Prednisolone inhibits inflammatory genes that are upregulated by LPS in PBMCs.

<p>Human PBMCs were incubated with varying concentrations of prednisolone (10 nM, 100 nM, 10 μM or DMSO control) with LPS concentrations of 0.01 μg/mL, 1 μg/mL or vehicle. PBMCs were incubated with prednisolone for 30 min at 37°C. Cells were then stimulated with LPS for 2 hr at 37°C. Cell lysate was sent to Covance Genetis Lab (Seattle, WA) for analysis. Gene expression was analyzed in cell lysates with the NanoString nCounter system. Representative genes are used in this figure: transrepressed genes IFN-γ (a) and IFIT3 (b), transactivated gene PER1 (d) and MGEA5 as a negative control (c). Y-axis: Background correction (2) positive control correction and (3) housekeeping gene correction. The housekeeping genes were GAPDH, TUBB, GUSB, HPRT1, and PGK1. Positive control is undisclosed by nanostring. Data points represent n = 3. Error bars represent standard deviation.</p

Inhibition of O-GlcNAcase by thiamet -G increases O-GlcNAcylated protein levels in CD3+ T-cells, CD14+ monocytes and HTS-43 cells.

<p>(a). HTS-32 cells (CD14+) were incubated overnight at varying concentrations of thiamet-G. The cells were then stained with a custom Alexa-fluor 647 RL2 antibody to detect O-GlcNAac levels via flow cytometry, with units being representative as median fluorescent intensity. (b,c) PBMCs were incubated with varying concentrations of thiamet-G overnight. The cells were stained for monocytes (CD14+/CD3-) and T-lymphocytes (CD3+/CD14-) and then stained with the RL2 antibody for O-GlcNAc detection. Units were measured as mean fluorescence intensity. Data points represent n = 3. Error bars represent standard deviation.</p

TNF-α secretion is inhibited in LPS-stimulated PBMCs treated with prednisolone.

<p>PBMCs were treated with increasing concentrations of prednisolone 30 min at 37°C. Cells were then treated with two concentrations (a) 0.01 μg/mL (b) 1 μg/mL of LPS for for four hours 37°C. Supernatant was tested for PBMC TNF-α cytokine release using a mesoscale kit. Data points represent n = 1</p

Thiamet-G does not alter potency of prednisolone in transactivated or transrepressed inflammatory genes in hPBMCs.

<p>PBMCs were incubated with a 10 point half- log titration of prednisolone starting at 3 μM and varying concentrations of OGA inhibitor for 30 min at 37°C. Cells were then stimulated with LPS for 2 hr, prior to lysis and shipment to CGL for analysis. Above is representative transrepressed genes (a) PTGS2 (c) TNF-α (e) IFN-γ (f) IFIT3 and transactivated genes (b) FKBP5 (d) TSC22D3 (h) PER1. MGEA5 has no response to prednisolone. Error of magnitude for the IC50 is represented as a 95% confidence interval. Dotted lines are the no-treatment control values. Data points represent n = 1</p

Average potency and efficacy of prednisolone are unchanged in the presence of thiamet -G in hPBMCs.

<p>Upper Panels: IC50 and EC50 values were calculated for transrepressed and transactivated genes for each OGA inhibitor dose at 1 μg/mL LPS. Average IC/EC 50 is indicated by the line in box. Thus, a lower IC50 with increasing thiamet-G dose would indicate potentiated transpression by the OGA inhibitor. Lower Panels: Efficacy was determined by taking the difference between top and bottom signals of a prednisolone dose titration curve, and the efficacy ratio represents the effect with OGA inhibitor vs the effect with OGA vehicle control. Thus, a ratio greater than 1 would indicate potentiated transpression by the OGA inhibitor. These values are plotted for all (a) transrepressed (CXCL2, IFIH1, IFIT2, IFIT3, IL1A, IL6, IL1RN, NFKBIZ, SOCS3, TRAF1, PTGS2, CD274, CD40, TNF-α, TNFSF15, IFN-γ, LTA, TRAF4) and (b) transactivated genes (FKBP5, PER1, TSC22D3, ZBTB16, GRASP, IRS2, LPL). Error bars represent standard deviation. IC50/ EC50 datapoints represent n = 1 for each gene.</p

Thiamet-G does not change the potency of prednisolone in HTS-43 cells expressing a TNF-α promoter.

<p>HTS-43 reporter cells were cultured with increasing concentrations of prednisolone and/or 10 μM thiamet-G for 30 min at 37°C. The cells were then stimulated with both TPA and LPS at concentrations of 5 ng/mL and 0.1 μg/mL respectively, for 20–24 hr. Activity was measured in a beta-lactamase assay as a blue/green ratio. High ratio value indicates increased TNF-α activity. Data points represent n = 2. Error bars represent standard deviation.</p

Thiamet-G does not potentiate the effect of dexamethasone-induced cell apoptosis in steroid resistant cell lines.

<p>Cancer cell lines were exposed to either only dexamethasone, or in addition to 1 μM OGA inhibitor or 50 nM ridaforolimus for 72 hr. Cell lysates were then tested using Cell Titer-Glo for a luminescent signal proportional to ATP presence. Data is represented as a percent viability from cell count from untreated cells. Error bars represent standard deviation. The calculated IC50 and 95% confidence interval are shown. Data points represent n = 3.</p

A human tissue-based functional assay platform to evaluate the immune function impact of small molecule inhibitors that target the immune system

<div><p>While the immune system is essential for the maintenance of the homeostasis, health and survival of humans, aberrant immune responses can lead to chronic inflammatory and autoimmune disorders. Pharmacological modulation of drug targets in the immune system to ameliorate disease also carry a risk of immunosuppression that could lead to adverse outcomes. Therefore, it is important to understand the ‘immune fingerprint’ of novel therapeutics as they relate to current and, clinically used immunological therapies to better understand their potential therapeutic benefit as well as immunosuppressive ability that might lead to adverse events such as infection risks and cancer. Since the mechanistic investigation of pharmacological modulators in a drug discovery setting is largely compound- and mechanism-centric but not comprehensive in terms of immune system impact, we developed a human tissue based functional assay platform to evaluate the impact of pharmacological modulators on a range of innate and adaptive immune functions. Here, we demonstrate that it is possible to generate a qualitative and quantitative immune system impact of pharmacological modulators, which might help better understand and predict the benefit-risk profiles of these compounds in the treatment of immune disorders.</p></div

Impact of SYK/ZAP-70 inhibitor and prednisolone on gene expression profiles in the T cell stimulation assay.

<p>(A) A nanostring gene expression panel was used to evaluate mRNA profiles of PBMCs 3, 6 and 24 h following treatment with three different concentrations of SYK/ZAP-70 inhibitor and prednisolone. The gene expression profiles of unstimulated and compound treated stimulated samples are shown. All data were normalized to housekeeping genes and stimulated DMSO control samples. Hierarchical agglomerative clustering of genes with greater than a 2-fold change (p-value<0.05) is shown. Transcript expression of IL-2 under unstimulated, stimulated, and stimulated plus SYK/ZAP-70 inhibitor or prednisolone treated conditions at the 3h (B), 6h (C) and 24h (D) time points in the T cell stimulation assay. The gene expression profiles in (A) is a composite of PBMCs from n = 3 donors, while the individual gene expression profiles in (B-D) are mean±SEM of mRNA expression from n = 3 PBMC donors.</p

A conceptual framework to develop an ‘immune impact score’ for compounds.

<p>The impact of compounds in immune system-based functional assays, -omics approaches, PK/PD information and chemical informatics (that classify and predict compound-function relationships based on available information) might be integrated with the aim of assigning an ‘immune impact score’ for individual compounds. Further integration of available or modeled efficacy and adverse event data for compounds with the immune impact score might be useful in better understanding and predicting the efficacy, adverse event profile and differentiation of these compounds in a clinical setting. PK/PD–Pharmacokinetics/Pharmacodynamics, AE–adverse events.</p