the cilium organization functional module; we deduced the potential antiviral role of an interaction between the virus-targeted NUP98 and the cilia-associated CHMP5. Functional enrichment analyses revealed promyelocytic leukaemia bodies, midbody, cell cycle checkpoints and tristetraprolin pathway as potential viral targets. Network proximity of diabetes and hypertension associated genes to host proteins indicated a mechanistic basis for these co-morbidities in critically ill/non-surviving patients. Twenty-four drugs were identified using comparative transcriptome analysis, which include those undergoing COVID-19 clinical trials, showing broad-spectrum antiviral properties or proven activity against SARS-CoV-2 or SARS-CoV/MERS-CoV in cell-based assays. The interactome is available on a webserver at http://severus.dbmi.pitt.edu/corona/.

Repurposing haloperidol for the treatment of rheumatoid arthritis: an integrative approach using data mining techniques

Introduction

Treatment of rheumatoid arthritis (RA) has advanced with the introduction of biological disease-modifying antirheumatic drugs. However, more than 20% of patients with RA still have moderate or severe disease activity. Hence, novel antirheumatic drugs are required. Recently, drug repurposing, a process of identifying new indications for existing drugs, has received great attention. Furthermore, a few reports have shown that antipsychotics are capable of affecting several cytokines that are also modulated by existing antirheumatic drugs. Therefore, we investigated the association between antipsychotics and RA by data mining using real-world data and bioinformatics databases.

Methods

Disproportionality and sequence symmetry analyses were employed to identify the associations between the investigational drugs and RA using the US Food and Drug Administration Adverse Event Reporting System (2004-2016) and JMDC administrative claims database (January 2005-April 2017; JMDC Inc., Tokyo, Japan), respectively. The reporting odds ratio (ROR) and information component (IC) were used in the disproportionality analysis to indicate a signal. The adjusted sequence ratio (SR) was used in the sequence symmetry analysis to indicate a signal. The bioinformatics analysis suite, BaseSpace Correlation Engine (Illumina, CA, USA) was employed to explore the molecular mechanisms associated with the potential candidates identified by the drug-repurposing approach.

Results

A potential inverse association between the antipsychotic haloperidol and RA, which exhibited significant inverse signals with ROR, IC, and adjusted SR, was found. Furthermore, the results suggested that haloperidol may exert antirheumatic effects by modulating various signaling pathways, including cytokine and chemokine signaling, major histocompatibility complex class-II antigen presentation, and Toll-like receptor cascade pathways.

Conclusion

Our drug-repurposing approach using data mining techniques identified haloperidol as a potential antirheumatic drug candidate.

Genome-wide study of copy number variation implicates multiple novel loci for schizophrenia risk in Han Chinese family trios

Schizophrenia (SCZ) is a severe neuropsychiatric disorder that affects 1% of the global population. Copy number variations (CNVs) have been shown to play a critical role in its pathophysiology; however, only case-control studies on SCZ susceptibility CNVs have been conducted in Han Chinese. Here, we performed an array comparative genomic hybridization-based genome-wide CNV analysis in 100 Chinese family trios with SCZ. Burden test suggested that the SCZ probands carried more duplications than their healthy parents and unrelated healthy controls. Besides, five CNV loci were firstly reported to be associated with SCZ here, including both unbalanced transmitted CNVs and enriched de novo CNVs. Moreover, two genes (CTDSPL and MGAM) in these CNVs showed significant SCZ relevance in the expression level. Our findings support the crucial role of CNVs in the etiology of SCZ and provide new insights into the underlying mechanism of SCZ pathogenesis.

In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis

We have performed theoretical calculations with 70 drugs that have been considered in 231 clinical trials as possible candidates to repurpose drugs for schizophrenia based on their interactions with the dopaminergic system. A hypothesis of shared pharmacophore features was formulated to support our calculations. To do so, we have used the crystal structure of the D2-like dopamine receptor in complex with risperidone, eticlopride, and nemonapride. Linagliptin, citalopram, flunarizine, sildenafil, minocycline, and duloxetine were the drugs that best fit with our model. Molecular docking calculations, molecular dynamics outcomes, blood-brain barrier penetration, and human intestinal absorption were studied and compared with the results. From the six drugs selected in the shared pharmacophore features input, flunarizine showed the best docking score with D2, D3, and D4 dopamine receptors and had high stability during molecular dynamics simulations. Flunarizine is a frequently used medication to treat migraines and vertigo. However, its antipsychotic properties have been previously hypothesized, particularly because of its possible ability to block the D2 dopamine receptors.

Carbonic anhydrase activity in the frontal lobe of human brain

Carbonic anhydrase (CA) plays an important role in many biological processes. Recent technological advances have demonstrated the feasibility of measuring CA activity in the occipital lobe of human subjects in vivo. In this work we report, for the first time, in vivo measurement of CA activity in the frontal lobe of human brain, where structural and function abnormalities are strongly associated with symptoms of major psychiatric disorders. Despite the much larger magnetic field distortion in the frontal lobe, the pseudo first-order bicarbonate dehydration rate constant was determined with high precision using in vivo ¹³ C magnetic resonance magnetization transfer spectroscopy following oral administration of [U-¹³ C₆ ]glucose. Nuclear Overhauser effect pulses were used to increase the signal-to-noise ratio; no proton decoupling was applied. The unidirectional dehydration rate constant of bicarbonate was found to be 0.26 ± 0.07 s^-1 , which is not statistically different from the dehydration rate constant in the occipital lobe determined in our previous study, indicating that CA activity in the two brain regions is essentially indistinguishable. These results demonstrate the feasibility of characterizing CA activity in the frontal lobe for future psychiatric studies.

Malignant Pleural Mesothelioma Interactome with 364 Novel Protein-Protein Interactions

Malignant pleural mesothelioma (MPM) is an aggressive cancer affecting the outer lining of the lung, with a median survival of less than one year. We constructed an 'MPM interactome' with over 300 computationally predicted protein-protein interactions (PPIs) and over 2400 known PPIs of 62 literature-curated genes whose activity affects MPM. Known PPIs of the 62 MPM associated genes were derived from Biological General Repository for Interaction Datasets (BioGRID) and Human Protein Reference Database (HPRD). Novel PPIs were predicted by applying the HiPPIP algorithm, which computes features of protein pairs such as cellular localization, molecular function, biological process membership, genomic location of the gene, and gene expression in microarray experiments, and classifies the pairwise features as interacting or non-interacting based on a random forest model. We validated five novel predicted PPIs experimentally. The interactome is significantly enriched with genes differentially ex-pressed in MPM tumors compared with normal pleura and with other thoracic tumors, genes whose high expression has been correlated with unfavorable prognosis in lung cancer, genes differentially expressed on crocidolite exposure, and exosome-derived proteins identified from malignant mesothelioma cell lines. 28 of the interactors of MPM proteins are targets of 147 U.S. Food and Drug Administration (FDA)-approved drugs. By comparing disease-associated versus drug-induced differential expression profiles, we identified five potentially repurposable drugs, namely cabazitaxel, primaquine, pyrimethamine, trimethoprim and gliclazide. Preclinical studies may be con-ducted in vitro to validate these computational results. Interactome analysis of disease-associated genes is a powerful approach with high translational impact. It shows how MPM-associated genes identified by various high throughput studies are functionally linked, leading to clinically translatable results such as repurposed drugs. The PPIs are made available on a webserver with interactive user interface, visualization and advanced search capabilities.

Signature-based approaches for informed drug repurposing: targeting CNS disorders

CNS disorders, and in particular psychiatric illnesses, lack definitive disease-altering therapeutics. The limited understanding of the mechanisms driving these illnesses with the slow pace and high cost of drug development exacerbates this issue. For these reasons, drug repurposing - both a less expensive and time-efficient practice compared to de novo drug development - has been a promising strategy to overcome the paucity of treatments available for these debilitating disorders. While empirical drug-repurposing has been a routine practice in clinical psychiatry, innovative, informed, and cost-effective repurposing efforts using big data ("omics") have been designed to characterize drugs by structural and transcriptomic signatures. These strategies, in conjunction with ontological integration, provide an important opportunity to address knowledge-based challenges associated with drug development for CNS disorders. In this review, we discuss various signature-based in silico approaches to drug repurposing, its integration with multiple omics platforms, and how this data can be used for clinically relevant, evidence-based drug repurposing. These tools provide an exciting translational avenue to merge omics-based drug discovery platforms with patient-specific disease signatures, ultimately facilitating the identification of new therapies for numerous psychiatric disorders.

Cilia interactome with predicted protein-protein interactions reveals connections to Alzheimer's disease, aging and other neuropsychiatric processes

Cilia are dynamic microtubule-based organelles present on the surface of many eukaryotic cell types and can be motile or non-motile primary cilia. Cilia defects underlie a growing list of human disorders, collectively called ciliopathies, with overlapping phenotypes such as developmental delays and cognitive and memory deficits. Consistent with this, cilia play an important role in brain development, particularly in neurogenesis and neuronal migration. These findings suggest that a deeper systems-level understanding of how ciliary proteins function together may provide new mechanistic insights into the molecular etiologies of nervous system defects. Towards this end, we performed a protein-protein interaction (PPI) network analysis of known intraflagellar transport, BBSome, transition zone, ciliary membrane and motile cilia proteins. Known PPIs of ciliary proteins were assembled from online databases. Novel PPIs were predicted for each ciliary protein using a computational method we developed, called High-precision PPI Prediction (HiPPIP) model. The resulting cilia "interactome" consists of 165 ciliary proteins, 1,011 known PPIs, and 765 novel PPIs. The cilia interactome revealed interconnections between ciliary proteins, and their relation to several pathways related to neuropsychiatric processes, and to drug targets. Approximately 184 genes in the cilia interactome are targeted by 548 currently approved drugs, of which 103 are used to treat various diseases of nervous system origin. Taken together, the cilia interactome presented here provides novel insights into the relationship between ciliary protein dysfunction and neuropsychiatric disorders, for e.g. interconnections of Alzheimer's disease, aging and cilia genes. These results provide the framework for the rational design of new therapeutic agents for treatment of ciliopathies and neuropsychiatric disorders.

Interactome of SARS-CoV-2 / nCoV19 modulated host proteins with computationally predicted PPIs

World over, people are looking for solutions to tackle the pandemic coronavirus disease (COVID-19) caused by the virus SARS-CoV-2/nCoV-19. Notable contributions in biomedical field have been characterizing viral genomes, host transcriptomes and proteomes, repurposable drugs and vaccines. In one such study, 332 human proteins targeted by nCoV19 were identified. We expanded this set of host proteins by constructing their protein interactome, including in it not only the known protein-protein interactions (PPIs) but also novel, hitherto unknown PPIs predicted with our High-precision Protein-Protein Interaction Prediction (HiPPIP) model that was shown to be highly accurate. In fact, one of the earliest discoveries made possible by HiPPIP is related to activation of immunity upon viral infection. We found that several interactors of the host proteins are differentially expressed upon viral infection, are related to highly relevant pathways, and that the novel interaction of NUP98 with CHMP5 may activate an antiviral mechanism leading to disruption of viral budding. We are making the interactions available as downloadable files to facilitate future systems biology studies and also on a web-server at http://hagrid.dbmi.pitt.edu/corona that allows not only keyword search but also queries such as "PPIs where one protein is associated with 'virus' and the interactors with 'pulmonary'".

Characterization of Carbonic Anhydrase In Vivo Using Magnetic Resonance Spectroscopy

Carbonic anhydrase is a ubiquitous metalloenzyme that catalyzes the reversible interconversion of CO2/HCO3-. Equilibrium of these species is maintained by the action of carbonic anhydrase. Recent advances in magnetic resonance spectroscopy have allowed, for the first time, in vivo characterization of carbonic anhydrase in the human brain. In this article, we review the theories and techniques of in vivo 13C magnetization (saturation) transfer magnetic resonance spectroscopy as they are applied to measuring the rate of exchange between CO2 and HCO3- catalyzed by carbonic anhydrase. Inhibitors of carbonic anhydrase have a wide range of therapeutic applications. Role of carbonic anhydrases and their inhibitors in many diseases are also reviewed to illustrate future applications of in vivo carbonic anhydrase assessment by magnetic resonance spectroscopy.