Development Of Quantitative Structure-activity Relationships Research Articles

Predicting Skin Permeability of Chemical Substances using a Quantitative Structure-activity Relationship

Abstract The health hazard arising from skin exposure to toxic chemical was conventionally evaluated by determining the skin permeation coefficient ( Kp ) of chemical in the stratum corneum of human or animal cadaver skin. However, limited by constraints inherent in the technique, to date the Kp evaluation has been performed only insufficiently. This study defined a quantitative structure-activity relationship (QSAR) for Kp prediction and for characterization of, at a molecular level, the physicochemical properties involved in transdermal transport of chemical. One hundred and fifty-eight chemical substances of known Kp determined in vitro using human skin were selected in the QSAR development. The final QSAR consisted of four molecular descriptors, including those describing electrostatic interactions between electric quadrupoles of van der Waals forces, octanol-water partitioning of solute, similarity in antineoplastic property, and frequency of carbon-nitrogen bonding at a constant topological distance. The four-descriptor multiple linear regression model fit the observed Kp data with a R 2 of 0.828 and a mean percentage error of 18.8%, suggesting a capacity of this QSAR to serve as an alterative source of Kp information and a potential tool of dermal hazard characterization, particularly when experimentally determined Kp are not readily available.

A Systematic Investigation of Quaternary Ammonium Ions as Asymmetric Phase-Transfer Catalysts. Application of Quantitative Structure Activity/Selectivity Relationships

Although the synthetic utility of asymmetric phase-transfer catalysis continues to expand, the number of proven catalyst types and design criteria remains limited. At the origin of this scarcity is a lack in understanding of how catalyst structural features affect the rate and enantioselectivity of phase transfer catalyzed reactions. Described in this paper is the development of quantitative structure-activity relationships (QSAR) and -selectivity relationships (QSSR) for the alkylation of a protected glycine imine with libraries of quaternary ammonium ion catalysts. Catalyst descriptors including ammonium ion accessibility, interfacial adsorption affinity, and partition coefficient were found to correlate meaningfully with catalyst activity. The physical nature of the descriptors was rationalized through differing contributions of the interfacial and extraction mechanisms to the reaction under study. The variation in the observed enantioselectivity was rationalized employing a comparative molecular field analysis (CoMFA) using both the steric and electrostatic fields of the catalysts. A qualitative analysis of the developed model reveals preferred regions for catalyst binding to afford both configurations of the alkylated product.

Using the TOPS-MODE approach to fit multi-target QSAR models for tyrosine kinases inhibitors

Tyrosine kinases constitute an eligible class of target for novel drug discovery. They resulted often overexpressed and/or deregulated in several cancer diseases. Thus, the development of novel tyrosine kinases inhibitors is of value, as well as the finding of novel cheminformatic tools for their design. Among the different ways to rationally design novel compounds, the Quantitative Structure-Activity Relationship (QSAR) plays a key role. The QSAR approach, in fact, allow the prediction of activity against a number of targets (multi-target QSAR), thus leading to models able to predict not only the activity of a compound, but also its selectivity versus a set of targets. Despite it is well known that tyrosine kinase inhibitors have to show multi-kinases inhibitory potency to be useful in anticancer therapy, only few multi-target computational tools have been developed to help medicinal chemists in the design of novel compounds. Herein we present the development of several multi-target classification QSAR (mtc-QSAR) models useful to assess the activity profile of the tyrosine kinases inhibitors.

Characterization of Dihydrofolate Reductases from Multiple Strains of Plasmodium falciparum Using Mathematical Descriptors of Their Inhibitors

Two classes of graph-theoretic molecular descriptors, viz., topological indices (TIs) and atom pairs (APs), have been used to derive high-quality quantitative structure-activity relationships (QSARs) for inhibitors of dihydrofolate reductases (DHFRs) isolated from the wild and four mutant strains of Plasmodium falciparum. Of the three methods used for QSAR formulation, viz., principal-components regression (PCR), partial least squares (PLS), and ridge regression (RR), the RR method outperformed the other two. Cohen's kappa values, based on the overlap of significant and insignificant structural descriptors calculated for the QSAR development, show that DHFR from the wild strain is substantially different from the four mutant strains. The differential QSAR approach reported in this study can be used in protocols for the development of drugs to combat drug-resistant pathogens arising continuously in nature due to mutations. The pairwise kappa values in conjunction with appropriate drug targets and their corresponding set of ligands may be a useful tool in gauging the evolving mutual similarities and dissimilarities of pathogenic organisms from purely computed mathematical descriptors of the ligands.

A QSAR Study of HIV Protease Inhibitors Using Theoretical Descriptors

This paper reports the development of quantitative structure-activity relationship (QSAR) models for a set of 170 chemicals using mathematical descriptors which can be calculated directly from molecular structure without the input of any other experimental data. The calculated descriptors include topostructural (TS), topochemical (TC), and quantum chemical (QC). Because the situation is rank deficient i.e. the number of independent variables (descriptors) is larger than the number of compounds, three robust linear statistical modeling methods capable of handling such situations, viz., principal components regression (PCR), partial least square (PLS), and ridge regression (RR) were used for QSAR formulation. Results show that PLS and RR gave better q2 values as compared to the PCR method. Of the three classes of descriptors, the TC indices were the best predictors of anti-HIV activity and the QC indices were the least effective.

Quantitative structure-activity relationship studies of boron-containing dipeptide proteasome inhibitors using calculated mathematical descriptors

Topological indices (TIs) and atom pairs (APs) were used to develop quantitative structure-activity relationship (QSAR) models of a set of 58 dipeptide boronic acids which are potent inhibitors of proteasome and have found applications in the treatment of various types of cancers. Of the three linear regression methods used for QSAR development, viz., principal components regression (PCR), partial least square (PLS), and ridge regression (RR), the last method gave the most satisfactory models whereas the remaining two methods yielded poor models. RR results obtained in this paper using TIs and APs are comparable to the CoMFA and CoMSIA results reported in the literature with the same set of compounds.

Reactivity of Alkyl Polyhalides toward Granular Iron: Development of QSARs and Reactivity Cross Correlations for Reductive Dehalogenation

Attempts to develop quantitative structure-activity relationships (QSARs) for reductive dehalogenation by granular iron have been hindered by the unavailability of high quality predictor variables, have included relatively few compounds, and on occasion have relied on data lacking internal consistency. We herein investigate the reduction of 24 alkyl polyhalides by granular iron and the better-defined, homogeneous reductants Cr(H(2)O)(6)(2+) and an Fe(II) porphyrin. QSARs were constructed with a new set of computationally derived gas phase homolytic carbon-halogen bond dissociation energies and solvated one-electron reduction potentials determined using a quantum chemistry composite method (G3MP2). Reactivity cross correlations between reductant systems were also developed. Reactivity trends were generally consistent among all reductants and revealed pronounced structural influences. Compounds reduced at C-Br were orders of magnitude more reactive than analogues reduced at C-Cl; the number and identity of α- (Br ∼ Cl > CH(3) > F > H) and β-substituents (Br > Cl) also influenced reactivity. Nonlinearities encountered during QSAR and cross correlation development suggest that reactions of highly halogenated alkyl polyhalides with granular iron are limited by mass transfer, as supported by estimates of mass transfer coefficients. For species not suspected to exhibit mass transfer limitations, reasonably strong cross correlations and comparable substituent effects are consistent with dissociative electron transfer as the rate-determining step.

Exploring QSARs with Extended Topochemical Atom (ETA) Indices for Modeling Chemical and Drug Toxicity

Development of quantitative structure-activity relationships (QSARs) and quantitative structure-property relationships (QSPRs) has been practiced for prediction of various toxicities and other relevant properties of chemicals including drug candidates to minimize animal testing, cost and time associated with risk assessment and management processes. This communication reviews published reports of QSARs/QSPRs with Extended Topochemical Atom (ETA) indices for modeling chemical and drug induced toxicities and some physicochemical properties relevant to such toxicities. In each study, ETA models have been compared to those developed using various non-ETA models and it was found that the quality of the QSARs involving ETA parameters were comparable to those involving non-ETA parameters. ETA descriptors were also found to increase statistical quality of the models involving non-ETA parameters when used in combination. On the basis of the reported studies, it can be concluded that the ETA descriptors are sufficiently rich in chemical information to encode the structural features contributing to the toxicities and these indices may be used in combination with other topological and physicochemical descriptors for development of predictive QSAR models. Such models may be used for virtual screening and in silico prediction of toxicities, and if appropriately used, these may be proved helpful for regulatory decision support and decision making processes.

Estimation of Aqueous‐Phase Reaction Rate Constants of Hydroxyl Radical with Phenols, Alkanes and Alcohols

AbstractA quantitative structure activity relationship (QSAR) model was developed for the aqueous‐phase hydroxyl radical reaction rate constants (kOH) employing quantum chemical descriptors and multiple linear regressions (MLR). The QSAR development followed the OECD guidelines, with special attention to validation, applicability domain (AD) and mechanistic interpretation. The established model yielded satisfactory performance: the correlation coefficient square (R2) was 0.905, the root mean squared error (RMSE) was 0.139, the leave‐many‐out cross‐validated QLMO2 was 0.806, and the external validated QEXT2 was 0.922 log units. The AD of the model covering compounds of phenols, alkanes and alcohols, was analyzed by Williams plot. The main molecular structural factors governing kOH are the energy of the highest occupied molecular orbital (EHOMO), average net atomic charges on hydrogen atoms ($\rm{ \overline {Q_{\rm{H}}^{} } }$), molecular surface area (MSA) and dipole moment (μ). It was concluded that kOH increased with increasing EHOMO and MSA, while decreased with increasing $\rm{ \overline {Q_{\rm{H}}^{} } }$ and μ.

Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses

The new European chemicals regulation Registration, Evaluation, Authorization and Restriction of Chemicals entered into force in June 2007 and accelerated the development of quantitative structure-activity relationship (QSAR) models for a variety of endpoints, including carcinogenicity. Here, we would like to present quantitative (continuous) and qualitative (categorical) models for non-congeneric chemicals for prediction of carcinogenic potency. A dataset of 805 substances was obtained after a preliminary screening of findings of rodent carcinogenicity for 1,481 chemicals accessible via Distributed Structure-Searchable Toxicity (DSSTox) Public Database Network originated from the Lois Gold Carcinogenic Potency Database (CPDB). Twenty seven two-dimensional MDL descriptors were selected using Kohonen mapping and principal component analysis. The counter propagation artificial neural network (CP ANN) technique was applied. Quantitative models were developed exploring the relationship between the experimental and predicted carcinogenic potency expressed as a tumorgenic dose TD(50) for rats. The obtained models showed low prediction power with correlation coefficient less than 0.5 for the test set. In the next step, qualitative models were developed. We found that the qualitative models exhibit good accuracy for the training set (92%). The model demonstrated good predicted performance for the test set. It was obtained accuracy (68%), sensitivity (73%), and specificity (63%). We believe that CP ANN method is a good in silico approach for modeling and predicting rodent carcinogenicity for non-congeneric chemicals and may find application for other toxicological endpoints.

Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans: Part B. Use of (Q)SAR systems for early detection of drug-induced hepatobiliary and urinary tract toxicities

This report describes the development of quantitative structure-activity relationship (QSAR) models for predicting rare drug-induced liver and urinary tract injury in humans based upon a database of post-marketing adverse effects (AEs) linked to ∼1600 chemical structures. The models are based upon estimated population exposure using AE proportional reporting ratios. Models were constructed for 5 types of liver injury (liver enzyme disorders, cytotoxic injury, cholestasis and jaundice, bile duct disorders, gall bladder disorders) and 6 types of urinary tract injury (acute renal disorders, nephropathies, bladder disorders, kidney function tests, blood in urine, urolithiases). Identical training data sets were configured for 4 QSAR programs (MC4PC, MDL-QSAR, BioEpisteme, and Predictive Data Miner). Model performance was optimized and was shown to be affected by the AE scoring method and the ratio of the number of active to inactive drugs. The best QSAR models exhibited an overall average 92.4% coverage, 86.5% specificity and 39.3% sensitivity. The 4 QSAR programs were demonstrated to be complementary and enhanced performance was obtained by combining predictions from 2 programs (average 78.4% specificity, 56.2% sensitivity). Consensus predictions resulted in better performance as judged by both internal and external validation experiments.

A new quality assurance system for the evaluation of ecotoxicity studies submitted under the new substances notification regulations in Canada

New substances destined for import into, or manufacture in, Canada must be reported to Environment Canada and Health Canada under the New Substances Notification Regulations (Chemicals and Polymers) (NSNR). With the use of information provided by the notifier, and other complementary information available to the 2 departments, the New Substances Program conducts ecological and human health risk assessments. Over the past 10 y, more than 750 ecotoxicity studies have been submitted to the New Substances Program of Environment Canada under the NSNR. Most of these experimental studies are not publicly available but are useful in the ecological risk assessment of new substances and for the development of Quantitative Structure-Activity Relationships (QSARs). In this paper, we describe the development and validation of a computer-based scoring system and our approach in the development of scoring methods used to assess the quality and usability of ecotoxicity studies with fish, Daphnia spp., and green algae. Results of ranking exercises conducted with these methods are described and discussed, together with the potential use of these results in a regulatory context. In addition, the methods are discussed in comparison with other similar evaluation schemes described in the literature.

Predicting rate constants of hydroxyl radical reactions with organic pollutants: Algorithm, validation, applicability domain, and mechanistic interpretation

The reaction with hydroxyl radical ( OH) is the most important removal process in the daytime for organic pollutants in the atmosphere, thus the OH reaction rate constants ( k OH) are important to assessing the fate of organic pollutants in the troposphere. In this study, experimental data for log k OH of 722 organic chemicals were employed to develop quantitative structure–activity relationships (QSARs) for k OH, applying 22 molecular structural descriptors and partial least squares (PLS) regression. The QSAR development followed the OECD guidelines, with special attention to validation, applicability domain and mechanistic interpretation. For the established model, the leave-many-out cross-validated Q CUM 2 = 0.865, R 2 = 0.878, and RMSE = 0.391 log units, indicating good robustness and predictivity. The predictive capability was also evaluated by external validation with Q EXT 2 = 0.872. The applicability domain of the model is composed of compounds containing C, H, N, O, S, F, Cl, Br, I, and Si atoms in various functional groups and analyzed by Williams plot. The main molecular structural factors governing k OH are the compactness of the molecule, the molecular ability of donating electrons and the number of halogen atoms in a molecule.

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.

A biology-based approach for quantitative structure-activity relationships (QSARs) in ecotoxicity

Quantitative structure-activity relationships (QSARs) for ecotoxicity can be used to fill data gaps and limit toxicity testing on animals. QSAR development may additionally reveal mechanistic information based on observed patterns in the data. However, the use of descriptive summary statistics for toxicity, such as the 4-day LC50 for fish, introduces bias and ignores valuable kinetic information in the data. Biology-based methods use all of the toxicity data in time to derive time-independent and unbiased parameter estimates. Such an approach offers whole new opportunities for mechanism-based QSAR development. In this paper, we apply the hazard model from DEBtox to analyse survival data for fathead minnows (Pimephales promelas). Different modes of action resulted in different patterns in the parameter estimates, and therefore, the toxicity data by themselves reveal insight into the actual mechanism of toxic action.

Application of QSARs and VFARs to the rapid risk assessment process at US EPA

With continued development of new chemicals and genetically engineered microbes as potential agents for terrorism and industrial development, there is a great need for the continued development and application of quantitative structure activity relationships (QSARs) and virulence factor activity relationships (VFARs). Development and application of QSARs and VFARs will facilitate efficient and streamlined use of dwindling resources and assessment of risks associated with exposures to chemical and biological agents. To facilitate the continued development of QSARs and VFARs at US Environmental Protection Agency, a two day workshop was organized June 20–21, 2006, in Cincinnati, OH, USA. This article summarizes the workshop report by highlighting the importance of continued QSAR research, the current state of VFAR science, and the guidance provided to the National Homeland Security Research Center and National Risk Management Research Laboratory by an expert panel for the continued use and development of computational approaches.

QSAR modeling of the antifungal activity against Candida albicans for a diverse set of organic compounds

The molecular structures of 83 diverse organic compounds are correlated by a quantitative structure–activity relationship (QSAR) to their minimum inhibitor concentrations (MIC expressed as log(1/MIC)), involving 6 descriptors with R 2 = 0.788, F = 47.140, s 2 = 0.130. A novel QSAR development technique is utilized combining advantages of the two frequently applied methods. The topological, electronic, geometrical, and hybrid type descriptors for the compounds were calculated by CODESSA PRO software.

Outliers in SAR and QSAR: 2. Is a flexible binding site a possible source of outliers?

Structure-activity relationship (SAR) and/or quantitative structure-activity relationship (QSAR) studies play an important role in a lead optimization of drug discovery research. When there is a lack of ligand-bound protein structural information, one of the assumptions in SAR and QSAR studies is that similar analogs bind to the same binding site in a similar binding mode. In such studies, outliers have often been observed, especially in QSAR. However, most of these studies have focused their attention on the development of QSAR and left outliers unattended. We searched ligand-bound X-ray crystal structures from the protein structure database to find evidences that could indicate a possible source of outliers in SAR or QSAR. Our results showed the possibility of conformational changes in a flexible binding site as one possible source of outliers.

Consensus QSAR Models: Do the Benefits Outweigh the Complexity?

This study has assessed the use of consensus regression, as compared to single multiple linear regression, models for the development of quantitative structure-activity relationships (QSARs). To provide a comparison, four data sets of varying size and complexity were analyzed: silastic membrane flux, toxicity of phenols to Tetrahymena pyriformis, acute toxicity to the fathead minnow and flash point. For each data set, a genetic algorithm was used to develop a model population and the performance of consensus models was compared to that of the best single model. Two consensus models were developed, one using the top 10 models, and the other using a subset of models chosen to provide maximal coverage of model space. The results highlight the ability of the genetic algorithm to develop predictive models from a large descriptor pool. However, the consensus models were shown to offer no significant improvements over single regression models, which are as statistically robust as the equivalent consensus models. Consensus models developed from a selection of the best QSARs were shown not to be superior to a selection of diverse in "model space" QSARs. For the data sets analyzed in this study, and in light of the Organization for Economic Cooperation and Development principles for the validation of QSARs, the increase in model complexity when using consensus models does not seem warranted given the minimal improvement in model statistics.

Measurement of biodegradability parameters for single unsubstituted and methylated polycyclic aromatic hydrocarbons in liquid bacterial suspensions

Substrate depletion experiments were conducted to characterize aerobic biodegradation of 20 single polycyclic aromatic hydrocarbons (PAHs) by induced Sphingomonas paucimobilis strain EPA505 in liquid suspensions. PAHs consisted of low molecular weight, unsubstituted, and methyl-substituted homologs. A material balance equation containing the Andrews kinetic model, an extension of the Monod model accounting for substrate inhibition, was numerically fitted to batch depletion data to estimate extant kinetic parameters including the maximal specific uptake rates, q(max), the affinity coefficients, K(S), and the substrate inhibition coefficients, K(I). Strain EPA505 degraded all PAHs tested. Applied kinetic models adequately simulated experimental data. A cell proliferation assay involving reduction of the tetrazolium dye WST-1 was used to evaluate the ability of strain EPA505 to utilize individual PAHs as sole energy and carbon sources. Of the 22 PAHs tested, 9 supported bacterial growth. Evaluation of the biokinetic data showed that q(max) correlated highly with transmembrane flux as theoretically estimated by a diffusion model, pointing to transmembrane transport as a potential rate-determining process. The biodegradability data generated in this study is essential for the development of quantitative structure-activity relationships (QSARs) for biodegradability and for modeling biodegradation of simple PAH mixtures.