Predictivity of QSAR

Quantitative Structure–Electrochemistry Relationship (QSER) Studies on Metal–Amino–Porphyrins for the Rational Design of CO2 Reduction Catalysts

The quantitative structure–electrochemistry relationship (QSER) method was applied to a series of transition-metal-coordinated porphyrins to relate their structural properties to their electrochemical CO2 reduction activity. Since the reactions mainly occur within the core of the metalloporphyrin catalysts, the cluster model was used to calculate their structural and electronic properties using density functional theory with the M06L exchange–correlation functional. Three dependent variables were employed in this work: the Gibbs free energies of H*, C*OOH, and O*CHO. QSER, with the genetic algorithm combined with multiple linear regression (GA–MLR), was used to manipulate the mathematical models of all three Gibbs free energies. The obtained statistical values resulted in a good predictive ability (R2 value) greater than 0.945. Based on our QSER models, both the electronic properties (charges of the metal and porphyrin) and the structural properties (bond lengths between the metal center and the nitrogen atoms of the porphyrin) play a significant role in the three Gibbs free energies. This finding was further applied to estimate the CO2 reduction activities of the metal–monoamino–porphyrins, which will prove beneficial in further experimental developments.

Novel sulfone derivatives containing a 1,3,4-oxadiazole moiety: design and synthesis based on the 3D-QSAR model as potential antibacterial agent

BACKGROUND

The rice bacterial leaf blight (BLB) is one of the most serious bacterial diseases caused by Xanthomonas oryzae pv. oryzae (Xoo), which can cause yield loss of rice up to 50%. The three-dimensional quantitative structure-activity relationship (3D-QSAR) is an important auxiliary method to find potential high-efficient pesticides active structures.

RESULTS

A series of novel 1,3,4-oxadiazole compounds were designed and synthesized based on the 3D-QSAR model, and their antibacterial activities in vitro against Xoo were evaluated. The results indicated that all the target compounds showed excellent in vitro antibacterial activities. For example, the compounds 6, 12, 13, 20, 21, and 23 exhibited excellent antibacterial activities against Xoo, with half-maximal effective concentration (EC₅₀ ) values of 0.24, 0.31, 0.36, 0.29, 0.19, and 0.31 mg/L, respectively, which were superior to the antibacterial agents thiodiazole copper (127.44 mg/L) and bismerthiazol (91.08 mg/L). Meanwhile, compound 21 showed good antibacterial activity in vivo against BLB, with curative and protective activities of 46.7% and 56.4%, respectively, which were superior to thiodiazole copper (28.5% and 32.5%) and bismerthiazol (37.6% and 38.4%). Compound 21 can significantly reduce the extracellular polysaccharides production of Xoo, increase the permeability of the cell membranes, and also can cause cell surface wrinkles, deformation and dryness.

CONCLUSION

The 3D-QSAR model can be used to find sulfone compounds containing a 1,3,4-oxadiazole moiety with higher antibacterial activity, and compound 21 can be used as a potential antibacterial agent in the future. © 2020 Society of Chemical Industry.

QSPR Models for the Prediction of Some Thermodynamic Properties of Cycloalkanes Using GA-MLR Method

AIM AND OBJECTIVE

Cycloalkanes have been largely used in the field of medicine, components of food, pharmaceutical drugs, and they are mainly used to produce fuel. In present study the relationship between molecular descriptors and thermodynamic properties such as the standard enthalpies of formation (∆H°_f), the standard enthalpies of fusion (∆H°_fus), and the standard Gibbs free energy of formation (∆G°_f)of the cycloalkanes is represented.

MATERIALS AND METHODS

The Genetic Algorithm (GA) and multiple linear regressions (MLR) were successfully used to predict the thermodynamic properties of cycloalkanes. A large number of molecular descriptors were obtained with the Dragon program. The Genetic algorithm and backward method were used to reduce and select suitable descriptors.

RESULTS

QSPR models were used to delineate the important descriptors responsible for the properties of the studied cycloalkanes. The multicollinearity and autocorrelation properties of the descriptors contributed in the models were tested by calculating the Variance Inflation Factor (VIF), Pearson Correlation Coefficient (PCC) and the Durbin-Watson (DW) statistics. The predictive powers of the MLR models were discussed using Leave-One-Out Cross-Validation (LOOCV) and test set validation methods. The statistical parameters of the training, and test sets for GA-MLR models were calculated.

CONCLUSION

The results of the present study indicate that the predictive ability of the models was satisfactory and molecular descriptors such as: the Functional group counts, Topological indices, GETAWAY descriptors, Constitutional indices, and molecular properties provide a promising route for developing highly correlated QSPR models for prediction the studied properties.

Quantitative Structure-Activity Relationship Study of Camptothecin Derivatives as Anticancer Drugs Using Molecular Descriptors

Aim and Objective:

A Quantitative Structure-Activity Relationship (QSAR) has been widely developed to derive a correlation between chemical structures of molecules to their known activities. In the present investigation, QSAR models have been carried out on 76 Camptothecin (CPT) derivatives as anticancer drugs to develop a robust model for the prediction of physicochemical properties.

Materials and Methods:

A training set of 60 structurally diverse CPT derivatives was used to construct QSAR models for the prediction of physiochemical parameters such as Van der Waals surface area (SvdW), Van der Waals Volume (VvdW), Molar Refractivity (MR) and Polarizability (α). The QSAR models were optimized using Multiple Linear Regression (MLR) analysis. A test set of 16 compounds was evaluated using the defined models.

:

The Genetic Algorithm And Multiple Linear Regression Analysis (GA-MLR) were used to select the descriptors derived from the Dragon software to generate the correlation models that relate the structural features to the studied properties.

Results:

QSAR models were used to delineate the important descriptors responsible for the properties of the CPT derivatives. The statistically significant QSAR models derived by GA-MLR analysis were validated by Leave-One-Out Cross-Validation (LOOCV) and test set validation methods. The multicollinearity and autocorrelation properties of the descriptors contributed in the models were tested by calculating the Variance Inflation Factor (VIF) and the Durbin–Watson (DW) statistics.

Conclusion:

The predictive ability of the models was found to be satisfactory. Thus, QSAR models derived from this study may be helpful for modeling and designing some new CPT derivatives and for predicting their activity.

Inhibition effect of natural flavonoids on red tide alga Phaeocystis globosa and its quantitative structure-activity relationship

Red tides that occur off coasts have become a worldwide phenomenon over the past decades. In order to mitigate the damage of the red tides on the aquatic ecosystems, it is crucial to develop a method for predicting algicidal activities that requires less labor and time, and most importantly, this method can quickly screen potential algicides to control red tides. In this study, we have investigated the algicidal activity of 19 natural flavonoids against a typical red tide alga, Phaeocystis globosa. Our results indicate that after 5 days of flavonoid exposure, the half inhibition concentrations (IC₅₀) ranged from 0.068 to 3.065 mg L^-1, which showed the strong algicidal activities of the flavonoids. Furthermore, quantitative structure activity relationship (QSAR) model has been carried out between negative scale logarithm (pIC₅₀) of the flavonoids and the corresponding molecular descriptors. The developed model was validated, both internally and externally, which displayed statistical robustness (R² = 0.867, p = 0.0002, Q²LOO = 0.825, RMSEC = 0.182, Q²_extF3 = 0.896, RMSEP = 0.161, CCC = 0.935). This indicates that the developed model was obtained successfully with satisfactory predictability and robustness for the future rapid screening of natural flavonoids with high inhibition activity on the red tide alga growth. Moreover, the main descriptors in the QSAR model were the molar refractivity, partition coefficient, lowest unoccupied molecular orbital, and highest occupied molecular orbital, illustrating that the molecular electro-chemical characteristics are significant in the algicidal actions of the flavonoids. Graphical abstract Red tides frequently occur worldwide and have become a global environment problem. Flavonoids showed great potential in allelopathic control of the excessive growth of red tide algae. In this study, the algicidal activity of 19 natural flavonoids was investigated on a typical red tide organism Phaeocystis globosa. Futhermore, we applied the quantitative structure-activity relationship (QSAR) model to the experimental data. The model between molecular descriptors of flavonoids and their antialgal activity displays statistical robustness, and 4 of 45 selected molecular descriptors were obtained by regression of training set. The numbers in the figure represent the half inhibition concentration (IC₅₀) of flavonoids. Our results show that the algicidal activity of flavonoids is closely related to molar refraction, partition coefficient, lowest unoccupied molecular orbital, and highest occupied molecular orbital. The QSAR model can efficaciously predict the algicidal activity and provide insights into the inhibitory mechanisms of flavonoids.

Best Practices for QSAR Model Reporting: Physical and Chemical Properties, Ecotoxicity, Environmental Fate, Human Health, and Toxicokinetics Endpoints

BACKGROUND

Quantitative and qualitative structure–activity relationships (QSARs) have been used to understand chemical behavior for almost a century. The main source of QSAR models is the scientific literature, but the open question is how well these models are documented.

OBJECTIVES

The main aim of this study was to critically analyze the publication practices of QSARs with regard to transparency, potential reproducibility, and independent verification. The focus was on the level of technical completeness of the published QSARs.

METHODS

A total of 1,533 QSAR articles reporting 79 individual endpoints, mostly in environmental and health science, were reviewed. The QSAR parameters required for technical completeness were grouped into five categories: chemical structures, experimental endpoint values, descriptor values, mathematical representation of the model, and predicted endpoint values. The data were summarized and discussed using Circos plots.

RESULTS

Altogether, 42.5% of the reviewed articles were found to be potentially reproducible. The potential reproducibility for different endpoint groups varied; the respective rates were 39% for physical and chemical properties, 52% for ecotoxicity, 56% for environmental fate, 30% for human health, and 32% for toxicokinetics. The reproducibility of QSARs is discussed and placed in the context of the reproducibility of the experimental methods. Included are 65 references to open QSAR datasets as examples of models restored from scientific articles.

DISCUSSION

Strikingly poor documentation of QSARs was observed, which reduces the transparency, availability, and consequently, the application of research results in scientific, industrial, and regulatory areas. A list of the components needed to ensure the best practices for QSAR reporting is provided, allowing long-term use and preservation of the models. This list also allows an assessment of the reproducibility of models by interested parties such as journal editors, reviewers, regulators, evaluators, and potential users. https://doi.org/10.1289/EHP3264.

Effect doses for protection of human health predicted from physicochemical properties of metals/metalloids

Effect doses (EDs) of metals/metalloids, usually obtained from toxicological experiments are required for developing environmental quality criteria/standards for use in assessment of hazard or risks. However, because in vivo tests are time-consuming, costly and sometimes impossible to conduct, among more than 60 metals/metalloids, there are sufficient data for development of EDs for only approximately 25 metals/metalloids. Hence, it was deemed a challenge to derive EDs for additional metals by use of alternative methods. This study found significant relationships between EDs and physicochemical parameters for twenty-five metals/metalloids. Elements were divided into three classes and then three individual empirical models were developed based on the most relevant parameters for each class. These parameters included log-βn, ΔE₀ and X_m²r, respectively (R² = 0.988, 0.839, 0.871, P < 0.01). Those models can satisfactorily predict EDs for another 25 metals/metalloids. Here, these alternative models for deriving thresholds of toxicity that could be used to perform preliminarily, screen-level health assessments for metals are presented.

Anti-Mycobacterial Evaluation of 7-Chloro-4-Aminoquinolines and Hologram Quantitative Structure-Activity Relationship (HQSAR) Modeling of Amino-Imino Tautomers

In an ongoing research program for the development of new anti-tuberculosis drugs, we synthesized three series (A, B, and C) of 7-chloro-4-aminoquinolines, which were evaluated in vitro against Mycobacterium tuberculosis (MTB). Now, we report the anti-MTB and cytotoxicity evaluations of a new series, D (D01–D21). Considering the active compounds of series A (A01–A13), B (B01–B13), C (C01–C07), and D (D01–D09), we compose a data set of 42 compounds and carried out hologram quantitative structure–activity relationship (HQSAR) analysis. The amino–imino tautomerism of the 4-aminoquinoline moiety was considered using both amino (I) and imino (II) forms as independent datasets. The best HQSAR model from each dataset was internally validated and both models showed significant statistical indexes. Tautomer I model: leave-one-out (LOO) cross-validated correlation coefficient (q²) = 0.80, squared correlation coefficient (r²) = 0.97, standard error (SE) = 0.12, cross-validated standard error (SE_cv) = 0.32. Tautomer II model: q² = 0.77, r² = 0.98, SE = 0.10, SE_cv = 0.35. Both models were externally validated by predicting the activity values of the corresponding test set, and the tautomer II model, which showed the best external prediction performance, was used to predict the biological activity responses of the compounds that were not evaluated in the anti-MTB trials due to poor solubility, pointing out D21 for further solubility studies to attempt to determine its actual biological activity.

New clues on carcinogenicity-related substructures derived from mining two large datasets of chemical compounds

In this study, new molecular fragments associated with genotoxic and nongenotoxic carcinogens are introduced to estimate the carcinogenic potential of compounds. Two rule-based carcinogenesis models were developed with the aid of SARpy: model R (from rodents' experimental data) and model E (from human carcinogenicity data). Structural alert extraction method of SARpy uses a completely automated and unbiased manner with statistical significance. The carcinogenicity models developed in this study are collections of carcinogenic potential fragments that were extracted from two carcinogenicity databases: the ANTARES carcinogenicity dataset with information from bioassay on rats and the combination of ISSCAN and CGX datasets, which take into accounts human-based assessment. The performance of these two models was evaluated in terms of cross-validation and external validation using a 258 compound case study dataset. Combining R and H predictions and scoring a positive or negative result when both models are concordant on a prediction, increased accuracy to 72% and specificity to 79% on the external test set. The carcinogenic fragments present in the two models were compared and analyzed from the point of view of chemical class. The results of this study show that the developed rule sets will be a useful tool to identify some new structural alerts of carcinogenicity and provide effective information on the molecular structures of carcinogenic chemicals.

Quantum-chemically-calculated mechanistically interpretable molecular descriptors for drug-action mechanism study – a case study of anthracycline anticancer antibiotics

Identification of drug-action mechanisms and understanding of chemical substituents affecting the anticancer activity of drugs are important for drug development.

Relating methanogen community structure and anaerobic digester function

Much remains unknown about the relationships between microbial community structure and anaerobic digester function. However, knowledge of links between community structure and function, such as specific methanogenic activity (SMA) and COD removal rate, are valuable to improve anaerobic bioprocesses. In this work, quantitative structure-activity relationships (QSARs) were developed using multiple linear regression (MLR) to predict SMA using methanogen community structure descriptors for 49 cultures. Community descriptors were DGGE demeaned standardized band intensities for amplicons of a methanogen functional gene (mcrA). First, predictive accuracy of MLR QSARs was assessed using cross validation with training (n = 30) and test sets (n = 19) for glucose and propionate SMA data. MLR equations correlating band intensities and SMA demonstrated good predictability for glucose (q(2) = 0.54) and propionate (q(2) = 0.53). Subsequently, data from all 49 cultures were used to develop QSARs to predict SMA values. Higher intensities of two bands were correlated with higher SMA values; high abundance of methanogens associated with these two bands should be encouraged to attain high SMA values. QSARs are helpful tools to identify key microorganisms or to study and improve many bioprocesses. Development of new, more robust QSARs is encouraged for anaerobic digestion or other bioprocesses, including nitrification, nitritation, denitrification, anaerobic ammonium oxidation, and enhanced biological phosphorus removal.

Alternative Toxicity Testing: Analyses on Skin Sensitization, ToxCast Phases I and II, and Carcinogenicity Provide Indications on How to Model Mechanisms Linked to Adverse Outcome Pathways

This article studies alternative toxicological approaches, with new (skin sensitization, ToxCast) and previous (carcinogenicity) analyses. Quantitative modeling of rate-limiting steps in skin sensitization and carcinogenicity predicts the majority of toxicants. Similarly, successful (Quantitative) Structure-Activity Relationships models exploit the quantification of only one, or few rate-limiting steps. High-throughput assays within ToxCast point to promising associations with endocrine disruption, whereas markers for pathways intermediate events have limited correlation with most endpoints. Since the pathways may be very different (often not simple linear chains of events), quantitative analysis is necessary to identify the type of mechanism and build the appropriate model.

QSAR study of the DPPH· radical scavenging activity of coumarin derivatives and xanthine oxidase inhibition by molecular docking

A Quantitative Structure-Activity Relationship (QSAR) of coumarins by genetic algorithms employing physicochemical, topological, lipophilic and electronic descriptors was performed. We have used experimental antioxidant activities of specific coumarin derivatives against the DPPH· radical molecule. Molecular descriptors such as Randic Path/Walk, hydrophilic factor and chemical hardness were selected to propose a mathematical model. We obtained a linear correlation with R2 = 96.65 and Q LOO2 = 93.14 values. The evaluation of the predictive ability of the model was performed by applying the Q ASYM2, $\hat r^2 $ and Δr m2 methods. Fukui functions were calculated here for coumarin derivatives in order to delve into the mechanics by which they work as primary antioxidants. We also investigated xanthine oxidase inhibition with these coumarins by molecular docking. Our results show that hydrophobic, electrostatic and hydrogen bond interactions are crucial in the inhibition of xanthine oxidase by coumarins.

Comparative pharmacophore modeling and QSAR studies for structural requirements of some substituted 2-aminopyridines derivatives as inhibitors nitric oxide synthases

The present studies are an attempt in this direction seeking for the development and comparison of QSAR models of substituted 2-aminopyridines derivatives as inhibitors of nitric oxide synthases by different feature selection methods. Comparing the two different feature selection methods, it is implicit that the model built with the selected variables by simulated annealing (SA) method gives better prediction in case of 2D and 3D QSAR modeling. The QSAR study was carried out on V-life Molecular Design Suite software and the derived best QSAR model was derived by partial component regression (PCR) method. The statistically significant best model with high correlation coefficient (r2 = 0.8408) was selected for further study. The model was further validated by means of crossed squared correlation coefficient (q2 = 0.7270 and pred r2 = 0.7889) which shows model has good predictive ability. 3D-QSAR analysis has been performed on a series of substituted 2-aminopyridines derivatives as which were screened as inhibitors of nitric oxide synthases, using the simulated annealing and step wise k-nearest neighbour Molecular Field Analysis. The best QSAR model showed q2 = 0.8377, r2 = 0.8739 and standard error = 0.1954. It was observed that steric properties predicted by k-nearest neighbour MFA contours can be related to inhibitors of nitric oxide synthases. The predictive ability of the resultant model was evaluated using a test set molecules and the predicted r2 = 0.8159. The distances between the pharmacophore sites were measured in order to confirm their significance to the activities. The results reveal that the acceptor (acc), donor (don), aliphatic and aromatic pharmacophore properties are favorable contours sites for both the activities. The two dimensional and k-nearest neighbour contour plots required for further understanding of the relationship between structural features of substituted 2-aminopyridines derivatives and their activities which should be applicable to design newer potential inducible nitric oxide synthases.

Structure-activity relationship models for rat carcinogenesis and assessing the role mutagens play in model predictivity

We previously demonstrated that fragment based cat-SAR carcinogenesis models consisting solely of mutagenic or non-mutagenic carcinogens varied greatly in terms of their predictive accuracy. This led us to investigate how well the rat cancer cat-SAR model predicted mutagens and non-mutagens in their learning set. Four rat cancer cat-SAR models were developed: Complete Rat, Transgender Rat, Male Rat and Female Rat, with leave-one-out (LOO) validation concordance values of 69%, 74%, 67% and 73%, respectively. The mutagenic carcinogens produced concordance values in the range 69-76% compared with only 47-53% for non-mutagenic carcinogens. As a surrogate for mutagenicity, comparisons between single site and multiple site carcinogen SAR models were analysed. The LOO concordance values for models consisting of 1-site, 2-site and 4+-site carcinogens were 66%, 71% and 79%, respectively. As expected, the proportion of mutagens to non-mutagens also increased, rising from 54% for 1-site to 80% for 4+-site carcinogens. This study demonstrates that mutagenic chemicals, in both SAR learning sets and test sets, are influential in assessing model accuracy. This suggests that SAR models for carcinogens may require a two-step process in which mutagenicity is first determined before carcinogenicity can be accurately predicted.

Performance of (Q)SAR models for predicting Ames mutagenicity of aryl azo and benzidine based compounds

Regulatory agencies worldwide are committed to the objectives of the Strategic Approach to International Chemicals Management to ensure that by 2020 chemicals are used and produced in ways that lead to the minimization of significant adverse effects on human health and the environment. Under the Government of Canada's Chemicals Management Plan, the commitment to address a large number of substances, many with limited data, has highlighted the importance of pursuing alternative hazard assessment methodologies that are able to accommodate chemicals with varying toxicological information. One such method is (Quantitative) Structure Activity Relationships ((Q)SAR) models. The current investigation into the predictivity of 20 (Q)SAR tools designed to model bacterial reverse mutation in Salmonella typhimurium is one of the first of this magnitude to be carried out using an external validation set comprised mainly of industrial chemicals which represent a diverse group of aromatic and benzidine-based azo dyes and pigments. Overall, this study highlights the value in challenging the predictivity of existing models using a small but representative subset of data-rich chemicals. Furthermore, external validation revealed that only a handful of models satisfactorily predicted for the test chemical space. The exercise also provides insight into using the Organisation for Economic Co-operation and Development (Q)SAR Toolbox as a read across tool.

An integrated approach to model the biomagnification of organic pollutants in aquatic food webs of the Yangtze Three Gorges Reservoir ecosystem using adapted pollution scenarios

The impounding of the Three Gorges Reservoir (TGR) at the Yangtze River caused large flooding of urban, industrial, and agricultural areas, and profound land use changes took place. Consequently, substantial amounts of organic and inorganic pollutants were released into the reservoir. Additionally, contaminants and nutrients are entering the reservoir by drift, drainage, and runoff from adjacent agricultural areas as well as from sewage of industry, aquacultures, and households. The main aim of the presented research project is a deeper understanding of the processes that determines the bioaccumulation and biomagnification of organic pollutants, i.e., mainly pesticides, in aquatic food webs under the newly developing conditions of the TGR. The project is part of the Yangtze-Hydro environmental program, financed by the German Ministry of Education and Science. In order to test combinations of environmental factors like nutrients and pollution, we use an integrated modeling approach to study the potential accumulation and biomagnification. We describe the integrative modeling approach and the consecutive adaption of the AQUATOX model, used as modeling framework for ecological risk assessment. As a starting point, pre-calibrated simulations were adapted to Yangtze-specific conditions (regionalization). Two exemplary food webs were developed by a thorough review of the pertinent literature. The first typical for the flowing conditions of the original Yangtze River and the Daning River near the city of Wushan, and the second for the stagnant reservoir characteristics of the aforementioned region that is marked by an intermediate between lake and large river communities of aquatic organisms. In close cooperation with German and Chinese partners of the Yangtze-Hydro Research Association, other site-specific parameters were estimated. The MINIBAT project contributed to the calibration of physicochemical and bathymetric parameters, and the TRANSMIC project delivered hydrodynamic models for water volume and flow velocity conditions. The research questions were firstly focused on the definition of scenarios that could depict representative situations regarding food webs, pollution, and flow conditions in the TGR. The food webs and the abiotic site conditions in the main study area near the city of Wushan that determine the environmental preconditions for the organisms were defined. In our conceptual approach, we used the pesticide propanil as a model substance.

Mutagenicity, carcinogenicity, and other end points

Aiming at understanding the structural and physical chemical basis of the biological activity of chemicals, the science of structure-activity relationships has seen dramatic progress in the last decades. Coarse-grain, qualitative approaches (e.g., the structural alerts), and fine-tuned quantitative structure-activity relationship models have been developed and used to predict the toxicological properties of untested chemicals. More recently, a number of approaches and concepts have been developed as support to, and corollary of, the structure-activity methods. These approaches (e.g., chemical relational databases, expert systems, software tools for manipulating the chemical information) have dramatically expanded the reach of the structure-activity work; at present, they are powerful and inescapable tools for computer chemists, toxicologists, and regulators. This chapter, after a general overview of traditional and well-known approaches, gives a detailed presentation of the latter more recent support tools freely available in the public domain.

Does electron-correlation has any role in the quantitative structure-activity relationships?

For developing quantitative structure-activity relationships (QSARs), quantum-mechanical molecular descriptors based on the state-of-the-art quantum-mechanical methods such as Hartree-Fock (HF) method and density-functional theory (DFT), are now routinely employed. The validity of these quantum-mechanical methods, however, rests on the accurate estimation of electron-correlation energy. This work analyses the role of electron-correlation, using correlation energy as a molecular descriptor, in the QSARs. In particular, QSAR models, for the mutagenic activity of a set of nitrated polycyclic aromatic hydrocarbons (nitro-PAHs), are examined for the role of electron-correlation through state-of-the-art external validation parameters such as concordance correlation coefficient and recently proposed predictive squared correlation coefficients, namely, QF1(2), QF2(2), and QF3(2) etc. The electron-correlation contribution to the highest occupied and lowest unoccupied molecular orbital (HOMO/LUMO) energies is also analyzed. QSAR models based on the semi-empirical quantum-mechanical methods like PM6 and RM1 are also compared. It is found that the models, developed using electron-correlation contribution of the quantum-mechanical descriptors, are not only robust but also relatively more predictive than those developed with the HF and DFT descriptors. The latter are found to be even less reliable than PM6 and RM1 descriptors based models, which show comparable robustness and predictivity with those developed using electron correlation based descriptors. The external predictivity of model based on semi-empirical descriptors can be improved if electron-correlation contribution of the quantum-mechanical descriptors is explicitly included in the model. This work reports the first-ever use of electron-correlation energy and its contribution to the HOMO/LUMO energies as molecular descriptors.

Predictive computational toxicology to support drug safety assessment

Use of predictive technologies is an important aspect of many efforts in today's research, development, and regulatory landscapes. Computational methods as predictive tools for supporting drug safety assessments is of widespread interest as the field of in silico assessments rapidly changes with emerging technologies and the large amount of existing data available for modeling. There are challenges associated with application of in silico analyses for drug toxicity predictions and need for strategies and harmonization to enable an acceptable in silico evaluation for prediction of specific toxicity assay outcomes. This chapter will provide an overview focused on computational tools using structure-activity relationships and will highlight initiatives for use of computational assessments and realistic applications for predictive modeling in evaluating potential toxicities of drug-related molecules.

Chemical structure determines target organ carcinogenesis in rats

SAR models were developed for 12 rat tumour sites using data derived from the Carcinogenic Potency Database. Essentially, the models fall into two categories: Target Site Carcinogen-Non-Carcinogen (TSC-NC) and Target Site Carcinogen-Non-Target Site Carcinogen (TSC-NTSC). The TSC-NC models were composed of active chemicals that were carcinogenic to a specific target site and inactive ones that were whole animal non-carcinogens. On the other hand, the TSC-NTSC models used an inactive category also composed of carcinogens but to any/all other sites but the target site. Leave one out (LOO) validations produced an overall average concordance value for all 12 models of 0.77 for the TSC-NC models and 0.73 for the TSC-NTSC models. Overall, these findings suggest that while the TSC-NC models are able to distinguish between carcinogens and non-carcinogens, the TSC-NTSC models are identifying structural attributes that associate carcinogens to specific tumour sites. Since the TSC-NTSC models are composed of active and inactive compounds that are genotoxic and non-genotoxic carcinogens, the TSC-NTSC models may be capable of deciphering non-genotoxic mechanisms of carcinogenesis. Together, models of this type may also prove useful in anticancer drug development since they essentially contain chemical moieties that target a specific tumour site.

Chemoinformatics and chemical genomics: potential utility of in silico methods

Computational life sciences and informatics are inseparably intertwined and they lie at the heart of modern biology, predictive quantitative modeling and high-performance computing. Two of the applied biological disciplines that are poised to benefit from such progress are pharmacology and toxicology. This review will describe in silico chemoinformatics methods such as (quantitative) structure-activity relationship modeling and will overview how chemoinformatic technologies are considered in applied regulatory research. Given the post-genomics era and large-scale repositories of omics data that are available, this review will also address potential applications of in silico techniques in chemical genomics. Chemical genomics utilizes small molecules to explore the complex biological phenomena that may not be not amenable to straightforward genetic approach. The reader will gain the understanding that chemoinformatics stands at the interface of chemistry and biology with enabling systems for mapping, statistical modeling, pattern recognition, imaging and database tools. The great potential of these technologies to help address complex issues in the toxicological sciences is appreciated with the applied goal of the protection of public health.

Role of in silico genotoxicity tools in the regulatory assessment of pharmaceutical impurities

The toxicological assessment of genotoxic impurities is important in the regulatory framework for pharmaceuticals. In this context, the application of promising computational methods (e.g. Quantitative Structure-Activity Relationships (QSARs), Structure-Activity Relationships (SARs) and/or expert systems) for the evaluation of genotoxicity is needed, especially when very limited information on impurities is available. To gain an overview of how computational methods are used internationally in the regulatory assessment of pharmaceutical impurities, the current regulatory documents were reviewed. The software recommended in the guidelines (e.g. MCASE, MC4PC, Derek for Windows) or used practically by various regulatory agencies (e.g. US Food and Drug Administration, US and Danish Environmental Protection Agencies), as well as other existing programs were analysed. Both statistically based and knowledge-based (expert system) tools were analysed. The overall conclusions on the available in silico tools for genotoxicity and carcinogenicity prediction are quite optimistic, and the regulatory application of QSAR methods is constantly growing. For regulatory purposes, it is recommended that predictions of genotoxicity/carcinogenicity should be based on a battery of models, combining high-sensitivity models (low rate of false negatives) with high-specificity ones (low rate of false positives) and in vitro assays in an integrated manner.

Alternatives to the carcinogenicity bioassay for toxicity prediction: are we there yet?

INTRODUCTION

For decades, traditional toxicology has been the ultimate source of information on the carcinogenic potential of chemicals; however, with increasing demand on regulation of chemicals and decreasing resources for testing, opportunities to accept 'alternative' approaches have dramatically expanded. The need for tools able to identify carcinogens in shorter times and at a lower cost in terms of animal lives and money is still an open issue, and the present strategies and regulations for carcinogenicity prescreening do not adequately protect human health.

AREAS COVERED

This paper briefly summarizes the theories on the early steps of carcinogenesis and presents alternative detection methods for carcinogens based on genetic toxicology, structure-activity relationships and cell transformation assays.

EXPERT OPINION

There is evidence that the combination of Salmonella and structural alerts for the DNA-reactive carcinogens, and in vitro cell transformation assays for nongenotoxic carcinogens, permits the identification of a very large proportion of carcinogens. If implemented, this alternative strategy could improve considerably the protection of human health.

Flexible use of QSAR models in predictive toxicology: a case study on aromatic amines

Over the last years, predictive toxicology approaches based on Structure-Activity Relationships have emerged as fundamental tools in the regulatory assessments of chemicals, especially in those programs where regulatory constraints and assessment schemes limit the amount of data available from experimental test methods. Both the qualitative (e.g., Structural Alerts) and the quantitative (Quantitative Structure-Activity Relationships, QSAR) approach can play important roles. However, the two approaches are not familiar to the same extent to the regulators that most often use only the qualitative approach, so that the potentiality of the more sophisticated QSAR approach is neglected. In fact, QSAR is a very flexible tool that allows the user to modulate its response according to different goals and requirements. Here, we present a non-naïve approach to the use of a QSAR relative to a dichotomous biological activity (such as mutagen/nonmutagen), and we show how the user can maximize alternatively the reliability of the prediction of negative compounds (i.e., safe chemicals) or that of positive chemicals (i.e., chemicals that pose high hazard). Because of the environmental and industrial importance of the class of aromatic amines, we apply the approach to a previously published QSAR on the Salmonella typhimurium mutagenicity of these chemicals.

Prediction of physicochemical properties

Physicochemical properties are key factors in controlling the interactions of xenobiotics with living organisms. Computational approaches to toxicity prediction therefore generally rely to a very large extent on the physicochemical properties of the query compounds. Consequently it is important that reliable in silico methods are available for the rapid calculation of physicochemical properties. The key properties are partition coefficient, aqueous solubility, and pKa and, to a lesser extent, melting point, boiling point, vapor pressure, and Henry's law constant (air-water partition coefficient). The calculation of each of these properties from quantitative structure-property relationships (QSPRs) and from available software is discussed in detail, and recommendations made. Finally, detailed consideration is given of guidelines for the development of QSPRs and QSARs.

Systematic computational analysis of structure-activity relationships: concepts, challenges and recent advances

The exploration of structure-activity relationships (SARs) of small molecules is a central aspect of medicinal chemistry. Typically, SARs are analyzed on a one-by-one basis, and chemical intuition and experience play an important role in this process. Since the 1960s, computational approaches have been developed to aid in SAR exploration that largely, but not exclusively, rely on the quantitative (Q)SAR paradigm. Accordingly, QSAR analysis has long been a mainstay of compound optimization efforts. However, the strong compound class dependence of SAR features and their intrinsic heterogeneity often pose severe constraints on the applicability of these methods. In addition to QSAR approaches, conceptually different molecular similarity methods are also applied to identify novel active compounds. In order to complement and further extend the current repertoire of computational methods, SAR analysis functions have recently been introduced that evaluate and compare SAR features on a large scale, extract SAR information from compound data sets and prioritize SARs that are promising targets for optimization. SAR analysis functions are designed to systematically profile and compare SARs contained in different data sets and characterize both global and local SAR features. Numerical SAR analysis is complemented by intuitive graphical representations of SAR landscapes.

Alternative (non-animal) methods for cosmetics testing: current status and future prospects-2010

The 7th amendment to the EU Cosmetics Directive prohibits to put animal-tested cosmetics on the market in Europe after 2013. In that context, the European Commission invited stakeholder bodies (industry, non-governmental organisations, EU Member States, and the Commission's Scientific Committee on Consumer Safety) to identify scientific experts in five toxicological areas, i.e. toxicokinetics, repeated dose toxicity, carcinogenicity, skin sensitisation, and reproductive toxicity for which the Directive foresees that the 2013 deadline could be further extended in case alternative and validated methods would not be available in time. The selected experts were asked to analyse the status and prospects of alternative methods and to provide a scientifically sound estimate of the time necessary to achieve full replacement of animal testing. In summary, the experts confirmed that it will take at least another 7-9 years for the replacement of the current in vivo animal tests used for the safety assessment of cosmetic ingredients for skin sensitisation. However, the experts were also of the opinion that alternative methods may be able to give hazard information, i.e. to differentiate between sensitisers and non-sensitisers, ahead of 2017. This would, however, not provide the complete picture of what is a safe exposure because the relative potency of a sensitiser would not be known. For toxicokinetics, the timeframe was 5-7 years to develop the models still lacking to predict lung absorption and renal/biliary excretion, and even longer to integrate the methods to fully replace the animal toxicokinetic models. For the systemic toxicological endpoints of repeated dose toxicity, carcinogenicity and reproductive toxicity, the time horizon for full replacement could not be estimated.

Developing the Applicability Domain of In Silico Models: Relevance, Importance and Methods

The past two decades has seen the rapid growth in the development and utilisation of computational technologies to predict the toxicity of chemicals. Most notably, widespread pressure to both reduce and replace current animal testing regimes has led to in silico modelling becoming a widely utilised tool in toxicological screening. Unfortunately, given that computational models are open to misuse, there has been, and still is, significant reluctance to accept them for regulatory use. In an effort to combat this, the validation of both model and predictions is now at the forefront of research, with the concept of applicability domain being central to the validation process.

In this chapter the applicability domain concept is defined and numerous methods for its characterisation are detailed and explored with the aid of a case study example. These approaches are shown to span from relatively simple descriptor-based methods to more complex approaches based upon structural similarity or mechanism of action. Given the wealth of differing approaches available and the different information each method yields about the model, a stepwise scheme which considers numerous methods is recommended. With appreciation of model architecture and subsequent utilisation, this chapter shows that a robust and multifaceted applicability domain can be generated. Once defined, the applicability domain serves as a critical screening stage ensuring that a model is fit-for-purpose and predictions are made with maximal confidence.

Data Quality Assessment for In Silico Methods: A Survey of Approaches and Needs

As indicated in Chapter 3, there are a large number of potential sources of data now available for modelling purposes. These range from historical literature references for a few compounds to highly curated databases of hundreds of thousands of compounds, available via the internet. Before including any data in an in silico model, the question of data quality must be addressed. Although it is difficult to define the quality of data in absolute terms, it is possible to assess the suitability of data for a given purpose. There are many reasons for variability within data and the degree of error that is acceptable for one model may not be the same as for another. For example generating a global model intended to pre-screen large numbers of compounds does not require the same degree of accuracy as performing an individual risk assessment for a chemical of interest. In this chapter, sources of data variability and error will be discussed and formal methods to score data quality, such as use of the Klimisch criteria, will be described. Examples of data quality issues will be given for specific endpoints relating to both environmental and human health effects. Mathematical approaches (Dempster-Schafer theory and Bayesian networks) demonstrating how this information relating to confidence in the data can be incorporated into in silico models is also discussed.

A self-adaptive genetic algorithm-artificial neural network algorithm with leave-one-out cross validation for descriptor selection in QSAR study

Based on the quantitative structure-activity relationships (QSARs) models developed by artificial neural networks (ANNs), genetic algorithm (GA) was used in the variable-selection approach with molecule descriptors and helped to improve the back-propagation training algorithm as well. The cross validation techniques of leave-one-out investigated the validity of the generated ANN model and preferable variable combinations derived in the GAs. A self-adaptive GA-ANN model was successfully established by using a new estimate function for avoiding over-fitting phenomenon in ANN training. Compared with the variables selected in two recent QSAR studies that were based on stepwise multiple linear regression (MLR) models, the variables selected in self-adaptive GA-ANN model are superior in constructing ANN model, as they revealed a higher cross validation (CV) coefficient (Q(2)) and a lower root mean square deviation both in the established model and biological activity prediction. The introduced methods for validation, including leave-multiple-out, Y-randomization, and external validation, proved the superiority of the established GA-ANN models over MLR models in both stability and predictive power. Self-adaptive GA-ANN showed us a prospect of improving QSAR model.

New public QSAR model for carcinogenicity

BACKGROUND

One of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration.

RESULTS

Models for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESAR's models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B.

CONCLUSION

Carcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions and conventional methods. However, we believe that combination of several methods will provide useful support to the overall evaluation of carcinogenicity. In present paper models for classification of carcinogenic compounds using MDL and Dragon descriptors were developed. Models could be used to set priorities among chemicals for further testing. The models at the CAESAR site were implemented in java and are publicly accessible.

New public QSAR model for carcinogenicity

BACKGROUND

One of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration.

RESULTS

Models for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESAR's models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B.

CONCLUSION

Carcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions and conventional methods. However, we believe that combination of several methods will provide useful support to the overall evaluation of carcinogenicity. In present paper models for classification of carcinogenic compounds using MDL and Dragon descriptors were developed. Models could be used to set priorities among chemicals for further testing. The models at the CAESAR site were implemented in java and are publicly accessible.

Mammary carcinogen-protein binding potentials: novel and biologically relevant structure-activity relationship model descriptors

Previously, SAR models for carcinogenesis used descriptors that are essentially chemical descriptors. Herein we report the development of models with the cat-SAR expert system using biological descriptors (i.e., ligand-receptor interactions) rat mammary carcinogens. These new descriptors are derived from the virtual screening for ligand-receptor interactions of carcinogens, non-carcinogens, and mammary carcinogens to a set of 5494 target proteins. Leave-one-out validations of the ligand mammary carcinogen-non-carcinogen model had a concordance between experimental and predicted results of 71%, and the mammary carcinogen-non-mammary carcinogen model was 72% concordant. The development of a hybrid fragment-ligand model improved the concordances to 85 and 83%, respectively. In a separate external validation exercise, hybrid fragment-ligand models had concordances of 81 and 76%. Analyses of example rat mammary carcinogens including the food mutagen and oestrogenic compound PhIP, the herbicide atrazine, and the drug indomethacin; the ligand model identified a number of proteins associated with each compound that had previously been referenced in Medline in conjunction with the test chemical and separately with association to breast cancer. This new modelling approach can enhance model predictivity and help bridge the gap between chemical structure and carcinogenic activity by descriptors that are related to biological targets.

A new strategy of outlier detection for QSAR/QSPR

The crucial step of building a high performance QSAR/QSPR model is the detection of outliers in the model. Detecting outliers in a multivariate point cloud is not trivial, especially when several outliers coexist in the model. The classical identification methods do not always identify them, because they are based on the sample mean and covariance matrix influenced by the outliers. Moreover, existing methods only lay stress on some type of outliers but not all the outliers. To avoid these problems and detect all kinds of outliers simultaneously, we provide a new strategy based on Monte-Carlo cross-validation, which was termed as the MC method. The MC method inherently provides a feasible way to detect different kinds of outliers by establishment of many cross-predictive models. With the help of the distribution of predictive residuals such obtained, it seems to be able to reduce the risk caused by the masking effect. In addition, a new display is proposed, in which the absolute values of mean value of predictive residuals are plotted versus standard deviations of predictive residuals. The plot divides the data into normal samples, y direction outliers and X direction outliers. Several examples are used to demonstrate the detection ability of MC method through the comparison of different diagnostic methods.