Biomedical subjects

Anton J Hopfinger

Publications and source records attributed to Anton J Hopfinger.

12 recordsLinked to original sources

PubMed2007

4D-fingerprint categorical QSAR models for skin sensitization based on the classification of local lymph node assay measures.

Currently, the only validated methods to identify skin sensitization effects are in vivo models, such as the local lymph node assay (LLNA) and guinea pig studies. There is a tremendous need, in particular due to novel legislation, to develop animal alternatives, for eaxample, quantitative structure-activity relationship (QSAR) models. Here, QSAR models for skin sensitization using LLNA data have been constructed. The descriptors used to generate these models are derived from the 4D-molecular similarity paradigm and are referred to as universal 4D-fingerprints. A training set of 132 structurally diverse compounds and a test set of 15 structurally diverse compounds were used in this study. The statistical methodologies used to build the models are logistic regression (LR) and partial least-square coupled logistic regression (PLS-LR), which prove to be effective tools for studying skin sensitization measures expressed in the two categorical terms of sensitizer and non-sensitizer. QSAR models with low values of the Hosmer-Lemeshow goodness-of-fit statistic, X(2)HL, are significant and predictive. For the training set, the cross-validated prediction accuracy of the logistic regression models ranges from 77.3% to 78.0%, whereas that of the PLS-logistic regression models ranges from 87.1% to 89.4%. For the test set, the prediction accuracy of logistic regression models ranges from 80.0% to 86.7%, whereas that of the PLS-logistic regression models ranges from 73.3% to 80.0%. The QSAR models are made up of 4D-fingerprints related to aromatic atoms, hydrogen bond acceptors, and negatively partially charged atoms.

Animals↗

PubMed2006

Free-energy force-field three-dimensional quantitative structure-activity relationship analysis of a set of p38-mitogen activated protein kinase inhibitors.

The p38-mitogen-activated protein kinases (p38-MAPKs) belong to a family of serine-threonine kinases activated by pro-inflammatory or stressful stimuli that are known to be involved in several diseases. Their biological importance, related to the release of inflammatory pro-cytokines such as tumor necrosis factor-alpha (TNF-alpha) and interleukin-1 (IL-1), has generated many studies aiming at the development of selective inhibitors for the treatment of inflammatory diseases. In this work, we developed receptor-based three dimensional (3D) quantitative structure-activity relationship (QSAR) models for a series of 33 pyridinyl imidazole compounds [Liverton et al. (1999) 42:2180], using a methodology named free-energy force-field (FEFF) [Tokarski and Hopfinger (1997) 37:792], in which scaled intra- and intermolecular energy terms of the Assisted Model Building Energy Refinement (AMBER) force field combined with a hydration-shell solvation model are the independent variables used in the QSAR studies. Multiple temperature molecular-dynamics simulations (MDS) of ligand-protein complexes and genetic-function approximation (GFA) were employed using partial least squares (PLS) as the fitting functions to develop FEFF-3D-QSAR models for the binding process. The best model obtained in the FEFF-3D-QSAR receptor-dependent (RD) method shows the importance of the van der Waals energy change upon binding and the electrostatic energy in the interaction of ligands with the receptor. The QSAR equations described here show good predictability and may be regarded as representatives of the binding process of ligands to p38-MAPK. Additionally, we have compared the top FEFF-3D-QSAR model with receptor independent (RI) 4D-QSAR models developed in a recent study [Romeiro et al. (2005) 19:385].

Hydrophobic and Hydrophilic Interactions↗

PubMed2005

Prediction of plasma protein binding of drugs using Kier-Hall valence connectivity indices and 4D-fingerprint molecular similarity analyses.

A 115 compound dataset for HSA binding is divided into the training set and the test set based on molecular similarity and cluster analyses. Both Kier-Hall valence connectivity indices and 4D-fingerprint similarity measures were applied to this dataset. Four different predictive schemes (SM, SA, SR, SC) were applied to the test set based on the similarity measures of each compound to the compounds in the training set. The first algorithmic scheme (SM) predicts the binding affinity of a test compound using only the most similar training set compound's binding affinity. This scheme has relatively poor predictivity based both on Kier-Hall valence connectivity indices similarity measures and 4D-fingerprints similarity analyses. The other three algorithmic schemes (SM SR, SC), which assign a weighting coefficient to each of the top-ten most similar training set compounds, have reasonable predictivity of a test set. The algorithmic scheme which categorizes the most similar compounds into different weighted clusters predicts the test set best. The 4D-fingerprints provide 36 different individual IPE/IPE type molecular similarity measures. This study supports that some types of similarity measures are highly similar to one another for this dataset. Both the Kier-Hall valence connectivity indices similarity measures and the 4D-fingerprints have nearly same predictivity for this particular dataset.

Algorithms↗

PubMed2005

Constructing plasma protein binding model based on a combination of cluster analysis and 4D-fingerprint molecular similarity analyses.

Based on 2D-connectivity molecular similarity and cluster analyses, a dataset for HSA binding is divided into the training set and the test set. 4D-fingerprint similarity measures were applied to this dataset. Four different predictive schemes (SM, SA, SR, and SC) were applied to the test set based on the similarity measures of each compound to the compounds in the training set. The first algorithmic scheme (SM), which only takes the most similar compound in the training set into consideration, predicts the binding affinity of a test compound. This scheme has relatively poor predictivity based on 4D-fingerprint similarity analyses. The other three algorithmic schemes (SM, SR, and SC), which assign a weighting coefficient to each of the top-ten most similar training set compounds, have reasonable predictivity of a test set. The algorithmic scheme which categorizes the most similar compounds into different weighted clusters predicts the test set best. The 4D-fingerprints provide 36 different individual IPE/IPE type molecular similarity measures. Further investigation shows that the NP/HA, HS/HA, and HA/HA IPE/IPE type measures predict the test set well. Moreover, these three IPE/IPE type similarity measures are very similar to one another for the particular training and test sets investigated. The 4D-fingerprints have relatively high predictivity for this particular dataset.

Algorithms↗

PubMed2005

Predicting permeability coefficient in ADMET evaluation by using different membranes-interaction QSAR.

Membrane-interaction quantitative structure activity relationship (MI-QSAR) analysis was applied to a data set with 18 compounds in 18 different membranes. MI-QSAR was used to estimate the ADMET properties including the transport of organic solutes through biological membranes. The most important descriptors are the aqueous solvation free energy, FH2O, and diffusion coefficient for all membranes. The correlation coefficient, r2, and cross-validation correlation coefficient, q2, for DMPG membrane is 0.850 and 0.770, respectively. The relationship between FH2O and permeability is nonlinear. But the detail effect of aqueous solvation free energy and diffusion coefficient to the permeability depends on the type of membrane. The final models also support the solution-diffusion mechanism of transport is important in membrane.

Caco-2 Cells↗

PubMed2005

Construction of 4D-QSAR models for use in the design of novel p38-MAPK inhibitors.

The p38-mitogen-activated protein kinase (p38-MAPK) plays a key role in lipopolysaccharide-induced tumor necrosis factor-alpha (TNF-alpha) and interleukin-1 (IL-1) release during the inflammatory process, emerging as an attractive target for new anti-inflammatory agents. Four-dimensional quantitative structure-activity relationship (4D-QSAR) analysis [Hopfinger et al., J. Am. Chem. Soc., 119 (1997) 10509] was applied to a series of 33 (a training set of 28 and a test set of 5) pyridinyl-imidazole and pyrimidinyl-imidazole inhibitors of p38-MAPK, with IC50 ranging from 0.11 to 2100 nM [Liverton et al., J. Med. Chem., 42 (1999) 2180]. Five thousand conformations of each analogue were sampled from a molecular dynamics simulation (MDS) during 50 ps at a constant temperature of 303 K. Each conformation was placed in a 2 angstroms grid cell lattice for each of three trial alignments. 4D-QSAR models were constructed by genetic algorithm (GA) optimization and partial least squares (PLS) fitting, and evaluated by leave-one-out cross-validation technique. In the best models, with three to six terms, the adjusted cross-validated squared correlation coefficients, Q2adj, ranged from 0.67 to 0.85. Model D (Q2adj = 0.84) was identified as the most robust model from alignment 1, and it is representative of the other best models. This model encompasses new molecular regions as containing pharmacophore sites, such as the amino-benzyl moiety of pyrimidine analogs and the N1-substituent in the imidazole ring. These regions of the ligands should be further explored to identify better anti-inflammatory inhibitors of p38-MAPK.

Anti-Inflammatory Agents, Non-Steroidal↗

PubMed2004

Rational design of new antituberculosis agents: receptor-independent four-dimensional quantitative structure-activity relationship analysis of a set of isoniazid derivatives.

A 4D-QSAR analysis was carried out for a set of 37 hydrazides whose minimum inhibitory concentrations against M. tuberculosis var. bovis were evaluated. These ligands are thought to act like isoniazid in mycolic acid biosynthesis. Results indicate that nonpolar groups in the acyl moiety of ligands markedly decrease biological activity. Molecular modifications of the ligand NAD moiety, including nonpolar groups and hydrogen bond donor and acceptor groups, seemingly improve ligand interactions with amino acid residues of the InhA active site.

Antitubercular Agents↗

PubMed2004

Characterization of a ligand-receptor binding event using receptor-dependent four-dimensional quantitative structure-activity relationship analysis.

Receptor-dependent four-dimensional quantitative structure-activity relationship (RD-4D-QSAR) analysis is used to map the ligand-receptor binding event characteristic of a set of 47 glucose analogue inhibitors of glycogen phosphorylase (GPb). Specifically, the geometric and energetic binding profiles are constructed, conformational changes are determined, and conformational couplings among structural units are identified for the composite set of ligand-receptor complexes. A pruned ligand-receptor model is used to estimate ligand-receptor thermodynamics. Rather than explicitly handling the large amount of structural data generated from each of the pruned ligand-receptor models, these complexes were divided into three subregions. The subregions consist of a "functional" region, the smallest region providing definitive information about inhibitor binding, and two "allosteric" regions that surround the "functional" region and are based on distances from the center of the catalytic site. Maximum information on inhibitor binding and/or inhibitor-receptor conformational changes is extracted from each of these subregions. The key sites for inhibitor binding and conformational changes in GPb are presented as grid cell occupancy descriptors (GCODs), which can be both numerically and graphically represented. An induced conformational change in both the inhibitor and the binding site of GPb occurs in a distinct manner for each complex. The inter-relationships (correlations) between GCODs from different regions are identified and probed. Such correlations validate the ligand-receptor interactions identified from the "functional" region. A long-range network of conformational associations involving ligands and the receptor is also found by exploring correlations among the GCODs for the set of inhibitors.

Allosteric Site↗

PubMed2004

Methods for applying the quantitative structure-activity relationship paradigm.

There are several Quantitative Structure-Activity Relationship (QSAR) methods to assist in the design of compounds for medicinal use. Owing to the different QSAR methodologies, deciding which QSAR method to use depends on the composition of system of interest and the desired results. The relationship between a compound's binding affinity/activity to its structural properties was first noted in the 1930s by Hammett and later refined by Hansch and Fujita in the mid-1960s. In 1988 Cramer and coworkers created Comparative Molecular Field Analysis (CoMFA) incorporating the three-dimensional (3D) aspects of the compounds, specifically the electrostatic fields of the compound, into the QSAR model. Hopfinger and coworkers included an additional dimension to 3D-QSAR methodology in 1997 that eliminated the question of "Which conformation to use in a QSAR study?", creating 4D-QSAR. In 1999 Chemical Computing Group Inc. (CCG) developed the Binary-QSAR methodology and added novel 3D-QSAR descriptors to the traditional QSAR model allowing the 3D properties of compounds to be incorporated into the traditional QSAR model. Recently CCG released Probabilistic Receptor Potentials to calculate the substrate's atomic preferences in the active site. These potentials are constructed by fitting analytical functions to experimental properties of the substrates using knowledge-based methods. An overview of these and other QSAR methods will be discussed along with an in-depth examination of the methodologies used to construct QSAR models. Also, included in this chapter is a case study of molecules used to create QSAR models utilizing different methodologies and QSAR programs.

Algorithms↗

PubMedDate not supplied

4D-QSAR analysis of a series of antifungal p450 inhibitors and 3D-pharmacophore comparisons as a function of alignment.

A training set of 55 antifungal p450 analogue inhibitors was used to construct receptor-independent four-dimensional quantitative structure-activity relationship (RI 4D-QSAR) models. Ten different alignments were used to build the models, and one alignment yields a significantly better model than the other alignments. Two different methodologies were used to measure the similarity of the best 4D-QSAR models of each alignment. One method compares the residual of fit between pairs of models using the cross-correlation coefficient of their residuals of fit as a similarity measure. The other method compares the spatial distributions of the IPE types (3D-pharmacophores) of pairs of 4D-QSAR models from different alignments. Optimum models from several different alignments have nearly the same correlation coefficients, r(2), and cross-validation correlation coefficients, xv-r(2), yet the 3D-pharmacophores of these models are very different from one another. The highest 3D-pharmacophore similarity correlation coefficient between any pair of 4D-QSAR models from the 10 alignments considered is only 0.216. However, the best 4D-QSAR models of each alignment do contain some proximate common pharmacorphore sites. A test set of 10 compounds was used to validate the predictivity of the best 4D-QSAR models of each alignment. The "best" model from the 10 alignments has the highest predictivity. The inferred active sites mapped out by the 4D-QSAR models suggest that hydrogen bond interactions are not prevalent when this class of P450 analogue inhibitors binds to the receptor active site. This feature of the 4D-QSAR models is in agreement with the crystal structure results that indicate no ligand-receptor hydrogen bonds are formed.

Algorithms↗

PubMedDate not supplied

Constructing optimum blood brain barrier QSAR models using a combination of 4D-molecular similarity measures and cluster analysis.

A new method, using a combination of 4D-molecular similarity measures and cluster analysis to construct optimum QSAR models, is applied to a data set of 150 chemically diverse compounds to build optimum blood-brain barrier (BBB) penetration models. The complete data set is divided into subsets based on 4D-molecular similarity measures using cluster analysis. The compounds in each cluster subset are further divided into a training set and a test set. Predictive QASAR models are constructed for each cluster subset using the corresponding training sets. These QSAR models best predict test set compounds which are assigned to the same cluster subset, based on the 4D-molecular similarity measures, from which the models are derived. The results suggest that the specific properties governing blood-brain barrier permeability may vary across chemically diverse compounds. Partitioning compounds into chemically similar classes is essential to constructing predictive blood-brain barrier penetration models embedding the corresponding key physiochemical properties of a given chemical class.

Blood-Brain Barrier↗

PubMedDate not supplied

Structure-based QSAR analysis of a set of 4-hydroxy-5,6-dihydropyrones as inhibitors of HIV-1 protease: an application of the receptor-dependent (RD) 4D-QSAR formalism.

Receptor-dependent (RD) 4D-QSAR models were constructed for a set of 39 4-hydroxy-5,6-dihydropyrone analogue HIV-1 protease inhibitors. The receptor model used in this QSAR analysis was derived from the HIV-1 protease (PDB ID ) crystal structure. The bound ligand in the active site of the enzyme, also a 4-hydroxy-5,6-dihydropyrone analogue, was used as the reference ligand for docking the data set compounds. The optimized RD 4D-QSAR models are not only statistically significant (r(2) = 0.86, q(2) = 0.80 for four- and greater-term models) but also possess reasonable predictivity based on test set predictions. The proposed "active" conformations of the docked analogues in the active site of the enzyme are consistent in overall molecular shape with those suggested from crystallographic studies. Moreover, the RD 4D-QSAR models also "capture" the existence of specific induced-fit interactions between the enzyme active site and each specific inhibitor. Hydrophobic interactions, steric shape requirements, and hydrogen bonding of the 4-hydroxy-5,6-dihydropyrone analogues with the HIV-1 protease binding site model dominate the RD 4D-QSAR models in a manner again consistent with experimental conclusions. Some possible hypotheses for the development of new lead HIV-1 protease inhibitors can be inferred from the RD 4D-QSAR models.

Computer Simulation↗