Biomedical subjects

Michal Vieth

Publications and source records attributed to Michal Vieth.

9 recordsLinked to original sources

PubMed2004

Aminoimidazo[1,2-a]pyridines as a new structural class of cyclin-dependent kinase inhibitors. Part 1: Design, synthesis, and biological evaluation.

We have identified a novel structural class of protein serine/threonine kinase inhibitors comprised of an aminoimidazo[1,2-a]pyridine nucleus. Compounds from this family are shown to potently inhibit cyclin-dependent kinases by competing with ATP for binding to a catalytic subunit of the protein. Structure-based design approach was used to direct this chemical scaffold toward generating potent and selective CDK2 inhibitors. The discovery of this new class of ATP-site directed protein kinase inhibitors, aminoimidazo[1,2-a]pyridines, provides the basis of new medicinal chemistry tool in search for an effective treatment of cancer and other diseases that involve protein kinase signaling pathways.

Cyclin-Dependent Kinases↗

PubMed2004

SDOCKER: a method utilizing existing X-ray structures to improve docking accuracy.

This paper introduces a new strategy for structure-based drug design that combines high-quality docking with data from existing ligand-protein cocrystal X-ray structures. The main goal of SDOCKER, a new algorithm that implements this strategy, is docking accuracy improvement. In this new paradigm, simulated annealing molecular dynamics is used for conformational sampling and optimization and an additional similarity force is applied on the basis of the positions of ligands from X-ray data that focus the sampling on relevant regions of the active site. Because the structural information from both the ligand and protein active site is included, this approach is more effective in finding the optimal conformation for a ligand-protein complex than the classical docking or similarity overlays. Interestingly, it was found that a 3D similarity-only approach gives comparable docking accuracy to the regular force field approach used in classical docking, given the final structures are minimized in the presence of the protein. The combination of both, as implemented in SDOCKER, is shown here to be more accurate. A significant improvement in docking accuracy has been observed for three different test systems. Specifically an improvement of 10%, 17.5%, and 10% is seen for 37 HIV-1 protease, 32 thrombin, and 23 CDK2 ligands, respectively, compared to docking using the force field alone. In addition, SDOCKER's accuracy performance dependence on the similarity template is discussed. The strategy of utilizing existing ligand X-ray information should prove effective in light of the multitude of structures available from structural genomics approaches.

Algorithms↗

PubMed2004

Kinomics-structural biology and chemogenomics of kinase inhibitors and targets.

Classifying kinases based entirely on small molecule selectivity data is a new approach to drug discovery that allows scientists to understand relationships between targets. This approach combines the understanding of small molecules and targets, and thereby assists the researcher in finding new targets for existing molecules or understanding selectivity and polypharmacology of molecules in related targets. Currently, structural information is available for relatively few of the protein kinases encoded in the human genome (7% of the estimated 518); however, even the current knowledge base, when paired with structure-based design techniques, can assist in the identification and optimization of novel kinase inhibitors across the entire protein class. Chemogenomics attempts to combine genomic data, structural biological data, classical dendrograms, and selectivity data to explore, define, and classify the medicinally relevant kinase space. Exploitation of this information in the discovery of kinase inhibitors defines practical kinase chemogenomics (kinomics). In this paper, we review the available information on kinase targets and their inhibitors, and present the relationships between the various classification schema for kinase space. In particular, we present the first dendrogram of kinases based entirely on small molecule selectivity data. We find that the selectivity dendrogram differs from sequence-based clustering mostly in the higher-level groupings of the smaller clusters, and remains very comparable for closely homologous targets. Highly homologous kinases are, on average, inhibited comparably by small molecules. This observation, although intuitive, is very important to the process of target selection, as one would expect difficulty in achieving inhibitor selectivity for kinases that share high sequence identity.

Adenosine Triphosphate↗

PubMed2004

Lessons in molecular recognition: the effects of ligand and protein flexibility on molecular docking accuracy.

The key to success for computational tools used in structure-based drug design is the ability to accurately place or "dock" a ligand in the binding pocket of the target of interest. In this report we examine the effect of several factors on docking accuracy, including ligand and protein flexibility. To examine ligand flexibility in an unbiased fashion, a test set of 41 ligand-protein cocomplex X-ray structures were assembled that represent a diversity of size, flexibility, and polarity with respect to the ligands. Four docking algorithms, DOCK, FlexX, GOLD, and CDOCKER, were applied to the test set, and the results were examined in terms of the ability to reproduce X-ray ligand positions within 2.0A heavy atom root-mean-square deviation. Overall, each method performed well (>50% accuracy) but for all methods it was found that docking accuracy decreased substantially for ligands with eight or more rotatable bonds. Only CDOCKER was able to accurately dock most of those ligands with eight or more rotatable bonds (71% accuracy rate). A second test set of structures was gathered to examine how protein flexibility influences docking accuracy. CDOCKER was applied to X-ray structures of trypsin, thrombin, and HIV-1-protease, using protein structures bound to several ligands and also the unbound (apo) form. Docking experiments of each ligand to one "average" structure and to the apo form were carried out, and the results were compared to docking each ligand back to its originating structure. The results show that docking accuracy falls off dramatically if one uses an average or apo structure. In fact, it is shown that the drop in docking accuracy mirrors the degree to which the protein moves upon ligand binding.

Algorithms↗

PubMed2004

Characteristic physical properties and structural fragments of marketed oral drugs.

An increasingly competitive pharmaceutical market demands improvement in the efficiency and probability of drug candidate discovery. Usually these new drug candidates are targeted for oral administration, so a detailed understanding of the molecular-level properties that relate to optimal pharmacokinetics is a critical step toward improving the probability of selecting successful clinical candidates. Although the characteristics of druglike molecules have been previously discussed in the literature, the importance of this topic sustains a continued interest for additional perspective and further detailed statistical analyses. In this contribution, we approach the analysis from the perspective of profiling distinguishing features of orally administered drugs. We have compiled both structural and route-administration information for a total of 1729 marketed drugs to provide a solid basis for developing a new perspective on the characteristics of over 1000 orally administered drugs. The molecular properties and most commonly occurring structural elements are statistically analyzed to capture the differences between routes of administration, as well as between marketed drugs and SAR or clinical compounds. We find that, with respect to other routes of administration, oral drugs tend to be lighter and have fewer H-bond donors, acceptors, and rotatable bonds than drugs with other routes of administration. These differences are particularly pronounced when comparing the mean values for oral vs injectable drugs. We also demonstrate that the mean property values for oral drugs do not vary substantially with respect to launch date, suggesting that the range of acceptable oral properties is independent of synthetic complexity or targeted receptor. Finally, we note that, while these properties are descriptive of each class, they are not necessarily predictive of what class any particular drug will reside in, since there is significant overlap in the acceptable ranges found for each drug class.

Administration, Oral↗

PubMed2004

The discovery of a new structural class of cyclin-dependent kinase inhibitors, aminoimidazo[1,2-a]pyridines.

The protein kinase family represents an enormous opportunity for drug development. However, the current limitation in structural diversity of kinase inhibitors has complicated efforts to identify effective treatments of diseases that involve protein kinase signaling pathways. We have identified a new structural class of protein serine/threonine kinase inhibitors comprising an aminoimidazo[1,2-a]pyridine nucleus. In this report, we describe the first successful use of this class of aza-heterocycles to generate potent inhibitors of cyclin-dependent kinases that compete with ATP for binding to a catalytic subunit of the protein. Co-crystal structures of CDK2 in complex with lead compounds reveal a unique mode of binding. Using this knowledge, a structure-based design approach directed this chemical scaffold toward generating potent and selective CDK2 inhibitors, which selectively inhibited the CDK2-dependent phosphorylation of Rb and induced caspase-3-dependent apoptosis in HCT 116 tumor cells. The discovery of this new class of ATP-site-directed protein kinase inhibitors, aminoimidazo[1,2-a]pyridines, provides the basis for a new medicinal chemistry tool to be used in the search for effective treatments of cancer and other diseases that involve protein kinase signaling pathways.

Adenosine Triphosphate↗

PubMed2004

Molecular properties that influence oral drug-like behavior.

Pharmaceutical companies are constantly racing to discover the next therapeutic blockbuster. The consensus in the industry is to focus on compounds that are by some measure drug-like, but in order to do this effectively a number of questions must be answered. For example, how should drug-like be defined and how might this definition be used to enhance drug discovery? Has the field moved beyond Lipinski's seminal 'rule-of-five' observations? This review offers a working definition of oral drug-likeness, describes various approaches used in its characterization and discusses its appropriate use. We will focus primarily on the use of computed molecular properties that attempt to predict the oral drug-like behavior of compounds and propose guidelines for the use of observations and trends established in existing datasets to support drug discovery efforts. In particular, this review will demonstrate how trends in simple properties of the data can be used prospectively to compare and prioritize groups of compounds, chemical libraries and different chemical series with greater reliability than for predicting drug-likeness of single compounds. It is the authors' belief, however, that properties or descriptors that will completely separate drug-like space from non-drug-like space are unlikely to be found; the focus should instead lie on overall distributions of drug-like and non-drug-like compounds in the property space that tend to overlap significantly.

Administration, Oral↗

PubMed2003

Synthesis and activity of new aryl- and heteroaryl-substituted pyrazole inhibitors of the transforming growth factor-beta type I receptor kinase domain.

Pyrazole-based inhibitors of the transforming growth factor-beta type I receptor kinase domain (TbetaR-I) are described. Examination of the SAR in both enzyme- and cell-based in vitro assays resulted in the emergence of two subseries featuring differing selectivity versus p38 MAP kinase. A common binding mode at the active site has been established by successful cocrystallization and X-ray analysis of potent inhibitors with the TbetaR-I receptor kinase domain.

3T3 Cells↗

PubMed2003

Detailed analysis of grid-based molecular docking: A case study of CDOCKER-A CHARMm-based MD docking algorithm.

The influence of various factors on the accuracy of protein-ligand docking is examined. The factors investigated include the role of a grid representation of protein-ligand interactions, the initial ligand conformation and orientation, the sampling rate of the energy hyper-surface, and the final minimization. A representative docking method is used to study these factors, namely, CDOCKER, a molecular dynamics (MD) simulated-annealing-based algorithm. A major emphasis in these studies is to compare the relative performance and accuracy of various grid-based approximations to explicit all-atom force field calculations. In these docking studies, the protein is kept rigid while the ligands are treated as fully flexible and a final minimization step is used to refine the docked poses. A docking success rate of 74% is observed when an explicit all-atom representation of the protein (full force field) is used, while a lower accuracy of 66-76% is observed for grid-based methods. All docking experiments considered a 41-member protein-ligand validation set. A significant improvement in accuracy (76 vs. 66%) for the grid-based docking is achieved if the explicit all-atom force field is used in a final minimization step to refine the docking poses. Statistical analysis shows that even lower-accuracy grid-based energy representations can be effectively used when followed with full force field minimization. The results of these grid-based protocols are statistically indistinguishable from the detailed atomic dockings and provide up to a sixfold reduction in computation time. For the test case examined here, improving the docking accuracy did not necessarily enhance the ability to estimate binding affinities using the docked structures.

Algorithms↗