Joanna Parkinson

Translational pharmacokinetic-pharmacodynamic modeling of QTc effects in dog and human.

INTRODUCTION Preclinical assessment of the heart rate corrected QT interval (QTc) is an important component of the cardiovascular safety evaluation in drug discovery. Here we aimed to quantify the translational relationship between QTc prolongation and shortening in the conscious telemetered dog and humans by a retrospective pharmacokinetic-pharmacodynamic (PKPD) analysis. METHODS QTc effects of 2 proprietary compounds and 2 reference drugs (moxifloxacin and dofetilide) were quantified in conscious dogs and healthy volunteers via a linear and Emax pharmacokinetic-pharmacodynamic models. The translational relationship was quantified by correlating the QTc response from dog and human at matching free drug concentrations. RESULTS A consistent translational relationship was found at low delta-QTc intervals indicating that a QTc change of 2.5-8 ms in dog would correspond to a 10 ms change in human. DISCUSSION The translational relationship developed here can be used to predict the QTc liability in human using preclinical dog data. It could therefore help protect the health of human volunteers, for example by appropriate clinical study design and dose selection, as well as improve future decision-making and help reduce compound attrition due to changes in QT interval.

Has inhibition of Aβ production adequately been tested as therapeutic approach in mild AD? A model-based meta-analysis of γ-secretase inhibitor data.

PurposeTo date, γ-secretase inhibition is the most frequently studied mechanism of reducing Aβ in clinical trials with as yet no therapeutic success for AD patients, as measured by the slowing down of cognitive decline or an improvement in cognitive function. The aims of this investigation were to evaluate whether the amyloid hypothesis has been tested clinically, and to explore whether preclinical data are predictive of clinical Aβ effects.MethodsA model-based-meta analysis on Aβ levels and drug exposure over time was performed on published and in-house (pre-)clinical data with γ-secretase inhibitors (GSIs; semagacestat, avagacestat, begacestat, PF-3074014, and MK0752).ResultsThe clinical data available did not show any significant or robust reduction of CNS Aβ over time at dose levels intended for AD patients. In contrast, these doses resulted in an average increase in plasma Aβ levels over a 24-h interval. A general agreement between preclinical and clinical data was found and allowed for interspecies extrapolations.ConclusionsMore substantially, CNS Aβ-lowering drugs are needed to test whether inhibition of Aβ production is efficacious in mild AD. Predictions based on preclinical data could assist in the selection of drug candidates and trial design.

Parameter Identifiability of Fundamental Pharmacodynamic Models

Issues of parameter identifiability of routinely used pharmacodynamics models are considered in this paper. The structural identifiability of 16 commonly applied pharmacodynamic model structures was analyzed analytically, using the input-output approach. Both fixed-effects versions (non-population, no between-subject variability) and mixed-effects versions (population, including between-subject variability) of each model structure were analyzed. All models were found to be structurally globally identifiable under conditions of fixing either one of two particular parameters. Furthermore, an example was constructed to illustrate the importance of sufficient data quality and show that structural identifiability is a prerequisite, but not a guarantee, for successful parameter estimation and practical parameter identifiability. This analysis was performed by generating artificial data of varying quality to a structurally identifiable model with known true parameter values, followed by re-estimation of the parameter values. In addition, to show the benefit of including structural identifiability as part of model development, a case study was performed applying an unidentifiable model to real experimental data. This case study shows how performing such an analysis prior to parameter estimation can improve the parameter estimation process and model performance. Finally, an unidentifiable model was fitted to simulated data using multiple initial parameter values, resulting in highly different estimated uncertainties. This example shows that although the standard errors of the parameter estimates often indicate a structural identifiability issue, reasonably “good” standard errors may sometimes mask unidentifiability issues.

Modeling of age-dependent amyloid accumulation and γ-secretase inhibition of soluble and insoluble Aβ in a transgenic mouse model of amyloid deposition

According to the “amyloid hypothesis,” accumulation of amyloid beta (Aβ) peptides in the brain is linked to the development of Alzheimers disease. The aims of this investigation were to develop a model for the age‐dependent amyloid accumulation and to quantify the age‐ and treatment‐duration‐dependent efficacy of the γ‐secretase inhibitor MRK‐560 in the Tg2576 transgenic mouse model of amyloid deposition. Soluble and insoluble Aβ40 and Aβ42 brain concentrations were compiled from multiple naïve, vehicle, and MRK‐560‐treated animals. The age of Tg2576 mice in the studies ranged between 3.5 and 26 months. Single doses of MRK‐560 inhibited soluble Aβ40 levels in animals up to 9 months old. In contrast, MRK‐560 did not cause significant acute effects on soluble Aβ40 levels in animals older than 13 months. Absolute levels of Aβ variants increased exponentially over age and reached a plateau at ~20 months. In the final model, it was assumed that MRK‐560 inhibited the Aβ production rate with an Aβ level‐dependent IC50.The age‐dependent increase in Aβ levels was best described by a logistic model that stimulated the production rate of soluble Aβ. The increase in insoluble Aβ was defined as a function of soluble Aβ by using a scaling factor and a different turnover rate. The turnover half‐life for insoluble Aβ was estimated at 30 days, explaining that at least a 4‐week treatment in young animals was required to demonstrate a reduction in insoluble Aβ. Taken together, the derived knowledge could be exploited for an improved design of new experiments in Tg2576 mice.

Effects of isopropanol on collagen fibrils in new parchment

BackgroundIsopropanol is widely used by conservators to relax the creases and folds of parchment artefacts. At present, little is known of the possible side effects of the chemical on parchments main structural component- collagen. This study uses X-ray Diffraction to investigate the effects of a range of isopropanol concentrations on the dimensions of the nanostructure of the collagen component of new parchment.ResultsIt is found in this study that the packing features of the collagen molecules within the collagen fibril are altered by exposure to isopropanol. The results suggest that this chemical treatment can induce a loss of structural water from the collagen within parchment and thus a rearrangement of intermolecular bonding. This study also finds that the effects of isopropanol treatment are permanent to parchment artefacts and cannot be reversed with rehydration using deionised water.ConclusionsThis study has shown that isopropanol induces permanent changes to the packing features of collagen within parchment artefacts and has provided scientific evidence that its use to remove creases and folds on parchment artefacts will cause structural change that may contribute to long-term deterioration of parchment artefacts. This work provides valuable information that informs conservation practitioners regarding the use of isopropanol on parchment artefacts.

PKPD modelling of PR and QRS intervals in conscious dogs using standard safety pharmacology data

INTRODUCTION Pharmacokinetic-pharmacodynamic (PKPD) modelling can improve safety assessment, but few PKPD models describing drug-induced QRS and PR prolongations have been published. This investigation aims to develop and evaluate PKPD models for describing QRS and PR effects in routine safety studies. METHODS Exposure and telemetry data from safety pharmacology studies in conscious beagle dogs were acquired. Mixed effects baseline and PK-QRS/PR models were developed for the anti-arrhythmic compounds AZD1305, flecainide, quinidine and verapamil and the anti-muscarinic compounds AZD8683 and AZD9164. RR interval correction and circadian rhythms were investigated for predicting baseline variability. Individual PK predictions were used to drive the pharmacological effects evaluating linear and non-linear direct and effect compartment models. RESULTS Conduction slowing induced by the tested anti-arrhythmics was direct and proportional at low exposures, whilst time delays and non-linear effects were evident for the tested anti-muscarinics. AZD1305, flecainide and quinidine induced QRS widening with 4.2, 10 and 5.6% μM(-1) unbound drug. AZD1305 and flecainide also prolonged PR with 13.5 and 11.5% μM(-1). PR prolongations induced by the anti-muscarinics and verapamil were best described by Emax models with maximal effects ranging from 55 to 95%. RR interval correction and circadian rhythm improved PR but not QRS modelling. However, circadian rhythm had minor impact on estimated drug effects. DISCUSSION Baseline and drug-induced effects on QRS and PR intervals can be effectively described with PKPD models using routine data, providing quantitative safety information to support drug discovery and development.

Comparison of the exposure–response relationship of dapagliflozin in adult and paediatric patients with type 2 diabetes mellitus

To quantitatively compare the exposure–response relationship of dapagliflozin in adult and paediatric patients with type 2 diabetes mellitus (T2DM) and to assess the potential impact of covariate effects.

Predicting QRS and PR interval prolongations in humans using nonclinical data

Risk of cardiac conduction slowing (QRS/PR prolongations) is assessed prior to clinical trials using in vitro and in vivo studies. Understanding the quantitative translation of these studies to the clinical situation enables improved risk assessment in the nonclinical phase.

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.

Pharmacometrics in Cardiovascular Safety

Approximately one third of all drug discontinuation from preclinical discovery to postapproval stage is caused by drug safety, with a large contribution of cardiovascular (CV) safety findings. Moreover, drug-induced QT prolongation and proarrhythmic liabilities have been the main reasons for labeling restrictions and drug withdrawals after approval. Pharmacometric (model-based) tools have become increasingly beneficial to assess CV liabilities, as they allow predictions under new circumstances (new dosing regimen or response in the alternative patient populations), and extrapolations across different systems (e.g., from in vitro or in vivo to clinical). This model-based analysis is particularly important for the pharmaceutical industry as it facilitates selection and progression of the best compounds at early stages, and understanding of the right dose and schedule that is safe to patients via the right clinical study design in clinical development. This chapter provides an overview on how pharmacometrics is used in drug industry to quantify the risk of CV liabilities, with the main focus on QT interval.