Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient lead discovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than the traditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledge of the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applications of Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initial stages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of topscoring hits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, induced fit and consensus docking are also discussed. The review highlights advances in the field within the framework of several success studies that have led to nM inhibition directly from VS and provides recent trends in library design as well as discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enable the discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteins are also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increase inhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed to inhibit the mutant H1047R PI3Kα kinase. Second, we discuss a strategy for the identification of selective binders for the RXRα nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on binding site shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein target through the SBVS process.

To the best of our knowledge, this is the first comprehensive field synopsis and systematic meta-analysis to identify genes associated with an increased susceptibility to CM.

Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.

Alarming epidemiological features of Alzheimer's disease impose curative treatment rather than symptomatic relief. Drug repurposing, that is reappraisal of a substance's indications against other diseases, offers time, cost and efficiency benefits in drug development, especially when in silico techniques are used. In this study, we have used gene signatures, where up- and down-regulated gene lists summarize a cell's gene expression perturbation from a drug or disease. To cope with the inherent biological and computational noise, we used an integrative approach on five disease-related microarray data sets of hippocampal origin with three different methods of evaluating differential gene expression and four drug repurposing tools. We found a list of 27 potential anti-Alzheimer agents that were additionally processed with regard to molecular similarity, pathway/ontology enrichment and network analysis. Protein kinase C, histone deacetylase, glycogen synthase kinase 3 and arginase inhibitors appear consistently in the resultant drug list and may exert their pharmacologic action in an epidermal growth factor receptor-mediated subpathway of Alzheimer's disease.

We have developed a model using Monte Carlo methods to simulate x-ray mammography. All possible physical processes of interaction of x-rays with matter have been taken into account. A simplified geometry of the mammographic apparatus has been considered along with a software phantom of compressed breast. The phantom may contain inhomogeneities of various compositions and sizes. We have used this model to produce Monte Carlo mammograms under realistic conditions. The validation of the simulation includes both the modelling of physical processes and the production of Monte Carlo mammograms. The first part is accomplished by the demonstration of the coincidence between Monte Carlo and theoretical data, whereas the second is accomplished by the comparison of real mammograms, taken from irradiation of a simplified breast phantom that we have constructed, and Monte Carlo mammograms taken from simulation of the above phantom under the corresponding exposure conditions. The limitations of the model as well as the future use of Monte Carlo mammograms are discussed.

gspyrou@bioacademy.gr

The emergence of powerful mass spectrometry-based proteomic techniques has added a new dimension to the field of biomedical research. Application of these high throughput methodologies in pregnancy-related pathology has contributed to the comprehension of the underlying pathophysiologies and the successful identification of relevant protein biomarkers that can potentially change early diagnosis and treatment of several medical conditions related to human pregnancy. Most of the existing research on human reproduction and gestation has focused on follicular fluid, cervical/vaginal fluid, and amniotic fluid. Although proteome technologies in reproductive medicine research are not as yet widely applied, characterization of the proteome of reproductive fluids can be expected to significantly improve maternal healthcare. This article aims to summarize the applications of mass spectrometry based technology on the most important and specific biological fluids related to reproduction and gestation.

Magnification mammography is a special technique used in the cases where breast complaints are noted by a woman or when an abnormality is found in a screening mammogram. The carcinogenic risk in mammography is related to the dose deposited in the glandular tissue of the breast rather than the adipose, and average glandular dose (AGD) is the quantity taken into consideration during a mammographic examination. Direct measurement of the AGD is not feasible during clinical practice and thus, the incident air KERMA on the breast surface is used to estimate the glandular dose, with the help of proper conversion factors. Additional conversion factors adapted for magnification and tube voltage are calculated, using Monte Carlo simulation. The effect of magnification degree, tube voltage, various anode/filter material combinations and glandularity on AGD is also studied, considering partial breast irradiation. Results demonstrate that the estimation of AGD utilizing conversion factors depends on these parameters, while the omission of correction factors for magnification and tube voltage can lead to significant underestimation or overestimation of AGD. AGD was found to increase with filter material's k-absorption edge, anode material's k-emission edge, tube voltage and magnification. Decrease of the glandularity of the breast leads to higher AGD due to the increased penetrating ability of the photon beam in thick breasts with low glandularity.