Arch Physiol Biochem. 2023 Jun 9:1-8. doi: 10.1080/13813455.2023.2219863. Online ahead of print.

ABSTRACT

Diabetes mellitus (DM) is a chronic metabolic condition linked to high blood sugar levels. Diabetes causes complications like neuropathy, nephropathy, and retinopathy. Diabetes foot ulcer (DFU) is a significant and serious wound healing issue resulting from uncontrolled DM. The main causes of the development of the DFU are oxidative stress brought on by the NO moiety, release of pro-inflammatory cytokines like tumour necrosis factor (TNF)-α and interleukin (IL-1), cellular dysfunction, and pathogenic microorganisms including staphylococcus and streptococcus species. The two main types of wounds that are prevalent in DFU patients are neuropathic and neuroischemic. If this wound is not properly treated or cared for, a lower limb may have to be amputated. There are several therapy options for DFU, including antibiotics, debridement, dressings, nano formulations, and growth factor preparations like PDGF-BB, to help the wound heal and prevent amputation. Other novel approaches involved the use of nerve taps, microneedle patches, nanotechnology-based formulations and stem cell applications to promote healing. There are possibilities of drug repurposing for the DFU treatment based on targeting specific enzymes. This article summarises the current pathophysiological aspects of DFU and its probable future targets.

PMID:37294861 | DOI:10.1080/13813455.2023.2219863

Laryngoscope. 2023 Jun 9. doi: 10.1002/lary.30768. Online ahead of print.

ABSTRACT

OBJECTIVES AND HYPOTHESIS: Vocal dysfunction, including hypophonia, in Parkinson disease (PD) manifests in the prodromal period and significantly impacts an individual's quality of life. Data from human studies suggest that pathology leading to vocal deficits may be structurally related to the larynx and its function. The Pink1-/- rat is a translational model used to study pathogenesis in the context of early-stage mitochondrial dysfunction. The primary objective of this work was to identify differentially expressed genes in the thyroarytenoid muscle and examine the dysregulated biological pathways in the female rat.

METHODS: RNA sequencing was used to determine thyroarytenoid (TA) muscle gene expression in adult female Pink1-/- rats compared with controls. A bioinformatic approach and the ENRICHR gene analysis tool were used to compare the sequencing dataset with biological pathways and processes, disease relationships, and drug-repurposing compounds. Weighted Gene Co-expression Network Analysis was used to construct biological network modules. The data were compared with a previously published dataset in male rats.

RESULTS: Significant upregulated pathways in female Pink1-/- rats included fatty acid oxidation and muscle contraction, synaptic transmission, and neuromuscular processes. Downregulated pathways included anterograde transsynaptic signaling, chemical synaptic transmission, and ion release. Several drug treatment options including cetuximab, fluoxetine, and resveratrol are hypothesized to reverse observed genetic dysregulation.

CONCLUSIONS: Data presented here are useful for identifying biological pathways that may underlie the mechanisms of peripheral dysfunction including neuromuscular synaptic transmission to the TA muscle. These experimental biomarkers have the potential to be targeted as sites for improving the treatment for hypophonia in early-stage PD.

LEVEL OF EVIDENCE: N/A Laryngoscope, 2023.

PMID:37293988 | DOI:10.1002/lary.30768

medRxiv. 2023 May 21:2023.05.15.23290002. doi: 10.1101/2023.05.15.23290002. Preprint.

ABSTRACT

Recently, computational drug repurposing has emerged as a promising method for identifying new pharmaceutical interventions (PI) for Alzheimer's Disease (AD). Non-pharmaceutical interventions (NPI), such as Vitamin E and Music therapy, have great potential to improve cognitive function and slow the progression of AD, but have largely been unexplored. This study predicts novel NPIs for AD through link prediction on our developed biomedical knowledge graph. We constructed a comprehensive knowledge graph containing AD concepts and various potential interventions, called ADInt, by integrating a dietary supplement domain knowledge graph, SuppKG, with semantic relations from SemMedDB database. Four knowledge graph embedding models (TransE, RotatE, DistMult and ComplEX) and two graph convolutional network models (R-GCN and CompGCN) were compared to learn the representation of ADInt. R-GCN outperformed other models by evaluating on the time slice test set and the clinical trial test set and was used to generate the score tables of the link prediction task. Discovery patterns were applied to generate mechanism pathways for high scoring triples. Our ADInt had 162,213 nodes and 1,017,319 edges. The graph convolutional network model, R-GCN, performed best in both the Time Slicing test set (MR = 7.099, MRR = 0.5007, Hits@1 = 0.4112, Hits@3 = 0.5058, Hits@10 = 0.6804) and the Clinical Trials test set (MR = 1.731, MRR = 0.8582, Hits@1 = 0.7906, Hits@3 = 0.9033, Hits@10 = 0.9848). Among high scoring triples in the link prediction results, we found the plausible mechanism pathways of (Photodynamic therapy, PREVENTS, Alzheimer's Disease) and (Choerospondias axillaris, PREVENTS, Alzheimer's Disease) by discovery patterns and discussed them further. In conclusion, we presented a novel methodology to extend an existing knowledge graph and discover NPIs (dietary supplements (DS) and complementary and integrative health (CIH)) for AD. We used discovery patterns to find mechanisms for predicted triples to solve the poor interpretability of artificial neural networks. Our method can potentially be applied to other clinical problems, such as discovering drug adverse reactions and drug-drug interactions.

PMID:37292731 | PMC:PMC10246059 | DOI:10.1101/2023.05.15.23290002

Adv Genet (Hoboken). 2023 Jan 17;4(2):2200024. doi: 10.1002/ggn2.202200024. eCollection 2023 Jun.

ABSTRACT

Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.

PMID:37288167 | PMC:PMC10242409 | DOI:10.1002/ggn2.202200024

Curr Med Chem. 2023 Jun 7. doi: 10.2174/0929867330666230607104441. Online ahead of print.

ABSTRACT

BACKGROUND: Prostate cancer is the most frequently diagnosed malignancy in 112 countries and is the leading cause of death in eighteen. In addition to continuing research on prevention and early diagnosis, improving treatments and making them more affordable is imperative. In this sense, the therapeutic repurposing of low-cost and widely available drugs could reduce global mortality from this disease. The malignant metabolic phenotype is becoming increasingly important due to its therapeutic implications. Cancer generally is characterized by hyperactivation of glycolysis, glutaminolysis, and fatty acid synthesis. However, prostate cancer is particularly lipidic; it exhibits increased activity in the pathways for synthesizing fatty acids, cholesterol, and fatty acid oxidation (FAO).

OBJECTIVE: Based on a literature review, we propose the PaSTe regimen (Pantoprazole, Simvastatin, Trimetazidine) as a metabolic therapy for prostate cancer. Pantoprazole and simvastatin inhibit the enzymes fatty acid synthase (FASN) and 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR), therefore, blocking the synthesis of fatty acids and cholesterol, respectively. In contrast, trimetazidine inhibits the enzyme 3-b-Ketoacyl-CoA thiolase (3-KAT), an enzyme that catalyzes the oxidation of fatty acids (FAO). It is known that the pharmacological or genetic depletion of any of these enzymes has antitumor effects in prostatic cancer.

RESULTS: Based on this information, we hypothesize that the PaSTe regimen will have increased antitumor effects and may impede the metabolic reprogramming shift. Existing knowledge shows that enzyme inhibition occurs at molar concentrations achieved in plasma at standard doses of these drugs.

CONCLUSION: We conclude that this regimen deserves to be preclinically evaluated because of its clinical potential for the treatment of prostate cancer.

PMID:37287286 | DOI:10.2174/0929867330666230607104441

Sci Rep. 2023 Jun 7;13(1):9238. doi: 10.1038/s41598-023-36438-0.

ABSTRACT

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

PMID:37286613 | DOI:10.1038/s41598-023-36438-0

Comput Biol Med. 2023 May 29;162:107095. doi: 10.1016/j.compbiomed.2023.107095. Online ahead of print.

ABSTRACT

Asthma is a chronic disease that is caused by a combination of genetic risks and environmental triggers and can affect both adults and children. Genome-wide association studies have revealed partly distinct genetic architectures for its two age-of-onset subtypes (namely, adult-onset and childhood-onset). We reason that identifying shared and distinct drug targets between these subtypes may inform the development of subtype-specific therapeutic strategies. In attempting this, we here introduce Priority Index for Asthma or PIA, a genetics-led and network-driven drug target prioritisation tool for asthma. We demonstrate the validity of the tool in improving drug target prioritisation for asthma compared to the status quo methods, as well as in capturing the underlying etiology and existing therapeutics for the disease. We also illustrate how PIA can be used to prioritise drug targets for adult- and childhood-onset asthma, as well as to identify shared and distinct pathway crosstalk genes. Shared crosstalk genes are mostly involved in JAK-STAT signaling, with clinical evidence supporting that targeting this pathway may be a promising drug repurposing opportunity for both subtypes. Crosstalk genes specific to childhood-onset asthma are enriched for PI3K-AKT-mTOR signaling, and we identify genes that are already targeted by licensed medications as repurposed drug candidates for this subtype. We make all our results accessible and reproducible at http://www.genetictargets.com/PIA. Collectively, our study has significant implications for asthma computational medicine research and can guide the future development of subtype-specific therapeutic strategies for the disease.

PMID:37285660 | DOI:10.1016/j.compbiomed.2023.107095

Front Pharmacol. 2023 May 22;14:1205144. doi: 10.3389/fphar.2023.1205144. eCollection 2023.

ABSTRACT

Introduction: Exploring the potential efficacy of a drug is a valid approach for drug development with shorter development times and lower costs. Recently, several computational drug repositioning methods have been introduced to learn multi-features for potential association prediction. However, fully leveraging the vast amount of information in the scientific literature to enhance drug-disease association prediction is a great challenge. Methods: We constructed a drug-disease association prediction method called Literature Based Multi-Feature Fusion (LBMFF), which effectively integrated known drugs, diseases, side effects and target associations from public databases as well as literature semantic features. Specifically, a pre-training and fine-tuning BERT model was introduced to extract literature semantic information for similarity assessment. Then, we revealed drug and disease embeddings from the constructed fusion similarity matrix by a graph convolutional network with an attention mechanism. Results: LBMFF achieved superior performance in drug-disease association prediction with an AUC value of 0.8818 and an AUPR value of 0.5916. Discussion: LBMFF achieved relative improvements of 31.67% and 16.09%, respectively, over the second-best results, compared to single feature methods and seven existing state-of-the-art prediction methods on the same test datasets. Meanwhile, case studies have verified that LBMFF can discover new associations to accelerate drug development. The proposed benchmark dataset and source code are available at: https://github.com/kang-hongyu/LBMFF.

PMID:37284317 | PMC:PMC10239876 | DOI:10.3389/fphar.2023.1205144

Infez Med. 2023 Jun 1;31(2):163-173. doi: 10.53854/liim-3102-4. eCollection 2023.

ABSTRACT

INTRODUCTION: Although fosfomycin is currently approved for treating urinary tract infections, it is increasingly being used as salvage therapy for various infectious syndromes outside the urinary tract. This systematic review evaluates clinical and microbiological cure rates in patients with bacterial infections not restricted to the urinary tract where fosfomycin was used off-label.

MATERIALS AND METHODS: Articles from two databases (Pubmed and Scopus) were reviewed. The dosage, route, and duration of fosfomycin therapy along with the details of adjunctive antimicrobial agents were noted. The final outcomes captured were clinical or microbiological cures.

RESULTS: A total of 649 articles, not including duplicates, were selected for the title and abstract screening. After title and abstract screening, 102 articles were kept for full-text screening. Of the 102 articles, 23 studies (n=1227 patients) were kept in the final analysis. Of the 1227 patients, 301 (25%) received fosfomycin as monotherapy, and the remaining 926 75%) received fosfomycin in combination with at least one other antimicrobial agent. Most of the patients received intravenous fosfomycin (n=1046, 85%). Staphylococcus spp and Enterobacteriaceae were the most common organisms. The pooled clinical and microbiological cure rates were 75% and 84%, respectively.

CONCLUSION: Fosfomycin has moderate clinical success in patients with non-urinary tract infections, especially when used with other antimicrobials. Due to the paucity of randomized controlled trials, fosfomycin's use should be limited to situations where no alternatives are supported by better clinical evidence.

PMID:37283634 | PMC:PMC10241401 | DOI:10.53854/liim-3102-4

J Stroke. 2023 May;25(2):266-271. doi: 10.5853/jos.2022.03230. Epub 2023 May 30.

ABSTRACT

BACKGROUND AND PURPOSE: We performed a two-sample Mendelian randomization (MR) analysis to evaluate the causal effect of genetically proxied AMP-activated protein kinase (AMPK) activation, which is the target of metformin, on functional outcome following ischemic stroke onset.

METHODS: A total of 44 AMPK-related variants associated with HbA1c (%) were used as instruments for AMPK activation. The primary outcome was the modified Rankin Scale (mRS) score at 3 months following the onset of ischemic stroke, evaluated as a dichotomous variable (3-6 vs. 0-2) and subsequently as an ordinal variable. Summary-level data for the 3-month mRS were obtained from the Genetics of Ischemic Stroke Functional Outcome network, including 6,165 patients with ischemic stroke. The inverse-variance weighted method was used to obtain causal estimates. The alternative MR methods were used for sensitivity analysis.

RESULTS: Genetically predicted AMPK activation was significantly associated with lower odds of poor functional outcome (mRS 3-6 vs. 0-2, odds ratio [OR]: 0.06, 95% confidence interval [CI]: 0.01-0.49, P=0.009). This association was maintained when 3-month mRS was analyzed as an ordinal variable. Similar results were observed in the sensitivity analyses, and there was no evidence of pleiotropy.

CONCLUSION: This MR study provided evidence that AMPK activation by metformin may exert beneficial effects on functional outcome following ischemic stroke.

PMID:37282373 | DOI:10.5853/jos.2022.03230

Mol Divers. 2023 Jun 6. doi: 10.1007/s11030-023-10666-y. Online ahead of print.

ABSTRACT

The global prevalence of obesity-related systemic disorders, including non-alcoholic fatty liver disease (NAFLD), and cancers are rapidly rising. Several of these disorders involve peroxisome proliferator-activated receptors (PPARs) as one of the key cell signaling pathways. PPARs are nuclear receptors that play a central role in lipid metabolism and glucose homeostasis. They can activate or suppress the genes responsible for inflammation, adipogenesis, and energy balance, making them promising therapeutic targets for treating metabolic disorders. In this study, an attempt has been made to screen novel PPAR pan-agonists from the ZINC database targeting the three PPAR family of receptors (α, γ, β/δ), using molecular docking and molecular dynamics (MD) simulations. The top scoring five ligands with strong binding affinity against all the three PPAR isoforms were eprosartan, canagliflozin, pralatrexate, sacubitril, olaparib. The ADMET analysis was performed to assess the pharmacokinetic profile of the top 5 molecules. On the basis of ADMET analysis, the top ligand was subjected to MD simulations, and compared with lanifibranor (reference PPAR pan-agonist). Comparatively, the top-scoring ligand showed better protein-ligand complex (PLC) stability with all the PPARs (α, γ, β/δ). When experimentally tested in in vitro cell culture model of NAFLD, eprosartan showed dose dependent decrease in lipid accumulation and oxidative damage. These outcomes suggest potential PPAR pan-agonist molecules for further experimental validation and pharmacological development, towards treatment of PPAR-mediated metabolic disorders.

PMID:37280404 | DOI:10.1007/s11030-023-10666-y

Sci Rep. 2023 Jun 6;13(1):9204. doi: 10.1038/s41598-023-35671-x.

ABSTRACT

The recent outbreak of the COVID-19 pandemic caused by severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) has shown the necessity for fast and broad drug discovery methods to enable us to react quickly to novel and highly infectious diseases. A well-known SARS-CoV-2 target is the viral main 3-chymotrypsin-like cysteine protease (Mpro), known to control coronavirus replication, which is essential for the viral life cycle. Here, we applied an interaction-based drug repositioning algorithm on all protein-compound complexes available in the protein database (PDB) to identify Mpro inhibitors and potential novel compound scaffolds against SARS-CoV-2. The screen revealed a heterogeneous set of 692 potential Mpro inhibitors containing known ones such as Dasatinib, Amodiaquine, and Flavin mononucleotide, as well as so far untested chemical scaffolds. In a follow-up evaluation, we used publicly available data published almost two years after the screen to validate our results. In total, we are able to validate 17% of the top 100 predictions with publicly available data and can furthermore show that predicted compounds do cover scaffolds that are yet not associated with Mpro. Finally, we detected a potentially important binding pattern consisting of 3 hydrogen bonds with hydrogen donors of an oxyanion hole within the active side of Mpro. Overall, these results give hope that we will be better prepared for future pandemics and that drug development will become more efficient in the upcoming years.

PMID:37280244 | DOI:10.1038/s41598-023-35671-x

Anticancer Agents Med Chem. 2023;23(10):1103. doi: 10.2174/187152062310230425170324.

NO ABSTRACT

PMID:37271990 | DOI:10.2174/187152062310230425170324

Br J Clin Pharmacol. 2023 Jun 5. doi: 10.1111/bcp.15814. Online ahead of print.

ABSTRACT

INTRODUCTION: The UK Prescribing Safety Assessment was modified for use in Australia and New Zealand (ANZ) as the Prescribing Skills Assessment (PSA). We investigated the implementation, student performance and acceptability of the ANZ PSA for final-year medical students.

METHODS: This study used a mixed-method approach involving student data (n=6440) for 2017-2019 (PSA overall score and eight domain sub-scores). Data were also aggregated by medical school and included student evaluation survey results. Quantitative data were analysed using descriptive and multivariate analyses. The pass rate was established by a modified Angoff method. Thematic analyses of open-ended survey comments were conducted.

RESULTS: The average pass rate was slightly higher in 2017 (89%) which used a different examination to 2018 (85%) and 2019 (86%). Little difference was identified between schools for the PSA overall performance or domain sub-scores. There was low intercorrelation between sub-scores. Most students provided positive feedback about the PSA regarding the interface and clarity of questions, but an average of 35% reported insufficient time for completion. Further, 70% on average felt unprepared by their school curricula for the PSA, which is in part explained by the low prescribing experience; 69% reported completing ≤10 prescriptions during training.

DISCUSSION: The ANZ PSA was associated with high pass rates and acceptability although student preparedness was highlighted as a concern for further investigation. We demonstrate how a collaboration of medical schools can adapt a medical education assessment resource (UK PSA) as a means for fulfilling an unmet need.

PMID:37276579 | DOI:10.1111/bcp.15814

Front Oncol. 2023 May 18;13:1198284. doi: 10.3389/fonc.2023.1198284. eCollection 2023.

ABSTRACT

Historically, the effort by academia and industry to develop new chemical entities into lifesaving drugs has limited success in meeting the demands of today's healthcare. Repurposing drugs that are originally approved by the United States Food and Drug Administration or by regulatory authorities around the globe is an attractive strategy to rapidly develop much-needed therapeutics for oncologic indications that extend from treating cancer to managing treatment-related complications. This review discusses computational approaches to harness existing drugs for new therapeutic use in oncology.

PMID:37274281 | PMC:PMC10233043 | DOI:10.3389/fonc.2023.1198284

Front Pharmacol. 2023 May 19;14:1137791. doi: 10.3389/fphar.2023.1137791. eCollection 2023.

ABSTRACT

Introduction: Preclinical studies have demonstrated the possible role of beta-adrenergic receptors in pancreatic ductal adenocarcinoma (PDAC) tumor invasion and migration. The current study aimed to explore the possible association between survival outcomes and beta-blocker (BB) exposure in patients with advanced PDAC. Methods: This retrospective single-center study included 182 patients with advanced PDAC. Clinical [age, sex, BMI, cardiovascular condition, presence (SBB) or absence (NSBB) of beta-1 selectivity of BB, exposure duration, and multimorbidity], oncological (stage and anticancer treatment regimen), and biological (renal and liver function) data were collected. The endpoints were overall survival (OS) and progression-free survival (PFS). Hazard ratios (HRs) and 95% confidence intervals (95% CIs) for survival outcomes associated with BB exposure were estimated using Cox regression model and propensity score (PS) methods. Results: Forty-one patients (22.5%) were exposed to BB. A total of 104 patients progressed (57.1%) to PDAC and 139 (76.4%) patients died at the end of follow-up (median, 320 days; IQR, 438.75 days). When compared to the non-exposed group, there was no increase in survival outcomes associated with BB use (OS: HR = 1.38, 95% CI = 0.80-2.39, p = 0.25; PFS: adjusted HR = 0.95, 95% CI = 0.48-1.88, p = 0.88). Similar results were obtained using the PS method. Compared to no BB usage, SBB use was associated with a significant decrease in OS (HR = 1.80, 95% CI = 1.16-2.80, p < 10-2). Conclusion: BB exposure was not associated with improved PDAC survival outcomes. Beta-1-selectivity was not independently associated with any differences.

PMID:37274119 | PMC:PMC10235451 | DOI:10.3389/fphar.2023.1137791

Front Pharmacol. 2023 May 18;14:1181711. doi: 10.3389/fphar.2023.1181711. eCollection 2023.

ABSTRACT

Diabetes mellitus (DM) increases the incidence of age-related cataracts. Currently, no medication is approved or known to delay clinical cataract progression. Using a novel approach based on AI, we searched for drugs with potential cataract surgery-suppressing effects. We developed a drug discovery strategy that combines AI-based potential candidate prediction among 2650 Food and Drug Administration (FDA)-approved drugs with clinical corroboration leveraging multicenter electronic health records (EHRs) of approximately 800,000 cataract patients from the TriNetX platform. Among the top-10 AI-predicted repurposed candidate drugs, we identified three DM diagnostic ICD code groups, such as cataract patients with type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM), or hyperglycemia, and conducted retrospective cohort analyses to evaluate the efficacy of these candidate drugs in reducing the risk of cataract extraction. Aspirin, melatonin, and ibuprofen were associated with a reduced 5-, 10-, and 20-year cataract extraction risk in all types of diabetes. Acetylcysteine was associated with a reduced 5-, 10-, and 20-year cataract extraction risk in T2DM and hyperglycemia but not in T1DM patient groups. The suppressive effects of aspirin, acetylcysteine, and ibuprofen waned over time, while those of melatonin became stronger in both genders. Thus, the four repositioned drugs have the potential to delay cataract progression in both genders. All four drugs share the ability to directly or indirectly inhibit cyclooxygenase-2 (COX-2), an enzyme that is increased by multiple cataractogenic stimuli.

PMID:37274099 | PMC:PMC10232753 | DOI:10.3389/fphar.2023.1181711

J Biomol Struct Dyn. 2023 Jun 5:1-16. doi: 10.1080/07391102.2023.2217923. Online ahead of print.

ABSTRACT

Drug repurposing is proved to be a groundbreaking concept in the field of cancer research, accelerating the pace of de novo drug discovery by investigating the anti-cancer activity of the already approved drugs. On the other hand, it got highly benefitted from the advancement in the in-silico tools and techniques, which are used to build up the initial "proof of concept" based on the drug-target interaction. Acalabrutinib (ACL) is a well-known drug for the treatment of hematological malignancies. But, the therapeutic ability of ACL against solid tumors is still unexplored. Thereby, the activity of ACL on breast cancer and lung cancer was evaluated utilizing different computational methods. A series of proteins such as VEGFR1, ALK, BCL2, CXCR-4, mTOR, AKT, PI3K, HER-2, and Estrogen receptors were selected based on their involvement in the progression of the breast as well as lung cancer. A multi-level computational study starting from protein-ligand docking to molecular dynamic (MD) simulations were performed to detect the binding potential of ACL towards the selected proteins. Results of the study led to the identification of ACL as a ligand that showed a high docking score and binding energy with HER-2, mTOR, and VEGFR-1 successively. Whereas, the MD simulations study has also shown good docked complex stability of ACL with HER2 and VEGFR1. Our findings suggest that interaction with those receptors can lead to preventive action on both breast and lung cancer, thus it can be concluded that ACL could be a potential molecule for the same purpose.Communicated by Ramaswamy H. Sarma.

PMID:37272883 | DOI:10.1080/07391102.2023.2217923

Curr Med Chem. 2023;30(35):3941. doi: 10.2174/092986733035230504164038.

NO ABSTRACT

PMID:37271987 | DOI:10.2174/092986733035230504164038

Transl Oncol. 2023 Jun 2;34:101707. doi: 10.1016/j.tranon.2023.101707. Online ahead of print.

ABSTRACT

Chemoresistance is a major obstacle in the clinical management of metastatic, castration-resistant prostate cancer (PCa). It is imperative to develop novel strategies to overcome chemoresistance and improve clinical outcomes in patients who have failed chemotherapy. Using a two-tier phenotypic screening platform, we identified bromocriptine mesylate as a potent and selective inhibitor of chemoresistant PCa cells. Bromocriptine effectively induced cell cycle arrest and activated apoptosis in chemoresistant PCa cells but not in chemoresponsive PCa cells. RNA-seq analyses revealed that bromocriptine affected a subset of genes implicated in the regulation of the cell cycle, DNA repair, and cell death. Interestingly, approximately one-third (50/157) of the differentially expressed genes affected by bromocriptine overlapped with known p53-p21- retinoblastoma protein (RB) target genes. At the protein level, bromocriptine increased the expression of dopamine D2 receptor (DRD2) and affected several classical and non-classical dopamine receptor signal pathways in chemoresistant PCa cells, including adenosine monophosphate-activated protein kinase (AMPK), p38 mitogen-activated protein kinase (p38 MAPK), nuclear factor kappa B (NF-κB), enhancer of zeste homolog 2 (EZH2), and survivin. As a monotherapy, bromocriptine treatment at 15 mg/kg, three times per week, via the intraperitoneal route significantly inhibited the skeletal growth of chemoresistant C4-2B-TaxR xenografts in athymic nude mice. In summary, these results provided the first preclinical evidence that bromocriptine is a selective and effective inhibitor of chemoresistant PCa. Due to its favorable clinical safety profiles, bromocriptine could be rapidly tested in PCa patients and repurposed as a novel subtype-specific treatment to overcome chemoresistance.

PMID:37271121 | DOI:10.1016/j.tranon.2023.101707