Computer-modeling-based QSARs for analyzing experimental data on biotransformation and toxicity

Active sites of peptides Asp-Asp-Asp-Tyr and Asp-Tyr-Asp-Asp protect against cellular oxidative stress

The protective effects of the peptides Asp-Asp-Asp-Tyr (DDDY) and Asp-Tyr-Asp-Asp (DYDD) against AAPH-induced HepG2 cells are unclear. Our objective was to investigate the active sites of these peptides and their cellular antioxidant mechanism. DDDY and DYDD show a direct free radical scavenging effect in reducing ROS levels and maintained cellular antioxidant enzymes at normal levels. The quantum chemistry analysis of the electronic properties of antioxidant activity showed that DYDD has a greater energy in the highest occupied molecular orbital than DDDY, and O₅₈-H₅₉ and N₁₀-H₁₂ were identified as the active antioxidant sites in DYDD and DDDY, respectively, indicating that the inconsistent arrangement of amino acids affects the distribution of the highest occupied orbital energy as well as the active sites; thus, influences the antioxidant activity of peptides. It provide valuable insights into the antioxidant active sites of peptides.

Toxicity of binary mixtures of pesticides and pharmaceuticals toward Vibrio fischeri: Assessment by quantitative structure-activity relationships

Pollutants in real aquatic systems commonly occur as chemical mixtures. Yet, the corresponding risk assessment is still mostly based on information on single-pollutant toxicity, accepting the assumption that pollutant mixtures exhibit additive toxicity effect which is often not the case. Therefore, it is still better to use the experimental approach. Unfortunately, experimental determination of toxicity for each mixture is practically unfeasible. In this study, quantitative structure-activity relationship (QSAR) models for the prediction of toxicity of binary mixtures towards bioluminescent bacteria Vibrio fischeri were developed at three toxicity levels (EC₁₀, EC₃₀ and EC₅₀). For model development, experimentally determined toxicity values of 14 pollutants (pharmaceuticals and pesticides) were correlated with their structural features, applying multiple linear regression together with genetic algorithm. Statistical analysis, internal validation and external validation of the models were carried out. The toxicity is accurately predicted by all three models. EC₃₀ and EC₅₀ values are mostly influenced by geometrical distances between nitrogen and sulfur atoms. Furthermore, the simultaneous presence of oxygen and chlorine atoms in mixture can induce the increase in toxicity. At lower effect levels (EC₁₀), nitrogen atom bonded to different groups has the highest impact on mixture toxicity. Thus, the analysis of the descriptors involved in the developed models can give insight into toxic mechanisms of the binary systems.

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.

Quantitative Structure-activity Relationships; Studying the Toxicity of Metal Nanoparticles

Background:

Metal nanomaterials are widely used in various fields, including targeted therapy and diagnosis. They are extensively used in targeted drug delivery and local treatments. However, the toxicity associated with these materials could lead to severe adverse health effects.

Methods:

In this study, we investigated the relationships between the toxicity and structures of metal nanoparticles by using theoretical calculations and quantitative structure-activity relationships. Twenty four physicochemical descriptors and toxicity data of 23 types of metal nanoparticles were selected as samples, and a multiple linear regression model was established to obtain a toxicity prediction equation with 5 descriptors with an R2 of 0.910. Structures of copper nanoparticles were designed based on the model, and the structure with low toxicity was searched. The multiple nonlinear regression model was used to further improve the prediction accuracy.

Results:

The R2 values were 0.995 in the training set and 0.988 in the test set, which indicated that the prediction accuracy improved. Based on the result of multiple linear regression, we designed copper nanoparticles with low toxicity.

Conclusion:

The study confirmed that the quantitative structure-activity relationship was a reasonable method for predicting the toxicity and designing the structures with low toxicity of metal nanoparticles.

Discovering active sites in peptide Ala-Val-Thr-Phe that counter 2,2-azobis(2-methylpropanimidamidine)dihydrochloride-induced oxidative stress in HepG2 cells

The Ala-Val-Thr-Phe (AVTF) peptide derived from edible Dendrobium aphyllum was co-incubated with Lactobacillus amylolyticus in a previous study. The aim of the present study was to further examine the antioxidative and protective effects of the AVTF peptides through the analysis of free-radical quenching in HepG2 cells subjected to 2,2-azobis(2-methylpropanimidamidine)dihydrochloride (AAPH)-induced oxidative stress and to determine the active sites within the peptide. Variations in intracellular malondialdehyde levels indicated that these peptides protect HepG2 cells by preventing ROS attack and lipid peroxidation. Antioxidant enzymes and Nrf2 were downregulated in AVTF-treated but not in AAPH-treated HepG2 cells, whereas the electrically sensitive Keap1 was not susceptible to free radical-induced damage after AVTF treatment. However, this did not result in the activation of the Nrf2/Keap1 signaling pathway, thus indicating that one potential mechanism by which AVTF maintains homeostasis in HepG2 cells is by directly scavenging free radicals. Furthermore, quantum chemical calculations and the assessment of electronic-related properties associated with antioxidant activity revealed that the active sites of AVTF included N9-H11, which was further confirmed by the assessment of ROS levels in methylated AVTF-treated cells. The results of this study provide valuable insights into the active site N9-H11 in the Ala residue of AVTF, which influences the antioxidant activity of the peptide.

Mechanistic insights into the specificity of human cytosolic sulfotransferase 2A1 (hSULT2A1) for hydroxylated polychlorinated biphenyls through the use of fluoro-tagged probes

Determining the relationships between the structures of substrates and inhibitors and their interactions with drug-metabolizing enzymes is of prime importance in predicting the toxic potential of new and legacy xenobiotics. Traditionally, quantitative structure activity relationship (QSAR) studies are performed with many distinct compounds. Based on the chemical properties of the tested compounds, complex relationships can be established so that models can be developed to predict toxicity of novel compounds. In this study, the use of fluorinated analogues as supplemental QSAR compounds was investigated. Substituting fluorine induces changes in electronic and steric properties of the substrate without substantially changing the chemical backbone of the substrate. In vitro assays were performed using purified human cytosolic sulfotransferase hSULT2A1 as a model enzyme. A mono-hydroxylated polychlorinated biphenyl (4-OH PCB 14) and its four possible mono-fluoro analogues were used as test compounds. Remarkable similarities were found between this approach and previously published QSAR studies for hSULT2A1. Both studies implicate the importance of dipole moment and dihedral angle as being important to PCB structure in respect to being substrates for hSULT2A1. We conclude that mono-fluorinated analogues of a target substrate can be a useful tool to study the structure activity relationships for enzyme specificity.

Understanding P450-mediated Bio-transformations into Epoxide and Phenolic Metabolites

Adverse drug reactions are commonly the result of cytochrome P450 enzymes (CYPs) converting the drugs into reactive metabolites. Thus, information about the CYP bioactivation of drugs would not only provide insight into metabolic stability, but also into the potential toxicity. For example, oxidation of phenyl rings may lead to either toxic epoxides or safer phenols. Herein, we demonstrate that the potential to form reactive metabolites is encoded primarily in the properties of the molecule to be oxidized. While the enzyme positions the molecule inside the binding pocket (selects the site of metabolism), the subsequent reaction is only dependent on the substrate itself. To test this hypothesis, we used this observation as a predictor of drug inherent toxicity. This approach was used to successfully identify the formation of reactive metabolites in over 100 drug molecules. These results provide a new perspective on the impact of functional groups on aromatic oxidation of drugs and their effects on toxicity.

Assessment of bromide-based ionic liquid toxicity toward aquatic organisms and QSAR analysis

The toxicities of 24 bromide-based ionic liquids (Br-ILs) towards Vibrio fischeri (V. fischeri) and Daphnia magna (D. magna) were determined. These Br-ILs are composed of a bromide ion and a generic cation (i.e., pyrrolidinium, piperidinium, pyridinium or imidazolium) with different alkyl side chains. QSAR models with relatively high correlation coefficients, R(2), of 0.954 and 0.895 were developed for V. fischeri and D. magna. The model for V. fischeri indicated that the Br-IL toxicity towards V. fischeri was negatively correlated with the energy of the lowest unoccupied molecular orbitals (ELUMO) which reflects the electron affinities (EAs) and positively correlated with the volumes of Br-IL cations. For the D. magna model, the Br-IL toxicity was positively correlated with the dipole moment (μ) and negatively correlated with the total energy (TE) that is highly correlated with the molecular volume (V). For Br-ILs with the same cation ring, the toxicity increased as the length of the alkyl chains increased. For the same alkyl chain length, the toxicity order for V. fischeri was pyridinium>imidazolium>piperidinium>pyrrolidinium, except for those containing octyl side chains, while the toxicity ranking for D. magna was imidazolium~pyridinium>piperidinium>pyrrolidinium.

Evaluation on joint toxicity of chlorinated anilines and cadmium to Photobacterium phosphoreum and QSAR analysis

The individual IC50 (the concentrations causing a 50% inhibition of bioluminescence after 15min exposure) of cadmium ion (Cd) and nine chlorinated anilines to Photobacterium phosphoreum (P. phosphoreum) were determined. In order to evaluate the combined effects of the nine chlorinated anilines and Cd, the toxicities of chlorinated anilines combined with different concentrations of Cd were determined, respectively. The results showed that the number of chlorinated anilines manifesting synergy with Cd decreased with the increasing Cd concentration, and the number manifesting antagonism decreased firstly and then increased. The joint toxicity of mixtures at low Cd concentration was weaker than that of most binary mixtures when combined with Cd at medium and high concentrations as indicated by TUTotal. QSAR analysis showed that the single toxicity of chlorinated anilines was related to the energy of the lowest unoccupied molecular orbital (ELUMO). When combined with different concentrations of Cd, the toxicity was related to the energy difference (EHOMO-ELUMO) with different coefficients. Van der Waals' force or the complexation between chlorinated anilines and Cd had an impact on the toxicity of combined systems, which could account for QSAR models with different physico-chemical descriptors.

Using molecular docking-based binding energy to predict toxicity of binary mixture with different binding sites

The flood of chemical substances in the environment result in the complexity of chemical mixtures, and one of the reasons for complexity is that their individual chemicals bind to different binding sites on different (or same) target proteins within the organism. A general approaches therefore are proposed in this study to predict the toxicity of chemical mixtures with different binding sites by using molecular docking-based binding energy (Ebinding). Aldehydes and cyanogenic toxicants were selected as the example of chemical mixtures with same binding site. Triazines and urea herbicide were selected as the example of chemical mixtures with different binding sites but on same target protein. Sulfonamides and trimethoprim toxicants were selected as the example of chemical mixtures with different target proteins. Although these chemical mixtures bind to their binding sites by different ways, there is a general relationship between their binary mixture toxicity (EC50M) and their corresponding Ebinding of individual chemicals and logKow(mix). By using the Ebinding to describe how the individual chemicals work in the different binding sites, the approach may provide a general and simply model to predict mixture toxicity to microorganism.

Toxicity of aromatic compounds to Tetrahymena estimated by microcalorimetry and QSAR

The toxicity of six organic aromatic chemicals to Tetrahymena growth metabolism was studied by microcalorimetry. The growth constant k, inhibitory ratio I, and half-inhibiting concentration IC(50) were calculated. The results suggested that the order of toxicity was aniline>nitrobenzene>chlorobenzene>toluene>benzene>phenol. Based on the molecular descriptors (such as K(OW), E(HOMO), E(LUMO), DeltaE, E(T) and logIC(50)), the QSAR equation is obtained by multiple linear regression analysis: logIC(50)=-3.360-1.545 E(HOMO)-0.6850 DeltaE-0.3019logK(OW) (R=0.8643, n=6, s=0.202, F=0.739, Sig.=0.041, R(CV)(2)=0.624). The equation indicates that the toxic action is a two-step process: the pass of the chemicals through the cell membrane (described by logK(OW)) and the electron-transfer reaction of the chemicals with biomolecules (described by E(HOMO) and DeltaE). The substituents on aromatic ring are crucial to the toxicity of the compounds and the reaction between the chemicals and biological macromolecules is important.

Toxicity evaluation and prediction of toxic chemicals on activated sludge system

The gaps of data for evaluating toxicity of new or overloaded organic chemicals on activated sludge system resulted in the requirements for methodology of toxicity estimation. In this study, 24 aromatic chemicals typically existed in the industrial wastewater were selected and classified into three groups of benzenes, phenols and anilines. Their toxicity on activated sludge was then investigated. Two indexes of IC(50-M) and IC(50-S) were determined respectively from the respiration rates of activated sludge with different toxicant concentration at mid-term (24h) and short-term (30min) time intervals. Experimental results showed that the group of benzenes was the most toxic, followed by the groups of phenols and anilines. The values of IC(50-M) of the tested chemicals were higher than those of IC(50-S). In addition, quantitative structure-activity relationships (QSARs) models developed from IC(50-M) were more stable and accurate than those of IC(50-S). The multiple linear models based on molecular descriptors and K(ow) presented better reliability than single linear models based on K(ow). Among these molecular descriptors, E(lumo) was the most important impact factor for evaluation of mid-term toxicity.

Toxicity testing in the 21st century: a vision and a strategy

With the release of the landmark report Toxicity Testing in the 21st Century: A Vision and a Strategy, the U.S. National Academy of Sciences, in 2007, precipitated a major change in the way toxicity testing is conducted. It envisions increased efficiency in toxicity testing and decreased animal usage by transitioning from current expensive and lengthy in vivo testing with qualitative endpoints to in vitro toxicity pathway assays on human cells or cell lines using robotic high-throughput screening with mechanistic quantitative parameters. Risk assessment in the exposed human population would focus on avoiding significant perturbations in these toxicity pathways. Computational systems biology models would be implemented to determine the dose-response models of perturbations of pathway function. Extrapolation of in vitro results to in vivo human blood and tissue concentrations would be based on pharmacokinetic models for the given exposure condition. This practice would enhance human relevance of test results, and would cover several test agents, compared to traditional toxicological testing strategies. As all the tools that are necessary to implement the vision are currently available or in an advanced stage of development, the key prerequisites to achieving this paradigm shift are a commitment to change in the scientific community, which could be facilitated by a broad discussion of the vision, and obtaining necessary resources to enhance current knowledge of pathway perturbations and pathway assays in humans and to implement computational systems biology models. Implementation of these strategies would result in a new toxicity testing paradigm firmly based on human biology.

Biokinetic modeling and in vitro-in vivo extrapolations

The introduction of in vitro methodologies in the toxicological risk assessment process requires a number of prerequisites regarding both the toxicodynamics and the biokinetics of the compounds under study. In vitro systems will need to be relevant for measuring those structural and physiological changes that are good indicators for adverse effects. Furthermore, the dose metric found to have an effect in the in vitro system should be relevant. One element in defining the appropriate dose metric is related to the kinetic behavior of the compound in the in vitro system: binding to proteins, binding to plastic, evaporation, and the interaction between the culture medium and the cells. Ways to measure and model "in vitro biokinetics" are described. Second, the appropriate dose metric in vitro, e.g., the effective concentration, will need to be extrapolated to relevant in vivo exposure scenarios. The application of physiologically based biokinetic modelling is essential in such extrapolations. The parameters needed to build these models often can be estimated based on nonanimal data, namely chemical properties (QSARs) and in vitro experiments.

Promises and pitfalls of quantitative structure-activity relationship approaches for predicting metabolism and toxicity

The description of quantitative structure-activity relationship (QSAR) models has been a topic for scientific research for more than 40 years and a topic within the regulatory framework for more than 20 years. At present, efforts on QSAR development are increasing because of their promise for supporting reduction, refinement, and/or replacement of animal toxicity experiments. However, their acceptance in risk assessment seems to require a more standardized and scientific underpinning of QSAR technology to avoid possible pitfalls. For this reason, guidelines for QSAR model development recently proposed by the Organization for Economic Cooperation and Development (OECD) [Organization for Economic Cooperation and Development (OECD) (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. OECD Environment Health and Safety Publications: Series on Testing and Assessment No. 69, Paris] are expected to help increase the acceptability of QSAR models for regulatory purposes. The guidelines recommend that QSAR models should be associated with (i) a defined end point, (ii) an unambiguous algorithm, (iii) a defined domain of applicability, (iv) appropriate measures of goodness-of-fit, robustness, and predictivity, and (v) a mechanistic interpretation, if possible [Organization for Economic Cooperation and Development (OECD) (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. The present perspective provides an overview of these guidelines for QSAR model development and their rationale, as well as the promises and pitfalls of using QSAR approaches and these guidelines for predicting metabolism and toxicity of new and existing chemicals.

Prediction of biodegradation rate constants of hydroxylated polychlorinated biphenyls by fungal laccases from Trametes versicolor and Pleurotus ostreatus

Quantitative structure-activity relationship (QSAR) models for fungal laccase-catalyzed degradation of different hydroxylated polychlorinated biphenyls (OH-PCBs) were developed using some fundamental quantum chemical descriptors. The cross-validated Q(2)(cum )values for the two optimal QSAR models are as high as 0.958 and 0.961 for laccases from Trametes versicolor and Pleurotus ostreatus, respectively, indicating good predictive abilities for laccase-catalyzed degradation of OH-PCBs. Results from this study show that increasing heat of formation (DeltaH(f)) and frontier molecular orbital energy (i.e. E(LUMO) + E(HOMO)) values or decreasing frontier molecular orbital energy (i.e. E(HOMO-1)) and core-core repulsion energy (CCR) values leads to the increase of OH-PCB degradation rates by laccases.

Structure-activity models of oral clearance, cytotoxicity, and LD50: a screen for promising anticancer compounds

BACKGROUND

Quantitative structure-activity relationship (QSAR) models have become popular tools to help identify promising lead compounds in anticancer drug development. Few QSAR studies have investigated multitask learning, however. Multitask learning is an approach that allows distinct but related data sets to be used in training. In this paper, a suite of three QSAR models is developed to identify compounds that are likely to (a) exhibit cytotoxic behavior against cancer cells, (b) exhibit high rat LD50 values (low systemic toxicity), and (c) exhibit low to modest human oral clearance (favorable pharmacokinetic characteristics). Models were constructed using Kernel Multitask Latent Analysis (KMLA), an approach that can effectively handle a large number of correlated data features, nonlinear relationships between features and responses, and multitask learning. Multitask learning is particularly useful when the number of available training records is small relative to the number of features, as was the case with the oral clearance data.

RESULTS

Multitask learning modestly but significantly improved the classification precision for the oral clearance model. For the cytotoxicity model, which was constructed using a large number of records, multitask learning did not affect precision but did reduce computation time. The models developed here were used to predict activities for 115,000 natural compounds. Hundreds of natural compounds, particularly in the anthraquinone and flavonoids groups, were predicted to be cytotoxic, have high LD50 values, and have low to moderate oral clearance.

CONCLUSION

Multitask learning can be useful in some QSAR models. A suite of QSAR models was constructed and used to screen a large drug library for compounds likely to be cytotoxic to multiple cancer cell lines in vitro, have low systemic toxicity in rats, and have favorable pharmacokinetic properties in humans.

Identification of individual structural fragments of N,N'-(bis-5-nitropyrimidyl)dispirotripiperazine derivatives for cytotoxicity and antiherpetic activity allows the prediction of new highly active compounds

OBJECTIVES

The objectives of this study were (i) to apply computer-based technologies to evaluate the structure of 48 N,N'-(bis-5-nitropyrimidyl)dispirotripiperazines which belong to a new class of highly active antiviral compounds binding to cell surface heparan sulphates, (ii) to understand the chemical- biological interactions governing their activities, and (iii) to design new compounds with strong antiviral activity.

METHODS

The logarithm of 50% cytotoxic concentration (CC(50)) in GMK cells, of 50% inhibitory concentration (IC(50)) against herpes simplex virus type 1, and of selectivity index (SI = CC(50)/IC(50)) was used to develop quantitative structure-activity relationships (QSARs) based on simplex representation of molecular structure. The QSAR model was applied to design new compounds. Two of these compounds were synthesized, physico-chemically characterized and tested for cytotoxicity and antiviral activity.

RESULTS

Statistic characteristics for partial least squares models allow the prediction of CC(50), IC(50) and SI values. The QSAR results demonstrate a high impact of individual structural fragments for antiviral activity. Molecular fragments that promote and interfere with antiviral activity were defined on the basis of the obtained models. Electrostatic factors (38%) and hydrophobicity (34%) were the most important determinants of antiherpetic activity. Using the established method, new potential dispirotripiperazine derivatives were computationally designed. Two of these computationally designed compounds were synthesized. The biological test results confirm the computationally predicted values of these compounds.

CONCLUSIONS

The established QSAR model is suitable for the design of new antiherpetic compounds and prediction of their activity.

Estimation of gas-phase reaction rate constants of alkylnaphthalenes with chlorine, hydroxyl and nitrate radicals

Quantitative structure-property relationship/quantitative structure-activity relationship (QSPR/QSAR) models were developed for rate constants (k) of alkylnaphthalene reactions with chlorine (Cl), hydroxyl (*OH) and nitrate (NO(3)) radicals using partial least squares (PLS) regression. Quantum chemical descriptors computed by Parametric Method 3 (PM3) Hamiltonian were used as predictor variables. The cross-validated Q(cum)(2)values for the optimal QSPR/QSAR models of alkylnaphthalenes are 0.896, 0.728 and 0.774 for Cl, *OH and NO(3) radicals, respectively. Results from this study showed that rate constants with Cl, *OH and NO(3) are governed by different molecular structural descriptors. In the developed optimal QSPR/QSAR models, frontier molecular orbital energies and atomic charges are major descriptors that affect log k values. When the highest occupied molecular orbital (E(HOMO)) energy, the lowest unoccupied molecular orbital (E(LUMO)) energy, E(LUMO)+E(HOMO), and the average of net atomic charges on carbon atoms (Q(Cave)) are higher, the corresponding alkylnaphthalene reaction rate constants would be higher. In contrast, higher values of the most positive net atomic charges on hydrogen atoms (Q(H)(+)) could lead to the decrease of log k values. H-atom abstraction may occur on the hydrogen atom with the highest atomic charge (Q(H)). Radical addition reaction may also occur on the carbon (e.g. the carbon bonded to the alkyl group) with higher atomic charge (Q(C)) values. Other descriptors such as molecular weight (M(w)), standard heat of formation (DeltaH(f)), total energy (TE), electronic energy (EE), core-core repulsion energy (CCR), average molecular polarizability (a) and dipole moment (mu) were also important descriptors in the QSPR/QSAR models.

Computer-based QSARs for predicting mixture toxicity of benzene and its derivatives

During the past decades, the Quantitative structure-activity relationships (QSARs) have been proven to be reliable tools when little or no empirical data are available in medicinal chemistry, biochemistry, toxicology, and environmental sciences. However, only few studies that quantitatively predict mixtures toxicity have been reported. In this study, the QASR models for the binary mixtures toxicity of 12 benzene and its derivatives, including eight non-polar-narcotic compounds and four polar narcotic compounds were developed, without reference to exact toxicity mechanisms of single compounds. All parameters for the QSAR studies were defined on the basis of quantum mechanical calculations and these parameters were selected by the stepwise procedure. The results of this study provided a simple means of predicting the binary mixtures toxicity from the chemical structure.

Quantitative structure-activity relationship study on the biodegradation of acid dyestuffs

Quantitative structure-biodegradability relationships (QSBRs) were established to develop predictive models and mechanistic explanations for acid dyestuffs as well as biological activities. With a total of four descriptors, molecular weight (M(W)), energies of the highest occupied molecular orbital (E(HOMO)), the lowest unoccupied molecular orbital (E(LUMO)), and the excited state (E(ES)), calculated using quantum chemical semi-empirical methodology, a series of models were analyzed between the dye biodegradability and each descriptor. Results showed that E(HOMO) and M(W) were the dominant parameters controlling the biodegradability of acid dyes. A statistically robust QSBR model was developed for all studied dyes, with the combined application of E(HOMO) and M(W). The calculated biodegradations fitted well with the experimental data monitored in a facultative-aerobic process, indicative of the reliable prediction and mechanistic character of the developed model.

Trends in the development of microfluidic cell biochips for in vitro hepatotoxicity

Current developments in the technological fields of liver tissue engineering, bioengineering, biomechanics, microfabrication and microfluidics have lead to highly complex and pertinent new tools called "cell biochips" for in vitro toxicology. The purpose of "cell biochips" is to mimic organ tissues in vitro in order to partially reduce the amount of in vivo testing. These "cell biochips" consist of microchambers containing engineered tissue and living cell cultures interconnected by a microfluidic network, which allows the control of microfluidic flows for dynamic cultures, by continuous feeding of nutrients to cultured cells and waste removal. Cell biochips also allow the control of physiological contact times of diluted molecules with the tissues and cells, for rapid testing of sample preparations or specific addressing. Cell biochips can be situated between in vitro and in vivo testing. These types of systems can enhance functionality of cells by mimicking the tissue architecture complexities when compared to in vitro analysis but at the same time present a more rapid and simple process when compared to in vivo testing procedures. In this paper, we first introduce the concepts of microfluidic and biochip systems based on recent progress in microfabrication techniques used to mimic liver tissue in vitro. This includes progress and understanding in biomaterials science (cell culture substrate), biomechanics (dynamic cultures conditions) and biology (tissue engineering). The development of new "cell biochips" for chronic toxicology analysis of engineered tissues can be achieved through the combination of these research domains. Combining these advanced research domains, we then present "cell biochips" that allow liver chronic toxicity analysis in vitro on engineered tissues. An extension of the "cell biochip" idea has also allowed "organ interactions on chip", which can be considered as a first step towards the replacement of animal testing using a combined liver/lung organ model.

Structure-toxicity relationships of nitroaromatic compounds

The toxicity data of 28 nitroaromatic compounds (nitrobenzenes and, for comparison, benzene and toluene) related to a 50% lethal dose concentration for rats (LD50) were used to develop quantitative structure-activity relationships (QSARs).A genetic algorithm and multiple regression analysis were applied to select the descriptors and to generate the correlation models. The obtained equations consist of one to three descriptors. A number of molecular descriptors was obtained from HF/6-31G(d) and DFT (B3LYP/6-311+G(d, p)) level calculations. The calculated molecular geometry and electronic properties were evaluated by comparison with the available experimental data (where applicable). All parameters obtained at the B3LYP/6-311+G(d, p) level and the topological descriptors derived from this geometry were found to be reliable, except for dipole moment, due to the large uncertainty of its estimation. Satisfactory relationships were observed for the one-parameter structure-toxicity models between topological (X5Av, Ms) and quantum-chemical (ELUMO) descriptors. For better predictability two- and three-parameter QSAR analyses were performed. These analyses resulted in much better equations with correlation coefficient values r = 0.872-0.924. These models have been obtained with a set of topological, fragment and quantum-chemical descriptors (Ms, PCR, PCD, BELe1, C-026 and ELUMO). The toxicity of nitroaromatic compounds appears to be governed by a number of factors, such as the number of nitrogroups, the electrotopological state, the presence of certain fragments and the electrophilicity/reactivity parameter (ELUMO). Nitrobenzenes exhibited electrophilic reactivity (as was shown by correlation of the toxicity with the energy of the lowest unoccupied orbital, ELUMO). The toxicity LD50 parameter for rats has been utilized for the first time for QSAR analysis of nitrobenzenes. The predictive ability of the models is determined by a cross-validation "leave-one-out" method.

Structure-activity relationship analysis of antioxidant ability and neuroprotective effect of gallic acid derivatives

Gallic acid and its derivatives are a group of naturally occurring polyphenol antioxidants which have recently been shown to have potential healthy effects. In order to understand the relationship between the structures of gallic acid derivatives, their antioxidant activities, and neuroprotective effects, we examined their free radical scavenging effects in liposome and anti-apoptotic activities in human SH-SY5Y cell induced by 6-hydrodopamine autooxidation. It was found that these polyphenol antioxidants exhibited different hydrophobicity and could cross through the liposome membrane to react with 1,1-diphenyl-2-picryl-hydrazyl (DPPH) free radical in a time and dose-dependent manner. At the same time, the structure-antioxidant activity relationship of gallic acid derivatives on scavenging DPPH free radical in the liposome was also analyzed based on theoretical investigations. Analysis of cell apoptosis, intracellular GSH levels, production of ROS and the influx of Ca(2+) indicated that the protective effects of gallic acid derivatives in cell systems under oxidative stress depend on both their antioxidant capacities and hydrophobicity. However, the neuroprotective effects of gallic acid derivatives seem to depend more on their molecular polarities rather than antioxidant activities in the human SH-SY5Y cell line. In conclusion, these results reveal that compounds with high antioxidant activity and appropriate hydrophobicity are generally more effective in preventing the injury of oxidative stress in neurodegenerative diseases.

The uridine diphosphate glucuronosyltransferases: quantitative structure–activity relationships for hydroxyl polychlorinated biphenyl substrates

Quantitative structure-activity relationships (QSARs), which relate the glucuronidation of hydroxyl polychlorinated biphenyls (OH-PCBs)-catalyzed by the uridine diphosphate glucuronosyltransferases (UGTs)-to their physicochemical properties and molecular structural parameters, can be used to predict the rate constants and interpret the mechanism of glucuronidation. In this study, QSARs have been developed that use 23 semi-empirical calculated quantum chemical descriptors to predict the logarithms of the constants 1/K(m) and V(max), related to enzyme kinetics. A partial least squares regression method was used to select the optimal set of descriptors to minimize the multicollinearity between the descriptors, as well as to maximize the cross-validated coefficient (Q(2) (cum)) values. The key descriptors affecting log(1/K(m)) were E(lumo)- E(homo) (the energy gap between the lowest unoccupied molecular orbital and the highest occupied molecular orbital) and q(C) (-) (the largest negative net atomic charge on a carbon atom), while the key descriptors affecting logV(max) were the polarizability alpha, the Connolly solvent-excluded volume (CSEV), and logP (the logarithm of the partition coefficient for octanol/water). From the results obtained it can be concluded that hydrophobic and electronic aspects of OH-PCBs are important in the glucuronidation of OH-PCBs.

Molecular structural characteristics governing biocatalytic oxidation of PAHs with hemoglobin

Based on some fundamental quantum chemical descriptors computed by PM3 hamiltonian, two quantitative structure-activity relationship (QSAR) models for biocatalytic oxidation specific activity of unmodified and chemically modified hemoglobin in the oxidation of different polycyclic aromatic hydrocarbons (PAHs) in 15% acetonitrile were developed, respectively, using partial least squares analysis (PLS). The cross-validated Q(cum)(2) values for the two optimal QSAR models are 0.785 and 0.747, respectively, indicating a good predictive ability for biocatalytic oxidation specific activity of PAHs. The main factors affecting specific activity of PAHs are most positive net atomic charges on a hydrogen atom (q(H)(+)), largest negative atomic charge on a carbon atom (q(C)(-)), dipole moment (μ), the energy of the highest occupied molecular orbital (E(HOMO)), and (E(LUMO) - E(HOMO))(2). The biocatalytic oxidation specific activity of PAHs with big q(C)(-) and (E(LUMO) - E(HOMO))(2) values tends to be slow. Increasing q(H)(+), μ, and E(HOMO) values of PAHs leads to increase of specific activity.

Mechanism and structure–reactivity relationships for aromatic hydroxylation by cytochrome P450

Cytochrome P450 enzymes play a central role in drug metabolism, and models of their mechanism could contribute significantly to pharmaceutical research and development of new drugs. The mechanism of cytochrome P450 mediated hydroxylation of aromatics and the effects of substituents on reactivity have been investigated using B3LYP density functional theory computations in a realistic porphyrin model system. Two different orientations of substrate approach for addition of Compound I to benzene, and also possible subsequent rearrangement pathways have been explored. The rate-limiting Compound I addition to an aromatic carbon atom proceeds on the doublet potential energy surface via a transition state with mixed radical and cationic character. Subsequent formation of epoxide, ketone and phenol products is shown to occur with low barriers, especially starting from a cation-like rather than a radical-like tetrahedral adduct of Compound I with benzene. Effects of ring substituents were explored by calculating the activation barriers for Compound I addition in the meta and para-position for a range of monosubstituted benzenes and for more complex polysubstituted benzenes. Two structure-reactivity relationships including 8 and 10 different substituted benzenes have been determined using (i) experimentally derived Hammett sigma-constants and (ii) a theoretical scale based on bond dissociation energies of hydroxyl adducts of the substrates, respectively. In both cases a dual-parameter approach that employs a combination of radical and cationic electronic descriptors gave good relationships with correlation coefficients R2 of 0.96 and 0.82, respectively. These relationships can be extended to predict the reactivity of other substituted aromatics, and thus can potentially be used in predictive drug metabolism models.

Towards a new age of virtual ADME/TOX and multidimensional drug discovery

With the continual pressure to ensure follow-up molecules to billion dollar blockbuster drugs, there is a hurdle in profitability and growth for pharmaceutical companies in the next decades. With each success and failure we increasingly appreciate that a key to the success of synthesized molecules through the research and development process is the possession of drug-like properties. These properties include an adequate bioactivity as well as adequate solubility, an ability to cross critical membranes (intestinal and sometimes blood-brain barrier), reasonable metabolic stability and of course safety in humans. Dependent on the therapeutic area being investigated it might also be desirable to avoid certain enzymes or transporters to circumvent potential drug-drug interactions. It may also be important to limit the induction of these same proteins that can result in further toxicities. We have clearly moved the assessment of in vitro absorption, distribution, metabolism, excretion and toxicity (ADME/TOX) parameters much earlier in the discovery organization than a decade ago with the inclusion of higher throughput systems. We are also now faced with huge amounts of ADME/TOX data for each molecule that need interpretation and also provide a valuable resource for generating predictive computational models for future drug discovery. The present review aims to show what tools exist today for visualizing and modeling ADME/TOX data, what tools need to be developed, and how both the present and future tools are valuable for virtual filtering using ADME/TOX and bioactivity properties in parallel as a viable addition to present practices.

Quantitative structure-toxicity relationships using TOPS-MODE. 3. Structural factors influencing the permeability of commercial solvents through living human skin

The permeability of a series of 12 commercial solvents through living human skin were studied by using a topological sub-structural approach (TOPS-MODE). We first analyzed the influence of several physicochemical parameters used in describing the skin permeability of the solvents. No single significant relationship was found between any of these physicochemical properties and the permeability of the solvents. A QSAR model using TOPS-MODE descriptors was obtained and validated. This model accounted for more than 95% of the variance in the experimental permeability of these solvents. Using the derived model, the structural factors responsible for the permeability of this series of solvents through living human skin were identified. Methyl groups bonded to heteroatoms or to CH2 groups resulted in the greatest contributions to skin permeability and these groups were considered to be "permeability enhancers". In contrast, groups of the type X = O (X = S, C) were found to be "permeability inhibitors" because they possessed negative contributions to the logarithm of permeability in all of the studied solvents. Drawing on the idea of permeability "enhancers" and "inhibitors", we hypothesized that the solvents needed to orientate themselves in front of the stratum corneum layer first before penetrating through the skin.

What is toxicology and how does toxicity occur?

Toxicology has matured since it was defined as the 'science of poisons'. Modern toxicology is no longer anthropocentric but takes on different views at various biological systems, including ecosystems. Each will interact specifically when exposed to defined chemical agents, including drugs. Adverse effects during drug therapy or after (accidental) poisoning are the result of some negative interactions between the agent and the exposed biological system. Toxicity is no longer a specific property of drugs and chemicals but an operative term to describe the adverse outcome of a specific drugs-host interaction. Newer developments in toxicology have focused on the host. Toxicogenetics continues to provide answers to variations of host response to xenobiotics, including drugs. Clinically relevant genetic polymorphisms and gene defects have been detected, and their number is rapidly growing. The key to understanding is in the host proteins that interact with the drug and mediate the cellular response. Hence, the proteom, i.e. the complete set of proteins of a cell, an individual or a species, determines how an exposed biological system may interact with the manifold of different xenobiotics. Structure-activity studies try to find out useful predictive parameters for risk and toxicity assessment.

The integration of data on physico-chemical properties, in vitro-derived toxicity data and physiologically based kinetic and dynamic as modelling a tool in hazard and risk assessment. A commentary

Toxicity of a compound for an organism is dependent on the route of exposure, the amount (or concentration), the way in which the compound is taken up, distributes and is eliminated from the organism (ADME, kinetics) and the intrinsic properties (reactivity; mode of action, dynamics) of the compound towards the organism. These three elements: exposure, kinetics and dynamics form the basis of hazard and risk evaluations. Developments in our knowledge of the way in which physico-chemical properties of chemicals (on the one side) and physiological processes in the organism (on the other side) determine a compound's toxicity have greatly increased our understanding of toxicological processes and our ability to interpret experimental results. This has now resulted in the development of model systems in which the above-mentioned processes can be described mathematically. Biokinetic modelling is currently of great interest, but the further development of toxicodynamic modelling is equally important. The combination of both allows the estimation of a compound's critical amount/concentration on the critical site of action, which ideally would be the basis for hazard and risk assessments. In vitro systems have been extremely useful in studying the molecular basis of a chemical's biological activity, including its mechanism(s) of toxic action. Other achievements include the prediction of biological reactivity on the basis of a compound's physico-chemical properties and the construction of quantitative structure-activity relationships (QSARs). However, for the incorporation of in vitro-derived data as well as the results of QSARs, kinetic modelling is indispensable. Thus, biokinetic and toxicodynamic modelling are important (if not crucial) tools in toxicological research and there are increasing opportunities to incorporate the results of this work in hazard and risk assessments. Their implementation will allow a much more scientifically-based and a better structured risk assessment, which will be to a much lesser extent relying on animal experimentation.

Towards a new age of virtual ADME/TOX and multidimensional drug discovery

With the continual pressure to ensure follow-up molecules to billion dollar blockbuster drugs, there is a hurdle in profitability and growth for pharmaceutical companies in the next decades. With each success and failure we increasingly appreciate that a key to the success of synthesized molecules through the research and development process is the possession of drug-like properties. These properties include an adequate bioactivity as well as adequate solubility, an ability to cross critical membranes (intestinal and sometimes blood-brain barrier), reasonable metabolic stability and of course safety in humans. Dependent on the therapeutic area being investigated it might also be desirable to avoid certain enzymes or transporters to circumvent potential drug-drug interactions. It may also be important to limit the induction of these same proteins that can result in further toxicities. We have clearly moved the assessment of in vitro absorption, distribution, metabolism, excretion and toxicity (ADME/TOX) parameters much earlier in the discovery organization than a decade ago with the inclusion of higher throughput systems. We are also now faced with huge amounts of ADME/TOX data for each molecule that need interpretation and also provide a valuable resource for generating predictive computational models for future drug discovery. The present review aims to show what tools exist today for visualizing and modeling ADME/TOX data, what tools need to be developed, and how both the present and future tools are valuable for virtual filtering using ADME/TOX and bioactivity properties in parallel as a viable addition to present practices.