Quantifying the integrated physiological effects of endothelin-1 on cardiovascular and renal function in healthy subjects: a mathematical modeling analysis.

Endothelin-1 (ET-1) is a potent vasoconstrictor with strong anti-natriuretic and anti-diuretic effects. While many experimental studies have elucidated the mechanisms of ET-1 through its two receptors, ETA and ETB, the complexity of responses and sometimes conflicting data make it challenging to understand the effects of ET-1, as well as potential therapeutic antagonism of ET-1 receptors, on human physiology. In this study, we aimed to develop an integrated and quantitative description of ET-1 effects on cardiovascular and renal function in healthy humans by coupling existing experimental data with a mathematical model of ET-1 kinetics and an existing mathematical model of cardiorenal function. Using a novel agnostic and iterative approach to incorporating and testing potential mechanisms, we identified a minimal set of physiological actions of endothelin-1 through ETA and ETB receptors by fitting the physiological responses (changes in blood pressure, renal blood flow, glomerular filtration rate (GFR), and sodium/water excretion) to ET-1 infusion, with and without ETA/ETB antagonism. The identified mechanisms align with previous experimental studies on ET-1 and offer novel insights into the relative magnitude and significance of endothelin's effects. This model serves as a foundation for further investigating the mechanisms of ET-1 and its antagonists.

Population Pharmacokinetic Modeling of Cotadutide: A Dual Agonist Peptide of Glucagon-Like Peptide and Glucagon Receptors Administered to Participants with Type II Diabetes Mellitus, Chronic Kidney Disease, Obesity and Non-Alcoholic Steatohepatitis.

BACKGROUND: Cotadutide is a dual glucagon-like peptide-1 (GLP-1) and glucagon (GCG) receptor agonist peptide. The objective of this analysis was to develop a population pharmacokinetic (popPK) model of cotadutide, and to identify any potential effect on the PK from intrinsic and extrinsic covariates. METHODS: The popPK analysis utilized a non-linear mixed-effects modeling approach using the data from 10 clinical studies in different participant categories following once-daily subcutaneous dose administration ranging from 20 to 600 µg. Additionally, the covariates affecting cotadutide exposure were quantified, and the model performance was evaluated through the prediction-corrected visual predictive checks. RESULTS: A one-compartment model with first-order absorption and elimination adequately described the data as confirmed via visual predictive check plots and parameter plausibility. The mean values for cotadutide apparent clearance (CL/F), apparent volume of distribution (V/F), absorption rate constant (Ka), and half-life were 1.05 L/h, 20.0 L, 0.38 h-1, and 13.3 hours, respectively. Covariate modeling identified body weight, alanine transaminase, albumin, anti-drug antibody (ADA) titer values, formulation strength and injection device, and participant categories as significant covariates on PK parameters, where ADAs have been identified to decrease cotadutide clearance. The model demonstrated that a 150-kg participant was estimated to have 30% lower for both AUC and Cmax and a 66 kg participant was estimated to have 35% higher for both AUC and Cmax relative to a reference individual with a median weight of 96 kg. CONCLUSIONS: A popPK model was developed for cotadutide with cotadutide clinical data, and the impact of the statistically significant covariates identified was not considered clinically meaningful. The popPK model will be used to evaluate exposure-response relationships for cotadutide clinical data.

Dapagliflozin pharmacokinetics is similar between patients with heart failure with reduced ejection fraction and patients with type 2 diabetes mellitus.

Dapagliflozin was recently approved for use in adults with chronic heart failure with reduced ejection fraction (HFrEF) with/without type 2 diabetes mellitus (T2DM). The objectives of this analysis were to characterize dapagliflozin pharmacokinetics in patients with HFrEF and to compare dapagliflozin systemic exposure between adults with HFrEF with/without T2DM and adults with T2DM. A nonlinear mixed-effects modelling approach was applied; the population-pharmacokinetic model was developed using 9735 dapagliflozin plasma concentrations from 2744 patients. The final two-compartmental model adequately described the observed dapagliflozin concentrations, with a similar estimated apparent clearance compared with a previous estimate in patients with T2DM without HF and in healthy subjects (23.0 [95% CI: 22.6-23.9] L/h vs. 22.9 [95% CI: 22.1-23.7] L/h). The model-predicted median area under the dapagliflozin concentration-time profile was ≤1.2-fold higher in patients with HFrEF vs. those with T2DM without HFrEF, which is not considered clinically relevant. Dapagliflozin exposure was similar between patients with HFrEF with/without T2DM and T2DM patients without HFrEF.

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.

Bilirubin levels and kidney function decline: An analysis of clinical trial and real world data.

OBJECTIVE: To evaluate if previously found associations between low serum bilirubin concentration and kidney function decline is independent of hemoglobin and other key confounders. RESEARCH DESIGN AND METHODS: Clinical trial data from the SAVOR-TIMI 53 trial as well as the UK primary care electronic healthcare records, Clinical Practice Research Datalink (CPRD), were used to construct three cohorts of patients at risk of chronic kidney disease (CKD). The randomized clinical trial (RCT) cohort from the subset of SAVOR-TIMI 53 trial consisted of 10,555 type-2 diabetic patients with increased risk of cardiovascular disease. The two observational data cohorts from CPRD consisted of 71,104 newly diagnosed type-2 diabetes (CPRD-DM2) and 82,065 newly diagnosed hypertensive (CPRD-HT) patients without diabetes. Cohorts were stratified according to baseline circulating total bilirubin levels to determine association on the primary end point of a 30% reduction from baseline in estimated glomerular filtration rate (eGFR) and the secondary end point of albuminuria. RESULTS: The confounder adjusted hazard ratios of the subpopulation with lower than median bilirubin levels compared to above median bilirubin levels for the primary end point were 1.18 (1.02-1.37), 1.12 (1.05-1.19) and 1.09 (1.01-1.17), for the secondary end point were 1.26 (1.06-1.52), 1.11 (1.01-1.21) and 1.18 (1.01-1.39) for SAVOR-TIMI 53, CPRD-DM2, CPRD-HT, respectively. CONCLUSION: Our findings are consistent across all cohorts and endpoints: lower serum bilirubin levels are associated with a greater kidney function decline independent of hemoglobin and other key confounders. This suggests that increased monitoring of kidney health in patients with lower bilirubin levels may be considered, especially for diabetic patients.

Dapagliflozin Pharmacokinetics Is Similar in Adults With Type 1 and Type 2 Diabetes Mellitus.

Dapagliflozin improves glycemic control in patients with type 2 diabetes mellitus (T2DM) and is approved in Japanese patients with type 1 diabetes mellitus (T1DM) with inadequate glycemic control. The objectives of this work were to characterize the dapagliflozin pharmacokinetics (PK) in patients with T1DM, assess the influence of covariates on dapagliflozin PK, and compare dapagliflozin systemic exposure between patients with T1DM and T2DM. Population PK analysis was performed using a nonlinear mixed-effect modeling approach. The analysis included 5793 dapagliflozin plasma concentrations from 1150 adult patients with T1DM (global population), who were on routine insulin therapy, collected from 1 phase 2 (NCT01498185) and 2 phase 3 (DEPICT-1, NCT02268214; DEPICT-2, NCT02460978) studies. Covariate effects were investigated using stepwise covariate modeling. Model-derived area under the concentration-time curve (AUC) in patients with T1DM was compared to AUC in patients with T2DM (using data from historical dapagliflozin studies). The final 2-compartmental model adequately described the dapagliflozin concentrations in patients with T1DM. The estimated apparent clearance was 20.5 L/h. Renal function (measured as estimated glomerular filtration rate), sex, and body weight were identified as covariates, where patients with better renal function, male patients, and heavier patients had lower dapagliflozin systemic exposure. Among the covariates studied, none of the covariates affected dapagliflozin systemic exposure >1.4-fold compared to a reference individual and were therefore deemed to be not clinically relevant. Dapagliflozin systemic exposure was comparable between patients with T1DM and T2DM.

Assessing reproducibility and utility of clustering of patients with type 2 diabetes and established CV disease (SAVOR -TIMI 53 trial).

OBJECTIVE: To assess the reproducibility and clinical utility of clustering-based subtyping of patients with type 2 diabetes (T2D) and established cardiovascular (CV) disease. METHODS: The cardiovascular outcome trial SAVOR-TIMI 53 (n = 16,492) was used. Analyses focused on T2D patients with established CV disease. Unsupervised machine learning technique called "k-means clustering" was used to divide patients into subtypes. K-means clustering including HbA1c, age of diagnosis, BMI, HOMA2-IR and HOMA2-B was used to assign clusters to the following diabetes subtypes: severe insulin deficient diabetes (SIDD); severe insulin-resistant diabetes (SIRD); mild obesity-related diabetes (MOD); mild age-related diabetes (MARD). We refer these subtypes as "clustering-based diabetes subtypes". A simulation study using randomly generated data was conducted to understand how correlations between the above variables influence the formation of the cluster-based diabetes subtypes. The predictive utility of clustering-based diabetes subtypes for CV events (3-point MACE), renal function reduction (eGFR decrease >30%) and diabetic disease progression (introduction of additional anti-diabetic medication) were compared with conventional risk scores. Hazard ratios (HR) were estimated by Cox-proportional hazard models. RESULTS: In the SAVOR-TIMI 53 trial based dataset, the percentage of the clustering-based T2D subtypes were; SIDD (18%), SIRD (17%), MOD (29%), MARD (37%). Using the simulated dataset, the diabetes subtypes could be largely reproduced from a log-normal distribution when including known correlations between variables. The predictive utility of clustering-based diabetic subtypes on CV events, renal function reduction, and diabetic disease progression did not show an advantage compared to conventional risk scores. CONCLUSIONS: The consistent reproduction of four clustering-based T2D subtypes can be explained by the correlations between the variables used for clustering. Subtypes of T2D based on clustering had limited advantage compared to conventional risk scores to predict clinical outcome in patients with T2D and established CV disease.

Pharmacokinetics of Roxadustat: A Population Analysis of 2855 Dialysis- and Non-Dialysis-Dependent Patients with Chronic Kidney Disease.

BACKGROUND: Roxadustat is a novel, small-molecule, first-in-class therapeutic that stimulates erythropoiesis by inhibiting hypoxia-inducible factor prolyl hydroxylase enzymes. This agent (roxadustat) is in clinical development for the treatment of anemia in patients with non-dialysis-dependent (NDD) and dialysis-dependent (DD) chronic kidney disease. A population pharmacokinetic analysis was undertaken to evaluate the effect of intrinsic and extrinsic factors on roxadustat pharmacokinetics. METHODS: Non-linear mixed-effects models implemented in NONMEM software were fitted to 8209 pharmacokinetic samples from 2855 DD and NDD subjects enrolled in four phase III studies with roxadustat dose concentrations of 20-400 mg as orally administered tablets. Effects of intrinsic and extrinsic factors were evaluated using a stepwise covariate modeling procedure in combination with the full covariate approach, and defined no-effect boundaries for exposure were based on the difference in exposure between 70 and 100 mg of roxadustat (i.e., - 30%, + 43%). RESULTS: A two-compartment model with first-order absorption adequately described roxadustat pharmacokinetics, with parameter estimates (relative standard error) for apparent clearance of 1.1 (0.0223) L/h in NDD subjects, and apparent central and peripheral volumes of distribution of 14.9 (0.0278) L and 9.5 (0.0872) L, respectively. Stepwise covariate modeling identified bodyweight, dialysis status, race, and dose as statistically significant covariates on apparent clearance, and bodyweight, sex, and albumin as statistically significant covariates on apparent central volume of distribution. However, the effects of these covariates did not result in roxadustat area under the curve or maximum plasma concentration changes outside of the defined no-effect boundaries. The effects of concomitant oral iron, clopidogrel, and staggered sevelamer, calcium carbonate, or calcium acetate were investigated using a full covariate approach but did not result in roxadustat area under the curve or maximum plasma concentration changes outside of the defined no-effect boundaries. CONCLUSIONS: A population pharmacokinetic model was developed for the pharmacokinetics of roxadustat in the target population. None of the investigated intrinsic or extrinsic factors resulted in a significant change in roxadustat exposure outside of the defined no-effect boundaries.

Dose-Response Mixed Models for Repeated Measures - a New Method for Assessment of Dose-Response.

PURPOSE: In this paper we investigated a new method for dose-response analysis of longitudinal data in terms of precision and accuracy using simulations. METHODS: The new method, called Dose-Response Mixed Models for Repeated Measures (DR-MMRM), combines conventional Mixed Models for Repeated Measures (MMRM) and dose-response modeling. Conventional MMRM can be applied for highly variable repeated measure data and is a way to estimate the drug effect at each visit and dose, however without any assumptions regarding the dose-response shape. Dose-response modeling, on the other hand, utilizes information across dose arms and describes the drug effect as a function of dose. Drug development in chronic kidney disease (CKD) is complicated by many factors, primarily by the slow progression of the disease and lack of predictive biomarkers. Recently, new approaches and biomarkers are being explored to improve efficiency in CKD drug development. Proteinuria, i.e. urinary albumin-to-creatinine ratio (UACR) is increasingly used in dose finding trials in patients with CKD. We use proteinuria to illustrate the benefits of DR-MMRM. RESULTS: The DR-MMRM had higher precision than conventional MMRM and less bias than a dose-response model on UACR change from baseline to end-of-study (DR-EOS). CONCLUSIONS: DR-MMRM is a promising method for dose-response analysis.

Comparative quantitative systems pharmacology modeling of anti-PCSK9 therapeutic modalities in hypercholesterolemia.

Since the discovery of proprotein convertase subtilisin/kexin type 9 (PCSK9) as an attractive target in the treatment of hypercholesterolemia, multiple anti-PCSK9 therapeutic modalities have been pursued in drug development. The objective of this research is to set the stage for the quantitative benchmarking of two anti-PCSK9 pharmacological modality classes, monoclonal antibodies (mAbs) and small interfering RNA (siRNA). To this end, we developed an integrative mathematical model of lipoprotein homeostasis describing the dynamic interplay between PCSK9, LDL-cholesterol (LDL-C), VLDL-cholesterol, HDL-cholesterol (HDL-C), apoB, lipoprotein a [Lp(a)], and triglycerides (TGs). We demonstrate that LDL-C decreased proportionally to PCSK9 reduction for both mAb and siRNA modalities. At marketed doses, however, treatment with mAbs resulted in an additional ∼20% LDL-C reduction compared with siRNA. We further used the model as an evaluation tool and determined that no quantitative differences were observed in HDL-C, Lp(a), TG, or apoB responses, suggesting that the disruption of PCSK9 synthesis would provide no additional effects on lipoprotein-related biomarkers in the patient segment investigated. Predictive model simulations further indicate that siRNA therapies may reach reductions in LDL-C levels comparable to those achieved with mAbs if the current threshold of 80% PCSK9 inhibition via siRNA could be overcome.

Population Pharmacokinetics/Pharmacodynamics of Ticagrelor in Children with Sickle Cell Disease.

BACKGROUND AND OBJECTIVE: Ticagrelor, a reversible P2Y12 platelet inhibitor, is under investigation as a sickle cell disease (SCD) therapy in children. HESTIA1 (NCT02214121) was the first ticagrelor study generating pharmacokinetic (PK), pharmacodynamic (PD, P2Y12 reactivity units [PRU]), and safety data in 45 pediatric SCD patients. Population PK and PK/PD relationships for ticagrelor were quantified using a PK approach. METHODS: An adult population PK model was refined to describe ticagrelor and AR-C124910XX (active metabolite) plasma concentration and time data over a wide range of single/repeated ticagrelor doses (0.125-2.25 mg/kg). Population PK/PD modeling was used to describe the time course and extent of platelet inhibition. Demographic covariate relationships were investigated. RESULTS: The final population PK model adequately described ticagrelor and AR-C124910XX plasma concentrations over time. An allometric body weight relationship between ticagrelor and AR-C124910XX clearances and volumes of distribution was used. Significant covariates for ticagrelor were sex (relative bioavailability) and cholecystectomy (central volume of distribution). Estimated oral clearances (35 kg patient; median bodyweight) were 22.8 L/h (ticagrelor) and 9.97 L/h (AR-C124910XX). The final population PK/PD model well-described the time course and extent of platelet inhibition. Estimated baseline PRU was 283, maximum PRU effect was fixed at 1, and the ticagrelor concentration for half-maximum PRU effect was 233 nmol/L. CONCLUSIONS: These analyses offer the first quantitative characterization of the dose-exposure-response relationship for ticagrelor in pediatric SCD patients. This model-based approach may be used to inform dose selection and design of subsequent studies that aim to define ticagrelor safety and efficacy in pediatric SCD patients.

Model-based characterization of the relationship between dapagliflozin systemic exposure and HbA1c response in patients with type 1 diabetes mellitus.

AIMS: To quantitatively describe the relationship between dapagliflozin systemic exposure and HbA1c response among patients with type 1 diabetes mellitus (T1DM) and assess the potential impact of covariate effects. MATERIALS AND METHODS: Individual longitudinal HbA1c data from two phase 3 studies in patients with T1DM (24-week treatment with once-daily dapagliflozin 5 or 10 mg or placebo, with adjustable insulin) were analyzed using a non-linear mixed effect modeling approach. Area under the concentration curve was used to measure dapagliflozin systemic exposure. Baseline HbA1c, estimated glomerular filtration rate, reduction in total insulin dose, baseline glucose concentrations, age, sex, race (Asian vs. non-Asian), and insulin administration method (multiple daily injections vs. insulin pump) were assessed as covariates. RESULTS: A maximum effect (Emax ) model identified a positive exposure-response relationship. Model-predicted placebo-corrected HbA1c reductions after 24 weeks for dapagliflozin 5- and 10-mg doses were - 0.42% [95% confidence interval (CI) -0.47 to -0.36) and - 0.45% (95% CI -0.50 to -0.40), respectively; baseline HbA1c was ~8.4%. This was in good agreement with actual observations from both studies. Baseline HbA1c was a significant covariate: patients with higher baseline HbA1c were predicted to have greater HbA1c reductions. CONCLUSIONS: The relationship between dapagliflozin systemic exposure and HbA1c response was successfully described in patients with T1DM. None of the tested covariates affected the efficacy of dapagliflozin to a clinically relevant extent. Therefore, no dose adjustment of dapagliflozin is required in patients with T1DM based on the tested covariates. ClinicalTrials.gov, NCT02268214; NCT02460978.

Pharmacokinetic-pharmacodynamic modelling of platelet response to ticagrelor in stable coronary artery disease and prior myocardial infarction patients.

AIMS: To characterize ticagrelor exposure-response relationship for platelet inhibition in patients with stable coronary artery disease (CAD) and a history of myocardial infarction (MI), using nonlinear mixed effects modelling and simulation. METHODS: Platelet function data were integrated with plasma concentration data of ticagrelor and its active metabolite AR-C1249010XX in a population pharmacokinetic (PK) and pharmacodynamic (PD) model, based on two clinical studies. In the ONSET/OFFSET study, PK and platelet function were assessed in 123 CAD patients receiving placebo, ticagrelor (180 mg followed by 90 mg twice daily) or clopidogrel (600 mg followed by 75 mg once daily). In the PEGASUS-TIMI 54 platelet function substudy, PK and platelet function were assessed during maintenance dosing in 180 prior MI patients receiving placebo, ticagrelor 60 mg or ticagrelor 90 mg twice daily. RESULTS: Platelet inhibition by ticagrelor was described by a sigmoidal Emax model. On average, half maximal inhibition was reached at ticagrelor concentrations of 116 (RSE: 5.3%) nmol l-1 . Simulations showed that near maximal platelet inhibition is achieved with both ticagrelor 60 and 90 mg twice daily. At simulated lower doses, platelet inhibition is overall reduced, more variable between patients, and show greater peak-to-trough variability. Ticagrelor antiplatelet response was similar between the studied patient populations. CONCLUSIONS: In patients with stable CAD or a history of MI, near maximal platelet inhibition is achieved with both ticagrelor 60 and 90 mg twice daily. At modelled doses <60 mg, the response is reduced overall, more variable between patients, and patients will display greater peak-to-trough variability.

Impact of a five-dimensional framework on R&D productivity at AstraZeneca.

In 2011, AstraZeneca embarked on a major revision of its research and development (R&D) strategy with the aim of improving R&D productivity, which was below industry averages in 2005-2010. A cornerstone of the revised strategy was to focus decision-making on five technical determinants (the right target, right tissue, right safety, right patient and right commercial potential). In this article, we describe the progress made using this '5R framework' in the hope that our experience could be useful to other companies tackling R&D productivity issues. We focus on the evolution of our approach to target validation, hit and lead optimization, pharmacokinetic/pharmacodynamic modelling and drug safety testing, which have helped improve the quality of candidate drug nomination, as well as the development of the right culture, where 'truth seeking' is encouraged by more rigorous and quantitative decision-making. We also discuss where the approach has failed and the lessons learned. Overall, the continued evolution and application of the 5R framework are beginning to have an impact, with success rates from candidate drug nomination to phase III completion improving from 4% in 2005-2010 to 19% in 2012-2016.

Integrating dose estimation into a decision-making framework for model-based drug development.

Model-informed drug discovery and development offers the promise of more efficient clinical development, with increased productivity and reduced cost through scientific decision making and risk management. Go/no-go development decisions in the pharmaceutical industry are often driven by effect size estimates, with the goal of meeting commercially generated target profiles. Sufficient efficacy is critical for eventual success, but the decision to advance development phase is also dependent on adequate knowledge of appropriate dose and dose-response. Doses which are too high or low pose risk of clinical or commercial failure. This paper addresses this issue and continues the evolution of formal decision frameworks in drug development. Here, we consider the integration of both efficacy and dose-response estimation accuracy into the go/no-go decision process, using a model-based approach. Using prespecified target and lower reference values associated with both efficacy and dose accuracy, we build a decision framework to more completely characterize development risk. Given the limited knowledge of dose response in early development, our approach incorporates a set of dose-response models and uses model averaging. The approach and its operating characteristics are illustrated through simulation. Finally, we demonstrate the decision approach on a post hoc analysis of the phase 2 data for naloxegol (a drug approved for opioid-induced constipation).

Model selection and averaging of nonlinear mixed-effect models for robust phase III dose selection.

Population model-based (pharmacometric) approaches are widely used for the analyses of phase IIb clinical trial data to increase the accuracy of the dose selection for phase III clinical trials. On the other hand, if the analysis is based on one selected model, model selection bias can potentially spoil the accuracy of the dose selection process. In this paper, four methods that assume a number of pre-defined model structure candidates, for example a set of dose-response shape functions, and then combine or select those candidate models are introduced. The key hypothesis is that by combining both model structure uncertainty and model parameter uncertainty using these methodologies, we can make a more robust model based dose selection decision at the end of a phase IIb clinical trial. These methods are investigated using realistic simulation studies based on the study protocol of an actual phase IIb trial for an oral asthma drug candidate (AZD1981). Based on the simulation study, it is demonstrated that a bootstrap model selection method properly avoids model selection bias and in most cases increases the accuracy of the end of phase IIb decision. Thus, we recommend using this bootstrap model selection method when conducting population model-based decision-making at the end of phase IIb clinical trials.

Drug-disease modeling in the pharmaceutical industry - where mechanistic systems pharmacology and statistical pharmacometrics meet.

Modeling & simulation (M&S) methodologies are established quantitative tools, which have proven to be useful in supporting the research, development (R&D), regulatory approval, and marketing of novel therapeutics. Applications of M&S help design efficient studies and interpret their results in context of all available data and knowledge to enable effective decision-making during the R&D process. In this mini-review, we focus on two sets of modeling approaches: population-based models, which are well-established within the pharmaceutical industry today, and fall under the discipline of clinical pharmacometrics (PMX); and systems dynamics models, which encompass a range of models of (patho-)physiology amenable to pharmacological intervention, of signaling pathways in biology, and of substance distribution in the body (today known as physiologically-based pharmacokinetic models) - which today may be collectively referred to as quantitative systems pharmacology models (QSP). We next describe the convergence - or rather selected integration - of PMX and QSP approaches into 'middle-out' drug-disease models, which retain selected mechanistic aspects, while remaining parsimonious, fit-for-purpose, and able to address variability and the testing of covariates. We further propose development opportunities for drug-disease systems models, to increase their utility and applicability throughout the preclinical and clinical spectrum of pharmaceutical R&D.

Population pharmacokinetics of ticagrelor and AR-C124910XX in patients with prior myocardial infarction
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OBJECTIVE: The population pharmacokinetics of ticagrelor and its active metabolite AR-C124910XX were characterized following ticagrelor 60 mg or 90 mg twice daily oral long-term treatment in 4,426 patients with a history of myocardial infarction. METHODS: The ticagrelor and AR-C124910XX plasma concentration-time data were described by one-compartment models with first-order absorption or metabolite formation and elimination. RESULTS: Systemic exposure to ticagrelor and AR-C124910XX were stable over time. Ticagrelor apparent clearance (CL/F) was 17 L/h for the 60-mg and 15.4 L/h for the 90-mg dose. The CL/F of AR-C124910XX was 11.1 L/h for the 60-mg and 9.95 L/h for the 90-mg dose. Both ticagrelor and AR-C124910XX CL/F were independently influenced by body weight, sex, age, smoking, and Japanese ethnicity. Female sex and age > 75 years were the only categorical covariates, having more than 20% effect on AR-C124910XX CL/F. Ticagrelor CL/F was 6% higher and 11% lower, whereas AR-C124910XX CL/F was 26% higher and 34% lower for patients weighing 110 and 50 kg, respectively, compared with an 83 kg patient. CONCLUSIONS: The small differences in exposure to both ticagrelor and AR-C124910XX between demographic subgroups were in accordance with the consistent efficacy and safety outcomes observed across the population. The results were similar to those observed previously in patients with acute coronary syndromes.
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Exposure-Response Analyses Supporting Ticagrelor Dosing Recommendation in Patients With Prior Myocardial Infarction.

The relationships between drug exposure and the composite risk of cardiovascular (CV) death, myocardial infarction (MI), and stroke as well as the risk of TIMI major bleeding were estimated following long-term treatment with ticagrelor 60 or 90 mg twice daily in 20,942 patients with prior MI. These analyses support the primary reported efficacy and safety evaluations by showing that there were clear separations from placebo early in treatment with both doses, regardless of ticagrelor exposure, for both endpoints. In addition, the exposure-response analyses provided new insight into the contribution of individual exposure levels, rather than dose, as a predictor of events and accounted for differences in the baseline risk between patients. The predicted risks of CV death/MI/stroke were similar despite an increase in the median predicted ticagrelor average steady-state concentration from 606 nmol/L with ticagrelor 60 mg to 998 nmol/L with ticagrelor 90 mg (hazard ratios vs placebo of 0.83 and 0.81, respectively). The corresponding predicted risk of TIMI major bleeding slightly increased (hazard ratios vs placebo of 2.4 and 2.6, respectively). Apart from Japanese patients, showing a lower risk of CV death/MI/stroke, the response to ticagrelor was consistent across the study population, as supported by the combination of relatively flat exposure-response relationships in the studied exposure range, similar sensitivity to ticagrelor exposure, and small exposure differences. Consequently, the present analyses support the selection of the 60-mg dose for all demographic subgroups of patients studied.

Application of the integrated glucose-insulin model for cross-study characterization of T2DM patients on metformin background treatment.

AIM: The integrated glucose-insulin (IGI) model is a semi-mechanistic physiological model which can describe the glucose-insulin homeostasis system following various glucose challenge settings. The aim of the present work was to apply the model to a large and diverse population of metformin-only-treated type 2 diabetes mellitus (T2DM) patients and identify patient-specific covariates. METHODS: Data from four clinical studies were pooled, including glucose and insulin concentration-time profiles from T2DM patients on stable treatment with metformin alone following mixed-meal tolerance tests. The data were collected from a wide range of patients with respect to the duration of diabetes and level of glycaemic control. RESULTS: The IGI model was expanded by four patient-specific covariates. The level of glycaemic control, represented by baseline glycosylated haemoglobin was identified as a significant covariate for steady-state glucose, insulin-dependent glucose clearance and the magnitude of the incretin effect, while baseline body mass index was a significant covariate for steady-state insulin levels. In addition, glucose dose was found to have an impact on glucose absorption rate. The developed model was used to simulate glucose and insulin profiles in different groups of T2DM patients, across a range of glycaemic control, and it was found accurately to characterize their response to the standard oral glucose challenge. CONCLUSIONS: The IGI model was successfully applied to characterize differences between T2DM patients across a wide range of glycaemic control. The addition of patient-specific covariates in the IGI model might be valuable for the future development of antidiabetic treatment and for the design and simulation of clinical studies.