Alert-QSAR. Implications for electrophilic theory of chemical carcinogenesis

Computational approaches for lead compound discovery in dipeptidyl peptidase-4 inhibition using machine learning and molecular dynamics techniques

The prediction of possible lead compounds from already-known drugs that may present DPP-4 inhibition activity imply a advantage in the drug development in terms of time and cost to find alternative medicines for the treatment of Type 2 Diabetes Mellitus (T2DM). The inhibition of dipeptidyl peptidase-4 (DPP-4) has been one of the most explored strategies to develop potential drugs against this condition. A diverse dataset of molecules with known experimental inhibitory activity against DPP-4 was constructed and used to develop predictive models using different machine-learning algorithms. Model M36 is the most promising one based on the internal and external performance showing values of Q2CV = 0.813, and Q2EXT = 0.803. The applicability domain evaluation and Tropsha's analysis were conducted to validate M36, indicating its robustness and accuracy in predicting pIC50 values for organic molecules within the established domain. The physicochemical properties of the ligands, including electronegativity, polarizability, and van der Waals volume were relevant to predict the inhibition process. The model was then employed in the virtual screening of potential DPP4 inhibitors, finding 448 compounds from the DrugBank and 9 from DiaNat with potential inhibitory activity. Molecular docking and molecular dynamics simulations were used to get insight into the ligand-protein interaction. From the screening and the favorable molecular dynamic results, several compounds including Skimmin (pIC50 = 3.54, Binding energy = -8.86 kcal/mol), bergenin (pIC50 = 2.69, Binding energy = -13.90 kcal/mol), and DB07272 (pIC50 = 3.97, Binding energy = -25.28 kcal/mol) seem to be promising hits to be tested and optimized in the treatment of T2DM. This results imply a important reduction in cost and time on the application of this drugs because all the information about the its metabolism is already available.

Searching glycolate oxidase inhibitors based on QSAR, molecular docking, and molecular dynamic simulation approaches

Primary hyperoxaluria type 1 (PHT1) treatment is mainly focused on inhibiting the enzyme glycolate oxidase, which plays a pivotal role in the production of glyoxylate, which undergoes oxidation to produce oxalate. When the renal secretion capacity exceeds, calcium oxalate forms stones that accumulate in the kidneys. In this respect, detailed QSAR analysis, molecular docking, and dynamics simulations of a series of inhibitors containing glycolic, glyoxylic, and salicylic acid groups have been performed employing different regression machine learning techniques. Three robust models with less than 9 descriptors-based on a tenfold cross (Q² _CV) and external (Q² _EXT) validation-were found i.e., MLR1 (Q² _CV = 0.893, Q² _EXT = 0.897), RF1 (Q² _CV = 0.889, Q² _EXT = 0.907), and IBK1 (Q² _CV = 0.891, Q² _EXT = 0.907). An ensemble model was built by averaging the predicted pIC₅₀ of the three models, obtaining a Q² _EXT = 0.933. Physicochemical properties such as charge, electronegativity, hardness, softness, van der Waals volume, and polarizability were considered as attributes to build the models. To get more insight into the potential biological activity of the compouds studied herein, docking and dynamic analysis were carried out, finding the hydrophobic and polar residues show important interactions with the ligands. A screening of the DrugBank database V.5.1.7 was performed, leading to the proposal of seven commercial drugs within the applicability domain of the models, that can be suggested as possible PHT1 treatment.

In Silico Antiprotozoal Evaluation of 1,4-Naphthoquinone Derivatives against Chagas and Leishmaniasis Diseases Using QSAR, Molecular Docking, and ADME Approaches

Chagas and leishmaniasis are two neglected diseases considered as public health problems worldwide, for which there is no effective, low-cost, and low-toxicity treatment for the host. Naphthoquinones are ligands with redox properties involved in oxidative biological processes with a wide variety of activities, including antiparasitic. In this work, in silico methods of quantitative structure–activity relationship (QSAR), molecular docking, and calculation of ADME (absorption, distribution, metabolism, and excretion) properties were used to evaluate naphthoquinone derivatives with unknown antiprotozoal activity. QSAR models were developed for predicting antiparasitic activity against Trypanosoma cruzi, Leishmania amazonensis, and Leishmania infatum, as well as the QSAR model for toxicity activity. Most of the evaluated ligands presented high antiparasitic activity. According to the docking results, the family of triazole derivatives presented the best affinity with the different macromolecular targets. The ADME results showed that most of the evaluated compounds present adequate conditions to be administered orally. Naphthoquinone derivatives show good biological activity results, depending on the substituents attached to the quinone ring, and perhaps the potential to be converted into drugs or starting molecules.

In Silico Approaches In Carcinogenicity Hazard Assessment: Current Status and Future Needs

Historically, identifying carcinogens has relied primarily on tumor studies in rodents, which require enormous resources in both money and time. In silico models have been developed for predicting rodent carcinogens but have not yet found general regulatory acceptance, in part due to the lack of a generally accepted protocol for performing such an assessment as well as limitations in predictive performance and scope. There remains a need for additional, improved in silico carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making. As part of an international effort to develop in silico toxicological protocols, a consortium of toxicologists, computational scientists, and regulatory scientists across several industries and governmental agencies evaluated the extent to which in silico models exist for each of the recently defined 10 key characteristics (KCs) of carcinogens. This position paper summarizes the current status of in silico tools for the assessment of each KC and identifies the data gaps that need to be addressed before a comprehensive in silico carcinogenicity protocol can be developed for regulatory use.

Acrolein but not its metabolite, 3-Hydroxypropylmercapturic acid (3HPMA), activates vascular transient receptor potential Ankyrin-1 (TRPA1): Physiological to toxicological implications

Acrolein, an electrophilic α,β-unsaturated aldehyde, is present in foods and beverages, and is a product of incomplete combustion, and thus, reaches high ppm levels in tobacco smoke and structural fires. Exposure to acrolein is linked with cardiopulmonary toxicity and cardiovascular disease risk. The hypothesis of this study is the direct effects of acrolein in isolated murine blood vessels (aorta and superior mesenteric artery, SMA) are transient receptor potential ankyrin-1 (TRPA1) dependent. Using isometric myography, isolated aorta and SMA were exposed to increasing levels of acrolein. Acrolein inhibited phenylephrine (PE)-induced contractions (approximately 90%) in aorta and SMA of male and female mice in a concentration-dependent (0.01-100 μM) manner. The major metabolite of acrolein, 3-hydroxypropylmercapturic acid (3HPMA), also relaxed PE-precontracted SMA. As the SMA was 20× more sensitive to acrolein than aorta (SMA EC50 0.8 ± 0.2 μM; aorta EC50 > 29.4 ± 4.4 μM), the mechanisms of acrolein-induced relaxation were studied in SMA. The potency of acrolein-induced relaxation was inhibited significantly by: 1) mechanically-impaired endothelium; 2) Nω-Nitro-L-arginine methyl ester hydrochloride (L-NAME); 3) guanylyl cyclase (GC) inhibitor (ODQ); and, 4) a TRPA1 antagonist (A967079). TRPA1 positive immunofluorescence was present in the endothelium. Compared with other known TRPA1 agonists, including allyl isothiocyanate (AITC), cinnamaldehyde, crotonaldehyde, and formaldehyde, acrolein stimulated a more potent TRPA1-dependent relaxation. Acrolein, at high concentration [100 μM], induced tension oscillations (spasms) independent of TRPA1 in precontracted SMA but not in aorta. In conclusion, acrolein is vasorelaxant at low levels (physiological) yet vasotoxic at high levels (toxicological).

In Silico Screening of the DrugBank Database to Search for Possible Drugs against SARS-CoV-2

Coronavirus desease 2019 (COVID-19) is responsible for more than 1.80 M deaths worldwide. A Quantitative Structure-Activity Relationships (QSAR) model is developed based on experimental pIC50 values reported for a structurally diverse dataset. A robust model with only five descriptors is found, with values of R2 = 0.897, Q2LOO = 0.854, and Q2ext = 0.876 and complying with all the parameters established in the validation Tropsha's test. The analysis of the applicability domain (AD) reveals coverage of about 90% for the external test set. Docking and molecular dynamic analysis are performed on the three most relevant biological targets for SARS-CoV-2: main protease, papain-like protease, and RNA-dependent RNA polymerase. A screening of the DrugBank database is executed, predicting the pIC50 value of 6664 drugs, which are IN the AD of the model (coverage = 79%). Fifty-seven possible potent anti-COVID-19 candidates with pIC50 values > 6.6 are identified, and based on a pharmacophore modelling analysis, four compounds of this set can be suggested as potent candidates to be potential inhibitors of SARS-CoV-2. Finally, the biological activity of the compounds was related to the frontier molecular orbitals shapes.

QSAR and molecular docking modelling of anti-leishmanial activities of organic selenium and tellurium compounds

Leishmaniasis affects mainly rural areas and the poorest people in the world. A computational study of the antileishmanial activity of organic selenium and tellurium compounds was performed. The 3D structures of the compounds were optimized at the wb97xd/lanl2dz level and used in the quantitative structure-activity relationship (QSAR) analysis. The antileishmanial activity was measured by L. donovani β carbonic anhydrase inhibition (Ki) and the half-maximal inhibitory concentration (IC₅₀) against L. infantum amastigotes. The dataset was divided into training (75%) and test sets (25%) by using a k-means clustering algorithm. For pKi prediction, model M3 with seven 3D topographic descriptors was characterized by the following statistical parameters: r ² = 0.879, Q ² _LOO = 0.822, and Q ² _ext = 0.840. For pIC₅₀ prediction, model M12 with six attributes was characterized by the following statistical parameters: r ² = 0.907, Q ² _LOO = 0.824, and Q ² _ext = 0.795. Both models met all the requirements of Tropsha´s test, which implies predictions of pIC₅₀ and pKi activities with high accuracy. Concomitantly, favourable interactions of the sulphonamide group with the Zn atom in the protein were revealed by the docking analysis.

Natural Compounds Therapeutic Features in Brain Disorders by Experimental, Bioinformatics and Cheminformatics Methods

BACKGROUND

Synthetic compounds with pharmaceutical applications in brain disorders are daily designed and synthesized, with well first effects but also seldom severe side effects. This imposes the search for alternative therapies based on the pharmaceutical potentials of natural compounds. The natural compounds isolated from various plants and arthropods venom are well known for their antimicrobial (antibacterial, antiviral) and antiinflammatory activities, but more studies are needed for a better understanding of their structural and pharmacological features with new therapeutic applications.

OBJECTIVES

Here we present some structural and pharmaceutical features of natural compounds isolated from plants and arthropods venom relevant for their efficiency and potency in brain disorders. We present the polytherapeutic effects of natural compounds belonging to terpenes (limonene), monoterpenoids (1,8-cineole) and stilbenes (resveratrol), as well as natural peptides (apamin, mastoparan and melittin).

METHODS

Various experimental and in silico methods are presented with special attention on bioinformatics (natural compounds database, artificial neural network) and cheminformatics (QSAR, drug design, computational mutagenesis, molecular docking).

RESULTS

In the present paper we reviewed: (i) recent studies regarding the pharmacological potential of natural compounds in the brain; (ii) the most useful databases containing molecular and functional features of natural compounds; and (iii) the most important molecular descriptors of natural compounds in comparison with a few synthetic compounds.

CONCLUSION

Our paper indicates that natural compounds are a real alternative for nervous system therapy and represents a helpful tool for the future papers focused on the study of the natural compounds.

Iron Release Profile of Silica-Modified Zero-Valent Iron NPs and Their Implication in Cancer Therapy

To evaluate the iron ion release profile of zero-valent iron (ZVI)-based nanoparticles (NPs) and their relationship with lysosomes in cancer cells, silica and mesoporous silica-coated ZVI NPs (denoted as ZVI@SiO2 and ZVI@mSiO2) were synthesized and characterized for the following study of cytotoxicity, intracellular iron ion release, and their underlying mechanisms. ZVI@mSiO2 NPs showed higher cytotoxicity than ZVI@SiO2 NPs in the OEC-M1 oral cancer cell line. In addition, internalized ZVI@mSiO2 NPs deformed into hollow and void structures within the cells after a 24-h treatment, but ZVI@SiO2 NPs remained intact after internalization. The intracellular iron ion release profile was also accordant with the structural deformation of ZVI@mSiO2 NPs. Burst iron ion release occurred in ZVI@mSiO2-treated cells within an hour with increased lysosome membrane permeability, which induced massive reactive oxygen species generation followed by necrotic and apoptotic cell death. Furthermore, inhibition of endosome-lysosome system acidification successfully compromised burst iron ion release, thereby reversing the cell fate. An in vivo test also showed a promising anticancer effect of ZVI@mSiO2 NPs without significant weight loss. In conclusion, we demonstrated the anticancer property of ZVI@mSiO2 NPs as well as the iron ion release profile in time course within cells, which is highly associated with the surface coating of ZVI NPs and lysosomal acidification.

QSAR by Minimal Topological Difference[s]: Post-Modern Perspectives

In the context of reconsidering the Quantitative Structure-Activity Relationship (QSAR) methods at the economical level, namely the optimization rules of OECD, the present review unfolds the key features of Minimal Sterical, Monte-Carlo and Minimal Topological Difference (MTD) methods, developed for quantitative treatment of the relations between biological activity of organic chemical compounds (drugs, pesticides, and so on) and their structures. The initial Minimal Steric Difference (MSD) is completed by the three-dimensional variant of the MTD method, being the last one referred to here, while the main principles of validating and guiding a viable QSAR method verified by the analytical-automated MTD, thus enlarging the perspectives of understanding the chemical-biological interaction at the level of ligand-receptor sites, cavity, and walls, with a true service to the future adaptive molecular design.

CORAL: QSAR models for carcinogenicity of organic compounds for male and female rats

Quantitative structure - activity relationships (QSARs) for carcinogenicity (rats, TD₅₀) have been built up using the CORAL software. Different molecular features, which are extracted from simplified molecular input-line entry system (SMILES) serve as the basis for building up a model. Correlation weights for the molecular features are calculated by means of the Monte Carlo optimization. Using the numerical data on the correlation weights, one can calculate a model of carcinogenicity as a mathematical function of descriptors, which are sum of the corresponding correlation weights. In other words, the correlation weights provide the maximal correlation coefficient between the descriptor and carcinogenicity, for the training set. This correlation was assessed via external validation set. Finally, lists of molecular alerts in aspects of carcinogenicity for male rats and for female rats were compared and their differences were discussed.

Quantitative structure–activity relationships study of potent pyridinone scaffold derivatives as HIV-1 integrase inhibitors with therapeutic applications

Human immunodeficiency virus-1 (HIV-1) integrase appears to be a crucial target for developing new anti-HIV-1 therapeutic agents. Different quantitative structure–activity relationships (QSARs) algorithms have been used in order to develop efficient model(s) to predict the activity of new pyridinone derivatives against HIV-1 integrase. Multiple linear regression (MLR) and combined principal component analysis (PCA) with MLR have been applied to build QSAR models for a set of new pyridinone derivatives as potent anti-HIV-1 therapeutic agents. Four different approaches based on MLR method including; concrete-MLR, stepwise-MLR, concrete PCA–MLR and stepwise PCA–MLR were utilized for this aim. Twenty two different sets of descriptors containing 1613 descriptors were constructed for each optimized molecule. Comparison between predictability of the “concrete” and “stepwise” procedure in two different algorithms of MLR and PCA models indicated the advantage of the stepwise procedure over that of the simple concrete method. Although the PCA was employed for dimension reduction, using stepwise PCA–MLR model showed that the method has higher ability to predict the compounds’ activity. The stepwise PCA–MLR model showed highly validated statistical results both in fitting and prediction processes ([Formula: see text] and [Formula: see text]). Therefore, using stepwise PCA approach is suitable to remove ineffective descriptors, which results in remaining efficient descriptors for building good predictability stepwise PCA–MLR. The stepwise hybrid approach of PCA–MLR may be useful in derivation of highly predictive and interpretable QSAR models.

QSPR/QSAR Analyses by Means of the CORAL Software

In this chapter, the methodology of building up quantitative structure—property/activity relationships (QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of the correlation weights for various features of the molecular structure. Hybrid models that are based on features extracted from both SMILES and a graph also can be built up by the CORAL software. The conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an endpoint prediction.

Eugenol and Its Role in Chronic Diseases

The active components in cloves are eugenol and isoeugenol. Eugenol has recently become a focus of interest because of its potential role in alleviating and preventing chronic diseases such as cancer, inflammatory reactions, and other conditions. The radical-scavenging and anti-inflammatory activities of eugenol have been shown to modulate chronic diseases in vitro and in vivo, but in humans, the therapeutic use of eugenol still remains to be explored. Based on a review of the recent literature, the antioxidant, anti-proliferative, and anti-inflammatory activities of eugenol and its related compounds are discussed in relation to experimentally determined antioxidant activity (stoichiometric factor n and inhibition rate constant) and theoretical parameters [phenolic O-H bond dissociation enthalpy (BDE), ionization potential (IP according to Koopman's theorem), and electrophilicity (ω)], calculated using a density functional theory method. Dimers of eugenol and its related compounds showed large antioxidant activities and high ω values and also exerted efficient anti-inflammatory activities. Eugenol appears to possess multiple antioxidant activities (dimerization, recycling, and chelating effect) in one molecule, thus having the potential to alleviate and prevent chronic diseases.

A Combined Pharmacophore Modeling, 3D QSAR and Virtual Screening Studies on Imidazopyridines as B-Raf Inhibitors

B-Raf kinase is an important target in treatment of cancers. In order to design and find potent B-Raf inhibitors (BRIs), 3D pharmacophore models were created using the Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database (GALAHAD). The best pharmacophore model obtained which was used in effective alignment of the data set contains two acceptor atoms, three donor atoms and three hydrophobes. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 39 imidazopyridine BRIs to build three dimensional quantitative structure-activity relationship (3D QSAR) models based on both pharmacophore and docking alignments. The CoMSIA model based on the pharmacophore alignment shows the best result (q² = 0.621, r²_pred = 0.885). This 3D QSAR approach provides significant insights that are useful for designing potent BRIs. In addition, the obtained best pharmacophore model was used for virtual screening against the NCI2000 database. The hit compounds were further filtered with molecular docking, and their biological activities were predicted using the CoMSIA model, and three potential BRIs with new skeletons were obtained.

Experimental and theoretical study of o-substituent effect on the fluorescence of 8-hydroxyquinoline

The synthesis and characterization of different ether and ester derivatives of 8-hydroxyquinoline have been made. UV-visible and fluorescence spectra of these compounds have revealed spectral dependence on both solvent and O-substituent. The fluorescence intensity of ether derivatives revealed higher intensity for 8-octyloxyquinoline compared with 8-methoxyquinoline, whereas those of ester derivatives had less fluorescence than 8-hydroxyquinoline. Theoretical calculations based on Time-dependent density functional theory (TD-DFT) were carried out for the quinolin-8-yl benzoate (8-OateQ) compound to understand the effect of O-substituent on the electronic absorption of 8-hydroxyquinaline (8-HQ). The calculations revealed comparable results with those obtained from the experimental data. Optimized geometrical structure was calculated with DFT at B3LYP/6-311++G** level of theory. The results indicated that 8-OateQ is not a coplanar structure. The absorption spectra of the compound were computed in gas-phase and solvent using B3LYP and CAM-B3LYP methods with 6-311++G ** basis set. The agreement between calculated and experimental wavelengths was very good at CAM-B3LYP/6-311++G** level of theory.

QSPR/QSAR Analyses by Means of the CORAL Software

In this chapter, the methodology of building up quantitative structure—property/activity relationships (QSPRs/QSARs)—by means of the CORAL software is described. The Monte Carlo method is the basis of this approach. Simplified Molecular Input-Line Entry System (SMILES) is used as the representation of the molecular structure. The conversion of SMILES into the molecular graph is available for QSPR/QSAR analysis using the CORAL software. The model for an endpoint is a mathematical function of the correlation weights for various features of the molecular structure. Hybrid models that are based on features extracted from both SMILES and a graph also can be built up by the CORAL software. The conceptually new ideas collected and revealed through the CORAL software are: (1) any QSPR/QSAR model is a random event; and (2) optimal descriptor can be a translator of eclectic information into an endpoint prediction.

Pattern recognition techniques applied to the study of leishmanial glyceraldehyde-3-phosphate dehydrogenase inhibition

Chemometric pattern recognition techniques were employed in order to obtain Structure-Activity Relationship (SAR) models relating the structures of a series of adenosine compounds to the affinity for glyceraldehyde 3-phosphate dehydrogenase of Leishmania mexicana (LmGAPDH). A training set of 49 compounds was used to build the models and the best ones were obtained with one geometrical and four electronic descriptors. Classification models were externally validated by predictions for a test set of 14 compounds not used in the model building process. Results of good quality were obtained, as verified by the correct classifications achieved. Moreover, the results are in good agreement with previous SAR studies on these molecules, to such an extent that we can suggest that these findings may help in further investigations on ligands of LmGAPDH capable of improving treatment of leishmaniasis.

Design, synthesis, antifungal activities and 3D-QSAR of new N,N'-diacylhydrazines containing 2,4-dichlorophenoxy moiety

A series of new N,N'-diacylhydrazine derivatives were designed and synthesized. Their structures were verified by 1H-NMR, mass spectra (MS) and elemental analysis. The antifungal activities of these N,N'-diacylhydrazines were evaluated. The bioassay results showed that most of these N,N'-diacylhydrazines showed excellent antifungal activities against Cladosporium cucumerinum, Corynespora cassiicola, Sclerotinia sclerotiorum, Erysiphe cichoracearum, and Colletotrichum orbiculare in vivo. The half maximal effective concentration (EC50) of one of the compounds was also determined, and found to be comparable with a commercial drug. To further investigate the structure-activity relationship, comparative molecular field analysis (CoMFA) was performed on the basis of antifungal activity data. Both the steric and electronic field distributions of CoMFA are in good agreement in this study.

Development of classification and regression based QSAR models to predict rodent carcinogenic potency using oral slope factor

Carcinogenicity is among the toxicological endpoints posing the highest concern for human health. Oral slope factors (OSFs) are used to estimate quantitatively the carcinogenic potency or the risk associated with exposure to the chemical by oral route. Regulatory agencies in food and drug administration and environmental protection are employing quantitative structure-activity relationship (QSAR) models to fill the data gaps related with properties of chemicals affecting the environment and human health. In this background, we have developed quantitative structure-carcinogenicity regression models for rodents based on the carcinogenic potential of 70 chemicals with wide diversity of molecular structures, spanning a large number of chemical classes and biological mechanisms. All the developed models have been assessed according to the Organization for Economic Cooperation and Development (OECD) principles for the validation of QSAR models. We have also attempted to develop a carcinogenicity classification model based on Linear Discriminant Analysis (LDA). Developed regression and LDA models are rigorously validated internally as well as externally. Our in silico studies make it possible to obtain a quantitative interpretation of the structural information of carcinogenicity along with identification of the discriminant functions between lower and higher carcinogenic compounds by LDA. Pharmacological distribution diagrams (PDDs) are used as a visualizing technique for the identification and selection of chemicals with lower carcinogenicity. Constructive, informative and comparable interpretations have been observed in both cases of classification and regression based modeling.

Relationships between base-catalyzed hydrolysis rates or glutathione reactivity for acrylates and methacrylates and their NMR spectra or heat of formation

The NMR chemical shift, i.e., the π-electron density of the double bond, of acrylates and methacrylates is related to the reactivity of their monomers. We investigated quantitative structure-property relationships (QSPRs) between the base-catalyzed hydrolysis rate constants (k1) or the rate constant with glutathione (GSH) (log k(GSH)) for acrylates and methacrylates and the (13)C NMR chemical shifts of their α,β-unsaturated carbonyl groups (δC(α) and δC(β)) or heat of formation (Hf) calculated by the semi-empirical MO method. Reported data for the independent variables were employed. A significant linear relationship between k1 and δC(β), but not δC(α), was obtained for methacrylates (r(2) = 0.93), but not for acrylates. Also, a significant relationship between k1 and Hf was obtained for both acrylates and methacrylates (r(2) = 0.89). By contrast, log k(GSH) for acrylates and methacrylates was linearly related to their δC(β) (r(2) = 0.99), but not to Hf. These findings indicate that the (13)C NMR chemical shifts and calculated Hf values for acrylates and methacrylates could be valuable for estimating the hydrolysis rate constants and GSH reactivity of these compounds. Also, these data for monomers may be an important tool for examining mechanisms of reactivity.

Mechanisms of action of (meth)acrylates in hemolytic activity, in vivo toxicity and dipalmitoylphosphatidylcholine (DPPC) liposomes determined using NMR spectroscopy

We investigated the quantitative structure-activity relationships between hemolytic activity (log 1/H(50)) or in vivo mouse intraperitoneal (ip) LD(50) using reported data for α,β-unsaturated carbonyl compounds such as (meth)acrylate monomers and their (13)C-NMR β-carbon chemical shift (δ). The log 1/H(50) value for methacrylates was linearly correlated with the δC(β) value. That for (meth)acrylates was linearly correlated with log P, an index of lipophilicity. The ipLD(50) for (meth)acrylates was linearly correlated with δC(β) but not with log P. For (meth)acrylates, the δC(β) value, which is dependent on the π-electron density on the β-carbon, was linearly correlated with PM3-based theoretical parameters (chemical hardness, η; electronegativity, χ; electrophilicity, ω), whereas log P was linearly correlated with heat of formation (HF). Also, the interaction between (meth)acrylates and DPPC liposomes in cell membrane molecular models was investigated using (1)H-NMR spectroscopy and differential scanning calorimetry (DSC). The log 1/H(50) value was related to the difference in chemical shift (ΔδHa) (Ha: H (trans) attached to the β-carbon) between the free monomer and the DPPC liposome-bound monomer. Monomer-induced DSC phase transition properties were related to HF for monomers. NMR chemical shifts may represent a valuable parameter for investigating the biological mechanisms of action of (meth)acrylates.

Introducing catastrophe-QSAR. Application on modeling molecular mechanisms of pyridinone derivative-type HIV non-nucleoside reverse transcriptase inhibitors

The classical method of quantitative structure-activity relationships (QSAR) is enriched using non-linear models, as Thom's polynomials allow either uni- or bi-variate structural parameters. In this context, catastrophe QSAR algorithms are applied to the anti-HIV-1 activity of pyridinone derivatives. This requires calculation of the so-called relative statistical power and of its minimum principle in various QSAR models. A new index, known as a statistical relative power, is constructed as an Euclidian measure for the combined ratio of the Pearson correlation to algebraic correlation, with normalized t-Student and the Fisher tests. First and second order inter-model paths are considered for mono-variate catastrophes, whereas for bi-variate catastrophes the direct minimum path is provided, allowing the QSAR models to be tested for predictive purposes. At this stage, the max-to-min hierarchies of the tested models allow the interaction mechanism to be identified using structural parameter succession and the typical catastrophes involved. Minimized differences between these catastrophe models in the common structurally influential domains that span both the trial and tested compounds identify the "optimal molecular structural domains" and the molecules with the best output with respect to the modeled activity, which in this case is human immunodeficiency virus type 1 HIV-1 inhibition. The best molecules are characterized by hydrophobic interactions with the HIV-1 p66 subunit protein, and they concur with those identified in other 3D-QSAR analyses. Moreover, the importance of aromatic ring stacking interactions for increasing the binding affinity of the inhibitor-reverse transcriptase ligand-substrate complex is highlighted.