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Verinurad, a uric acid transporter 1 (URAT1) inhibitor, lowers serum uric acid by promoting its urinary excretion. Co-administration with a xanthine oxidase inhibitor (XOI) to simultaneously reduce uric acid production rate reduces the potential for renal tubular precipitation of uric acid, which can lead to acute kidney injury. The combination is currently in development for chronic kidney disease and heart failure. The aim of this work was to apply and extend a previously developed semi-mechanistic exposure–response model for uric acid kinetics to include between-subject variability to verinurad and its combinations with XOIs, and to provide predictions to support future treatment strategies. The model was developed using data from 12 clinical studies from a total of 434 individuals, including healthy volunteers, patients with hyperuricemia, and renally impaired subjects. The model described the data well, taking into account the impact of various patient characteristics such as renal function, baseline fractional excretion of uric acid, and race. The potencies (EC50s) of verinurad (reducing uric acid reuptake), febuxostat (reducing uric acid production), and oxypurinol (reducing uric acid production) were: 29, 128, and 13,030 ng/mL, respectively. For verinurad, symptomatic hyperuricemic (gout) subjects showed a higher EC50 compared with healthy volunteers (37 ng/mL versus 29 ng/mL); while no significant difference was found for asymptomatic hyperuricemic patients. Simulations based on the uric acid model were performed to assess dose–response of verinurad in combination with XOI, and to investigate the impact of covariates. The simulations demonstrated application of the model to support dose selection for verinurad. Supplementary Information The online version contains supplementary material available at 10.1007/s10928-021-09747-y.

Nonlinear mixed effects (NLME) modeling is a powerful tool to analyze timeseries data from several individual entities in an experiment. In this paper, we give a brief overview of a package for NLME modeling in Wolfram Mathematica entitled NLMEModeling, implementing the first-order conditional estimation method with sensitivity equation-based gradients for parameter estimation. NLMEModeling supports mixed effects modeling of dynamical systems where the underlying dynamics are described by either ordinary or stochastic differential equations combined with observation equations with flexible observation error models. Moreover, NLMEModeling is a user-friendly package with functionality for parameter estimation, model diagnostics (such as goodness-of-fit analysis and visual predictive checks), and model simulation. The package is freely available and provides an extensible add-on to Wolfram Mathematica.

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.

Summary 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 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.

Febuxostat is a xanthine oxidase inhibitor indicated for gout and hyperuricemia. This work investigates potential clinically relevant covariates for febuxostat pharmacokinetics with a special focus on Asian race and bodyweight. Febuxostat plasma concentrations from 141 male subjects were obtained from two phase II studies in patients with hyperuricemia/gout (NCT02246673, NCT02317861) and one study in healthy volunteers (NCT01883167). Subjects were administered febuxostat oral doses from 10 to 80 mg. The pharmacokinetics of febuxostat was analyzed using non-linear mixed-effects modeling as implemented in NONMEM 7.3.0. The dataset consisted of racially diverse subjects, 40% being Japanese, 10% of unknown Asian origin, 39% Caucasian, and 10% Black. Most subjects (n = 92, 63%) had normal creatinine clearance (90 mL/min), while 52 subjects (36%) had mild renal impairment (creatinine clearance > 60 to

Nonlinear mixed effects (NLME) models based on stochastic differential equations (SDEs) have evolved into a mature approach for analysis of PKPD data [1-3], but parameter estimation remains challenging. We present an exact-gradient version of the first order conditional estimation (FOCE) method for SDE-NLME models, and investigate whether it enables faster estimation and better gradient precision/accuracy compared to finite difference gradients.

AZD9898 is an oral small molecule inhibitor of LTC4 synthase (LTC4s) developed for the treatment of asthma driven by cysteinyl leukotrienes (CysLTs). It is expected to inhibit the downstream production of leukotrienes LTC4, LTD4 and LTE4, which are important mediators of airway inflammation and bronchoconstriction. The leukotriene LTE4 is a stable metabolite, measurable in urine and has been used as a proof of mechanism biomarker in several clinical studies. We planned to evaluate AZD9898 in an integrated, multi-part, randomized, single-blind, placebo controlled, first-in-human (FIH), Phase I (Ph I) study. In Part 1, single ascending dosing in four cohorts (3 mg, 9 mg, 15 mg and 45 mg) versus placebo was evaluated in healthy subjects to assess safety, tolerability, pharmacokinetic (PK) and pharmacodynamic (PD) profiles. Overall, single doses of AZD9898 were well tolerated in Part 1 and no safety concerns were raised. Measurement of urinary LTE4 (uLTE4) normalised to creatinine was included as a PD marker to evaluate inhibition of LTC4s. The PD data showed that, at the highest dose tested (45 mg), AZD9898 reduced uLTE4 levels for 14 hours post dose with a maximum reduction at 6 hours post dose. The 45 mg dose was the highest dose possible due to exposure limitation margins. The required magnitude and duration of PD effect at a dose of 45 mg was lower than expected. Furthermore, modelling and simulation of data did not support that the required level of effect on uLTE4 inhibition could be reached even with twice daily dosing. Therefore, the decision was made to stop the multi-part Ph I study after only Part 1 and not progress into multiple dosing in healthy subjects or single dosing in asthmatic patients. These data provide an example of how early evaluation of a PD biomarker can be utilized for decision making in early clinical development.

Background: AZD9567 is a novel oral selective glucocorticoid receptor modulator (SGRM) developed to have an improved safety profile versus prednisolone while maintaining efficacy. Objectives: To show clinical evidence of safety differentiation for AZD9567 compared to prednisolone with respect to glucose homeostasis and to investigate the underlying effects on the glucose metabolism in human cell systems in vitro. Methods: In a dose escalation study (NCT02760316), healthy volunteers were randomized to a 5-day once-daily treatment with AZD9567 (20, 40, 80, 125 mg) or prednisolone (5, 20, 40 mg). We monitored the anti-inflammatory effect by TNFα release from ex vivo LPS stimulated whole blood and modelled the relationship with pharmacokinetics (PKPD). Plasma glucose was measured (AUC0–4h) during an oral glucose tolerance test (OGTT) before and after four days of treatment. We also studied the effects of AZD9567 and prednisolone on mRNA expression of key gluconeogenesis enzymes (tyrosine aminotransferease (TAT), phosphoenolpyruvate carboxykinase (PEPCK) and glucose 6-phosphatase (G6Pase)) in primary human hepatocytes as well as on insulin secretion in human islets microtissues. Results: The inhibition of TNFα release increased with the plasma concentrations of AZD9567. PKPD modeling of TNFα inhibition data showed that 40 mg AZD9567 results in an anti-inflammatory activity similar to that predicted for 20 mg prednisolone. A significantly smaller increase of the glucose AUC was observed following treatment with 20 to 125 mg AZD9567 compared to 20 mg prednisolone (p We found significant induction of TAT, PEPCK, and G6Pase mRNA in hepatocytes after prednisolone treatment. In contrast, AZD9567 did not change the expression of these enzymes and inhibited the effect of prednisolone when co-administered. Furthermore, AZD9567 showed less suppression of insulin secretion in human islets microtissues compared to prednisolone at concentrations with comparable inhibition of TNFα release, resulting in a 12-fold better therapeutic ratio. Conclusions: In healthy individuals, AZD9567 showed significantly reduced effect on glucose homeostasis versus prednisolone at doses with similar anti-inflammatory activity. Thus, AZD9567 shows potential as an anti-inflammatory treatment with an improved metabolic safety profile compared to prednisolone. Disclosure of Interest: T. Hegelund Myrback Employee of: AstraZeneca, S. Prothon Employee of: AstraZeneca, M. Dearman Employee of: AstraZeneca, G. Edenro Employee of: AstraZeneca (former emplyee), J. Leander Employee of: AstraZeneca, M. Hashemi Employee of: AstraZeneca, G. de Miquel Employee of: AstraZeneca (former emplyee), R. Fuhr Employee of: Parexel International GmbH, T. Kornicke Employee of: Parexel International GmbH, Z. Tahib Employee of: AstraZeneca, E.-M. Andersson Employee of: AstraZeneca, P. Svanberg Employee of: AstraZeneca, K. Edman Employee of: AstraZeneca, R. Hendrickx Employee of: AstraZeneca, C. Keen Employee of: AstraZeneca, M. Kraan Employee of: AstraZeneca (former emplyee), K. Johansson Employee of: AstraZeneca (former emplyee), U. Eriksson Employee of: AstraZeneca, B. Carlsson Employee of: AstraZeneca, O. Vaarala Employee of: AstraZeneca

Background: Glucocorticoids (GC) are highly effective in the treatment of inflammatory diseases but chronic treatment is limited by severe adverse effects including hyperglycemia and bone re-modelling. AZD9567 is a novel, orally delivered, non-steroidal Selective Glucocorticoid Receptor Modulator (SGRM) with the potential to demonstrate an improved therapeutic ratio (TR) compared to steroidal GC such as prednisolone. Objectives: To investigate the effects of AZD9567 and prednisolone on biomarkers of inflammation, glucose metabolism and bone re-modelling in pre-clinical models. To confirm the inhibition of inflammatory biomarker production and to evaluate safety and pharmacokinetics (PK) of AZD9567 in a first clinical study. Methods: The effects on biomarkers of gluconeogenesis (tyrosine aminotransferase, TAT mRNA), bone re-modelling (osteoprotegerin, OPG mRNA) and anti-inflammatory activity (TNFα) were evaluated in vitro using human hepatocytes, an osteoblast cell line and whole blood, respectively. In vivo, effects on plasma insulin and osteocalcin levels were compared with inhibition of whole blood TNFα release in rats. Efficacy was evaluated in an adjuvant-induced rat arthritis model. In a human single ascending dose study the effect of AZD9567 on TNFα inhibition was investigated, together with assessment of safety profile and PK. Results: Potent in vitro anti-inflammatory activity (IC50,u6.2 nM, 7-fold more potent than prednisolone) was observed, whilst no effect on TAT mRNA expression in human hepatocytes was detected for AZD9567 (prednisolone EC5092 nM). This resulted in a substantially better TR compared to prednisolone. Furthermore, AZD9567 showed a 7-fold superior TR compared to prednisolone based on OPG mRNA expression in human osteoblasts. An improved profile for AZD9567 was also demonstrated in vivo in the rat (TR of 7.5 for osteocalcin and 3.6 for insulin). Efficacy was demonstrated in the rat arthritis model where an inhibition of joint inflammation was observed (ED500.1 mg/kg). In human, AZD9567 was safe and well tolerated after single doses (2–155 mg). The PK properties showed a fast absorption with a median tmax of 0.50 to 1.25 hour and a dose-dependent increase in exposure, with a mean terminal half-life of 3.9 to 6.4 hours, suitable for a once daily dosing regimen. TNFa release was inhibited in a concentration-dependent manner (IC50,u5.2 nM), consistent with pre-clinical findings. Conclusions: In pre-clinical models, AZD9567 demonstrated anti-inflammatory activity with a reduced effect on gluconeogenesis and biomarkers of bone re-modelling compared to prednisolone. Single oral dosing of AZD9567 was well tolerated and showed good PK properties in healthy subjects. These results support that AZD9567 has the potential to improve the treatment of several inflammatory diseases with a better TR compared to prednisolone. AZD9567 is currently in clinical evaluation in rheumatoid arthritis. Disclosure of Interest: R. Hendrickx Employee of: AstraZeneca, T. Hegelund-Myrback Employee of: AstraZeneca, M. Dearman Employee of: AstraZeneca, S. Prothon Employee of: AstraZeneca, G. Edenro Employee of: AstraZeneca (former employee), J. Leander Employee of: AstraZeneca, R. Fuhr Employee of: Parexel International GmbH, T. Kornicke Employee of: Parexel International GmbH, P. Svanberg Employee of: AstraZeneca, A. Berggren Employee of: AstraZeneca, T. Drmota Employee of: AstraZeneca (former employee), C. Keen Employee of: AstraZeneca, U. Eriksson Employee of: AstraZeneca

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.

In this paper we consider the problem of estimating parameters in ordinary differential equations given discrete time experimental data. The impact of going from an ordinary to a stochastic differential equation setting is investigated as a tool to overcome the problem of local minima in the objective function. Using two different models, it is demonstrated that by allowing noise in the underlying model itself, the objective functions to be minimized in the parameter estimation procedures are regularized in the sense that the number of local minima is reduced and better convergence is achieved. The advantage of using stochastic differential equations is that the actual states in the model are predicted from data and this will allow the prediction to stay close to data even when the parameters in the model is incorrect. The extended Kalman filter is used as a state estimator and sensitivity equations are provided to give an accurate calculation of the gradient of the objective function. The method is illustrated using in silico data from the FitzHugh-Nagumo model for excitable media and the Lotka-Volterra predator-prey system. The proposed method performs well on the models considered, and is able to regularize the objective function in both models. This leads to parameter estimation problems with fewer local minima which can be solved by efficient gradient-based methods.

In my dissertation, I study how firms with competing technologies are competing first to shape the market's cognition about what those technologies really are. When a new technology is brought to market, potential buyers may be unsure as to what the product is, and why it exists. For the technology to succeed, market participants have to develop some sort of understanding of it. Participants' technological frames form the cognitive underpinnings of purchase, research, and investment decisions, and can influence the technology's development trajectory (Kaplan & Tripsas, 2008). These frames are often heavily influenced by discourse in the marketplace. In these framing contests, firms battle to shape how participants frame the technology. By shaping the collective cognition of the market, firms aim to make their design dominant, and ultimately triumph in the economic contest. Prior research on dominant design has emphasized technical and economic factors, only recently considering cognitive factors. I identify factors in technology contests that make these markets act like social movements, and theorize that some of the previously identified technical and economic factors are likely mediated by the participants' frames. Empirically, I examine the smartphone industry, with emergence of the dominant keyboard and then multitouch designs. I make a novel contribution to by using latent semantic analysis (LSA) {Landauer, 2006 #2073} to study framing discourse. I use LSA in multiple ways to explore how the technology and market evolved. Various kinds of visualizations offer distinct advantages and disadvantages in exploring and understanding the meanings extant in a semantic space. I find four distinct periods in the evolution of smartphones, from 1992-2010. These are consistent with both framing and technology lifecycle theories. I find that LSA can be used to unpack the meanings of terms within a set of text, and that the content of a document can be used to identify the firm which produced it, indicating differences in content between firms. The data suggests the importance of both discursive and material framing in technology battles. It also suggests ways in which discursive opportunity can be manipulated as part of a framing contest.

Inclusion of stochastic differential equations in mixed effects models provides means to quantify and distinguish three sources of variability in data. In addition to the two commonly encountered sources, measurement error and interindividual variability, we also consider uncertainty in the dynamical model itself. To this end, we extend the ordinary differential equation setting used in nonlinear mixed effects models to include stochastic differential equations. The approximate population likelihood is derived using the first-order conditional estimation with interaction method and extended Kalman filtering. To illustrate the application of the stochastic differential mixed effects model, two pharmacokinetic models are considered. First, we use a stochastic one-compartmental model with first-order input and nonlinear elimination to generate synthetic data in a simulated study. We show that by using the proposed method, the three sources of variability can be successfully separated. If the stochastic part is neglected, the parameter estimates become biased, and the measurement error variance is significantly overestimated. Second, we consider an extension to a stochastic pharmacokinetic model in a preclinical study of nicotinic acid kinetics in obese Zucker rats. The parameter estimates are compared between a deterministic and a stochastic NiAc disposition model, respectively. Discrepancies between model predictions and observations, previously described as measurement noise only, are now separated into a comparatively lower level of measurement noise and a significant uncertainty in model dynamics. These examples demonstrate that stochastic differential mixed effects models are useful tools for identifying incomplete or inaccurate model dynamics and for reducing potential bias in parameter estimates due to such model deficiencies.

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.

Data were pooled from several studies on nicotinic acid (NiAc) intervention of fatty acid turnover in normal Sprague–Dawley and obese Zucker rats in order to perform a joint PKPD of data from more than 100 normal Sprague–Dawley and obese Zucker rats, exposed to several administration routes and rates. To describe the difference in pharmacodynamic parameters between obese and normal rats, we modified a previously published nonlinear mixed effects model describing tolerance and oscillatory rebound effects of NiAc on nonesterified fatty acids plasma concentrations. An important conclusion is that planning of experiments and dose scheduling cannot rely on pilot studies on normal animals alone. The obese rats have a less-pronounced concentration–response relationship and need higher doses to exhibit desired response. The relative level of fatty acid rebound after cessation of NiAc administration was also quantified in the two rat populations. Building joint normal-disease models with scaling parameter(s) to characterize the “degree of disease” can be a useful tool when designing informative experiments on diseased animals, particularly in the preclinical screen. Data were analyzed using nonlinear mixed effects modeling, for the optimization, we used an improved method for calculating the gradient than the usually adopted finite difference approximation. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 103:2571–2584, 2014