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We investigated the effects of mineral oil on statin pharmacokinetics and inflammatory markers in animal models. A new synthesis strategy produced regioisomers that facilitated the characterization of the main metabolite (M1) of atorvastatin, a lipophilic statin, in C57BL/6NCrl mice. The chemical structure of M1 in mice was confirmed as ortho-hydroxy β-oxidized atorvastatin. Atorvastatin and M1 pharmacokinetics and inflammatory markers were assessed in C57BL6/J mice given atorvastatin 5 mg/kg/day or 10 mg/kg/day, as a single dose or for 21 days, with or without 10 µL or 30 µL mineral oil. No consistent differences in plasma exposure of atorvastatin or M1 were observed in mice after single or repeat dosing of atorvastatin with or without mineral oil. However, mice administered atorvastatin 10 mg/kg with 30 µL mineral oil for 21 days had significantly increased plasma levels of serum amyloid A (mean 9.6 µg/mL vs 7.9 µg/mL without mineral oil; p0.01) and significantly increased proportions of C62L

Drugs can affect the cardiovascular (CV) system either as an intended treatment or as an unwanted side effect. In both cases, drug-induced cardiotoxicities such as arrhythmia and unfavourable hemodynamic effects can occur, and be described using mathematical models; such a model informed approach can provide valuable information during drug development and can aid decision-making. However, in order to develop informative models, it is vital to understand CV physiology. The aims of this tutorial are to present (1) key background biological and medical aspects of the CV system, (2) CV electrophysiology, (3) CV safety concepts, (4) practical aspects of development of CV models and (5) regulatory expectations with a focus on using model informed and quantitative approaches to support nonclinical and clinical drug development. In addition, we share several case studies to provide practical information on project strategy (planning, key questions, assumptions setting, and experimental design) and mathematical models development that support decision-making during drug discovery and development.

Background and Purpose 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. Experimental Approach Four compounds that prolong QRS and/or PR (AZD1305, flecainide, quinidine and verapamil) were characterized using in vitro (sodium/calcium channels), in vivo (guinea pigs/dogs) and clinical data. Concentration-matched translational relationships were developed based on in vitro and in vivo modelling, and the in vitro to clinical translation of AZD1305 was quantified using an in vitro model. Key Results Meaningful (10%) human QRS/PR effects correlated with low levels of in vitro Nav1.5 block (3–7%) and Cav1.2 binding (13–21%) for all compounds. The in vitro model developed using AZD1305 successfully predicted QRS/PR effects for the remaining drugs. Meaningful QRS/PR changes in humans correlated with small effects in guinea pigs and dogs (QRS 2.3–4.6% and PR 2.3–10%), suggesting that worst-case human effects can be predicted by assuming four times greater effects at the same concentration from dog/guinea pig data. Conclusion and Implications Small changes in vitro and in vivo consistently translated to meaningful PR/QRS changes in humans across compounds. Assuming broad applicability of these approaches to assess cardiovascular safety risk for non–arrhythmic drugs, this study provides a means of predicting human QRS/PR effects of new drugs from effects observed in nonclinical studies.

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.\ud \ud

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 antiarrhythmic 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 antimuscarinics. 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.

Systems pharmacology modeling and pharmacokinetic-pharmacodynamic (PK/PD) analysis of drug-induced effects on cardiovascular (CV) function plays a crucial role in understanding the safety risk of new drugs. The aim of this review is to outline the current modeling and simulation (M&S) approaches to describe and translate drug-induced CV effects, with an emphasis on how this impacts drug safety assessment. Current limitations are highlighted and recommendations are made for future effort in this vital area of drug research.