A QSAR study of acute toxicity of N-substituted fluoroacetamides to rats

QSAR models for predicting toxicity of polychlorinated dibenzo-p-dioxins and dibenzofurans using quantum chemical descriptors

By partial least square regression, simple quantitative structure-activity relationship (QSAR) models were developed for the toxicity of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs). Quantum chemical descriptors computed by semi-empirical PM3 method were used as predictor variables. Three optimal QSAR models are developed for 25 PCDDs, 35 PCDFs, 25 PCDDs and 35 PCDFs together, respectively. The cross-validated Q (cum) (2) values for the three QSAR models of 25 PCDDs, 35 PCDFs, 25 PCDDs and 35 PCDFs together are 0.816, 0.629 and 0.603, respectively, indicating good predictive capabilities for the biological toxicity of these PCDD/Fs. The present study suggests that quantum chemical descriptors of POPs indeed govern the binding affinity of these chemicals for aryl hydrocarbon receptors. Moreover, different models contain different molecular descriptors to define respective equation, which suggests that the relationship between molecular structure and the binding affinity of these chemicals for aryl hydrocarbon receptors is complex.

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 acute mammalian toxicity from QSARs and interspecies correlations

With the ever-growing number of xenobiotics that can potentially contaminate the environment, the determination of their mammalian toxicity is of prime importance. In this context, LD50 tests on rats and mice have been used for a long time to express the relative hazard associated with the acute toxicity of inorganic and organic chemicals. However, these laboratory tests encounter important hurdles. They are costly, time consuming and actively opposed by animal rights activists. Moreover, new legislation policies, such as REACH (Registration, Evaluation, Authorization and Restriction of Chemicals), aim at reducing the use of toxicity tests on vertebrates. Consequently, there is a need to find alternative methods for estimating the acute mammalian toxicity of chemicals. The quantitative structure-activity relationships (QSARs) and interspecies correlations appear particularly suited to reaching this goal. In this context, this paper reviews more than 150 models aiming at predicting rat and mouse LD50 values from molecular descriptors or (and) ecotoxicity data. The interest of these computational tools is discussed.

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.

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.

Prediction of Acute in vivo Toxicity of Some Amine and Amide Drugs to Rats by Multiple Linear Regression, Partial Least Squares and an Artificial Neural Network

The oral acute in vivo toxicity of 32 amine and amide drugs was related to their structural-dependent properties. Genetic algorithm-partial least-squares and stepwise variable selection was applied to select of meaningful descriptors. Multiple linear regression (MLR), artificial neural network (ANN) and partial least square (PLS) models were created with selected descriptors. The predictive ability of all three models was evaluated and compared on a set of five drugs, which were not used in modeling steps. Average errors of 0.168, 0.169 and 0.259 were obtained for MLR, ANN and PLS, respectively.