Development of a high-throughput dual-stream liquid chromatography-tandem mass spectrometry method to screen for inhibitors of glutamate carboxypeptidase II.

RATIONALE: Glutamate carboxypeptidase II (GCPII) catalyzes the hydrolysis of N-acetylaspartylglutamate (NAAG) to yield glutamate (Glu) and N-acetylaspartate (NAA). Inhibition of GCPII has been shown to remediate the neurotoxicity of excess Glu in a variety of cell and animal disease models. A robust high-throughput liquid chromatography-tandem mass spectrometry (LC/MS/MS) method was needed to quantify GCPII enzymatic activity in a biochemical high-throughput screening assay. METHODS: A dual-stream LC/MS/MS method was developed. Two parallel eluent streams ran identical HILIC gradient methods on BEH-Amide (2 × 30 mm) columns. Each LC channel was run independently, and the cycle time was 2 min per channel. Overall throughput was 1 min per sample for the dual-channel integrated system. Multiply injected acquisition files were split during data review, and batch metadata were automatically paired with raw data during the review process. RESULTS: Two LC sorbents, BEH-Amide and Penta-HILIC, were tested to separate the NAAG cleavage product Glu from isobaric interference and ion suppressants in the bioassay matrix. Early elution of NAAG and NAA on BEH-Amide allowed interfering species to be diverted to waste. The limit of quantification was 0.1 pmol for Glu. The Z-factor of this assay averaged 0.85. Over 36 000 compounds were screened using this method. CONCLUSIONS: A fast gradient dual-stream LC/MS/MS method for Glu quantification in GCPII biochemical screening assay samples was developed and validated. HILIC separation chemistry offers robust performance and unique selectivity for targeted positive mode quantification of Glu, NAA, and NAAG.

Target Class Profiling of Small-Molecule Methyltransferases.

Target class profiling (TCP) is a chemical biology approach to investigate understudied biological target classes. TCP is achieved by developing a generalizable assay platform and screening curated compound libraries to interrogate the chemical biological space of members of an enzyme family. In this work, we took a TCP approach to investigate inhibitory activity across a set of small-molecule methyltransferases (SMMTases), a subclass of methyltransferase enzymes, with the goal of creating a launchpad to explore this largely understudied target class. Using the representative enzymes nicotinamide N-methyltransferase (NNMT), phenylethanolamine N-methyltransferase (PNMT), histamine N-methyltransferase (HNMT), glycine N-methyltransferase (GNMT), catechol O-methyltransferase (COMT), and guanidinoacetate N-methyltransferase (GAMT), we optimized high-throughput screening (HTS)-amenable assays to screen 27,574 unique small molecules against all targets. From this data set, we identified a novel inhibitor which selectively inhibits the SMMTase HNMT and demonstrated how this platform approach can be leveraged for a targeted drug discovery campaign using the example of HNMT.

Determining molecular properties with differential mobility spectrometry and machine learning.

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.

Correction to "Assessing Physicochemical Properties of Drug Molecules via Microsolvation Measurements with Differential Mobility Spectrometry".

[This corrects the article DOI: 10.1021/acscentsci.6b00297.].

Discovery of Compounds that Positively Modulate the High Affinity Choline Transporter.

Cholinergic hypofunction is associated with decreased attention and cognitive deficits in the central nervous system in addition to compromised motor function. Consequently, stimulation of cholinergic neurotransmission is a rational therapeutic approach for the potential treatment of a variety of neurological conditions. High affinity choline uptake (HACU) into acetylcholine (ACh)-synthesizing neurons is critically mediated by the sodium- and pH-dependent high-affinity choline transporter (CHT, encoded by the SLC5A7 gene). This transporter is comparatively well-characterized but otherwise unexplored as a potential drug target. We therefore sought to identify small molecules that would enable testing of the hypothesis that positive modulation of CHT mediated transport would enhance activity-dependent cholinergic signaling. We utilized existing and novel screening techniques for their ability to reveal both positive and negative modulation of CHT using literature tools. A screening campaign was initiated with a bespoke compound library comprising both the Pfizer Chemogenomic Library (CGL) of 2,753 molecules designed specifically to help enable the elucidation of new mechanisms in phenotypic screens and 887 compounds from a virtual screening campaign to select molecules with field-based similarities to reported negative and positive allosteric modulators. We identified a number of previously unknown active and structurally distinct molecules that could be used as tools to further explore CHT biology or as a starting point for further medicinal chemistry.

Assessing Physicochemical Properties of Drug Molecules via Microsolvation Measurements with Differential Mobility Spectrometry.

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.

Using differential mobility spectrometry to measure ion solvation: an examination of the roles of solvents and ionic structures in separating quinoline-based drugs.

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.

High throughput ADME screening: practical considerations, impact on the portfolio and enabler of in silico ADME models.

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.

Perspectives on bioanalytical mass spectrometry and automation in drug discovery.

The use of high speed synthesis technologies has resulted in a steady increase in the number of new chemical entities active in the drug discovery research stream. Large organizations can have thousands of chemical entities in various stages of testing and evaluation across numerous projects on a weekly basis. Qualitative and quantitative measurements made using LC/MS are integrated throughout this process from early stage lead generation through candidate nomination. Nearly all analytical processes and procedures in modern research organizations are automated to some degree. This includes both hardware and software automation. In this review we discuss bioanalytical mass spectrometry and automation as components of the analytical chemistry infrastructure in pharma. Analytical chemists are presented as members of distinct groups with similar skillsets that build automated systems, manage test compounds, assays and reagents, and deliver data to project teams. The ADME-screening process in drug discovery is used as a model to highlight the relationships between analytical tasks in drug discovery. Emerging software and process automation tools are described that can potentially address gaps and link analytical chemistry related tasks. The role of analytical chemists and groups in modern 'industrialized' drug discovery is also discussed.

In vitro P-glycoprotein assays to predict the in vivo interactions of P-glycoprotein with drugs in the central nervous system.

Thirty-one structurally diverse marketed central nervous system (CNS)-active drugs, one active metabolite, and seven non-CNS-active 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 brain-to-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.