Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders

Advances in bioinformatic methods for the acceleration of the drug discovery from nature

BACKGROUND

Drug discovery from nature has a long, ethnopharmacologically-based background. Today, natural resources are undeniably vital reservoirs of active molecules or drug leads. Advances in (bio)informatics and computational biology emphasized the role of herbal medicines in the drug discovery pipeline.

PURPOSE

This review summarizes bioinformatic approaches applied in recent drug discovery from nature.

STUDY DESIGN

It examines advancements in molecular networking, pathway analysis, network pharmacology within a systems biology framework and AI for assessing the therapeutic potential of herbal preparations.

METHODS

A comprehensive literature search was conducted using Pubmed, SciFinder, and Google Database. Obtained data was analyzed and organized in subsections: AI, systems biology integrative approach, network pharmacology, pathway analysis, molecular networking, structure-based virtual screening.

RESULTS

Bioinformatic approaches is now essential for high-throughput data analysis in drug target identification, mechanism-based drug discovery, drug repurposing and side-effects prediction. Large datasets obtained from "omics" approaches require bioinformatic calculations to unveil interactions, and patterns in disease-relevant conditions. These tools enable databases annotations, pattern-matching, connections discovery, molecular relationship exploration, and data visualisation.

CONCLUSION

Despite the complexity of plant metabolites, bioinformatic approaches assist in characterization of herbal preparations and selection of bioactive molecule. It is perceived as powerful tool for uncovering multi-target effects and potential molecular mechanisms of compounds. By integrating multiple networks that connect gene-disease, drug-target and gene-drug-target, drug discovery from natural sources is experiencing a remarkable comeback.

Leveraging pleiotropy for the improved treatment of psychiatric disorders

Over 90% of drug candidates fail in clinical trials, while it takes 10-15 years and one billion US dollars to develop a single successful drug. Drug development is more challenging for psychiatric disorders, where disease comorbidity and complex symptom profiles obscure the identification of causal mechanisms for therapeutic intervention. One promising approach for determining more suitable drug candidates in clinical trials is integrating human genetic data into the selection process. Genome-wide association studies have identified thousands of replicable risk loci for psychiatric disorders, and sophisticated statistical tools are increasingly effective at using these data to pinpoint likely causal genes. These studies have also uncovered shared or pleiotropic genetic risk factors underlying comorbid psychiatric disorders. In this article, we argue that leveraging pleiotropic effects will provide opportunities to discover novel drug targets and identify more effective treatments for psychiatric disorders by targeting a common mechanism rather than treating each disease separately.

Genome-wide association study implicates lipid pathway dysfunction in antipsychotic-induced weight gain: multi-ancestry validation

Antipsychotic-induced weight gain (AIWG) is a common side effect of antipsychotic medication and may contribute to diabetes and coronary heart disease. To expand the unclear genetic mechanism underlying AIWG, we conducted a two-stage genome-wide association study in Han Chinese patients with schizophrenia. The study included a discovery cohort of 1936 patients and a validation cohort of 534 patients, with an additional 630 multi-ancestry patients from the CATIE study for external validation. We applied Mendelian randomization (MR) analysis to investigate the relationship between AIWG and antipsychotic-induced lipid changes. Our results identified two novel genome-wide significant loci associated with AIWG: rs10422861 in PEPD (P = 1.373 × 10-9) and rs3824417 in PTPRD (P = 3.348 × 10-9) in Chinese Han samples. The association of rs10422861 was validated in the European samples. Fine-mapping and functional annotation revealed that PEPD and PTPRD are potentially causal genes for AIWG, with their proteins being prospective therapeutic targets. Colocalization analysis suggested that AIWG and type 2 diabetes (T2D) shared a causal variant in PEPD. Polygenic risk scores (PRSs) for AIWG and T2D significantly predicted AIWG in multi-ancestry samples. Furthermore, MR revealed a risky causal effect of genetically predicted changes in low-density lipoprotein cholesterol (P = 7.58 × 10-4) and triglycerides (P = 2.06 × 10-3) caused by acute-phase of antipsychotic treatment on AIWG, which had not been previously reported. Our model, incorporating antipsychotic-induced lipid changes, PRSs, and clinical predictors, significantly predicted BMI percentage change after 6-month antipsychotic treatment (AUC = 0.79, R2 = 0.332). Our results highlight that the mechanism of AIWG involves lipid pathway dysfunction and may share a genetic basis with T2D through PEPD. Overall, this study provides new insights into the pathogenesis of AIWG and contributes to personalized treatment of schizophrenia.

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.

Current approaches in identification of a novel drug targets for drug repurposing

Currently, millions of drugs and their licence have been expired or will be expiring in near future. Therefore, existing USFDA approved drug can be used for treating another disease. The above-mentioned approach falls under the category of drug repurposing. Drug repurposing is an alternative strategy for finding new applications of existing USFDA approved drugs. Identification of a novel drug target is one of the go to way for drug repurposing so that new therapeutic applications of USFDA approved drugs could be determined. Recent advances in computational biology and bioinformatics can help to accelerate the same. Drug repurposing can save time and resource as compared to discovery of an entirely new drug molecule. In this chapter, we explore different strategies for discovery of a novel drug target and its uses for drug repurposing to treat disease.

Use of gene regulatory network analysis to repurpose drugs to treat bipolar disorder

BACKGROUND

Bipolar disorder (BD) presents significant challenges in drug discovery, necessitating alternative approaches. Drug repurposing, leveraging computational techniques and expanding biomedical data, holds promise for identifying novel treatment strategies.

METHODS

This study utilized gene regulatory networks (GRNs) to identify significant regulatory changes in BD, using network-based signatures for drug repurposing. Employing the PANDA algorithm, we investigated the variations in transcription factor-GRNs between individuals with BD and unaffected individuals, incorporating binding motifs, protein interactions, and gene co-expression data. The differences in edge weights between BD and controls were then used as differential network signatures to identify drugs potentially targeting the disease-associated gene signature, employing the CLUEreg tool in the GRAND database.

RESULTS

Using a large RNA-seq dataset of 216 post-mortem brain samples from the CommonMind consortium, we constructed GRNs based on co-expression for individuals with BD and unaffected controls, involving 15,271 genes and 405 TFs. Our analysis highlighted significant influences of these TFs on immune response, energy metabolism, cell signalling, and cell adhesion pathways in the disorder. By employing drug repurposing, we identified 10 promising candidates potentially repurposed as BD treatments.

LIMITATIONS

Non-drug-naïve transcriptomics data, bulk analysis of BD samples, potential bias of GRNs towards well-studied genes.

CONCLUSIONS

Further investigation into repurposing candidates, especially those with preclinical evidence supporting their efficacy, like kaempferol and pramocaine, is warranted to understand their mechanisms of action and effectiveness in treating BD. Additionally, novel targets such as PARP1 and A2b offer opportunities for future research on their relevance to the disorder.

Human protein-protein interaction networks: A topological comparison review

Protein-Protein Interaction Networks aim to model the interactome, providing a powerful tool for understanding the complex relationships governing cellular processes. These networks have numerous applications, including functional enrichment, discovering cancer driver genes, identifying drug targets, and more. Various databases make protein-protein networks available for many species, including Homo sapiens. This work topologically compares four Homo sapiens networks using a coarse-to-fine approach, comparing global characteristics, sub-network topology, specific nodes centrality, and interaction significance. Results show that the four human protein networks share many common protein-encoding genes and some global measures, but significantly differ in the interactions and neighbourhood. Small sub-networks from cancer pathways performed better than the whole networks, indicating an improved topological consistency in functional pathways. The centrality analysis shows that the same genes play different roles in different networks. We discuss how studies and analyses that rely on protein-protein networks for humans should consider their similarities and distinctions.

CaMKK2 as an emerging treatment target for bipolar disorder

Current pharmacological treatments for bipolar disorder are inadequate and based on serendipitously discovered drugs often with limited efficacy, burdensome side-effects, and unclear mechanisms of action. Advances in drug development for the treatment of bipolar disorder remain incremental and have come largely from repurposing drugs used for other psychiatric conditions, a strategy that has failed to find truly revolutionary therapies, as it does not target the mood instability that characterises the condition. The lack of therapeutic innovation in the bipolar disorder field is largely due to a poor understanding of the underlying disease mechanisms and the consequent absence of validated drug targets. A compelling new treatment target is the Ca2+-calmodulin dependent protein kinase kinase-2 (CaMKK2) enzyme. CaMKK2 is highly enriched in brain neurons and regulates energy metabolism and neuronal processes that underpin higher order functions such as long-term memory, mood, and other affective functions. Loss-of-function polymorphisms and a rare missense mutation in human CAMKK2 are associated with bipolar disorder, and genetic deletion of Camkk2 in mice causes bipolar-like behaviours similar to those in patients. Furthermore, these behaviours are ameliorated by lithium, which increases CaMKK2 activity. In this review, we discuss multiple convergent lines of evidence that support targeting of CaMKK2 as a new treatment strategy for bipolar disorder.

Metabolic regulation to treat bipolar depression: mechanisms and targeting by trimetazidine

Bipolar disorder's core feature is the pathological disturbances in mood, often accompanied by disrupted thinking and behavior. Its complex and heterogeneous etiology implies that a range of inherited and environmental factors are involved. This heterogeneity and poorly understood neurobiology pose significant challenges to existing drug development paradigms, resulting in scarce treatment options, especially for bipolar depression. Therefore, novel approaches are needed to discover new treatment options. In this review, we first highlight the main molecular mechanisms known to be associated with bipolar depression-mitochondrial dysfunction, inflammation and oxidative stress. We then examine the available literature for the effects of trimetazidine in said alterations. Trimetazidine was identified without a priori hypothesis using a gene-expression signature for the effects of a combination of drugs used to treat bipolar disorder and screening a library of off-patent drugs in cultured human neuronal-like cells. Trimetazidine is used to treat angina pectoris for its cytoprotective and metabolic effects (improved glucose utilization for energy production). The preclinical and clinical literature strongly support trimetazidine's potential to treat bipolar depression, having anti-inflammatory and antioxidant properties while normalizing mitochondrial function only when it is compromised. Further, trimetazidine's demonstrated safety and tolerability provide a strong rationale for clinical trials to test its efficacy to treat bipolar depression that could fast-track its repurposing to address such an unmet need as bipolar depression.

DEDTI versus IEDTI: efficient and predictive models of drug-target interactions

Drug repurposing is an active area of research that aims to decrease the cost and time of drug development. Most of those efforts are primarily concerned with the prediction of drug-target interactions. Many evaluation models, from matrix factorization to more cutting-edge deep neural networks, have come to the scene to identify such relations. Some predictive models are devoted to the prediction's quality, and others are devoted to the efficiency of the predictive models, e.g., embedding generation. In this work, we propose new representations of drugs and targets useful for more prediction and analysis. Using these representations, we propose two inductive, deep network models of IEDTI and DEDTI for drug-target interaction prediction. Both of them use the accumulation of new representations. The IEDTI takes advantage of triplet and maps the input accumulated similarity features into meaningful embedding corresponding vectors. Then, it applies a deep predictive model to each drug-target pair to evaluate their interaction. The DEDTI directly uses the accumulated similarity feature vectors of drugs and targets and applies a predictive model on each pair to identify their interactions. We have done a comprehensive simulation on the DTINet dataset as well as gold standard datasets, and the results show that DEDTI outperforms IEDTI and the state-of-the-art models. In addition, we conduct a docking study on new predicted interactions between two drug-target pairs, and the results confirm acceptable drug-target binding affinity between both predicted pairs.