Calibrating from Within: Multipoint Internal Calibration of a Quantitative Mass Spectrometric Assay of Serum Methotrexate.

BACKGROUND:Clinical LC-MS/MS assays traditionally require that samples be run in batches with calibration curves in each batch. This approach is inefficient and presents a barrier to random access analysis. We developed an alternative approach called multipoint internal calibration (MPIC) that eliminated the need for batch-mode analysis. METHODS:The new approach used 4 variants of 13C-labeled methotrexate (0.026-10.3 µM) as an internal calibration curve within each sample. One site carried out a comprehensive validation, which included an evaluation of interferences and matrix effects, lower limit of quantification (LLOQ), and 20-day precision. Three sites evaluated assay precision and linearity. MPIC was also compared with traditional LC-MS/MS and an immunoassay. RESULTS:Recovery of spiked analyte was 93%-102%. The LLOQ was validated to be 0.017 µM. Total variability, determined in a 20-day experiment, was 11.5%CV. In a 5-day variability study performed at each site, total imprecision was 3.4 to 16.8%CV. Linearity was validated throughout the calibrator range (r2 > 0.995, slopes = 0.996-1.01). In comparing 40 samples run in each laboratory, the median interlaboratory imprecision was 6.55%CV. MPIC quantification was comparable to both traditional LC-MS/MS and immunoassay (r2 = 0.96-0.98, slopes = 1.04-1.06). Bland-Altman analysis of all comparisons showed biases rarely exceeding 20% when MTX concentrations were >0.4 µM. CONCLUSION:The MPIC method for serum methotrexate quantification was validated in a multisite proof-of-concept study and represents a big step toward random-access LC-MS/MS analysis, which could change the paradigm of mass spectrometry in the clinical laboratory

GSTO1-1 plays a pro-inflammatory role in models of inflammation, colitis and obesity

Glutathione transferase Omega 1 (GSTO1-1) is an atypical GST reported to play a pro-inflammatory role in response to LPS. Here we show that genetic knockout of Gsto1 alters the response of mice to three distinct inflammatory disease models. GSTO1-1 deficiency ameliorates the inflammatory response stimulated by LPS and attenuates the inflammatory impact of a high fat diet on glucose tolerance and insulin resistance. In contrast, GSTO1-1 deficient mice show a more severe inflammatory response and increased escape of bacteria from the colon into the lymphatic system in a dextran sodium sulfate mediated model of inflammatory bowel disease. These responses are similar to those of TLR4 and MyD88 deficient mice in these models and confirm that GSTO1-1 is critical for a TLR4-like pro-inflammatory response in vivo. In wild-type mice, we show that a small molecule inhibitor that covalently binds in the active site of GSTO1-1 can be used to ameliorate the inflammatory response to LPS. Our findings demonstrate the potential therapeutic utility of GSTO1-1 inhibitors in the modulation of inflammation and suggest their possible application in the treatment of a range of inflammatory conditions.This work was supported by a grant from the Gretel and Gordon Bootes Medical Research Foundation to D.M. and P.B. The National Health and Medical Research Council of Australia (NHMRC) is thanked for Project Grant APP1124673 to PB, MC, LO, AO, and Fellowship support for J.B. (2012–2016 Senior Research Fellowship #1020411). J.B. acknowledges the Australian Federal Government Education Investment Fund Super Science Initiative and the Victorian State Government, Victoria Science Agenda Investment Fund for infrastructure support, and Translating Health Discovery (THD) NCRIS soft infrastructure support through Terapeutic Innovation Australia (TIA)

The promise and peril of chemical probes

Chemical probes are powerful reagents with increasing impacts on biomedical research. However, probes of poor quality or that are used incorrectly generate misleading results. To help address these shortcomings, we will create a community-driven wiki resource to improve quality and convey current best practice

Reviewing Hit Discovery Literature for Difficult Targets: Glutathione Transferase Omega-1 as an Example

Early stage drug discovery reporting on relatively new or difficult targets is often associated with insufficient hit triage. Literature reviews of such targets seldom delve into the detail required to critically analyze the associated screening hits reported. Here we take the enzyme glutathione transferase omega-1 (GSTO1-1) as an example of a relatively difficult target and review the associated literature involving small-molecule inhibitors. As part of this process we deliberately pay closer-than-usual attention to assay interference and hit quality aspects. We believe this Perspective will be a useful guide for future development of GSTO1-1 inhibitors, as well serving as a template for future review formats of new or difficult targets.This work was supported by Project Grant APP1124673 from the National Health and Medical Research Council of Australia (NHMRC) to Philip G. Board, Marco G. Casarotto, and Aaron J. Oakley. The NHMRC is thanked for Fellowship support for Jonathan B. Baell (2012-2016 Senior Research Fellowship No. 1020411, 2017- Principal Research Fellowship No. 1117602)

Reviewing Hit Discovery Literature for Difficult Targets: Glutathione Transferase Omega-1 as an Example

Early stage drug discovery reporting on relatively new or difficult targets is often associated with insufficient hit triage. Literature reviews of such targets seldom delve into the detail required to critically analyze the associated screening hits reported. Here we take the enzyme glutathione transferase omega-1 (GSTO1-1) as an example of a relatively difficult target and review the associated literature involving small-molecule inhibitors. As part of this process we deliberately pay closer-than-usual attention to assay interference and hit quality aspects. We believe this Perspective will be a useful guide for future development of GSTO1-1 inhibitors, as well serving as a template for future review formats of new or difficult targets

Modeling Small-Molecule Reactivity Identifies Promiscuous Bioactive Compounds

Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that appear active in many high-throughput screening experiments against diverse targets but are often false-positives which may not be easily developed into successful probes. These compounds can exhibit bioactivity due to nonspecific, intractable mechanisms of action and/or by interference with specific assay technology readouts. Such “frequent hitters” are now commonly identified using substructure filters, including pan assay interference compounds (PAINS). Herein, we show that mechanistic modeling of small-molecule reactivity using deep learning can improve upon PAINS filters when modeling promiscuous bioactivity in PubChem assays. Without training on high-throughput screening data, a deep learning model of small-molecule reactivity achieves a sensitivity and specificity of 18.5% and 95.5%, respectively, in identifying promiscuous bioactive compounds. This performance is similar to PAINS filters, which achieve a sensitivity of 20.3% at the same specificity. Importantly, such reactivity modeling is complementary to PAINS filters. When PAINS filters and reactivity models are combined, the resulting model outperforms either method alone, achieving a sensitivity of 24% at the same specificity. However, as a probabilistic model, the sensitivity and specificity of the deep learning model can be tuned by adjusting the threshold. Moreover, for a subset of PAINS filters, this reactivity model can help discriminate between promiscuous and nonpromiscuous bioactive compounds even among compounds matching those filters. Critically, the reactivity model provides mechanistic hypotheses for assay interference by predicting the precise atoms involved in compound reactivity. Overall, our analysis suggests that deep learning approaches to modeling promiscuous compound bioactivity may provide a complementary approach to current methods for identifying promiscuous compounds

The Essential Medicinal Chemistry of Curcumin

Curcumin is a constituent (up to ∼5%) of the traditional medicine known as turmeric. Interest in the therapeutic use of turmeric and the relative ease of isolation of curcuminoids has led to their extensive investigation. Curcumin has recently been classified as both a PAINS (pan-assay interference compounds) and an IMPS (invalid metabolic panaceas) candidate. The likely false activity of curcumin in vitro and in vivo has resulted in >120 clinical trials of curcuminoids against several diseases. No double-blinded, placebo controlled clinical trial of curcumin has been successful. This manuscript reviews the essential medicinal chemistry of curcumin and provides evidence that curcumin is an unstable, reactive, nonbioavailable compound and, therefore, a highly improbable lead. On the basis of this in-depth evaluation, potential new directions for research on curcuminoids are discussed

Development of Benzenesulfonamide Derivatives as Potent Glutathione Transferase Omega-1 Inhibitors

Glutathione transferase omega-1 (GSTO1-1) is an enzyme whose function supports the activation of interleukin (IL)-1β and IL-18 that are implicated in a variety of inflammatory disease states for which small-molecule inhibitors are sought. The potent reactivity of the active-site cysteine has resulted in reported inhibitors that act by covalent labeling. In this study, structure-activity relationship (SAR) elaboration of the reported GSTO1-1 inhibitor C1-27 was undertaken. Compounds were evaluated for inhibitory activity toward purified recombinant GSTO1-1 and for indicators of target engagement in cell-based assays. As covalent inhibitors, the kinact/KI values of selected compounds were determined, as well as in vivo pharmacokinetics analysis. Cocrystal structures of key novel compounds in complex with GSTO1-1 were also solved. This study represents the first application of a biochemical assay for GSTO1-1 to determine kinact/KI values for tested inhibitors and the most extensive set of cell-based data for a GSTO1-1 inhibitor SAR series reported to date. Our research culminated in the discovery of 25, which we propose as the preferred biochemical tool to interrogate cellular responses to GSTO1-1 inhibition