QSAR modeling of in vitro inhibition of cytochrome P450 3A4

We report the QSAR modeling of cytochrome P450 3A4 (CYP3A4) enzyme inhibition using four large data sets of in vitro data. These data sets consist of marketed drugs and drug-like compounds all tested in four assays measuring the inhibition of the metabolism of four different substrates by the CYP3A4 enzyme. The four probe substrates are benzyloxycoumarin, testosterone, benzyloxyresorufin, and midazolam. We first show that using state-of-the-art QSAR modeling approaches applied to only one of these four data sets does not lead to predictive models that would be useful for in silico filtering of chemical libraries. We then present the development and the testing of a multiple pharmacophore hypothesis (MPH) that is formulated as a conceptual extension of the traditional QSAR approach to modeling the promiscuous binding of a large variety of drugs to CYP3A4. In the simplest form, the MPH approach takes advantage of the multiple substrate data sets and identifies the binding of test compounds as either proximal or distal relative to that of a given substrate. Application of the approach to the in silico filtering of test compounds for potential inhibitors of CYP3A4 is also presented. In addition to an improvement in the QSAR modeling for the inhibition of CYP3A4, the results from this modeling approach provide structural insights into the drug-enzyme interactions. The existence of multiple inhibition data sets in the BioPrint database motivates the original development of the concept of a multiple pharmacophore hypothesis and provides a unique opportunity for formulating alternative strategies of QSAR modeling of the inhibition of the in vitro metabolism of CYP3A4.     

Numerical and empirical modeling of peak deceleration and stress analysis of polyurethane elastomer under impact loading test

In this work, we present the modeling of the peak deceleration (PD) using data of the experimental drop test. Specimens with different thicknesses and areas tested in the drop test device which has adaptable height and weight. In the empirical modeling of the PD, the thickness, area, drop mass and drop height considered as separable functions. An analytical model and Neural Network (NN) was used as the empirical models. Further, the stress on the material was calculated using differential equations and the Finite Element Method (FEM). The Obtained PD from the experimental test, analytical and NN models was converted to the stress on the material using a derived differential equation. Finally, the best model for analyzing the PD and Stress on the material was presented.     

Toward realistic modeling of dynamic processes in cell signaling: quantification of macromolecular crowding effects

One of the major factors distinguishing molecular processes in vivo from biochemical experiments in vitro is the effect of the environment produced by macromolecular crowding in the cell. To achieve a realistic modeling of processes in the living cell based on biochemical data, it becomes necessary, therefore, to consider such effects. We describe a protocol based on Brownian dynamics simulation to characterize and quantify the effect of various forms of crowding on diffusion and bimolecular association in a simple model of interacting hard spheres. We show that by combining the elastic collision method for hard spheres and the mean field approach for hydrodynamic interaction (HI), our simulations capture the correct dynamics of a monodisperse system. The contributions from excluded volume effect and HI to the crowding effect are thus quantified. The dependence of the results on size distribution of each component in the system is illustrated, and the approach is applied as well to the crowding effect on electrostatic-driven association in both neutral and charged environments; values for effective diffusion constants and association rates are obtained for the specific conditions. The results from our simulation approach can be used to improve the modeling of cell signaling processes without additional computational burdens.     

Hard modeling of ion chromatography separations on hydroxide-selective stationary phase

In the present paper the development and testing of the computer software for the simulation of the on-column ion chromatography separation processes are presented. The computer algorithm based exclusively on the selectivity coefficients of the tested analytes has been upgraded to cope with the 2:1 and 1:1 ratios of the analyte-to-eluent ion charge. The analytical solution of the cubic equation, which is needed for calculation of chromatograms for doubly charged analytes in the presence of singly charged eluent, is presented. The developed modeling approach was tested on the data sets produced by the system composed of hydroxide-selective stationary phase in combination with on-line electrolytically generated OH(-)-based eluents. Retention behavior of the selected anions on the AS15 (DIONEX, USA) stationary phase was investigated. The study of the dependence of the peak widths on the number of theoretical column segments considered in the calculated chromatograms enabled us to choose the optimal number of column segments. The average error in the retention time of the calculated chromatograms for the data set used in the study, i.e. seven different ions at eight different eluent concentrations was found to be 1.4%. A good match with the experimental chromatograms allows us to use the information of the intermediate states of calculations to get a detailed insight into the time-dependent on-column analyte distribution.     

Nonparametric assessment of comparability of analytical results obtained in proficiency testing based on a metrological approach

A nonparametric sign test is implemented for assessment of comparability of proficiency testing (PT) results when their distribution differs from the normal or other known distribution. It allows testing the null hypothesis about insignificance of the bias of median of results obtained in PT from the traceable certified value of the reference material used in PT as test items, i.e., the hypothesis stating that comparability of the PT results is successful. Probability of type I error of rejecting the null hypothesis when it is true, and probability of type II error of it not rejecting when it is false (the alternative hypothesis about unsuccessful comparability is true) are considered. The test can be helpful for PT providers and laboratory accreditation bodies in analysis of PT results when the comparability criterion developed for a normal results distribution (Accred. Qual. Assur. 10:466–470) is not applicable.     

Introduction of a new critical p value correction method for statistical significance analysis of metabonomics data

Nuclear magnetic resonance (NMR) spectroscopy-based metabonomics is of growing importance for discovery of human disease biomarkers. Identification and validation of disease biomarkers using statistical significance analysis (SSA) is critical for translation to clinical practice. SSA is performed by assessing a null hypothesis test using a derivative of the Student's t test, e.g., a Welch's t test. Choosing how to correct the significance level for rejecting null hypotheses in the case of multiple testing to maintain a constant family-wise type I error rate is a common problem in such tests. The multiple testing problem arises because the likelihood of falsely rejecting the null hypothesis, i.e., a false positive, grows as the number of tests applied to the same data set increases. Several methods have been introduced to address this problem. Bonferroni correction (BC) assumes all variables are independent and therefore sacrifices sensitivity for detecting true positives in partially dependent data sets. False discovery rate (FDR) methods are more sensitive than BC but uniformly ascribe highest stringency to lowest p value variables. Here, we introduce standard deviation step down (SDSD), which is more sensitive and appropriate than BC for partially dependent data sets. Sensitivity and type I error rate of SDSD can be adjusted based on the degree of variable dependency. SDSD generates fundamentally different profiles of critical p values compared with FDR methods potentially leading to reduced type II error rates. SDSD is increasingly sensitive for more concentrated metabolites. SDSD is demonstrated using NMR-based metabonomics data collected on three different breast cancer cell line extracts.     

Generalized extended empirical bond-order dependent force fields including nonbond interactions

We present a general approach for describing chemical processes (bond breaking and bond formation) in materials using force fields (FF) that properly describe multiple bonds at small distances while describing nonbond (Coulomb and van der Waals) interactions at long distances. This approach is referred to as the generalized extended empirical bond-order dependent FF. In this paper we use the Brenner empirical bond-order dependent FF for the short-range interactions and report applications on the energetics and structures of graphite crystal, dynamics of molecular crystals, and distortions of bucky tubes. Received: 7 August 1998 / Accepted: 26 October 1998 / Published online: 16 March 1999     

Equivalent dynamic thermoviscoelastic modeling of ionic polymers

Ionic polymers have attracted considerable attention due to their interesting sensing and actuating behavior which make them a proper choice for use in a wide range of applications including biomimetic robots and biomedical devices. The complicated electro‐chemo‐mechanical dynamics of ionic polymer actuators is a drawback for their applications in functional devices. Therefore, establishing a mathematical model which could effectively predict the actuators' dynamic behavior is of great interest. In this paper, a mathematical model, named equivalent dynamic thermoviscoelastic (EDT) model, based on thermal analogy and beam theory is proposed for dynamic analysis of bending‐type ionic polymer actuators. Then, the developed model is extended for analyzing the performance of the actuator in finite element software. The finite element analysis of the actuator enables consideration of material and geometric nonlinearities and facilitates modeling of functional devices based on the ionic polymer actuators. The proposed modeling approach is validated using experimental data. Copyright © 2015 John Wiley & Sons, Ltd.     

Sensitivity analysis and parameter estimation in dynamic modeling of chemical kinetics

A new approach to the dynamic modeling of chemical kinetics is presented. The technique is based on systematic planning of computer experiments, which allows an empirical model for the computed responses to be developed in the space of parameters. The empirical equations which are obtained provide complete information on the sensitivities with respect to various rate constants, disclosing their interrelationships. Utilization of these equations instead of numerical integration of the differential equations associated with the chemical reactions makes parameter estimation a trivial task. As a consequence the adequacy of the mechanism can be tested. The technique is applied to the thermal decomposition of propane.     

Comparability of analytical results obtained in proficiency testing based on a metrological approach

A “yes–no” type of criterion is proposed for the assessment of comparability of proficiency testing (PT) results when the PT scheme is based on a metrological approach, i.e. on the use of a reference material as the test sample, etc. The criterion tests a null hypothesis concerning the insignificance of a bias of the mean of the results from a traceable value certified in the reference material used for the PT. Reliability of such assessment is determined by the probabilities of not rejecting the null hypothesis when it is true, and rejecting it when it is false (the alternative hypothesis is true). It is shown that a number of chemical, metrological and statistical reasons should be taken into account for careful formulation of the hypotheses, enabling the avoidance of an erroneous assessment of the comparability. The criterion can be helpful for PT providers and laboratory accreditation bodies in analysis of PT results.     

Multiscale geometric modeling of macromolecules II: Lagrangian representation

Geometric modeling of biomolecules plays an essential role in the conceptualization of biolmolecular structure, function, dynamics, and transport. Qualitatively, geometric modeling offers a basis for molecular visualization, which is crucial for the understanding of molecular structure and interactions. Quantitatively, geometric modeling bridges the gap between molecular information, such as that from X‐ray, NMR, and cryo‐electron microscopy, and theoretical/mathematical models, such as molecular dynamics, the Poisson–Boltzmann equation, and the Nernst–Planck equation. In this work, we present a family of variational multiscale geometric models for macromolecular systems. Our models are able to combine multiresolution geometric modeling with multiscale electrostatic modeling in a unified variational framework. We discuss a suite of techniques for molecular surface generation, molecular surface meshing, molecular volumetric meshing, and the estimation of Hadwiger's functionals. Emphasis is given to the multiresolution representations of biomolecules and the associated multiscale electrostatic analyses as well as multiresolution curvature characterizations. The resulting fine resolution representations of a biomolecular system enable the detailed analysis of solvent–solute interaction, and ion channel dynamics, whereas our coarse resolution representations highlight the compatibility of protein‐ligand bindings and possibility of protein–protein interactions. © 2013 Wiley Periodicals, Inc.     

Ab initio modeling of CW-ESR spectra of the double spin labeled peptide Fmoc-(Aib-Aib-TOAC)2-Aib-OMe in acetonitrile

In this work we address the interpretation, via an ab initio integrated computational approach, of the CW-ESR spectra of the double spin labeled, 310-helical, peptide Fmoc-(Aib-Aib-TOAC)2-Aib-OMe dissolved in acetonitrile. Our approach is based on the determination of geometric and local magnetic parameters of the heptapeptide by quantum mechanical density functional calculations taking into account solvent and, when needed, vibrational averaging contributions. The system is then described by a stochastic Liouville equation for the two electron spins interacting with each other and with two 14N nuclear spins, in the presence of diffusive rotational dynamics. Parametrization of the diffusion rotational tensor is provided by a hydrodynamic model. CW-ESR spectra are simulated with minimal resorting to fitting procedures, proving that the combination of sensitive ESR spectroscopy and sophisticated modeling can be highly helpful in providing 3D structural and dynamic information on molecular systems.     

Multivariate range modeling, a new technique for multivariate class modeling: the uncertainty of the estimates of sensitivity and specificity

MRM, multivariate range modeling, is based on models built as parallelepipeds in the space of the original variables and/or of discriminant variables as those of linear discriminant analysis. The ranges of these variables define the boundary of the model. The ranges are increased by a "tolerance" factor to take into account the uncertainty of their estimate. MRM is compared with UNEQ (the modeling technique based on the hypothesis of multivariate normal distribution) and with SIMCA (based on principal components) by means of the sensitivities and specificities of the models, the estimates of type I (sensitivity) and II error rates (specificity) evaluated both with the final model built with all the available objects and by means of cross validation. UNEQ and SIMCA models were obtained with the usual critical significance value of 5% and with the model forced to accept all the objects of the modeled category. The performance parameters of the class models are critically discussed focusing on their uncertainty.     

Bayesian predictive modeling and comparison of oil samples

Statistical comparison of oil samples is an integral part of oil spill identification, which deals with the process of linking an oil spill with its source of origin. In current practice, a frequentist hypothesis test is often used to evaluate evidence in support of a match between a spill and a source sample. As frequentist tests are only able to evaluate evidence against a hypothesis but not in support of it, we argue that this leads to unsound statistical reasoning. Moreover, currently only verbal conclusions on a very coarse scale can be made about the match between two samples, whereas a finer quantitative assessment would often be preferred. To address these issues, we propose a Bayesian predictive approach for evaluating the similarity between the chemical compositions of two oil samples. We derive the underlying statistical model from some basic assumptions on modeling assays in analytical chemistry, and to further facilitate and improve numerical evaluations, we develop analytical expressions for the key elements of Bayesian inference for this model. The approach is illustrated with both simulated and real data and is shown to have appealing properties in comparison with both standard frequentist and Bayesian approaches. Copyright © 2013 John Wiley & Sons, Ltd.     

Efficient simultaneous reverse Monte Carlo modeling of pair-distribution functions and extended x-ray-absorption fine structure spectra of crystalline disordered materials

An efficient implementation of simultaneous reverse Monte Carlo (RMC) modeling of pair distribution function (PDF) and EXAFS spectra is reported. This implementation is an extension of the technique established by Krayzman et al. [J. Appl. Cryst. 42, 867 (2009)] in the sense that it enables simultaneous real-space fitting of x-ray PDF with accurate treatment of Q-dependence of the scattering cross-sections and EXAFS with multiple photoelectron scattering included. The extension also allows for atom swaps during EXAFS fits thereby enabling modeling the effects of chemical disorder, such as migrating atoms and vacancies. Significant acceleration of EXAFS computation is achieved via discretization of effective path lengths and subsequent reduction of operation counts. The validity and accuracy of the approach is illustrated on small atomic clusters and on 5500-9000 atom models of bcc-Fe and α-Fe(2)O(3). The accuracy gains of combined simultaneous EXAFS and PDF fits are pointed out against PDF-only and EXAFS-only RMC fits. Our modeling approach may be widely used in PDF and EXAFS based investigations of disordered materials.     

Estimating and modeling charge transfer from the SAPT induction energy

Recent studies using quantum mechanics energy decomposition methods, for example, SAPT and ALMO, have revealed that the charge transfer energy may play an important role in short ranged inter‐molecular interactions, and have a different distance dependence comparing with the polarization energy. However, the charge transfer energy component has been ignored in most current polarizable or non‐polarizable force fields. In this work, first, we proposed an empirical decomposition of SAPT induction energy into charge transfer and polarization energy that mimics the regularized SAPT method (ED‐SAPT). This empirical decomposition is free of the divergence issue, hence providing a good reference for force field development. Then, we further extended this concept in the context of AMOEBA polarizable force field, proposed a consistent approach to treat the charge transfer phenomenon. Current results show a promising application of this charge transfer model in future force field development. © 2017 Wiley Periodicals, Inc.     

Applied mathematical modeling for calculating the probability of the cell killing per hit in the human lung

The calculating the probability of the cell killing per hit, from radon progeny, requires the development of morphometric model of the human airway system. This study is focused on the different modeling concept. For example, several morphometric lung models have been published which differ in terms of airway structure and lung volume, there by affecting the particle deposition efficiencies. The present variety of modeling concepts suggests that the choice of specific modeling assumptions is as important for dose risk estimates as the choice of proper parameter values. The model of human lung analysed in the present study differ from those employed in the ICRP66 model, dose estimates will consequently differ from ICRP66 predictions, because its included the area of the branching the cylinders (airways tube) in the human lung. A analytical model cylinder bifurcation was created to simulate the geometry of human lung with the geometric distribution of cell nuclei in the airway wall of the tracheobronchial tree. Reported probabilities are calculated for various targets and alpha particle energies in order to show dependence of the probability of cell killing per hit from alpha particle energies and the geometry of tracheobronchial tree for the human lung, created in this study.     

Accurate and interpretable computational modeling of chemical mutagenicity

We describe a method for modeling chemical mutagenicity in terms of simple rules based on molecular features. A classification model was built using a rule-based ensemble method called RuleFit, developed by Friedman and Popescu. We show how performance compares favorably against literature methods. Performance was measured through the use of cross-validation and testing on external test sets. All data sets used are publicly available. The method automatically generated transparent rules in terms of molecular structure that agree well with known toxicology. While we have focused on chemical mutagenicity in demonstrating this method, we anticipate that it may be more generally useful in modeling other molecular properties such as other types of chemical toxicity.