Pharmacokinetics-Pharmacodynamics Modeling and Evaluation of Tumor Response to Bortezomib Proteasome Inhibitor in Waldenstrom Macroglobulinemia.

BACKGROUND: Waldenstrom's macroglobulinemia (WM), also known as lymphoplasmacytic lymphoma, is a type of non-Hodgkin's lymphoma in which the malignant cells produce many macroglobulin proteins. It originates from B cells and develops in the bone marrow, where Wm cells combine to produce distinct types of blood cells, resulting in reduced volumes of red blood cells, white blood cells, and platelets, making it harder for the body to fight diseases. Chemoimmunotherapy is being used for the clinical management of WM, but new targeted agents, the BTK inhibitor ibrutinib and the proteasome inhibitor bortezomib, have shown significant improvements in patients with relapsed/refractory WM. However, given its effectiveness, drug resistance and relapse are normal, and there is little research on the pathways responsible for drug effects on the tumor. METHODS: In this study, Pharmacokinetics-pharmacodynamic simulations were done to assess the effect of the proteasome inhibitor bortezomib on the tumor. For this purpose, the Pharmacokinetics-pharmacodynamic model was developed. The model parameters were determined and calculated using the Ordinary Differential Equation solver toolbox and the least-squares function. Pharmacokinetic profiles and pharmacodynamic analysis were performed to determine the change in tumor weight associated with the use of proteasome inhibitors. RESULTS: Bortezomib and ixazomib have been found to reduce tumor weight briefly, but once the dose is reduced, the tumor begins to grow again. Carfilzomib and oprozomib had better results, and rituximab reduced tumor weight more effectively. CONCLUSION: Once validated, it is proposed that a combination of selected drugs can be evaluated in the laboratory to treat WM.

Therapeutic Potential of Selenium Nanoparticles on Letrozole-Induced Polycystic Ovarian Syndrome in Female Wistar Rats.

Polycystic ovarian syndrome (PCOS) is considered the most frequent gynecological endocrine disorder that causes anovulatory infertility. The current study aimed to investigate the potential significance of selenium nanoparticles (SeNPs), an IL-1 inhibitor, in the treatment of letrozole-induced PCOS in rats that satisfied the metabolic and endocrine parameters found in PCOS patients. Letrozole (2 ppm, per orally, p.o.) was given orally to female Wistar rats for 21 days to develop PCOS. After PCOS induction, rats were given SeNPs (25 ppm/day, p.o.), SeNPs (50 ppm/day, p.o.), or metformin (2 ppm/day, p.o.) for 14 days. PCOS was associated with an increase in body weight, ovarian weight, ovarian size, and cysts, as well as an increase in blood testosterone, luteinizing hormone (LH), and insulin, glycaemia, and lipid profile levels. The SeNP administration decreased all of these variables. Furthermore, SeNPs significantly reduced letrozole-induced oxidative stress in the ovaries, muscles, and liver by decreasing elevated levels of malondialdehyde and total nitrite while raising suppressed levels of superoxide dismutase and catalase. SeNPs increased the amounts of the protective proteins Kelch-like ECH-associated protein 1 (Keap-1), nuclear factor erythroid 2-related factor 2 (Nrf2), and OH-1. It was depicted from the study that SeNPs reduce the upregulation of inflammatory cytokines that are interleukin 6 (IL-6), tumour necrosis factor α (TNF-α), and the interleukin 1 (IL-1). Our findings show that SeNPs, through their antioxidant and anti-inflammatory characteristics, alleviate letrozole-induced PCOS.

Gene expression profiling utilizing extremely sensitive CDNA arrays and enrichment-based network study of major bone cancer genes.

BACKGROUND: The gene interaction network is a set of genes interconnected by functional interactions among the genes. The gene interaction networks are studied to determine pathways and regulatory mechanisms in model organisms. In this research, the enrichment study of bone cancer-causing genes is undertaken to identify several hub genes associated to the development of bone cancer. MATERIALS AND METHODS: Data on bone cancer is obtained from mutated gene samples; highly mutated genes are selected for the enrichment analysis. Due to certain interactions with each other the interaction network model for the hub genes is developed and simulations are produced to determine the levels of expression. For the array analyses, a total of 100 tumor specimens are collected. Cell cultures are prepared, RNA is extracted, cDNA arrays probes are generated, and the expressions analysis of Hub genes is determined. RESULTS: Out of cDNA array findings, only 7 genes: CDKN2A, AKT1, NRAS, PIK3CA, RB1, BRAF, and TP53 are differentially expressed and shown as significant in the development of bone tumors, approximately 15 pathways have been identified, including pathways for non-small cell lung cancer, prostate cancer, pancreatic cancer, chronic myeloid leukemia, and glioma, consisting of all the identified 7 genes. After clinical validations of tumor samples, the IDH1 and TP53 gene revealed significant number of mutations similar to other genes. Specimens analysis showed that RB1, P53, and NRAS are amplified in brain tumor, while BRAF, CDKN2A, and AKT1 are amplified in sarcoma. Maximum deletion mutations of the PIK3CA gene are observed in leukemia. CDKN2A gene amplifications have been observed in virtually all tumor specimens. CONCLUSION: This study points to a recognizable evidence of novel superimposed pathways mechanisms strongly linked to cancer.

Identification of differentially expressed genes and pathways crosstalk analysis in Rheumatoid and Osteoarthritis using next-generation sequencing and protein-protein networks.

Osteoarthritis occurs when protective cartilage of bones worn out. Similarlty, cartilage damage occurs mainly in the pannus cartilage in rheumatoid arthritis. It is a potentially debilitating condition, affecting women two to three times more often than men. The cause and prognosis of rheumatoid and osteoarthritis are still poorly known. However, advances in the study of disease pathogenesis have encouraged the creation of new therapeutics with improved outcomes. The purpose of this study is to investigate the differentially expressed genes potentially involved in dysregulated rheumatoid arthritis (RA) and their association to other types of arthritis, including osteoarthritis (OA). Complete RNAs were isolated for RNA expression profiling using next-generation sequencing from human primary cultured normal and RA chondrocytes. From RNA sequencing results 250 differentially expressed genes were identified using bioinformatics analysis, of which 32 were found to be significantly playing role in RA pathogenesis and its associated diseases. Molecular ontologies of the identified genes showed they are connected to Innate immune response, Protein phosphorylation, Transcription initiation from RNA polymerase II promoter, Immune response, Neoplasms of bones, as well as osteorthritis, and Rheumatoid arthritis. Among the identified genes, TRAF1, TRAF2, BAMP, STX11, MEOX2, AES, REL, FHL3, PNMA1, SGTA, LZTS2, SIAH2, PNMA1, and TFCP2 were found to be highly enriched in the protein-protein interaction network. The significant cross talks were found in Hypertrophic cardiomyopathy, Small cell lung cancer, Proteasome, p53 signaling pathway, Arrhythmogenic right ventricular cardiomyopathy, Small cell lung cancer, SNARE interactions in vesicular transport, RIG-I-like receptor signaling pathway, and Hypertrophic cardiomyopathy pathways. The results offer new opportunities for target gene control in RA and OA cartilage destruction.

Proteomic Analysis of Medicinal Plant Calotropis Gigantea by In Silico Peptide Mass Fingerprinting.

Medicinal plants are the basic source of medicinal compounds traditionally used for the treatment of human diseases. Calotropis gigantea is a medicinal plant belonging to the family of Apocynaceae in the plant kingdom and subfamily Asclepiadaceae usually bearing multiple medicinal properties to cure a variety of diseases. BACKGROUND: The Peptide Mass Fingerprinting (PMF) identifies the proteins from a reference protein database by comparing the amino acid sequence that is previously stored in the database and identified. OBJECTIVE: The purpose of the study is to identify the peptides having anti-cancerous properties by in silico peptide mass fingerprinting. METHODS: The calculation of in silico peptide masses is done through the ExPASy PeptideMass and these masses are used to identify the peptides from the MASCOT online server. Anticancer probability is calculated by iACP server, docking of active peptides is done by CABS-dock the server. RESULTS: The anti-cancer peptides are identified with the MASCOT peptide mass fingerprinting server, the identified peptides are screened and only the anti-cancer are selected. De-novo peptide structure prediction is used for 3D structure prediction by PEP-FOLD 3 server. The docking results confirm strong bonding with the interacting amino acids of the receptor protein of breast cancer BRCA1 which shows the best peptide binding to the active chain, the human leukemia protein docking with peptides shows the accurate binding. CONCLUSION: These peptides are stable and functional and are the best way for the treatment of cancer and many other deadly diseases.

nCOV-19 peptides mass fingerprinting identification, binding, and blocking of inhibitors flavonoids and anthraquinone of Moringa oleifera and hydroxychloroquine.

An rare pandemic of viral pneumonia occurs in December 2019 in Wuhan, China, which is now recognized internationally as Corona Virus Disease 2019 (COVID-19), the etiological agent classified as Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2). According to the World Health Organization (WHO), it has so far expanded to more than 213 countries/territories worldwide. Our study aims to find the viral peptides of SARS-COV-2 by peptide mass fingerprinting (PMF) in order to predict its novel structure and find an inhibitor for each viral peptide. For this reason, we calculated the mass of amino acid sequences translated from the SARS-CoV2 whole genome and identify the peptides that may be a target for inhibition. Molecular peptide docking with Moringa oleifera, phytochemicals (aqueous and ethanolic) leaf extracts of flavonoids (3.56 ± 0.03), (3.83 ± 0.02), anthraquinone (11.68 ± 0.04), (10.86 ± 0.06) and hydroxychloroquine present therapy of COVID-19 in Pakistan for comparative study. Results indicate that 15 peptides of SARS-CoV2 have been identified from PMF, which is then used as a selective inhibitor. The maximum energy obtained from AutoDock Vina for hydroxychloroquine is -5.1 kcal/mol, kaempferol (flavonoid) is -6.2 kcal/mol, and for anthraquinone -6 kcal/mol. Visualization of docking complex, important effects are observed regarding the binding of peptides to drug compounds. In conclusion, it is proposed that these compounds are effective antiviral agents against COVID-19 and can be used in clinical trials.Communicated by Ramaswamy H. Sarma.

In silico authentication of amygdalin as a potent anticancer compound in the bitter kernels of family Rosaceae.

Amygdalin a naturally occurring compound, predominantly in the bitter kernels of apricot, almond, apple and other members of Rosaceae family. Though, amygdalin is used as an alternative therapy to treat various types of cancer but its role in cancer pathways has rarely been explored yet. Therefore, present study was intended with the aim to investigate the alleged anti-cancerous effects of amygdalin specifically on PI3K-AKT-mTOR and Ras pathways of cancer in human body. Computational modelling and simulation techniques were used to assess the effect of amygdalin on PI3K-AKT-mTOR and Ras pathways using different level of dosage. It was observed that amygdalin had direct and substantial contribution to regulate PI3K-mTOR activities on threshold levels while the other caner pathways were effected indirectly. Consequently, amygdalin is a down-regulator of a cancer within a specified amount and contribute considerably to reduce various types of cancer in human. Furthermore, in-vitro and in-vivo analyses of amygdalin could be of helpful to authenticate its pharmacological effects.

Silver nanoparticles conjugated with Neurotrophin 3 upregulate myelin gene transcription pathway.

Mathematical modeling is the art of converting problems from the biological area into handy mathematical formulations whose theoretical and numerical analysis provides understandings about the directions and solutions to the particular problem. Recently, the combination therapy treatments have been revealed exceptionally fruitful by using mathematical modeling technique. The human nervous system is composed of axons, covered by the myelin sheath. Axons carry signals and promote myelin development. The abnormalities in myelination formation due to mutations in myelin gene result in memory disorders and impaired cognitive activities. The ERBb gene family is responsible for causing abnormalities in myelin gene. Using this knowledge, the pathway of mutated myelin gene was retrieved and its model was developed. Modeling and simulation analysis was performed to determine the level of expression of several genes. The Neurotrophin 3 ligand-coated with silver nanoparticle was induced in the model to normalize the transcription of myelin gene. It was observed that the myelin gene expression level increases from 0 after two days of NT3 induction and reaches to the maximum level on the 10th day of drug induction along with an increase in ERBb expression. This research work can be used in the future as a part of drug discovery and formulation.

Clustering based drug-drug interaction networks for possible repositioning of drugs against EGFR mutations: Clustering based DDI networks for EGFR mutations.

EGFRs are a vast group of receptor tyrosine kinases playing an important role in a number of tumors, including lungs, head and neck, breast, and esophageal cancers. A couple of techniques are being used in the process of drug design. Drug repositioning or repurposing is a rising idea that consists of distinguishing modern remedial indications for officially existing dynamic pharmaceutical compounds. Here, a novel approach of analyzing drug-drug interaction networks, based on clustering methodology is used to reposition effective compounds against mutant EGFR having G719X, exon 19 deletions/insertions, L858R, and L861Q mutations. Data about 2062 drugs are obtained, and mining is performed to filter only those drugs which fulfill Lipinski rule of five. Clustering is performed, and DDIs are built on the clusters to identify effective drug compounds. Only 1052 compounds fulfill Lipinski rule. 12 clusters are formed for 1052 drugs compounds. DDIs are developed for each cluster. Only 15 drugs are suggested to be more effective assuming strong interactions in a DDI.