Development of quantitative structure-activity relationship (QSAR) models to predict the carcinogenic potency of chemicals. II. Using oral slope factor as a measure of carcinogenic potency

The overall risk associated with exposure to a chemical is determined by combining quantitative estimates of exposure to the chemical with their known health effects. For chemicals that cause carcinogenicity, oral slope factors (OSFs) and inhalation unit risks are used to quantitatively estimate the carcinogenic potency or the risk associated with exposure to the chemical by oral or inhalation route, respectively. Frequently, there is a lack of animal or human studies in the literature to determine OSFs. This study aims to circumvent this problem by developing quantitative structure-activity relationship (QSAR) models to predict the OSFs of chemicals. The OSFs of 70 chemicals based on male/female human, rat, and mouse bioassay data were obtained from the United States Environmental Protection Agency's Integrated Risk Information System (IRIS) database. A global QSAR model that considered all 70 chemicals as well as species and/or sex-specific QSARs were developed in this study. Study results indicate that the species and sex-specific QSARs (r(2)>0.8, q(2)>0.7) had a better predictive abilities than the global QSAR developed using data from all species and sexes (r(2)=0.77, q(2)=0.73). The QSARs developed in this study were externally validated, and demonstrated reasonable predictive abilities.     

A comprehensive model for reproductive and developmental toxicity hazard identification: II. Construction of QSAR models to predict activities of untested chemicals

This report describes the construction, optimization and validation of a battery of quantitative structure-activity relationship (QSAR) models to predict reproductive and developmental (reprotox) hazards of untested chemicals. These models run with MC4PC software to predict seven general reprotox classes: male and female reproductive toxicity, fetal dysmorphogenesis, functional toxicity, mortality, growth, and newborn behavioral toxicity. The reprotox QSARs incorporate a weight of evidence paradigm using rats, mice, and rabbit reprotox study data and are designed to identify trans-species reprotoxicants. The majority of the reprotox QSARs exhibit good predictive performance properties: high specificity (>80%), low false positives (<20%), significant receiver operating characteristic (ROC) values (>2.00), and high coverage (>80%) in 10% leave-many-out validation experiments. The QSARs are based on 627-2023 chemicals and exhibited a wide applicability domain for FDA regulated organic chemicals for which they were designed. Experiments were also performed using the MC4PC multiple module prediction technology, and ROC statistics, and adjustments to the ratio of active to inactive (A/I ratio) chemicals in training data sets were made to optimize the predictive performance of QSAR models. Results revealed that an A/I ratio of approximately 40% was optimal for MC4PC. We discuss specific recommendations for the application of the reprotox QSAR battery.     

Quantitative structure-activity relationship (QSAR) analysis to predict drug-drug interactions of ABC transporter ABCG2

Quantitative structure-activity relationship (QSAR) analysis is a practical approach by which chemical structure is quantitatively correlated with biological activity or chemical reactivity. Human ABC transporter ABCG2 exhibits broad substrate specificity toward structurally diverse compounds. To gain insight into the relationship between the molecular structures of compounds and the interaction with ABCG2, we have developed an algorithm that analyzes QSAR to evaluate ABCG2-drug interactions. In addition, to support QSAR analysis, we developed a high-speed screening method for analyzing the drug-drug interactions of ABCG2. Based on both experimental results and computational QSAR analysis data, we propose a hypothetical mechanism underlying ABC-mediated drug transport and its interaction with drugs.     

Three-dimensional structure-activity relationships and receptor mapping of N1-substituents of quinolone antibacterials

Quantitative structure-activity relationships for quinolone antibacterials have been previously examined and a steric parameter L for the N1-substituents found to be important in QSAR equations. But some compounds for which previous QSAR equations could not predict the activity have appeared recently. In this study, conformations of a variety of N1-substituents of quinolone antibacterials were analyzed by a molecular modeling method. An active conformation of each of the compounds was estimated with information of the energy profile calculated by molecular orbital methods and of its biological activity. A model of a receptor corresponding to the N1-substituents was constructed by superposing van der Waals volumes of active conformer of highly active compounds. As a result of these conformational analyses and receptor mapping, it is proposed that there are two different optimum volumes for increasing the activity of quinolone antibacterials and two unfavorable regions for reducing the activity. It is suggested that the steric parameter L which appeared in previous QSAR equations corresponds to one of the optimum volumes of the proposed receptor model. With this receptor model, a relation between structure and activity of the compounds, including those mispredicted compounds in previous QSAR equations is able to be rationalized qualitatively and elegantly. We believe that this receptor model is useful for a prediction of the activity of compounds not yet synthesized as well as for designing new quinolone antibacterials.     

Advances in quantitative structure-activity relationship models of antimalarials

Importance of the field: Malaria still remains one of the deadliest infectious diseases having a tremendous morbidity and mortality impact in the developing world. Computational tools such as quantitative structure-activity relationship (QSAR) studies help medicinal chemists to understand the consistent relationship between antimalarial activity and molecular properties, and design new potent and selective ligands that may act on different classes of antimalarial drug targets so that these compounds may eventually be synthesized and assayed. Area covered in this review: In the present review, we focus on the current knowledge of QSARs and pharmacophore models of different classes of antimalarial drugs. In this context, we also review the reported docking studies of antimalarial compounds acting on different targets to explore the interaction pattern at the molecular level. What the reader will gain: The reader will gain an overview of advances of QSAR and related theoretical models of antimalarial drug compounds. Take home message: This review infers that most of the reported QSAR models are analog based QSARs with a limited applicability domain, but QSAR models based on diverse chemical structures acting on a particular target have been reported in very few cases.     

MO-Rechnungen zur Struktur von muscarinartig wirkenden Piperidinium-und Thioniacyclohexan-Verbindungen

Conformational Energies of Piperidine and Thiacyclohexane Derivatives Exhibiting Muscarine-Type Activity The conformational energies of all possible conformers of the 3-acetoxy-1-methyl derivatives of the aza and thia analogues of cyclohexane were calculated using the semiempirical CNDO/2 MO method. The results show that the high muscarine-type activity of the trans-thiacyclohexane compound is due to the conformational stability of the pharmacologically active conformer. This result is in agreement with recent NMR data.     

Structure-activity relationships for thiol reactivity and rat or human hepatocyte toxicity induced by substituted p-benzoquinone compounds

Covalent binding of toxic chemicals to cellular targets is a molecular interaction that initiates a wide array of adverse biological effects. The creation of a covalent bond can be cited as a key initiating step along many toxicity pathways which must be predicted in order to predict the potential of a chemical to cause specific harmful effects. Currently, quantitative structure-activity relationship (QSAR) models are being improved by focusing on endpoints such as simple electrophile reactivity for covalent interactions rather than on commonly used complex toxicity endpoints. The cytotoxicity and electrophilic reactivity of 10 p-substituted benzoquinone derivatives, which are well known electrophilic alkylating agents, were investigated under the premise that QSAR toxicity models can be improved when the molecular triggering event is considered. Hepatocyte toxicity was determined by incubation of individual compounds with freshly isolated rat or cryopreserved human hepatocyte suspensions. The potential for chemical reactivity between a chemical and cellular target was measured by determining non-enzymic reactivity with glutathione, representing thiol nucleophiles. The decline in free thiol moieties was measured to characterize the electrophile reactivity. It was found that the degree of rat hepatotoxicity induced by benzoquinones correlated with the rate at which they non-enzymically react with glutathione and to various global and atomic electronic frontier orbital parameters which described electrophilicity. Human hepatocytes showed similar results but the statistical significance was much lower. The QSAR expressions suggest that covalent binding reactivity serves as a good correlate to hepatotoxicity and could improve QSAR modeling for potential toxicity risks.     

QSAR analysis of Delta(8)-THC analogues: relationship of side-chain conformation to cannabinoid receptor affinity and pharmacological potency

A novel quantitative structure-activity relationship (QSAR) for the side-chain region of Delta(8)-tetrahydrocannabinol (Delta(8)-THC) analogues is reported. A series of 36 side-chain-substituted Delta(8)-THCs with a wide range of pharmacological potency and CB1 receptor affinity was investigated using computational molecular modeling and QSAR analyses. The conformational mobility of each compound's side chain was characterized using a quenched molecular dynamics approach. The QSAR techniques included a modified active analogue approach (MAA), multiple linear regression analyses (MLR), and comparative molecular field analysis (CoMFA) studies. All three approaches yielded consistent results. The MAA approach applied to a set of alkene/alkyne pairs identified the most active conformers as those with conformational mobility constrained within an approximately 8 A radius. MLR analyses (restricted to 15 hydrocarbon side-chain analogues) identified two variables describing side-chain length and terminus position that were able to fit the pharmacological data for receptor affinity with a correlation coefficient for pK(D) of 0.82. While chain length was found to be directly related to receptor affinity, the angle made by the side chain from its attachment point to its terminus (angle defined by C3-C1'-side-chain terminus carbon, see Figure 1) was found to be inversely related to affinity. These results suggest that increased side-chain length and increased side-chain ability to wrap around the ring system are predicted to increase affinity. Therefore, the side chain's conformational mobility must not restrict the chain straight away from the ring system but must allow the chain to wrap back around toward the ring system. Finally, the CoMFA analyses involved all 36 analogues; they also provided data to support the hypothesis that for optimum affinity and potency the side chain must have conformational freedom that allows its terminus to fold back and come into proximity with the phenolic ring.     

QSARs in ecotoxicological risk assessment

The need for more ecotoxicological data encourages the use of QSARs because of the reduction of (animal) testing, time and cost. QSARs may however only be used if they prove to be reliable and accurate. In this paper, four QSARs were attempted to predict toxicity for 170 compounds from a broad chemical class, using them as a black-box. Predictions were obtained for 122 compounds, indicating an important drawback of QSARs, i.e., for 28% of the compounds QSARs cannot be used at all. Ecosar, Topkat, and QSARs for non-polar and polar narcosis generated predictions for 120, 39, 24, and 11 compounds, respectively. Correlations between experimental and predicted effect concentrations were significant for Topkat and the QSAR for polar narcosis, but generally poor for Ecosar and the QSAR for non-polar narcosis. When predicted effect concentrations for fish were allowed to deviate from experimental values by a factor of 5, correct predictions were generated for 77%, 54%, 68%, and 91% of the compounds using Ecosar, Topkat, and the QSARs for non-polar and polar narcosis, respectively. It was impossible to indicate specific chemical classes for which a QSAR should be used or not. The results show that currently available QSARs cannot be used as a black-box.     

Modelling acute oral mammalian toxicity. 1. Definition of a quantifiable baseline effect

Quantitative structure–activity relationships (QSARs) provide a useful tool to define a relationship between chemical structure and toxicity and allow for the prediction of the toxicity of untested chemicals. QSAR models based upon an anaesthetic or narcosis mechanism represent a baseline, or minimum, toxicity, i.e. unless a chemical acts by another, more specific, mechanism, its toxicity will be predicted by such models. The aim of this investigation was to develop baseline models for the acute toxicity of chemicals to mammals (rat and mouse) following the oral route of administration. The availability of such baseline toxicity models for mammalian species can provide a probe for testing new chemicals with respect to their molecular mechanism of toxicity. Multiple-regression type structure–toxicity relationships were derived . (i.e., from oral log data for mammalian species (rat and mouse) and the 1-octanol/water partition coefficient (log P) of classic non-polar narcotics). Subsequently, these models were used to distinguish between reactive chemicals of different mechanistic domains and baseline toxic chemicals. Comparison of measured toxicity data for oral rat and mouse LD₅₀ with predictions from baseline QSAR provides a means of identifying mechanistic categories and for categorising more specific acute mechanisms.     

Conformational templates for rational drug design: flexibility of cyclo(D-Pro1-Ala2-Ala3-Ala4-Ala5) in DMSO solution

Long MD simulations (100 ns) for the important model cyclopentapeptide cyclo(D-Pro1-Ala2-Ala3-Ala4-Ala5) were performed in explicit DMSO solution using both OPLS-AA and AMBER03 force fields. Simulations revealed conformational transitions between two main conformers, a predominant one (population 93-99%) and a minor conformer (population 0.4-6.7%). These results are in excellent agreement with 20 experimental proton-proton distances estimated for this cyclopentapeptide. The previously discussed gamma-turn-like conformation for Ala4 was present only in a minor conformer.