Today classification of drug candidates on the Biopharmaceutics Classification System (BCS) has become an important issue in pharmaceutical researches. In this work, we provide a potential in silico approach to predict this system using two separately classification models of Dose number and Caco-2 cell permeability. 18 statistical linear and nonlinear models have been constructed based on 803 0-2D Dragon and 126 Volsurf+ molecular descriptors to classify the solubility and permeability properties. The voting consensus model of solubility (VoteS) showed a high accuracy of 88.7% in training and 92.3% in test set. Likewise, for the permeability model (VoteP), accuracy was 85.3% in training and 96.9% in test set. A combination of VoteS and VoteP appropriately predicts the BCS class of drugs (overall 73% with class I precision of 77.2%). This consensus system predicts the BCS allocations of 57 drugs appeared in the WHO Model List of Essential Medicines with 87.5% of accuracy. A simulation of a biopharmaceutical screening assay has been proved in a large data set of 37,377 compounds in different drug development phases (1, 2, 3 and launched), and NMEs. Distributions of BCS forecasts illustrate the current status in drug discovery and development. It is anticipated that developed QSPR models could offer the best estimation of BCS for NMEs in early stages of drug discovery.

Abstract:

     Hemagglutinin (HA) and neuraminidase (NA) are glycoproteins encoded by several types of viral particles.   Most notably, they exercise complementary chemical functions during infection and propagation of influenza A:  infection of a host is initiated by HA while NA catalyzes the release of newly-made viral particles. The antibodies of the molecules form the means of classifying the influenza A subtypes: H1N1, H2N2, H3N2, etc.. Given the risks of viral exposure to global host populations, intense effort is directed toward understanding the molecular mechanisms. Further, the design and formulation of drugs which subvert the mechanisms are on-going challenges.  This research focuses on the primary structure information expressed by the two proteins, applying an information theoretic model from previous research.   The amino acid sequences for HA and NA such as MKARLLILLCALSATD….. and MNPNQKIITIGSICMAI……  are parsed for their correlated information, both the total accumulation and fluctuations.   Data for the HA and NA of multiple influenza A subtypes are illustrated via information signatures and phase plots. This enables sharp contrasts to be drawn between seasonal infectious proteins and ones with high pandemic potential.    Overall, the analysis illuminates new ways of evaluating HA and NA molecules for their subtype and virulence based on information properties.  Just as important, the results point to mutation strategies for re-directing and attenuating the protein functions. 

Cytosine DNA methylation (CDM) is a highly abundant epigenetic heritable but reversible chemical modification to the genome. Herein, a machine learning approach, was applied to analyze the accumulation of epigenetic marks in 150 methylomes from Arabidopsis thaliana ecotypes. We hypothesize that these marks are chromosomal footprints that account for different ontogenetic and phylogenetic and histories of individual members of the sampling population. Our results support this hypothesis and suggest a statistical-physical relationship between CDM changes and single nucleotide polymorphism (SNPs). Furthermore, the genome-wide redistribution of CDM changes ensures the thermal stability of the DNA molecule preserving the integrity of the genetic message continuously stressed by thermal fluctuations in the cell environment.

The presence of human pharmaceutical compounds in surface waters is an emerging issue in environmental science. Low levels of many active pharmaceutical ingredients are detected in the aquatic environment as a result of pharmaco-chemical industrial waste spill-offs in draining water.   In the manufacturing of pharmaceutical drug substances azides are used as reagents or when they are generated somehow in the synthesis, it may be necessary to demonstrate that these impurities are sufficiently removed to levels below an appropriate safety threshold.  Sodium azide is an example of an azide for which the environmental exposure limits have been reasonably well characterized. The treatment of waste and industrial water can be conducted by removing dissolved materials and ions in water using membrane separation technology with ultra- and nanofiltration (NF) and reverse osmosis (RO) membranes. To achieve better effluent water quality, tertiary treatment with activated carbon adsorption is used.  To analyze the risk of pharmaceuticals in the environment, a proposed validated methodology by NMR spectroscopy will support the evaluation of the eco-toxicological hazards during the early development process of pharmaceuticals.

Site-occupancy disorder, defined as the non-periodic occupation of lattice sites in a crystal structure, is a ubiquitous phenomenon in solid-state physics and chemistry. Examples are mineral solid solutions, synthetic non-stoichiometric compounds and metal alloys. The experimental investigation of these materials using diffraction techniques only provides averaged information of their structure. However, many properties of interest in these solids are determined by the local geometry and degree of disorder, which escape an “average crystal” description, either from experiments or from theory.  In this paper, I will introduce a methodology for the computer simulation of site-disordered solids, based on the consideration of configurational ensembles and statistical mechanics, where the number of occupancy configurations is reduced by taking advantage of the crystal symmetry of the lattice [1, 2]. Thermodynamics and non-thermodynamic properties are then defined from the statistics in the symmetry-adapted configurational ensemble. I will briefly summarize and discuss some recent applications of this type of methodology to problems in mineralogy and materials science [3-7].

1. R Grau-Crespo, S Hamad, CRA Catlow, NH De Leeuw. Symmetry-adapted configurational modelling of fractional site occupancy in solids. Journal of Physics-Condensed Matter, 2007. 19: 256201.
2. R Grau-Crespo and UV Waghmare. Simulation of crystals with chemical disorder at lattice sites. In Molecular Modeling for the Design of Novel Performance Chemicals and Materials. Edited by Beena Rai. CRC Press Inc. ISBN 9781439840788 (2012).
3. R Grau-Crespo, KC Smith, TS Fisher, NH De Leeuw, UV Waghmare. Thermodynamics of hydrogen vacancies in MgH₂ from first-principles calculations and grand-canonical statistical mechanics. Physical Review B, 2009. 80: 174117
4. KC Smith, TS Fisher, UV Waghmare, R Grau-Crespo, Dopant-vacancy binding effects in Li-doped magnesium hydride. Physical Review B, 2010. 82: 134109.
5. R Grau-Crespo, NH de Leeuw, S Hamad, UV Waghmare. Phase separation and surface segregation in ceria-zirconia solid solutions. Proceedings of the Royal Society A-Mathematical Physical and Engineering Sciences, 2011. 467: 1925-1938.
6. SE Ruiz‐Hernandez, R Grau‐Crespo, N Almora‐Barrios, M Wolthers, AR Ruiz‐Salvador, N Fernandez, NH de Leeuw. Mg/Ca partitioning between aqueous solution and aragonite mineral: a molecular dynamics study. Chemistry - A European Journal, 2012. 18: 9828-9833.
7. Corps, Paz Vaqueiro, A Aziz, R Grau-Crespo, W Kockelmann, J-C Jumas, AV Powell. The Interplay of Metal-Atom Ordering, Fermi Level Tuning and Thermoelectric Properties in Cobalt Shandites Co3M2S2 (M = Sn, In). Chemistry of Materials, 2015. 27: 3946–3956.

Prediction models are gaining importance in many areas such as medicine, meteorology, finance, toxicology, etc. In this context, a common distribution for the response variable is the binomial distribution and hence the logistic regression model is a commonly used regression modelling approach. Although it is not recommended from a statistical points of view due to loss of information and power, the categorisation of continuous variables is a common practice in the development of prediction models. However, there are no unified criteria for the selection of the cut points in the categorisation process. In order to provide valid cut points whenever a categorisation is going to be performed, we have developed a valid methodology to categorise continuous variables in a logistic regression model based on the maximisation of the AUC. This methodology has been implemented in an R package called CatPredi . This is a package of R functions that allows the user to categorise a continuous predictor variable in a univariate or multiple logistic regression model. It provides the optimal location of cut points for a chosen number of cut points, fits the prediction model with the categorised predictor variable and returns the estimated and bias-corrected discriminative ability index for this model. Additionally, it allows a comparison of two categorisation proposals for different number of cut points and the selection of the optimal number of cut points.

The use of in-silico methods for identifying new drugs to a target of interest is a step of a process called Rational Drug Design. This process consider a target protein, also known as receptor, which the three-dimensional structure is use to determine the binding site, and a set of drug candidates that are tested in order to establish a stable complex. The in-silico analysis of a set of drug candidates is performed by a computational technique defined as Virtual Screening (VS). In a previous work we developed a novel web tool for  configuring different types of VS experiments using AutoDock Vina docking software. The presented tool is a framework that generates a python script to run a VS experiment in the users’ computer according to the users configuration on the framework web interface. In this paper we propose to apply the developed framework to a specific VS experiment considering one target receptor and a set of ligands. For this VS experiment the researcher informs the location of receptor and ligands files as well as their formats. It is also possible to set receptor and ligand flexibility.  After this, the user indicates the output folder where all the results in the user’s computer will be stored after the script execution.  Then, the user should configure the box area that indicates where ligands will be docked in the receptor molecule. The box size and the center must be configured, the variation of box center could be configured if user wants to execute an experiment that search the binding site in all molecule. For results analysis, the framework uses LigPlot software that describes the interactions between ligand and receptor amino-acids atoms after the performed molecular docking. In this way, this paper demonstrates the usage of the proposed framework for VS where we considered as receptor the structure of the human voltage-dependent anion channel (PDB code: 2JK4) and as ligands different types of carbon nanotubes. In the performed experiment we defined both receptor and ligands as rigid and considered only one box for representing the receptor binding site.

The growing increase in the amount and type of nutrients in food created the necessity for a more efficient use applied to dietetics and nutrition. Flavonoids are exogenous dietetic antioxidants and contribute to the total antioxidant capacity of the food. This paper aims to explore the data using different algorithms of artificial intelligence to find the one that best predict the total antioxidant capacity of food by the oxygen radical absorbance capacity (ORAC) method. A record of composition data based on the Database for the Flavonoid Content of Selected Foods and the Database for the Isoflavone Content of Selected Foods, was created. The KNN (K-Nearest Neighbors) and supervised unidirectional networks MLP (MultiLayer Perceptron) technics were used. The attributes were: a) amount of flavonoid (mean), b) class of flavonoid, c) Trolox equivalent antioxidant capacity (TEAC) value of each flavonoid, d) probability of clastogenicity and clastogenicity classification by Quantitative Structure-Activity Relationship (QSAR) method and e) total polyphenol (TP) value. The variable to predict the activities was the ORAC value. For the prediction, a cross-validation method was used. For the KNN algorithm the optimal K value was 3, making clear the importance of the similarity between objects for the success of the results. It was concluded the successful use of the MLP and KNN techniques to predict the antioxidant capacity in the studied food groups.

Protein Data Bank (PDB) is a public web database with more than 100,000 biological macromolecular structures. With this large amount of protein structures available on PDB the use of tools for acquisition and analysis of specific sets of biological macromolecules is a necessity. Hence, in this work we propose the development of a tool for acquiring, storing and analyzing specific sets of proteins from the PDB database. The proposed tool runs on desktop environment allowing the user to acquire the structures from the RESTful web-service provided by PDB server. After the acquisition of a set of interesting PDBs the user can manipulate these data in an off-line environment through a local database that stores the information about the characteristics of the structures, for example, ligands, mutations, residues, sequences and docking results. The protein files are locally stored in the users’ computer and can be used, for instance, for molecular docking simulations and alignment of sequences and structures. Having a set of proteins of interest available locally and using our proposed tool the user can perform analysis related to alignments and visualize important proteins characteristics improving the knowledge about specific target. Besides, the user can select PDB files to be visualized on a graphical environment that is integrated in our tool. Other features are related to the exporting of sequence alignments results in csv (comma separated value) format or exporting sequences that have a similar identity in a format that can be easily loaded on graph tools. These alignments allow the user to visualize which proteins are similar and discard those that are not.

The antiviral QSAR models today have an important limitation. Only they predict the biological activity of drugs against only one viral species. This is determined due the fact that most of the current reported molecular descriptors encode only information about the molecular structure. As a result, predicting the probability with which a drug is active against different viral species with a single unifying model is a goal of major importance. In this we use the Markov Chain theory to calculate new multi-target entropy to fit a QSAR model that predict by the first time a ms-QSAR model for 900 drugs tested in the literature against 40 viral species and other 207 drugs no tested in the literature using entropy QSAR. We used Linear Discriminant Analysis (LDA) to classify drugs into two classes as active or non-active against the different tested viral species whose data we processed. The model correctly classifies 31 188 out of 31 213 non-active compounds (99.92%) and 432 out of 434 active compounds (99.54%). Overall training predictability was 98.56%. Validation of the model was carried out by means of external predicting series, the model classifying, thus, 15 588 out of 15 606 non-active compounds and 213 out of 217 active compounds. Overall validation predictability was 98.54%. The present work report the first attempts to calculate within a unify framework probabilities of antiviral drugs against different virus species based on entropy analysis.