Biomedical subjects

Ulf Norinder

Publications and source records attributed to Ulf Norinder.

6 recordsLinked to original sources

PubMed2005

Exploring the role of different drug transport routes in permeability screening.

The influence of different drug transport routes in intestinal drug permeability screening assays was studied. Three experimental models were compared: the small-intestine-like 2/4/A1 cell model, which has a leaky paracellular pathway, the Caco-2 cell model, which has a tighter paracellular pathway, and artificial hexadecane membranes (HDMs), which exclusively model the passive transcellular pathway. The models were investigated regarding their ability to divide passively and actively transported compounds into two permeability classes and to rank compounds according to human intestinal absorption. In silico permeability models based on two-dimensional (2D) and three-dimensional (3D) molecular descriptors were also developed and validated using external test sets. The cell-based models classified 80% of the acceptably absorbed compounds (FA >/= 30%) correctly, compared to 60% correct classifications using the HDM model. The best compound ranking was obtained with 2/4/A1 (r(s) = 0.74; r(s) = 0.95 after removing actively transported outliers). The in silico model based on 2/4/A1 permeability gave results of similar quality to those obtained when using experimental permeability, and it was also better than the experimental HDM model at compound ranking (r(s) = 0.85 and 0.47, respectively). We conclude that the paracellular transport pathway present in the cell models plays a significant role in models used for intestinal permeability screening and that 2/4/A1 in vitro and in silico models are promising alternatives for drug discovery permeability screening.

Administration, Oral↗

PubMed2003

Improving structure-based virtual screening by multivariate analysis of scoring data.

Three different multivariate statistical methods, PLS discriminant analysis, rule-based methods, and Bayesian classification, have been applied to multidimensional scoring data from four different target proteins: estrogen receptor alpha (ERalpha), matrix metalloprotease 3 (MMP3), factor Xa (fXa), and acetylcholine esterase (AChE). The purpose was to build classifiers able to discriminate between active and inactive compounds, given a structure-based virtual screen. Seven different scoring functions were used to generate the scoring matrices. The classifiers were compared to classical consensus scoring and single scoring functions. The classifiers show a superior performance, with rule-based methods being most effective. The precision of correctly predicting an active compound is about 90% for three of the targets and about 25% for acetylcholine esterase. On the basis of these results, a new two-stage approach is suggested for structure-based virtual screening where limited activity information is available.

Acetylcholinesterase↗

PubMed2002

Computational approaches to the prediction of the blood-brain distribution.

This review attempts to summarise present knowledge related to the theoretical modelling of drug transport across the blood-brain barrier. Several computational protocols are described ranging from quantum mechanics-based approaches through molecular mechanics-related techniques to simple and fast procedures based on only the 2-D graph of the investigated structures. Amazingly, few descriptors have been shown to influence the derived relationships in a significant manner and a cornerstone in most of the described models are terms describing hydrogen bonding. A very quick quantitative assessment of the brain partitioning of a compound has also been devised using the following two rules: If N+O (the number of nitrogen and oxygen atoms) in a molecule is less than or equal to five, it has a high chance of entering the brain. The second rule predicts that if log P-(N+O) is positive then log BB is positive.

Algorithms↗

PubMed2002

Experimental and computational screening models for prediction of aqueous drug solubility.

PURPOSE: To devise experimental and computational models to predict aqueous drug solubility. METHODS: A simple and reliable modification of the shake flask method to a small-scale format was devised, and the intrinsic solubilities of 17 structurally diverse drugs were determined. The experimental solubility data were used to investigate the accuracy of commonly used theoretical and semiexperimental models for prediction of aqueous drug solubility. Computational models for prediction of intrinsic solubility, based on lipophilicity and molecular surface areas, were developed. RESULTS: The intrinsic solubilities ranged from 0.7 ng/mL to 6.0 mg/ mL, covering a range of almost seven log10 units, and the values determined with the new small-scale shake flask method agreed well with published solubility data. Solubility data computed with established theoretical models agreed poorly with the experimentally determined solubilities, but the correlations improved when experimentally determined melting points were included in the models. A new, fast computational model based on lipophilicity and partitioned molecular surface areas, which predicted intrinsic drug solubility with a good accuracy (R2 of 0.91 and RMSEtr of 0.61) was devised. CONCLUSIONS: A small-scale shake flask method for determination of intrinsic drug solubility was developed, and a promising alternative computational model for the theoretical prediction of aqueous drug solubility was proposed.

Chemical Phenomena↗

PubMedDate not supplied

Molecular descriptors influencing melting point and their role in classification of solid drugs.

The aim of the study was to investigate whether easily and rapidly calculated 2D and 3D molecular descriptors could predict the melting point of drug-like compounds, to allow a melting point classification of solid drugs. The melting points for 277 structurally diverse model drugs were extracted from the 12th edition of the Merck Index. 2D descriptors mainly representing electrotopology and electron accessibilities were calculated by Molconn-Z and the AstraZeneca in-house program Selma. 3D descriptors for molecular surface areas were generated using the programs MacroModel and Marea. Correlations between the calculated descriptors and the melting point values were established with partial least squares projection to latent structures (PLS) using training and test sets. Three different descriptor matrixes were studied, and the models obtained were used for consensus modeling. The calculated properties were shown to explain 63% of the melting point. Descriptors for hydrophilicity, polarity, partial atom charge, and molecular rigidity were found to be positively correlated with melting point, whereas nonpolar atoms and high flexibility within the molecule were negatively correlated to this solid-state characteristic. Moreover, the studied descriptors were successful in providing a qualitative ranking of compounds into classes displaying a low, intermediate, or high melting point. Finally, a mechanism for the relation between the molecular descriptors and their effect on the melting point and the aqueous solubility was proposed.

Chemistry, Physical↗

PubMedDate not supplied

Global and local computational models for aqueous solubility prediction of drug-like molecules.

The aim of this study was to develop in silico protocols for the prediction of aqueous drug solubility. For this purpose, high quality solubility data of 85 drug-like compounds covering the total drug-like space as identified with the ChemGPS methodology were used. Two-dimensional molecular descriptors describing electron distribution, lipophilicity, flexibility, and size were calculated by Molconn-Z and Selma. Global minimum energy conformers were obtained by Monte Carlo simulations in MacroModel and three-dimensional descriptors of molecular surface area properties were calculated by Marea. PLS models were obtained by use of training and test sets. Both a global drug solubility model (R(2) = 0.80, RMSE(te) = 0.83) and subset specific models (after dividing the 85 compounds into acids, bases, ampholytes, and nonproteolytes) were generated. Furthermore, the final models were successful in predicting the solubility values of external test sets taken from the literature. The results showed that homologous series and subsets can be predicted with high accuracy from easily comprehensible models, whereas consensus modeling might be needed to predict the aqueous drug solubility of datasets with large structural diversity.

Computer Simulation↗