Search PubMed⌕ Search

Biomedical subjects

William J Welsh

Publications and source records attributed to William J Welsh.

At least 37 records · Page 2Linked to original sources
PubMed2004

Identification of possible kinetically significant anion-binding sites in human serum transferrin using molecular modeling strategies.

Certain anions have been shown experimentally to influence the rate of iron release from human serum transferrin (HST), implying the existence of one or more allosteric kinetically significant anion-binding (KISAB) sites on or near the surface of the protein. A rank-ordered selection of potential HST KISAB sites has been obtained using a novel three-stage molecular modeling strategy. The crystal structure of HST (1A8E.pdb) was first subjected to a heuristic analysis, in which positively charged and hydrogen-bonding residues on or near the surface of the protein were identified. In this stage, a preliminary electrostatic potential map was also calculated, yielding six preliminary sites. Next, energy-grid calculations were conducted in order to identify anion-protein interaction energy minima, which resulted in the inclusion of three additional sites. Finally, three anions already shown experimentally to demonstrate varied effects on HST iron-release kinetics were placed at each potential site; molecular dynamics and molecular mechanics calculations were performed in order to elucidate the hydrogen-bonding environment around each anion of the protein as well as to calculate anion-protein-binding energies.

Anions↗
PubMed2004

Gaussian mixture clustering and imputation of microarray data.

MOTIVATION: In microarray experiments, missing entries arise from blemishes on the chips. In large-scale studies, virtually every chip contains some missing entries and more than 90% of the genes are affected. Many analysis methods require a full set of data. Either those genes with missing entries are excluded, or the missing entries are filled with estimates prior to the analyses. This study compares methods of missing value estimation. RESULTS: Two evaluation metrics of imputation accuracy are employed. First, the root mean squared error measures the difference between the true values and the imputed values. Second, the number of mis-clustered genes measures the difference between clustering with true values and that with imputed values; it examines the bias introduced by imputation to clustering. The Gaussian mixture clustering with model averaging imputation is superior to all other imputation methods, according to both evaluation metrics, on both time-series (correlated) and non-time series (uncorrelated) data sets.

Algorithms↗
PubMed2004

Characteristics of the Plasmodium falciparum PK5 ATP-binding site: implications for the design of novel antimalarial agents.

Increasing worldwide resistance of Plasmodium falciparum (P. falciparum) to traditional chemotherapy strategies such as chloroquine and mefloquine demonstrates the urgent need for the discovery of novel chemotherapeutic agents in the fight against malaria. The recent discovery of P. falciparum Protein Kinase 5 (PfPK5) invites the possibility of selectively targeting the life cycle of P. falciparum in order to prevent cerebral malaria. PfPK5 bears a high degree of sequence identity (>58%) to a structurally conserved family of mammalian kinases known as the cyclin-dependent kinases (CDKs). The CDKs are the key regulatory elements governing the ordered progression of the mammalian cell cycle. With numerous X-ray crystal structures of CDK2 to provide a structural template, here we present a three-dimensional structural model of PfPK5 constructed using computer-based homology modeling techniques. Our model was used to compare the ATP binding site of PfPK5 with that of the mammalian kinase CDK2. Furthermore, kinase-ligand interactions of PfPK5 with known inhibitors were investigated and compared to available crystal structures of CDK2 with inhibitors bound. The focus of the study is to identify similarities and differences between the ATP binding sites of the two kinases that can be exploited for future rational drug design.

Adenosine Triphosphate↗
PubMed2004

Detecting hidden sequence propensity for amyloid fibril formation.

The preponderance of evidence implicates protein misfolding in many unrelated human diseases. In all cases, normal correctly folded proteins transform from their proper native structure into an abnormal beta-rich structure known as amyloid fibril. Here we introduce a computational algorithm to detect nonnative (hidden) sequence propensity for amyloid fibril formation. Analyzing sequence-structure relationships in terms of tertiary contact (TC), we find that the hidden beta-strand propensity of a query local sequence can be quantitatively estimated from the secondary structure preferences of template sequences of known secondary structure found in regions of high TC. The present method correctly pinpoints the minimal peptide fragment shown experimentally as the likely local mediator of amyloid fibril formation in beta-amyloid peptide, islet amyloid polypeptide (hIAPP), alpha-synuclein, and human acetylcholinesterase (AChE). It also found previously unrecognized beta-strand propensities in the prototypical helical protein myoglobin that has been reported as amyloidogenic. Analysis of 2358 nonhomologous protein domains provides compelling evidence that most proteins contain sequences with significant hidden beta-strand propensity. The present method may find utility in many medically relevant applications, such as the engineering of protein sequences and the discovery of therapeutic agents that specifically target these sequences for the prevention and treatment of amyloid diseases.

Acetylcholinesterase↗
PubMed2004

Highly chlorinated PCBs inhibit the human xenobiotic response mediated by the steroid and xenobiotic receptor (SXR).

Polychlorinated biphenyls (PCBs) are a family of persistent organic contaminants suspected to cause adverse effects in wildlife and humans. In rodents, PCBs bind to the aryl hydrocarbon (AhR) and pregnane X receptors (PXR) inducing the expression of catabolic cytochrome p450 enzymes of the CYP1A and 3A families. We found that certain highly chlorinated PCBs are potent activators of rodent PXR but antagonize its human ortholog, the steroid and xenobiotic receptor (SXR), inhibiting target gene induction. Thus, exposure to PCBs may blunt the human xenobiotic response, inhibiting the detoxification of steroids, bioactive dietary compounds, and xenobiotics normally mediated by SXR. The antagonistic PCBs are among the most stable and abundant in human tissues. These findings have important implications for understanding the biologic effects of PCB exposure and the use of animal models to predict the attendant risk.

Animals↗
PubMed2003

Shape signatures: a new approach to computer-aided ligand- and receptor-based drug design.

A unifying principle of rational drug design is the use of either shape similarity or complementarity to identify compounds expected to be active against a given target. Shape similarity is the underlying foundation of ligand-based methods, which seek compounds with structure similar to known actives, while shape complementarity is the basis of most receptor-based design, where the goal is to identify compounds complementary in shape to a given receptor. These approaches can be extended to include molecular descriptors in addition to shape, such as lipophilicity or electrostatic potential. Here we introduce a new technique, which we call shape signatures, for describing the shape of ligand molecules and of receptor sites. The method uses a technique akin to ray-tracing to explore the volume enclosed by a ligand molecule, or the volume exterior to the active site of a protein. Probability distributions are derived from the ray-trace, and can be based solely on the geometry of the reflecting ray, or may include joint dependence on properties, such as the molecular electrostatic potential, computed over the surface. Our shape signatures are just these probability distributions, stored as histograms. They converge rapidly with the length of the ray-trace, are independent of molecular orientation, and can be compared quickly using simple metrics. Shape signatures can be used to test for both shape similarity between compounds and for shape complementarity between compounds and receptors and thus can be applied to problems in both ligand- and receptor-based molecular design. We present results for comparisons between small molecules of biological interest and the NCI Database using shape signatures under two different metrics. Our results show that the method can reliably extract compounds of shape (and polarity) similar to the query molecules. We also present initial results for a receptor-based strategy using shape signatures, with application to the design of new inhibitors predicted to be active against HIV protease.

Binding Sites↗
PubMed2003

Three-dimensional structure-activity relationship modeling of cocaine binding to two monoclonal antibodies by comparative molecular field analysis.

Successful immunotherapy of cocaine addiction and overdoses requires cocaine-binding antibodies with specific properties, such as high affinity and selectivity for cocaine. We have determined the affinities of two cocaine-binding murine monoclonal antibodies (mAb: clones 3P1A6 and MM0240PA) for cocaine and its metabolites by [3H]-radioligand binding assays. mAb 3P1A6 (K(d) = 0.22 nM) displayed a 50-fold higher affinity for cocaine than mAb MM0240PA (K(d) = 11 nM) and also had a greater specificity for cocaine. For the systematic exploration of both antibodies' binding specificities, we used a set of approximately 35 cocaine analogues as structural probes by determining their relative binding affinities (RBAs) using an enzyme-linked immunosorbent competition assay. Three-dimensional quantitative structure-activity relationship (3D-QSAR) models on the basis of comparative molecular field analysis (CoMFA) techniques correlated the binding data with structural features of the ligands. The analysis indicated that despite the mAbs' differing specificities for cocaine, the relative contributions of the steric (approximately 80%) and electrostatic (approximately 20%) field interactions to ligand-binding were similar. Generated three-dimensional CoMFA contour plots then located the specific regions about cocaine where the ligand/receptor interactions occurred. While the overall binding patterns of the two mAbs had many features in common, distinct differences were observed about the phenyl ring and the methylester group of cocaine. Furthermore, using previously published data, a 3D-QSAR model was developed for cocaine binding to the dopamine reuptake transporter (DAT) that was compared to the mAb models. Although the relative steric and electrostatic field contributions were similar to those of the mAbs, the DAT cocaine-binding site showed a preference for negatively charged ligands. Besides establishing molecular level insight into the interactions that govern cocaine binding specificity by biopolymers, the three-dimensional images obtained reflect the properties of the mAbs binding pockets and provide the initial information needed for the possible design of novel antibodies with properties optimized for immunotherapy.

Animals↗
PubMed2003

Homology model of the CB1 cannabinoid receptor: sites critical for nonclassical cannabinoid agonist interaction.

Association of cannabimimetic compounds such as cannabinoids, aminoalkylindoles (AAIs), and arachidonylethanolamide (anandamide) with the brain cannabinoid (CB(1)) receptor activates G-proteins and relays signals to regulate neuronal functions. A CB(1) receptor homology model was constructed using the published x-ray crystal structure of bovine rhodopsin (Palczewski et al., Science, 2000, Vol. 289, pp. 739-745) in the conformation most likely to represent the "high-affinity" state for agonist binding to G-protein coupled receptors (GPCRs). A molecular docking approach that combined Monte Carlo and molecular dynamics simulations was used to identify the putative binding conformations of nonclassical cannabinoid agonists, including AC-bicyclic CP47497 and CP55940, and ACD-tricyclic CP55244. Placement of these ligands was based upon the assumption of a critical hydrogen bond between the A-ring OH and the side chain N of Lys192 in transmembrane helix 3. We evaluated two alternative binding conformations, C3-in and C3-out, denoting the directionality of the ligand C3 side chain within the receptor with respect to the inside or the outside of the cell. Assuming both the C3-in or C3-out conformation, the calculated ligand-receptor binding energy (DeltaE(bind)) was correlated with the experimentally observed binding affinity (K(i)) for a series of nonclassical cannabinoid agonists. The C3-in conformation was marginally better than the alternative C3-out conformation in predicting the rank order of the tested nonclassical cannabinoid analogs. Adopting the C3-in conformation due to the greater number of receptor interactions with known pharmacophoric elements of the ligand, key residues were identified comprising the presumed hydrophobic pocket that interacts with the C3 side chain of cannabinoid agonists. Key hydrogen bonds would form between both K3.28(192) and E(258) and the A-ring OH, and between Q(261) and the C-ring C-12 hydroxypropyl. In summary, the present study represents one of the first attempts to construct a homology model of the CB(1) cannabinoid receptor based upon the published bovine rhodopsin x-ray crystal structure and to elucidate the putative ligand binding site for nonclassical cannabinoid agonists. We postulated sites of the CB(1) receptor critical for the ligand interaction, including the hydrophobic pocket interacting with the key pharmacophoric moiety, the C3 side chain. More work is needed to delineate between two alternative (and possibly other) binding conformations of the nonclassical cannabinoid ligands within the CB(1) receptor. The present study provides a consistent framework for further investigation of the CB(1) receptor-ligand interaction and for the study of CB(1) receptor activation.

Amino Acid Sequence↗
PubMed2003

Computational models for predicting the binding affinities of ligands for the wild-type androgen receptor and a mutated variant associated with human prostate cancer.

In the present study, values of the binding energy (BE) were calculated for the rat androgen receptor on a data set of 25 steroidal and nonsteroidal compounds for which published values of the observed binding affinity (K(i)) are available. A correlation between BE and pK(i) was evident (r(2) = 0.50) for the entire data set and became more pronounced when the steroids and nonsteroids were plotted separately (r(2) congruent with 0.76). Including BE as an additional descriptor to supplement the default steric-electrostatic descriptors in comparative molecular field analysis dramatically improved the predictive ability of the resulting three-dimensional quantitative structure-activity relationship models. We also demonstrate that the observed loss in ligand specificity between the wild-type (wt) AR and the T877A mutant AR associated with androgen-independent prostate cancer is reflected in decreased BE values (i.e., higher binding affinity) for the antiandrogen pharmaceutical hydroxyflutamide and for several nonandrogenic endogenous steroids, most notably cortisol, corticosterone, 17beta-estradiol, progesterone, and 17alpha-hydroxyprogesterone.

Amino Acid Substitution↗
PubMed2003

Interaction of organophosphate pesticides and related compounds with the androgen receptor.

Identification of several environmental chemicals capable of binding to the androgen receptor (AR) and interfering with its normal function has heightened concern about adverse effects across a broad spectrum of environmental chemicals. We previously demonstrated AR antagonist activity of the organophosphate (OP) pesticide fenitrothion. In this study, we characterized AR activity of analogues of fenitrothion to probe the structural requirements for AR activity among related chemicals. AR activity was measured using HepG2 human hepatoma cells transfected with human AR plus an androgen-responsive luciferase reporter gene, MMTV-luc. AR antagonist activity decreased as alkyl chain length of the phosphoester increased, whereas electron-donating properties of phenyl substituents of the tested compounds did not influence AR activity. Oxon derivatives of fenitrothion, which are more likely to undergo hydrolytic degradation, had no detectable AR antagonist activity. Molecular modeling results suggest that hydrogen-bond energies and the maximum achievable interatomic distance between two terminal H-bond capable sites may influence both the potential to interact with the AR and the nature of the interaction (agonist vs. antagonist) within this series of chemicals. This hypothesis is supported by the results of recent AR homology modeling and crystallographic studies relative to agonist- and antagonist-bound AR complexes. The present results are placed in the context of structure-activity knowledge derived from previous modeling studies as well as studies aimed toward designing nonsteroidal antiandrogen pharmaceuticals. Present results extend understanding of the structural requirements for AR activity to a new class of nonsteroidal, environmental, OP-related chemicals.

Carcinoma, Hepatocellular↗
PubMed2003

Quantitative structure-activity relationship methods: perspectives on drug discovery and toxicology.

Quantitative structure-activity relationships (QSARs) attempt to correlate chemical structure with activity using statistical approaches. The QSAR models are useful for various purposes including the prediction of activities of untested chemicals. Quantitative structure-activity relationships and other related approaches have attracted broad scientific interest, particularly in the pharmaceutical industry for drug discovery and in toxicology and environmental science for risk assessment. An assortment of new QSAR methods have been developed during the past decade, most of them focused on drug discovery. Besides advancing our fundamental knowledge of QSARs, these scientific efforts have stimulated their application in a wider range of disciplines, such as toxicology, where QSARs have not yet gained full appreciation. In this review, we attempt to summarize the status of QSAR with emphasis on illuminating the utility and limitations of QSAR technology. We will first review two-dimensional (2D) QSAR with a discussion of the availability and appropriate selection of molecular descriptors. We will then proceed to describe three-dimensional (3D) QSAR and key issues associated with this technology, then compare the relative suitability of 2D and 3D QSAR for different applications. Given the recent technological advances in biological research for rapid identification of drug targets, we mention several examples in which QSAR approaches are employed in conjunction with improved knowledge of the structure and function of the target receptor. The review will conclude by discussing statistical validation of QSAR models, a topic that has received sparse attention in recent years despite its critical importance.

Drug Design↗
PubMed2003

Structure-activity relationship approaches and applications.

New techniques and software have enabled ubiquitous use of structure-activity relationships (SARs) in the pharmaceutical industry and toxicological sciences. We review the status of SAR technology by using examples to underscore the advances as well as the unique technical challenges. Applying SAR involves two steps: Characterization of the chemicals under investigation, and application of chemometric approaches to explore data patterns or to establish the relationships between structure and activity. We describe generally but not exhaustively the SAR methodologies popular use in toxicology, including representation of chemical structure, and chemometric techniques where models are both unsupervised and supervised. The utility of SAR technology is most evident when supervised methods are used to predict toxicity of untested chemicals based only on chemical structure. Such models can predict on both an ordinal scale (e.g., active vs inactive) or a continuouis scale (e.g., median lethal dose [LD50] dose). The reader is also referred to a companion paper in this issue that discusses quantitative structure-activity relationship (QSAR) methods that have advanced markedly over the past decade.

Forecasting↗
PubMed2002

Three-dimensional quantitative structure-activity relationship analysis of ligand binding to human sequence antidigoxin monoclonal antibodies using comparative molecular field analysis.

The present study indicates that the newly generated human sequence antidigoxin monoclonal antibody (mAb), 1B3, binds digoxin with a different fine specificity binding than our previously obtained human sequence monoclonal antibodies (mAbs) (Ball, W. J.; et al. J. Immunol. 1999, 163, 2291-2298). Uniquely, 1B3 has a higher affinity for digitoxin than digoxin, the immunizing hapten, and a strong requirement for at least one sugar residue linked to the aglycone (-genin). By means of comparative molecular field analysis (CoMFA), the results of competition binding studies for 56 cardiotonic and hormonal steroids were employed to develop three-dimensional quantitative structure-activity relationship (3D-QSAR) models for ligand binding to 1B3 and to three additional human sequence mAbs, as well as the murine antidigoxin mAb 40-50 (Mudgett-Hunter, M.; et al. Mol. Immunol. 1985, 22, 447-488). All five 3D-QSAR models yielded cross-validated q(2) values greater than 0.5, which indicates that they have significant predictive ability. The CoMFA StDevCoeff contour plots, as well as the competition results, indicate that 1B3 binds ligands in a manner distinct from the other four mAbs. The CoMFA contour plots for 40-50 were also compared with the known X-ray crystallographic structure of the 40-50-ouabain complex (Jeffrey, P. D.; et al. J. Mol. Biol. 1995, 248, 344-360) in order to identify correlations between residues in the mAb binding site and specific contour plot regions. These 3D-QSAR models and their respective contour plots should be useful tools to further understand the molecular nature of antibody-antigen interactions and to aid in the redesign or enhancement of therapeutic antibodies.

Amino Acid Sequence↗
PubMed2002

Molecular interaction of the antagonist N-(piperidin-1-yl)-5-(4-chlorophenyl)-1- (2,4-dichlorophenyl)-4-methyl-1H-pyrazole-3-carboxamide with the CB1 cannabinoid receptor.

N-(piperidin-1-yl)-5-(4-chlorophenyl)-1-(2,4-dichlorophenyl)-4-methyl-1H-pyrazole-3-carboxamide (SR141716; 1) is a potent and selective antagonist for the CB1 cannabinoid receptor. Using the AM1 molecular orbital method, conformational analysis of 1 around the pyrazole C3 substituent identified four distinct conformations designated Tg, Ts, Cg, and Cs. The energetic stability of these conformers followed the order Tg > Cg > Ts > Cs for the neutral (unprotonated) form of 1 and Ts > Tg > Cs > Cg for its piperidine N-protonated form. Unified pharmacophore models for the CB1 receptor ligands were developed by incorporating the protonated form of 1 into the superimposition model for the cannabinoid agonists 4-[4-(1,1-dimethylheptyl)-2-hydroxyphenyl]perhydro-2alpha,6beta-dihydroxynaphthalene (CP55244; 2) and the protonated form of (R)-[2,3-dihydro-5-methyl-3-[(4-morpholinyl)methyl]pyrrolo[1,2,3-de]-1,4-benzoxazin-6-yl](1-naphthalenyl)methanone (WIN55212-2; 3) reported previously (Shim et al. In Rational Drug Design Symposium Series; Parrill, A. L., Reddy, M. R., Eds.; American Chemical Society: Washington, DC, 1999; pp 165-184). Values of K(i) for 1 and a series of 31 structural analogues were determined from radioligand binding analyses by competitive displacement of [3H]CP55940 from cannabinoid receptors in a rat brain membrane preparation. Comparative molecular field analysis (CoMFA) was employed to construct three-dimensional (3D)-quantitative structure-activity relationship (QSAR) models for this data set as unprotonated species assuming the Tg, Cg, and Ts conformers and for the protonated species assuming the Ts, Tg, and Cs conformers. Values of the conventional r2 and cross-validated r2 (r(cv)2) associated with these CoMFA models exceeded the threshold for statistical robustness (r2 > or = 0.90) and internal predictive ability (r(cv)2 > or = 0.50) in each of these six cases except for the protonated species assuming the Tg conformer (i.e., r2 = 0.97; r(cv)2 = 0.36). Results from conformational analyses, superimposition models, and 3D-QSAR models suggest that the N1 aromatic ring moiety of 1 dominates the steric binding interaction with the receptor in much the same way as does the C3 alkyl side chain of cannabinoid agonists and the C3 aroyl ring of the aminoalkylindole agonists. We also determined that several of the conformers considered in this study possess the proper spatial orientation and distinct electrostatic character to bind to the CB1 receptor. We propose that the unique region in space occupied by the C5 aromatic ring of 1 might contribute to conferring antagonist activity. We further propose that the pyrazole C3 substituent of 1 might contribute to conferring either neutral antagonist or inverse agonist activity, depending upon the interaction with the receptor.

Algorithms↗
PubMed2002

Computational studies on tetrahydropyrimidine-2-one HIV-1 protease inhibitors: improving three-dimensional quantitative structure-activity relationship comparative molecular field analysis models by inclusion of calculated inhibitor- and receptor-based properties.

A computational chemistry study has been performed on a series of tetrahydropyrimidine-2-ones (THPs) as HIV-1 protease (HIV-1 PR) inhibitors. The present investigation focuses on the correlation of inhibitor-enzyme complexation energies (E(compl)), inhibitor solvation energies E(solv)[I], and both polar and nonpolar buried surface areas (BSAs) with the observed values of the binding affinity (pK(I)). Various combinations of these specific inhibitor- and receptor-based properties were also evaluated as additional descriptors to three-dimensional quantitative structure-activity relationship (3D-QSAR) models constructed using comparative molecular field analysis (CoMFA). Linear regression of the observed pK(I) values with E(compl), E(solv)[I], and the BSAs yielded a strong correlation in terms of both self-consistency (r(2) approximately equal to 0.90) and internal predictive ability (r(cv)(2) > 0.50). The 3D-QSAR models obtained from CoMFA using standard partial least-squares (PLS) analysis also yielded a strong correlation between the CoMFA fields and the experimental pK(i) (r(2) = 0.96; r(cv)(2) = 0.58). Various "enhanced" 3D-QSAR models were constructed in which different combinations of the E(compl), E(solv)[I], and BSAs were added as additional descriptors to the default steric-electrostatic CoMFA fields. Inclusion of E(solv)[I] in particular yielded significant improvement in the predictive ability (r(cv)(2) approximately equal to 0.80) of the resultant 3D-QSAR model.

HIV Protease↗
PubMed2002

Three-dimensional quantitative structure-activity relationship study of the inhibition of Na(+),K(+)-ATPase by cardiotonic steroids using comparative molecular field analysis.

Na(+),K(+)-ATPase is a transmembrane protein that transports sodium and potassium ions across cell membranes during an activity cycle that uses the energy released by ATP hydrolysis. Cardiotonic steroids (digitalis) inhibit this activity and consequently produce a positive inotropic response in the heart. To identify the structural features of the steroids that are important for this inhibition, we have tested the inhibitory properties of 47 cardiotonic and hormonal steroids and developed a three-dimensional quantitative structure-activity relationship (3D-QSAR) model for the inhibition of Na(+),K(+)-ATPase using comparative molecular field analysis (CoMFA). We also developed a 3D-QSAR model for the binding of digoxin to the murine anti-digoxin monoclonal antibody (mAb) 26-10 because we have previously shown that the environment of the binding sites of 26-10 and the enzyme are similar (Kasturi et al. (1998) Biochemistry 37, 6658-6666). These statistically predictive 3D-QSAR models indicate that both binding sites are about 20 A long and have a close fit or complementarity about the beta side of the lactone ring of digitalis. Furthermore, steric bulk about the lactone ring and the alpha sugar may be critical for drug binding. However, the binding site of Na(+),K(+)-ATPase differs from that of mAb in that it has a greater number of electrostatic interactions along the alpha-sugar, steroid, and lactone moieties. In addition, the availability of the structure of the 26-10 Fab-digoxin complex (Jeffrey et al. (1993) Proc. Natl. Acad. Sci. U.S.A. 90, 10310-10314) enabled us to compare the CoMFA-derived contour maps with the known locations for amino acid residues comprising the mAb ligand binding site.

Animals↗
PubMed2002

Influence of the structural diversity of data sets on the statistical quality of three-dimensional quantitative structure-activity relationship (3D-QSAR) models: predicting the estrogenic activity of xenoestrogens.

Federal legislation has resulted in the two-tiered in vitro and in vivo screening of some 80 000 structurally diverse chemicals for possible endocrine disrupting effects. To maximize efficiency and minimize expense, prioritization of these chemicals with respect to their estrogenic disrupting potential prior to this time-consuming and labor-intensive screening process is essential. Computer-based quantitative structure-activity relationship (QSAR) models, such as those obtained using comparative molecular field analysis (CoMFA), have been demonstrated as useful for risk assessment in this application. In general, however, CoMFA models to predict estrogenicity have been developed from data sets with limited structural diversity. In this study, we constructed CoMFA models based on biological data for a structurally diverse set of compounds spanning eight chemical families. We also compared two standard alignment schemes employed in CoMFA, namely, atom-fit and flexible field-fit, with respect to the predictive capabilities of their respective models for structurally diverse data sets. The present analysis indicates that flexible field-fit alignment fares better than atom-fit alignment as the structural diversity of the data set increases. Values of log(RP), where RP = relative potency, predicted by the final flexible field-fit CoMFA models are in good agreement with the corresponding experimental values. These models should be effective for predicting the endocrine disrupting potential of existing chemicals as well as prospective and newly prepared chemicals before they enter the environment.

Animals↗