Injectable Biodegradable Silica Depot for Controlled Subcutaneous Delivery of Antisense Oligonucleotides with beyond Monthly Administration.

Single-stranded antisense oligonucleotides (ASOs) are typically administered subcutaneously once per week or monthly. Less frequent dosing would have strong potential to improve patient convenience and increase adherence and thereby for some diseases result in more optimal therapeutic outcomes. Several technologies are available to provide sustained drug release via subcutaneous (SC) administration. ASOs have a high aqueous solubility and require relatively high doses, which limits the options available substantially. In the present work, we show that an innovative biodegradable, nonporous silica-based matrix provides zero-order release in vivo (rats) for at least 4 weeks for compositions with ASO loads of up to about 100 mg/mL (0.5 mL injection) without any sign of initial burst. This implies that administration beyond once monthly can be feasible. For higher drug loads, substantial burst release was observed during the first week. The concentrations of unconjugated ASO levels in the liver were found to be comparable to corresponding bolus doses. Additionally, infusion using a minipump shows a higher liver exposure than SC bolus administration at the same dose level and, in addition, clear mRNA knockdown and circulating protein reduction comparable to SC bolus dosing, hence suggesting productive liver uptake for a slow-release administration.

Digital twin predicting diet response before and after long-term fasting.

Today, there is great interest in diets proposing new combinations of macronutrient compositions and fasting schedules. Unfortunately, there is little consensus regarding the impact of these different diets, since available studies measure different sets of variables in different populations, thus only providing partial, non-connected insights. We lack an approach for integrating all such partial insights into a useful and interconnected big picture. Herein, we present such an integrating tool. The tool uses a novel mathematical model that describes mechanisms regulating diet response and fasting metabolic fluxes, both for organ-organ crosstalk, and inside the liver. The tool can mechanistically explain and integrate data from several clinical studies, and correctly predict new independent data, including data from a new study. Using this model, we can predict non-measured variables, e.g. hepatic glycogen and gluconeogenesis, in response to fasting and different diets. Furthermore, we exemplify how such metabolic responses can be successfully adapted to a specific individual's sex, weight, height, as well as to the individual's historical data on metabolite dynamics. This tool enables an offline digital twin technology.

Integrated experimental-computational analysis of a HepaRG liver-islet microphysiological system for human-centric diabetes research.

Microphysiological systems (MPS) are powerful tools for emulating human physiology and replicating disease progression in vitro. MPS could be better predictors of human outcome than current animal models, but mechanistic interpretation and in vivo extrapolation of the experimental results remain significant challenges. Here, we address these challenges using an integrated experimental-computational approach. This approach allows for in silico representation and predictions of glucose metabolism in a previously reported MPS with two organ compartments (liver and pancreas) connected in a closed loop with circulating medium. We developed a computational model describing glucose metabolism over 15 days of culture in the MPS. The model was calibrated on an experiment-specific basis using data from seven experiments, where HepaRG single-liver or liver-islet cultures were exposed to both normal and hyperglycemic conditions resembling high blood glucose levels in diabetes. The calibrated models reproduced the fast (i.e. hourly) variations in glucose and insulin observed in the MPS experiments, as well as the long-term (i.e. over weeks) decline in both glucose tolerance and insulin secretion. We also investigated the behaviour of the system under hypoglycemia by simulating this condition in silico, and the model could correctly predict the glucose and insulin responses measured in new MPS experiments. Last, we used the computational model to translate the experimental results to humans, showing good agreement with published data of the glucose response to a meal in healthy subjects. The integrated experimental-computational framework opens new avenues for future investigations toward disease mechanisms and the development of new therapies for metabolic disorders.

Inhibition of the prostaglandin D2-GPR44/DP2 axis improves human islet survival and function.

AIMS/HYPOTHESIS: Inflammatory signals and increased prostaglandin synthesis play a role during the development of diabetes. The prostaglandin D2 (PGD2) receptor, GPR44/DP2, is highly expressed in human islets and activation of the pathway results in impaired insulin secretion. The role of GPR44 activation on islet function and survival rate during chronic hyperglycaemic conditions is not known. In this study, we investigate GPR44 inhibition by using a selective GPR44 antagonist (AZ8154) in human islets both in vitro and in vivo in diabetic mice transplanted with human islets. METHODS: Human islets were exposed to PGD2 or proinflammatory cytokines in vitro to investigate the effect of GPR44 inhibition on islet survival rate. In addition, the molecular mechanisms of GPR44 inhibition were investigated in human islets exposed to high concentrations of glucose (HG) and to IL-1ß. For the in vivo part of the study, human islets were transplanted under the kidney capsule of immunodeficient diabetic mice and treated with 6, 60 or 100 mg/kg per day of a GPR44 antagonist starting from the transplantation day until day 4 (short-term study) or day 17 (long-term study) post transplantation. IVGTT was performed on mice at day 10 and day 15 post transplantation. After termination of the study, metabolic variables, circulating human proinflammatory cytokines, and hepatocyte growth factor (HGF) were analysed in the grafted human islets. RESULTS: PGD2 or proinflammatory cytokines induced apoptosis in human islets whereas GPR44 inhibition reversed this effect. GPR44 inhibition antagonised the reduction in glucose-stimulated insulin secretion induced by HG and IL-1ß in human islets. This was accompanied by activation of the Akt-glycogen synthase kinase 3ß signalling pathway together with phosphorylation and inactivation of forkhead box O-1and upregulation of pancreatic and duodenal homeobox-1 and HGF. Administration of the GPR44 antagonist for up to 17 days to diabetic mice transplanted with a marginal number of human islets resulted in reduced fasting blood glucose and lower glucose excursions during IVGTT. Improved glucose regulation was supported by increased human C-peptide levels compared with the vehicle group at day 4 and throughout the treatment period. GPR44 inhibition reduced plasma levels of TNF-α and growth-regulated oncogene-α/chemokine (C-X-C motif) ligand 1 and increased the levels of HGF in human islets. CONCLUSIONS/INTERPRETATION: Inhibition of GPR44 in human islets has the potential to improve islet function and survival rate under inflammatory and hyperglycaemic stress. This may have implications for better survival rate of islets following transplantation.

Structural and functional characterization of a specific antidote for ticagrelor.

Ticagrelor is a direct-acting reversibly binding P2Y12 antagonist and is widely used as an antiplatelet therapy for the prevention of cardiovascular events in acute coronary syndrome patients. However, antiplatelet therapy can be associated with an increased risk of bleeding. Here, we present data on the identification and the in vitro and in vivo pharmacology of an antigen-binding fragment (Fab) antidote for ticagrelor. The Fab has a 20 pM affinity for ticagrelor, which is 100 times stronger than ticagrelor's affinity for its target, P2Y12. Despite ticagrelor's structural similarities to adenosine, the Fab is highly specific and does not bind to adenosine, adenosine triphosphate, adenosine 5'-diphosphate, or structurally related drugs. The antidote concentration-dependently neutralized the free fraction of ticagrelor and reversed its antiplatelet activity both in vitro in human platelet-rich plasma and in vivo in mice. Lastly, the antidote proved effective in normalizing ticagrelor-dependent bleeding in a mouse model of acute surgery. This specific antidote for ticagrelor may prove valuable as an agent for patients who require emergency procedures.

Input estimation for drug discovery using optimal control and Markov chain Monte Carlo approaches.

Input estimation is employed in cases where it is desirable to recover the form of an input function which cannot be directly observed and for which there is no model for the generating process. In pharmacokinetic and pharmacodynamic modelling, input estimation in linear systems (deconvolution) is well established, while the nonlinear case is largely unexplored. In this paper, a rigorous definition of the input-estimation problem is given, and the choices involved in terms of modelling assumptions and estimation algorithms are discussed. In particular, the paper covers Maximum a Posteriori estimates using techniques from optimal control theory, and full Bayesian estimation using Markov Chain Monte Carlo (MCMC) approaches. These techniques are implemented using the optimisation software CasADi, and applied to two example problems: one where the oral absorption rate and bioavailability of the drug eflornithine are estimated using pharmacokinetic data from rats, and one where energy intake is estimated from body-mass measurements of mice exposed to monoclonal antibodies targeting the fibroblast growth factor receptor (FGFR) 1c. The results from the analysis are used to highlight the strengths and weaknesses of the methods used when applied to sparsely sampled data. The presented methods for optimal control are fast and robust, and can be recommended for use in drug discovery. The MCMC-based methods can have long running times and require more expertise from the user. The rigorous definition together with the illustrative examples and suggestions for software serve as a highly promising starting point for application of input-estimation methods to problems in drug discovery.

Modeling energy intake by adding homeostatic feedback and drug intervention.

Energy intake (EI) is a pivotal biomarker used in quantification approaches to metabolic disease processes such as obesity, diabetes, and growth disorders. Eating behavior is however under both short-term and long-term control. This control system manifests itself as tolerance and rebound phenomena in EI, when challenged by drug treatment or diet restriction. The paper describes a model with the capability to capture physiological counter-regulatory feedback actions triggered by energy imbalances. This feedback is general as it handles tolerance to both increases and decreases in EI, and works in both acute and chronic settings. A drug mechanism function inhibits (or stimulates) EI. The deviation of EI relative to a reference level (set-point) serves as input to a non-linear appetite control signal which in turn impacts EI in parallel to the drug intervention. Three examples demonstrate the potential usefulness of the model in both acute and chronic dosing situations. The model shifts the predicted concentration-response relationship rightwardly at lower concentrations, in contrast to models that do not handle functional adaptation. A fourth example further shows that the model may qualitatively explain differences in rate and extent of adaptation in observed EI and its concomitants in both rodents and humans.

Tissue pharmacokinetics of antisense oligonucleotides.

Pharmacokinetics (PK) of antisense oligonucleotides (ASOs) is characterized by rapid distribution from plasma to tissue and slow terminal plasma elimination driven by re-distribution from tissue. Quantitative understanding of tissue PK and RNA knockdown for various ASO chemistries, conjugations, and administration routes is critical for successful drug discovery. Here, we report concentration-time and RNA knockdown profiles for a gapmer ASO with locked nucleic acid ribose chemistry in mouse liver, kidney, heart, and lung after subcutaneous and intratracheal administration. Additionally, the same ASO with liver targeting conjugation (galactosamine-N-acetyl) is evaluated for subcutaneous administration. Data indicate that exposure and knockdown differ between tissues and strongly depend on administration route and conjugation. In a second study, we show that tissue PK is similar between the three different ribose chemistries locked nucleic acid, constrained ethyl and 2'-O-methoxyethyl, both after subcutaneous and intratracheal administration. Further, we show that the half-life in mouse liver may vary with ASO sequence. Finally, we report less than dose-proportional increase in liver concentration in the dose range of 3-30 µmol/kg. Overall, our studies contribute pivotal data to support design and interpretation of ASO in vivo studies, thereby increasing the probability of delivering novel ASO therapies to patients.

A unified framework for prediction of liver steatosis dynamics in response to different diet and drug interventions.

BACKGROUND & AIMS: Non-alcoholic fatty liver disease (NAFLD) is a common metabolic disorder, characterized by the accumulation of excess fat in the liver, and is a driving factor for various severe liver diseases. These multi-factorial and multi-timescale changes are observed in different clinical studies, but these studies have not been integrated into a unified framework. In this study, we aim to present such a unified framework in the form of a dynamic mathematical model. METHODS: For model training and validation, we collected data for dietary or drug-induced interventions aimed at reducing or increasing liver fat. The model was formulated using ordinary differential equations (ODEs) and the mathematical analysis, model simulation, model formulation and the model parameter estimation were all performed in MATLAB. RESULTS: Our mathematical model describes accumulation of fat in the liver and predicts changes in lipid fluxes induced by both dietary and drug interventions. The model is validated using data from a wide range of drug and dietary intervention studies and can predict both short-term (days) and long-term (weeks) changes in liver fat. Importantly, the model computes the contribution of each individual lipid flux to the total liver fat dynamics. Furthermore, the model can be combined with an established bodyweight model, to simulate even longer scenarios (years), also including the effects of insulin resistance and body weight. To help prepare for corresponding eHealth applications, we also present a way to visualize the simulated changes, using dynamically changing lipid droplets, seen in images of liver biopsies. CONCLUSION: In conclusion, we believe that the minimal model presented herein might be a useful tool for future applications, and to further integrate and understand data regarding changes in dietary and drug induced changes in ectopic TAG in the liver. With further development and validation, the minimal model could be used as a disease progression model for steatosis.

Normoglycemia and physiological cortisone level maintain glucose homeostasis in a pancreas-liver microphysiological system.

Current research on metabolic disorders and diabetes relies on animal models because multi-organ diseases cannot be well studied with standard in vitro assays. Here, we have connected cell models of key metabolic organs, the pancreas and liver, on a microfluidic chip to enable diabetes research in a human-based in vitro system. Aided by mechanistic mathematical modeling, we demonstrate that hyperglycemia and high cortisone concentration induce glucose dysregulation in the pancreas-liver microphysiological system (MPS), mimicking a diabetic phenotype seen in patients with glucocorticoid-induced diabetes. In this diseased condition, the pancreas-liver MPS displays beta-cell dysfunction, steatosis, elevated ketone-body secretion, increased glycogen storage, and upregulated gluconeogenic gene expression. Conversely, a physiological culture condition maintains glucose tolerance and beta-cell function. This method was reproducible in two laboratories and was effective in multiple pancreatic islet donors. The model also provides a platform to identify new therapeutic proteins, as demonstrated with a combined transcriptome and proteome analysis.

Population pharmacokinetic modeling and deconvolution of enantioselective absorption of eflornithine in the rat.

Enantioselective pharmacokinetics and absorption of eflornithine in the rat was investigated using population pharmacokinetic modeling and a modified deconvolution method. Bidirectional permeability of L- and D-eflornithine was investigated in Caco-2 cells. The rat was administered racemic eflornithine hydrochloride as a single oral dose [40-3,000 mg/kg bodyweight (BW)] or intravenously (IV) (100-2,700 mg/kg BW infused over 60-400 min). Serial arterial blood samples were collected and L- and D-eflornithine were quantitated with a previously published chiral bioanalysis method. The D:L concentration ratio was determined in rat faeces. Intravenous L-and D-eflornithine plasma concentration-time data was analyzed using population pharmacokinetic modeling and described with a 3-compartment pharmacokinetic model with saturable binding to one of the peripheral compartments. Oral plasma concentration-time data was analyzed using a modified deconvolution method accounting for nonlinearities in the eflornithine pharmacokinetics. Clearance was similar for both enantiomers (3.36 and 3.09 mL/min). Oral bioavailability was estimated by deconvolution at 30 and 59% for L- and D-eflornithine. The D:L concentration ratio in feces was 0.49 and the Caco-2 cell permeability was similar for both enantiomers (6-10 × 10(-8) cm/s) with no evident involvement of active transport or efflux. The results presented here suggest that the difference in the bioavailability between eflornithine enantiomers is caused by a stereoselective difference in extent rather than rate of absorption. The presented modified deconvolution method made it possible to account for the non-linear component in the suggested three-compartment pharmacokinetic model thus rapidly estimating eflornithine oral bioavailability.

A modeling approach for compounds affecting body composition.

Body composition and body mass are pivotal clinical endpoints in studies of welfare diseases. We present a combined effort of established and new mathematical models based on rigorous monitoring of energy intake (EI) and body mass in mice. Specifically, we parameterize a mechanistic turnover model based on the law of energy conservation coupled to a drug mechanism model. Key model variables are fat-free mass (FFM) and fat mass (FM), governed by EI and energy expenditure (EE). An empirical Forbes curve relating FFM to FM was derived experimentally for female C57BL/6 mice. The Forbes curve differs from a previously reported curve for male C57BL/6 mice, and we thoroughly analyse how the choice of Forbes curve impacts model predictions. The drug mechanism function acts on EI or EE, or both. Drug mechanism parameters (two to three parameters) and system parameters (up to six free parameters) could be estimated with good precision (coefficients of variation typically <20 % and not greater than 40 % in our analyses). Model simulations were done to predict the EE and FM change at different drug provocations in mice. In addition, we simulated body mass and FM changes at different drug provocations using a similar model for man. Surprisingly, model simulations indicate that an increase in EI (e.g. 10 %) was more efficient than an equal lowering of EI. Also, the relative change in body mass and FM is greater in man than in mouse at the same relative change in either EI or EE. We acknowledge that this assumes the same drug mechanism impact across the two species. A set of recommendations regarding the Forbes curve, vehicle control groups, dual action on EI and loss, and translational aspects are discussed. This quantitative approach significantly improves data interpretation, disease system understanding, safety assessment and translation across species.

Plasma Pharmacokinetics of N-Acetylgalactosamine-Conjugated Small-Interfering Ribonucleic Acids (GalNAc-Conjugated siRNAs).

Small-interfering ribonucleic acids (siRNAs) with N-acetylgalactosamine (GalNAc) conjugation for improved liver uptake represent an emerging class of drugs that modulate liver-expressed therapeutic targets. The pharmacokinetics of GalNAc-siRNAs are characterized by a rapid distribution from plasma to tissue (hours) and a long terminal plasma half-life, analyzed in the form of the antisense strand, driven by redistribution from tissue (weeks). Understanding how clinical pharmacokinetics relate to the dose and type of siRNA chemical stabilizing method used is critical, e.g., to design studies, to investigate safety windows, and to predict the pharmacokinetics of new preclinical assets. To this end, we collected and analyzed pharmacokinetic data from the literature regarding nine GalNAc-siRNAs. Based on this analysis, we showed that the clinical plasma pharmacokinetics of GalNAc-siRNAs are approximately dose proportional and similar between chemical stabilizing methods. This holds for both the area under the concentration-time curve (AUC) and the maximum plasma concentration (Cmax). Corresponding rat and monkey pharmacokinetic data for a subset of the nine GalNAc-siRNAs show dose-proportional Cmax, supra-dose-proportional AUC, and similar pharmacokinetics between chemical stabilizing methods​. Together, the animal and human pharmacokinetic data indicate that plasma clearance divided by bioavailability follows allometric principles and scales between species with an exponent of 0.75. Finally, the clinical plasma concentration-time profiles can be empirically described by standard one-compartment kinetics with first-order absorption up to 24 h after subcutaneous dosing, and by three-compartment kinetics with first-order absorption in general. To describe the system more mechanistically, we report a corrected and unambiguously defined version of a previously published physiologically based pharmacokinetic model.

A multi-scale digital twin for adiposity-driven insulin resistance in humans: diet and drug effects.

BACKGROUND: The increased prevalence of insulin resistance is one of the major health risks in society today. Insulin resistance involves both short-term dynamics, such as altered meal responses, and long-term dynamics, such as the development of type 2 diabetes. Insulin resistance also occurs on different physiological levels, ranging from disease phenotypes to organ-organ communication and intracellular signaling. To better understand the progression of insulin resistance, an analysis method is needed that can combine different timescales and physiological levels. One such method is digital twins, consisting of combined mechanistic mathematical models. We have previously developed a model for short-term glucose homeostasis and intracellular insulin signaling, and there exist long-term weight regulation models. Herein, we combine these models into a first interconnected digital twin for the progression of insulin resistance in humans. METHODS: The model is based on ordinary differential equations representing biochemical and physiological processes, in which unknown parameters were fitted to data using a MATLAB toolbox. RESULTS: The interconnected twin correctly predicts independent data from a weight increase study, both for weight-changes, fasting plasma insulin and glucose levels, and intracellular insulin signaling. Similarly, the model can predict independent weight-change data in a weight loss study with the weight loss drug topiramate. The model can also predict non-measured variables. CONCLUSIONS: The model presented herein constitutes the basis for a new digital twin technology, which in the future could be used to aid medical pedagogy and increase motivation and compliance and thus aid in the prevention and treatment of insulin resistance.

Network modeling of the transcriptional effects of copy number aberrations in glioblastoma.

DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease-driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long- and short-term survivors. Our method constructs causal network models of gene expression by combining genome-wide DNA- and RNA-level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease-relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large-scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.

Cross-Species Translation of Biophase Half-Life and Potency of GalNAc-Conjugated siRNAs.

Small interfering RNAs (siRNAs) with N-acetylgalactosamine (GalNAc) conjugation for improved liver uptake represent an emerging class of drugs to treat liver diseases. Understanding how pharmacokinetics and pharmacodynamics translate is pivotal for in vivo study design and human dose prediction. However, the literature is sparse on translational data for this modality, and pharmacokinetics in the liver is seldom measured. To overcome these difficulties, we collected time-course biomarker data for 11 GalNAc-siRNAs in various species and applied the kinetic-pharmacodynamic modeling approach to estimate the biophase (liver) half-life and the potency. Our analysis indicates that the biophase half-life is 0.6-3 weeks in mouse, 1-8 weeks in monkey, and 1.5-14 weeks in human. For individual siRNAs, the biophase half-life is 1-8 times longer in human than in mouse, and generally 1-3 times longer in human than in monkey. The analysis indicates that the siRNAs are more potent in human than in mouse and monkey.

Inhibition of SGLT2 Preserves Function and Promotes Proliferation of Human Islets Cells In Vivo in Diabetic Mice.

Dapagliflozin is a sodium-glucose co-transporter 2 (SGLT2) inhibitor used for the treatment of diabetes. This study examines the effects of dapagliflozin on human islets, focusing on alpha and beta cell composition in relation to function in vivo, following treatment of xeno-transplanted diabetic mice. Mouse beta cells were ablated by alloxan, and dapagliflozin was provided in the drinking water while controls received tap water. Body weight, food and water intake, plasma glucose, and human C-peptide levels were monitored, and intravenous arginine/glucose tolerance tests (IVarg GTT) were performed to evaluate islet function. The grafted human islets were isolated at termination and stained for insulin, glucagon, Ki67, caspase 3, and PDX-1 immunoreactivity in dual and triple combinations. In addition, human islets were treated in vitro with dapagliflozin at different glucose concentrations, followed by insulin and glucagon secretion measurements. SGLT2 inhibition increased the animal survival rate and reduced plasma glucose, accompanied by sustained human C-peptide levels and improved islet response to glucose/arginine. SGLT2 inhibition increased both alpha and beta cell proliferation (Ki67+glucagon+ and Ki67+insulin+) while apoptosis was reduced (caspase3+glucagon+ and caspase3+insulin+). Alpha cells were fewer following inhibition of SGLT2 with increased glucagon/PDX-1 double-positive cells, a marker of alpha to beta cell transdifferentiation. In vitro treatment of human islets with dapagliflozin had no apparent impact on islet function. In summary, SGLT2 inhibition supported human islet function in vivo in the hyperglycemic milieu and potentially promoted alpha to beta cell transdifferentiation, most likely through an indirect mechanism.

A case-study of model-informed drug development of a novel PCSK9 anti sense oligonucleotide. Part 1: First time in man to phase II.

Here, we show model-informed drug development (MIDD) of a novel antisense oligonucleotide, targeting PCSK9 for treatment of hypocholesteremia. The case study exemplifies use of MIDD to analyze emerging data from an ongoing first-in-human study, utility of the US Food and Drug Administration MIDD pilot program to accelerate timelines, innovative use of competitor data to set biomarker targets, and use of MIDD to optimize sample size and dose selection, as well as to accelerate and de-risk a phase IIb study. The focus of the case-study is on the cross-functional collaboration and other key MIDD enablers that are critical to maximize the value of MIDD, rather than the technical application of MIDD.

Hybrid modelling for stroke care: Review and suggestions of new approaches for risk assessment and simulation of scenarios.

Stroke is an example of a complex and multi-factorial disease involving multiple organs, timescales, and disease mechanisms. To deal with this complexity, and to realize Precision Medicine of stroke, mathematical models are needed. Such approaches include: 1) machine learning, 2) bioinformatic network models, and 3) mechanistic models. Since these three approaches have complementary strengths and weaknesses, a hybrid modelling approach combining them would be the most beneficial. However, no concrete approach ready to be implemented for a specific disease has been presented to date. In this paper, we both review the strengths and weaknesses of the three approaches, and propose a roadmap for hybrid modelling in the case of stroke care. We focus on two main tasks needed for the clinical setting: a) For stroke risk calculation, we propose a new two-step approach, where non-linear mixed effects models and bioinformatic network models yield biomarkers which are used as input to a machine learning model and b) For simulation of care scenarios, we propose a new four-step approach, which revolves around iterations between simulations of the mechanistic models and imputations of non-modelled or non-measured variables. We illustrate and discuss the different approaches in the context of Precision Medicine for stroke.

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.