Discovery of potent, selective chymase inhibitors via fragment linking strategies.

Chymase plays an important and diverse role in the homeostasis of a number of cardiovascular processes. Herein, we describe the identification of potent, selective chymase inhibitors, developed using fragment-based, structure-guided linking and optimization techniques. High-concentration biophysical screening methods followed by high-throughput crystallography identified an oxindole fragment bound to the S1 pocket of the protein exhibiting a novel interaction pattern hitherto not observed in chymase inhibitors. X-ray crystallographic structures were used to guide the elaboration/linking of the fragment, ultimately leading to a potent inhibitor that was >100-fold selective over cathepsin G and that mitigated a number of liabilities associated with poor physicochemical properties of the series it was derived from.

Correlation of Ecom50 values between mass spectrometers: effect of collision cell radiofrequency voltage on calculated survival yield.

RATIONALE: The determination of the center-of-mass energy at which 50% of a precursor ion decomposes (Ecom(50)) during collision-induced dissociation (CID) is dependent on the chemical structure of the ion as well as the physical and electrical characteristics of the collision cell. The current study was designed to identify variables influencing Ecom(50) values measured on four different mass spectrometers. METHODS: Fifteen test compounds were protonated using + ve electrospray ionization and the resulting ions were fragmented across a range of collision energies by CID. Survival yield versus collision energy curves were then used to calculate Ecom(50) values for each of these [M+H](+) ions on four different mass spectrometers. In addition, the relative recovery of the [M+H](+) ions of eight compounds ranging in molecular weight from 46 to 854 Da were determined at collision cell radiofrequency (RF) voltages ranging from 0 to 600 V. RESULTS: Ecom(50) values determined on the four instruments were highly correlated (r(2) values ranged from 0.953 to 0.992). Although these overall correlations were high, we found different maximum ion recoveries depending on collision cell RF voltage. High-mass ions had greater recovery at higher collision cell RF voltages, whereas low-mass ions had greater recovery at lower collision cell RF voltages as well as a broader range of ion recoveries. CONCLUSIONS: Ecom(50) values measured on four different instruments correlated surprisingly well given the differences in electrical and physical characteristics of the collision cells. However, our results suggest caution when comparing Ecom(50) values or CID spectra between instruments without correcting for the effects of RF voltage on ion transfer efficiency.

Time-dependent inhibition and estimation of CYP3A clinical pharmacokinetic drug-drug interactions using plated human cell systems.

The current studies assessed the utility of freshly plated hepatocytes, cryopreserved plated hepatocytes, and cryopreserved plated HepaRG cells for the estimation of inactivation parameters k(inact) and K(I) for CYP3A. This was achieved using a subset of CYP3A time-dependent inhibitors (fluoxetine, verapamil, clarithromycin, troleandomycin, and mibefradil) representing a range of potencies. The estimated k(inact) and K(I) values for each time-dependent inhibitor were compared with those obtained using human liver microsomes and used to estimate the magnitude of clinical pharmacokinetic drug-drug interaction (DDI). The inactivation kinetic parameter, k(inact), was most consistent across systems tested for clarithromycin, verapamil, and troleandomycin, with a high k(inact) of 0.91 min(-1) observed for mibefradil in HepaRG cells. The apparent K(I) estimates derived from the various systems displayed a range of variability from 3-fold for clarithromycin (5.4-17.7 µM) to 6-fold for verapamil (1.9-12.6 µM). In general, the inactivation kinetic parameters derived from the cell systems tested fairly replicated what was observed in time-dependent inhibition studies using human liver microsomes. Despite some of the observed differences in inactivation kinetic parameters, the estimated DDIs derived from each of the tested systems generally agreed with the clinically reported DDI within approximately 2-fold. In addition, a plated cell approach offered the ability to conduct longer primary incubations (greater than 30 min), which afforded improved ability to identify the weak time-dependent inhibitor fluoxetine. Overall, results from these studies suggest that in vitro inactivation parameters generated from plated cell systems may be a practical approach for identifying time-dependent inhibitors and for estimating the magnitude of clinical DDIs.

Benzimidazolone as potent chymase inhibitor: modulation of reactive metabolite formation in the hydrophobic (P1) region.

A new class of chymase inhibitor featuring a benzimidazolone core with an acid side chain and a P(1) hydrophobic moiety is described. Incubation of the lead compound with GSH resulted in the formation of a GSH conjugate on the benzothiophene P(1) moiety. Replacement of the benzothiophene with different heterocyclic systems such as indoles and benzoisothiazole is feasible. Among the P(1) replacements, benzoisothiazole prevents the formation of GSH conjugate and an in silico analysis of oxidative potentials agreed with the experimental outcome.

Prediction of HPLC retention index using artificial neural networks and IGroup E-state indices.

A back-propagation artificial neural network (ANN) was used to create a 10-fold leave-10%-out cross-validated ensemble model of high performance liquid chromatography retention index (HPLC-RI) for a data set of 498 diverse druglike compounds. A 10-fold multiple linear regression (MLR) ensemble model of the same data was developed for comparison. Molecular structure was described using IGroup E-state indices, a novel set of structure-information representation (SIR) descriptors, along with molecular connectivity chi and kappa indices and other SIR descriptors previously reported. The same input descriptors were used to develop models by both learning algorithms. The MLR model yielded marginally acceptable statistics with training correlation r(2) = 0.65, mean absolute error (MAE) = 83 RI units. External validation of 104 compounds not used for model development yielded validation v(2) = 0.49 and MAE = 73 RI units. The distribution of residuals for the fit and validate data sets suggest a nonlinear relationship between retention index and molecular structure as described by the SIR indices. Not surprisingly, the ANN model was significantly more accurate for both training and validation with training set r(2) = 0.93, MAE = 30 RI units and validation v(2) = 0.84, MAE = 41 RI units. For the ANN model, a total of 91% of validation predictions were within 100 RI units of the experimental value.

Database searching for structural identification of metabolites in complex biofluids for mass spectrometry-based metabonomics.

MS and HPLC are commonly used for compound characterization and obtaining structural information; in the field of metabonomics, these two analytical techniques are often combined to characterize unknown endogenous or exogenous metabolites present in complex biological samples. Since the structures of a majority of these metabolites are not actually identified, the result of most metabonomic studies is a list of m/z values and retention times. However, without knowing actual structures, the biological significance of these 'features' cannot be determined. The process of identifying the structures of unknown compounds can be time intensive, costly and frequently requires the use of multiple orthogonal analytical techniques - this laborious procedure seems insurmountable for the long lists of unknowns that must be identified for each study. In addition, the limited sample volume and the extremely low concentration of most endogenous analytes frequently make purification and identification by other instrumentation nearly impossible. This review is intended to explore the problems and progress with current tools that are available for MS-based structure identification for both endogenous and exogenous metabolites.