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The Emerging Discipline of Quantitative Systems Pharmacology

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889196425 Year: Pages: 97 DOI: 10.3389/978-2-88919-642-5 Language: English
Publisher: Frontiers Media SA
Subject: Science (General) --- Therapeutics
Added to DOAB on : 2016-08-16 10:34:25
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In 2011, the National Institutes of Health (NIH), in collaboration with leaders from the pharmaceutical industry and the academic community, published a white paper describing the emerging discipline of Quantitative Systems Pharmacology (QSP), and recommended the establishment of NIH-supported interdisciplinary research and training programs for QSP. QSP is still in its infancy, but has tremendous potential to change the way we approach biomedical research. QSP is really the integration of two disciplines that have been increasingly useful in biomedical research; “Systems Biology” and “Quantitative Pharmacology”. Systems Biology is the field of biomedical research that seeks to understand the relationships between genes and biologically active molecules to develop qualitative models of these systems; and Quantitative Pharmacology is the field of biomedical research that seeks to use computer aided modeling and simulation to increase our understanding of the pharmacokinetics (PK) and pharmacodynamics (PD) of drugs, and to aid in the design of pre-clinical and clinical experiments. The purpose of QSP modeling is to develop quantitative computer models of biological systems and disease processes, and the effects of drug PK and PD on those systems. QSP models allow testing of numerous potential experiments “in-silico” to eliminate those associated with a low probability of success, avoiding the potential costs of evaluating all of those failed experiments in the real world. At the same time, QSP models allow us to develop our understanding of the interaction between drugs and biological systems in a more systematic and rigorous manner. As the need to be more cost-efficient in the use of research funding increases, biomedical researchers will be required to gain the maximum insight from each experiment that is conducted. This need is even more acute in the pharmaceutical industry, where there is tremendous competition to develop innovative therapies in a highly regulated environment, combined with very high research and development (R&D) costs for bringing new drugs to market (~$1.3 billion/drug). Analogous modeling & simulation approaches have been successfully integrated into other disciplines to improve the fundamental understanding of the science and to improve the efficiency of R&D (e.g., physics, engineering, economics, etc.). The biomedical research community has been slow to integrate computer aided modeling & simulation for many reasons: including the perception that biology and pharmacology are “too complex” and “too variable” to be modeled with mathematical equations; a lack of adequate graduate training programs; and the lack of support from government agencies that fund biomedical research. However, there is an active community of researchers in the pharmaceutical industry, the academic community, and government agencies that develop QSP and quantitative systems biology models and apply them both to better characterize and predict drug pharmacology and disease processes; as well as to improve efficiency and productivity in pharmaceutical R&D.

Epitope Discovery and Synthetic Vaccine Design

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889455225 Year: Pages: 284 DOI: 10.3389/978-2-88945-522-5 Language: English
Publisher: Frontiers Media SA
Subject: Medicine (General) --- Allergy and Immunology
Added to DOAB on : 2019-01-23 14:53:42
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Abstract

Since variolation, conventional approaches to vaccine development are based on live-attenuated, inactivated or purified pathogen-derived components. However, effective vaccines against global health threats such as HIV, parasite infections and tumors are difficult to achieve. On the other hand, synthetic vaccines based on immunogenic epitopes offer advantages over traditional vaccines since they are chemically defined antigens free from deleterious effects. Additionally, in contrast to live-attenuated vaccines, they do not revert to virulence in immunocompromised subjects, and different from genetic vaccines, they do not involve ethical questions. Traditional vaccines contain PAMPs and induce strong immune responses, while recombinant vaccines are less potent. In spite of the immunogenic weakness previously attributed to epitope-based vaccines a synthetic vaccine containing a 17 amino acid-epitope of the Pseudomonas aeruginosa Type IV pilus exceeded the protective potential of its cognate protein composed of 115 amino acids. Therefore, the efficacy yield of a synthetic vaccine can be potentiated by using the proper combination of target epitopes. Recent advances in adjuvant development, immunogen platforms for DNA vaccines and viral vectors also contributed to optimize immunogenicity. Another constraint to the use of epitope vaccines was their restriction to some MHC or HLA phenotypes. However, epitopes containing 20 or less amino acids of Plasmodium falciparum and Leishmania donovani bind to multiple HLA-DR and MHC receptors. Thus synthetic epitope vaccines may better meet the requirements of the regulatory agencies since they have lower costs and are easier to produce. The classical experimental approach for the development of an epitope-based vaccine involves the use of recombinant domains or overlapping 15-mer peptides spanning the full length of the target antigen, and the analysis of the induced antibody and/or T cell immune responses in vitro or in vivo. On the other hand, in silico tools can select peptides that are more likely to contain epitopes, reducing the number of sequence candidates. T cell epitope prediction dates back to 1980s, when the first algorithm was developed based on the identification of amphipathic helical regions on protein antigens. Since then, new methods based on MHC peptide-binding motifs or MHC-binding properties have been developed. The recent reverse vaccinology concept uses high-throughput genome sequencing and bioinformatics tools to identify potential targets of immune responses. The feasibility of this approach was shown for the first time in the design of a vaccine against Neisseria meningitides that is now in phase III clinical trials. In addition, different computational tools allow the determination of crucial gene(s) through comparative analyses between different pathogenic strains Alternatively, carbohydrates have been considered as key targets in developing safe and effective vaccines to combat cancer, bacterial and viral infections. Tumor associated carbohydrate antigens can be coupled covalently to protein carriers to target MHC receptors and improve immunogenicity and have reached already pre-clinical and clinical studies. In light of the recent availability of genomic tools, we believe that in the near future an increasing number of vaccine candidates, composed of defined epitopes, will be available for synthetic vaccines showing improved protection.

Molecular Modeling in Drug Design

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ISBN: 9783038976141 Year: Pages: 220 DOI: 10.3390/books978-3-03897-615-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Chemistry (General) --- Science (General)
Added to DOAB on : 2019-04-05 10:34:31
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Abstract

This book is a printed edition of the Special Issue Molecular Modeling in Drug Design that was published in Molecules

Keywords

hyperlipidemia --- squalene synthase (SQS) --- molecular modeling --- drug discovery --- Traditional Chinese Medicine --- molecular dynamics simulation --- biophenols --- natural compounds --- amyloid fibrils --- Alzheimer’s disease --- ligand–protofiber interactions --- adhesion --- FimH --- rational drug design --- molecular dynamics --- molecular docking --- ligand binding --- EphA2-ephrin A1 --- PPI inhibition --- interaction energy --- in silico screening --- adenosine --- boron cluster --- adenosine receptors --- AR ligands --- aggregation --- promiscuous mechanism --- human ecto-5?-nucleotidase --- virtual screening --- enzymatic assays --- turbidimetry --- dynamic light scattering --- docking --- solvent effect --- binding affinity --- scoring function --- molecular dynamics --- target-focused pharmacophore modeling --- density-based clustering --- structure-based drug design --- AutoGrid --- grid maps --- probe energies --- method development --- steered molecular dynamics --- all-atom molecular dynamics simulation --- resultant dipole moment --- mechanical stability --- protein-peptide interactions --- molecular dynamics --- proteins --- molecular recognition --- protein protein interactions --- artificial intelligence --- deep learning --- neural networks --- property prediction --- quantitative structure-activity relationship (QSAR) --- quantitative structure-property prediction (QSPR) --- de novo design --- adenosine receptor --- metadynamics --- extracellular loops --- allosterism --- molecular dynamics --- cosolvent molecular dynamics --- drug design --- fragment screening --- docking

Lignans

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ISBN: 9783038979081 / 9783038979098 Year: Pages: 384 DOI: 10.3390/books978-3-03897-909-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Chemistry (General) --- Organic Chemistry
Added to DOAB on : 2019-06-26 10:09:00
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Lignans are a class of natural products found mainly in plants. They have a wide variety of structures and exhibit a range of potent biological activities. Lignans are also well-known components of a number of widely eaten foods and are frequently studied for their dietary impact. Owing to these factors, lignans have been extensively studied by scientists from a large number of disciplines. This collection of research and review articles describes topics ranging in scope from the recent isolation and structural elucidation of new lignans, strategies towards the chemical synthesis of lignans, assessment of their biological activities and potential for further therapeutic development. Research showing the impact of lignans in the food and agricultural industries is also presented.

Keywords

lignans --- chemometrics --- neolignans --- flavonolignans --- chemical space --- drug-like --- Lespedeza cuneata --- lignan glycoside --- flavonoid glycoside --- cytotoxicity --- adipocyte and osteoblast differentiation --- Bursera --- Burseraceae --- lignans --- lignan --- molecular dynamics --- intermolecular interactions --- graph theory --- lignans --- heilaohu --- tujia ethnomedicine --- chemical characterization --- cytotoxicity --- antioxidant --- cultivar --- environment --- flax --- flavonol --- genetic --- hydroxycinnamic acid --- lignan --- seed --- aryldihydronaphthalene lignan --- arylnaphthalene lignan --- oxidation --- synthesis --- lignans --- dibenzyl butyrolactones --- anti-proliferative --- acyl-Claisen --- stereoselective synthesis --- Schisandra rubriflora --- Schisandra chinensis --- red-flowered Chinese magnolia vine --- Chinese magnolia vine --- lignans --- phytochemical analysis --- UHPLC-MS/MS --- anti-inflammatory activity --- LOX --- COX --- sPLA2 --- lignans --- neolignans --- Lauraceae --- chemical components --- chemical structures --- dietary lignans --- national databases --- food groups --- dietary intake --- harmonized databases --- lignans --- in silico studies --- podophyllotoxin --- antibacterial activity --- acetylcholinesterase inhibitors --- antioxidant activity --- cytotoxicity --- natural products --- total synthesis --- lignan --- Bursera fagaroides --- podophyllotoxin-type lignans --- cell cycle --- cell migration --- epiboly --- microtubules --- F-actin --- cancer --- lignans --- animal health --- cattle --- enterolignan --- human health --- pharmacokinetic --- ruminant --- secoisolariciresinol diglucoside --- Lignan --- bitterness --- taste-active compound --- quantification --- oak ageing --- lignans --- norlignans --- 9-norlignans --- semisynthesis --- hydroxymatairesinol --- bioactivity --- lignans --- oxidation --- lignans --- diet --- antioxidants --- health promotion --- chronic diseases --- Schisandrae Chinensis Fructus --- wild --- cultivated --- multiple bioactive components --- simultaneous quantitation

Molecular Computing and Bioinformatics

Authors: --- ---
ISBN: 9783039211951 / 9783039211968 Year: Pages: 390 DOI: 10.3390/books978-3-03921-196-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- Biotechnology
Added to DOAB on : 2019-08-28 11:21:27
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Abstract

This text will provide the most recent knowledge and advances in the area of molecular computing and bioinformatics. Molecular computing and bioinformatics have a close relationship, paying attention to the same object but working towards different orientations. The articles will range from topics such as DNA computing and membrane computing to specific biomedical applications, including drug R&D and disease analysis.

Keywords

prostate cancer --- Mycoplasma hominis --- endoplasmic reticulum --- systems biology --- protein targeting --- biomedical text mining --- big data --- Tianhe-2 --- parallel computing --- load balancing --- bacterial computing --- bacteria and plasmid system --- Turing universality --- recursively enumerable function --- miRNA biogenesis --- structural patterns --- DCL1 --- protein–protein interaction (PPI) --- clustering --- protein complex --- penalized matrix decomposition --- avian influenza virus --- interspecies transmission --- amino acid mutation --- machine learning --- Bayesian causal model --- causal direction learning --- K2 --- brain storm optimization --- line graph --- Cartesian product graph --- join graph --- atom-bond connectivity index --- geometric arithmetic index --- P-glycoprotein --- efflux ratio --- in silico --- machine learning --- hierarchical support vector regression --- absorption --- distribution --- metabolism --- excretion --- toxicity --- image encryption --- chaotic map --- DNA coding --- Hamming distance --- Stenotrophomonas maltophilia --- iron acquisition systems --- iron-depleted --- RAST server --- NanoString Technologies --- siderophores --- gene fusion data --- gene susceptibility prioritization --- evaluating driver partner --- gene networks --- drug-target interaction prediction --- machine learning --- drug discovery --- microRNA --- environmental factor --- structure information --- similarity network --- bioinformatics --- identification of Chinese herbal medicines --- biochip technology --- DNA barcoding technology --- DNA strand displacement --- cascade --- 8-bit adder/subtractor --- domain label --- Alzheimer’s disease --- gene coding protein --- sequence information --- support vector machine --- classification --- adverse drug reaction prediction --- heterogeneous information network embedding --- stacking denoising auto-encoder --- meta-path-based proximity --- Panax ginseng --- oligopeptide transporter --- flowering plant --- phylogeny --- transcription factor --- multiple interaction networks --- function prediction --- multinetwork integration --- low-dimensional representation --- dihydrouridine --- nucleotide physicochemical property --- pseudo dinucleotide composition --- RNA secondary structure --- ensemble classifier --- diabetes mellitus --- hypoxia-inducible factor-1? --- angiogenesis --- bone formation --- osteogenesis --- protein transduction domain --- membrane computing --- edge detection --- enzymatic numerical P system --- resolution free --- molecular computing --- molecular learning --- DNA computing --- self-organizing systems --- pattern classification --- machine learning --- laccase --- Brassica napus --- lignification --- stress --- molecular computing --- bioinformatics --- machine learning --- protein --- DNA --- RNA --- drug --- bio-inspired

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