Biomedical subjects

Peter Wolschann

Publications and source records attributed to Peter Wolschann.

14 recordsLinked to original sources

PubMed2006

Three-dimensional quantitative structure: activity relationship studies on diverse structural classes of HIV-1 integrase inhibitors using CoMFA and CoMSIA.

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques, were applied to a set of 89 HIV-1 integrase (IN) inhibitors (training set=61, test set=28), belonging to 11 structurally different classes. The biological data for 3' processing mechanism were used. For CoMFA calculations, three different fitting methods for alignment process were investigated. The best CoMFA model yielded the cross-validated r(2) r(2)(cv) =0.698 and the non-cross-validated r(2) (r(2))=0.947. The derived model indicated the importance of steric (60.8%) as well as electrostatic (39.2%) contributions. For CoMSIA calculations, different combinations of the fields were tested. The best CoMSIA model gave r(2)(cv) =0.724 and r(2)=0.864. This model showed that steric (30.3%), hydrogen bond donor (43.4%) and hydrogen bond acceptor (26.3%) properties played major roles in HIV-1 IN inhibition. The mapping of hydrogen bond interaction fields with the HIV-1 IN active site gave details on hydrogen bond forming between ligands and enzyme. These obtained results agree well with the experimental observations that there should be hydrogen bond interactions between ligands and Glu152, Lys156 and Lys159 residues. The results not only lead to a better understanding of structural requirements of HIV-1 IN inhibitors but also can help in the design of new IN inhibitors.

HIV Integrase Inhibitors↗

PubMed2005

Validation of fluid bed granulation utilizing artificial neural network.

Three innovative components (an annular gap spray system, a booster bottom and an outlet filter) have been developed by Innojet Technologies to improve fluid bed technology and to reduce the common interference factors (clogging of nozzles and outlet filters, spray loss, spray drying and fluidized bed heterogeneity). In a fluid bed granulator, three conventional components have been replaced with these innovative components. Validation of the modified fluid bed granulator has been conducted using a generalized regression neural network (GRNN). Under different operating conditions (by variation of inlet air temperature, liquid-binder spray rate, atomizing air pressure, air velocity, amount and concentration of binder solution and batch size), sucrose was granulated and the properties of size, size distribution, flow rate, repose angle and bulk and tapped volumes of granules were measured. To confirm the method's validity, the trained network has been used to predict new granulation parameters as well as granule properties. These forecasts were then compared with the corresponding experimental results. Good correlation has been obtained between the predicted and the experimental data. From these findings, we conclude that the GRNN may serve as a reliable method to validate the modified fluid bed apparatus.

Chemistry, Pharmaceutical↗

PubMed2005

Molecular mechanics PBSA ligand binding energy and interaction of Efavirenz derivatives with HIV-1 reverse transcriptase.

In order to evaluate the properties of several HIV-1 reverse transcripase(RT) inhibitors, Efavirenz (SUSTIVA) and a set of its derivatives (benzoxazinones) have been placed into the nonnucleoside analogue binding site of the enzyme by molecular docking. The resulting geometries were used for a molecular dynamics simulation and binding energy calculations. The enzyme-inhibitor binding energies were estimated from experimental inhibitory activities (IC90). The correlation of the predicted and experimental binding energies were satisfactory acceptable as indicated by r2 = 0.865. Based on MD simulations, the obtained results indicate that the tight association of the ligand to the HIV-1 RT binding pocket was based on hydrogen bonding between Efavirenz's N1 and the oxygen of the backbone of Lys 101, with an estimated average distance of 1.88 A. Moreover, electrostatic interaction was mainly contributed by two amino acid residues in the binding site; Lys 101 and His 235. MD simulations open the possibility to study the reaction of the flexible enzyme to those substances as well as the overall affinity.

Adipates↗

PubMed2004

Structure-activity relationships of sandalwood odorants: synthesis and odor of tricyclo beta-santalol.

In a series of structure-odor relationship investigations the synthesis of a new tricyclic beta-santalol derivative is described. The product of a multistep synthesis appears in an olfactive evaluation more or less odorless, may be slightly creamy but definitely with no sandalwood odor. This modification with a bulky aliphatic bridge in the neighborhood of the quaternary C3-atom demonstrated the sensitivity of sandalwood odor on the structure of beta-santalol analogues.

Humans↗

PubMed2003

Molecular dynamics simulations of beta-cyclodextrin in aqueous solution.

Molecular dynamics (MD) simulations of beta-cyclodextrin (beta-CD) have been carried out in aqueous solution at 300 K over a period of about 200 ps using Tripos force field. The atomic trajectories obtained by these simulations have been analysed by means of the occurrence of hydrogen bonds. The MD simulations lead to an association of seven water molecules into the beta-CD cavity which is in a good agreement with X-ray crystallography experiments. This demonstrates that the force field used in the simulations is able to reproduce the experimentally observed hydrophilic-hydrophobic characteristics of beta-CD molecule.

Crystallography, X-Ray↗

PubMed2003

Solubility enhancement of low soluble biologically active compounds--temperature and cosolvent dependent inclusion complexation.

The solubility enhancement of biologically active compounds was analysed in dependence on temperature, amount of cosolvents and on beta-cyclodextrin as complexing agent. The analysis was performed for the systemic fungicide triflumizole (TF), a poorly water soluble compound. All parameters lead to an improvement of the solubility, the largest effect was obtained for host-guest complexation with beta-cyclodextrin. Generally, the combination of cosolvents and beta-cyclodextrin does not increase the solubility of the compound, because cosolvents destabilize the inclusion complex. At higher cosolvent concentrations the solubility of TF is mainly determined by the solubility of the free non-complexed compound in the solvent mixtures. Raising temperature enhances the solubility in any cases. It could be shown that the temperature dependence of the solubility of TF is controlled by the solubility in the solvent mixtures and not by the inclusion reaction.

Cyclodextrins↗

PubMed2003

Hologram quantitative structure-activity relationships investigations of non-nucleoside reverse transcriptase inhibitors.

Non-nucleoside reverse transcriptase inhibitors (NNRTIs) such as TIBO, HEPT and dipyridodiazepinone are effective against HIV-1 RT. These NNRTIs are chemically and structurally diverse, but they all bind to a common allosteric site of HIV-1 RT. These inhibitors exhibit high potency, low cytotoxicity and produce few side effects. However, the emergency of drug-resistance viral strain has limited the therapeutic efficiency of the NNRTIs. Several different QSAR studies were reported to identify important structural features responsible for the inhibitory activity of these NNRTIs. In this study, hologram quantitative structure-activity relationships (HQSAR) was applied to three different data sets, 70 TIBO, 101 HEPT and 125 dipyridodiazepinone derivatives. Starting geometries of compounds were taken from available X-ray crystallographic data. Modification and full geometry optimization of all derivatives were performed, based on quantum chemical calculations at the HF/3-21G level of theory. All derived HQSAR models produce satisfying predictive ability and yield r(2)(cv) values ranging from 0.62-0.84. Moreover, it was also found that the quality of models enhances as the size of fragments increases. The obtained HQSAR results indicate the similarity of the interactions of these three different NNRTIs with the inhibition pocket of the enzyme. Comparisons of different QSAR methods on these NNRTIs data sets were also considered and it could be shown that HQSAR results yield superior predictive models than other 2D-QSAR approaches. In particular, the predictive ability of the models derived from dipyridodiazepinone analogues was significantly improved and apparently revealed differentiating structural requirements between WT and Y181C HIV RT inhibition. Additionally, the quality of QSAR models constructed by CoMFA and HQSAR methods are comparable and the interpretations of the models reinforce each other. It suggests an advantage of HQSAR as a useful tool in designing new potent inhibitors with enhanced HIV-1 RT inhibition activity, especially against mutant enzyme.

Anti-HIV Agents↗

PubMed2002

Aroma quality differentiation of pyrazine derivatives using self-organizing molecular field analysis and artificial neural network.

The encoding of various aroma impressions and the distinction between different aroma qualities are unsolved problems, as differences between aroma impressions can be described only in a qualitative but not in a quantitative manner. As a consequence, classifications of various aroma qualities cannot easily be performed by standard QSAR methods. To find a proper way to encode aroma impressions for SAR studies, a total of 50 pyrazine-based aroma compounds showing the aroma quality of earthy, green-earthy, or green are analyzed. Special attention is thereby turned on the mixed aroma impression green-earthy. Classifications on the whole data set as well as on smaller subsets are calculated using self-organizing molecular field analysis (SOMFA) and artificial neural networks (ANNs). SOMFA classifies between two or three aroma impressions, leading to models satisfying in predictive power. ANN analysis using multilayer perceptron network architecture with one hidden layer and nominal output as well as genetic regression neural network) with two hidden layers and numerical output both lead to a rather good performance rate of 94%.

Neural Networks, Computer↗

PubMed2002

Internally defined distances in 3D-quantitative structure-activity relationships.

A new type of 3D-QSAR descriptors is introduced. For each molecule under consideration an internal coordinate system is defined relative to molecular points, such as positions of atoms in the molecule or centers of mass or certain substructures. From the origin of this system distances to the solvent accessible surface are calculated at defined spherical coordinate angles, theta and phi. The distances represent steric features, while the molecular electrostatic potentials at the intersection points with the surface represent the electrostatic contributions. The approach is called IDA (internal distances analysis). Matrices obtained by varying the spherical coordinate angles by fixed increments are correlated with the biological activity by partial least squares (PLS). The descriptors, tested with the benchmark steroids and an also well characterized benzodiazepine data set, turn out to be highly predictive. Additionally, they share the advantage of grid-based methods that the obtained models can be visualized, and thus be directly used in a rational drug design approach.

Benzodiazepines↗

PubMedDate not supplied

Bayesian neural networks for aroma classification.

Bayesian Neural Networks (BNNs) are investigated to test their potential to distinguish between different aroma impressions. Special attention is thereby drawn on mixed aroma impressions, resulting from the flavor description of a single compound with more than one aroma quality. The structures of 133 pyrazine-derived aroma compounds as well as their aroma descriptions are selected for comparison. The information fed into the neural networks is based on molecular descriptors calculated from the geometrically optimized chemical structures. While in the case of the Probabilistic Neural Network (PNN) the networks' output consists of a categorical variable, the output for the General Regression Neural Network (GRNN) is defined in a numerical way. The best models attain comparable performance with a correct prediction of 90.8% of the cases for PNN and 89.9% for GRNN, respectively. Comparison of the BNN results to those obtained by Multiple Linear Regression (MLR) points out that the nonlinear methods work significantly better on the studied problem and that BNNs can be applied to multiple-category problems in structure-flavor relationships with good accuracy.

Bayes Theorem↗

PubMedDate not supplied

QSAR modeling of alpha-campholenic derivatives with sandalwood odor.

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for a series of 44 synthetic alpha-campholenic derivatives with sandalwood odor. These compounds have complex stereochemistry as they contain up to five chiral atoms. To address stereospecificity of odor intensity, a 3D-QSAR method was developed, which does not require spatial alignment of molecules. In this method, compounds are represented as derivatives of several common structural templates with several substituents, which are numbered according to their relative spatial positions in the molecule. Both wholistic and substituent descriptors calculated with the TSAR software were used as independent variables. Based on published experimental data of sandalwood odor intensities, two discrete scales of the odor intensity with equal or unequal intervals between the threshold values were developed. The data set was divided into a training set of 38 compounds and a test set of six compounds. To build QSAR models, a stepwise multiple linear regression method was used. The best model was obtained using the unequal scale of odor intensity: for the training set, the leave one out cross-validated R(2) (q(2)) was 0.80, the correlation coefficient R between actual and predicted odor intensities was 0.93, and the correlation coefficient for the test set was 0.95. The QSAR models developed in this study contribute to the better understanding of structural, electronic, and lipophilic properties responsible for sandalwood odor. Furthermore, the QSAR approach reported herein can be applied to other data sets that include compounds with complex stereochemistry.

Journal Article↗

PubMedDate not supplied

Structural flexibility of non-nucleoside HIV-1 reverse transcriptase inhibitor: 9-Cl TIBO as explained by potential energy surface and (13)C and (1)H NMR calculations, based on ab initio and density functional study.

The conformational analysis of the HIV-1 reverse transcriptase inhibitor, (+)-(s)-4,5,6,7-tetrahydro-9-chloro-5-methyl-6-(3-methyl-2-butenyl)imidazol[4,5,1-jk][1,4]benzodiazepin-2(1H)-thione or 9-Cl TIBO, has been investigated using high level of calculations, ab initio, and DFT theory. The potential energy surface as the function of two important rotatable dihedral angles of the 9-Cl TIBO side chain was generated by the Hartree-Fock method at the 3-21G basis set. Eight pronounced local minima were found to exist within an energy difference of less than 10 kJ/mol. The energy barriers between the different local minima are lower than 15 kJ/mol. A second derivative (frequency) analysis showed that all conformers are stable at this level of theory. These structures were used as starting points for full geometry optimizations at the HF/6-31G and B3LYP/6-31G levels of theory to obtain the absolute geometries and structural information. The comparisons of calculated conformers with the bound conformer in the X-ray structure were sequentially considered. Additionally, to obtain some structural information and to correlate between calculated structures and the structure in solution, NMR chemical shift calculations were also performed on all eight local minimum structures at B3LYP/6-311++G level, using the GIAO approach. The calculated (1)H NMR and (13)C NMR chemical shifts for the lowest energetic conformer give the greatest correspondence with the experimental results.

Benzodiazepines↗

PubMedDate not supplied

Combinatorial QSAR of ambergris fragrance compounds.

A combinatorial quantitative structure-activity relationships (Combi-QSAR) approach has been developed and applied to a data set of 98 ambergris fragrance compounds with complex stereochemistry. The Combi-QSAR approach explores all possible combinations of different independent descriptor collections and various individual correlation methods to obtain statistically significant models with high internal (for the training set) and external (for the test set) accuracy. Seven different descriptor collections were generated with commercially available MOE, CoMFA, CoMMA, Dragon, VolSurf, and MolconnZ programs; we also included chirality topological descriptors recently developed in our laboratory (Golbraikh, A.; Bonchev, D.; Tropsha, A. J. Chem. Inf. Comput. Sci. 2001, 41, 147-158). CoMMA descriptors were used in combination with MOE descriptors. MolconnZ descriptors were used in combination with chirality descriptors. Each descriptor collection was combined individually with four correlation methods, including k-nearest neighbors (kNN) classification, Support Vector Machines (SVM), decision trees, and binary QSAR, giving rise to 28 different types of QSAR models. Multiple diverse and representative training and test sets were generated by the divisions of the original data set in two. Each model with high values of leave-one-out cross-validated correct classification rate for the training set was subjected to extensive internal and external validation to avoid overfitting and achieve reliable predictive power. Two validation techniques were employed, i.e., the randomization of the target property (in this case, odor intensity) also known as the Y-randomization test and the assessment of external prediction accuracy using test sets. We demonstrate that not every combination of the data modeling technique and the descriptor collection yields a validated and predictive QSAR model. kNN classification in combination with CoMFA descriptors was found to be the best QSAR approach overall since predictive models with correct classification rates for both training and test sets of 0.7 and higher were obtained for all divisions of the ambergris data set into the training and test sets. Many predictive QSAR models were also found using a combination of kNN classification method with other collections of descriptors. The combinatorial QSAR affords automation, computational efficiency, and higher probability of identifying significant QSAR models for experimental data sets than the traditional approaches that rely on a single QSAR method.

Algorithms↗

PubMedDate not supplied

Molecular dynamic and free energy studies of primary resistance mutations in HIV-1 protease-ritonavir complexes.

To understand the basis of drug resistance of the HIV-1 protease, molecular dynamic (MD) and free energy calculations of the wild-type and three primary resistance mutants, V82F, I84V, and V82F/I84V, of HIV-1 protease complexed with ritonavir were carried out. Analysis of the MD trajectories revealed overall structures of the protein and the hydrogen bonding of the catalytic residues to ritonavir were similar in all four complexes. Substantial differences were also found near the catalytic binding domain, of which the double mutant complex has the greatest impact on conformational changes of the protein and the inhibitor. The tip of the HIV-1 protease flap of the double mutant has the greater degree of opening with respect to that of the others. Additionally, the phenyl ring of Phe82 moves away from the binding pocket S1', and the conformational change of ritonavir subsite P1' consequently affects the cavity size of the protein and the conformational energy of the inhibitor. Calculations of binding free energy using the solvent continuum model were able to reproduce the same trend of the experimental inhibition constant. The results show that the resistance mutants require hydrophobic residues to maintain the interactions in the binding pocket. Changes of the cavity volume correlate well with free energy penalties due to the mutation and are responsible for the loss of drug susceptibility.

Catalytic Domain↗