Induction of Mitochondrial Cell Death and Reversal of Anticancer Drug Resistance via Nanocarriers Composed of a Triphenylphosphonium Derivative of Tocopheryl Polyethylene Glycol Succinate.

We have devised a nanocarrier using "tocopheryl polyethylene glycol succinate (TPGS) conjugated to triphenylphosphonium cation" (TPP-TPGS) for improving the efficacy of doxorubicin hydrochloride (DOX). Triphenylphosphonium cation (TPP) has affinity for an elevated transmembrane potential gradient (mitochondrial), which is usually high in cancer cells. Consequently, when tested in molecular docking and cytotoxicity assays, TPP-TPGS, owing to its structural similarity to mitochondrially directed anticancer compounds of the "tocopheryl succinate" family, interferes specifically in mitochondrial CII enzyme activity, increases intracellular oxidative stress, and induces apoptosis in breast cancer cells. DOX loaded nanocarrier (DTPP-TPGS) constructed using TPP-TPGS was positively charged, spherical in shape, sized below 100 nm, and had its drug content distributed evenly. DTPP-TPGS offers greater intracellular drug delivery due to its rapid endocytosis and subsequent endosomal escape. DTPP-TPGS also efficiently inhibits efflux transporter P glycoprotein (PgP), which, along with greater cell uptake and inherent cytotoxic activity of the construction material (TPP-TPGS), cumulatively results in 3-fold increment in anticancer activity of DOX in resistant breast cancer cells as well as greater induction of necroapoptosis and arrest in all phases of the cell cycle. DTPP-TPGS after intravenous administration in Balb/C mice with breast cancer accumulates preferentially in tumor tissue, which produces significantly greater antitumor activity when compared to DOX solution. Toxicity evaluation was also performed to confirm the safety of this formulation. Overall TPP-TPGS is a promising candidate for delivery of DOX.

Alternative methods in toxicology: CFU assays application, limitation and future prospective.

Blood is a fluid connective tissue which plays a vital role for normal body function. It consist different type of blood cells which is continuously reproduce inside the bone marrow from hematopoietic system. Xenobiotics could be specifically toxic to the hematopoietic system and they can cause hematological disorders by disturbing the normal functions. In vitro hematopoietic colony-forming cell assays play a crucial role to evaluate potential toxic effects of new xenobiotics and also helpful in bridging the gap between preclinical toxicology studies in animal models and clinical investigations. Use of these assays in conjunction with, high-throughput screening reduces the cost and time associated with these assays. This article provides a critical view over in vitro hematopoietic colony-forming cell assays in assessment of hematotoxicity.

Next generation sequencing: potential and application in drug discovery.

The world has now entered into a new era of genomics because of the continued advancements in the next generation high throughput sequencing technologies, which includes sequencing by synthesis-fluorescent in situ sequencing (FISSEQ), pyrosequencing, sequencing by ligation using polony amplification, supported oligonucleotide detection (SOLiD), sequencing by hybridization along with sequencing by ligation, and nanopore technology. Great impacts of these methods can be seen for solving the genome related problems of plant and animal kingdom that will open the door of a new era of genomics. This may ultimately overcome the Sanger sequencing that ruled for 30 years. NGS is expected to advance and make the drug discovery process more rapid.

MicroRNAs as powerful tool against COVID-19: Computational perspective.

Severe acute respiratory syndrome coronavirus 2 is the virus that is responsible for the current pandemic, COVID-19 (SARS-CoV-2). MiRNAs, a component of RNAi technology, belong to the family of short, noncoding ssRNAs, and may be crucial in the battle against this global threat since they are involved in regulating complex biochemical pathways and may prevent viral proliferation, translation, and host expression. The complicated metabolic pathways are modulated by the activity of many proteins, mRNAs, and miRNAs working together in miRNA-mediated genetic control. The amount of omics data has increased dramatically in recent years. This massive, linked, yet complex metabolic regulatory network data offers a wealth of opportunity for iterative analysis; hence, extensive, in-depth, but time-efficient screening is necessary to acquire fresh discoveries; this is readily performed with the use of bioinformatics. We have reviewed the literature on microRNAs, bioinformatics, and COVID-19 infection to summarize (1) the function of miRNAs in combating COVID-19, and (2) the use of computational methods in combating COVID-19 in certain noteworthy studies, and (3) computational tools used by these studies against COVID-19 in several purposes. This article is categorized under: Infectious Diseases > Computational Models.

An integrated approach of network-based systems biology, molecular docking, and molecular dynamics approach to unravel the role of existing antiviral molecules against AIDS-associated cancer.

A serious challenge in cancer treatment is to reposition the activity of various already known drug candidates against cancer. There is a need to rewrite and systematically analyze the detailed mechanistic aspect of cellular networks to gain insight into the novel role played by various molecules. Most Human Immunodeficiency Virus infection-associated cancers are caused by oncogenic viruses like Human Papilloma Viruses and Epstein-Bar Virus. As the onset of AIDS-associated cancers marks the severity of AIDS, there might be possible interconnections between the targets and mechanism of both the diseases. We have explored the possibility of certain antiviral compounds to act against major AIDS-associated cancers: Kaposi's Sarcoma, Non-Hodgkin Lymphoma, and Cervical Cancer with the help of systems pharmacology approach that includes screening for targets and molecules through the construction of a series of drug-target and drug-target-diseases network. Two molecules (Calanolide A and Chaetochromin B) and the target "HRAS" were finally screened with the help of molecular docking and molecular dynamics simulation. The results provide novel antiviral molecules against HRAS target to treat AIDS defining cancers and an insight for understanding the pharmacological, therapeutic aspects of similar unexplored molecules against various cancers.

Molecular cloning and characterization of protein disulfide isomerase of Brugia malayi, a human lymphatic filarial parasite.

Lymphatic filariasis results in an altered lymphatic system and the abnormal enlargement of body parts, causing pain, serious disability and social stigma. Effective vaccines are still not available nowadays, drugs against the disease is required. Protein disulfide isomerase (PDI) is an essential catalyst of the endoplasmic reticulum which is involved in folding and chaperone activities in different biological systems. Here, we report the enzymatic characterization of a Brugia malayi Protein disulfide isomerase (BmPDI), which was expressed and purified from Escherichia coli BL21 (DE3). Western blotting analysis showed the recombinant BmPDI could be recognized by anti-BmPDI Rabbit serum. The rBmPDI exhibited an optimum activity at pH 8 and 40 °C. The enzyme was inhibited by aurin and PDI inhibitor. Recombinant BmPDI showed interaction with recombinant Brugia malayi calreticulin (rBmCRT). The three-dimensional model for BmPDI and BmCRT was generated by homology modelling. A total of 25 hydrogen bonds were found to be formed between two interfaces. There are 259 non-bonded contacts present in the BmPDI-BmCRT complex and 12 salt bridges were formed in the interaction.

Explicit Drug Re-positioning: Predicting Novel Drug-Target Interactions of the Shelved Molecules with QM/MM Based Approaches.

With the demand to enhance the speed of the drug discovery process there has been an increased usage of computational approaches in drug discovery studies. However because of their probabilistic outcomes, the challenge is to exactly mimic the natural environment which can provide the exact charge polarization effect while estimating the binding energy between protein and ligand. There has been a large number of scoring functions from simple one to the complex one available for estimating binding energy. The quantum mechanics/molecular mechanics (QM/MM) hybrid approach has been the preferred choice of interest since last decade for modeling reactions in biomolecular systems. The application of QM/MM approach has been expanded right from rescoring the already known complexes and depicting the correct position of some novel molecule to ranking a large number of molecules. It is expected that the application of QM/MM-based scoring will grow in all areas of drug discovery. However, the most promising area will be its application in repositioning, that is, assigning novel functions or targets to the already existing drugs, as this would stop the rising attrition rates as well as reduce the overall time and cost of drug discovery procedure.

microRNAs: role in leukemia and their computational perspective.

MicroRNAs (miRNAs) belong to the family of noncoding RNAs (ncRNAs) and had gained importance due to its role in complex biochemical pathways. Changes in the expression of protein coding genes are the major cause of leukemia. Role of miRNAs as tumor suppressors has provided a new insight in the field of leukemia research. Particularly, the miRNAs mediated gene regulation involves the modulation of multiple mRNAs and cooperative action of different miRNAs to regulate a particular gene expression. This highly complex array of regulatory pathway network indicates the great possibility in analyzing and identifying novel findings. Owing to the conventional, slow experimental identification process of miRNAs and their targets, the last decade has witnessed the development of a large amount of computational approaches to deal with the complex interrelations present within biological systems. This article describes the various roles played by miRNAs in regulating leukemia and the role of computational approaches in exploring new possibilities.

Molecular insights of protein contour recognition with ligand pharmacophoric sites through combinatorial library design and MD simulation in validating HTLV-1 PR inhibitors.

Retroviruses HIV-1 and HTLV-1 are chiefly considered to be the most dangerous pathogens in Homo sapiens. These two viruses have structurally unique protease (PR) enzymes, which are having common function of its replication mechanism. Though HIV PR drugs failed to inhibit HTLV-1 infections, they emphatically emphasise the need for designing new lead compounds against HTLV-1 PR. Therefore, we tried to understand the binding level interactions through the charge environment present in both ligand and protein active sites. The domino effect illustrates that libraries of purvalanol-A are attuned to fill allosteric binding site of HTLV-1 PR through molecular recognition and shows proper binding of ligand pharmacophoric features in receptor contours. Our screening evaluates seven compounds from purvalanol-A libraries, and these compounds' pharmacophore searches for an appropriate place in the binding site and it places well according to respective receptor contour surfaces. Thus our result provides a platform for the progress of more effective compounds, which are better in free energy calculation, molecular docking, ADME and molecular dynamics studies. Finally, this research provided novel chemical scaffolds for HTLV-1 drug discovery.

Saraca indica bark extract shows in vitro antioxidant, antibreast cancer activity and does not exhibit toxicological effects.

Medicinal plants are used as a complementary and alternative medicine in treatment of various diseases including cancer worldwide, because of their ease of accessibility and cost effectiveness. Multicomposed mixture of compounds present in a plant extract has synergistic activity, increases the therapeutic potential many folds, compensates toxicity, and increases bioavailability. Saraca indica (family Caesalpiniaceae) is one of the most ancient sacred plants with medicinal properties, exhibiting a number of pharmacological effects. Antioxidant, antibreast cancer activity and toxicological evaluation of Saraca indica bark extract (SIE) were carried out in the present study. The results of the study indicated that this herbal preparation has antioxidant and antibreast cancer activity. Toxicological studies suggest that SIE is safer to use and may have a potential to be used as complementary and alternative medicine for breast cancer therapy.

An overview of data mining algorithms in drug induced toxicity prediction.

The growth in chemical diversity has increased the need to adjudicate the toxicity of different chemical compounds raising the burden on the demand of animal testing. The toxicity evaluation requires time consuming and expensive undertaking, leading to the deprivation of the methods employed for screening chemicals pointing towards the need to develop more efficient toxicity assessment systems. Computational approaches have reduced the time as well as the cost for evaluating the toxicity and kinetic behavior of any chemical. The accessibility of a large amount of data and the intense need of turning this data into useful information have attracted the attention towards data mining. Machine Learning, one of the powerful data mining techniques has evolved as the most effective and potent tool for exploring new insights on combinatorial relationships among various experimental data generated. The article accounts on some sophisticated machine learning algorithms like Artificial Neural Networks (ANN), Support Vector Machine (SVM), k-mean clustering and Self Organizing Maps (SOM) with some of the available tools used for classification, sorting and toxicological evaluation of data, clarifying, how data mining and machine learning interact cooperatively to facilitate knowledge discovery. Addressing the association of some commonly used expert systems, we briefly outline some real world applications to consider the crucial role of data set partitioning.

Hematological malignancies: role of miRNAs and their in silico aspects.

Hematological malignancies is a broad term that includes blood cell cancers including chronic lymphocytic leukemia (CLL), chronic myeloid leukemia (CML), acute myeloid leukemia (AML), Myelodysplastic syndrome, acute lymphocytic leukemia (ALL), multiple myelomas (MM) and lymphomas. miRNAs are ~22-nt long non-coding RNAs that play a very important role in gene regulation by binding to mRNA at their complementary sequence. These miRNAs are conceptually connected with various signal and pathway networks that make them capable of regulating various diseases including hematological malignancies. These miRNAs are not only playing regulatory roles in hematological malignancies, but are also providing new potent markers for efficient diagnosis and prognosis for hematological malignancies patients. Since the discovery of very first miRNA, the importance and role of miRNAs have been established in various fields, and there is a need to search for new potent miRNAs and their targets. A large amount of sequence data have been generated in last few years, which has further generated the need to develop efficient and reliable computational tools to analyze and extract out relevant information promptly from raw data. Here, we review various possible roles played by miRNA in hematological malignancies, principles involved in miRNA gene identification, target prediction and their preceding role in hematological malignancies research.

Designing allosteric modulators for active conformational state of m-glutamate G-protein coupled receptors.

G-protein coupled receptors (GPCRs) are found to be attractive drug targets for the treatment of various neuronal diseases. Allosteric modulators have their role in enhancing or suppressing the effect of glutamate on mGluRs. Structure of mGluR1 was generated with the help of Modeller software by considering human B2-adrenergic GPCR protein as template. Structure of various already known drug molecules were used for similarity search in the ZINC database and a large number of similar molecules were obtained, than filtering of these molecules were done by applying drug features. Molecules were screened by Molegro Virtual Docking program and numbers of novel molecules were generated by using LigBuilder software. Finally 16 novel drug candidates were selected, which were showing better results than the seed molecule and previously known modulators. These results will help in designing and synthesis of better drugs against diseases like Epilepsy and Parkinson's.

Identification of miRNAs in C. roseus and their potential targets.

MicroRNAs are small (20-22 nucleotides) none coding, regulatory RNAs, whose pivotal role in gene expression has been associated in number of diseases, therefore prediction of miRNA is an essential yet challenging field. In this study miRNAs of C. roseus are predicted along with their possible target genes. A total of 19,899 ESTs were downloaded from dbEST database and processed and trimmed through SeqClean. Nine sequences were trashed and 31 sequences were trimmed by the program and the resulting sequences were submitted to Repeatmasker and TGICL for clustering and assembly. This contig database was now used to find the putative miRNAs by performing a local BLAST with the miRNAs of B. rapa retrieved from miRBase. The targets were scanned by hybridizing screened ESTs with the UTRs of human using miRanda software. Finally, 7 putative miRNAs were found to hybridize with the various targets of signal transduction and apoptosis that may play significant role in preventing diseases like Leukemia, Arthritis and Alzheimer.