It has been recognized that drug-induced QT prolongation is related to blockage of the human ether-a-go-go-related gene (hERG) ion channel. Therefore, it is prudent to evaluate the hERG binding of active compounds in early stages of drug discovery. In silico approaches provide an economic and quick method to screen for potential hERG liability. A diverse set of 90 compounds with hERG IC(50) inhibition data was collected from literature references. Fragment-based QSAR descriptors and three different statistical methods, support vector regression, partial least squares, and random forests, were employed to construct QSAR models for hERG binding affinity. Important fragment descriptors relevant to hERG binding affinity were identified through an efficient feature selection method based on sparse linear support vector regression. The support vector regression predictive model built upon selected fragment descriptors outperforms the other two statistical methods in this study, resulting in an r(2) of 0.912 and 0.848 for the training and testing data sets, respectively. The support vector regression model was applied to predict hERG binding affinities of 20 in-house compounds belonging to three different series. The model predicted the relative binding affinity well for two out of three compound series. The hierarchical clustering and dendrogram results show that the compound series with the best prediction has much higher structural similarity and more neighbors of training compounds than the other two compound series, demonstrating the predictive scope of the model. The combination of a QSAR model and postprocessing analysis, such as clustering and visualization, provides a way to assess the confidence level of QSAR prediction results on the basis of similarity to the training set.

Quantitative Structure-Retention Relationship (QSRR) models are developed for the prediction of protein retention times in anion-exchange chromatography systems. Topological, subdivided surface area, and TAE (Transferable Atom Equivalent) electron-density-based descriptors are computed directly for a set of proteins using molecular connectivity patterns and crystal structure geometries. A novel algorithm based on Support Vector Machine (SVM) regression has been employed to obtain predictive QSRR models using a two-step computational strategy. In the first step, a sparse linear SVM was utilized as a feature selection procedure to remove irrelevant or redundant information. Subsequently, the selected features were used to produce an ensemble of nonlinear SVM regression models that were combined using bootstrap aggregation (bagging) techniques, where various combinations of training and validation data sets were selected from the pool of available data. A visualization scheme (star plots) was used to display the relative importance of each selected descriptor in the final set of "bagged" models. Once these predictive models have been validated, they can be used as an automated prediction tool for virtual high-throughput screening (VHTS).

A series of novel indolizine 2-oxoacetamides were designed and synthesized as PDE4 inhibitors. Preliminary SAR of this new class of compounds revealed key structural features required for high potency. Compounds 1ab and 2a are among the most potent inhibitors of PDE4 with low single nM IC 50 . Cellular activity was demonstrated by the inhibition of TNFa production from human PBMC with IC 50 ranging from 14 to 72 nM. Docking analyses suggest the OH group in 1ab enhance the binding via an H-bond interaction with the PDE4 enzyme.

Sum frequency generation (SFG) vibrational spectroscopy was applied to study molecular interactions between amantadine and substrate supported lipid bilayers serving as model cell membranes. Both isotopically asymmetric and symmetric lipid bilayers were used in the research. SFG results elucidated how the water-soluble drug, amantadine, influenced the packing state of each leaflet of a lipid bilayer and how the drugs affected the lipid flip-flop process. It is difficult to achieve such detailed molecular-level information using other analytical techniques. Especially, from the flip-flop rate change of isotopically asymmetric lipid bilayer induced by amantadine, important information on the drug-membrane interaction mechanism can be derived. The results show that amantadine can be associated with zwitterionic PC bilayers but has a negligible influence on the flip-flop behavior of PC molecules unless at high concentrations. Different effects of amantadine on the lipid bilayer were observed for the negatively charged DPPG bilayer; low concentration amantadine (e.g., 0.20 mM) in the subphase could immediately disturb the outer lipid leaflet and then the lipid associated amantadine molecules gradually reorganize to cause the outer leaflet to return to the original orderly packed state. Higher concentration amantadine (e.g., 5.0 mM) immediately disordered the packing state of the outer lipid leaflet. For both the high and low concentration cases, amantadine molecules only bind to the outer PG leaflet and cannot translocate to the inner layer. The presence of amantadine within the negatively charged lipid layers has certain implications for using liposomes as drug delivery carriers for amantadine. Besides, by using PC or PG bilayers with both leaflets deuterated, we were able to examine how amantadine is distributed and/or oriented within the lipid bilayer. The present work demonstrates that SFG results can provide an in-depth understanding of the molecular mechanisms of interactions between water-soluble drugs and model cell membranes.

Tandem mass spectrometry methods were used to study the sites of protonation and for identification of 3-amino-1,2,4-benzotriazine 1,4-dioxide (1, tirapazamine), and its metabolites (3-amino-1,2,4-benzotriazine 1-oxide (3), 3-amino-1,2,4-benzotriazine 4-oxide (4), 3-amino-1,2,4-benzotriazine (5), and a related isomer 3-amino-1,2,4-benzotriazine 2-oxide (6). Fragmentation pathways of 3 and 5 indicated the 4-N-atom as the most likely site of protonation. Among the N-oxides studied, the 4-oxide (4) showed the highest degree of protonation at the oxygen atom. The differences in collision-induced dissociation of isomeric protonated 1-, 2-and 4-oxides allowed for their identification by LC/MS/MS. Gas phase and liquid phase protonation of tirapazamine occurred exclusively at the oxygen in the 4-position. A loss of OH radical from these ions (2 ϩ ) resulted in ionized 3. Neutralization-reionization mass spectrometry (NR MS) experiments demonstrated the stability of the neutral analogue of protonated tirapazamine in the gas phase in the s time-frame. A significant portion of the neutral tirapazamine radicals (2) dissociated by loss of hydroxyl radical during the NR MS event, which indicates that previously proposed mechanisms for redox-activated DNA damage are reasonable. The activation energy for loss of hydroxyl radical from activated tirapazamine (2) was estimated to be ϳ14 kcal mol Ϫ1 . Stable neutral analogues of [3 ϩ H] ϩ and [5 ϩ H] ϩ ions were also generated in the course of NR MS experiments. Structures of these radicals were assigned to the molecules having an extra hydrogen atom at one of the ring N-atoms. Quantum chemical calculations of protonated 1, 3, 4 and 5 and the corresponding neutrals were performed to assist in the interpretation of experimental results and to help identify their structures. (J Am Soc Mass Spectrom 2003, 14, 881-892)