The effect of AZD9567 vs. prednisolone on glycaemic control in patients with type 2 diabetes mellitus: Results from a phase 2a clinical trial.

AIMS: Corticosteroids are the treatment of choice for many inflammatory diseases but often lead to adverse effects, including hyperglycaemia. This study investigated the mechanisms driving differential effects on glucose control for AZD9567, an oral nonsteroidal selective glucocorticoid receptor modulator vs. prednisolone in 46 patients with type 2 diabetes mellitus. METHODS: In this randomized, double-blind, 2-way cross-over study (NCT04556760), participants received either AZD9567 72 mg and prednisolone 40 mg daily (cohort 1); AZD9567 40 mg and prednisolone 20 mg daily (cohort 2); or placebo and prednisolone 5 mg daily (cohort 3). Treatment duration was 3 days with a 3-week washout between treatment periods. Glycaemic control was assessed after a standardized meal and with continuous glucose monitoring. RESULTS: A significant difference between AZD9567 and prednisolone in favour of AZD9567 was observed for the change from baseline to Day 4 glucose excursions postmeal in cohort 1 (glucose area under the curve from 0 to 4 h -4.54%; 95% confidence interval [CI]: -8.88, -0.01; P = .049), but not in cohort 2 (-5.77%; 95% CI: -20.92, 12.29; P = .435). In cohort 1, significant differences between AZD9567 and prednisolone were also seen for the change from baseline to day 4 in insulin and glucagon secretion postmeal (P < .001 and P = .005, respectively) and change from baseline to Day 4 in GLP-1 response (P = .022). Significant differences between AZD9567 and prednisolone for 24-h glucose control were observed for both cohort 1 (-1.507 mmol/L; 95% CI: -2.0820, -0.9314; P < .001) and cohort 2 (-1.110 mmol/L; 95% CI -1.7257, -0.4941; P < .001). CONCLUSION: AZD9567 significantly reduced treatment-induced hyperglycaemia compared with prednisolone.

A Phase 2a, Double-Blind, Placebo-controlled Randomized Trial of Inhaled TLR9 Agonist AZD1419 in Asthma.

Rationale: To examine the potential of TLR9 (Toll-like receptor 9) activation to modulate the type 2 immune response in asthma.Objectives: To evaluate efficacy and safety of AZD1419, an inhaled TLR9 agonist, in a phase 2a, randomized, double-blind trial.Methods: Adult patients with asthma with a history of elevated eosinophils (>250 cells/µl) were randomized 1:1 to receive 13 once-weekly doses of inhaled AZD1419 (1, 4, or 8 mg; n = 40) or placebo (n = 41). Inhaled corticosteroids and long-acting ß2-agonist were tapered down and then discontinued. The last four doses of AZD1419 were given without maintenance medication, followed by a 40-week observation period. Primary endpoint was time to loss of asthma control (LOC).Measurements and Main Results: AZD1419 induced a T-helper cell type 1-type IFN response with a sustained reduction in markers of type 2 inflammation. However, there were no statistically significant differences between AZD1419 and placebo for time to LOC, proportion of patients with LOC, changes in Asthma Control Questionnaire-five-item version, exacerbations, reliever use, FEV1, peak expiratory flow, or fractional exhaled nitric oxide (FeNO). LOC was predicted by an early rise in FeNO in 63% of patients. Despite withdrawal of maintenance treatment, 24 patients completed the study without LOC; AZD1419 n = 11, placebo n = 13. Adverse events were balanced across groups, with no deaths or serious adverse events judged as causally related to AZD1419.Conclusions: AZD1419 was safe and well tolerated but did not lead to improved asthma control, despite reducing markers of type 2 inflammation. Results suggest that a novel accelerated step-down approach based on FeNO is possible for patients with well-controlled asthma.

Exposure-response modeling improves selection of radiation and radiosensitizer combinations.

A central question in drug discovery is how to select drug candidates from a large number of available compounds. This analysis presents a model-based approach for comparing and ranking combinations of radiation and radiosensitizers. The approach is quantitative and based on the previously-derived Tumor Static Exposure (TSE) concept. Combinations of radiation and radiosensitizers are evaluated based on their ability to induce tumor regression relative to toxicity and other potential costs. The approach is presented in the form of a case study where the objective is to find the most promising candidate out of three radiosensitizing agents. Data from a xenograft study is described using a nonlinear mixed-effects modeling approach and a previously-published tumor model for radiation and radiosensitizing agents. First, the most promising candidate is chosen under the assumption that all compounds are equally toxic. The impact of toxicity in compound selection is then illustrated by assuming that one compound is more toxic than the others, leading to a different choice of candidate.

Modeling of free fatty acid dynamics: insulin and nicotinic acid resistance under acute and chronic treatments.

Nicotinic acid (NiAc) is a potent inhibitor of adipose tissue lipolysis. Acute administration results in a rapid reduction of plasma free fatty acid (FFA) concentrations. Sustained NiAc exposure is associated with tolerance development (drug resistance) and complete adaptation (FFA returning to pretreatment levels). We conducted a meta-analysis on a rich pre-clinical data set of the NiAc-FFA interaction to establish the acute and chronic exposure-response relations from a macro perspective. The data were analyzed using a nonlinear mixed-effects framework. We also developed a new turnover model that describes the adaptation seen in plasma FFA concentrations in lean Sprague-Dawley and obese Zucker rats following acute and chronic NiAc exposure. The adaptive mechanisms within the system were described using integral control systems and dynamic efficacies in the traditional [Formula: see text] model. Insulin was incorporated in parallel with NiAc as the main endogenous co-variate of FFA dynamics. The model captured profound insulin resistance and complete drug resistance in obese rats. The efficacy of NiAc as an inhibitor of FFA release went from 1 to approximately 0 during sustained exposure in obese rats. The potency of NiAc as an inhibitor of insulin and of FFA release was estimated to be 0.338 and 0.436 [Formula: see text], respectively, in obese rats. A range of dosing regimens was analyzed and predictions made for optimizing NiAc delivery to minimize FFA exposure. Given the exposure levels of the experiments, the importance of washout periods in-between NiAc infusions was illustrated. The washout periods should be [Formula: see text]2 h longer than the infusions in order to optimize 24 h lowering of FFA in rats. However, the predicted concentration-response relationships suggests that higher AUC reductions might be attained at lower NiAc exposures.

Yeast AMP-activated protein kinase monitors glucose concentration changes and absolute glucose levels.

Analysis of the time-dependent behavior of a signaling system can provide insight into its dynamic properties. We employed the nucleocytoplasmic shuttling of the transcriptional repressor Mig1 as readout to characterize Snf1-Mig1 dynamics in single yeast cells. Mig1 binds to promoters of target genes and mediates glucose repression. Mig1 is predominantly located in the nucleus when glucose is abundant. Upon glucose depletion, Mig1 is phosphorylated by the yeast AMP-activated kinase Snf1 and exported into the cytoplasm. We used a three-channel microfluidic device to establish a high degree of control over the glucose concentration exposed to cells. Following regimes of glucose up- and downshifts, we observed a very rapid response reaching a new steady state within less than 1 min, different glucose threshold concentrations depending on glucose up- or downshifts, a graded profile with increased cell-to-cell variation at threshold glucose concentrations, and biphasic behavior with a transient translocation of Mig1 upon the shift from high to intermediate glucose concentrations. Fluorescence loss in photobleaching and fluorescence recovery after photobleaching data demonstrate that Mig1 shuttles constantly between the nucleus and cytoplasm, although with different rates, depending on the presence of glucose. Taken together, our data suggest that the Snf1-Mig1 system has the ability to monitor glucose concentration changes as well as absolute glucose levels. The sensitivity over a wide range of glucose levels and different glucose concentration-dependent response profiles are likely determined by the close integration of signaling with the metabolism and may provide for a highly flexible and fast adaptation to an altered nutritional status.

Using sensitivity equations for computing gradients of the FOCE and FOCEI approximations to the population likelihood.

The first order conditional estimation (FOCE) method is still one of the parameter estimation workhorses for nonlinear mixed effects (NLME) modeling used in population pharmacokinetics and pharmacodynamics. However, because this method involves two nested levels of optimizations, with respect to the empirical Bayes estimates and the population parameters, FOCE may be numerically unstable and have long run times, issues which are most apparent for models requiring numerical integration of differential equations. We propose an alternative implementation of the FOCE method, and the related FOCEI, for parameter estimation in NLME models. Instead of obtaining the gradients needed for the two levels of quasi-Newton optimizations from the standard finite difference approximation, gradients are computed using so called sensitivity equations. The advantages of this approach were demonstrated using different versions of a pharmacokinetic model defined by nonlinear differential equations. We show that both the accuracy and precision of gradients can be improved extensively, which will increase the chances of a successfully converging parameter estimation. We also show that the proposed approach can lead to markedly reduced computational times. The accumulated effect of the novel gradient computations ranged from a 10-fold decrease in run times for the least complex model when comparing to forward finite differences, to a substantial 100-fold decrease for the most complex model when comparing to central finite differences. Considering the use of finite differences in for instance NONMEM and Phoenix NLME, our results suggests that significant improvements in the execution of FOCE are possible and that the approach of sensitivity equations should be carefully considered for both levels of optimization.

Bridging the gaps in systems biology.

Systems biology aims at creating mathematical models, i.e., computational reconstructions of biological systems and processes that will result in a new level of understanding-the elucidation of the basic and presumably conserved "design" and "engineering" principles of biomolecular systems. Thus, systems biology will move biology from a phenomenological to a predictive science. Mathematical modeling of biological networks and processes has already greatly improved our understanding of many cellular processes. However, given the massive amount of qualitative and quantitative data currently produced and number of burning questions in health care and biotechnology needed to be solved is still in its early phases. The field requires novel approaches for abstraction, for modeling bioprocesses that follow different biochemical and biophysical rules, and for combining different modules into larger models that still allow realistic simulation with the computational power available today. We have identified and discussed currently most prominent problems in systems biology: (1) how to bridge different scales of modeling abstraction, (2) how to bridge the gap between topological and mechanistic modeling, and (3) how to bridge the wet and dry laboratory gap. The future success of systems biology largely depends on bridging the recognized gaps.

Kinetic models in industrial biotechnology - Improving cell factory performance.

An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed.

Clinical Pharmacokinetics, Pharmacodynamics, and Immunogenicity of Anifrolumab.

The type I interferon (IFN) signaling pathway is implicated in the pathogenesis of systemic lupus erythematosus (SLE). Anifrolumab is a monoclonal antibody that targets the type I IFN receptor subunit 1. Anifrolumab is approved in several countries for patients with moderate to severe SLE receiving standard therapy. The approved dosing regimen of anifrolumab is a 300-mg dose administered intravenously every 4 weeks; this was initially based on the results of the Phase 2b MUSE and further confirmed in the Phase 3 TULIP-1 and TULIP-2 trials, in which anifrolumab 300-mg treatment was associated with clinically meaningful improvements in disease activity with an acceptable safety profile. There have been several published analyses of the pharmacokinetic and pharmacodynamic profile of anifrolumab, including a population-pharmacokinetic analysis of 5 clinical studies of healthy volunteers and patients with SLE, in which body weight and type I IFN gene expression were significant covariates identified for anifrolumab exposure and clearance. Additionally, the pooled Phase 3 SLE population has been used to evaluate how serum exposure may be related to clinical responses, safety risks, and pharmacodynamic effects of the 21-gene type I IFN gene signature (21-IFNGS). The relevance of 21-IFNGS with regard to clinical efficacy outcomes has also been analyzed. Herein, the clinical pharmacokinetics, pharmacodynamics, and immunogenicity of anifrolumab as well as results of population-pharmacokinetics and exposure-response analyses are reviewed.

AZD9567 versus prednisolone in patients with active rheumatoid arthritis: A phase IIa, randomized, double-blind, efficacy, and safety study.

Oral corticosteroid use is limited by side effects, some caused by off-target actions on the mineralocorticoid receptor that disrupt electrolyte balance. AZD9567 is a selective, nonsteroidal glucocorticoid receptor modulator. The efficacy, safety, and tolerability of AZD9567 and prednisolone were assessed in a phase IIa study. Anti-inflammatory mechanism of action was also evaluated in vitro in monocytes from healthy donors. In this randomized, double-blind, parallel-group, multicenter study, patients with active rheumatoid arthritis were randomized 1:1 to AZD9567 40 mg or prednisolone 20 mg once daily orally for 14 days. The primary end point was change from baseline in DAS28-CRP at day 15. Secondary end points included components of DAS28-CRP, American College of Rheumatology (ACR) response criteria (ACR20, ACR50, and ACR70), and safety end points, including serum electrolytes. Overall, 21 patients were randomized to AZD9567 (n = 11) or prednisolone (n = 10), and all completed the study. As anticipated, AZD9567 had a similar efficacy profile to prednisolone, with no clinically meaningful (i.e., >1.0) difference in change from baseline to day 15 in DAS28-CRP between AZD9567 and prednisolone (least-squares mean difference: 0.47, 95% confidence interval: -0.49 to 1.43). Similar results were observed for the secondary efficacy end points. In vitro transcriptomic analysis showed that anti-inflammatory responses were similar for AZD9567, prednisolone, and dexamethasone. Unlike prednisolone, AZD9567 had no effect on the serum sodium:potassium ratio. The safety profile was not different from that of prednisolone. Larger studies of longer duration are required to determine whether AZD9567 40 mg may in the future be an alternative to prednisolone in patients with inflammatory disease.

Nonlinear Population Pharmacokinetics of Anifrolumab in Healthy Volunteers and Patients With Systemic Lupus Erythematosus.

We characterized the population pharmacokinetics of anifrolumab, a type I interferon receptor-blocking antibody. Pharmacokinetic data were analyzed from the anifrolumab (intravenous [IV], every 4 weeks) arms from 5 clinical trials in patients with systemic lupus erythematosus (SLE) (n = 664) and healthy volunteers (n = 6). Population pharmacokinetic modeling was performed using a 2-compartment model with parallel linear and nonlinear elimination pathways. The impact of covariates (demographics, interferon gene signature [IFNGS, high/low], disease characteristics, renal/hepatic function, SLE medications, and antidrug antibodies) on pharmacokinetics was evaluated. Time-varying clearance (CL) was characterized using an empirical sigmoidal time-dependent function. Anifrolumab exposure increased more than dose-proportionally from 100 to 1000 mg IV every 4 weeks. Based on population pharmacokinetics modeling, the baseline median linear CL was 0.193 L/day in IFNGS-high patients and 0.153 L/day in IFNGS-low/healthy volunteers. After a year, median anifrolumab linear CL decreased by 8.4% from baseline. Body weight and IFNGS were significant pharmacokinetic covariates, whereas age, sex, race, disease activity, SLE medications, and presence of antidrug antibodies had no significant effect on anifrolumab pharmacokinetics. Anifrolumab at a concentration of 300 mg IV every 4 weeks was predicted to be below the lower limit of quantitation in 95% of patients ≈10 weeks after a single dose and ≈16 weeks after stopping dosing at steady state. To conclude, anifrolumab exhibited nonlinear pharmacokinetics and time-varying linear CL; doses ≥300 mg IV every 4 weeks provided sustained anifrolumab concentrations. This study provides further evidence to support the use of anifrolumab 300 mg IV every 4 weeks in patients with moderate to severe SLE.

Mathematical Model Predicts that Acceleration of Diabetic Wound Healing is Dependent on Spatial Distribution of VEGF-A mRNA (AZD8601).

INTRODUCTION: Pharmacologic approaches for promoting angiogenesis have been utilized to accelerate healing of chronic wounds in diabetic patients with varying degrees of success. We hypothesize that the distribution of proangiogenic drugs in the wound area critically impacts the rate of closure of diabetic wounds. To evaluate this hypothesis, we developed a mathematical model that predicts how spatial distribution of VEGF-A produced by delivery of a modified mRNA (AZD8601) accelerates diabetic wound healing. METHODS: We modified a previously published model of cutaneous wound healing based on coupled partial differential equations that describe the density of sprouting capillary tips, chemoattractant concentration, and density of blood vessels in a circular wound. Key model parameters identified by a sensitivity analysis were fit to data obtained from an in vivo wound healing study performed in the dorsum of diabetic mice, and a pharmacokinetic model was used to simulate mRNA and VEGF-A distribution following injections with AZD8601. Due to the limited availability of data regarding the spatial distribution of AZD8601 in the wound bed, we performed simulations with perturbations to the location of injections and diffusion coefficient of mRNA to understand the impact of these spatial parameters on wound healing. RESULTS: When simulating injections delivered at the wound border, the model predicted that injections delivered on day 0 were more effective in accelerating wound healing than injections delivered at later time points. When the location of the injection was varied throughout the wound space, the model predicted that healing could be accelerated by delivering injections a distance of 1-2 mm inside the wound bed when compared to injections delivered on the same day at the wound border. Perturbations to the diffusivity of mRNA predicted that restricting diffusion of mRNA delayed wound healing by creating an accumulation of VEGF-A at the wound border. Alternatively, a high mRNA diffusivity had no effect on wound healing compared to a simulation with vehicle injection due to the rapid loss of mRNA at the wound border to surrounding tissue. CONCLUSIONS: These findings highlight the critical need to consider the location of drug delivery and diffusivity of the drug, parameters not typically explored in pre-clinical experiments, when designing and testing drugs for treating diabetic wounds. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12195-021-00678-9.

Modeling the effect of Kv1.5 block on the canine action potential.

A wide range of ion channels have been considered as potential targets for pharmacological treatment of atrial fibrillation. The Kv1.5 channel, carrying the I(Kur) current, has received special attention because it contributes to repolarization in the atria but is absent or weakly expressed in ventricular tissue. The dog serves as an important animal model for electrophysiological studies of the heart and mathematical models of the canine atrial action potential (CAAP) have been developed to study the interplay between ionic currents. To enable more-realistic studies on the effects of Kv1.5 blockers on the CAAP in silico, two continuous-time Markov models of the guarded receptor type were formulated for Kv1.5 and subsequently inserted into the Ramirez-Nattel-Courtemanche model of the CAAP. The main findings were: 1), time- and state-dependent Markov models of open-channel Kv1.5 block gave significantly different results compared to a time- and state-independent model with a downscaled conductance; 2), the outcome of Kv1.5 block on the macroscopic system variable APD(90) was dependent on the precise mechanism of block; and 3), open-channel block produced a reverse use-dependent prolongation of APD(90). This study suggests that more-complex ion-channel models are a prerequisite for quantitative modeling of drug effects.

Estimation of Equipotent Doses for Anti-Inflammatory Effects of Prednisolone and AZD9567, an Oral Selective Nonsteroidal Glucocorticoid Receptor Modulator.

AZD9567 is a potent and selective nonsteroidal oral glucocorticoid receptor modulator. It is developed as an anti-inflammatory drug with improved safety profile compared with steroids like prednisolone. Throughout the clinical development of AZD9567, dose selection and data interpretation require a method for determining doses with the same anti-inflammatory effect as prednisolone. Equipotent doses of AZD9567 and prednisolone were defined by the same average inhibition of TNFα release, a biomarker of anti-inflammatory effect, measured in a lipopolysaccharide-stimulated whole blood ex vivo assay. Based on pharmacokinetic-pharmacodynamic models, TNFα dose-response relationships for AZD9567 and prednisolone were established. A comparison of the dose-response curves enabled estimation of an equipotency relationship. Specifically, 20 mg prednisolone was estimated to be equipotent to 40 mg AZD9567 (95% confidence interval: 29-54 mg). Static concentration-response analyses showed that the relative potencies for inhibition of TNFα release of AZD9567 and prednisolone were well aligned with several other pro-inflammatory cytokines.

Model-Based Analysis Reveals a Sustained and Dose-Dependent Acceleration of Wound Healing by VEGF-A mRNA (AZD8601).

Intradermal delivery of AZD8601, an mRNA designed to produce vascular endothelial growth factor A (VEGF-A), has previously been shown to accelerate cutaneous wound healing in a murine diabetic model. Here, we develop population pharmacokinetic and pharmacodynamic models aiming to quantify the effect of AZD8601 injections on the dynamics of wound healing. A dataset of 584 open wound area measurements from 131 mice was integrated from 3 independent studies encompassing different doses, dosing timepoints, and number of doses. Evaluation of several candidate models showed that wound healing acceleration is not likely driven directly by time-dependent VEGF-A concentration. Instead, we found that administration of AZD8601 induced a sustained acceleration of wound healing depending on the accumulated dose, with a dose producing 50% of the maximal effect of 92 µg. Simulations with this model showed that a single dose of 200 µg AZD8601 can reduce the time to reach 50% wound healing by up to 5 days.

Effects of a selective glucocorticoid receptor modulator (AZD9567) versus prednisolone in healthy volunteers: two phase 1, single-blind, randomised controlled trials.

BACKGROUND: Glucocorticoids are highly effective and widely used anti-inflammatory drugs, but their use is limited by serious side-effects, including glucocorticoid-induced hyperglycaemia and diabetes. AZD9567 is a non-steroidal, selective glucocorticoid receptor modulator that aims to reduce side-effects. We aimed to assess the safety, tolerability, and pharmacokinetics of AZD9567 in healthy volunteers. METHODS: Two phase 1 clinical studies were done. First, a randomised, placebo-controlled, single-blind, single-ascending dose study was done in healthy men who received single oral doses of AZD9567 2 mg, 10 mg, 20 mg, 40 mg, 80 mg, 100 mg, 125 mg, or 155 mg, or prednisolone 60 mg (n=8 per dose group, randomly assigned [6:2] to receive active drug or placebo). Second, a randomised, active-controlled, single-blind, multiple-ascending dose study was done, in which men and women received oral AZD9567 or prednisolone once daily for 5 days. One cohort of volunteers with prediabetes received AZD9567 10 mg (n=7) or prednisolone 20 mg (n=2). All other cohorts comprised healthy volunteers, receiving AZD9567 20 mg, 40 mg, 80 mg, or 125 mg (n=7 per dose group), or prednisolone 5 mg (n=13), 20 mg (n=16), or 40 mg (n=13). Participants and study centre staff were masked to treatment assignment for each cohort, although data were unmasked for safety review between cohorts. The primary outcome of the single-ascending dose study was the safety, tolerability, and pharmacokinetics of single ascending doses of AZD9567; for the multiple-ascending dose study it was the safety and tolerability of AZD9567 following multiple ascending doses. As a secondary outcome, effects on glycaemic control were ascertained with oral glucose tolerance tests (OGTTs) done at baseline and on day 1 of the single-ascending dose study, and at baseline and on day 4 of the multiple-ascending dose study. These trials are registered at ClinicalTrials.gov, NCT02512575 and NCT02760316. FINDINGS: In the single-ascending dose study, between Nov 18, 2015, and Sept 26, 2016, 72 healthy white men were enrolled, and all completed the study. In the multiple-ascending dose study, between May 2, 2016, and Sept 13, 2017, 77 predominantly white male volunteers (including nine individuals with prediabetes and eight women) were enrolled and 75 completed the study. All doses of AZD9567 and prednisolone were well tolerated, with no serious adverse events or events suggesting adrenal insufficiency. In the single-ascending dose study, nine adverse events of mild intensity were reported (five with AZD9567 and four with placebo); no adverse event was reported by more than one person. In the multiple-ascending dose study, 44 adverse events of mild or moderate intensity were reported (18 with AZD9567 and 26 with prednisolone). The most common were headache and micturition. Apparent clearance, volume of distribution, and half-life of AZD9567 were consistent across doses and for single versus repeated dosing. In the multiple-ascending dose study, OGTTs showed no significant difference with AZD9567 doses up to 80 mg compared with prednisolone 5 mg in glucose area under the curve from 0 h to 4 h post-OGTT (AUC0-4h) from baseline to day 4; the increase in glucose AUC0-4h from baseline to day 4 was significantly lower with all AZD9567 doses versus prednisolone 20 mg (AZD9567 20 mg p<0·0001, 40 mg p=0·0001, 80 mg p=0·0001, and 125 mg p=0·0237). INTERPRETATION: AZD9567 appears to be safe and well tolerated in healthy, predominantly white male volunteers and shows promising initial evidence for improved post-prandial glucose control. Studies of longer duration, with a greater proportion of women and other ethnic groups, and in patients requiring anti-inflammatory treatment are needed to characterise the clinical efficacy and safety profile of AZD9567. FUNDING: AstraZeneca.

Challenging the dose-response-time data approach: Analysis of a complex system.

This study presents an extensive dose-response-time (DRT) meta-analysis of the nicotinic acid-induced inhibition of free fatty acids and insulin release. The purpose was to quantify the implications of lacking exposure data when analysing complex pharmacodynamic systems. The DRT model successfully characterised various response behaviours-including time-delays, rebound, feedback mechanisms, and adaptation-on both the individual and the population level. Comparing the fitted DRT model to an exposure-driven reference analysis showed that bias and uncertainty were introduced in the parameter estimates. However, most estimates were within one standard error from the reference. In both approaches, a few parameters suffered from practical identifiability issues, likely due to large differences in half-lives of the different rate processes. Moreover, the optimal dosing strategies predicted by the DRT model differed slightly from those of the exposure-driven analysis, having a lower optimal steady-state reduction of free fatty acids exposure.

Modeling long-term tumor growth and kill after combinations of radiation and radiosensitizing agents.

PURPOSE: Radiation therapy, whether given alone or in combination with chemical agents, is one of the cornerstones of oncology. We develop a quantitative model that describes tumor growth during and after treatment with radiation and radiosensitizing agents. The model also describes long-term treatment effects including tumor regrowth and eradication. METHODS: We challenge the model with data from a xenograft study using a clinically relevant administration schedule and use a mixed-effects approach for model-fitting. We use the calibrated model to predict exposure combinations that result in tumor eradication using Tumor Static Exposure (TSE). RESULTS: The model is able to adequately describe data from all treatment groups, with the parameter estimates taking biologically reasonable values. Using TSE, we predict the total radiation dose necessary for tumor eradication to be 110 Gy, which is reduced to 80 or 30 Gy with co-administration of 25 or 100 mg kg-1 of a radiosensitizer. TSE is also explored via a heat map of different growth and shrinkage rates. Finally, we discuss the translational potential of the model and TSE concept to humans. CONCLUSIONS: The new model is capable of describing different tumor dynamics including tumor eradication and tumor regrowth with different rates, and can be calibrated using data from standard xenograft experiments. TSE and related concepts can be used to predict tumor shrinkage and eradication, and have the potential to guide new experiments and support translations from animals to humans.

Exact Gradients Improve Parameter Estimation in Nonlinear Mixed Effects Models with Stochastic Dynamics.

Nonlinear mixed effects (NLME) modeling based on stochastic differential equations (SDEs) have evolved into a promising approach for analysis of PK/PD data. SDE-NLME models go beyond the realm of standard population modeling as they consider stochastic dynamics, thereby introducing a probabilistic perspective on the state variables. This article presents a summary of the main contributions to SDE-NLME models found in the literature. The aims of this work were to develop an exact gradient version of the first-order conditional estimation (FOCE) method for SDE-NLME models and to investigate whether it enabled faster estimation and better gradient precision/accuracy compared to the use of gradients approximated by finite differences. A simulation-estimation study was set up whereby finite difference approximations of the gradients of each level were interchanged with the exact gradients. Following previous work, the uncertainty of the state variables was accounted for using the extended Kalman filter (EKF). The exact gradient FOCE method was implemented in Mathematica 11 and evaluated on SDE versions of three common PK/PD models. When finite difference gradients were replaced by exact gradients at both FOCE levels, relative runtimes improved between 6- and 32-fold, depending on model complexity. Additionally, gradient precision/accuracy was significantly better in the exact gradient case. We conclude that parameter estimation using FOCE with exact gradients can successfully be applied to SDE-NLME models.

Evaluation and translation of combination therapies in oncology - A quantitative approach.

Quantitative techniques improve our understanding of tumor volume data for combination treatments and its translation across in vivo models and species. The focus of this paper is therefore on understanding in vivo data, highlighting key structural elements of pharmacodynamic tumor models, and challenging these methods from a translational point of view. We introduce the concept of Tumor Static Exposure (TSE) both for single and multiple combined anticancer agents. The TSE curve separates all possible exposure combinations into regions of tumor growth and tumor shrinkage. Moreover, the degree of curvature of the TSE curve indicates the degree of synergy or antagonism. We demonstrate the TSE approach by two case studies. The first examines a combination of the drugs cetuximab and cisplatin. The TSE curve associated with this combination reveals a weak synergistic effect, suggesting only modest gains from combination therapy. The second case study examines combinations of ionizing radiation and a radiosensitizing agent. In this case, the TSE curve exhibits a pronounced curvature, indicating a strong synergistic effect; tumor regression can be achieved at significantly lower exposure levels and/or radiation doses. Finally, an allometric approach to human dose prediction demonstrates the translational power of the model and the TSE concept. We conclude that the TSE approach, which embodies model-based measures of both drug (potency) and target properties (tumor growth rate), has a strong potential for ranking of compounds, supporting compound selection, and translating preclinical findings to humans.