Binary Quantitative Structure−Activity Relationship (QSAR) Analysis of Estrogen Receptor Ligands

Gao, Hua; Williams, Christopher I.; Labute, Paul; Bajorath, Jürgen

doi:10.1021/ci980140g

Cited by 107 publications

(68 citation statements)

References 32 publications

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“…The BCUT metrics 36 and the interclass distance parameter 38 represent recent efforts for developing descriptors more suitable for classification. However, the use of BCUT metrics with the binary QSAR method 13,14 produced models of predictive accuracy similar to those obtained with traditional descriptors. 39 …”

Section: Discussionmentioning

confidence: 61%

Spline-Fitting with a Genetic Algorithm: A Method for Developing Classification Structure−Activity Relationships

Sutherland

O’Brien

Weaver

2003

J. Chem. Inf. Comput. Sci.

159

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Classification methods allow for the development of structure-activity relationship models when the target property is categorical rather than continuous. We describe a classification method which fits descriptor splines to activities, with descriptors selected using a genetic algorithm. This method, which we identify as SFGA, is compared to the well-established techniques of recursive partitioning (RP) and soft independent modeling by class analogy (SIMCA) using five series of compounds: cyclooxygenase-2 (COX-2) inhibitors, benzodiazepine receptor (BZR) ligands, estrogen receptor (ER) ligands, dihydrofolate reductase (DHFR) inhibitors, and monoamine oxidase (MAO) inhibitors. Only 1-D and 2-D descriptors were used. Approximately 40% of compounds in each series were assigned to a test set, "cherry-picked" from the complete set such that they lie outside the training set as much as possible. SFGA produced models that were more predictive for all but the DHFR set, for which SIMCA was most predictive. RP gave the least predictive models for all but the MAO set. A similar trend was observed when using training and test sets to which compounds were randomly assigned and when gradually eliminating compounds from the (designed) training set. The stability of models was examined for the random and reduced sets, where stability means that classification statistics and the selected descriptors are similar for models derived from different sets. Here, SIMCA produced the most stable models, followed by SFGA and RP. We show that a consensus approach that combines all three methods outperforms the single best model for all data sets.

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Section: Discussionmentioning

confidence: 61%

Spline-Fitting with a Genetic Algorithm: A Method for Developing Classification Structure−Activity Relationships

Sutherland

O’Brien

Weaver

2003

J. Chem. Inf. Comput. Sci.

159

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“…Some of the descriptors such as Kier shape indices contain implicit three-dimensional information. Explicit three-dimensional molecular descriptors were not used to avoid bias of the analysis due to predicted conformational effects and speed of calculation for fast prediction as stated in a previous publication (Gao et al, 1999). cLogD and pK a calculations in our analysis were performed using ACD software (version 9.03; ACD/Labs, Toronto, ON, Canada).…”

Section: Methodsmentioning

confidence: 99%

In Silico Modeling of Nonspecific Binding to Human Liver Microsomes

Gao

Yao²,

Mathieu³

et al. 2008

Drug Metab Dispos

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ABSTRACT:Estimation of unbound fraction of substrate in microsomal incubation media is important in accurately predicting hepatic intrinsic clearance and drug-drug interactions. In this study, the unbound fraction of 1223 drug-like molecules in human liver microsomal incubation media has been determined using equilibrium dialysis. These compounds, which include 27 marketed drug molecules, cover a much broader range of physiochemical properties such as hydrophobicity, molecular weight, ionization state, and degree of binding than those examined in previous work. In developing the in silico model, we have used two-dimensional molecular descriptors including cLogP, Kier connectivity, shape, and E-state indices, a subset of MOE descriptors, and a set of absorption, disposition, metabolism, and excretion structural keys used for our in-house absorption, disposition, metabolism, excretion, and toxicity modeling. Hydrophobicity is the most important molecular property contributing to the nonspecific binding of substrate to microsomes. The prediction accuracy of the model is validated using a subset of 100 compounds, and 92% of the variance is accounted for by the model with a root mean square error (RMSE) of 0.10. For the training set of compounds, 99% of variance is accounted for by the model with a RMSE of 0.02. The performance of the developed model has been further tested using the 27 marketed drug molecules with a RMSE of 0.10 between the observed and the predicted unbound fraction values.In drug discovery, the ultimate goal of a medicinal chemist is to rationally design compounds that are safe and efficacious at a marketable dose regimen. Such chemical design efforts frequently rely upon intrinsic clearance (CL int ) and P450 inhibitory potency (IC 50 ) estimates derived from human liver microsomal incubations. With relatively few assumptions, these estimates can be expected to have proportional implications for oral dose requirements and therapeutic windows against pharmacokinetic drug-drug interactions. For example, the classic well stirred model of hepatic clearance can be used to illustrate the directly proportional relationship between CL int and oral dose by eq. 1 (Wagner et al., 1965). Other oral dose determinants include the unbound steady-state average concentration necessary for the desired pharmacologic response (C ss, u ), dose interval (), and fraction of dose absorbed (fa):In addition, by assuming that inhibition of P450 occurs via a competitive mechanism, eq. 2 is a valid and commonly used approach to calculate the therapeutic window against pharmacokinetic drug-drug interactions:Therapeutic window ϭ C ss, u IC 50It has now been well established in the scientific literature that nonspecific binding of compounds into microsomal phospholipids will confound the experimental estimation of CL int and IC 50 by decreasing the fraction of unbound drug (fu mic ) to metabolizing enzymes within the incubation (Kalvass et al., 2001;Margolis and Obach, 2003;Obach, 1997Obach, , 1999Tran et al., 2002). In such ca...

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“…Using selective agonists or antagonists to modulate this biology is the focus of significant activity in the pharmaceutical industry (5)(6)(7)(8)(9). To date, a ligand's binding properties for ER have mainly been determined by equilibrium binding assays that often employ radiolabeled compounds and require overnight incubations.…”

Section: S Urface Plasmon Resonance (Spr) Biosensor Technology Hasmentioning

confidence: 99%

Kinetic analysis of estrogen receptor/ligand interactions

Rich

Hoth²,

Geoghegan³

et al. 2002

Proc. Natl. Acad. Sci. U.S.A.

209

126

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Surface plasmon resonance biosensor technology was used to directly measure the binding interactions of small molecules to the ligand-binding domain of human estrogen receptor. In a screening mode, specific ligands of the receptor were easily discerned from nonligands. In a high-resolution mode, the association and dissociation phase binding responses were shown to be reproducible and could be fit globally to a simple interaction model to extract reaction rate constants. On average, antagonist ligands (such as tamoxifen and nafoxidine) were observed to bind to the receptor with association rates that were 500-fold slower than agonists (such as estriol and ␤-estradiol). This finding is consistent with these antagonists binding to an altered conformation of the receptor. The biosensor assay also could identify subtle differences in how the same ligand interacted with two different isoforms of the receptor (␣ and ␤). The biosensor's ability to determine kinetic rate constants for small molecule͞protein interactions provides unique opportunities to understand the mechanisms associated with complex formation as well as new information to drive the optimization of drug candidates.S urface plasmon resonance (SPR) biosensor technology has advanced to the point where it is possible to measure directly small molecules interacting with immobilized macromolecular targets (1, 2). This development suggests that biosensor analysis will become an important secondary screening tool in drug discovery, confirming hits from primary screens and providing detailed kinetics for lead optimization (3, 4). To illustrate the utility of current SPR technology, the binding properties of small compounds (200-500 Da) interacting with human estrogen receptor (ER) were analyzed.Ligand binding to ER is responsible for controlling the basic biology of estrogen-sensitive tissues. Using selective agonists or antagonists to modulate this biology is the focus of significant activity in the pharmaceutical industry (5-9). To date, a ligand's binding properties for ER have mainly been determined by equilibrium binding assays that often employ radiolabeled compounds and require overnight incubations. Here, we demonstrate how optical biosensors may be used to determine both kinetic and equilibrium binding constants for compounds interacting with ER in real time without labeling either binding partner.SPR biosensor experiments require immobilizing one reactant on a surface and monitoring its binding to a second reactant in solution. An antibody-capturing method was used to study the dynamics of ER͞ligand interactions. This assay format created a chemically homogenous receptor surface and allowed us to determine rapidly the binding properties of a variety of compounds. We examined the binding of 12 compounds (shown in Fig. 1) having differing receptor activities: both estrogen and non-estrogen agonists, SERMs (selective ER modulators, which for this discussion are referred to as antagonists), and nonbinding control compounds that possess core structures sim...

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Binary Quantitative Structure−Activity Relationship (QSAR) Analysis of Estrogen Receptor Ligands

Cited by 107 publications

References 32 publications

Spline-Fitting with a Genetic Algorithm: A Method for Developing Classification Structure−Activity Relationships

Spline-Fitting with a Genetic Algorithm: A Method for Developing Classification Structure−Activity Relationships

In Silico Modeling of Nonspecific Binding to Human Liver Microsomes

Kinetic analysis of estrogen receptor/ligand interactions

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