Pharmaceuticals (Basel). 2022 Feb 9;15(2):208. doi: 10.3390/ph15020208.

ABSTRACT

Cisplatin is a well-known chemotherapeutic agent used to treat various types of cancers; however, it can also induce anorexia, which results in reduced food intake, loss of body weight, and lower quality of life. Although drugs such as megestrol acetate and cyproheptadine are used to decrease this severe feeding disorder, they can also induce side effects, such as diarrhea and somnolence, which limit their widespread use. Various types of herbal medicines have long been used to prevent and treat numerous gastrointestinal tract diseases; however, to date, no study has been conducted to analyze and summarize their effects on cisplatin-induced anorexia. In this paper, we analyze 12 animal studies that used either a single herbal medicine extract or mixtures thereof to decrease cisplatin-induced anorexia. Among the herbal medicines, Ginseng Radix was the most used, as it was included in seven studies, whereas both Glycyrrhizae Radix et Rhizoma and Angelicae Gigantis Radix were used in four studies. As for the mechanisms of action, the roles of serotonin and its receptors, cytokines, white blood cells, ghrelin, and leptin were investigated. Based on these results, we suggest that herbal medicines could be considered a useful treatment method for cisplatin-induced anorexia.

PMID:35215322 | DOI:10.3390/ph15020208

Pharmaceuticals (Basel). 2022 Jan 21;15(2):124. doi: 10.3390/ph15020124.

ABSTRACT

In the field of drug repurposing, the use of statins for treating dyslipidemia is considered promising in ovarian cancer treatment based on epidemiological studies and basic research findings. Biomarkers should be established to identify patients who will respond to statin treatment to achieve clinical application. In the present study, we demonstrated that statins have a multifaceted mode of action in ovarian cancer and involve pathways other than protein prenylation. To identify biomarkers that predict the response to statins, we subjected ovarian cancer cells to microarray analysis and calculated Pearson's correlation coefficients between gene expression and cell survival after statin treatment. The results showed that VDAC1 and LDLRAP1 were positively and negatively correlated with the response to statins, respectively. Histoculture drug response assays revealed that statins were effective in clinical samples. We also confirmed the synergistic effects of statins with paclitaxel and panobinostat and determined that statins are hematologically safe to administer to statin-treated mice. Future clinical trials based on the expression of the biomarkers identified in this study for repurposing statins for ovarian cancer treatment are warranted.

PMID:35215239 | DOI:10.3390/ph15020124

Pathogens. 2022 Feb 19;11(2):270. doi: 10.3390/pathogens11020270.

ABSTRACT

BACKGROUND: Obesity is a risk factor for the development of influenza by leading to a chronic inflammatory state and T-cell dysfunction. Based upon preclinical research, metformin has influenza activity by restoring T-cell function and improving the immune response.

OBJECTIVE: We aimed to evaluate the potential drug repurposing of metformin for the management of influenza among patients with obesity utilizing national claims data in an electronic health record database.

METHODS: The VA Informatics and Computing Infrastructure (VINCI) was utilized to obtain individual-level information on demographics, administrative claims, and pharmacy dispensation. A cohort was created among individuals with laboratory confirmed diagnosis of influenza with a diagnosis of fever, cough, influenza, or acute upper respiratory infection in an outpatient setting. The study outcome was death after diagnosis of influenza. Cohorts were formed using diabetes status and metformin exposure prior to a positive influenza diagnosis. Hazard ratios for mortality were estimated using a cox proportional hazards model adjusting for baseline covariates and a sub-analysis was conducted utilizing propensity score matching. A greedy nearest neighbor algorithm was utilized to match 1 to 1 non-metformin diabetic controls and non-diabetic controls to diabetic patients receiving metformin.

RESULTS: A total of 3551 patients met the inclusion criteria and were evaluated in our study. The cohorts consisted of 1461 patients in the non-diabetic cohort, 1597 patients in the diabetic / metformin cohort, and 493 patients in the diabetic no metformin cohort. Compared to non-diabetic patients, diabetic patients with metformin had a lower rate of death (aHR 0.78, 95% CI 0.609-0.999). There was not a statistical difference between the non-diabetic patients and the diabetic patients without metformin (aHR 1.046, 95% CI 0.781-1.400). The propensity score matched cohorts revealed consistent results with the primary analysis.

CONCLUSION: Our results demonstrated patients with obesity and a history of metformin treatment have lower influenza mortality.

PMID:35215211 | DOI:10.3390/pathogens11020270

Pathogens. 2022 Feb 17;11(2):259. doi: 10.3390/pathogens11020259.

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is accountable for the protracted COVID-19 pandemic. Its high transmission rate and pathogenicity led to health emergencies and economic crisis. Recent studies pertaining to the understanding of the molecular pathogenesis of SARS-CoV-2 infection exhibited the indispensable role of ion channels in viral infection inside the host. Moreover, machine learning (ML)-based algorithms are providing a higher accuracy for host-SARS-CoV-2 protein-protein interactions (PPIs). In this study, PPIs of SARS-CoV-2 proteins with human ion channels (HICs) were trained on the PPI-MetaGO algorithm. PPI networks (PPINs) and a signaling pathway map of HICs with SARS-CoV-2 proteins were generated. Additionally, various U.S. food and drug administration (FDA)-approved drugs interacting with the potential HICs were identified. The PPIs were predicted with 82.71% accuracy, 84.09% precision, 84.09% sensitivity, 0.89 AUC-ROC, 65.17% Matthews correlation coefficient score (MCC) and 84.09% F1 score. Several host pathways were found to be altered, including calcium signaling and taste transduction pathway. Potential HICs could serve as an initial set to the experimentalists for further validation. The study also reinforces the drug repurposing approach for the development of host directed antiviral drugs that may provide a better therapeutic management strategy for infection caused by SARS-CoV-2.

PMID:35215201 | DOI:10.3390/pathogens11020259

Pharmaceutics. 2022 Feb 10;14(2):391. doi: 10.3390/pharmaceutics14020391.

ABSTRACT

The transcriptional regulators YAP (Yes-associated protein) and TAZ (transcriptional co-activator with PDZ-binding motif) are the major downstream effectors in the Hippo pathway and are involved in cancer progression through modulation of the activity of TEAD (transcriptional enhanced associate domain) transcription factors. To exploit the advantages of drug repurposing in the search of new drugs, we developed a similar approach for the identification of new hits interfering with TEAD target gene expression. In our study, a 27-member in-house library was assembled, characterized, and screened for its cancer cell growth inhibition effect. In a secondary luciferase-based assay, only seven compounds confirmed their specific involvement in TEAD activity. IA5 bearing a p-quinoid structure reduced the cytoplasmic level of phosphorylated YAP and the YAP-TEAD complex transcriptional activity and reduced cancer cell growth. IA5 is a promising hit compound for TEAD activity modulator development.

PMID:35214125 | DOI:10.3390/pharmaceutics14020391

Pharmaceutics. 2022 Jan 20;14(2):234. doi: 10.3390/pharmaceutics14020234.

ABSTRACT

Identification of the correct targets is a key element for successful drug development. However, there are limited approaches for predicting drug targets for specific diseases using omics data, and few have leveraged expression profiles from gene perturbations. We present a novel computational approach for drug target discovery based on machine learning (ML) models. ML models are first trained on drug-induced expression profiles with outcomes defined as whether the drug treats the studied disease. The goal is to "learn" the expression patterns associated with treatment. Then, the fitted ML models were applied to expression profiles from gene perturbations (overexpression (OE)/knockdown (KD)). We prioritized targets based on predicted probabilities from the ML model, which reflects treatment potential. The methodology was applied to predict targets for hypertension, diabetes mellitus (DM), rheumatoid arthritis (RA), and schizophrenia (SCZ). We validated our approach by evaluating whether the identified targets may 're-discover' known drug targets from an external database (OpenTargets). Indeed, we found evidence of significant enrichment across all diseases under study. A further literature search revealed that many candidates were supported by previous studies. For example, we predicted PSMB8 inhibition to be associated with the treatment of RA, which was supported by a study showing that PSMB8 inhibitors (PR-957) ameliorated experimental RA in mice. In conclusion, we propose a new ML approach to integrate the expression profiles from drugs and gene perturbations and validated the framework. Our approach is flexible and may provide an independent source of information when prioritizing drug targets.

PMID:35213968 | DOI:10.3390/pharmaceutics14020234

PLoS Comput Biol. 2022 Feb 25;18(2):e1009909. doi: 10.1371/journal.pcbi.1009909. Online ahead of print.

ABSTRACT

Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two case studies showing how one can deconvolute the predictions made by RPath as well as predict novel targets.

PMID:35213534 | DOI:10.1371/journal.pcbi.1009909

BMC Cancer. 2022 Feb 24;22(1):205. doi: 10.1186/s12885-022-09272-2.

ABSTRACT

BACKGROUND: Cancer-associated fibroblasts (CAFs) are an important component of the tumour microenvironment. Recent studies revealed CAFs are heterogeneous and CAF subset(s) that suppress cancer progression (cancer-restraining CAFs [rCAFs]) must exist in addition to well-characterised cancer-promoting CAFs (pCAFs). However, the identity and specific markers of rCAFs are not yet reported. We recently identified Meflin as a specific marker of rCAFs in pancreatic and colon cancers. Our studies revealed that rCAFs may represent proliferating resident fibroblasts. Interestingly, a lineage tracing experiment showed Meflin-positive rCAFs differentiate into α-smooth muscle actin-positive and Meflin-negative CAFs, which are generally hypothesised as pCAFs, during cancer progression. Using a pharmacological approach, we identified AM80, a synthetic unnatural retinoid, as a reagent that effectively converts Meflin-negative pCAFs to Meflin-positive rCAFs. We aimed to investigate the efficacy of a combination of AM80 and gemcitabine (GEM) and nab-paclitaxel (nab-PTX) in patients with advanced pancreatic cancer.

METHODS: The phase I part is a 3 + 3 design, open-label, and dose-finding study. The dose-limiting toxicity (DLT) of these combination therapies would be evaluated for 4 weeks. After the DLT evaluation period, if no disease progression is noted based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 or if the patient has no intolerable toxicity, administration of AM80 with GEM and nab-PTX would be continued for up to 24 weeks. The phase II part is an open-label, single-arm study. The maximum tolerated dose (MTD) of AM80 with GEM and nab-PTX, determined in phase I, would be administered until intolerable toxicity or disease progression occurs, up to a maximum of 24 weeks, to confirm efficacy and safety. The primary endpoints are frequency of DLT and MTD of AM80 with GEM and nab-PTX in the phase I part and response rate based on the RECIST in the phase II part. Given the historical control data, we hope that the response rate will be over 23% in phase II.

DISCUSSION: Strategies to convert pCAFs into rCAFs have been developed in recent years. We hypothesised that AM80 would be a promising enhancer of chemosensitivity and drug distribution through CAF conversion in the stroma.

TRIAL REGISTRATION: Clinicaltrial.gov: NCT05064618 , registered on 1 October 2021. jRCT: jRCT2041210056 , registered on 27 August 2021.

PMID:35209871 | DOI:10.1186/s12885-022-09272-2

Molecules. 2022 Feb 19;27(4):1414. doi: 10.3390/molecules27041414.

ABSTRACT

Schistosomiasis is a neglected tropical disease affecting more than 200 million people worldwide. Chemotherapy relies on one single drug, praziquantel, which is safe but ineffective at killing larval stages of this parasite. Furthermore, concerns have been expressed about the rise in resistance against this drug. In the absence of an antischistosomal vaccine, it is, therefore, necessary to develop new drugs against the different species of schistosomes. Protein kinases are important molecules involved in key cellular processes such as signaling, growth, and differentiation. The kinome of schistosomes has been studied and the suitability of schistosomal protein kinases as targets demonstrated by RNA interference studies. Although protein kinase inhibitors are mostly used in cancer therapy, e.g., for the treatment of chronic myeloid leukemia or melanoma, they are now being increasingly explored for the treatment of non-oncological conditions, including schistosomiasis. Here, we discuss the various approaches including screening of natural and synthetic compounds, de novo drug development, and drug repurposing in the context of the search for protein kinase inhibitors against schistosomiasis. We discuss the status quo of the development of kinase inhibitors against schistosomal serine/threonine kinases such as polo-like kinases (PLKs) and mitogen-activated protein kinases (MAP kinases), as well as protein tyrosine kinases (PTKs).

PMID:35209202 | DOI:10.3390/molecules27041414

Molecules. 2022 Feb 18;27(4):1404. doi: 10.3390/molecules27041404.

ABSTRACT

Drug repurposing identifies new clinical indications for existing drugs. It can be used to overcome common problems associated with cancers, such as heterogeneity and resistance to established therapies, by rapidly adapting known drugs for new treatment. In this study, we utilized a recommendation system learning model to prioritize candidate cancer drugs. We designed a drug-drug pathway functional similarity by integrating multiple genetic and epigenetic alterations such as gene expression, copy number variation (CNV), and DNA methylation. When compared with other similarities, such as SMILES chemical structures and drug targets based on the protein-protein interaction network, our approach provided better interpretable models capturing drug response mechanisms. Furthermore, our approach can achieve comparable accuracy when evaluated with other learning models based on large public datasets (CCLE and GDSC). A case study about the Erlotinib and OSI-906 (Linsitinib) indicated that they have a synergistic effect to reduce the growth rate of tumors, which is an alternative targeted therapy option for patients. Taken together, our computational method characterized drug response from the viewpoint of a multi-omics pathway and systematically predicted candidate cancer drugs with similar therapeutic effects.

PMID:35209193 | DOI:10.3390/molecules27041404

Molecules. 2022 Feb 14;27(4):1283. doi: 10.3390/molecules27041283.

ABSTRACT

Bacteria expressing New Delhi metallo-β-lactamase-1 (NDM-1) can hydrolyze β-lactam antibiotics (penicillins, cephalosporins, and carbapenems) and, thus, mediate multidrug resistance. The worldwide dissemination of NDM-1 poses a serious threat to public health, imposing a huge economic burden in the development of new antibiotics. Thus, there is an urgent need for the identification of novel NDM-1 inhibitors from a pool of already-known drug molecules. Here, we screened a library of FDA-approved drugs to identify novel non-β-lactam ring-containing inhibitors of NDM-1 by applying computational as well as in vitro experimental approaches. Different steps of high-throughput virtual screening, molecular docking, molecular dynamics simulation, and enzyme kinetics were performed to identify risedronate and methotrexate as the inhibitors with the most potential. The molecular mechanics/generalized Born surface area (MM/GBSA) and molecular dynamics (MD) simulations showed that both of the compounds (risedronate and methotrexate) formed a stable complex with NDM-1. Furthermore, analyses of the binding pose revealed that risedronate formed two hydrogen bonds and three electrostatic interactions with the catalytic residues of NDM-1. Similarly, methotrexate formed four hydrogen bonds and one electrostatic interaction with NDM-1's active site residues. The docking scores of risedronate and methotrexate for NDM-1 were -10.543 kcal mol-1 and -10.189 kcal mol-1, respectively. Steady-state enzyme kinetics in the presence of risedronate and methotrexate showed a decreased catalytic efficiency (i.e., kcat/Km) of NDM-1 on various antibiotics, owing to poor catalytic proficiency and affinity. The results were further validated by determining the MICs of imipenem and meropenem in the presence of risedronate and methotrexate. The IC50 values of the identified inhibitors were in the micromolar range. The findings of this study should be helpful in further characterizing the potential of risedronate and methotrexate to treat bacterial infections.

PMID:35209073 | DOI:10.3390/molecules27041283

Cancers (Basel). 2022 Feb 20;14(4):1067. doi: 10.3390/cancers14041067.

ABSTRACT

The dysregulation of PI3K, HDACs, and MYCN are well known for promoting multiple cancer types, including neuroblastoma (NB). Targeting the upstream regulators of MYCN, including HDACs and PI3K, was shown to suppress cancer growth. In the present study, we analyze different NB patient datasets to reveal that high PI3K and HDAC expression is correlated with overall poor NB patient survival. High PI3K level is also found to be associated with high MYCN level and NB stage progression. We repurpose a dual inhibitor CUDC-907 as a single agent to directly target both PI3K and HDAC in NB. We use in vitro methodologies to determine the efficacy and selectivity of CUDC-907 using six NB and three control fibroblast cell lines. Our results show that CUDC-907 significantly inhibits NB proliferation and colony growth, induces apoptosis, blocks cell cycle progression, inhibits MYCN, and enhances H3K9Ac levels by inhibiting the PI3K/AKT signaling pathway and HDAC function. Furthermore, CUDC-907 significantly inhibits NB tumor growth in a 3D spheroid tumor model that recapitulates the in vivo tumor growth. Overall, our findings highlight that the dual inhibition of PI3K and HDAC by CUDC-907 is an effective therapeutic strategy for NB and other MYC-dependent cancers.

PMID:35205815 | DOI:10.3390/cancers14041067

Cancers (Basel). 2022 Feb 18;14(4):1043. doi: 10.3390/cancers14041043.

ABSTRACT

Colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide. Perhexiline, a prophylactic anti-anginal drug, has been reported to have anti-tumour effects both in vitro and in vivo. Perhexiline as used clinically is a 50:50 racemic mixture ((R)-P) of (-) and (+) enantiomers. It is not known if the enantiomers differ in terms of their effects on cancer. In this study, we examined the cytotoxic capacity of perhexiline and its enantiomers ((-)-P and (+)-P) on CRC cell lines, grown as monolayers or spheroids, and patient-derived organoids. Treatment of CRC cell lines with (R)-P, (-)-P or (+)-P reduced cell viability, with IC50 values of ~4 µM. Treatment was associated with an increase in annexin V staining and caspase 3/7 activation, indicating apoptosis induction. Caspase 3/7 activation and loss of structural integrity were also observed in CRC cell lines grown as spheroids. Drug treatment at clinically relevant concentrations significantly reduced the viability of patient-derived CRC organoids. Given these in vitro findings, perhexiline, as a racemic mixture or its enantiomers, warrants further investigation as a repurposed drug for use in the management of CRC.

PMID:35205791 | DOI:10.3390/cancers14041043

Genes (Basel). 2022 Jan 29;13(2):271. doi: 10.3390/genes13020271.

ABSTRACT

BACKGROUND: Cholangiocarcinoma (CCA) has a complex immune microenvironment architecture, thus possessing challenges in its characterization and treatment. This study aimed to repurpose FDA-approved drugs for cholangiocarcinoma by transcriptomic-driven bioinformatic approach.

METHODS: Cox-proportional univariate regression was applied to 3017 immune-related genes known a priori to identify a list of mortality-associated genes, so-called immune-oncogenic gene signature, in CCA tumor-derived RNA-seq profiles of two independent cohorts. Unsupervised clustering stratified CCA tumors into two groups according to the immune-oncogenic gene signature expression, which then confirmed its clinical relevance by Kaplan-Meier curve. Molecularly guided drug repurposing was performed by an integrative connectivity map-prioritized drug-gene network analysis.

RESULTS: The immune-oncogenic gene signature consists of 26 mortality-associated immune-related genes. Patients with high-expression signature had a poorer overall survival (log-rank p < 0.001), while gene enrichment analysis revealed cell-cycle checkpoint regulation and inflammatory-immune response signaling pathways affected this high-risk group. The integrative drug-gene network identified eight FDA-approved drugs as promising candidates, including Dasatinib a multi-kinase inhibitor currently investigated for advanced CCA with isocitrate-dehydrogenase mutations.

CONCLUSION: This study proposes the use of the immune-oncogenic gene signature to identify high-risk CCA patients. Future preclinical and clinical studies are required to elucidate the therapeutic efficacy of the molecularly guided drugs as the adjunct therapy, aiming to improve the survival outcome.

PMID:35205315 | DOI:10.3390/genes13020271

Biomolecules. 2022 Jan 24;12(2):196. doi: 10.3390/biom12020196.

ABSTRACT

Alzheimer's disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States and incurring a substantial global healthcare cost. Unfortunately, current treatments are only palliative and do not cure AD. There is an urgent need to develop novel anti-AD therapies; however, drug discovery is a time-consuming, expensive, and high-risk process. Drug repositioning, on the other hand, is an attractive approach to identify drugs for AD treatment. Thus, we developed a novel deep learning method called DOTA (Drug repositioning approach using Optimal Transport for Alzheimer's disease) to repurpose effective FDA-approved drugs for AD. Specifically, DOTA consists of two major autoencoders: (1) a multi-modal autoencoder to integrate heterogeneous drug information and (2) a Wasserstein variational autoencoder to identify effective AD drugs. Using our approach, we predict that antipsychotic drugs with circadian effects, such as quetiapine, aripiprazole, risperidone, suvorexant, brexpiprazole, olanzapine, and trazadone, will have efficacious effects in AD patients. These drugs target important brain receptors involved in memory, learning, and cognition, including serotonin 5-HT2A, dopamine D2, and orexin receptors. In summary, DOTA repositions promising drugs that target important biological pathways and are predicted to improve patient cognition, circadian rhythms, and AD pathogenesis.

PMID:35204697 | DOI:10.3390/biom12020196

Biomolecules. 2022 Jan 23;12(2):190. doi: 10.3390/biom12020190.

ABSTRACT

Despite the progressive research and recent advances in drug therapy to treat solid tumours, the number of cases and deaths in patients with cancer is still a major health problem. Drug repurposing coupled to drug combination strategies has been gaining interest among the scientific community. Recently, our group proposed novel drug combinations for breast and colon cancer using repurposed drugs from different classes (antimalarial and central nervous system (CNS)) and chemotherapeutic agents such as 5-fluorouracil (5-FU), paclitaxel (PTX), and found promising results. Here, we proposed a novel drug combination using different CNS drugs and doxorubicin (DOX), an antineoplastic used in breast cancer therapy, and studied their anticancer potential in MCF-7 breast cancer cells. Cells were treated with each drug alone and combined with increasing concentrations of DOX and cell viability was evaluated by MTT and SRB assays. Studies were also complemented with morphological evaluation. Assessment of drug interaction was performed using the CompuSyn and SynergyFinder software. We also compiled our previously studied drug pairs and selected the most promising ones for evaluation of the expression of EMT biomarkers (E-cadherin, P-cadherin, vimentin, and β-catenin) by immunohistochemistry (IHC) to assess if these drug combinations affect the expression of these proteins and eventually revert EMT. These results demonstrate that combination of DOX plus fluoxetine, benztropine, and thioridazine at their IC50 can improve the anticancer effect of DOX but to a lesser degree than when combined with PTX (previous results), resulting in most of the drug interactions being antagonist or additive. This suggests that the choice of the antineoplastic drug influences the success of the drug combination. Collectively, these results also allow us to conclude that antimalarial drugs as repurposed drugs have enhanced effects in MCF-7 breast cancer cells, while combination with CNS drugs seems to be more effective in HT-29 colon cancer cells. The IHC results demonstrate that combination treatments increase E-cadherin expression while reducing P-cadherin, vimentin, and β-catenin, suggesting that these treatments could induce EMT reversal. Taken together, these results could provide promising approaches to the design of novel drug combinations to treat breast and colon cancer patients.

PMID:35204691 | DOI:10.3390/biom12020190

Biomolecules. 2022 Jan 22;12(2):188. doi: 10.3390/biom12020188.

ABSTRACT

The sudden outbreak and worldwide spread of the SARS-CoV-2 pandemic pushed the scientific community to find fast solutions to cope with the health emergency. COVID-19 complexity, in terms of clinical outcomes, severity, and response to therapy suggested the use of multifactorial strategies, characteristic of the network medicine, to approach the study of the pathobiology. Proteomics and interactomics especially allow to generate datasets that, reduced and represented in the forms of networks, can be analyzed with the tools of systems biology to unveil specific pathways central to virus-human host interaction. Moreover, artificial intelligence tools can be implemented for the identification of druggable targets and drug repurposing. In this review article, we provide an overview of the results obtained so far, from a systems biology perspective, in the understanding of COVID-19 pathobiology and virus-host interactions, and in the development of disease classifiers and tools for drug repurposing.

PMID:35204689 | DOI:10.3390/biom12020188

Physiol Res. 2021 Dec 30;70(Suppl4):S715-S722.

ABSTRACT

Development of a new dug is a very lengthy and highly expensive process since only preclinical, pharmacokinetic, pharmacodynamic and toxicological studies include a multiple of in silico, in vitro, in vivo experimentations that traditionally last several years. In the present review, we briefly report some examples that demonstrate the power of the computer-assisted drug discovery process with some examples that are published and revealing the successful applications of artificial intelligence (AI) technology on this vivid area. Besides, we address the situation of drug repositioning (repurposing) in clinical applications. Yet few success stories in this regard that provide us with a clear evidence that AI will reveal its great potential in accelerating effective new drug finding. AI accelerates drug repurposing and AI approaches are altogether necessary and inevitable tools in new medicine development. In spite of the fact that AI in drug development is still in its infancy, the advancements in AI and machine-learning (ML) algorithms have an unprecedented potential. The AI/ML solutions driven by pharmaceutical scientists, computer scientists, statisticians, physicians and others are increasingly working together in the processes of drug development and are adopting AI-based technologies for the rapid discovery of medicines. AI approaches, coupled with big data, are expected to substantially improve the effectiveness of drug repurposing and finding new drugs for various complex human diseases.

PMID:35199553

Oncotarget. 2022 Feb 18;13:408-424. doi: 10.18632/oncotarget.28191. eCollection 2022.

ABSTRACT

Glucocorticoids (Gcs) are widely used to treat inflammatory diseases and hematological malignancies, and despite the introduction of novel anti-inflammatory and anti-cancer biologics, the use of inexpensive and effective Gcs is expected to grow. Unfortunately, chronic treatment with Gcs results in multiple atrophic and metabolic side effects. Thus, the search for safer glucocorticoid receptor (GR)-targeted therapies that preserve therapeutic potential of Gcs but result in fewer adverse effects remains highly relevant. Development of selective GR agonists/modulators (SEGRAM) with reduced side effects, based on the concept of dissociation of GR transactivation and transrepression functions, resulted in limited success, and currently focus has shifted towards partial GR agonists. Additional approach is the identification and inhibition of genes associated with Gcs specific side effects. Others and we recently identified GR target genes REDD1 and FKBP51 as key mediators of Gcs-induced atrophy, and selected and validated candidate molecules for REDD1 blockage including PI3K/Akt/mTOR inhibitors. In this review, we summarized classic and contemporary approaches to safer GR-mediated therapies including unique concept of Gcs combination with REDD1 inhibitors. We discussed protective effects of REDD1 inhibitors against Gcs-induced atrophy in skin and bone and underlined the translational potential of this combination for further development of safer and effective Gcs-based therapies.

PMID:35198100 | PMC:PMC8858080 | DOI:10.18632/oncotarget.28191

Sci Rep. 2022 Feb 23;12(1):3080. doi: 10.1038/s41598-022-07151-1.

ABSTRACT

Fibrosis is a leading cause of morbidity and mortality worldwide. Although fibrosis may involve different organ systems, transforming growth factor-β (TGFβ) has been established as a master regulator of fibrosis across organs. Pirfenidone and Nintedanib are the only currently-approved drugs to treat fibrosis, specifically idiopathic pulmonary fibrosis, but their mechanisms of action remain poorly understood. To identify novel drug targets and uncover potential mechanisms by which these drugs attenuate fibrosis, we performed an integrative 'omics analysis of transcriptomic and proteomic responses to TGFβ1-stimulated lung fibroblasts. Significant findings were annotated as associated with pirfenidone and nintedanib treatment in silico via Coremine. Integrative 'omics identified a co-expressed transcriptomic and proteomic module significantly correlated with TGFβ1 treatment that was enriched (FDR-p = 0.04) with genes associated with pirfenidone and nintedanib treatment. While a subset of genes in this module have been implicated in fibrogenesis, several novel TGFβ1 signaling targets were identified. Specifically, four genes (BASP1, HSD17B6, CDH11, and TNS1) have been associated with pirfenidone, while five genes (CLINT1, CADM1, MTDH, SYDE1, and MCTS1) have been associated with nintedanib, and MYDGF has been implicated with treatment using both drugs. Using the Clue Drug Repurposing Hub, succinic acid was highlighted as a metabolite regulated by the protein encoded by HSD17B6. This study provides new insights into the anti-fibrotic actions of pirfenidone and nintedanib and identifies novel targets for future mechanistic studies.

PMID:35197532 | DOI:10.1038/s41598-022-07151-1