ABSTRACT:Thirty-one structurally diverse marketed central nervous system (CNS)-active drugs, one active metabolite, and seven non-CNSactive compounds were tested in three P-glycoprotein (P-gp) in vitro assays: transwell assays using MDCK, human MDR1-MDCK, and mouse Mdr1a-MDCK cells, ATPase, and calcein AM inhibition. Additionally, the permeability for these compounds was measured in two in vitro models: parallel artificial membrane permeation assay and apical-to-basolateral apparent permeability in MDCK. The exposure of the same set of compounds in brain and plasma was measured in P-gp knockout (KO) and wild-type (WT) mice after subcutaneous administration. One drug and its metabolite, risperidone and 9-hydroxyrisperidone, of the 32 CNS compounds, and 6 of the 7 non-CNS drugs were determined to have positive efflux using ratio of ratios in MDR1-MDCK versus MDCK transwell assays. Data from transwell studies correlated well with the brainto-plasma area under the curve ratios between P-gp KO and WT mice for the 32 CNS compounds. In addition, 3300 Pfizer compounds were tested in MDR1-MDCK and Mdr1a-MDCK transwell assays, with a good correlation (R 2 ‫؍‬ 0.92) between the efflux ratios in human MDR1-MDCK and mouse Mdr1a-MDCK cells. Permeability data showed that the majority of the 32 CNS compounds have moderate to high passive permeability. This work has demonstrated that in vitro transporter assays help in understanding the role of P-gp-mediated efflux activity in determining the disposition of CNS drugs in vivo, and the transwell assay is a valuable in vitro assay to evaluate human P-gp interaction with compounds for assessing brain penetration of new chemical entities to treat CNS disorders.Human P-glycoprotein (P-gp, MDR1) is known to be a determinant of drug absorption, distribution, and excretion of a number of clinically important drugs (Ambudkar et al., 1999;Fromm, 2000). P-gp is widely expressed in major organs, and, more specifically, P-gp is highly expressed in the capillaries of the blood brain barrier (BBB) and poses a barrier to brain penetration of its substrates (Schinkel, 1999). Given that P-gp efflux liability can be a major hurdle for CNS therapeutic drugs to cross the BBB and reach the target, the interactions of CNS compounds with P-gp may lead to the lack of CNS activity as a result of the decreased brain penetration. Thus, the prediction and understanding of the relevance of P-gp-mediated efflux transport have become important activities in the discovery and development of CNS drugs. In attempts to predict the effects of P-gp in vivo, a variety of in vitro P-gp assays have been developed to classify compounds as P-gp substrates. For instance, transwell-based assays using polarized cell lines such as the Madin-Darby canine kidney (MDCK) cell line. The MDCK cell line can be stably transfected with human MDR1 or mouse Mdr1a (MDR1-MDCK or Mdr1a-MDCK, respectively). Comparison of the efflux ratios between MDR1-MDCK and MDCK transwell assays can provide a measure of the specific human P-gp-mediated e...

The usefulness of MALDI for small-molecule work has been limited by matrix chemical interference in the mass range of interest, tedious sample preparation, and various crystallization and sample deposition issues. We report instrument characterization and small-molecule quantification performance data from a high repetition rate laser MALDI ion source coupled to a triple quadrupole mass spectrometer. The high repetition rate laser improves sensitivity and precision and allows a proportional increase in sample throughput. Tandem mass spectrometry is used to discriminate the signal from the high chemical background caused by the MALDI matrix. Successful quantification requires use of an internal standard and a means of sample cleanup for typical in vitro sample compositions. This instrument combination and analysis technique is relatively insensitive to sample crystal quality and spot homogeneity. Quantitative performance results are characterized for 53 small-molecule pharmaceutical compounds and compared to those obtained by ESI-MS/MS. Further comparison between MALDI and ESI is examined, and the potential for high-throughput MALDI-MS/MS quantification is demonstrated.

Understanding the mechanisms and energetics of ion solvation is critical in many scientific areas. Here, we present a methodlogy for studying ion solvation using differential mobility spectrometry (DMS) coupled to mass spectrometry. While in the DMS cell, ions experience electric fields established by a high frequency asymmetric waveform in the presence of a desired pressure of water vapor. By observing how a specific ion's behavior changes between the high- and low-field parts of the waveform, we gain knowledge about the aqueous microsolvation of that ion. In this study, we applied DMS to investigate the aqueous microsolvation of protonated quinoline-based drug candidates. Owing to their low binding energies with water, the clustering propensity of 8-substituted quinolinium ions was less than that of the 6- or 7-substituted analogues. We attribute these differences to the steric hinderance presented by subtituents in the 8-position. In addition, these experimental DMS results were complemented by extensive computational studies that determined cluster structures and relative thermodynamic stabilities.

HPLC/MS is a linear technique characterized by serial injection and analysis of individual samples. Parallel-format high-throughput screens for druglike properties present a significant analytical challenge. Analysis speed and system ruggedness are key requirements for bioanalysis of thousands of samples per day. The tasks involved in LC/MS analysis are readily divided into three areas, sample preparation/liquid handling, LC/MS method building/sample analysis, and data processing. Several automation and multitasking strategies were developed and implemented to minimize plating and liquid handling errors, reduce dead times within the analysis cycle, and allow for comprehensive review of data. Delivering multiple samples to multiple injectors allows the autosampler time to complete its wash cycles and aspirate the next set of samples while the previous set is being analyzed. A dual-column chromatography system provides column cycling and peak stacking and allows rapid throughput using conventional LC equipment. Collecting all data for a compound into a single file greatly reduces the number of data files collected, increases the speed of data collection, allows rugged and complete review of all data, and provides facile data management. The described systems have analyzed over 40 000 samples per month for two years and have the capacity for over 2000 samples per instrument per day.

The microsolvated state of a molecule, represented by its interactions with only a small number of solvent molecules, can play a key role in determining the observable bulk properties of the molecule. This is especially true in cases where strong local hydrogen bonding exists between the molecule and the solvent. One method that can probe the microsolvated states of charged molecules is differential mobility spectrometry (DMS), which rapidly interrogates an ion’s transitions between a solvated and desolvated state in the gas phase (i.e., few solvent molecules present). However, can the results of DMS analyses of a class of molecules reveal information about the bulk physicochemical properties of those species? Our findings presented here show that DMS behaviors correlate strongly with the measured solution phase pKa and pKb values, and cell permeabilities of a set of structurally related drug molecules, even yielding high-resolution discrimination between isomeric forms of these drugs. This is due to DMS’s ability to separate species based upon only subtle (yet predictable) changes in structure: the same subtle changes that can influence isomers’ different bulk properties. Using 2-methylquinolin-8-ol as the core structure, we demonstrate how DMS shows promise for rapidly and sensitively probing the physicochemical properties of molecules, with particular attention paid to drug candidates at the early stage of drug development. This study serves as a foundation upon which future drug molecules of different structural classes could be examined.

The fast and accurate determination of molecular properties is highly desirable for many facets of chemical research, particularly in drug discovery where pre-clinical assays play an important role in paring down large sets of drug candidates. Here, we present the use of supervised machine learning to treat differential mobility spectrometry – mass spectrometry data for ten topological classes of drug candidates. We demonstrate that the gas-phase clustering behavior probed in our experiments can be used to predict the candidates’ condensed phase molecular properties, such as cell permeability, solubility, polar surface area, and water/octanol distribution coefficient. All of these measurements are performed in minutes and require mere nanograms of each drug examined. Moreover, by tuning gas temperature within the differential mobility spectrometer, one can fine tune the extent of ion-solvent clustering to separate subtly different molecular geometries and to discriminate molecules of very similar physicochemical properties.

Evaluation and optimization of drug metabolism and pharmacokinetic data plays an important role in drug discovery and development and several reliable in vitro ADME models are available. Recently higher throughput in vitro ADME screening facilities have been established in order to be able to evaluate an appreciable fraction of synthesized compounds. The ADME screening process can be dissected in five distinct steps: (1) plate management of compounds in need of in vitro ADME data, (2) optimization of the MS/MS method for the compounds, (3) in vitro ADME experiments and sample clean up, (4) collection and reduction of the raw LC-MS/MS data and (5) archival of the processed ADME data. All steps will be described in detail and the value of the data on drug discovery projects will be discussed as well. Finally, in vitro ADME screening can generate large quantities of data obtained under identical conditions to allow building of reliable in silico models.

Drug-drug interactions involving cytochrome P(450) (CYP) are an important factor in whether a new chemical entity will survive through to the development stage. Therefore, the identification of this potential as early as possible in vitro could save considerable future unnecessary investment. In vitro CYP interaction screening data generated for CYP2C9, CYP2D6, and CYP3A4 were initially analyzed to determine the correlation of IC(50) from 10- and 3-point determinations. A high correlation (r = 0.99) prompted the further assessment of predicting the IC(50) by a single value of percent inhibition at either 10, 3, or 1 microM. Statistical analysis of the initial proprietary compounds showed that there was a strong linear relationship between log IC(50) and percent inhibition at 3 microM, and that it was possible to predict a compound's IC(50) by the percent inhibition value obtained at 3 microM. Additional data for CYP1A2, CYP2C19, and the recombinant CYP2D6 were later obtained and used together with the initial data to demonstrate that a single statistical model could be applicable across different CYPs and different in vitro microsomal systems. Ultimately, the data for all five CYPs and the recombinant CYP2D6 were used to build a statistical model for predicting the IC(50) with a single point. The 95% prediction boundary for the region of interest was about +/- 0.37 on log(10) scale, comparable to the variability of in vitro determinations for positive control IC(50) data. The use of a single inhibitor concentration would enable determination of more IC(50) values on a 96-well plate and result in more economical use of compounds, human liver or expressed enzyme microsomes, substrates, and reagents. This approach would offer the opportunity to increase screening for CYP-mediated drug-drug interactions, which may be important given the challenges provided by the generation of orders of magnitude more new chemical entities in the field of combinatorial chemistry. In addition, the algorithmic approach we propose would obviously be applicable for other in vitro bioactivity and therapeutic target enzyme and receptor screens.