Predicting Skin Permeability by means of Computational Approaches : Reliability and Caveats in Pharmaceutical Studies

© 2019 American Chemical Society.The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.Peer reviewedFinal Accepted Versio

Modeling the vapor-liquid equilibrium and association of nitrogen dioxide/dinitrogen tetroxide and its mixtures with carbon dioxide

We have used in this work the crossover soft-SAFT equation of state to model nitrogen dioxide/dinitrogen tetraoxide (NO2/N2O4), carbon dioxide (CO2) and their mixtures. The prediction of the vapor – liquid equilibrium of this mixture is of utmost importance to correctly assess the NO2 monomer amount that is the oxidizing agent of vegetal macromolecules in the CO2 + NO2 / N2O4 reacting medium under supercritical conditions. The quadrupolar effect was explicitly considered when modeling carbon dioxide, enabling to obtain an excellent description of the vapor-liquid equilibria diagrams. NO2 was modeled as a self associating molecule with a single association site to account for the strong associating character of the NO2 molecule. Again, the vapor-liquid equilibrium of NO2 was correctly modeled. The molecular parameters were tested by accurately predicting the very few available experimental data outside the phase equilibrium. Soft-SAFT was also able to predict the degree of dimerization of NO2 (mimicking the real NO2/N2O4 situation), in good agreement with experimental data. Finally, CO2 and NO2 pure compound parameters were used to predict the vapor – liquid coexistence of the CO2 + NO2 / N2O4 mixture at different temperatures. Experimental pressure – CO2 mass fraction isotherms recently measured were well described using a unique binary parameter, independent of the temperature, proving that the soft-SAFT model is able to capture the non-ideal behavior of the mixture

Spin Hamiltonian Parameters for Cu(II)−Prion Peptide Complexes from L-Band Electron Paramagnetic Resonance Spectroscopy

Cu(II) is an essential element for life but is also associated with numerous and serious medical conditions, particularly neurodegeneration. Structural modeling of crystallization-resistant biological Cu(II) species relies on detailed spectroscopic analysis. Electron paramagnetic resonance (EPR) can, in principle, provide spin Hamiltonian parameters that contain information on the geometry and ligand atom complement of Cu(II). Unfortunately, EPR spectra of Cu(II) recorded at the traditional X-band frequency are complicated by (i) strains in the region of the spectrum corresponding to the g∥ orientation and (ii) potentially very many overlapping transitions in the g⊥ region. The rapid progress of density functional theory computation as a means to correlate EPR and structure, and the increasing need to study Cu(II) associated with biomolecules in more biologically and biomedically relevant environments such as cells and tissue, have spurred the development of a technique for the extraction of a more complete set of spin Hamiltonian parameters that is relatively straightforward and widely applicable. EPR at L-band (1−2 GHz) provides much enhanced spectral resolution and straightforward analysis via computer simulation methods. Herein, the anisotropic spin Hamiltonian parameters and the nitrogen coordination numbers for two hitherto incompletely characterized Cu(II)-bound species of a prion peptide complex are determined by analysis of their L-band EPR spectra

The Journal of Computer-Aided Molecular Design: a bibliometric note

Summarizes the articles in, and the citations to, volumes 2-24 of the Journal of Computer-Aided Molecular Design. The citations to the journal come from almost 2000 different sources that span a very wide range of academic subjects, with the most heavily cited articles being descriptions of software systems and of computational methods

Dynamic and multi-pharmacophore modeling for designing polo-box domain inhibitors.

The polo-like kinase 1 (Plk1) is a critical regulator of cell division that is overexpressed in many types of tumors. Thus, a strategy in the treatment of cancer has been to target the kinase activity (ATPase domain) or substrate-binding domain (Polo-box Domain, PBD) of Plk1. However, only few synthetic small molecules have been identified that target the Plk1-PBD. Here, we have applied an integrative approach that combines pharmacophore modeling, molecular docking, virtual screening, and in vitro testing to discover novel Plk1-PBD inhibitors. Nine Plk1-PBD crystal structures were used to generate structure-based hypotheses. A common pharmacophore model (Hypo1) composed of five chemical features was selected from the 9 structure-based hypotheses and used for virtual screening of a drug-like database consisting of 159,757 compounds to identify novel Plk1-PBD inhibitors. The virtual screening technique revealed 9,327 compounds with a maximum fit value of 3 or greater, which were selected and subjected to molecular docking analyses. This approach yielded 93 compounds that made good interactions with critical residues within the Plk1-PBD active site. The testing of these 93 compounds in vitro for their ability to inhibit the Plk1-PBD, showed that many of these compounds had Plk1-PBD inhibitory activity and that compound Chemistry_28272 was the most potent Plk1-PBD inhibitor. Thus Chemistry_28272 and the other top compounds are novel Plk1-PBD inhibitors and could be used for the development of cancer therapeutics

Modeling, Simulating, and Parameter Fitting of Biochemical Kinetic Experiments

In many chemical and biological applications, systems of differential equations containing unknown parameters are used to explain empirical observations and experimental data. The DEs are typically nonlinear and difficult to analyze, requiring numerical methods to approximate the solutions. Compounding this difficulty are the unknown parameters in the DE system, which must be given specific numerical values in order for simulations to be run. Estrogen receptor protein dimerization is used as an example to demonstrate model construction, reduction, simulation, and parameter estimation. Mathematical, computational, and statistical methods are applied to empirical data to deduce kinetic parameter estimates and guide decisions regarding future experiments and modeling. The process demonstrated serves as a pedagogical example of quantitative methods being used to extract parameter values from biochemical data models.Comment: 23 pages, 9 figures, to be published in SIAM Revie

Entropy scaling based viscosity predictions for hydrocarbon mixtures and diesel fuels up to extreme conditions

An entropy scaling based technique using the Perturbed-Chain Statistical Associating Fluid Theory is described for predicting the viscosity of hydrocarbon mixtures and diesel fuels up to high temperatures and high pressures. The compounds found in diesel fuels or hydrocarbon mixtures are represented as a single pseudo-component. The model is not fit to viscosity data but is predictive up to high temperatures and pressures with input of only two calculated or measured mixture properties: the number averaged molecular weight and hydrogen to carbon ratio. Viscosity is predicted less accurately when the mixture contains high concentrations of iso-alkanes and cyclohexanes. However, it is shown that predictions for these mixtures are improved by fitting a third parameter to a single viscosity data point at a chosen reference state. For hydrocarbon mixtures, viscosity is predicted with average mean absolute percent deviations (MAPDs) of 12.2% using the two-parameter model and 7.3% using the three-parameter model from 293 to 353 K and up to 1000 bar. For two different diesel fuels, viscosity is predicted with an average MAPD of 21.4% using the two-parameter model and 9.4% using the three-parameter model from 323 to 423 K and up to 3500 bar