Life Sci. 2023 Oct 17:122181. doi: 10.1016/j.lfs.2023.122181. Online ahead of print.

ABSTRACT

BACKGROUND: The anti-apoptotic protein B-Cell Lymphoma 2 (Bcl-2) is a key target for the development of anti-cancer agents, as its overexpression can render cancer cells resistant to chemotherapeutic treatments.

AIMS AND OBJECTIVES: The current study has systematically evaluated a library of FDA-approved drugs for Bcl-2 inhibition using a drug repurposing strategy via in vitro, biophysical, and in-silico techniques.

MATERIALS AND METHODS: In vitro anticancer activity was performed, followed by apoptosis assay. The selected compounds were subjected to Saturation Transfer Difference Nuclear Magnetic Resonance (STD-NMR) spectroscopy, molecular docking, and molecular dynamic simulation for ligand-protein interactions.

KEY FINDINGS: In the initial screening, seventy-five (75) drugs were evaluated against the HL-60 (human blood promyelocytic leukemia) cancer cell line. Among them, paroxetine HCl, carvedilol, clomipramine HCl, and clomifene citrate showed significant anti-proliferative activity (IC50 = 9.733 ± 0.524, 11.940 ± 0.079, 12.376 ± 1.242, and 6.155 ± 0.363 μM, respectively), in comparison to the reference drug venetoclax (IC50 = 7.086 ± 0.041 μM). This indicated that the test drugs have comparable IC50 values to the standard drug. Furthermore, the drugs were able to induce apoptosis in HL-60 cells. These drugs showed interactions with Bcl-2 protein in STD-NMR analysis. Docking and MD simulation studies further supported the interaction of these drugs with Bcl-2 protein, mainly via hydrophobic contacts leading to stable drug-Bcl-2 complexes.

SIGNIFICANCE: This study, identifies paroxetine HCl, carvedilol, clomipramine HCl, and clomifene citrate as significant Bcl-2 inhibitors and needs further pre-clinical and clinical studies for potential anti-cancer agents' evaluation.

PMID:37858717 | DOI:10.1016/j.lfs.2023.122181

Sci Rep. 2023 Oct 19;13(1):17887. doi: 10.1038/s41598-023-44264-7.

NO ABSTRACT

PMID:37857641 | DOI:10.1038/s41598-023-44264-7

J Biomol Struct Dyn. 2023 Oct 19:1-12. doi: 10.1080/07391102.2023.2270086. Online ahead of print.

ABSTRACT

Emerging evidence portray the involvement of epigenomic reprogramming in the onset and progression of several malignancies, including breast cancer. Histone acetyltransferase (HAT) p300 is a critical epigenetic regulator that acts as a transcription co-activator and regulates various cellular processes. p300 is overexpressed in breast cancer and promotes cellular invasion and survival, making it a promising druggable target. In this study, the relevance of p300 in different cancer pathways was established. Virtual screening of the FDA-approved drug library was carried out using molecular docking, and the top 10 potential repurposed drugs were identified. Further, recalculation of binding free energy of drug-p300 complexes was carried out using molecular mechanics Poisson-Boltzmann and surface area (MM-PBSA) method after molecular dynamic simulation. Based on molecular dynamic simulation parameters and binding free energy analysis, two drugs, namely Netarsudil (-305.068 kJ/mol) and Imatinib (-260.457 kJ/mol), were identified as potential repurposed drugs to inhibit the activity of p300. In conclusion, these findings suggest, Netarsudil and Imatinib might be a potential repurposed drug to combat breast cancer via p300 inhibition.Communicated by Ramaswamy H. Sarma.

PMID:37855370 | DOI:10.1080/07391102.2023.2270086

Fish Shellfish Immunol Rep. 2023 Oct 8;5:100120. doi: 10.1016/j.fsirep.2023.100120. eCollection 2023 Dec 15.

ABSTRACT

Drug repurposing is a methodology of identifying new therapeutic use for existing drugs. It is a highly efficient, time and cost-saving strategy that offers an alternative approach to the traditional drug discovery process. Past in-silico studies involving molecular docking have been successful in identifying potential repurposed drugs for the various treatment of diseases including aquaculture diseases. The emerging shrimp hemocyte iridescent virus (SHIV) or Decapod iridescent virus 1 (DIV1) is a viral pathogen that causes severe disease and high mortality (80 %) in farmed shrimps caused serious economic losses and presents a new threat to the shrimp farming industry. Therefore, effective antiviral drugs are critically needed to control DIV1 infections. The aim of this study is to investigate the interaction of potential existing antiviral drugs, Chloroquine, Rimantadine, and CAP-1 with DIV1 major capsid protein (MCP) with the intention of exploring the potential of drug repurposing. The interaction of the DIV1 MCP and three antivirals were characterised and analysed using molecular docking and molecular dynamics simulation. The results showed that CAP-1 is a more promising candidate against DIV1 with the lowest binding energy of -8.46 kcal/mol and is more stable compared to others. We speculate that CAP-1 binding may induce the conformational changes in the DIV1 MCP structure by phosphorylating multiple residues (His123, Tyr162, and Thr395) and ultimately block the viral assembly and maturation of DIV1 MCP. To the best of our knowledge, this is the first report regarding the structural characterisation of DIV1 MCP docked with repurposing drugs.

PMID:37854946 | PMC:PMC10579962 | DOI:10.1016/j.fsirep.2023.100120

Cell Rep Med. 2023 Oct 17;4(10):101227. doi: 10.1016/j.xcrm.2023.101227.

ABSTRACT

Drug repositioning seeks to leverage existing clinical knowledge to identify alternative clinical settings for approved drugs. However, repositioning efforts fail to demonstrate improved success rates in late-stage clinical trials. Focusing on 11 approved kinase inhibitors that have been evaluated in 139 repositioning hypotheses, we use data mining to characterize the state of clinical repurposing. Then, using a simple experimental correction with human serum proteins in in vitro pharmacodynamic assays, we develop a measurement of a drug's effective exposure. We show that this metric is remarkably predictive of clinical activity for a panel of five kinase inhibitors across 23 drug variant targets in leukemia. We then validate our model's performance in six other kinase inhibitors for two types of solid tumors: non-small cell lung cancer (NSCLC) and gastrointestinal stromal tumors (GISTs). Our approach presents a straightforward strategy to use existing clinical information and experimental systems to decrease the clinical failure rate in drug repurposing studies.

PMID:37852183 | DOI:10.1016/j.xcrm.2023.101227

Curr Comput Aided Drug Des. 2023 Oct 17. doi: 10.2174/0115734099264119230925054833. Online ahead of print.

ABSTRACT

BACKGTOUND: The interconnection between different fields of research has gained interest due to its cutting-edge perspectives in solving scientific problems. Terminalia arjuna is indigenously used in India for curing several diseases and its pharmacological activities are being revisited in recent drug-repurposing researches.

OBJECTIVE: Efficient ultrasound-assisted extraction of phytochemicals from the bark of Terminalia arjuna is highlighted in this study. Following the optimization of the extraction process, the crude hydroethanolic extract is subjected to phytochemical profiling and an in-silico investigation on its anti-cancer property.

METHODOLOGY: A three-level four-factor Box-Behnken design is exploited to optimize four operational parameters namely extraction time, ultrasonic power, ethanol concentration (as the extracting solvent) and solute (in g) : solvent (in mL) ratio. At the optimum parametric condition, the crude extract is obtained and its GC-MS analysis is done. An analysis of network pharmacology (by constructing and visualizing biological networks using Cytoscape) combined with molecular docking reveals the potential antineoplastic targets of the crude extract.

RESULTS: The ANOVA table exhibits the significance, adequacy and reliability of the proposed second-order polynomial model with a R² value of 0.917 and adjusted R² of 0.865. Experimental results portray significant antioxidant potential of the prepared extract in its crude form. The GC-MS analysis of the crude extract predicts the extracted phytochemicals while the constructed biological networks highlight its multi-targeted activity in colorectal cancer.

CONCLUSION: The study identifies three phytochemicals viz. luteolin, β-sitosterol and arjunic acid as potent anti-cancer agents and can be extended with in-vitro and in-vivo experiments to validate the in-silico results, thus establishing lead phytochemicals in multi-targeted colorectal cancer therapies with minimal side effects.

PMID:37850546 | DOI:10.2174/0115734099264119230925054833

J Biomol Struct Dyn. 2023 Oct 18:1-12. doi: 10.1080/07391102.2023.2269280. Online ahead of print.

ABSTRACT

The identification of druggable proteins (DPs) is significant for the development of new drugs, personalized medicine, understanding of disease mechanisms, drug repurposing, and economic benefits. By identifying new druggable targets, researchers can develop new therapies for a range of diseases, leading to better patient outcomes. Identification of DPs by machine learning strategies is more efficient and cost-effective than conventional methods. In this study, a computational predictor, namely Drug-LXGB, is introduced to enhance the identification of DPs. Features are discovered by composition, transition, and distribution (CTD), composition of K-spaced amino acid pair (CKSAAP), pseudo-position-specific scoring matrix (PsePSSM), and a novel descriptor, called multi-block pseudo amino acid composition (MB-PseAAC). The dimensions of CTD, CKSAAP, PsePSSM, and MB-PseAAC are integrated and utilized the sequential forward selection as feature selection algorithm. The best characteristics are provided by random forest, extreme gradient boosting, and light eXtreme gradient boosting (LXGB). The predictive analysis of these learning methods is measured via 10-fold cross-validation. The LXGB-based model secures the highest results than other existing predictors. Our novel protocol will perform an active role in designing novel drugs and would be fruitful to explore the potential target. This study will help better to capture a more universal view of a potential target.Communicated by Ramaswamy H. Sarma.

PMID:37850427 | DOI:10.1080/07391102.2023.2269280

Sci Rep. 2023 Oct 17;13(1):17684. doi: 10.1038/s41598-023-44956-0.

ABSTRACT

Bruton's tyrosine kinase (BTK) is a non-receptor protein kinase that plays a crucial role in various biological processes, including immune system function and cancer development. Therefore, inhibition of BTK has been proposed as a therapeutic strategy for various complex diseases. In this study, we aimed to identify potential inhibitors of BTK by using a drug repurposing approach. To identify potential inhibitors, we performed a molecular docking-based virtual screening using a library of repurposed drugs from DrugBank. We then used various filtrations followed by molecular dynamics (MD) simulations, principal component analysis (PCA), and Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA) analysis to further evaluate the binding interactions and stability of the top-ranking compounds. Molecular docking-based virtual screening approach identified several repurposed drugs as potential BTK inhibitors, including Eltrombopag and Alectinib, which have already been approved for human use. All-atom MD simulations provided insights into the binding interactions and stability of the identified compounds, which will be helpful for further experimental validation and optimization. Overall, our study demonstrates that drug repurposing is a promising approach to identify potential inhibitors of BTK and highlights the importance of computational methods in drug discovery.

PMID:37848584 | DOI:10.1038/s41598-023-44956-0

Cancer Inform. 2023 Oct 14;22:11769351231202588. doi: 10.1177/11769351231202588. eCollection 2023.

ABSTRACT

The aim of this study was to utilize a computational methodology based on Gene Reversal Rate (GRR) scoring to repurpose existing drugs for a rare and understudied cancer: inflammatory breast cancer (IBC). This method uses IBC-related gene expression signatures (GES) and drug-induced gene expression profiles from the LINCS database to calculate a GRR score for each candidate drug, and is based on the idea that a compound that can counteract gene expression changes of a disease may have potential therapeutic applications for that disease. Genes related to IBC with associated differential expression data (265 up-regulated and 122 down-regulated) were collated from PubMed-indexed publications. Drug-induced gene expression profiles were downloaded from the LINCS database and candidate drugs to treat IBC were predicted using their GRR scores. Thirty-two (32) drug perturbations that could potentially reverse the pre-compiled list of 297 IBC genes were obtained using the LINCS Canvas Browser (LCB) analysis. Binary combinations of the 32 perturbations were assessed computationally to identify combined perturbations with the highest GRR scores, and resulted in 131 combinations with GRR greater than 80%, that reverse up to 264 of the 297 genes in the IBC-GES. The top 35 combinations involve 20 unique individual drug perturbations, and 19 potential drug candidates. A comprehensive literature search confirmed 17 of the 19 known drugs as having either anti-cancer or anti-inflammatory activities. AZD-7545, BMS-754807, and nimesulide target known IBC relevant genes: PDK, Met, and COX, respectively. AG-14361, butalbital, and clobenpropit are known to be functionally relevant in DNA damage, cell cycle, and apoptosis, respectively. These findings support the use of the GRR approach to identify drug candidates and potential combination therapies that could be used to treat rare diseases such as IBC.

PMID:37846218 | PMC:PMC10576937 | DOI:10.1177/11769351231202588

Comput Biol Med. 2023 Oct 12;166:107572. doi: 10.1016/j.compbiomed.2023.107572. Online ahead of print.

ABSTRACT

Cholangiocarcinoma (CCA) is a subtype of liver cancer with increasing incidence, poor prognosis, and limited treatment modalities. It is, therefore, imperative to identify novel therapeutic targets for better management of the disease. Chaperones are known to be significant regulators of carcinogenesis, however, their role in CCA remains unclear. This study aims to screen chaperones involved in CCA pathogenesis and identify drugs targeting key chaperones to improve the therapeutic response to the disease. To achieve this, first we mined the literature to create an atlas of human chaperone proteins. Next, their expression in CCA was determined by publicly available datasets of patients at mRNA and protein levels. In addition, our analysis involving protein-protein interaction and pathway analysis of eight key dysregulated chaperones revealed that they control crucial cancer-related pathways. Furthermore, topology analysis of the CCA network identified crystallin alpha-B protein (CRYAB) and prolyl-4-hydroxylase subunit 2 (P4HA2) as novel therapeutic targets for the disease. Finally, drug repurposing of 286 clinically approved anti-cancer drugs against these two chaperones performed by molecular docking and molecular dynamics simulations showed that tucatinib and regorafenib had a modulatory effect on them and could be potential inhibitors of CRYAB and P4HA2, respectively. Overall, our study, for the first time, provides insights into the pan-chaperone expression in CCA and explains the pathways that might drive CCA pathogenesis. Further, our identification of potential therapeutic targets and their inhibitors could provide new and complementary approaches to CCA treatment.

PMID:37844407 | DOI:10.1016/j.compbiomed.2023.107572

Comput Biol Chem. 2023 Oct 7;107:107968. doi: 10.1016/j.compbiolchem.2023.107968. Online ahead of print.

ABSTRACT

Predicting drug-target interaction (DTI) is a crucial step in the process of drug repurposing and new drug development. Although the attention mechanism has been widely used to capture the interactions between drugs and targets, it mainly uses the Simplified Molecular Input Line Entry System (SMILES) and two-dimensional (2D) molecular graph features of drugs. In this paper, we propose a neural network model called MdDTI for DTI prediction. The model searches for binding sites that may interact with the target from the multiple dimensions of drug structure, namely the 2D substructures and the three-dimensional (3D) spatial structure. For the 2D substructures, we have developed a novel substructure decomposition strategy based on drug molecular graphs and compared its performance with the SMILES-based decomposition method. For the 3D spatial structure of drugs, we constructed spatial feature representation matrices for drugs based on the Cartesian coordinates of heavy atoms (without hydrogen atoms) in each drug. Finally, to ensure the search results of the model are consistent across multiple dimensions, we construct a consistency loss function. We evaluate MdDTI on four drug-target interaction datasets and three independent compound-protein affinity test sets. The results indicate that our model surpasses a series of state-of-the-art models. Case studies demonstrate that our model is capable of capturing the potential binding regions between drugs and targets, and it shows efficacy in drug repurposing. Our code is available at https://github.com/lhhu1999/MdDTI.

PMID:37844375 | DOI:10.1016/j.compbiolchem.2023.107968

Mol Oncol. 2023 Oct 16. doi: 10.1002/1878-0261.13536. Online ahead of print.

ABSTRACT

Microtubule-targeting agents represent one of the most successful classes of anticancer agents. However, the development of drug resistance and the appearance of adverse effects hamper their clinical implementation. Novel microtubule-targeting agents without such limitations are urgently needed. By employing a gene expression-based drug repositioning strategy, this study identifies VU-0365114, originally synthesized as a positive allosteric modulator of human muscarinic acetylcholine receptor M5 (M5 mAChR), as a novel type of tubulin inhibitor by destabilizing microtubules. VU-0365114 exhibits a broad-spectrum in vitro anticancer activity, especially in colorectal cancer cells. A tumor xenograft study in nude mice shows that VU-0365114 slowed the in vivo colorectal tumor growth. The anticancer activity of VU-0365114 is not related to its original target, M5 mAChR. In addition, VU-0365114 does not serve as a substrate of multidrug resistance (MDR) proteins, and thus it can overcome MDR. Furthermore, a kinome analysis shows that VU-0365114 did not exhibit other significant off-target effects. Taken together, our study suggests that VU-0365114 primarily targets microtubules, offering potential for repurposing in cancer treatment, although more studies are needed before further drug development.

PMID:37842807 | DOI:10.1002/1878-0261.13536

Front Microbiol. 2023 Sep 29;14:1266261. doi: 10.3389/fmicb.2023.1266261. eCollection 2023.

ABSTRACT

Mycobacterium ulcerans causes Buruli Ulcer, a neglected infectious skin disease that typically progresses from an early non-ulcerative lesion to an ulcer with undermined edges. If not promptly treated, these lesions can lead to severe disfigurement and disability. The standard antibiotic regimen for Buruli Ulcer treatment has been oral rifampicin combined with intramuscular streptomycin administered daily for 8 weeks. However, there has been a recent shift toward replacing streptomycin with oral clarithromycin. Despite the advantages of this antibiotic regimen, it is limited by low compliance, associated side effects, and refractory efficacy for severe ulcerative lesions. Therefore, new drug candidates with a safer pharmacological spectrum and easier mode of administration are needed. Statins are lipid-lowering drugs broadly used for dyslipidemia treatment but have also been reported to have several pleiotropic effects, including antimicrobial activity against fungi, parasites, and bacteria. In the present study, we tested the susceptibility of M. ulcerans to several statins, namely atorvastatin, simvastatin, lovastatin and fluvastatin. Using broth microdilution assays and cultures of M. ulcerans-infected macrophages, we found that atorvastatin, simvastatin and fluvastatin had antimicrobial activity against M. ulcerans. Furthermore, when using the in vitro checkerboard assay, the combinatory additive effect of atorvastatin and fluvastatin with the standard antibiotics used for Buruli Ulcer treatment highlighted the potential of statins as adjuvant drugs. In conclusion, statins hold promise as potential treatment options for Buruli Ulcer. Further studies are necessary to validate their effectiveness and understand the mechanism of action of statins against M. ulcerans.

PMID:37840746 | PMC:PMC10570734 | DOI:10.3389/fmicb.2023.1266261

Naunyn Schmiedebergs Arch Pharmacol. 2023 Oct 14. doi: 10.1007/s00210-023-02722-z. Online ahead of print.

ABSTRACT

Colon cancer is one of the most common cancers and one of the main causes of death worldwide. Therefore, new treatment methods with better efficiency and fewer risks are very necessary. Mebendazole (MBZ), a drug commonly used for helminthic infections, has recently received attention as a suitable candidate for the treatment of various cancers. This study aimed to investigate, in vitro and in vivo, anticancer activity and selectivity Index of MBZ on colon cancer. HT-29 (human colorectal adenocarcinoma) and MCF-10 (non-tumorigenic epithelial) cell lines were treated with MBZ and Doxorubicin (DOX; positive control drug). IC50 values were estimated using methyl thiazole diphenyl-tetrazolium bromide (MTT) assay. We employed flow cytometry using annexin V-FITC and propidium iodide dyes. For the animal study, colon cancer was subcutaneously induced by CT26 cells (mouse colon cancer) in Bulb/C mice. The mice were treated with 0.05 of LD50, intraperitoneal, every other day for 35 days. Finally, the survival rate, tumor volume, and tumor weight were calculated. Our results demonstrated that IC50 values after 72 h for HT29 and MCF-10 cell lines were 0.29 ± 0.04 µM and 0.80 ± 0.02 µM, respectively. MBZ was more selective than DOX in inhibiting the proliferation of cancer cells compared to normal cells (2. 75 vs. 2.45). Annexin V/PI staining demonstrated that MBZ treatment at IC50 concentrations induced (78 ± 12%) apoptosis in the HT29 cancer cell line after 48 h (P ≤ 0.0001). Also, in mice bearing colon cancer, MBZ significantly reduced the tumor volume (1177 ± 1109 mm3; P ≤ 0.001) and tumor weight (2.30 ± 1.97 g; P ≤ 0.0001) compared to the negative control group (weight 12.45 ± 2.0 g; volume 7346 ± 1077). Also, MBZ increases mean survival time (MST) and increase life span (ILS) percentage in the animal study (51.2 ± 37% vs 93%, respectively). This study suggests that mebendazole strongly and selectively inhibits proliferation and induces apoptosis in colon cancer cells. It may be, accordingly, a promising drug for clinical research and application.

PMID:37837472 | DOI:10.1007/s00210-023-02722-z

Alzheimers Dement. 2023 Oct 14. doi: 10.1002/alz.13463. Online ahead of print.

ABSTRACT

INTRODUCTION: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding.

METHODS: ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field.

RESULTS: Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics.

DISCUSSION: ML is not yet widely used but has considerable potential to enhance precision in dementia prevention.

HIGHLIGHTS: Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk-profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk-management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.

PMID:37837420 | DOI:10.1002/alz.13463

J Biomol Struct Dyn. 2023 Oct 14:1-11. doi: 10.1080/07391102.2023.2268176. Online ahead of print.

ABSTRACT

Breast cancer (BC) accounts for 30% of all diagnosed cases of cancer in women and remains a leading cause of cancer-related deaths among women worldwide. The current study looks for a protein from the anti-apoptotic/pro-survival BCL-2 family whose overexpression reduces survivability in BC patients and a potential inhibitor for the protein. We found BCL-2A1/BFL1 protein with high expression linked to low survivability in BC. The protein shows prognosis in 8 out of 29 categories, whereas no other family member manifests this property. Out of 7379 compounds, three small molecules (CHEMBL9509, CHEMBL2104550 and CHEMBL3545011) form an H-bond with BCL-2A1/BFL1 protein's unique residue Cys55. Of the three small molecules, we found CHEMBL9509 (Silibinin) to be a potent inhibitor. The compound forms a stable H-bond with the residue Cys55 with the lowest binding energy compared to the other two compounds. It remains stable in the BH3 binding region for more than 100 ns, whereas the other two detach from the region. Additionally, the compound is found to be better than Venetoclax and Nematoclax. We firmly believe in the compound CHEMBL9509 potency to halt BC's progression by inhibiting the BCL-2A1/BFL1 protein, increasing patients' survivability.Communicated by Ramaswamy H. Sarma.

PMID:37837418 | DOI:10.1080/07391102.2023.2268176

Pharmacol Res. 2023 Oct 11:106960. doi: 10.1016/j.phrs.2023.106960. Online ahead of print.

ABSTRACT

Opioid Use Disorder (OUD) is a chronic and relapsing condition characterized by the misuse of opioid drugs, causing significant morbidity and mortality in the United States. Existing medications for OUD are limited, and there is an immediate need to discover treatments with enhanced safety and efficacy. Drug repurposing aims to find new indications for existing medications, offering a time-saving and cost-efficient alternative strategy to traditional drug discovery. Computational approaches have been developed to further facilitate the drug repurposing process. In this paper, we reviewed state-of-the-art data-driven computational drug repurposing approaches for OUD and discussed their advantages and potential challenges. We also highlighted promising repurposed candidate drugs for OUD that were identified by computational drug repurposing techniques and reviewed studies supporting their potential mechanisms of action in treating OUD.

PMID:37832859 | DOI:10.1016/j.phrs.2023.106960

Nucleic Acids Res. 2023 Oct 13:gkad832. doi: 10.1093/nar/gkad832. Online ahead of print.

ABSTRACT

Leveraging genetics insights to promote drug repurposing has become a promising and active strategy in pharmacology. Indeed, among the 50 drugs approved by FDA in 2021, two-thirds have genetically supported evidence. In this regard, the increasing amount of widely available genome-wide association studies (GWAS) datasets have provided substantial opportunities for drug repurposing based on genetics discoveries. Here, we developed PharmGWAS, a comprehensive knowledgebase designed to identify candidate drugs through the integration of GWAS data. PharmGWAS focuses on novel connections between diseases and small-molecule compounds derived using a reverse relationship between the genetically-regulated expression signature and the drug-induced signature. Specifically, we collected and processed 1929 GWAS datasets across a diverse spectrum of diseases and 724 485 perturbation signatures pertaining to a substantial 33609 molecular compounds. To obtain reliable and robust predictions for the reverse connections, we implemented six distinct connectivity methods. In the current version, PharmGWAS deposits a total of 740 227 genetically-informed disease-drug pairs derived from drug-perturbation signatures, presenting a valuable and comprehensive catalog. Further equipped with its user-friendly web design, PharmGWAS is expected to greatly aid the discovery of novel drugs, the exploration of drug combination therapies and the identification of drug resistance or side effects. PharmGWAS is available at https://ngdc.cncb.ac.cn/pharmgwas.

PMID:37831083 | DOI:10.1093/nar/gkad832

Aust N Z J Psychiatry. 2023 Oct 13:48674231203898. doi: 10.1177/00048674231203898. Online ahead of print.

ABSTRACT

In this paper, the case study of ketamine as a new treatment for severe depression is used to outline the challenges of repurposing established medicines and we suggest potential solutions. The antidepressant effects of generic racemic ketamine were identified over 20 years ago, but there were insufficient incentives for commercial entities to pursue its registration, or support for non-commercial entities to fill this gap. As a result, the evaluation of generic ketamine was delayed, piecemeal, uncoordinated, and insufficient to gain approval. Meanwhile, substantial commercial investment enabled the widespread registration of a patented, intranasal s-enantiomeric ketamine formulation (Spravato®) for depression. However, Spravato is priced at $600-$900/dose compared to ~$5/dose for generic ketamine, and the ~AUD$100 million annual government investment requested in Australia (to cover drug costs alone) has been rejected twice, leaving this treatment largely inaccessible for Australian patients 2 years after Therapeutic Goods Administration approval. Moreover, emerging evidence indicates that generic racemic ketamine is at least as effective as Spravato, but no comparative trials were required for regulatory approval and have not been conducted. Without action, this story will repeat regularly in the next decade with a new wave of psychedelic-assisted psychotherapy treatments, for which the original off-patent molecules could be available at low-cost and reduce the overall cost of treatment. Several systemic reforms are required to ensure that affordable, effective options become accessible; these include commercial incentives, public and public-private funding schemes, reduced regulatory barriers and more coordinated international public funding schemes to support translational research.

PMID:37830221 | DOI:10.1177/00048674231203898

Chem Sci. 2023 Aug 8;14(39):10684-10701. doi: 10.1039/d3sc02139d. eCollection 2023 Oct 11.

ABSTRACT

Traditional Chinese Medicine (TCM) has long been viewed as a precious source of modern drug discovery. AI-assisted drug discovery (AIDD) has been investigated extensively. However, there are still two challenges in applying AIDD to guide TCM drug discovery: the lack of a large amount of standardized TCM-related information and AIDD is prone to pathological failures in out-of-domain data. We have released TCM Database@Taiwan in 2011, and it has been widely disseminated and used. Now, we developed TCMBank, the largest systematic free TCM database, which is an extension of TCM Database@Taiwan. TCMBank contains 9192 herbs, 61 966 ingredients (unduplicated), 15 179 targets, 32 529 diseases, and their pairwise relationships. By integrating multiple data sources, TCMBank provides 3D structure information of ingredients and provides a standard list and detailed information on herbs, ingredients, targets and diseases. TCMBank has an intelligent document identification module that continuously adds TCM-related information retrieved from the literature in PubChem. In addition, driven by TCMBank big data, we developed an ensemble learning-based drug discovery protocol for identifying potential leads and drug repurposing. We take colorectal cancer and Alzheimer's disease as examples to demonstrate how to accelerate drug discovery by artificial intelligence. Using TCMBank, researchers can view literature-driven relationship mapping between herbs/ingredients and genes/diseases, allowing the understanding of molecular action mechanisms for ingredients and identification of new potentially effective treatments. TCMBank is available at https://TCMBank.CN/.

PMID:37829020 | PMC:PMC10566508 | DOI:10.1039/d3sc02139d