Receptor-dependent productive uptake of GLP1-conjugated antisense oligonucleotides occurs selectively in pancreatic β-cells.
BackgroundStudies of cell-to-cell variation have in recent years grown in interest, due to improved bioanalytical techniques which facilitates determination of small changes with high uncertainty. Like much high-quality data, single-cell data is best analysed using a systems biology approach. The most common systems biology approach to single-cell data is the standard two-stage (STS) approach. In STS, data from each cell is analysed in a separate sub-problem, meaning that only data from the same cell is used to calculate the parameter values within that cell. Because only parts of the data are considered, problems with parameter unidentifiability are exaggerated in STS. In contrast, a related approach to data analysis has been developed for the studies of patient-to-patient variations. This approach, called nonlinear mixed-effects modelling (NLME), makes use of all data, when estimating the patient-specific parameters. NLME would therefore be advantageous compared to STS also for the study of cell-to-cell variation. However, no such systematic evaluation of the two approaches exists.ResultsHerein, such a systematic comparison between STS and NLME has been performed. Different examples, both linear and nonlinear, and both simulated and real experimental data, have been examined. With informative data, there is no significant difference in the results for either parameter or noise estimation. However, when data becomes uninformative, NLME is significantly superior to STS. These results hold independently of whether the loss of information is due to a low signal-to-noise ratio, too few data points, or a bad input signal. The improvement is shown to come from both the consideration of a joint likelihood (JLH) function, describing all parameters and data, and from an a priori postulated form of the population parameters. Finally, we provide a small tutorial that shows how to use NLME for single-cell analysis, using the free and user-friendly software Monolix.ConclusionsWhen considering uninformative single-cell data, NLME yields more accurate parameter and noise estimates, compared to more traditional approaches, such as STS and JLH.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0203-x) contains supplementary material, which is available to authorized users.
1114-1123, 1986. OVERALL left ventricular systolic performance is inversely related to the force opposing ventricular fiber shortening (i.e., afterload). This fundamental property of the myocardium becomes critically important in interpreting variables of left ventricular shortening in patients suspected of having contractile abnormalities. 1-3 In the clinical setting, the most commonly used measure of ventricular afterload is systemic vascular resistance (SVR). However, SVR is a measure of vasomotor tone that reflects only the nonpulsatile component of peripheral load. 1-2, 4,5 In contrast, left ventricular systolic wall stress reflects the combined effects of peripheral loading conditions and factors internal to the heart.6' 7 Recently, the wall stress at end-systole (Ges) has been shown to be directly related to end- Received April 9, 1986; revision accepted Aug. 7, 1986. systolic dimension and volume.7 8 As such, aes is a major determinant of overall left ventricular performance and can be considered the afterload that limits ventricular fiber shortening at end-ejection.2 7-9To determine the relationship between total left ventricular muscle load (i.e., wall stress) and SVR, an instrumented canine preparation was studied during alterations in left ventricular afterload alone (with nitroprusside and methoxamine) and during combined changes in left ventricular afterload and contractile state (with dobutamine and norepinephrine). MethodsAnimal preparation and instrumentation. Eight closedchest mongrel dogs (18.5 to 35 kg) were premedicated with subcutaneous morphine (5 mg/kg) followed by anesthesia with intravenous a-chloralose (100 mg/kg). After endotracheal intubation, room air ventilation was maintained with a Harvard volume respirator. Arterial blood gases (Coming 165.
The extra hepatic delivery of antisense oligonucleotides (ASOs) remains a challenge and hampers the widespread application of this powerful class of therapeutic agents. In that regard, pancreatic beta cells are a particularly attractive but challenging cell type because of their pivotal role in diabetes and the fact that they are refractory to uptake of unconjugated ASOs. To circumvent this, we have expanded our understanding of the structure activity relationship of ASOs conjugated to Glucagon Like Peptide 1 Receptor (GLP1R) agonist peptide ligands. We demonstrate the key role of the linker chemistry and its optimization to design maleimide based conjugates with improved in vivo efficacy. In addition, truncation studies and scoping of a diverse set of GLP1R agonists proved fruitful to identify additional targeting ligands efficacious in vivo including native hGLP1(7−36)NH 2 . Variation of the carrier peptide also shed some light on the dramatic impact of subtle sequence differences on the corresponding ASO conjugate performance in vivo, an area which clearly warrant further investigations. We have confirmed the remarkable potential of GLP1R agonist conjugation for the delivery of ASOs to pancreatic beta cell by effectively knocking down islet amyloid polypeptide (IAPP) mRNA, a potential proapoptotic target, in mice.
Issues of parameter identifiability of routinely used pharmacodynamics models are considered in this paper. The structural identifiability of 16 commonly applied pharmacodynamic model structures was analyzed analytically, using the input-output approach. Both fixed-effects versions (non-population, no between-subject variability) and mixed-effects versions (population, including between-subject variability) of each model structure were analyzed. All models were found to be structurally globally identifiable under conditions of fixing either one of two particular parameters. Furthermore, an example was constructed to illustrate the importance of sufficient data quality and show that structural identifiability is a prerequisite, but not a guarantee, for successful parameter estimation and practical parameter identifiability. This analysis was performed by generating artificial data of varying quality to a structurally identifiable model with known true parameter values, followed by re-estimation of the parameter values. In addition, to show the benefit of including structural identifiability as part of model development, a case study was performed applying an unidentifiable model to real experimental data. This case study shows how performing such an analysis prior to parameter estimation can improve the parameter estimation process and model performance. Finally, an unidentifiable model was fitted to simulated data using multiple initial parameter values, resulting in highly different estimated uncertainties. This example shows that although the standard errors of the parameter estimates often indicate a structural identifiability issue, reasonably “good” standard errors may sometimes mask unidentifiability issues.
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