Biomedical subjects

Ola Engkvist

Publications and source records attributed to Ola Engkvist.

4 recordsLinked to original sources

PubMed2004

Characterization of a conserved structural determinant controlling protein kinase sensitivity to selective inhibitors.

Some protein kinases are known to acquire resistance to selective small molecule inhibitors upon mutation of a conserved threonine at the ATP binding site to a larger residue. Here, we performed a comprehensive mutational analysis of this structural element and determined the cellular sensitivities of several disease-relevant tyrosine kinases against various inhibitors. Mutant kinases possessing a larger side chain at the critical site showed resistance to most compounds tested, such as ZD1839, PP1, AG1296, STI571, and a pyrido[2,3-d]pyrimidine inhibitor. In contrast, indolinones affected both wild-type and mutant kinases with similar potencies. Resistant mutants were established for pharmacological analysis of betaPDGF receptor-mediated signaling and allowed the generation of a drug-inducible system of cellular Src kinase activity. Our data establish a conserved structural determinant of protein kinase sensitivity relevant for both signal transduction research and drug development.

Amino Acid Sequence↗

PubMedDate not supplied

Prediction of CNS activity of compound libraries using substructure analysis.

An in silico ADME/Tox prediction tool based on substructural analysis has been developed. The tool called SUBSTRUCT has been used to predict CNS activity. Data sets with CNS active and nonactive drugs were extracted from the World Drug Index (WDI). The SUBSTRUCT program predicts CNS activity as good as a much more complicated artificial neural network model. SUBSTRUCT separates the data sets with approximately 80% accuracy. Substructural analysis also shows surprisingly large differences in substructure profiles between CNS active and nonactive drugs.

Blood-Brain Barrier↗

PubMedDate not supplied

High-throughput, in silico prediction of aqueous solubility based on one- and two-dimensional descriptors.

An aqueous solubility model has been developed. The model is based solely on one- and two-dimensional descriptors and an artificial neural network to ensure fast execution. 63 descriptors expressing physicochemical and topological properties were used. The final model consisted of a training set of 3042 molecules, a test set of 309 molecules and an independent validation set of 307 molecules. The squared correlation coefficients were 0.91 for the training set, 0.89 for the test set and 0.86 for the independent validation set.

Journal Article↗

PubMedDate not supplied

Multifingerprint based similarity searches for targeted class compound selection.

Molecular fingerprints are widely used for similarity-based virtual screening in drug discovery projects. In this paper we discuss the performance and the complementarity of nine two-dimensional fingerprints (Daylight, Unity, AlFi, Hologram, CATS, TRUST, Molprint 2D, ChemGPS, and ALOGP) in retrieving active molecules by similarity searching against a set of query compounds. For this purpose, we used biological data from HTS screening campaigns of four protein families (GPCRs, kinases, ion channels, and proteases). We have established threshold values for the similarity index (Tanimoto index) to be used as starting points for similarity searches. Based on the complementarities between the selections made by using different fingerprints we propose a multifingerprint approach as an efficient tool to balance the strengths and weaknesses of various fingerprints.

Journal Article↗