Species scaling and extrapolation.

The various scaling methodologies and molecular features analysis were applied to new dataset to predict human pharmacokinetics studies. Whereas the predictive accuracies demonstrated across all of the various methodologies were lower for this higher clearance compound dataset, scaling from species continued to be an accurate methodology, and human volume of distribution was similarly well predicted regardless of scaling methodology. Also, extrapolation is the method for constructing new data points given a set of discrete data points. Methods estimate is reasonably reliable for short times, but for longer times, the estimate is liable to become less accurate. Species Scaling and Extrapolation are useful for acquiring toxicological data- epidemiological and experimental study. Animal studies help us to understand toxicity characteristics of a chemical before human exposure is allowed, whereas the epidemiological method generally does not. Species scaling and extrapolation from animals is necessary in many cases which helps in dealing with the so-called human risks more properly.

Epigenomics.

'Epigenomics' can be termed as the study of the effects of chromatin structure, including the higher order of chromatin folding and attachment to the nuclear matrix, packaging of DNA around nucleosomes, covalent modifications of histone tails and DNA methylation. This has evolved to include any process that alters gene activity without changing the DNA sequence, and leads to modifications that can be transmitted to daughter cells. It also leads to a better knowledge of the changes in the regulation of genes and genomes that occur in major psychosis. It may also aid in understanding why the same gene sequence may predispose an individual to schizophrenia or bipolar disorder and in other cases does not, and elucidate the molecular mechanisms of how harmful; environmental factors interact with the genome. Results from the work may further lead to new diagnostics and effective therapies.

Blood stage parasites: sufficient to induce protective immunity.

Merozoites are the surface antigens and variant antigens expressed on the surface of malaria-infected erythrocytes (including PfEMP1) are both targets of protective antibody responses. The mechanism of the modified immune response was observed after subpatent infections. Subpatently infected mice had increased antigen-specific T-cell responses; they were not better protected than patently infected mice. The study of human volunteers, the absence of detectable malaria-specific antibodies probably reflects the extremely low parasite doses used for immunization. Induction of this type of immunity by immunizing with low doses of purified antigens from whole parasites may be an alternative but highly effective vaccine strategy.

RNAi--a tool for target finding in new drug development.

RNAi (RNA interference) refers to the introduction of homologous double stranded RNA (dsRNA) to specifically target a gene's product, resulting in null or hypomorphic phenotypes. Long double-stranded RNAs (dsRNAs; typically >200 nt) can be used to silence the expression of target genes in a variety of organisms and cell types (e.g., worms, fruit flies, and plants). The long dsRNAs enter a cellular pathway that is commonly referred to as the RNA interference (RNAi) pathway. RNAi is being considered as an important tool not only for functional genomics, but also for gene-specific therapeutic activities that target the mRNAs of disease-related genes. RNAi plays a very important role in endogenous cellular processes, such as heterochromatin formation, developmental control and serves as an antiviral defense mechanism. RNAi has shown great potential for use as a tool for target finding in new drug development, molecular biological discovery, analysis and therapeutics. RNAi pathway is involved in post-transcription silencing, transcriptional silencing and epigenetic silencing as well as its use as a tool for forward genetics and therapeutics.

Transcriptomics.

Transcriptomics, a genome-wide measurement of mRNA expression levels based on DNA microarray technology is one of the prominent fields of study. This is the term given to the set of all transcripts or messenger RNA (mRNA) molecules produced in cells. It can also be applied to the specific subset of transcripts present in a particular cell or the total set of transcripts in a given organism. Transcriptomics has evolved from a variety of already present technologies and areas. These areas include proteomics, genomics, and environmental science.

Toxicogenomics.

Toxicogenomics is defined as an integration of genomics (transcriptomics, proteomics and metabolomics) and toxicology. It is a scientific field that studies how the genome is involved in responses to environmental stressors and toxicants. It combines studies of mRNA expression, cell and tissue-wide protein expression and metabonomics to understand the role of gene-environment interactions in disease. One of the important aspects of toxicogenomics research is the development and application of bioinformatics tools and databases in order to facilitate the analysis, mining, visualizing and sharing of the vast amount of biological information being generated in this field. This rapidly growing area promises to have a large impact on many other scientific and medical disciplines as scientists could now generate complete descriptions of how components of biological systems work together in response to various stresses, drugs, or toxicants.

Kinomics.

Kinomics is derived from the word kinome that is the kinase part of the proteome. Kinomics is a merger between genomics and proteomics. Defining the kinase complement of the human genome, the kinome, has provided an excellent starting point for understanding the scale of the problem. This approach combines the understanding of small molecules and targets, and thereby assists the researcher in finding new targets for existing molecules or understanding selectivity and poly-pharmacology of molecules in related targets. Deciphering the complex network of phosphorylation-based signaling is necessary for a thorough and therapeutically applicable understanding of the functioning of a cell in physiological and pathological states.

Physiomics.

Physiomics is that branch of omics that uses large scale databases and experimental databases along with computer algorithms to study the physiological phenotypes of genes, proteins and their relationships. It deals with studying the physiome, the total integration of genome, proteome and metabolome, from cells to organisms. It is a very useful branch that has been actively used in studying drug development, various interactions and biosensor as well as biochip development.

Cytomics.

Cytomics is the branch of omics that takes into account the various bioinformatic techniques for understanding the functions and molecular architecture of the cytome. Cytomics, the multi-molecular cytometric analysis of the cellular heterogeneity of cytomes, access a maximum of information on the apparent molecular cell phenotype as it results from cell genotype and exposure. This has been done using various cytometrical procedures including microscopic techniques allowing the various components of a cell to be visualized as they interact in vivo.

Microarray: an approach for current drug targets.

Microarrays are a powerful tool has multiple applications both in clinical and cellular and molecular biology arenas. Early assessment of the probable biological importance of drug targets, pharmacogenomics, toxicogenomics and single nucleotide polymorphisms (SNPs). A list of new drug candidates along with proposed targets for intervention is described. Recent advances in the knowledge of microarrays analysis of organisms and the availability of the genomics sequences provide a wide range of novel targets for drug design. This review gives different process of microarray technologies; methods for comparative gene expression study, applications of microarrays in medicine and pharmacogenomics and current drug targets in research, which are relevant to common diseases as they relate to clinical and future perspectives.

Clonetics.

Work on human immortalized cell lines is not considered research on human subjects, but does involve biohazards. It has also been estimated that about 80% of human cell lines are the kind of cells that they are expected. Cells that are cultured directly from a subject are referred to as primary cells. Clonetics is the term can be used to describe Human Immortalized Cell Lines. Using Clonetics, the process of drug discovery and development can be accelerated. It is expected to contribute to drug development in metabolic diseases. These can be successfully used in many medical treatments.

Oncogenomics.

The rapid developments in the field of genomics and proteomics are expected to lead to a further increase in the potential for early diagnosis, the fine-tuning of prognostic features of specific tumors and the detection of cancer predisposition. Oncogenomics has identified new drug targets for genotype-specific treatments and provided strategies to validate these targets and to develop drugs. With the potential need to stratify patients by genotype, clinical testing of targeted drugs has become more complicated while expectations of patients, investors, and funding agencies have become accelerated. Oncogenomics has progressed logically from molecular profiling to model systems, cancer pharmacology and clinical trials. Oncogenomics covers cutting-edge issues such as array-based diagnostics, pharmacogenomics, pharmacoproteomics and molecularly targeted therapeutics includes discussions of ethical, legal, and social issues related to cancer genomics and clinical trials.

Pharmacogenomics.

Pharmacogenetics is the intersection of the fields of pharmacology and genetics. Simply stated, pharmacogenetics is the study of how genetic variations affect the ways in which people respond to drugs. These variations can manifest themselves as differences in the drug targets or as differences in the enzymes that metabolize drugs. A difference in the target will usually lead to differences in how well the drug works, whereas differences in metabolizing enzymes can result in differences in either efficacy or toxicity. It's also possible that genes not directly involved in a particular pathway could end up being predictive of clinical outcomes. Although pharmacogenomics has the potential to radically change the way health care is provided, it is only in its infancy. In the future, pharmacogenomics could find uses along the entire drug discovery and development timeline, all the way from target discovery and validation to late-stage clinical trials. Beyond that, pharmacogenomics tests could find their way into the doctor's office as a means to get the right medicine to the right patient at the right time. While genetics and genomics are often used synonymously, pharmacogenetics is more focused in scope than and is viewed as a subset of pharmacogenomics, which encompasses factors beyond those that are inherited.

Proteomics: technologies for protein analysis.

Proteomics technologies have produced an abundance of drug targets, which is creating a bottleneck in drug development process. There is an increasing need for better target validation for new drug development and proteomic technologies are contributing to it. Identifying a potential protein drug target within a cell is a major challenge in modern drug discovery; techniques for screening the proteome are, therefore, an important tool. Major difficulties for target identification include the separation of proteins and their detection. These technologies are compared to enable the selection of the one by matching the needs of a particular project. There are prospects for further improvement, and proteomics technologies will form an important addition to the existing genomic and chemical technologies for new target validation. Proteomics is applicable for protein analysis and bioinformatics based analysis gives the comprehensive molecular description of the actual protein component. Bioinformatics is being increasingly used to support target validation by providing functionally predictive information mined from databases and experimental datasets using a variety of computational tools. This review is focused on key technologies for proteomics strategy and their application in protein analysis.

Prediction of MHC binding peptides and epitopes from alfalfa mosaic virus.

Peptide fragments from alfalfa mosaic virus involved multiple antigenic components directing and empowering the immune system to protect the host from infection. MHC molecules are cell surface proteins, which take active part in host immune reactions and involvement of MHC class-I & II in response to almost all antigens. Coat protein of alfalfa mosaic virus contains 221 aa residues. Analysis found five MHC ligands in coat protein as 64-LSSFNGLGV-72; 86- RILEEDLIY-94; 96-MVFSITPSY-104; 100- ITPSYAGTF-108; 110- LTDDVTTED-118; having rescaled binding affinity and c-terminal cleavage affinity more than 0.5. The predicted binding affinity is normalized by the 1% fractil. The MHC peptide binding is predicted using neural networks trained on c-terminals of known epitopes. In analysis predicted MHC/peptide binding is a log transformed value related to the IC50 values in nM units. Total numbers of peptides found are 213. Predicted MHC binding regions act like red flags for antigen specific and generate immune response against the parent antigen. So a small fragment of antigen can induce immune response against whole antigen. This theme is implemented in designing subunit and synthetic peptide vaccines. The sequence analysis method allows potential drug targets to identify active sites against plant diseases. The method integrates prediction of peptide MHC class I binding; proteosomal c-terminal cleavage and TAP transport efficiency.

Prediction of antigenic epitopes of neurotoxin Bmbktx1 from Mesobuthus martensii.

Gene therapy or recombinant DNA vaccines targeting multiple antigenic components to direct empower the immune system. Antigenic epitopes on neurotoxin Mesobuthus martensii (Buthus martensii) are important determinant of protection against cardiovascular disorder. Small segments 4-YSSDCRVKCVAM-15, 18-SSGKCINSKC-27 of neuro-toxin protein called the antigenic epitopes is sufficient for eliciting the desired immune response. In analysis predicted antigenic epitopes neurotoxin protein is seen. Immunization cassettes should be capable of immunizing of broad immunity against both humoral and cellular epitope thus giving vaccines the maximum ability to deal with neurotoxin protein of M. martensii. We have predicted a successful immunization.

Prediction of Brugia malayi antigenic peptides: candidates for synthetic vaccine design against lymphatic filariasis.

Brugia malayi is a threadlike nematode cause's swelling of lymphatic organs, condition well known as lymphatic filariasis; till date no invention made to effectively address lymphatic filariasis. In this analysis we a have predicted suitable antigenic peptides from Brugia malayi antigen protein for peptide vaccine design against lymphatic filariasis based on cross protection phenomenon as, an ample immune response can be generated with a single protein subunit. We found MHC class II binding peptides of Brugia malayi antigen protein are important determinant against the diseased condition. The analysis shows Brugia malayi antigen protein having 505 amino acids, which shows 497 nonamers. In this assay, we have predicted MHC-I binding peptides for 8mer_H2_Db (optimal score- 15.966), 9mer_H2_Db (optimal score- 15.595), 10mer_H2_Db (optimal score- 19.405), 11mer_H2_Dballeles (optimal score- 23.801). We also predicted the SVM based MHCII-IAb nonamers, 51-FQQIDPLDA, 442-FAAIACLVH, 206-YLNPFGHQF, 167-WYVIMAACY, 367-YAMIVIRLL, 434- LVITTAANF, 176-LDSYCLWKP, 435-VITTAANFA, 364-WPGYAMIVI (optimal score- 13.963); MHCII-IAd nonamers, 52-QQIDPLDAE, 171-MAACYLDSY, 239-QWRSVILCN, 168-YVIMAACYL, 3-QYLSVHSLS, 322-EILLHAKVV, 417- LGIIASFVS, 396-KAIFLAHFG, 167-WYVIMAACY, 269-LALHCINVI, 93-FINKAAPKQ, 259-NCIIVLKAF, 79- QGVLLIIPR, 22-TILQRSQAI, 63-RGFVYGNVS, 109-NISSLAFET,(optimal score- 16.748); and MHCII-IAg7 nonamers 171-MAACYLDSY, 73-KIVNGAQGV, 259-NCIIVLKAF, 209-PFGHQFSFE, 102-SCDTLLKNI, 25-QRSQAIRIV, 444- AIACLVHLF, 88-SLVNGFINK, 252-FPRHQLLNC, 471-RFVLANDNE, 52-QQIDPLDAE, 469-HRRFVLAND, 457- SNRHYFLAD, 362-KSWPGYAMI, 476-NDNEGEDFE, 370-IVIRLLQAL (optimal score- 19.847) which represents potential binders from Brugia malayi antigen protein. The method integrates prediction of MHC class I binding proteasomal C-terminal cleavage peptides and Eighteen potential antigenic peptides at average propensity 1.063 having highest local hydrophilicity. Thus a small antigen fragment can induce immune response against whole antigen. This approach can be applied for designing subunit and synthetic peptide vaccines.

microRNA: human disease and development.

microRNAs or miRNAs are an abundant class of highly conversed, small non-coding RNAs that present an entirely new theme of post-transcriptional gene regulation. miRNAs play a key role in diverse biological systems, such as virology, embryogenesis, differentiation, inflammation and cancer research. Research showed the importance of these non-coding small RNAs on immune system development and response. It plays important regulatory roles in various metabolic pathways in most eukaryotes. miRNAs are found to be involved in the regulation of immunity, including the development and differentiation of immune cells, antibody production and the inflammatory mediator release.

Phylogenomics: evolution and genomics intersection.

Phylogenomics is the analysis of genomes of a group of closely related species. Almost all functional prediction methods rely on the identification, characterisation and quantification of sequence similarity between the gene of interest and genes for which functional information is available. This is the new evolved branch that is developed from the ongoing genome sequencing projects that have led to a phylogenetic approach based on genome-scale data. The use of large data sets in phylogenomic analysis results in a global increase in resolution owing to a decrease in sampling error.