Comparative QSAR and the radical toxicity of various functional groups

Reaction rate constant: a theoretical description from local temperature

Application of various descriptors based on electron density and its associated quantities to quantify chemical reactivity within the conceptual density functional theory has recently come into spotlight. Among others and particularly relevant to our study, local temperature based on electron density as well as kinetic energy density, as a measure of the kinetic energy of an electron moving in the Kohn-Sham potential of systems, should be mentioned. In this work, we propose to use the local temperature for describing the reaction rate constant, where our main idea originates from the point that the smaller the local temperature at the reaction center, the easier the electron removal, leading to a larger rate constant. On the basis of theoretical considerations, it is proved that the rate constant variations caused by the substituent effects can well be proportional to the local temperature at the reaction center. In order to numerically validate our proposed approach, we have taken the phenol derivatives with the available experimental rate constants of their O-methylation reaction as working models. The reason for this choice is that one of the most versatile approaches for labeling biologically active compounds with the 11C nuclide for positron emission tomography (PET) is methylation by methyl iodide including 11C nuclide, [11C]MeI, where methylation of phenolic oxygen with [11C]MeI is utilized to label some important tracers for PET studies. Our results unveil that the local temperature changes at the reaction center of the aforementioned reaction are reasonably correlated with the rate constant variations. Hopefully, incorporating the proposed correlations between the local temperature and the kinetics data into a computer control algorithm not only provides a simple tool for predicting the rate constant of the O-methylation reaction for other substituted phenols, but also, as a part of the chemical artificial intelligence, the optimum [11C]MeI labeling conditions for a wide variety of phenol derivatives can be controlled.

Temperature-Dependent Density and Viscosity Prediction for Hydrocarbons: Machine Learning and Molecular Dynamics Simulations

Machine learning-based predictive models allow rapid and reliable prediction of material properties and facilitate innovative materials design. Base oils used in the formulation of lubricant products are complex hydrocarbons of varying sizes and structure. This study developed Gaussian process regression-based models to accurately predict the temperature-dependent density and dynamic viscosity of 305 complex hydrocarbons. In our approach, strongly correlated/collinear predictors were trimmed, important predictors were selected by least absolute shrinkage and selection operator (LASSO) regularization and prior domain knowledge, hyperparameters were systematically optimized by Bayesian optimization, and the models were interpreted. The approach provided versatile and quantitative structure-property relationship (QSPR) models with relatively simple predictors for determining the dynamic viscosity and density of complex hydrocarbons at any temperature. In addition, we developed molecular dynamics simulation-based descriptors and evaluated the feasibility and versatility of dynamic descriptors from simulations for predicting the material properties. It was found that the models developed using a comparably smaller pool of dynamic descriptors performed similarly in predicting density and viscosity to models based on many more static descriptors. The best models were shown to predict density and dynamic viscosity with coefficient of determination (R2) values of 99.6% and 97.7%, respectively, for all data sets, including a test data set of 45 molecules. Finally, partial dependency plots (PDPs), individual conditional expectation (ICE) plots, local interpretable model-agnostic explanation (LIME) values, and trimmed model R2 values were used to identify the most important static and dynamic predictors of the density and viscosity.

PyL3dMD: Python LAMMPS 3D molecular descriptors package

Molecular descriptors characterize the biological, physical, and chemical properties of molecules and have long been used for understanding molecular interactions and facilitating materials design. Some of the most robust descriptors are derived from geometrical representations of molecules, called 3-dimensional (3D) descriptors. When calculated from molecular dynamics (MD) simulation trajectories, 3D descriptors can also capture the effects of operating conditions such as temperature or pressure. However, extracting 3D descriptors from MD trajectories is non-trivial, which hinders their wide use by researchers developing advanced quantitative-structure-property-relationship models using machine learning. Here, we describe a suite of open-source Python-based post-processing routines, called PyL3dMD, for calculating 3D descriptors from MD simulations. PyL3dMD is compatible with the popular simulation package LAMMPS and enables users to compute more than 2000 3D molecular descriptors from atomic trajectories generated by MD simulations. PyL3dMD is freely available via GitHub and can be easily installed and used as a highly flexible Python package on all major platforms (Windows, Linux, and macOS). A performance benchmark study used descriptors calculated by PyL3dMD to develop a neural network and the results showed that PyL3dMD is fast and efficient in calculating descriptors for large and complex molecular systems with long simulation durations. PyL3dMD facilitates the calculation of 3D molecular descriptors using MD simulations, making it a valuable tool for cheminformatics studies.

Interdisciplinary analysis of drugs: Structural features and clinical data

Background:

Chemical structure is a vital consideration early in the drug development process. Its role in analysis of safety and efficacy is relatively diminished after drugs are approved for clinical use. This interdisciplinary study explores a strategy by which readily available clinical data may be used along with structural features of drugs to identify associations with potential utility for both clinical decision-making and drug development.

Methods:

Chemical functional groups and structural groups (SGs) of 261 drugs were manually classified in tiers, and their incidence of gastrointestinal (GI) and central nervous system (CNS) adverse drug reactions (ADRs) were obtained from a clinical database. Drugs with an GI or CNS ADR incidence of at least 10% were analyzed for correlations with their functional and SGs.

Results:

Eight statistically significant associations were detected by preliminary analysis: piperazine and methylene groups were associated with higher rate of CNS ADRs; while amides, secondary alcohols, and di-substituted phenyl groups were associated with lower rates of GI or CNS ADRs or both.

Conclusions:

Although further study is necessary to understand these associations and build upon this strategy, this exploratory analysis establishes a methodology by which chemical properties of drugs may be used to aid in clinical decision-making when choosing between otherwise equivalent drug therapy options, as the presence of specific groups on drugs may be associated with increased or decreased risks of specific ADRs.

Polarizability: a promising descriptor to study chemical-biological interactions

Recently, we have defined atomic polarizability, a Conceptual Density Functional Theory (CDFT)-based reactivity descriptor, through an empirical method. Though the method is empirical, it is competent enough to meet the criteria of periodic descriptors and exhibit relativistic effect. Since the atomic data are very accurate, we have applied them to determine molecular polarizability. Molecular polarizability is an electronic parameter and has an impact on chemical-biological interactions. Thus, it plays a pivotal role in explaining such interactions through Structure Activity Relationships (SAR). In the present work, we have explored the application of polarizability in the real field through investigation of chemical-biological interactions in terms of molecular polarizability. A Quantitative Structure-Activity Relationship (QSAR) model is constructed to account for electronic effects owing to polarizability in ligand-substrate interactions. The study involves the prediction of various biological activities in terms of minimum block concentration, relative biological response, inhibitory growth concentration or binding affinity. Superior results are presented for the predicted and observed activities which support the accuracy of the proposed polarizability-QSAR model. Further, the results are considered from a biological viewpoint in order to understand the mechanism of interactions. The study is performed to explore the efficacy of the computational model based on newly proposed polarizability and not to establish the finest QSAR. For future studies, it is suggested that the descriptor polarizability should be contrasted with the use of other drug-like descriptors.

Growth Inhibition and DNA Damage Induced by X-Phenols in Yeast: A Quantitative Structure-Activity Relationship Study

Phenolic compounds and their derivatives are ubiquitous constituents of numerous synthetic and natural chemicals that exist in the environment. Their toxicity is mostly attributed to their hydrophobicity and/or the formation of free radicals. In a continuation of the study of phenolic toxicity in a systematic manner, we have examined the biological responses of Saccharomyces cerevisiae to a series of mostly monosubstituted phenols utilizing a quantitative structure-activity relationship (QSAR) approach. The biological end points included a growth assay that determines the levels of growth inhibition induced by the phenols as well as a yeast deletion (DEL) assay that assesses the ability of X-phenols to induce DNA damage or DNA breaks. The QSAR analysis of cell growth patterns determined by IC₅₀ and IC₈₀ values indicates that toxicity is delineated by a hydrophobic, parabolic model. The DEL assay was then utilized to detect genomic deletions in yeast. The increase in the genotoxicity was enhanced by the electrophilicity of the phenolic substituents that were strong electron donors as well as by minimal hydrophobicity. The electrophilicities are represented by Brown's sigma plus values that are a variant of the Hammett sigma constants. A few mutant strains of genes involved in DNA repair were separately exposed to 2,6-di-tert-butyl-4-methyl-phenol (BHT) and butylated hydroxy anisole (BHA). They were subsequently screened for growth phenotypes. BHA-induced growth defects in most of the DNA repair null mutant strains, whereas BHT was unresponsive.

Molecular design of flotation collectors: A recent progress

The nature of froth flotation is to selectively hydrophobize valuable minerals by collector adsorption so that the hydrophobized mineral particles can attach air bubbles. In recent years, the increasing commercial production of refractory complex ores has been urgent to develop special collectors for enhancing flotation separation efficiency of valuable minerals from these ores. Molecular design methods offer an effective way for understanding the structure-property relationship of flotation collectors and developing new ones. The conditional stability constant (CSC), molecular mechanics (MM), quantitative structure-activity relationship (QSAR), and first-principle theory, especially density functional theory (DFT), have been adopted to build the criteria for designing flotation collectors. Azole-thiones, guanidines, acyl thioureas and thionocarbamates, amide-hydroxamates, and double minerophilic-group surfactants such as Gemini, dithiourea and dithionocarbamate molecules have been recently developed as high-performance collectors. To design hydrophobic groups, the hydrophilic-hydrophobic balance parameters have been extensively used as criteria. The replacement of aryl group with aliphatic group or CC single bond(s) with CC double bond(s), reduction of carbon numbers, introduction of oxygen atom(s) and addition of trisiloxane to the tail terminal have been proved to be useful approaches for adjusting the surface activity of collectors. The role of molecular design of collectors in practical flotation applications was also summarized. Based on the critical review, some comments and prospects for further research on molecular design of flotation collectors were also presented in the paper.

QSPR modeling of detonation parameters and sensitivity of some energetic materials: DFT vs. PM3 calculations

The quantitative structure-property relationship (QSPR) methodology was applied to describe and seek the relationship between the structures and energetic properties (and sensitivity) for some common energy compounds. An extended series of structural and energetic descriptors was obtained with density functional theory (DFT) B3LYP and semi-empirical PM3 approaches. Results indicate that QSPR model constructed using quantum descriptors can be applied to verify the confidence of calculation results compared with experimental data. It can be extended to predict the properties of similar compounds.

Curation and analysis of multitargeting agents for polypharmacological modeling

In drug discovery and development, the conventional “single drug, single target” concept has been shifted to “single drug, multiple targets” – a concept coined as polypharmacology. For studies in this emerging field, dedicated and high-quality databases of multitargeting ligands would be exceedingly beneficial. To this end, we conducted a comprehensive analysis of the structural and chemical/biological profiles of polypharmacological agents and present a Web-based database (Polypharma). All of these compounds curated herein have been cocrystallized with more than one unique protein with intensive reports of their multitargeting activities. The present study provides more insight of drug multitargeting and is particularly useful for polypharmacology modeling. This specialized curation has been made publically available at http:/imdlab.org/polypharma/

QSAR of caffeines by similarity cluster prediction

A novel QSAR approach based on correlation weighting and alignment over a hypermolecule that mimics the investigated correlational space was performed on a set of 40 caffeines downloaded from the PubChem database. The best models describing log P and LD50 values of this set of caffeine derivatives were validated against the external test set and in a new predictive model by using clusters of similarity.

In vitro activity of natural phenolic compounds against fluconazole-resistant Candida species: a quantitative structure-activity relationship analysis

AIMS

To evaluate the antifungal activity and to analyse the structure-activity relationship of eleven natural phenolic compounds against four Candida species which are resistant to fluconazole.

METHODS AND RESULTS

Four different species of Candida isolates were used: Candida albicans, Candida krusei, Candida tropicalis and Candida dubliniensis. The phenolic compound carvacrol showed the highest anti-Candida bioactivity, followed by thymol and isoeugenol. The obtained minimum inhibitory concentration (MIC) values obtained were used in a quantitative structure-activity relationship (QSAR) analysis where the electronic, steric, thermodynamic and topological descriptors served as dependent variables. According to the descriptors obtained in this QSAR study, the antifungal activity of phenols has a first action specific character which is based on their interaction with plasma or mitochondrial membranes. The second action is based on a steric descriptor-the maximal and minimal projection of the area-which could explain the inability of some phenolic compounds to be biotransformed to quinones methylene by Candida species.

CONCLUSIONS

According to the descriptors obtained in this QSAR study, the anti-Candida activity of ortho-substituted phenols is due to more than one action mechanism. The anti-Candida activity of phenolic compounds can be predicted by their molecular properties and structural characteristics.

SIGNIFICANCE AND IMPACT OF THE STUDY

These results could be employed to predict the anti-Candida activity of new phenolic compounds in the search for new alternatives or complementary therapies to combat against candidiasis.

NMR assignment in regioisomeric hydroquinones

A set of regioisomeric pairs of tricyclic hydroquinones, analogues of antitumor 9,10-dihydroxy-4,4-dimethyl-5,8-dihydroanthracen-1(4H)-one (1) and other derivatives, were synthesized and their regiochemistry and NMR spectra assigned by using (1)H-detected one-bond (C-H) HMQC and long-range C-H HMBC, in good agreement with theoretical O3LYP/Alhrichs-pVTZ calculations. The 5-hydroxymethyl derivatives (11, 15, 19) showed a (3)J(H, H) coupling constant of methylene protons evidencing the presence of a seven-membered intramolecular hydrogen bonded ring, not observed for the 8-hydroxymethyl isomers.

QSPR modeling of thermal stability of nitroaromatic compounds: DFT vs. AM1 calculated descriptors

The quantitative structure-property relationship (QSPR) methodology was applied to predict the decomposition enthalpies of 22 nitroaromatic compounds, used as indicators of thermal stability. An extended series of descriptors (constitutional, topological, geometrical charge related and quantum chemical) was calculated at two different levels of theory: density functional theory (DFT) and semi-empirical AM1 approaches. Reliable models have been developed for each level, leading to similar correlations between calculated and experimental data (R(2) > 0.98). Hence, both of them can be employed as screening tools for the prediction of thermal stability of nitroaromatic compounds. If using the AM1 model presents the advantage to be less time consuming, DFT allows the calculation of more accurate molecular quantum properties, e.g., conceptual DFT descriptors. In this study, our best QSPR model is based on such descriptors, providing more chemical comprehensive relationships with decomposition reactivity, a particularly complex property for the specific class of nitroaromatic compounds.

On the prediction of thermal stability of nitroaromatic compounds using quantum chemical calculations

This work presents a new approach to predict thermal stability of nitroaromatic compounds based on quantum chemical calculations and on quantitative structure-property relationship (QSPR) methods. The data set consists of 22 nitroaromatic compounds of known decomposition enthalpy (taken as a macroscopic property related to explosibility) obtained from differential scanning calorimetry. Geometric, electronic and energetic descriptors have been selected and computed using density functional theory (DFT) calculation to describe the 22 molecules. First approach consisted in looking at their linear correlations with the experimental decomposition enthalpy. Molecular weight, electrophilicity index, electron affinity and oxygen balance appeared as the most correlated descriptors (respectively R(2)=0.76, 0.75, 0.71 and 0.64). Then multilinear regression was computed with these descriptors. The obtained model is a six-parameter equation containing descriptors all issued from quantum chemical calculations. The prediction is satisfactory with a correlation coefficient R(2) of 0.91 and a predictivity coefficient R(cv)(2) of 0.84 using a cross validation method.

Overcoming tumor drug resistance with C2-modified 10-deacetyl-7-propionyl cephalomannines: a QSAR study

The microtubule-stabilizing taxanes such as paclitaxel and docetaxel are the two most important anticancer drugs currently used in clinics for the treatment of various types of cancers. However, the major common drawbacks of these two drugs are drug resistance, neurotoxicity, substrate for drug transporter P-gp, cross-resistance with other chemotherapeutic agents, low oral bioavailability, and no penetration in the blood-brain barrier (BBB). These limitations have led to the search for new taxane derivatives with improved biological activity. In the present paper, we discuss the quantitative structure-activity relationship (QSAR) studies on a series of C2-modified 10-deacetyl-7-propionyl cephalomannines (IV) with respect to their binding affinities toward beta-tubulin and cytotoxic activities against both drug-sensitive and drug-resistant tumor cells, in which resistance is mediated through either P-gp overexpression or beta-tubulin mutation mechanisms, by the formulation of five QSARs. Hydrophobicity and molar refractivity of the substituents (pi(X) and MR(X)) are found to be the most important determinants for the activity. Parabolic correlations in terms of MR(X) (eqs 2 and 4 ) are encouraging examples in which the optimum values of MR(X) are well-defined. We believe that these two QSAR models may prove to be adequate predictive models that can help to provide guidance in design and synthesis, and subsequently yield very specific cephalomannine derivatives (IV) that may have high biological activities. On the basis of these two QSAR models, 10 cephalomannine analogues (IV-21 to IV-30) are suggested as potential synthetic targets. Internal (cross-validation (q(2)), quality factor (Q), Fischer statistics (F), and Y-randomization) and external validation tests have validated all the QSAR models.

Taxane analogues against lung cancer: a quantitative structure-activity relationship study

Lung cancer is the second most common cancer in both men (after prostate cancer) and women (after breast cancer). The microtubule-stabilizing taxane such as docetaxel is the only agent currently approved for both first- and second-line treatment of advanced non-small cell lung cancer. Although docetaxel has made significant progress in the treatment of lung cancers either using alone or in combination with various novel targeted agents, its use often results in various undesired side-effects. These limitations have led to the search for new taxane derivatives with fewer side-effects, superior pharmacological properties, and improved anticancer activity to maximize the induced benefits for lung cancer patients. Herein, four series of taxane derivatives were used to correlate their inhibitory activities against lung cancer cells with hydrophobic and steric descriptors to gain a better understanding of their chemical-biological interactions. A parabolic correlation with MR(Y) is the most encouraging example, in which the optimum value of this parameter is well defined. On the basis of this quantitative structure-activity relationship model, six compounds (3-23 to 3-28) are suggested as potential synthetic targets. Internal (cross-validation (q(2)), quality factor (Q), Fischer statistics (F ) and Y-randomization) and external validation tests have validated all the quantitative structure-activity relationship models.

Classification of toxicity of phenols to Tetrahymena pyriformis and subsequent derivation of QSARs from hydrophobic, ionization and electronic parameters

Phenolic compounds were classified into different groups based on the structure and functional groups of the phenol. Quantitative structure-activity relationship (QSAR) analysis was performed between the toxicity and octanol/water partition coefficient (logP) for these groups. The results showed that the toxicity of non-ionisable phenols is dependent on their hydrophobicity. Poor relationships were found between the toxicity and logP for ionisable compounds, and the use of methods based on logP to predict the toxicity of ionisable compounds can result in considerable errors. Ionized and unionized forms have different contributions to toxicity; the unionized form plays a more important role than the ionized form because the toxicity of organic acids and phenols decreases as the pH increases. In order to investigate the effect of ionization, the fraction of ionized and unionized forms of phenols at different pH values were calculated from the pK(a) values, and a corrected distribution partition coefficient (D(T)) was derived from QSAR analysis for ionisable compounds. The prediction of toxicity of non-reactive ionisable compounds was improved remarkably by using the D(T) parameter. Ionization not only affects the bio-uptake of ionisable compounds, but interaction with the receptor micromolecule can also depend on the electronic situation, which is also related to the ionization. Stepwise regression showed that the reactivity of ionisable phenols was strongly correlated with the fraction of negatively charged form (F(-)). Interpretable QSAR equations with good statistical fits were developed from hydrophobic, ionization and electronic parameters for 207 phenols.

Taxane analogues against breast cancer: a quantitative structure-activity relationship study

Breast cancer is the second leading cause of cancer death among women in the United States. Two taxane analogues, taxol and taxotere, are the most important antimitotic drugs currently in clinical use for the treatment of breast cancers. However, recent reports have indicated that the use of these drugs often results in various undesired side effects as well as multi-drug resistance. These limitations have led to the development of new taxane derivatives with fewer side effects, superior pharmacological properties, and improved anticancer activity to maximize the induced benefits for breast cancer patients. Herein, four series of taxane derivatives were used to correlate their inhibitory activities against breast cancer cells with their hydrophobic and steric properties in order to understand their chemical-biological interactions. The resulting QSARs show that the inhibitory activities of taxane analogues against breast cancers are mainly dependent either on their hydrophobicity or the hydrophobic/molar refractivity descriptor of their substituents. A parabolic correlation with MR(Y) is the most encouraging example, in which the optimum value of this parameter is well defined. We believe this correlation may prove to be an adequate predictive model that can help provide guidance in design and synthesis and subsequently yield highly specific compounds that may have high anti-breast-cancer activity with fewer side effects and superior pharmacological properties. On the basis of this QSAR model, five compounds are suggested as potential synthetic targets. Internal (cross-validation (LOO-q(2) and LMO-q(2)), quality factor (Q), Fischer statistics (F), and Y-randomization) and external validation tests have validated all the QSAR models.

Understanding tubulin/microtubule-taxane interactions: a quantitative structure-activity relationship study

For years, the microtubule-stabilizing agents paclitaxel and docetaxel (progenitors of the family of taxanes) have been the most successful anticancer drugs currently used in clinics. However, both drugs are associated with notorious side effects, drug resistance, and cross resistance with other chemotherapeutic agents. These limitations have led to the search for new drugs with improved biological activity. In the present paper, we discuss the interaction of taxanes with the tubulin/microtubule system by the formulation of 6 QSARs. Hydrophobicity of the substituents (pi) is found to be one of the most important determinants of the activity followed by steric parameters. Parabolic correlations (eqs 3 and 7) with B5 and pi are the most encouraging examples, where the optimum values of these parameters are well defined. We believe that these two QSARs may prove to be adequate predictive models that can help to provide guidance in design/synthesis and subsequently yield very specific compounds (IV and VIII) that may have high biological activities. On the basis of these two QSARs 3 and 7, 18 compounds (IV-12- IV-22 and VIII-16- VIII-22) are suggested as potential synthetic targets. Cross-validation, quality factor (Q), Fischer statistics (F), and Y-randomization tests have validated all the QSAR models.

Investigation of DNA‐Binding Properties of Organic Molecules Using Quantitative Structure‐Activity Relationship (QSAR) Models

Due to the great potential of DNA as a receptor, many classes of synthetic and naturally occurring molecules exert their anticancer activities through DNA-binding. In the field of antitumor DNA-binding agents, a number of acridine and anthracycline derivatives are in the market as chemotherapeutic agents. However, the clinical application of such classes of compounds has encountered problems such as multi-drug resistance and secondary and/or collateral effects. Thus, there has been increasing interest in discovering and developing small molecules that are capable of DNA-binding, which will be expected to be used either in place of or in conjunction with, the existing compounds. The interest in the application of the QSAR paradigm has steadily increased in recent decades and we hope it may be useful in the design and development of DNA-binding molecules as new anticancer agents. In the present review, an attempt has been made to understand the DNA-binding properties of different compound series and discussed using 27 QSAR models, which reveal a number of interesting points. The most important determinants for the activity in these models are Hammett electronic (sigma and sigma+), hydrophobic, molar refractivity, and Sterimol width parameters.

20-(S)-Camptothecin Analogues as DNA Topoisomerase I Inhibitors: A QSAR Study

The interest in the application of the quantitative structure-activity relationships (QSAR) has steadily increased in recent decades because it has repeatedly proven itself to be a low-cost, high-return investment. Potential use of QSAR models for screening of chemical databases or virtual libraries before their synthesis appears equally attractive to chemical manufacturers, pharmaceutical companies, and government agencies. We hope it may also be useful in the design and development of new camptothecin derivatives as DNA topoisomerase I (topo I) inhibitors. In this paper, two series of camptothecin derivatives were undertaken to correlate DNA topo I inhibition with their hydrophobic and steric properties, to understand their chemical-biological interactions. The resulting QSAR have shown that the inhibitory activity of camptothecin analogues 4 toward DNA topo I is mainly dependent on their hydrophobic and steric descriptors, whereas the same activity of 10,11-methylenedioxy- camptothecin analogues 5 is largely dependent on their hydrophobicity at position-7. Using internal [cross-validation, quality factor (Q), Fischer statistics (F), and Y-randomization tests] and external validation tests both of these QSAR models have been validated. Both series of these camptothecin derivatives are also filtered by Lipinski's rule of five to check their oral bioavailability. On the basis of these QSAR models, five compounds (4-35, 4-36, 5-20, 5-21, and 5-22) have been predicted that may be the next synthetic target. These molecules also fulfill the conditions of Lipinski's rule of five.

Antioxidant properties and free radical-scavenging reactivity of a family of hydroxynaphthalenones and dihydroxyanthracenones

This study was undertaken to investigate the free radical-scavenging and antioxidant activities of various structurally related hydroquinones including hydroxynaphthalenones and dihydroxyanthracenones. Electron spin resonance spectroscopy and spin trapping techniques were used to evaluate the ability of hydroquinones to scavenge hydroxyl, diphenylpicrylhydrazyl, and galvinoxyl radicals. In addition, the oxygen radical absorbing capacity assay using fluorescein (ORAC-FL) was used to obtain the relative antioxidant capacity of these radicals. The rate constants of the first H atom abstraction by 2,2-diphenyl-2-picrylhydrazyl (k(2)), were obtained under pseudo-first-order conditions. The free radical-scavenging activities and k(2) values discriminate well between hydroxynaphthalenones and dihydroxyanthracenones, showing that the latter have better antioxidant properties. The aforementioned experimental data agree with quantum-chemical results demonstrating the relevance of intramolecular H bonding to radical-scavenging activities.

Comparative QSAR studies on PAMPA/modified PAMPA for high throughput profiling of drug absorption potential with respect to Caco-2 cells and human intestinal absorption

Despite the dramatic increase in speed of synthesis and biological evaluation of new chemical entities, the number of compounds that survive the rigorous processes associated with drug development is low. Thus, an increased emphasis on thorough ADMET (absorption, distribution, metabolism, excretion and toxicity) studies based on in vitro and in silico approaches allows for early evaluation of new drugs in the development phase. Artificial membrane permeability measurements afford a high throughput, relatively low cost but labor intensive alternative for in vitro determination of drug absorption potential; parallel artificial membrane permeability assays have been extensively utilized to determine drug absorption potentials. The present study provides comparative QSAR analysis on PAMPA/modified PAMPA for high throughput profiling of drugs with respect to Caco-2 cells and human intestinal absorption.

Matrix metalloproteinases (MMPs): chemical-biological functions and (Q)SARs

Matrix metalloproteinases (MMPs) are a large family of calcium-dependent zinc-containing endopeptidases, which are responsible for the tissue remodeling and degradation of the extracellular matrix (ECM), including collagens, elastins, gelatin, matrix glycoproteins, and proteoglycan. They are regulated by hormones, growth factors, and cytokines, and are involved in ovarian functions. MMPs are excreted by a variety of connective tissue and pro-inflammatory cells including fibroblasts, osteoblasts, endothelial cells, macrophages, neutrophils, and lymphocytes. These enzymes are expressed as zymogens, which are subsequently processed by other proteolytic enzymes (such as serine proteases, furin, plasmin, and others) to generate the active forms. Matrix metalloproteinases are considered as promising targets for the treatment of cancer due to their strong involvement in malignant pathologies. Clinical/preclinical studies on MMP inhibition in tumor models brought positive results raising the idea that the development of strategies to inhibit MMPs may be proved to be a powerful tool to fight against cancer. However, the presence of an inherent flexibility in the MMP active-site limits dramatically the accurate modeling of MMP-inhibitor complexes. The interest in the application of quantitative structure-activity relationships (QSARs) has steadily increased in recent decades and we hope it may be useful in elucidating the mechanisms of chemical-biological interactions for this enzyme. In the present review, an attempt has been made to explore the in-depth knowledge from the classification of this enzyme to the clinical trials of their inhibitors. A total number of 92 QSAR models (44 published and 48 new formulated QSAR models) have also been presented to understand the chemical-biological interactions. QSAR results on the inhibition of various compound series against MMP-1, -2, -3, -7, -8, -9, -12, -13, and -14 reveal a number of interesting points. The most important of these are hydrophobicity and molar refractivity, which are the most important determinants of the activity.

Application of quantum topological molecular similarity descriptors in QSPR study of the O-methylation of substituted phenols

The usefulness of a novel type of electronic descriptors called quantum topological molecular similarity (QTMS) indices for describing the quantitative effects of molecular electronic environments on the O-methylation kinetic of substituted phenols has been investigated. QTMS theory produces for each molecule a matrix of descriptors, containing bond (or structure) information in one dimension and electronic effects in another dimension, instead of other methods producing a vector of descriptors for each molecule. A collection of chemometrics tools including principal component analysis (PCA), partial least squares (PLS), and genetic algorithms (GA) were used to model the structure-kinetic data. PCA separated the bond and descriptor effects, and PLS modeled the effects of these parameters on the rate constant data, and GA selected the most relevant subset of variables. The model performances were validated by both cross-validation and external validation. The results indicated that the proposed models could explain about 95% of variances in the rate constant data. The significant effects of variables on the reaction kinetic were identified by calculating variable important in projection (VIP). It was found that the rate constant of esterification of phenols is highly influenced by the electronic properties of the C2--C1--O--H fragment of the parent molecule. Indeed, the C2--X and C4--X bonds (corresponding to ortho and para substituents) were found as highly influential parameters. All of the eight calculated QTMS indices were found significant however, lambda1, lambda2, lambda3, epsilon, and K(r) were detected as highly influential parameters.