Characterization of cell-to-cell variation in nuclear transport rates and identification of its sources.

Nuclear transport is an essential part of eukaryotic cell function. Here, we present scFRAP, a model-assisted fluorescent recovery after photobleaching (FRAP)- based method to determine nuclear import and export rates independently in individual live cells. To overcome the inherent noise of single-cell measurements, we performed sequential FRAPs on the same cell. We found large cell-to-cell variation in transport rates within isogenic yeast populations. For passive transport, the variability in NPC number might explain most of the variability. Using this approach, we studied mother-daughter cell asymmetry in the active nuclear shuttling of the transcription factor Ace2, which is specifically concentrated in daughter cell nuclei in early G1. Rather than reduced export in the daughter cell, as previously hypothesized, we found that this asymmetry is mainly due to an increased import in daughters. These results shed light on cell-to-cell variation in cellular dynamics and its sources.

Evaluation in pig of an intestinal administration device for oral peptide delivery.

The bioavailability of peptides co-delivered with permeation enhancers following oral administration remains low and highly variable. Two factors that may contribute to this are the dilution of the permeation enhancer in the intestinal fluid, as well as spreading of the released permeation enhancer and peptide in the lumen by intestinal motility. In this work we evaluated an Intestinal Administration Device (IAD) designed to reduce the luminal dilution of drug and permeation enhancer, and to minimize movement of the dosage form in the intestinal lumen. To achieve this, the IAD utilizes an expanding design that holds immediate release mini tablets and places these in contact with the intestinal epithelium, where unidirectional drug release can occur. The expanding conformation limits movement of the IAD in the intestinal tract, thereby enabling drug release at a single focal point in the intestine. A pig model was selected to study the ability of the IAD to promote intestinal absorption of the peptide MEDI7219 formulated together with the permeation enhancer sodium caprate. We compared the IAD to intestinally administered enteric coated capsules and an intestinally administered solution. The IAD restricted movement of the immediate release tablets in the small intestine and histological evaluation of the mucosa indicated that high concentrations of sodium caprate were achieved. Despite significant effect of the permeation enhancer on the integrity of the intestinal epithelium, the bioavailability of MEDI7219 was of the same order of magnitude as that achieved with the solution and enteric coated capsule formulations (2.5-3.8%). The variability in plasma concentrations of MEDI7219 were however lower when delivered using the IAD as compared to the solution and enteric coated capsule formulations. This suggests that dosage forms that can limit intestinal dilution and control the position of drug release can be a way to reduce the absorptive variability of peptides delivered with permeation enhancers but do not offer significant benefits in terms of increasing bioavailability.

Antisense Therapy Attenuates Phospholamban p.(Arg14del) Cardiomyopathy in Mice and Reverses Protein Aggregation.

Inherited cardiomyopathy caused by the p.(Arg14del) pathogenic variant of the phospholamban (PLN) gene is characterized by intracardiomyocyte PLN aggregation and can lead to severe dilated cardiomyopathy. We recently reported that pre-emptive depletion of PLN attenuated heart failure (HF) in several cardiomyopathy models. Here, we investigated if administration of a Pln-targeting antisense oligonucleotide (ASO) could halt or reverse disease progression in mice with advanced PLN-R14del cardiomyopathy. To this aim, homozygous PLN-R14del (PLN-R14 Δ/Δ) mice received PLN-ASO injections starting at 5 or 6 weeks of age, in the presence of moderate or severe HF, respectively. Mice were monitored for another 4 months with echocardiographic analyses at several timepoints, after which cardiac tissues were examined for pathological remodeling. We found that vehicle-treated PLN-R14 Δ/Δ mice continued to develop severe HF, and reached a humane endpoint at 8.1 ± 0.5 weeks of age. Both early and late PLN-ASO administration halted further cardiac remodeling and dysfunction shortly after treatment start, resulting in a life span extension to at least 22 weeks of age. Earlier treatment initiation halted disease development sooner, resulting in better heart function and less remodeling at the study endpoint. PLN-ASO treatment almost completely eliminated PLN aggregates, and normalized levels of autophagic proteins. In conclusion, these findings indicate that PLN-ASO therapy may have beneficial outcomes in PLN-R14del cardiomyopathy when administered after disease onset. Although existing tissue damage was not reversed, further cardiomyopathy progression was stopped, and PLN aggregates were resolved.

Distribution and biotransformation of therapeutic antisense oligonucleotides and conjugates.

Drug properties of antisense oligonucleotides (ASOs) differ significantly from those of traditional small-molecule therapeutics. In this review, we focus on ASO disposition, mainly as characterized by distribution and biotransformation, of nonconjugated and conjugated ASOs. We introduce ASO chemistry to allow the following in-depth discussion on bioanalytical methods and determination of distribution and elimination kinetics at low concentrations over extended periods of time. The resulting quantitative data on the parent oligonucleotide, and the identification and quantification of formed metabolites define the disposition. Proper quantitative understanding of disposition is pivotal for nonclinical to clinical predictions, supports communication with health agencies, and increases the probability of delivering optimal ASO therapy to patients.

Glucagon Like Peptide 1 Receptor Agonists for Targeted Delivery of Antisense Oligonucleotides to Pancreatic Beta Cell.

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)NH2. 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.

In Vitro and In Vivo Evaluation of 3D Printed Capsules with Pressure Triggered Release Mechanism for Oral Peptide Delivery.

In this study a 3D printed capsule designed to break from the physiological pressures in the antropyloric region was evaluated for its ability to deliver the synthetic octapeptide octreotide in beagle dogs when co-formulated with the permeation enhancer sodium caprate. The pressure sensitive capsules were compared to traditional enteric coated hard gelatin capsules and enteric coated tablets. Paracetamol, which is completely absorbed in dogs, was included in the formulations and used as an absorption marker to give information about the in vivo performance of the dosage forms. The pressure sensitive capsules released drug in 50% of the dogs. In the cases where drug was released, there was no difference in octreotide bioavailability or Cmax compared to the enteric coated dosage forms. When comparing all dosage forms, a correlation was seen between paracetamol Cmax and octreotide bioavailability, suggesting that a high drug release rate may be beneficial for peptide absorption when delivered together with sodium caprate.

Model-Based Assessment of Variability in Isoniazid Pharmacokinetics and Metabolism in Patients Co-Infected With Tuberculosis and HIV: Implications for a Novel Dosing Strategy.

Tuberculosis is the most common cause of death in HIV-infected patients. Isoniazid is used as a first-line drug to treat tuberculosis infection. However, variability in isoniazid pharmacokinetics can result in hepatotoxicity or treatment failure. Determination of clinical factors affecting isoniazid pharmacokinetics and metabolic pathways in HIV co-infected patients is therefore critical. Plasma levels of isoniazid, acetyl-isoniazid, and isonicotinic acid from 63 patients co-infected with tuberculosis and HIV were analyzed by liquid chromatography with tandem mass spectrometry followed by nonlinear mixed-effects modeling. Patients were genotyped to determine acetylator status. Patients were either on concomitant efavirenz-based antiretroviral therapy or HIV treatment naïve. Clearances of isoniazid were 1.3-fold and 2.3-fold higher in intermediate and rapid acetylators, respectively, compared with slow acetylators. Patients on concomitant efavirenz-based antiretroviral therapy had 64% and 80% higher population predicted clearances of acetyl-isoniazid and isonicotinic acid, respectively, compared with patients who were HIV treatment naïve. Both sex and CD4 cell count affected the bioavailability of isoniazid. Variability in isoniazid exposure could be reduced by dose adaptions based on acetylator type and sex in addition to the currently used weight bands. A novel dosing strategy that has the potential to reduce isoniazid-related toxicity and treatment failure is presented.

Three novel approaches to structural identifiability analysis in mixed-effects models.

BACKGROUND AND OBJECTIVE: Structural identifiability is a concept that considers whether the structure of a model together with a set of input-output relations uniquely determines the model parameters. In the mathematical modelling of biological systems, structural identifiability is an important concept since biological interpretations are typically made from the parameter estimates. For a system defined by ordinary differential equations, several methods have been developed to analyse whether the model is structurally identifiable or otherwise. Another well-used modelling framework, which is particularly useful when the experimental data are sparsely sampled and the population variance is of interest, is mixed-effects modelling. However, established identifiability analysis techniques for ordinary differential equations are not directly applicable to such models. METHODS: In this paper, we present and apply three different methods that can be used to study structural identifiability in mixed-effects models. The first method, called the repeated measurement approach, is based on applying a set of previously established statistical theorems. The second method, called the augmented system approach, is based on augmenting the mixed-effects model to an extended state-space form. The third method, called the Laplace transform mixed-effects extension, is based on considering the moment invariants of the systems transfer function as functions of random variables. RESULTS: To illustrate, compare and contrast the application of the three methods, they are applied to a set of mixed-effects models. CONCLUSIONS: Three structural identifiability analysis methods applicable to mixed-effects models have been presented in this paper. As method development of structural identifiability techniques for mixed-effects models has been given very little attention, despite mixed-effects models being widely used, the methods presented in this paper provides a way of handling structural identifiability in mixed-effects models previously not possible.

Physiologically Based Pharmacokinetic Model of Itraconazole and Two of Its Metabolites to Improve the Predictions and the Mechanistic Understanding of CYP3A4 Drug-Drug Interactions.

Physiologically based pharmacokinetic (PBPK) modeling for itraconazole using a bottom-up approach is challenging, not only due to complex saturable pharmacokinetics (PK) and the presence of three metabolites exhibiting CYP3A4 inhibition, but also because of discrepancies in reported in vitro data. The overall objective of this study is to provide a comprehensive mechanistic PBPK model for itraconazole in order to increase the confidence in its drug-drug interaction (DDI) predictions. To achieve this, key in vitro and in vivo data for itraconazole and its major metabolites were generated. These data were crucial to developing a novel bottom-up PBPK model in Simcyp (Simcyp Ltd., Certara, Sheffield, United Kingdom) for itraconazole and two of its major metabolites: hydroxy-itraconazole (OH-ITZ) and keto-itraconazole (keto-ITZ). Performance of the model was validated using prespecified acceptance criteria against different dosing regimens, formulations for 29 PK, and DDI studies with midazolam and other CYP3A4 substrates. The main outcome is an accurate PBPK model that simultaneously predicts the PK profiles of itraconazole, OH-ITZ, and keto-ITZ. In addition, itraconazole DDIs with midazolam and other CYP3A4 substrates were successfully predicted within a 2-fold error. Prediction precision and bias of DDI expressed as geometric mean fold error were for the area under the concentration-time curve and peak concentration, 1.06 and 0.96, respectively. To conclude, in this paper a comprehensive data set for itraconazole and its metabolites is provided that enables bottom-up mechanism-based PBPK modeling. The presented model is applicable for studying the contribution from the metabolites and allows improved assessments of itraconazole DDI.

Extending existing structural identifiability analysis methods to mixed-effects models.

The concept of structural identifiability for state-space models is expanded to cover mixed-effects state-space models. Two methods applicable for the analytical study of the structural identifiability of mixed-effects models are presented. The two methods are based on previously established techniques for non-mixed-effects models; namely the Taylor series expansion and the input-output form approach. By generating an exhaustive summary, and by assuming an infinite number of subjects, functions of random variables can be derived which in turn determine the distribution of the system's observation function(s). By considering the uniqueness of the analytical statistical moments of the derived functions of the random variables, the structural identifiability of the corresponding mixed-effects model can be determined. The two methods are applied to a set of examples of mixed-effects models to illustrate how they work in practice.

Translational model to predict pulmonary pharmacokinetics and efficacy in man for inhaled bronchodilators.

Translational pharmacokinetic (PK) models are needed to describe and predict drug concentration-time profiles in lung tissue at the site of action to enable animal-to-man translation and prediction of efficacy in humans for inhaled medicines. Current pulmonary PK models are generally descriptive rather than predictive, drug/compound specific, and fail to show successful cross-species translation. The objective of this work was to develop a robust compartmental modeling approach that captures key features of lung and systemic PK after pulmonary administration of a set of 12 soluble drugs containing single basic, dibasic, or cationic functional groups. The model is shown to allow translation between animal species and predicts drug concentrations in human lungs that correlate with the forced expiratory volume for different classes of bronchodilators. Thus, the pulmonary modeling approach has potential to be a key component in the prediction of human PK, efficacy, and safety for future inhaled medicines.

Input Estimation for Extended-Release Formulations Exemplified with Exenatide.

Estimating the in vivo absorption profile of a drug is essential when developing extended-release medications. Such estimates can be obtained by measuring plasma concentrations over time and inferring the absorption from a model of the drug's pharmacokinetics. Of particular interest is to predict the bioavailability-the fraction of the drug that is absorbed and enters the systemic circulation. This paper presents a framework for addressing this class of estimation problems and gives advice on the choice of method. In parametric methods, a model is constructed for the absorption process, which can be difficult when the absorption has a complicated profile. Here, we place emphasis on non-parametric methods that avoid making strong assumptions about the absorption. A modern estimation method that can address very general input-estimation problems has previously been presented. In this method, the absorption profile is modeled as a stochastic process, which is estimated using Markov chain Monte Carlo techniques. The applicability of this method for extended-release formulation development is evaluated by analyzing a dataset of Bydureon, an injectable extended-release suspension formulation of exenatide, a GLP-1 receptor agonist for treating diabetes. This drug is known to have non-linear pharmacokinetics. Its plasma concentration profile exhibits multiple peaks, something that can make parametric modeling challenging, but poses no major difficulties for non-parametric methods. The method is also validated on synthetic data, exploring the effects of sampling and noise on the accuracy of the estimates.

Parameter Identifiability of Fundamental Pharmacodynamic Models.

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.

Nonlinear mixed-effects modelling for single cell estimation: when, why, and how to use it.

BACKGROUND: Studies 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. RESULTS: Herein, 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. CONCLUSIONS: When considering uninformative single-cell data, NLME yields more accurate parameter and noise estimates, compared to more traditional approaches, such as STS and JLH.