Predictive Quantitative Structure-activity Relationship Research Articles

Health-effects related structure–toxicity relationships: a paradigm for the first decade of the new millennium

Prediction of the effects of industrial chemicals to humans will be an area of increasing concern in the next century. The role of quantitative structure–activity relationships (QSARs) is to aid in the prediction of effects by determination of the limits of variation in structure that are consistent with the production of a specific toxic effect and define the ways in which alterations in structure influences toxicity. The paradigm followed in the development of QSARs for ecotoxic-endpoints has been successful as a direct result of the availability of in vivo toxicity data in which to build initial QSARs and validate surrogate test systems. However, the lack of quantitative in vivo toxicity data means this paradigm cannot be used in the prediction of human health-effect endpoints. Therefore, a new paradigm, which provides guidance in the use of predictive QSARs for health-effects that serves to circumvent the problems associated with the lack of whole-mammal toxicity data must be established. A scenario is given that provides for the development, standardization, and validation of health-effects related QSARs in the first decade of the 21st century. Due to the structural diversity and sheer number of industrial organic chemicals, assays used to garner health-effects data must be based on quantifiable, rapid, reliable, and inexpensive surrogate endpoints. New ‘biotools’ developed using modern molecular techniques will aid in the circumvention of in vivo health-effects data and provide measurable endpoints, which can be used as surrogates for health-effects endpoints. This has particular application in receptor-mediated toxicity and gene expression. The lack of whole-mammal health-effects data means that the standardization and validation, of these endpoints will be accomplished in novel ways. Databases garnered using modern biotools will allow the derivation of QSARs developed for validated surrogate health-effect endpoints. If QSARs are to be useful in bridging gaps in health-effects data, they must be based on accurate, reproducible data for a robust series of non-congeneric chemicals. The latter applies to both toxicity and descriptor values. Because health-effects are often the result of metabolic activation, if these new QSARs are to be meaningful, validated software for predicting metabolite formation must be incorporated. Lastly, once QSARs and software are developed and validated, they will need to be linked into some type of expert system.

Quantitative structure-activity relationships: sulfonate esters in the local lymph node assay.

The biological activity of skin-sensitizing chemicals is related to their ability to react, either directly or after metabolic activation, with appropriate skin proteins. For direct acting electrophilic compounds, this ability can be modelled, using the RAI (relative alkylation index) approach, by a combination of electrophilicity and hydrophobicity parameters. The development of predictive quantitative structure-activity relationships (QSAR) models of skin sensitization, using mechanism-based physicochemical parameters, has been greatly facilitated by the introduction of the murine local lymph node assay (LLNA), which is able to describe the extent of the biological response in objective and quantitative terms. In the present work, sensitization response data in the LLNA is generated for a series of 6 sulfonate esters. An RAI-based hybrid QSAR/dose-response relationship is derived using a negative hydrophobicity coefficient in the RAI expression, to model the effect of retention of the hydrophobic test chemicals in the stratum corneum. Dose-response analyses are used to estimate EC3 and EC20 values as quantitative indices of skin sensitization potential for each compound, and regression analysis is applied to develop QSARs correlating these EC3 and EC20 values with an RAI-based parameter. The high statistical quality of these QSARs demonstrates both the consistency of the LLNA method for generating high quality skin sensitization data, and the value of the RAI approach in development of mathematical models for skin sensitization.

Toxicity of aryl‐ and benzylhalides to Daphnia magna and classification of their mode of action based on quantitative structure–activity relationship

AbstractThe acute toxicity of aryl‐ and benzylhalides to Daphnia magna was investigated to test the validity of existing classification schemes for chemicals by mode of action, mainly based on fish studies, and the applicability of predictive quantitative structure–activity relationship (QSAR) models. Halobenzenes and halotoluenes are generally agreed to be unambiguous baseline toxicants (class I) with the major exception of the benzylic structures, which are reactive in fish tests (class III). Eighty‐nine percent of the arylhalides tested in this study match a log Pow‐dependent QSAR, including fluorinated, chlorinated, brominated, and iodinated derivatives, thereby confirming the validity of the baseline models also for variously halogenated compounds (other than only‐chloro compounds). The toxicities of the benzylhalides relative to baseline QSARs clearly indicate that these compounds belong to two classes by mode of action, i.e., they either act as narcotic toxicants (class I) or reveal excess toxicity due to unspecific reactivity (class III). On some occasions, the assignment to the two classes in D. magna deviates from the structural rules derived from fish, i.e., iodinated compounds as well as α,α‐Cl2‐toluenes lack reactive excess toxicity but behave as nonpolar nonspecific toxicants. The QSARs derived during this study reveal lower slopes and higher intercepts than typical baseline models and, together with the analysis of mixture toxicity studies, behavioral studies, and critical body burden, advocate the hypothesis that there are several different ways to produce baseline toxicity. Most halobenzenes and halotoluenes are actually baseline chemicals with some extra reactivity and as such form a subgroup, whose limits still have to be defined. Different primary sites of action could explain why the chemicals are discriminated by different classification systems, but still they must have some rate‐limiting interaction in common (e.g., lipid diffusion) as they fit the same log Pow‐dependent baseline QSAR.

Preprocessing peptide sequences for multivariate sequence-property analysis

The increasing number of peptide sequences with different lengths, available from synthesised peptide libraries and sequenced proteins are potentially valuable for evaluating structure–activity relationships. However, in order to apply multivariate classification or Quantitative Structure–Activity Relationship (QSAR) analyses on such sequences, it is necessary to have a preprocessing method that translates them into a uniform set of variables. By describing each amino acid by principal properties ( z-scales) and then calculating auto cross covariances (ACCs) for each sequence, a new uniform matrix is generated, i.e., each sequence is described by a vector with equal length. The ACC approach has been used before for classification of peptides, but here, a QSAR analysis based on 20 peptide sequences of different lengths is presented. The results show that it is possible to obtain a predictive multivariate QSAR model ( R 2 Y cum=86.2%, Q cum 2=60.3%) based on the ACC preprocessing method, together with Orthogonal Signal Correction (OSC) and Partial Least Squares (PLS). The model generated was further validated by permutation tests and found to be valid. The new variables generated by ACCs can also be interpreted, i.e., used to identify important features in the original sequences.

Further evaluation of the quantitative structure-activity relationship for skin-sensitizing alkyl transfer agents.

The biological activity of skin-sensitizing chemicals can be expressed in terms of physicochemical properties which relate to the propensity of those chemicals to behave as electrophiles and which describe their ability to partition into the epidermis and between compartments within it. For defined series of chemicals, it has proved possible to express such structure-activity relationships quantitatively. Such quantitative relationships can provide valuable insights into the mechanisms of skin sensitization and/or are of use in predictive toxicology. In the present work the quantitative structure-activity relationship (QSAR) previously derived for a series of alkyl transfer agents based on alkanesulfonate leaving groups has been critically examined in the light of skin sensitization data obtained for new members of that series and also for alkyl transfer agents based on different leaving groups. The QSAR predictions were broadly accurate, but demonstrated that further refinement was both necessary and possible. In particular, the physicochemical parameters which relate to the disposition of the chemical in the epidermis, i.e., its penetration through the stratum corneum, cell surface/cytoplasmic distribution and the associated dynamics, will need to be understood more fully in order to enhance the precision of the QSAR and its predictive power.

3D-quantitative structure-activity relationships of human immunodeficiency virus type-1 proteinase inhibitors: comparative molecular field analysis of 2-heterosubstituted statine derivatives-implications for the design of novel inhibitors.

A set of 100 novel 2-heterosubstituted statine derivatives inhibiting human immunodeficiency virus type-1 proteinase has been investigated by comparative molecular field analysis. In order to combine the structural information available from X-ray analyses with a predictive quantitative structure-activity relationship (QSAR) model, docking experiments of a prototype compound into the receptor were performed, and the 'active conformation' was determined. The structure of the receptor was taken from the published X-ray analysis of the proteinase with bound MVT-101, the latter compound exhibiting high structural similarity with the inhibitors investigated. The validity of the resulting QSARs was confirmed in four different ways. (1) The common parameters, namely, the cross-validated r2 values obtained by the leave-one-out (LOO) method (r2cv = 0.572-0.593), and (2) the accurate prediction of a test set of 67 compounds (q2 = 0.552-0.569) indicated a high consistency of the models. (3) Repeated analyses with two randomly selected cross-validation groups were performed and the cross-validated r2 values monitored. The resulting average r2 values were of similar magnitudes compared to those obtained by the LOO method. (4) The coefficient fields were compared with the steric and electrostatic properties of the receptor and showed a high level of compatibility. Further analysis of the results led to the design of a novel class of highly active compounds containing an additional linkage between P1' and P3'. The predicted activities of these inhibitors were also in good agreement with the experimentally determined values.

Theoretical quantitative structure-activity relationship analysis of congeneric and non-congeneric α1-adrenoceptor antagonists: a chemometric study

Molecular orbital calculations (AM1) and molecular modelling procedures ( quanta/charmm) have been performed both on a congeneric (prazosin analogs) and on a non-congeneric series of α 1-adrenergic antagonists. A large variety of theoretical molecular descriptors has been obtained and compared by principal component analysis (PCA). The generating optimal least squares estimations (GOLPE) procedure has been used to derive quantitative structure-activity relationships (QSARs). Good predictive QSAR models with a restricted pool of informative theoretical descriptors have been obtained. These results support the generality of the theoretical QSAR approach proposed; in fact both congeneric and non-congeneric molecular series were satisfactorily modeled. Moreover, the high and well-defined physical information content encoded in the theoretical descriptors considered allows the rationalization of the structural heterogeneity of the molecules examined as differences in the complementary intermolecular interactions of the studied ligands towards their common receptor.

QSAR in Toxicology. 2. Prediction of Acute Mammalian Toxicity and Interspecies Correlations

AbstractA critical review is given of quantitative structure‐activity relationships (QSAR) for the prediction of mammalian toxicological endpoints. QSAR predictions for mammalian toxicity are generally poorly developed although hydrophobicity correlates well with the toxicity of unreactive chemicals. Interspecies correlations of acute toxicity are discussed and when the mode of toxic action is taken into account these may provide a valuable source of information for comparative toxicity and the potential use of surrogate species for testing.

Predictive QSAR models for estimating ecotoxic hazard of plant-protecting agents: target and non-target toxicity

Quantitative structure-activity relationships (QSARs) are evaluated for predicting pesticides' effects on non-target and target organisms: aquatic biota, mammals, soil microorganisms, and plants. Satisfactory estimates of pesticides' fish toxicity are obtained from log P ow-dependent QSARs derived using chemicals of similar polarity and reactivity. Algal toxicity of herbicidal compounds reflects interactions with the electron transport chain in photosystem II and can be modelled by QSARs describing the Hill reaction inhibition. The ranking in effects on soil micro-organisms is evaluated from models derived using in-vitro bacteria systems. Mammalian toxicity can be estimated by QSARs using the partition coefficient log P ow and electronic terms derived by MNDO quantum mechanical calculations. In general, the targets are more susceptible than the non-target species towards phenylurea herbicides. Plants and algae constitute the most sensitive populations, corresponding to the same mode of action. Differences in mode of action towards bacteria, rats, and fish, which are similarly sensitive organisms, are revealed by QSAR analyses.

Outliers: their origin and use in the classification of molecular mechanisms of toxicity

Quantitative structure-activity relationships (QSARs) provide a useful tool for defining a mathematical relationship between chemical structure and toxicity, and for applying such statistically derived models for predicting the toxicity of untested chemicals. Outlier chemicals can be encountered both in the derivation of QSAR models as well as in their application. Information regarding the relationship between molecular descriptors and molecular mechanism of toxicity can provide insight into the origin of outlier behavior as well as guidance regarding the predictive limitations of such models. Comparison of measured toxicity data for fish and rats with baseline QSAR prediction provides a means of identifying outliers and for categorizing more specific molecular mechanisms.

Predictive QSAR models estimating ecotoxic hazard of phenylureas: Mammalian toxicity

Quantitative structure-activity relationships (QSARs) are analyzed for evaluating phenylurea compounds side effects on mammals. Available QSARs derived from substructures or solely partition properties significantly underestimate their toxicity (oral rat LD 50). Predictive QSARs are presented using partitioning and electronic descriptors derived from quantum mechanical calculations. The combination of log P OW and the ionization potential results in satisfactory estimates of phenylurea rat LD 50 values for hazard assessment purposes. None of the phenylureas was significantly underestimated in toxicity. Using log P OW and hardness, a model was derived very well suited for screening of experimental data in order to recognize outliers, for which either spurious test results or a different mode of action have to be assumed.

Comparative toxicity and structure-activity inChlorella andTetrahymena: Monosubstituted phenols

The relative toxicity of selected monosubstituted phenols has been assessed by Kramer and Truemper in the Chlorella vulgaris assay. The authors examined population growth inhibition of this simple green algae under short-term static conditions for 33 derivatives. However, efforts to develop a strong predictive quantitative structure-activity relationship (QSAR) met with limited success because they modeled across modes of toxic action or segregated derivatives such as positional isomers (i.e., ortho-, meta-, para-). In an effort to further their understanding of the relationships of ecotoxic effects of phenols, the authors have evaluated the same derivatives reported by Kramer and Truemper in the Tetrahymena pyriformis population growth assay, compared the responses in both systems and developed QSARs for the Chlorella vulgaris data based on mechanisms of action.

Comparison of fish toxicity screening data for 55 alcohols with the quantitative structure‐activity relationship predictions of minimum toxicity for nonreactive nonelectrolyte organic compounds

AbstractA comparison was made between the fish toxicity screening test results at concentrations of 5 to 10 mg/L and the LC50 values predicted by the narcotic quantitative structure‐activity relationship (QSAR) equation of Könemann for 55 alcohols containing no additional heteroatom functional groups. The majority of the QSAR‐predicted toxicities are consistent with the test data. The results for two chemicals predicted to be toxic but showing no effects were explained by a water solubility cutoff. Five chemicals exhibited toxicity at concentrations 20 to more than 10,000 times below those predicted. Four of these contained a double or triple bond attached to a carbon atom bearing a primary or secondary alcohol moiety. Their toxicity was explained by a proposed pro‐electrophile mechanism in which an electrophilic toxicant is produced metabolically by the enzyme alcohol dehydrogenase.