Quantitative Structure-activity Relationship Techniques Research Articles

Ecotoxicity quantitative structure–activity relationships for alcohol ethoxylate mixtures based on substance-specific toxicity predictions

Traditionally, ecotoxicity quantitative structure–activity relationships (QSARs) for alcohol ethoxylate (AE) surfactants have been developed by assigning the measured ecotoxicity for commercial products to the average structures (alkyl chain length and ethoxylate chain length) of these materials. Acute Daphnia magna toxicity tests for binary mixtures indicate that mixtures are more toxic than the individual AE substances corresponding with their average structures (due to the nonlinear relation of toxicity with structure). Consequently, the ecotoxicity value (expressed as effects concentration) attributed to the average structures that are used to develop the existing QSARs is expected to be too low. A new QSAR technique for complex substances, which interprets the mixture toxicity with regard to the “ethoxymers” distribution (i.e., the individual AE components) rather than the average structure, was developed. This new technique was then applied to develop new AE ecotoxicity QSARs for invertebrates, fish, and mesocosms. Despite the higher complexity, the fit and accuracy of the new QSARs are at least as good as those for the existing QSARs based on the same data set. As expected from typical ethoxymer distributions of commercial AEs, the new QSAR generally predicts less toxicity than the QSARs based on average structure.

Modeling Ligand−Receptor Interaction for Some MHC Class II HLA-DR4 Peptide Mimetic Inhibitors Using Several Molecular Docking and 3D QSAR Techniques

The ligand-receptor interaction between some peptidomimetic inhibitors and a class II MHC peptide presenting molecule, the HLA-DR4 receptor, was modeled using some three-dimensional (3D) quantitative structure-activity relationship (QSAR) methods such as the Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Indices Analysis (CoMSIA), and a pharmacophore building method, the Catalyst program. The structures of these peptidomimetic inhibitors were generated theoretically, and the conformations used in the 3D QSAR studies were defined by docking them into the known structure of HLA-DR4 receptor through the GOLD, GLIDE Rigidly, GLIDE Flexible, and Xscore programs. Some of the parameters used in these docking programs were selected by docking an X-ray ligand into the receptor and comparing the root-means-square difference (RMSD) computed between the coordinates of the X-ray and docked structure. However, the goodness of a docking result for docking a series of peptidomimetic inhibitors into the HLA-DR4 receptor was judged by comparing the Spearman's rank correlation coefficient computed between each docking result and the activity data taken from the literature. The best CoMFA and CoMSIA models were constructed using the aligned structures of the best docking result. The CoMSIA was conducted in a stepwise manner to identify some important molecular features that were further employed in a pharmacophore building process by the Catalyst program. It was found that most inhibitors of the training set were accurately predicted by the best pharmacophore model, the Hypo1 hypothesis constructed. The deviation or conflict found between the actual and predicted activities of some inhibitors of both the training and the test sets were also investigated by mapping the Hypo1 hypothesis onto the corresponding structures of the inhibitors.

CoMFA, CoMSIA, and Molecular Hologram QSAR Studies of Novel Neuronal nAChRs Ligands-Open Ring Analogues of 3-Pyridyl Ether

3-Pyridyl ethers are excellent nAChRs ligands, which show high subtype selectivity and binding affinity to alpha4beta2 nAChR. Although the quantitative structure-activity relationship (QSAR) of nAChRs ligands has been widely investigated using various classes of compounds, the open ring analogues of 3-pyridyl ethers have been less involved in these studies due to the greater flexibility of this kind of molecule. In this study, two three-dimensional QSAR techniques and one two-dimensional QSAR technique were used to correlate the molecular structure with the biological activity of 64 analogues of 3-pyridyl ethers. Three different QSAR models were established. Their performances in the QSAR studies of open ring analogues of 3-pyridyl ethers were evaluated by the statistical values in the corresponding models. All models exhibited satisfactory predictive power. Of these models, the HQSAR behaved optimally in terms of the statistical values with q2=0.845, r2=0.897. Finally, graphic interpretation of three different models provided coincident information about the interaction of the ligand-receptor complex and supplied useful guidelines for the synthesis of novel, potent ligands.

A Comparative QSAR Study Using CoMFA, HQSAR, and FRED/SKEYS Paradigms for Estrogen Receptor Binding Affinities of Structurally Diverse Compounds.

The three-dimensional quantitative structure-activity relationship (QSAR) technique of comparative molecular field analysis (CoMFA) has demonstrated the ability to provide accurate predictions for diverse chemical compounds when trained with molecules of diverse chemical type. Although predictive, the derivation and utilization of models of this type are quite computationally and person power intensive. It is this intensity that pragmatically limits the widespread implementation of these models as predictive tools. In this study, two newer QSAR techniques were evaluated as possible alternatives to CoMFA based QSAR models for the purpose of rapidly identifying estrogen receptor ligands from diverse collections of molecules. The first of these is Hologram QSAR, or HQSAR. HQSAR utilizes Tripos molecular fingerprints as descriptors in conjunction with partial least squares (PLS) regression and cross-validation routines. The HQSAR technique demonstrated the ability to rapidly develop QSAR models independent of the intense user input (i.e. geometry optimization, conformational analysis, and molecular superposition were not required). Second, a newly developed QSAR paradigm that utilizes Molecular Design Limited (MDL) substructure keys (SKEYS) as descriptors in combination with an evolutionary algorithm, Fast Random Elimination of Descriptors (FRED), was evaluated. By utilizing the FRED/SKEYS algorithm, a simple substructure-based QSAR model was derived that was comparable in statistical robustness and predictive ability to both CoMFA and HQSAR derived models. A comparison of the utility of these three approaches as computational tools for the rapid identification of estrogen receptor ligands as potential endocrine disruptors as assessed by model predictive ability will be described.

Development of a Quantitative Structure—Activity Relationship Model for Inhibition of Gram‐Positive Bacterial Cell Growth by Biarylamides.

A set of compounds consisting of a new and diverse collection of biarylamides was examined using quantitative structure-activity relationship techniques for the purpose of developing a model to describe inhibition of gram-positive bacterial growth (minimum inhibition concentration). The model was sought in order to obtain insight for designing new molecules. A detailed analysis of the underlying structure-activity relationship helped provide insight concerning which structural features of the molecules modulated the activity of the compounds against gram-positive organisms.

3D-QSAR analysis of metallocene-based catalysts used in ethylene polymerisation

Single site olefin polymerisation catalysts are suitable candidates for modelling purposes. Their well-defined structure and the almost complete elucidation of their polymerisation mechanisms, make these organometallic complexes ideal for studies based on quantitative structure-activity relationships (QSAR). Although the QSAR technique is extensively used in drug design, there are very few reports on its application to metallocene-based polymerisation catalysis. This probably has something to do with the difficulties inherent in controlling experimental conditions during the polymerisation process. In the present study, we obtained ethylene polymerisation data using a number of zirconocene catalysts under carefully controlled experimental conditions, i.e. keeping all polymerisation variables constant except catalyst structure. The catalytic activity and molecular weight of the resulting polyethylenes were experimentally determined. We then applied 3D-QSAR methodology to explain the experimental data in terms of three-dimensional (3D) field descriptors related to the structure of the metallocene catalyst. Our results provide useful correlations between experimental ethylene polymerisation activities and the steric, LUMO and local softness fields of the catalysts. The molecular weights calculated from comparative molecular field analysis (CoMFA) models including LUMO and local softness fields correlate well with the experimental ones. The predictive capacity of the models was also tested. Based on the proposed models, steric and electronic factors affecting polymerisation performance are discussed.

Development of a quantitative structure-activity relationship model for inhibition of gram-positive bacterial cell growth by biarylamides.

A set of compounds consisting of a new and diverse collection of biarylamides was examined using quantitative structure-activity relationship techniques for the purpose of developing a model to describe inhibition of gram-positive bacterial growth (minimum inhibition concentration). The model was sought in order to obtain insight for designing new molecules. A detailed analysis of the underlying structure-activity relationship helped provide insight concerning which structural features of the molecules modulated the activity of the compounds against gram-positive organisms.

3-D QSAR investigations of the inhibition of Leishmania major farnesyl pyrophosphate synthase by bisphosphonates.

We report the activities of 62 bisphosphonates as inhibitors of the Leishmania major mevalonate/isoprene biosynthesis pathway enzyme, farnesyl pyrophosphate synthase. The compounds investigated exhibit activities (IC(50) values) ranging from approximately 100 nM to approximately 80 microM (corresponding to K(i) values as low as 10 nM). The most active compounds were found to be zoledronate (whose single-crystal X-ray structure is reported), pyridinyl-ethane-1-hydroxy-1,1-bisphosphonates or picolyl aminomethylene bisphosphonates. However, N-alicyclic aminomethylene bisphosphonates, such as incadronate (N-cycloheptyl aminomethylene bisphosphonate), as well as aliphatic aminomethylene bisphosphonates containing short (n = 4, 5) alkyl chains, were also active, with IC(50) values in the 200-1700 nM range (corresponding to K(i) values of approximately 20-170 nM). Bisphosphonates containing longer or multiple (N,N-) alkyl substitutions were inactive, as were aromatic species lacking an o- or m-nitrogen atom in the ring, or possessing multiple halogen substitutions or a p-amino group. To put these observations on a more quantitative structural basis, we used three-dimensional quantitative structure-activity relationship techniques: comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA), to investigate which structural features correlated with high activity. Training set results (N = 62 compounds) yielded good correlations with each technique (R(2) = 0.87 and 0.88, respectively), and were further validated by using a training/test set approach. Test set results (N = 24 compounds) indicated that IC(50) values could be predicted within factors of 2.9 and 2.7 for the CoMFA and CoMSIA methods, respectively. The CoMSIA fields indicated that a positive charge in the bisphosphonate side chain and a hydrophobic feature contributed significantly to activity. Overall, these results are of general interest since they represent the first detailed quantitative structure-activity relationship study of the inhibition of an expressed farnesyl pyrophosphate synthase enzyme by bisphosphonate inhibitors and that the activity of these inhibitors can be predicted within about a factor of 3 by using 3D-QSAR techniques.

Powerspan closes $20 M in new financing

Severe acute respiratory syndrome (SARS), caused by SARS-coronavirus (SARS-CoV), is a dreadful infection worldwide having economic and medical importance and a global threat for health. It was turned into an epidemic in South China followed by a chain of infections across three generations. A number of pathogeneses in human may occur due to the virus. This infection has not been taken into account before the SARS outbreak, and still it is a neglected one. Therefore, there is an urgent need to develop small molecule antivirals to combat the SARS-CoV. No vaccines are available till date though a number of SARS-CoV 3C-like and 3C protease inhibitors were reported. In this chapter, quantitative structure–activity relationship technique is used for development of anti-SARS and anti-HRV drugs and outcome discussed in details. This approach may be a useful strategy to design novel and potential anti-SARS drugs to combat these dreadful viral diseases.

Mapping property distributions of molecular surfaces: algorithm and evaluation of a novel 3D quantitative structure-activity relationship technique.

A novel molecular descriptor called MaP (mapping property distributions of molecular surfaces) is presented. It combines facile computation, translational and rotational invariance, and straightforward interpretability of the computed models. A three-step procedure is used to compute the MaP descriptor. First, an approximation to the molecular surface with equally distributed surface points is computed. Next, molecular properties are projected onto this surface. Finally, the distribution of surface properties is encoded into a translationally and rotationally invariant molecular descriptor that is based on radial distribution functions (distance-dependent count statistics). The calculated descriptor is correlated with biological data through chemometric regression techniques in combination with a variable selection. The latter is used to identify variables that are highly relevant for the model and hence for its interpretation. Three applications of the new descriptor are presented, each representing a different area of 3D-QSAR. For reasons of comparability, the new descriptor was tested on the steroid "benchmark" data set. Furthermore, a highly diverse data set with potentially eye-irritating compounds was studied, and third, a set of flexible structures with a modulating effect on the muscarinic M(2) receptor were studied. Not only were all models highly predictive but interpretation of the back-projected variables into the original molecular space led to biologically and chemically relevant conclusions.

Quantitative structure–activity relationships (QSARs) in toxicology: a historical perspective

The history of the use of quantitative structure–activity relationships (QSARs) in toxicology, both for environmental, and human health effects is described. A particular emphasis is made on the science in response to the United States Toxic Substance Control Act of 1976. Specifically, the basic concepts and objectives of QSARs for toxicity are reviewed. QSARs for environmental and human health effects are discussed separately. Environmental, and more specifically, ecotoxicity, QSARs have focused historically on modeling congeneric series and non-specific effects in aquatic organisms through the use of the logarithm of the 1-octanol/water partition coefficient to describe hydrophobicity, and hence uptake. Compounds that do not fit these QSARs (namely the outliers) have been explained by differences in mechanism of acute toxicity, especially as a result of electro(nucleo)philic interactions. In light of this, mechanisms of acute toxicity are discussed. QSAR approaches to receptor-mediated effects, such as those exhibited by environmental estrogens, and competitive binding to the estrogen receptor, are different from those typically applied to model acute toxic endpoints. Several of these approaches, including three-dimensional QSAR techniques, are reviewed. Human health effects include both local and systemic effects. Local effects (e.g. corrosivity and skin sensitization) are often modeled by multivariate QSAR methods such as linear regression and discriminant analysis. The prediction of systemic effects such as mutagenesis and carcinogenesis requires consideration of the endpoint and a more mechanistic basis for modeling. Approaches to predict these endpoints include the use of expert systems.

Current state and perspectives of 3D-QSAR.

Quantitative structure-activity relationships (QSAR) have played an important role in the design of pharmaceuticals and agrochemicals. All QSAR techniques assume that all the compounds used in analyses bind to the same site of the same biological target. However, each method differs in how it describes structural properties of compounds and how it finds the quantitative relationships between the properties and activities. The Hansch-Fujita approach, the so-called classical QSAR, is a representative of QSAR methods. Despite the usefulness, classical QSAR techniques cannot be applied to all datasets due to the lack of availability of physicochemical parameters of the whole molecule or its substituents and often it is difficult to estimate those values. In addition, molecular properties based on the three dimensional (3D) structure of compounds may be useful in describing the ligand-receptor interactions. Recently, a variety of ligand-based 3D-QSAR methods such as Comparative Molecular Field Analysis (CoMFA) have been developed and widely used in medicinal chemistry. This review describes different 3D-QSAR techniques and indicates their advantages and disadvantages. Several studies about 3D-QSAR of ADME-toxicity and perspective of 3D-QSAR are also described in this review.

Dynamic QSAR techniques: applications in drug design and toxicology.

The basic principles of 3-D quantitative structure-activity relationships (QSARs) analysis are discussed in the light of the fuzzy logic concept. According to that concept, the traditionally one chemical - one structure - one parameter value relationship in QSAR is suggested to be modified into one chemical finite set of structures - range of parameter values principle. In this respect, two recently developed techniques accounting for conformational flexibility in 3-D QSARs are reviewed. A basic assumption underlying both methods is that chemical behavior in complex biological systems is context-dependent. A molecule can exist and interact in a variety of conformations depending on the specificity of the endpoint under investigation and reaction media. It was demonstrated that selection of active conformer(s) in QSAR studies is a task as important as the selection of relevant molecular parameters. Specifically selected active conformers, rather than the lowest-energy states of the chemicals are suggested to be used in the correlative QSARs. The method for recognition the common reactivity pattern (COREPA) of structurally heterogeneous compounds that elicit similar biological behaviour is based on all energetically reasonable conformers of chemicals. The principle assumption of the method is that biologically similar chemicals should possess a commonality in their stereoelectronic (reactivity) pattern. Originally developed algorithms for conformer generation are presented in association with the QSAR methods accounting for conformational flexibility of chemicals. Applicability of the QSAR technique for selection active conformers is illustrated by presenting QSAR models derived for Ah binding affinity of PCBs and antimicrobial activity of rifamicin derivatives. Models for predicting estrogenic activity of structurally diverse chemicals and ACE inhibition exemplified the applicability of the COREPA method. The model performance is analyzed by the 3D screening exercise of large chemical inventories with subsequent experimental validation within the EDAEP project. Besides the impact of conformational flexibility of chemicals in 3D QSAR the role of different molecular descriptors is discussed with respect to their ability to describe molecular interactions with different specificity.

Anticancer Activity of Estradiol Derivatives: A Quantitative Structure-Activity Relationship Approach

Commercial packages to implement modern QSAR (quantitative structure-activity relationship) techniques are highly priced; however, the essence of QSAR can be taught without them. Microsoft Excel was used to analyze published data on anticancer activities of estradiol analogs by a QSAR approach. The resulting QSAR equations highly correlate the structural features and physicochemical properties of the analogs with the observed biological activities by multiple linear regression.

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.

Computer Assisted Molecular Design: Overview of cModern Techniques in Quantitative Structure Activity Relationships (QSAR)

This paper gives an overview of computer assisted molecule design using quantitative structure activity relationships (QSAR). In deriving a QSAR for a set of compounds, one should be aware of possible properties one can use to correlate with the activity of this set of compounds. The paper describes some of the traditional and also more recently used properties that can be calculated. The traditionally used properties have been those calculated for the substituent groups that were varied on a particular scaffold, whereas the more recently used properties are those calculated on the molecule as a whole. One of the most recently used properties for comparative purposes are fields around the molecules. The technique is termed comparative molecular field analysis (CoMFA). The statistical techniques used to correlate the properties with the activity are also described and, finally, a case study is described. The case study shows the application of traditional and 3D QSAR (CoMFA) techniques on some photosystem II inhibitors.

New techniques enhance structure activity work

A group of industrial chemists and several academic collaborators are fomenting revolution in the normally sedate field of quantitative structureactivity relationships (QSAR). They are developing a series of QSAR techniques they hope will replace some traditional QSAR concepts that they believe are over the hill. In QSAR, mathematical descriptors of molecular structure are used as a basis for predicting chemical and physical properties or biological activity. QSAR is widely used for drug discovery, among other applications. For example, it can be employed to predict partition coefficients, lipophilicities, or relative reactivities with biological receptors to support drug-design efforts. But Jack W. Frazer of Tuolumne, Calif., team leader of a scientific computing group that consults for Eastman Kodak, believes QSAR techniques up to now have not realized their true potential. Several years ago, Frazer's group was asked to bring new insights and computer technology to the drug discovery process at Koda...

Quantitative structure-activity relationships and ecological risk assessment: an overview of predictive aquatic toxicology research

In the field of aquatic toxicology, quantitative structure-activity relationships (QSARs) have developed as scientifically credible tools for predicting the toxicity of chemicals when little or no empirical data are available. A fundamental understanding of toxicological principles has been considered an important component to the acceptance and application of QSAR approaches as biologically relevant in ecological risk assessments. As a consequence, there has been an evolution of QSAR development and application from that of a chemical-class perspective to one that is more consistent with assumptions regarding modes of toxic action. In this review, techniques to assess modes of toxic action from chemical structure are discussed, with consideration that toxicodynamic knowledge bases must be clearly defined with regard to exposure regimes, biological models/endpoints and compounds that adequately span the diversity of chemicals anticipated for future applications. With such knowledge bases, classification systems, including rule-based expert systems, have been established for use in predictive aquatic toxicology applications. The establishment of QSAR techniques that are based on an understanding of toxic mechanisms is needed to provide a link to physiologically based toxicokinetic and toxicodynamic models, which can provide the means to extrapolate adverse effects across species and exposure regimes.

A 2 agonists: structure-activity relationships of 2-(cycloalkylalkynyl)adenosine derivatives

Quantitative structure-activity relationships (QSAR) analysis of 2-(cycloalkylalkynyl)adenosine derivatives were developed. It is shown that a sharp discrimination of the adenosine receptor subtypes A 1 and A 2 is difficult due to biological and chemical reasons. The biological responses are mainly determined by the lipophilicity of the substituents, and the van der Waals and electrostatic energies of the compounds. A novel combination of 3-dimensional analysis and the QSAR technique is proposed. It allows the reduction of the matrix of Cartesian coordinates to a 3-dimensional vector observation.

Conformational analysis, molecular modeling, and quantitative structure-activity relationship studies of agents for the inhibition of astrocytic chloride transport.

Molecular modeling studies were carried out on a series of 1-oxoisoindolines which are pharmacologically active as inhibitors of astrocytic chloride transport. Conformational analysis revealed that the halogen substituent exerted a pronounced steric directing effect on the acid side chain. The 4-substituted analogs apparently provided for the best spatial arrangement of pharamacophoric elements of the molecules. Conventional quantitative structure-activity relationship (QSAR) studies using lipophilic and dipole moment characteristics of the molecules as physical descriptor variables in the regression equation yielded a statistically significant model. Comparative molecular field analysis (CoMFA) was utilized as a three-dimensional QSAR technique to explore changes in the steric and electrostatic fields of the molecules that can account for differences in biological activity values. A highly predictive model was attained which supported the results from the qualitative and conventional quantitative structure-activity relationship analyses. These modeling techniques represent the evolutionary process by which structure-activity methods were employed to aid in the development of novel more potent inhibitors of astrocytic chloride transport.