A model-based approach to predict individual weight loss with semaglutide in people with overweight or obesity.

AIMS: To determine the relationship between exposure and weight-loss trajectories for the glucagon-like peptide-1 analogue semaglutide for weight management. MATERIALS AND METHODS: Data from one 52-week, phase 2, dose-ranging trial (once-daily subcutaneous semaglutide 0.05-0.4 mg) and two 68-week phase 3 trials (once-weekly subcutaneous semaglutide 2.4 mg) for weight management in people with overweight or obesity with or without type 2 diabetes were used to develop a population pharmacokinetic (PK) model describing semaglutide exposure. An exposure-response model describing weight change was then developed using baseline demographics, glycated haemoglobin and PK data during treatment. The ability of the exposure-response model to predict 1-year weight loss based on weight data collected at baseline and after up to 28 weeks of treatment, was assessed using three independent phase 3 trials. RESULTS: Based on population PK, exposure levels over time consistently explained the weight-loss trajectories across trials and dosing regimens. The exposure-response model had high precision and limited bias for predicting body weight loss at 1 year in independent datasets, with increased precision when data from later time points were included in the prediction. CONCLUSION: An exposure-response model has been established that quantitatively describes the relationship between systemic semaglutide exposure and weight loss and predicts weight-loss trajectories for people with overweight or obesity who are receiving semaglutide doses up to 2.4 mg once weekly.

Optimal Monitoring of Weekly IGF-I Levels During Growth Hormone Therapy With Once-Weekly Somapacitan.

CONTEXT: Somapacitan is a long-acting growth hormone (GH) in development for once-weekly treatment of GH deficiency (GHD). Optimal monitoring of insulin-like growth factor-I (IGF-I) levels must account for weekly IGF-I fluctuations following somapacitan administration. OBJECTIVE: To develop and assess the reliability of linear models for predicting mean and peak IGF-I levels from samples taken on different days after dosing. DESIGN: A pharmacokinetic/pharmacodynamic model was used to simulate IGF-I data in adults and children following weekly somapacitan treatment of GHD. SETTING AND PATIENTS: 39 200 IGF-I profiles were simulated with reference to data from 26 adults and 23 children with GHD. INTERVENTION(S): The simulated dose range was 0.02 to 0.12 mg/kg for adults and 0.02 to 0.16 mg/kg for children. Simulated data with >4 average standard deviation score were excluded. MAIN OUTCOME MEASURE(S): Linear models for predicting mean and peak IGF-I levels based on IGF-I samples from different days after somapacitan dose. RESULTS: Robust linear relationships were found between IGF-I sampled on any day after somapacitan dose and the weekly mean (R2 > 0.94) and peak (R2 > 0.84). Prediction uncertainties were generally low when predicting mean from samples taken on any day (residual standard deviation [RSD] ≤ 0.36) and peak from samples taken on day 1 to 4 (RSD ≤ 0.34). IGF-I monitoring on day 4 and day 2 after dose provided the most accurate estimate of IGF-I mean (RSD < 0.2) and peak (RSD < 0.1), respectively. CONCLUSIONS: Linear models provided a simple and reliable tool to aid optimal monitoring of IGF-I by predicting mean and peak IGF-I levels based on an IGF-I sample following dosing of somapacitan. A short visual summary of our work is available (1).

Impact of dose-escalation schemes and drug discontinuation on weight loss outcomes with liraglutide 3.0 mg: A model-based approach.

AIMS: To investigate the impact on weight loss of the treatment changes in overweight or obese people that may be needed in case of gastrointestinal (GI) tolerability issues during escalation of the glucagon-like peptide-1 analogue liraglutide. MATERIALS AND METHODS: The individual longitudinal body weight data from the main trial periods of three phase II/III trials in overweight or obese patients (56-week treatment with once-daily liraglutide 1.2, 1.8, 2.4 or 3.0 mg or placebo, n = 4952) were analysed using a non-linear mixed-effect modelling approach. Individual pharmacokinetic profiles were derived based on published pharmacokinetic models. Baseline body weight, baseline glycated haemoglobin (HbA1c), age, gender, diabetes status (no diabetes, prediabetes or type 2 diabetes), race and trial region were investigated as covariates. As a form of external validation, the model was used to predict the weight regain after treatment cessation at week 56 (data not included in model development). RESULTS: A pharmacokinetic/pharmacodynamic model provided an adequate description of the weight loss trajectories for all studied doses. Gender and diabetes status were identified as the most influential covariates, and an underlying seasonal weight fluctuation was identified. Slower than that recommended, one-week dose-escalation algorithms led up to 2 weeks slower initial weight loss but similar long-term weight loss trajectories. CONCLUSIONS: The relationship between liraglutide systemic exposure and weight loss was successfully established in overweight or obese people. The model could predict the time course of weight regain after treatment cessation and suggests that GI tolerability can be mitigated by slower escalation with only minor impact on the weight loss trajectory.

Optimizing Dose-Finding Studies for Drug Combinations Based on Exposure-Response Models.

Combinations of pharmacological treatments are increasingly being investigated for potentially higher clinical benefit, especially when the combined drugs are expected to act via synergistic interactions. The clinical development of combination treatments is particularly challenging, particularly during the dose-selection phase, where a vast range of possible combination doses exists. The purpose of this work was to evaluate the added value of using optimal design for guiding the dose allocation in drug combination dose-finding studies as compared with a typical drug-combination trial. Optimizations were performed using local [D(s)-optimality] and global [ED(s)-optimality] optimal designs to maximize the precision of model parameters in a number of potential exposure-response (E-R) surfaces. A compound criterion [D(s)/V-optimality] was used to optimize the precision of model predictions in specific parts of the E-R surfaces. Optimal designs provided unbiased estimates and significantly improved the accuracy of results relative to the typical design. It was possible to improve the efficiency and overall parameter precision up to 7832% and 96.6% respectively. When the compound criterion was used, the probability to accurately identify the optimal dose-combination increased from 71% for the typical design up to 91%. These results indicate that optimal design methodology in tandem with E-R analyses is a beneficial tool that can be used for appropriate dose allocation in dose-finding studies for drug combinations.

Pharmacokinetics and Pharmacodynamics of Once-Weekly Somapacitan in Children and Adults: Supporting Dosing Rationales with a Model-Based Analysis of Three Phase I Trials.

BACKGROUND: Somapacitan, a long-acting growth hormone (GH) derivative, has been well-tolerated in children with GH deficiency (GHD) and adults (healthy and adult GHD), in phase I, single- and multiple-dose trials, respectively, and has pharmacokinetic and pharmacodynamic properties supporting a once-weekly dosing regimen. OBJECTIVE: In the absence of a multiple-dose phase I trial in children with GHD, the aim was to develop a pharmacokinetic/pharmacodynamic model to predict somapacitan exposure and insulin-like growth factor-I (IGF-I) response after once-weekly multiple doses in both children and adults with GHD. METHODS: Pharmacokinetic/pharmacodynamic models were developed from pharmacokinetic and IGF-I profiles in three phase I trials of somapacitan (doses: healthy adults, 0.01-0.32 mg/kg; adult with GHD, 0.02-0.12 mg/kg; children with GHD, 0.02-0.16 mg/kg) using non-linear mixed-effects modeling. Pharmacokinetics were described using a non-linear one-compartment model with dual first- and zero-order absorption through a transit compartment, with saturable elimination. IGF-I profiles were described using an indirect response pharmacokinetic/pharmacodynamic model, with sigmoidal-effect relationship. RESULTS: The non-linear pharmacokinetic and IGF-I data were well-described in order to confidently predict pharmacokinetic/pharmacodynamic profiles after multiple doses in adults and children with GHD. Body weight was found to be a significant covariate, predictive of the differences observed in the pharmacokinetics and pharmacodynamics between children and adults. Weekly dosing of somapacitan provided elevated IGF-I levels throughout the week, despite little or no accumulation of somapacitan, in both adults and children with GHD. CONCLUSION: This analysis of somapacitan pharmacokinetic/pharmacodynamic data supports once-weekly dosing in adults and children with GHD. TRIAL REGISTRATION: ClinicalTrials.gov identifier numbers NCT01514500, NCT01706783, NCT01973244.

Semaglutide s.c. Once-Weekly in Type 2 Diabetes: A Population Pharmacokinetic Analysis.

INTRODUCTION: Semaglutide, a new treatment option approved for the treatment of patients with type 2 diabetes mellitus, is a glucagon-like peptide-1 receptor agonist to be injected subcutaneously once weekly. This analysis used a population pharmacokinetic model of semaglutide to identify clinically relevant covariates for exposure. METHODS: A total of 1612 patients with up to seven pharmacokinetic observations each were included in the analysis. All subjects had type 2 diabetes mellitus and were enrolled in one of five trials in the phase III development program for subcutaneous semaglutide once weekly (the SUSTAIN program). The treatment duration of the trials varied from 30 to 104 weeks. RESULTS: No clinically relevant effects on the exposure were seen for sex, age, race, ethnicity, renal function, or injection site used, and semaglutide exposure was stable over time. Of the covariates chosen, only body weight had a relevant effect on the exposure of semaglutide. Few subjects developed semaglutide antibodies, and the antibodies had no effect on exposure. Dose proportionality was shown for the 0.5 mg and 1.0 mg maintenance doses of semaglutide. CONCLUSION: The population pharmacokinetic study showed that semaglutide exposure is not affected by covariates other than body weight at either a maintenance dose of 0.5 or 1.0 mg semaglutide. Therefore, we conclude that no semaglutide dose adjustments are needed in different populations. This finding is to be further explored in an exposure-response analysis. TRIAL REGISTRATION: The trials were registered at ClinicalTrials.gov (identifiers: NCT02054897, NCT01930188, NCT01885208, NCT01720446 and NCT02207374). FUNDING: Novo Nordisk A/S, Bagsværd, Denmark.

Feasibility of Exposure-Response Analyses for Clinical Dose-Ranging Studies of Drug Combinations.

The exposure-response relationship of combinatory drug effects can be quantitatively described using pharmacodynamic interaction models, which can be used for the selection of optimal dose combinations. The aim of this simulation study was to evaluate the reliability of parameter estimates and the probability for accurate dose identification for various underlying exposure-response profiles, under a number of different phase II designs. An efficacy variable driven by the combined exposure of two theoretical compounds was simulated and model parameters were estimated using two different models, one estimating all parameters and one assuming that adequate previous knowledge for one drug is readily available. Estimation of all pharmacodynamic parameters under a realistic, in terms of sample size and study design, phase II trial, proved to be challenging. Inaccurate estimates were found in all exposure-response scenarios, except for situations where no pharmacodynamic interaction was present, with the drug potency and interaction parameters being the hardest to estimate. When previous knowledge of the exposure-response relationship of one of the monocomponents is available, such information should be utilized, as it enabled relevant improvements in parameter estimation and in correct dose identification. No general trends for classification of the performance of the tested study designs across different scenarios could be identified. This study shows that pharmacodynamic interactions models can be used for the exposure-response analysis of clinical endpoints especially when accompanied by appropriate dose selection in regard to the expected drug potencies and appropriate trial size and if information regarding the exposure-response profile of one monocomponent is available.

Anti-NKG2D monoclonal antibody (NNC0142-0002) in active Crohn's disease: a randomised controlled trial.

OBJECTIVE: Anti-NKG2D (NNC0142-0002) is an antagonising human immunoglobulin G4 monoclonal antibody that binds to natural killer group 2 member D (NKG2D) receptors, which are expressed by T cells and innate lymphoid cells, and may be linked to mucosal damage in Crohn's disease (CD). DESIGN: Seventy-eight patients (aged ≥18 and ≤75 years) with CD for ≥3 months, Crohn's Disease Activity Index (CDAI) ≥220 and ≤450 and either C-reactive protein ≥10 mg/L or endoscopic evidence of inflammation, were randomised 1:1 to a single subcutaneous (SC) dose of 2 mg/kg anti-NKG2D or placebo. Primary endpoint was change in CDAI (ΔCDAI) from baseline to week 4. Prespecified significance level was 10% for CDAI endpoints. A futility analysis was instituted due to slow recruitment. RESULTS: Primary endpoint was not significantly different between anti-NKG2D and placebo (week 4 ΔCDAI=-16); however, there was a significant difference by week 12 (ΔCDAI=-55; p≤0.10). Significant improvements were noted in the non-failure to biologics subgroup (treated with anti-NKG2D (n=28)) from week 1 onward. Greater effects of anti-NKG2D were also observed in patients with baseline CDAI ≥330. Frequencies of adverse events (AEs) were comparable between anti-NKG2D and placebo. Most AEs were mild (49%) or moderate (43%). No antidrug antibodies were observed. CONCLUSIONS: A single SC dose of 2 mg/kg anti-NKG2D did not reduce disease activity at week 4 versus placebo, but the difference was significant at week 12, and effects were evident in key subgroups. These data support further development of anti-NKG2D in IBD. TRIAL REGISTRATION NUMBER: NCT01203631.

Clinical pharmacokinetics of the anti-interleukin-20 monoclonal antibody NNC0109-0012 in healthy volunteers and patients with psoriasis or rheumatoid arthritis.

INTRODUCTION: NNC0109-0012, a novel human monoclonal antibody that binds to and neutralizes the activity of interleukin-20, was investigated as a potential treatment for inflammatory diseases. Pharmacokinetic (PK) modeling was performed using data from four completed clinical phase 1/2 trials to better understand the clinical PK of NNC0109-0012. METHODS: The populations included were patients with rheumatoid arthritis (RA), chronic plaque psoriasis, and healthy volunteers. NNC0109-0012 was administered subcutaneously at various dose levels (0.01-3 mg/kg) as single dose, once weekly, or multiple doses every second week for up to 12 doses. Noncompartmental methods were used to describe the PK parameters. Population PK was analyzed using nonlinear mixed-effects modeling, with body weight as the main covariate and gender, age, and population as additional covariates. RESULTS: Across studies (N = 116), mean age and body weight ranged from 38 to 58 years and 72 to 96 kg, respectively. NNC0109-0012 displays linear PK. Time to maximum plasma concentration occurred at approximately 1 week, and the terminal half-life was approximately 3 weeks. Clearance and volume of distribution increased proportionally to body weight. No difference in clearance or volume of distribution was observed between gender or different age groups; however, clearance was slightly lower in healthy volunteers than in patients with RA. CONCLUSION: The PK profile of NNC0109-0012 is similar to other monoclonal antibodies directed against soluble targets.

Pharmacokinetics of an anti-TFPI monoclonal antibody (concizumab) blocking the TFPI interaction with the active site of FXa in Cynomolgus monkeys after iv and sc administration.

INTRODUCTION: Concizumab (mAb 2021) is a monoclonal IgG4 antibody (mAb) that binds to the Kunitz-type protease inhibitor (KPI) 2 domain of TFPI thereby blocking the interaction of this domain with the active site of FXa. The objective of the present study was to characterize the pharmacokinetics of concizumab in Cynomolgus monkeys after intravenous (iv) and subcutaneous (sc) administration. METHODS: Data from two studies were included in the modelling, all in all data from 52 monkeys distributed into 9 groups. Three groups received three escalating sc doses of concizumab with a one week dosing interval, two groups were administered a single dose, and four groups received multiple doses over 13 weeks of concizumab. The plasma concentration was measured using a standard ELISA, and pharmacokinetic data were analysed using NONMEM. RESULTS: The pharmacokinetics of concizumab were characterised by a high bioavailability (93%) after sc administration. The time course of the elimination of concizumab from the circulation was well described by the proposed target mediated drug disposition (TMDD) model. The clearance of concizumab was estimated to be 0.14 ml/h/kg, the target clearance was characterized by a 50% saturation level of 0.54 µg/ml (Km), and the clearance at target saturation was estimated to be 11 µg/h/kg. CONCLUSION: Concizumab displays a typical TMDD profile with important implications for a putative treatment regime in haemophilia patients. Compared to current standard haemophilia treatment, concizumab has a high bioavailability after sc administration and may provide a viable alternative to intravenous dosing for the treatment of haemophilia.

An integrated disease/pharmacokinetic/pharmacodynamic model suggests improved interleukin-21 regimens validated prospectively for mouse solid cancers.

Interleukin (IL)-21 is an attractive antitumor agent with potent immunomodulatory functions. Yet thus far, the cytokine has yielded only partial responses in solid cancer patients, and conditions for beneficial IL-21 immunotherapy remain elusive. The current work aims to identify clinically-relevant IL-21 regimens with enhanced efficacy, based on mathematical modeling of long-term antitumor responses. For this purpose, pharmacokinetic (PK) and pharmacodynamic (PD) data were acquired from a preclinical study applying systemic IL-21 therapy in murine solid cancers. We developed an integrated disease/PK/PD model for the IL-21 anticancer response, and calibrated it using selected "training" data. The accuracy of the model was verified retrospectively under diverse IL-21 treatment settings, by comparing its predictions to independent "validation" data in melanoma and renal cell carcinoma-challenged mice (R(2)>0.90). Simulations of the verified model surfaced important therapeutic insights: (1) Fractionating the standard daily regimen (50 µg/dose) into a twice daily schedule (25 µg/dose) is advantageous, yielding a significantly lower tumor mass (45% decrease); (2) A low-dose (12 µg/day) regimen exerts a response similar to that obtained under the 50 µg/day treatment, suggestive of an equally efficacious dose with potentially reduced toxicity. Subsequent experiments in melanoma-bearing mice corroborated both of these predictions with high precision (R(2)>0.89), thus validating the model also prospectively in vivo. Thus, the confirmed PK/PD model rationalizes IL-21 therapy, and pinpoints improved clinically-feasible treatment schedules. Our analysis demonstrates the value of employing mathematical modeling and in silico-guided design of solid tumor immunotherapy in the clinic.

Population stochastic modelling (PSM)--an R package for mixed-effects models based on stochastic differential equations.

The extension from ordinary to stochastic differential equations (SDEs) in pharmacokinetic and pharmacodynamic (PK/PD) modelling is an emerging field and has been motivated in a number of articles [N.R. Kristensen, H. Madsen, S.H. Ingwersen, Using stochastic differential equations for PK/PD model development, J. Pharmacokinet. Pharmacodyn. 32 (February(1)) (2005) 109-141; C.W. Tornøe, R.V. Overgaard, H. Agersø, H.A. Nielsen, H. Madsen, E.N. Jonsson, Stochastic differential equations in NONMEM: implementation, application, and comparison with ordinary differential equations, Pharm. Res. 22 (August(8)) (2005) 1247-1258; R.V. Overgaard, N. Jonsson, C.W. Tornøe, H. Madsen, Non-linear mixed-effects models with stochastic differential equations: implementation of an estimation algorithm, J. Pharmacokinet. Pharmacodyn. 32 (February(1)) (2005) 85-107; U. Picchini, S. Ditlevsen, A. De Gaetano, Maximum likelihood estimation of a time-inhomogeneous stochastic differential model of glucose dynamics, Math. Med. Biol. 25 (June(2)) (2008) 141-155]. PK/PD models are traditionally based ordinary differential equations (ODEs) with an observation link that incorporates noise. This state-space formulation only allows for observation noise and not for system noise. Extending to SDEs allows for a Wiener noise component in the system equations. This additional noise component enables handling of autocorrelated residuals originating from natural variation or systematic model error. Autocorrelated residuals are often partly ignored in PK/PD modelling although violating the hypothesis for many standard statistical tests. This article presents a package for the statistical program R that is able to handle SDEs in a mixed-effects setting. The estimation method implemented is the FOCE(1) approximation to the population likelihood which is generated from the individual likelihoods that are approximated using the Extended Kalman Filter's one-step predictions.

PKPD model of interleukin-21 effects on thermoregulation in monkeys--application and evaluation of stochastic differential equations.

PURPOSE: To describe the pharmacodynamic effects of recombinant human interleukin-21 (IL-21) on core body temperature in cynomolgus monkeys using basic mechanisms of heat regulation. A major effort was devoted to compare the use of ordinary differential equations (ODEs) with stochastic differential equations (SDEs) in pharmacokinetic pharmacodynamic (PKPD) modelling. METHODS: A temperature model was formulated including circadian rhythm, metabolism, heat loss, and a thermoregulatory set-point. This model was formulated as a mixed-effects model based on SDEs using NONMEM. RESULTS: The effects of IL-21 were on the set-point and the circadian rhythm of metabolism. The model was able to describe a complex set of IL-21 induced phenomena, including 1) disappearance of the circadian rhythm, 2) no effect after first dose, and 3) high variability after second dose. SDEs provided a more realistic description with improved simulation properties, and further changed the model into one that could not be falsified by the autocorrelation function. CONCLUSIONS: The IL-21 induced effects on thermoregulation in cynomolgus monkeys are explained by a biologically plausible model. The quality of the model was improved by the use of SDEs.