Deciphering resistance mechanisms and novel strategies to overcome drug resistance in ovarian cancer: a comprehensive review.

Ovarian cancer is among the most lethal gynecological cancers, primarily due to the lack of specific symptoms leading to an advanced-stage diagnosis and resistance to chemotherapy. Drug resistance (DR) poses the most significant challenge in treating patients with existing drugs. The Food and Drug Administration (FDA) has recently approved three new therapeutic drugs, including two poly (ADP-ribose) polymerase (PARP) inhibitors (olaparib and niraparib) and one vascular endothelial growth factor (VEGF) inhibitor (bevacizumab) for maintenance therapy. However, resistance to these new drugs has emerged. Therefore, understanding the mechanisms of DR and exploring new approaches to overcome them is crucial for effective management. In this review, we summarize the major molecular mechanisms of DR and discuss novel strategies to combat DR.

The Application of Artificial Intelligence and Drug Repositioning for the Identification of Fibroblast Growth Factor Receptor Inhibitors: A Review.

Artificial intelligence talks about modeling intelligent behavior through a computer with the least human involvement. Drug repositioning techniques based on artificial intelligence accelerate the research process and decrease the cost of experimental studies. Dysregulation of fibroblast growth factor (FGF) receptors as the tyrosine kinase family of receptors plays a vital role in a wide range of malignancies. Because of their functional significance, they were considered promising drug targets for the therapy of various cancers. This review has summarized small molecules capable of inhibiting FGF receptors that progressed using artificial intelligence and repositioning drugs examined in clinical trials associated with cancer therapy. This review is based on a literature search in PubMed, Web of Science, Scopus EMBASE, and Google Scholar databases to gather the necessary information in each chapter by employing keywords like artificial intelligence, computational drug design, drug repositioning, and FGF receptor inhibitors. To achieve this goal, a spacious literature review of human studies in these fields-published over the last 20 decades-was performed. According to published reports, nonselective FGF receptor inhibitors can be used for cancer management, and multitarget kinase inhibitors are the first drug class approved due to more advanced clinical studies. For example, AZD4547 and BGJ398 are gradually entering the consumption cycle and are good options as combined treatments. Artificial intelligence and drug repositioning methods can help preselect suitable drug targets more successfully for future inhibition of carcinogenicity.

CTLA-4 expression and polymorphisms in Schizophrenia; a systematic review of literature.

Schizophrenia constitutes a severe psychiatric disorder with detrimental impacts on individuals, their support systems, and the broader economy. Extensive research has revealed a notable association between variations in the Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) gene and an increased susceptibility to schizophrenia.This study represents the first systematic review of the literature investigating the impact of CTLA-4 polymorphisms and expression on the development and progression of schizophrenia.Our investigation involved a comprehensive search strategy, using a combination of title, abstract, and MESH terms in four databases, including PubMed, Scopus, Web of Science, and Google Scholar, until August 29th, 2023. The complete texts of the identified records were obtained and rigorously assessed based on predefined exclusion and inclusion criteria. Out of the numerous records, a total of 88 were identified through the databases. 10 studies met the criteria; therefore, their quality was assessed and included in this systematic study. The records were then categorized into polymorphism and expression groups. Our investigation emphasizes an association between rs3087243, rs231779, rs231777, rs16840252, rs5742909, and rs231775 polymorphisms and the development of schizophrenia. The results demonstrate a correlation between CTLA-4 polymorphisms and schizophrenia, compelling the need for further research to thoroughly examine the role of CTLA-4 in schizophrenia and other psychiatric disorders.

Identification of new potential candidates to inhibit EGF via machine learning algorithm.

One of the cost-effective alternative methods to find new inhibitors has been the repositioning approach of existing drugs. The advantage of computational drug repositioning method is saving time and cost to remove the pre-clinical step and accelerate the drug discovery process. Hence, an ensemble computational-experimental approach, consisting of three steps, a machine learning model, simulation of drug-target interaction and experimental characterization, was developed. The machine learning type used here was a different tree classification method, which is one of the best randomize machine learning model to identify potential inhibitors from weak inhibitors. This model was trained more than one-hundred times, and forty top trained models were extracted for the drug repositioning step. The machine learning step aimed to discover the approved drugs with the highest possible success rate in the experimental step. Therefore, among all the identified molecules with more than 0.9 probability in more than 70% of the models, nine compounds, were selected. Besides, out of the nine chosen drugs, seven compounds have been confirmed to inhibit EGF in the published articles since 2019. Hence, two identified compounds, in addition to gefitinib, as a positive control, five weak-inhibitors and one neutral, were considered via molecular docking study. Finally, the eight proposed drugs, including gefitinib, were investigated using MTT assay and In-Cell ELISA to characterize the drugs' effect on A431 cell growth and EGF-signaling. From our experiments, we could conclude that salicylic acid and piperazine could play an EGF-inhibitor role like gefitinib.

Oncogenic alterations of metabolism associated with resistance to chemotherapy.

Metabolic reprogramming in cancer cells is a strategy to meet high proliferation rates, invasion, and metastasis. Also, several researchers indicated that the cellular metabolism changed during the resistance to chemotherapy. Since glycolytic enzymes play a prominent role in these alterations, the ability to reduce resistance to chemotherapy drugs is promising for cancer patients. Oscillating gene expression of these enzymes was involved in the proliferation, invasion, and metastasis of cancer cells. This review discussed the roles of some glycolytic enzymes associated with cancer progression and resistance to chemotherapy in the various cancer types.

A GU-Net-Based Architecture Predicting Ligand-Protein-Binding Atoms.

Background: The first step in developing new drugs is to find binding sites for a protein structure that can be used as a starting point to design new antagonists and inhibitors. The methods relying on convolutional neural network for the prediction of binding sites have attracted much attention. This study focuses on the use of optimized neural network for three-dimensional (3D) non-Euclidean data. Methods: A graph, which is made from 3D protein structure, is fed to the proposed GU-Net model based on graph convolutional operation. The features of each atom are considered as attributes of each node. The results of the proposed GU-Net are compared with a classifier based on random forest (RF). A new data exhibition is used as the input of RF classifier. Results: The performance of our model is also examined through extensive experiments on various datasets from other sources. GU-Net could predict the more number of pockets with accurate shape than RF. Conclusions: This study will enable future works on a better modeling of protein structures that will enhance knowledge of proteomics and offer deeper insight into drug design process.

Thymoquinone enhanced the antitumor activity of cisplatin in human bladder cancer 5637 cells in vitro.

PURPOSE: Cisplatin-based chemotherapy is a primary alternative for treating bladder cancer. But drug resistance and various side effects are the main unsightliness challenges. In search of a novel chemotherapeutic approach, this study was conducted to investigate whether thymoquinone (TQ) chemosensitize 5637 bladder cancer cells to cisplatin (CDDP). METHODS: The IC50 for each drug was first determined. The cells were then pre-exposed to 40 µM of TQ for 24 h before being treated with 6 µM of cisplatin. The viability and the sub-G1 population of the 5673 cells were respectively evaluated by alamar blue assay and propidium iodide staining. RT-qPCR was also applied to analyze the expression profile of the apoptosis-related genes (Bax, Bcl-2, p53). RESULTS: The viability of the cells treated with the combination of TQ and CDDP was significantly decreased compared to CDDP- or TQ-treated cells. TQ at the concentration of 40 µM increased the cytotoxicity of 6 µM CDDP by 35.5%. Moreover, flow cytometry analysis indicated that TQ pre-treatment of the cells resulted in a 55.5% increase in the population of 5637 cells in the sub-G1 phase compared to cells treated with CDDP alone. The results from RT-qPCR exhibited that the exposure of the cells to both TQ and CDDP significantly elevated Bax/Bcl-2 ratio by down-regulating Bcl-2 expression. CONCLUSION: TQ significantly increased the cytotoxicity of CDDP in 5637 cells and induced apoptosis by down-regulation of the Bcl-2. Therefore, TQ and CDDP might be an effective therapeutic combination for TCC bladder cancer treatment.

Covid-19 prevention and treatment by targeting Fc-fusion proteins: An experience to fight emerging diseases.

The coronavirus disease 2019 (Covid-19) pandemic has been considered a major threat to human health. Effective therapeutic approaches are urgently required. Spike protein and the Angiotensin-converting enzyme 2 (ACE2) receptors have critical roles in SARS-CoV-2 infection. As a result, these two proteins are considered potential targets for the development of a wide variety of biotherapeutics and vaccines for controlling Covid-19. The fusion proteins have desirable medicinal properties, including high serum half-life, stability, and solubility in the body. Moreover, other Fc-fusion proteins used to treat other diseases have no known side effects. These Fc-fusion proteins are valuable biopharmaceuticals and have been proposed as therapeutic candidates for the treatment and prevention of Covid-19 owing to their potential therapeutic benefits.

Nanostructured multifunctional stimuli-responsive glycopolypeptide-based copolymers for biomedical applications.

Inspired by natural resources, such as peptides and carbohydrates, glycopolypeptide biopolymer has recently emerged as a new form of biopolymer being recruited in various biomedical applications. Glycopolypeptides with well-defined secondary structures and pendant glycosides on the polypeptide backbone have sparked lots of research interest and they have an innate ability to self-assemble in diverse structures. The nanostructures of glycopolypeptides have also opened up new perspectives in biomedical applications due to their stable three-dimensional structures, high drug loading efficiency, excellent biocompatibility, and biodegradability. Although the development of glycopolypeptide-based nanocarriers is well-studied, their clinical translation is still limited. The present review highlights the preparation and characterization strategies related to glycopolypeptides-based copolymers, followed by a comprehensive discussion on their biomedical applications with a specific focus on drug delivery by various stimuli-responsive (e.g., pH, redox, conduction, and sugar) nanostructures, as well as their beneficial usage in diagnosis and regenerative medicine.

A Versatile Biomimic Nanotemplating Fluidic Assay for Multiplex Quantitative Monitoring of Viral Respiratory Infections and Immune Responses in Saliva and Blood.

The last pandemic exposed critical gaps in monitoring and mitigating the spread of viral respiratory infections at the point-of-need. A cost-effective multiplexed fluidic device (NFluidEX), as a home-test kit analogous to a glucometer, that uses saliva and blood for parallel quantitative detection of viral infection and body's immune response in an automated manner within 11 min is proposed. The technology integrates a versatile biomimetic receptor based on molecularly imprinted polymers in a core-shell structure with nano gold electrodes, a multiplexed fluidic-impedimetric readout, built-in saliva collection/preparation, and smartphone-enabled data acquisition and interpretation. NFluidEX is validated with Influenza A H1N1 and SARS-CoV-2 (original strain and variants of concern), and achieves low detection limit in saliva and blood for the viral proteins and the anti-receptor binding domain (RBD) Immunoglobulin G (IgG) and Immunoglobulin M (IgM), respectively. It is demonstrated that nanoprotrusions of gold electrodes are essential for the fine templating of antibodies and spike proteins during molecular imprinting, and differentiation of IgG and IgM in whole blood. In the clinical setting, NFluidEX achieves 100% sensitivity and 100% specificity by testing 44 COVID-positive and 25 COVID-negative saliva and blood samples on par with the real-time quantitative polymerase chain reaction (p < 0.001, 95% confidence) and the enzyme-linked immunosorbent assay.

MicroRNAs Dysregulation as Potential Biomarkers for Early Diagnosis of Endometriosis.

Endometriosis is a benign chronic disease in women that is characterized by the presence of active foci of the endometrium or endometrial tissue occurring outside of the uterus. The disease causes disabling symptoms such as pelvic pain and infertility, which negatively affect a patient's quality of life. In addition, endometriosis imposes an immense financial burden on the healthcare system. At present, laparoscopy is the gold standard for diagnosing the disease because other non-invasive diagnostic tests have less accuracy. In addition, other diagnostic tests have low accuracy. Therefore, there is an urgent need for the development of a highly sensitive, more specific, and non-invasive test for the early diagnosis of endometriosis. Numerous researchers have suggested miRNAs as potential biomarkers for endometriosis diagnosis due to their specificity and stability. However, the greatest prognostic force is the determination of several miRNAs, the expression of which varies in a given disease. Despite the identification of several miRNAs, the studies are investigatory in nature, and there is no consensus on them. In the present review, we first provide an introduction to the dysregulation of miRNAs in patients with endometriosis and the potential use of miRNAs as biomarkers in the detection of endometriosis. Then we will describe the role of the mir-200 family in endometriosis. Several studies have shown that the expression of the mir-200 family changes in endometriosis patients, suggesting that they could be used as a diagnostic biomarker and therapeutic target for endometriosis.

Nanobodies; new molecular instruments with special specifications for targeting, diagnosis and treatment of triple-negative breast cancer.

Breast cancer is the most common type of cancer in women and the second leading cause of cancer death in female. Triple-negative breast cancer has a more aggressive proliferation and a poorer clinical diagnosis than other breast cancers. The most common treatments for TNBC are chemotherapy, surgical removal, and radiation therapy, which impose many side effects and costs on patients. Nanobodies have superior advantages, which makes them attractive for use in therapeutic agents and diagnostic kits. There are numerous techniques suggested by investigators for early detection of breast cancer. Nevertheless, there are fewer molecular diagnostic methods in the case of TNBC due to the lack of expression of famous breast cancer antigens in TNBC. Although conventional antibodies have a high ability to detect tumor cell markers, their large size, instability, and costly production cause a lot of problems. Since the HER-2 do not express in TNBC diagnosis, the production of nanobodies for the diagnosis and treatment of cancer cells should be performed against other antigens expressed in TNBC. In this review, nanobodies which developed against triple negative breast cancer, were classified based on type of antigen.

In silico and in vitro analysis of a rational mutation in gIII signal peptide and its effects on periplasmic expression of rhGH in E. coli.

The secretion efficiency of a heterologous protein in E. coli is mainly dictated by the N-terminal signal peptide fused to the desired protein. In this study, we aimed to select and introduce mutations into the - 1, - 2 and - 3 positions of the gIII signal peptide (originated from filamentous phage fd Gene III) fused to the N-terminus of the human growth hormone (hGH), and study its effect on the secretion efficiency of the recombinant hGH into the periplasmic space of E. coli Top10. Bioinformatics software such as SignalP-5.0 and PrediSi were employed to predict the effects of the mutations on the secretion efficiency of the recombinant hGH. Site-directed mutagenesis was applied to introduce the desired mutations into the C-terminus of the gIII signal peptide. The periplasmic expression and the secretion efficiency of the recombinant hGH using the native and mutant gIII signal peptides were compared in E. coli Top10 under the control of araBAD promoter. Our results from bioinformatics analysis indicated that the mutant gIII signal peptide was more potent than the native one for secretion of the recombinant hGH in E. coli. While our experimental results revealed that the mutation had no effect on hGH secretion. This result points to the importance of experimental validation of bioinformatics predictions.

Graphene oxide quantum dot-chitosan nanotheranostic platform as a pH-responsive carrier for improving curcumin uptake internalization: In vitro & in silico study.

We herein fabricated a cancer nanotheranostics platform based on Graphene Oxide Quantum Dot-Chitosan-polyethylene glycol nanoconjugate (GOQD-CS-PEG), which were targeted with MUC-1 aptamer towards breast and colon tumors. The interaction between aptamer and MUC-1 receptor on the desired cells was investigated utilizing molecular docking. The process of curcumin release was investigated, as well as the potential of the produced nanocomposite in targeted drug delivery, specific detection, and photoluminescence imaging. The fluorescence intensity of GOQD-CS-PEG was reduced due to transferred energy between (cytosine-guanin) base pairs in the hairpin structure of the aptamer, resulting in an "on/off" photoluminescence bio-sensing. Interestingly, the integration of pH-responsive chitosan nanoparticles in the nanocomposite results in a smart nanocomposite capable of delivering more curcumin to desired tumor cells. When selectively binds to the MUC-1 receptor, the two strands of aptamer separate in acidic conditions, resulting in a sustained drug release and photoluminescence recovery. The cytotoxicity results also revealed that the nanocomposite was more toxic to MUC-1-overexpressed tumor cells than to negative control cell lines, confirming its selective targeting. As a result, the proposed nanocomposite could be used as an intelligent cancer nanotheranostic platform for tracing MUC-1-overexpressed tumor cells and targeting them with great efficiency and selectivity.

Comparison of the efficacy of bone morphogenetic protein-4 on in vitro differentiation of murine adipose and bone marrow mesenchymal stem cells into primordial germ cells.

Background and purpose: In vitro development of functional gametes from pluripotent stem cells is a promising prospect to treat infertility. Mesenchymal stem cells with a high degree of plasticity and less tumorigenicity are a reliable source of stem cells for the generation of gametes. The present study aimed to compare the differentiation potential in the mesenchymal stem cells that are derived from bone marrow (BMDMSCs) and adipose tissue derived mesenchymal stem cells (AD-MSCs) into germ cells in a culture medium containing bone morphogenic protein-4 (BMP-4). Experimental approach: In this study, MSCs were isolated from both bone marrow and adipose tissue of murine samples. To further verify the nature of the harvested stem cells, their multipotency and surface marker were examined. The identified stem cells were cultured in a medium supplemented with 0 and 25 ng/mL of BMP-4 for 4 days. Flow cytometry analysis, immunofluorescence staining, and real RT-PCR were used to assess the expression levels in germ cell-specific biomarkers (Mvh, Dazl, Stra8, and Scp3). Findings/Results: CD44+, CD45-, CD31-, BMD-MSCs, and AD-MSCs showed to be capable of differentiating to osteo-adipogenic lineages. The flow cytometry, immunofluorescence, and RT-PCR results indicated that early germ cell markers (Mvh and Dazl) were expressed in both types of cells but they were significantly higher in BMD-MSCs than AD-MSCs. Conclusion and implications: Based on our results, the addition of exogenous BMP4 to the culture medium could differentiate BMD-MSCs and AD-MSCs into primordial germ cells, but it is inadequate to further develop into late germ cells in vitro. Moreover, the results revealed that, although AD-MSCs were easier to collect and had faster growth and proliferation rates than BMD-MSCs, the BMD-MSCs were better capable of differentiation into primordial germ cells. They may serve to be considered a more suitable source of MSC for in vitro generation of gametes than AD-MSCs.

Effects of exposure in utero to buprenorphine on oxidative stress and apoptosis in the hippocampus of rat pups.

The study investigated the effect of buprenorphine (BUP) on oxidative indices and gene expression of apoptotic molecules in the hippocampus of neonates during the fetal stage. BUP (1 or 0.5 mg/kg) was subcutaneously administrated to pregnant rat dams. After parturition, the pups were maintained to the end of breastfeeding period, then hippocampi were assessed for oxidative stress indices [glutathione (GSH), thiobarbituric acid reactive substances (TBARS), superoxide dismutase (SOD), total antioxidant capacity (TAC)] and mRNA expression of apoptotic markers (Bax, Bcl2 and caspase 3). Our data indicated that BUP (0.5 mg/kg) administration during gestation significantly increased GSH and TAC concentrations in the hippocampus of pups versus control group (p < 0.05). BUP (0.5 and 1 mg/kg) administration significantly elevated the expression levels of Bcl2 in the hippocampus of neonates compared with controls. BUP injection (0.5 and 1 mg/kg) to pregnant rats markedly reduced the expression levels of caspase 3 in the hippocampus of neonates in BUP 0.5 group (p < 0.01) and BUP 1 group (p < 0.05) versus the controls. Our study indicated that BUP may potentiate antioxidant system and inhibit apoptosis and oxidative stress in the hippocampus of neonates received this drug during the fetal stage.

Accelerating Big Data Analysis through LASSO-Random Forest Algorithm in QSAR Studies.

MOTIVATION: The aim of quantitative structure-activity prediction (QSAR) studies is to identify novel drug-like molecules that can be suggested as lead compounds by means of two approaches, which are discussed in this article. First, to identify appropriate molecular descriptors by focusing on one feature-selection algorithms; and second to predict the biological activities of designed compounds. Recent studies have shown increased interest in the prediction of a huge number of molecules, known as Big Data, using deep learning models. However, despite all these efforts to solve critical challenges in QSAR models, such as over-fitting, massive processing procedures, is major shortcomings of deep learning models. Hence, finding the most effective molecular descriptors in the shortest possible time is an ongoing task. One of the successful methods to speed up the extraction of the best features from big datasets is the use of least absolute shrinkage and selection operator (LASSO). This algorithm is a regression model that selects a subset of molecular descriptors with the aim of enhancing prediction accuracy and interpretability because of removing inappropriate and irrelevant features. RESULTS: To implement and test our proposed model, a random forest was built to predict the molecular activities of Kaggle competition compounds. Finally, the prediction results and computation time of the suggested model were compared with the other well-known algorithms, i.e. Boruta-random forest, deep random forest and deep belief network model. The results revealed that improving output correlation through LASSO-random forest leads to appreciably reduced implementation time and model complexity, while maintaining accuracy of the predictions. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effects of Methadone on the Toll-like Receptor 4 Expression in Human Non-Small Cell Lung Carcinoma A549 Cell Line Using In-silico and In vitro Techniques.

Background: In this study, the effects of methadone and naloxone on the expression of toll-like receptor 4 (TLR4) gene have been evaluated in human non-small cell lung carcinoma A549 cell line migration using in-silico and in vitro techniques. Materials and Methods: Lung cancer A549 cell cultures were stimulated for 24 h with methadone (5, 10, and 20 µM) and naloxone (20 and 40 µM) concentrations. The level of TLR4 expression was determined by the quantitative real-time polymerase chain reaction. Migration of the A549 cells was investigated after a 4-h incubation period with methadone using the Boyden Chamber assay. Results: Migration rate of the A549 cells treated with 5 (P < 0.05) and 20 (P < 0.01) µM methadone was, respectively, increased and decreased with 20 µM naloxone (P < 0.05). Furthermore, the TLR4 expression was enhanced with 5 (P < 0.05) and 20 (P < 0.01) µM methadone and decreased with 20 (P < 0.05) and 40 µM naloxone (P < 0.01). In addition, in silico docking analysis revealed docking of methadone to MD-2 and TLR4. Conclusion: According to the present DATA, methadone affects the TLR4 expression. It may however cause adverse consequences by increasing the TLR4 expression. Therefore, the useful analgesic properties of methadone should be separated from the unwanted TLR4-mediated side effects.

Drug Repurposing Against Angiotensin-Converting Enzyme-Related Carboxypeptidase (ACE2) Through Computational Approach.

Ongoing novel coronavirus (COVID-19) with high mortality is an infectious disease in the world which epidemic in 2019 with human-human transmission. According to the literature, S-protein is one of the main proteins of COVID-19 that bind to the human cell receptor angiotensin-converting enzyme 2 (ACE2). In this study, it was attempted to identify the main effective drugs approved that may be repurposed to the binding site of ACE2. High throughput virtual screening based on the docking study was performed to know which one of the small-molecules had a potential interaction with ACE2 structure. Forasmuch as investigating and identifying the best ACE2 inhibitors among more than 3,500 small-molecules is time-consuming, supercomputer was utilized to apply docking-based virtual screening. Outputs of the proposed computational model revealed that vincristine, vinbelastin and bisoctrizole can significantly bind to ACE2 and may interface with its normal activity.

Studying the Expression Efficiencies of Human Clotting Factor IX Analogs, Rationally-designed for Hyper-glycosylation.

Glyco-engineering has attracted lots of interest in studies dealing with the pharmacokinetics of therapeutic proteins. Based on our previous in-silico studies, two sites were selected in the N-terminal gamma-carboxy glutamic acid-rich (Gla) domain of the human clotting factor IX (hFIX) to add new N-glycosylation sites. Site-directed mutagenesis was employed to conduct K22N and R37N substitutions and introduce new N-glycosylation sites in the mature hFIX. The expression efficiencies of the mutants, in parallel with the wild-type hFIX (hFIXwt), were assessed in suspension adapted Chinese hamster ovary (CHO-s) cells at transcriptional, translational, and post-translational levels. The transcription levels of both N-glycosylation mutants were significantly lower than that of the hFIXwt. In contrast, at the protein level, the two hFIX mutants showed higher expression. The occurrence of hyper-glycosylation was only confirmed in the case of the hFIXR37N mutant, which decreased the clotting activity. The higher expression of the hFIX mutants at protein level was evidenced, which could be attributed to higher protein stability, via omitting certain protease cleavage sites. The coagulation activity decline in the hyper-glycosylated hFIXR37N mutant is probably due to the interference of the new N-glycan with protein-protein interactions in the coagulation cascade.