Drug repositioning using in silico compound profiling

Structure and dynamic simulation-based interactions of benzenoids, pyrroles and organooxygen compounds for effective targeting of GPX4 in ischemic stroke

The discovery of a novel drug for ischemic stroke is plagued by expensive and unsuccessful outcomes. FDA-approved drugs could be a viable repurposing strategy for stroke therapy. Emerging evidence suggests the regulating role of Glutathione peroxidase (GPX4) in stroke and attracts as a potential target. To overcome limited therapeutic interventions, a drug repurposing in silico investigation of FDA-approved drugs is proposed for the GPX4 receptor in distinctive species (Homo sapiens and Mus musculus). The GPX4 UniProt wild type ids, that is, P36969 (Homo sapiens), P36970 (Rattus norvegicus) and O70325 (Mus musculus) are Swiss modelled, and resultant templates are 2OBI and 6HN3 for Homo sapiens, and 5L71 for Mus musculus with a sequence identity of ∼88%. Enrichment analysis reveals high sensitivity and ranked actives with ROC and AUC values of 0.59 and 0.61, respectively. Virtual screening at extra precision resulted hit Acarbosum, is similar between 2OBI and 6HN3, demonstrating a multiple-target specificity and Iopromide, targeting 2OBI. MD simulation at 100 ns following trajectory analysis provides RMSD (∼1.2-1.8Å), RMSF (∼1.6-2.7Å), Rgyr (∼15-15.6Å) depicting stabilisation of receptor-ligand complexes. Furthermore, average B-factor value of 2OBI, 6HN3 and 5L71 is 25Å, 24Å and 60Å with a defined resolution of 1.55Å, 1.01Å and 1.80Å, respectively, depicting the thermodynamic stability of the protein structures. The dynamic cross-correlation and principal component analysis of residual fluctuations reveal more positive correlation, high atomic displacements and greater residual clustering of residues from atomic coordinates. Therefore, Acarbosum, an FDA-approved drug, could act as a potential repurposing drug with a multi-target approach translating from preclinical to clinical stages.Communicated by Ramaswamy H. Sarma.

Knowledge Mapping of Drug Repositioning's Theme and Development

Background

In recent years, the emergence of new diseases and resistance to known diseases have led to increasing demand for new drugs. By means of bibliometric analysis, this paper studied the relevant articles on drug repositioning in recent years and analyzed the current research foci and trends.

Methodology

The Web of Science database was searched to collect all relevant literature on drug repositioning from 2001 to 2022. These data were imported into CiteSpace and bibliometric online analysis platforms for bibliometric analysis. The processed data and visualized images predict the development trends in the research field.

Results

The quality and quantity of articles published after 2011 have improved significantly, with 45 of them cited more than 100 times. Articles posted by journals from different countries have high citation values. Authors from other institutions have also collaborated to analyze drug rediscovery. Keywords found in the literature include molecular docking (N=223), virtual screening (N=170), drug discovery (N=126), machine learning (N=125), and drug-target interaction (N=68); these words represent the core content of drug repositioning.

Conclusion

The key focus of drug research and development is related to the discovery of new indications for drugs. Researchers are starting to retarget drugs after analyzing online databases and clinical trials. More and more drugs are being targeted at other diseases to treat more patients, based on saving money and time. It is worth noting that researchers need more financial and technical support to complete drug development.

In silico activity and ADMET profiling of phytochemicals from Ethiopian indigenous aloes using pharmacophore models

In silico profiling is used in identification of active compounds and guide rational use of traditional medicines. Previous studies on Ethiopian indigenous aloes focused on documentation of phytochemical compositions and traditional uses. In this study, ADMET and drug-likeness properties of phytochemicals from Ethiopian indigenous aloes were evaluated, and pharmacophore-based profiling was done using Discovery Studio to predict therapeutic targets. The targets were examined using KEGG pathway, gene ontology and network analysis. Using random-walk with restart algorithm, network propagation was performed in CODA network to find diseases associated with the targets. As a result, 82 human targets were predicted and found to be involved in several molecular functions and biological processes. The targets also were linked to various cancers and diseases of immune system, metabolism, neurological system, musculoskeletal system, digestive system, hematologic, infectious, mouth and dental, and congenital disorder of metabolism. 207 KEGG pathways were enriched with the targets, and the main pathways were metabolism of steroid hormone biosynthesis, lipid and atherosclerosis, chemical carcinogenesis, and pathways in cancer. In conclusion, in silico target fishing and network analysis revealed therapeutic activities of the phytochemicals, demonstrating that Ethiopian indigenous aloes exhibit polypharmacology effects on numerous genes and signaling pathways linked to many diseases.

Repurposing FDA Approved Drugs as JNK3 Inhibitor for Prevention of Neuroinflammation Induced by MCAO in Rats

Background

Stress-associated kinases are considered major pathological mediators in several incurable neurological disorders. Importantly, among these stress kinases, the c-Jun NH2-terminal kinase (JNK) has been linked to numerous neuropathological conditions, including oxidative stress, neuroinflammation, and brain degeneration associated with brain injuries such as ischemia/reperfusion injury. In this study, we adopted a drug repurposing/reprofiling approach to explore novel JNK3 inhibitors from FDA-approved medications to supplement existing therapeutic strategies.

Materials and Methods

We performed in silico docking analysis and molecular dynamics simulation to screen potential candidates from the FDA approved drug library using the standard JNK inhibitor SP600125 as a reference. After the virtual screening, dabigatran, estazolam, leucovorin, and pitavastatin were further examined in ischemic stroke using an animal rodent model of focal cerebral ischemia using transient middle cerebral artery occlusion (t-MCAO). The selected drugs were probed for neuroprotective effectiveness by measuring the infarct area (%) and neurological deficits using a 28-point composite score. Biochemical assays including ELISA and immunohistochemical experiments were performed.

Results

We obtained structural insights for dabigatran, estazolam, and pitavastatin binding to JNK3, revealing a significant contribution of the hydrophobic regions and significant residues of active site regions. To validate the docking results, the pharmacological effects of dabigatran, estazolam, leucovorin, and pitavastatin on MCAO were tested in parallel with the JNK inhibitor SP600125. After MCAO surgery, severe neurological deficits were detected in the MCAO group compared with the sham controls, which were significantly reversed by dabigatran, estazolam, and pitavastatin treatment. Aberrant morphological features and brain damage were observed in the ipsilateral cortex and striatum of the MCAO groups. The drugs restored the anti-oxidant enzyme activity and reduced the levels of oxidative stress-induced p-JNK and neuroinflammatory mediators such as NF-kB and TNF-ɑ in rats subjected to MCAO.

Conclusion

Our results demonstrated that the novel FDA-approved medications attenuate ischemic stroke-induced neuronal degeneration, possibly by inhibiting JNK3. Being FDA-approved safe medications, the use of these drugs can be clinically translated for ischemic stroke-associated brain degeneration and other neurodegenerative diseases associated with oxidative stress and neuroinflammation.

Drug Repurposing in Medulloblastoma: Challenges and Recommendations

OPINION STATEMENT

Medulloblastoma is the most frequently diagnosed primary malignant brain tumor among children. Currently available therapeutic strategies are based on surgical resection, chemotherapy, and/or radiotherapy. However, majority of patients quickly develop therapeutic resistance and are often left with long-term therapy-related side effects and sequelae. Therefore, there remains a dire need to develop more effective therapeutics to overcome the acquired resistance to currently available therapies. Unfortunately, the process of developing novel anti-neoplastic drugs from bench to bedside is highly time-consuming and very expensive. A wide range of drugs that are already in clinical use for treating non-cancerous diseases might commonly target tumor-associated signaling pathways as well and hence be of interest in treating different cancers. This is referred to as drug repurposing or repositioning. In medulloblastoma, drug repurposing has recently gained a remarkable interest as an alternative therapy to overcome therapy resistance, wherein existing non-tumor drugs are being tested for their potential anti-neoplastic effects outside the scope of their original use.

In silico identification of therapeutic compounds against microRNA targets in drug-resistant pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a major health issue that has been eluding efforts to identify viable therapeutic treatment options. Besides having the lowest survival rate among all types of cancer, almost all conventional methods of treatment are futile against this condition, leaving patients to succumb to this ailment faster than ever. As it is increasingly becoming difficult to come up with new compounds for the treatment of various diseases, alternative solutions are required for tackling these problems. In this study, publically available miRNA and gene expression data were used to identify common elements that were present in gemcitabine-resistant PDAC cell lines. By selecting overexpressed genes involved in pancreatic cancer and cancer pathways in general, potential drug candidates for the treatment of PDAC were identified. In this study, 21 differentially expressed miRNAs were identified from PANC-1 cell line treated with gemcitabine. Pathway analysis revealed that MET and PPARG were overexpressed in cancer-related pathways, including pancreatic cancer, and could be targeted for PDAC treatment. Using CMap, fisetin was identified a likely candidate drug for the treatment of PDAC. Docking studies indicated that fisetin was bound to c-Met and PPARG with an XP G score of -12.819 and -7.021 kcal/mol, respectively. As miRNAs have increasingly been shown to part take in important cancer-related processes and pathways, researching drug development methods based on miRNA targets could be beneficial for pharmaceutical industries. Communicated by Ramaswamy H. Sarma.

In Silico Drug-Target Profiling

Pharmacological science is trying to establish the link between chemicals, targets, and disease-related phenotypes. A plethora of chemical proteomics and structural data have been generated, thanks to the target-based approach that has dominated drug discovery at the turn of the century. There is an invaluable source of information for in silico target profiling. Prediction is based on the principle of chemical similarity (similar drugs bind similar targets) or on first principles from the biophysics of molecular interactions. In the first case, compound comparison is made through ligand-based chemical similarity search or through classifier-based machine learning approach. The 3D techniques are based on 3D structural descriptors or energy-based scoring scheme to infer a binding affinity of a compound with its putative target. More recently, a new approach based on compound set metric has been proposed in which a query compound is compared with a whole of compounds associated with a target or a family of targets. This chapter reviews the different techniques of in silico target profiling and their main applications such as inference of unwanted targets, drug repurposing, or compound prioritization after phenotypic-based screening campaigns.

Current Screening Methodologies in Drug Discovery for Selected Human Diseases

The increase of many deadly diseases like infections by multidrug-resistant bacteria implies re-inventing the wheel on drug discovery. A better comprehension of the metabolisms and regulation of diseases, the increase in knowledge based on the study of disease-born microorganisms’ genomes, the development of more representative disease models and improvement of techniques, technologies, and computation applied to biology are advances that will foster drug discovery in upcoming years. In this paper, several aspects of current methodologies for drug discovery of antibacterial and antifungals, anti-tropical diseases, antibiofilm and antiquorum sensing, anticancer and neuroprotectors are considered. For drug discovery, two different complementary approaches can be applied: classical pharmacology, also known as phenotypic drug discovery, which is the historical basis of drug discovery, and reverse pharmacology, also designated target-based drug discovery. Screening methods based on phenotypic drug discovery have been used to discover new natural products mainly from terrestrial origin. Examples of the discovery of marine natural products are provided. A section on future trends provides a comprehensive overview on recent advances that will foster the pharmaceutical industry.

The relative resistance of children to sepsis mortality: from pathways to drug candidates

Attempts to develop drugs that address sepsis based on leads developed in animal models have failed. We sought to identify leads based on human data by exploiting a natural experiment: the relative resistance of children to mortality from severe infections and sepsis. Using public datasets, we identified key differences in pathway activity (Pathprint) in blood transcriptome profiles of septic adults and children. To find drugs that could promote beneficial (child) pathways or inhibit harmful (adult) ones, we built an in silico pathway drug network (PDN) using expression correlation between drug, disease, and pathway gene signatures across 58,475 microarrays. Specific pathway clusters from children or adults were assessed for correlation with drug‐based signatures. Validation by literature curation and by direct testing in an endotoxemia model of murine sepsis of the most correlated drug candidates demonstrated that the Pathprint‐PDN methodology is more effective at generating positive drug leads than gene‐level methods (e.g., CMap). Pathway‐centric Pathprint‐PDN is a powerful new way to identify drug candidates for intervention against sepsis and provides direct insight into pathways that may determine survival.

A structure- and chemical genomics-based approach for repositioning of drugs against VCP/p97 ATPase

Valosin-containing protein (VCP/p97) ATPase (a.k.a. Cdc48) is a key member of the ER-associated protein degradation (ERAD) pathway. ERAD and VCP/p97 have been implicated in a multitude of human diseases, such as neurodegenerative diseases and cancer. Inhibition of VCP/p97 induces proteotoxic ER stress and cell death in cancer cells, making it an attractive target for cancer treatment. However, no drugs exist against this protein in the market. Repositioning of drugs towards new indications is an attractive alternative to the de novo drug development due to the potential for significantly shorter time to clinical translation. Here, we employed an integrative strategy for the repositioning of drugs as novel inhibitors of the VCP/p97 ATPase. We integrated structure-based virtual screening with the chemical genomics analysis of drug molecular signatures, and identified several candidate inhibitors of VCP/p97 ATPase. Importantly, experimental validation with cell-based and in vitro ATPase assays confirmed three (ebastine, astemizole and clotrimazole) out of seven tested candidates (~40% true hit rate) as direct inhibitors of VCP/p97 and ERAD. This study introduces an effective integrative strategy for drug repositioning, and identified new drugs against the VCP/p97/ERAD pathway in human diseases.

Gene-set analysis based on the pharmacological profiles of drugs to identify repurposing opportunities in schizophrenia

Genome-wide association studies (GWAS) have identified thousands of novel genetic associations for complex genetic disorders, leading to the identification of potential pharmacological targets for novel drug development. In schizophrenia, 108 conservatively defined loci that meet genome-wide significance have been identified and hundreds of additional sub-threshold associations harbour information on the genetic aetiology of the disorder. In the present study, we used gene-set analysis based on the known binding targets of chemical compounds to identify the 'drug pathways' most strongly associated with schizophrenia-associated genes, with the aim of identifying potential drug repositioning opportunities and clues for novel treatment paradigms, especially in multi-target drug development. We compiled 9389 gene sets (2496 with unique gene content) and interrogated gene-based p-values from the PGC2-SCZ analysis. Although no single drug exceeded experiment wide significance (corrected p<0.05), highly ranked gene-sets reaching suggestive significance including the dopamine receptor antagonists metoclopramide and trifluoperazine and the tyrosine kinase inhibitor neratinib. This is a proof of principle analysis showing the potential utility of GWAS data of schizophrenia for the direct identification of candidate drugs and molecules that show polypharmacy.

A Practical Guide for Exploring Opportunities of Repurposing Drugs for CNS Diseases in Systems Biology

Systems biology has shown its potential in facilitating pathway-focused therapy development for central nervous system (CNS) diseases. An integrated network can be utilized to explore the multiple disease mechanisms and to discover repositioning opportunities. This review covers current therapeutic gaps for CNS diseases and the role of systems biology in pharmaceutical industry. We conclude with a Multiple Level Network Modeling (MLNM) example to illustrate the great potential of systems biology for CNS diseases. The system focuses on the benefit and practical applications in pathway centric therapy and drug repositioning.

Drug repositioning approaches for the discovery of new therapeutics for Alzheimer's disease

Alzheimer's disease (AD) is the most common cause of dementia and represents one of the highest unmet needs in medicine today. Drug development efforts for AD have been encumbered by largely unsuccessful clinical trials in the last decade. Drug repositioning, a process of discovering a new therapeutic use for existing drugs or drug candidates, is an attractive and timely drug development strategy especially for AD. Compared with traditional de novo drug development, time and cost are reduced as the safety and pharmacokinetic properties of most repositioning candidates have already been determined. A majority of drug repositioning efforts for AD have been based on positive clinical or epidemiological observations or in vivo efficacy found in mouse models of AD. More systematic, multidisciplinary approaches will further facilitate drug repositioning for AD. Some experimental approaches include unbiased phenotypic screening using the library of available drug collections in physiologically relevant model systems (e.g. stem cell-derived neurons or glial cells), computational prediction and selection approaches that leverage the accumulating data resulting from RNA expression profiles, and genome-wide association studies. This review will summarize several notable strategies and representative examples of drug repositioning for AD.

Early repositioning through compound set enrichment analysis: a knowledge-recycling strategy

Despite famous serendipitous drug repositioning success stories, systematic projects have not yet delivered the expected results. However, repositioning technologies are gaining ground in different phases of routine drug development, together with new adaptive strategies. We demonstrate the power of the compound information pool, the ever-growing heterogeneous information repertoire of approved drugs and candidates as an invaluable catalyzer in this transition. Systematic, computational utilization of this information pool for candidates in early phases is an open research problem; we propose a novel application of the enrichment analysis statistical framework for fusion of this information pool, specifically for the prediction of indications. Pharmaceutical consequences are formulated for a systematic and continuous knowledge recycling strategy, utilizing this information pool throughout the drug-discovery pipeline.

Systematic drug repurposing through text mining

Drug development remains a time-consuming and highly expensive process with high attrition rates at each stage. Given the safety hurdles drugs must pass due to increased regulatory scrutiny, it is essential for pharmaceutical companies to maximize their return on investment by effectively extending drug life cycles. There have been many effective techniques, such as phenotypic screening and compound profiling, which identify new indications for existing drugs, often referred to as drug repurposing or drug repositioning. This chapter explores the use of text mining leveraging several publicly available knowledge resources and mechanism of action representations to link existing drugs to new diseases from biomedical abstracts in an attempt to generate biologically meaningful alternative drug indications.

Recent advances in drug repositioning for the discovery of new anticancer drugs

Drug repositioning (also referred to as drug repurposing), the process of finding new uses of existing drugs, has been gaining popularity in recent years. The availability of several established clinical drug libraries and rapid advances in disease biology, genomics and bioinformatics has accelerated the pace of both activity-based and in silico drug repositioning. Drug repositioning has attracted particular attention from the communities engaged in anticancer drug discovery due to the combination of great demand for new anticancer drugs and the availability of a wide variety of cell- and target-based screening assays. With the successful clinical introduction of a number of non-cancer drugs for cancer treatment, drug repositioning now became a powerful alternative strategy to discover and develop novel anticancer drug candidates from the existing drug space. In this review, recent successful examples of drug repositioning for anticancer drug discovery from non-cancer drugs will be discussed.

Accidental interaction between PDZ domains and diclofenac revealed by NMR-assisted virtual screening

In silico approaches have become indispensable for drug discovery as well as drug repositioning and adverse effect prediction. We have developed the eF-seek program to predict protein–ligand interactions based on the surface structure of proteins using a clique search algorithm. We have also developed a special protein structure prediction pipeline and accumulated predicted 3D models in the Structural Atlas of the Human Genome (SAHG) database. Using this database, genome-wide prediction of non-peptide ligands for proteins in the human genome was performed, and a subset of predicted interactions including 14 PDZ domains was then confirmed by NMR titration. Surprisingly, diclofenac, a non-steroidal anti-inflammatory drug, was found to be a non-peptide PDZ domain ligand, which bound to 5 of 15 tested PDZ domains. The critical residues for the PDZ–diclofenac interaction were also determined. Pharmacological implications of the accidental PDZ–diclofenac interaction are further discussed.

Chemogenomics of allosteric binding sites in GPCRs

Chemogenomic techniques connect the chemical and biological domains to establish ligand and target relationships not evident from the individual disciplines. Chemogenomics has been applied in lead generation, target classification, focused library design as well as selectivity and polypharmacology profiling. This review describes recent developments structured into ligand-, target- and combined chemogenomic techniques and applications to allosteric GPCR ligands. It also outlines relative strengths and limitations of these techniques and the impact of the increasing crystallographic data.

Traditional chinese medicine-based network pharmacology could lead to new multicompound drug discovery

Current strategies for drug discovery have reached a bottleneck where the paradigm is generally “one gene, one drug, one disease.” However, using holistic and systemic views, network pharmacology may be the next paradigm in drug discovery. Based on network pharmacology, a combinational drug with two or more compounds could offer beneficial synergistic effects for complex diseases. Interestingly, traditional chinese medicine (TCM) has been practicing holistic views for over 3,000 years, and its distinguished feature is using herbal formulas to treat diseases based on the unique pattern classification. Though TCM herbal formulas are acknowledged as a great source for drug discovery, no drug discovery strategies compatible with the multidimensional complexities of TCM herbal formulas have been developed. In this paper, we highlighted some novel paradigms in TCM-based network pharmacology and new drug discovery. A multiple compound drug can be discovered by merging herbal formula-based pharmacological networks with TCM pattern-based disease molecular networks. Herbal formulas would be a source for multiple compound drug candidates, and the TCM pattern in the disease would be an indication for a new drug.

Identifying novel drug indications through automated reasoning

Background

With the large amount of pharmacological and biological knowledge available in literature, finding novel drug indications for existing drugs using in silico approaches has become increasingly feasible. Typical literature-based approaches generate new hypotheses in the form of protein-protein interactions networks by means of linking concepts based on their cooccurrences within abstracts. However, this kind of approaches tends to generate too many hypotheses, and identifying new drug indications from large networks can be a time-consuming process.

Methodology

In this work, we developed a method that acquires the necessary facts from literature and knowledge bases, and identifies new drug indications through automated reasoning. This is achieved by encoding the molecular effects caused by drug-target interactions and links to various diseases and drug mechanism as domain knowledge in AnsProlog, a declarative language that is useful for automated reasoning, including reasoning with incomplete information. Unlike other literature-based approaches, our approach is more fine-grained, especially in identifying indirect relationships for drug indications.

Conclusion/Significance

To evaluate the capability of our approach in inferring novel drug indications, we applied our method to 943 drugs from DrugBank and asked if any of these drugs have potential anti-cancer activities based on information on their targets and molecular interaction types alone. A total of 507 drugs were found to have the potential to be used for cancer treatments. Among the potential anti-cancer drugs, 67 out of 81 drugs (a recall of 82.7%) are indeed known cancer drugs. In addition, 144 out of 289 drugs (a recall of 49.8%) are non-cancer drugs that are currently tested in clinical trials for cancer treatments. These results suggest that our method is able to infer drug indications (original or alternative) based on their molecular targets and interactions alone and has the potential to discover novel drug indications for existing drugs.