GLP1R signaling pathways and pharmacology in vitro potency data (Fig. 2 and table S1) were analyzed with a four-parameter logistic fit using the equation y = Bottom + (Top − Bottom)/(1 + 10^((LogEC50x)*HillSlope)), where y is the response, x is the base 10 logarithm of the drug concentration, Bottom is no activation, and Top is full activation using GraphPad Prism version 7.00 for Windows (GraphPad Software, La Jolla, CA, USA; www.graphpad.com). Potency estimates were reported as mean ± SEM derived from independent experiments, as indicated in table S1.

In vitro potency estimates of the effect on gene expression in cell lines (Fig. 3) were estimated with a three-parameter logistic fit using the equation y = Bottom + (Top − Bottom)/(1 + 10^((Log Drug Concentration − LogIC50))), where y is the response, Bottom corresponds to the maximum reduction achieved, Top is the least level achieved, and LogIC50 and Log Drug Concentration were stated in molar concentrations using GraphPad Prism. From each incubation concentration, the average and SD of three biological replicates were calculated and used as input variables in the regression analysis. Data were presented as mean and 95% asymptotic CIs.

In vivo potency estimations for RNA knockdown in islet cells (Fig. 5B and table S3) were based on fitting an Imax model (Hill equation), according to the equation RNA knockdown = Baseline × (1 – (Imax × Dosen)/(ED50n + Dosen), where the baseline is RNA from untreated animals, n is the Hill factor that was set to 1, and ED50 is the dose needed to achieve an effect equal to 50% of its maximum effect. Imax is the estimated maximum reduction in knockdown achievable. The islet data were simultaneously modeled for eGLP1-MALAT1 ASO and MALAT1-ASO (Fig. 5A) or eGLP1-FOXO1 ASO and FOXO1-ASO (Fig. 5B), where ED50 values were estimated as separate parameters for the drugs in the model and Imax was assumed to be the same. Data were modeled using a nonlinear mixed effects approach, as implemented in Phoenix WinNonlin 7.0 and NLME 7.0 (Pharsight Certara, Princeton, NJ). Interindividual variability was allowed for the baseline value, and an exponential error model was applied [Observed (EObs = E * exp(Residual Error), where EObs is the observed response, E is the model predicted, and Residual Error is the residual error estimate]. To evaluate the model and its parameters, the input experimental values were used to generate 1000 randomly generated datasets by nonparametric bootstrap. The model was then fitted to these datasets to derive the parameter estimates. For visual assessment, the experimental data were normalized to vehicle levels and presented as mean ± SEM, using log-normal distribution, and the model fit curve was derived from the bootstrap model fit and normalized to vehicle levels.

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