Carbohydrate-derived amphiphilic macromolecules: a biophysical structural characterization and analysis of binding behaviors to model membranes.

The design and synthesis of enhanced membrane-intercalating biomaterials for drug delivery or vascular membrane targeting is currently challenged by the lack of screening and prediction tools. The present work demonstrates the generation of a Quantitative Structural Activity Relationship model (QSAR) to make a priori predictions. Amphiphilic macromolecules (AMs) "stealth lipids" built on aldaric and uronic acids frameworks attached to poly(ethylene glycol) (PEG) polymer tails were developed to form self-assembling micelles. In the present study, a defined set of novel AM structures were investigated in terms of their binding to lipid membrane bilayers using Quartz Crystal Microbalance with Dissipation (QCM-D) experiments coupled with computational coarse-grained molecular dynamics (CG MD) and all-atom MD (AA MD) simulations. The CG MD simulations capture the insertion dynamics of the AM lipophilic backbones into the lipid bilayer with the PEGylated tail directed into bulk water. QCM-D measurements with Voigt viscoelastic model analysis enabled the quantitation of the mass gain and rate of interaction between the AM and the lipid bilayer surface. Thus, this study yielded insights about variations in the functional activity of AM materials with minute compositional or stereochemical differences based on membrane binding, which has translational potential for transplanting these materials in vivo. More broadly, it demonstrates an integrated computational-experimental approach, which can offer a promising strategy for the in silico design and screening of therapeutic candidate materials

Molecular modeling for physical property prediction

Multiscale modeling is becoming the standard approach for process study in a broader framework that promotes computer aided integrated product and process design. In addition to usual purity requirements, end products must meet new constraints in terms of environmental impact, safety of goods and people, specific properties. This chapter adresses the use of molecular modeling tools for the prediction of physical property usefull for chemical engineering practice

How the parts organize in the whole : a top-downview of molecular descriptors and properties for QSARand drug design

Sometimes the complexity of a system, or the properties derived from it, do depend neither on the individual characteristics of the components of the system nor on the nature of the physical forces that hold them together. In such cases the properties derived from the 'organization' of the system given by the connectivity of its elements can be determinant for explaining the structure of such systems. Here we explore the necessity of accounting for these structural characteristics in the molecular descriptors. We show that graph theory is the most appropriate mathematical theory to account for such molecular features. We review a method (TOPS-MODE) that is able to transform simple molecular descriptors, such as logP, polar surface area, molar refraction, charges, etc., into series of descriptors that account for the distribution of these characteristics (hydrophobicity, polarity, steric effects, etc) across the molecule. We explain the mathematical and physical principles of the TOPS-MODE method and develop three examples covering the description and interpretation of skin sensitisation of chemicals, chromosome aberration produced by organic molecules and drug binding to human serum albumin

QSAR study for carcinogenicity in a large set of organic compounds

In our continuing efforts to find out acceptable Absorption, Distribution, Metabolization, Elimination and Toxicity (ADMET) properties of organic compounds, we establish linear QSAR models for the carcinogenic potential prediction of 1464 compounds taken from the "Galvez data set", that include many marketed drugs. More than a thousand of geometry-independent molecular descriptors are simultaneously analyzed, obtained with the softwares E-Dragon and Recon. The variable subset selection method employed is the Replacement Method, and also the improved version Enhanced Replacement Method. The established models are properly validated through an external test set of compounds, and by means of the Leave-Group-Out Cross Validation method. In addition, we apply the Y-Randomization strategy and analyze the Applicability Domain of the developed model. Finally, we compare the results obtained in present study with the previous ones from the literature. The novelty of present work relies on the development of an alternative predictive structure-carcinogenicity relationship in a large heterogeneous set of organic compounds, by only using a reduced number of geometry independent molecular descriptors.Fil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaFil: Comelli, Nieves Carolina. Universidad Nacional de Catamarca. Facultad de Ciencias Agrarias; ArgentinaFil: Ortiz, Erlinda del Valle. Universidad Nacional de Catamarca. Facultad de Tecnología y Ciencias Aplicadas; ArgentinaFil: Castro, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentin

Lipophilicity in drug design: an overview of lipophilicity descriptors in 3D-QSAR studies

The pharmacophore concept is a fundamental cornerstone in drug discovery, playing a critical role in determining the success of in silico techniques, such as virtual screening and 3D-QSAR studies. The reliability of these approaches is influenced by the quality of the physicochemical descriptors used to characterize the chemical entities. In this context, a pivotal role is exerted by lipophilicity, which is a major contribution to host-guest interaction and ligand binding affinity. Several approaches have been undertaken to account for the descriptive and predictive capabilities of lipophilicity in 3D-QSAR modeling. Recent efforts encode the use of quantum mechanical-based descriptors derived from continuum solvation models, which open novel avenues for gaining insight into structure-activity relationships studies