PKPD modelling of the interrelationship between mean arterial BP, cardiac output and total peripheral resistance in conscious rats

Evaluation of a Cardiovascular Systems Model for Design and Analysis of Hemodynamic Safety Studies

Early prediction, quantification and translation of cardiovascular hemodynamic drug effects is essential in pre-clinical drug development. In this study, a novel hemodynamic cardiovascular systems (CVS) model was developed to support these goals. The model consisted of distinct system- and drug-specific parameter, and uses data for heart rate (HR), cardiac output (CO), and mean atrial pressure (MAP) to infer drug mode-of-action (MoA). To support further application of this model in drug development, we conducted a systematic analysis of the estimation performance of the CVS model to infer drug- and system-specific parameters. Specifically, we focused on the impact on model estimation performance when considering differences in available readouts and the impact of study design choices. To this end, a practical identifiability analysis was performed, evaluating model estimation performance for different combinations of hemodynamic endpoints, drug effect sizes, and study design characteristics. The practical identifiability analysis showed that MoA of drug effect could be identified for different drug effect magnitudes and both system- and drug-specific parameters can be estimated precisely with minimal bias. Study designs which exclude measurement of CO or use a reduced measurement duration still allow the identification and quantification of MoA with acceptable performance. In conclusion, the CVS model can be used to support the design and inference of MoA in pre-clinical CVS experiments, with a future potential for applying the uniquely identifiable systems parameters to support inter-species scaling.

A novel cardiovascular systems model to quantify drugs effects on the inter‐relationship between contractility and other hemodynamic variables

The use of systems‐based pharmacological modeling approaches to characterize mode‐of‐action and concentration‐effect relationships for drugs on specific hemodynamic variables has been demonstrated. Here, we (i) expand a previously developed hemodynamic system model through integration of cardiac output (CO) with contractility (CTR) using pressure‐volume loop theory, and (ii) evaluate the contribution of CO data for identification of system‐specific parameters, using atenolol as proof‐of‐concept drug. Previously collected experimental data was used to develop the systems model, and included measurements for heart rate (HR), CO, mean arterial pressure (MAP), and CTR after administration of atenolol (0.3–30 mg/kg) from three in vivo telemetry studies in conscious Beagle dogs. The developed cardiovascular (CVS)‐contractility systems model adequately described the effect of atenolol on HR, CO, dP/dtmax, and MAP dynamics and allowed identification of both system‐ and drug‐specific parameters with good precision. Model parameters were structurally identifiable, and the true mode of action can be identified properly. Omission of CO data did not lead to a significant change in parameter estimates compared to a model that included CO data. The newly developed CVS‐contractility systems model characterizes short‐term drug effects on CTR, CO, and other hemodynamic variables in an integrated and quantitative manner. When the baseline value of total peripheral resistance is predefined, CO data was not required to identify drug‐ and system‐specific parameters. Confirmation of the consistency of system‐specific parameters via inclusion of data for additional drugs and species is warranted. Ultimately, the developed model has the potential to be of relevance to support translational CVS safety studies.

Modeling approaches for reducing safety-related attrition in drug discovery and development: a review on myelotoxicity, immunotoxicity, cardiovascular toxicity, and liver toxicity

Introduction:Safety and tolerability is a critical area where improvements are needed to decrease the attrition rates during development of new drug candidates. Modeling approaches, when smartly implemented, can contribute to this aim.Areas covered:The focus of this review was on modeling approaches applied to four kinds of drug-induced toxicities: hematological, immunological, cardiovascular (CV) and liver toxicity. Papers, mainly published in the last 10 years, reporting models in three main methodological categories - computational models (e.g., quantitative structure-property relationships, machine learning approaches, neural networks, etc.), pharmacokinetic-pharmacodynamic (PK-PD) models, and quantitative system pharmacology (QSP) models - have been considered.Expert opinion:The picture observed in the four examined toxicity areas appears heterogeneous. Computational models are typically used in all areas as screening tools in the early stages of development for hematological, cardiovascular and liver toxicity, with accuracies in the range of 70-90%. A limited number of computational models, based on the analysis of drug protein sequence, was instead proposed for immunotoxicity. In the later stages of development, toxicities are quantitatively predicted with reasonably good accuracy using either semi-mechanistic PK-PD models (hematological and cardiovascular toxicity), or fully exploited QSP models (immuno-toxicity and liver toxicity).

Semi-mechanistic PK/PD model to assess pulmonary targeting of beclomethasone dipropionate and its active metabolite

PURPOSE

The objective of this study was to describe the pulmonary targeting of beclomethasone dipropionate (BDP) and its active metabolite beclomethasone 17-monopropionate (BMP) in rats using a semi-mechanistic PK/PD model.

METHODS

Rat plasma and tissue concentrations of BDP and BMP, and tissue receptor occupancies of BMP after systemic and pulmonary delivery of BDP and BMP were integrated in a newly developed semi-mechanistic PK/PD model.

RESULTS

After IV administration of BDP, 95.4% of BDP was converted to BMP, while after pulmonary delivery of BDP, 46.6% of deposited BDP was absorbed as BMP. The developed semi-mechanistic PK model described plasma and tissue concentrations of BDP and BMP as well as receptor occupancies sufficiently well. The model incorporated dissolution, metabolic activation, and drug absorption processes to describe the local fate of BDP and BMP after systemic and pulmonary delivery. Dissolution rate constants of BDP and BMP were estimated to be 0.47/h and 2.01/h, respectively, and the permeabilities in central lung were estimated to be 15.0 and 2.9 × 10⁶ cm/s for BDP and BMP, respectively. The EC₅₀ of the binding of BMP to to the receptor was estimated to be 0.0017 ng/ml. Overall, receptor occupancies in the lung were more pronounced than those in the systemic circulation after pulmonary delivery of BDP or BMP. Simulations using the developed semi-mechanistic PK/PD model demonstrated that a slow dissolution rate and low permeability can improve pulmonary targeting.

CONCLUSIONS

A semi-mechanistic model was developed to describe the fate of an inhaled glucocorticoid pro-drug and its active metabolite in lung and the systemic circulation, both after pulmonary and systemic administration , thereby facilitating the understanding of the complex interplay between drug, prodrug and pharmacodynamic properties for quantifying the degree pulmonary targeting.

A minimal model to describe short-term haemodynamic changes of the cardiovascular system

AIMS

Current pharmacokinetic-pharmacodynamic models describing the haemodynamic changes often do not include necessary feedback mechanisms. These models provide adequate description of current data but may fail to adequately extrapolate to additional scenarios. This study aims to develop a minimal model to describe the short-term changes of haemodynamics that can be used as the basis for model development by future researchers.

METHODS

A minimal haemodynamic model was developed to describe the influence of drugs on blood pressure components. The model structure was defined based on known mechanisms and previously published models. The model was evaluated under 2 different simulation settings. The model parameters were calibrated to describe (without estimation) the haemodynamics of 2 antihypertensive drugs with data extracted from the literature. Structural identifiability analysis was done using various combinations of the observed variable.

RESULTS

The proposed model structure includes mean arterial pressure, heart rate and stroke volume and is composed of 4 states described by differential equations. Model evaluation showed flexibility in describing the haemodynamics at different target perturbations. Overlay plots of model predictions and literature data showed a good description without data fitting. The structural identifiability analysis revealed all model parameters and initial conditions were identifiable only when heart rate, mean arterial pressure and cardiac output were measured together.

CONCLUSIONS

A minimal model of the haemodynamic system was developed and evaluated. The model accounted for short-term haemodynamic feedback processes. We propose that this model can be used as the basis for future pharmacometric analyses of drugs acting on the haemodynamic system.

Semi-mechanistic modelling platform to assess cardiac contractility and haemodynamics in preclinical cardiovascular safety profiling of new molecular entities

BACKGROUND AND PURPOSE

Cardiovascular safety is one of the most frequent causes of safety-related attrition both preclinically and clinically. Preclinical cardiovascular safety is routinely assessed using dog telemetry monitoring key cardiovascular functions. The present research was to develop a semi-mechanistic modelling platform to simultaneously assess changes in contractility (dPdt_max ), heart rate (HR) and mean arterial pressure (MAP) in preclinical studies.

EXPERIMENTAL APPROACH

Data from dPdt_max , HR, preload (left ventricular end-diastolic pressure [LVEDP]) and MAP were available from dog telemetry studies after dosing with atenolol (n = 27), salbutamol (n = 5), L-N^G -nitroarginine methyl ester (L-NAME; n = 4), milrinone (n = 4), verapamil (n = 12), dofetilide (n = 8), flecainide (n = 4) and AZ001 (n = 14). Literature model for rat CV function was used for the structural population pharmacodynamic model development. LVEDP was evaluated as covariate to account for the effect of preload on dPdt_max .

KEY RESULTS

The model was able to describe drug-induced changes in dPdt_max , HR and MAP for all drugs included in the developed framework adequately, by incorporating appropriate drug effects on dPdt_max , HR and/or total peripheral resistance. Consistent with the Starling's law, incorporation of LVEDP as a covariate on dPdt_max to correct for the preload effect was found to be statistically significant.

CONCLUSIONS AND IMPLICATIONS

The contractility and haemodynamics semi-mechanistic modelling platform accounts for diurnal variation, drug-induced changes and inter-animal variation. It can be used to hypothesize and evaluate pharmacological effects and provide a holistic cardiovascular safety profile for new drugs.

Optimization of Allosteric With-No-Lysine (WNK) Kinase Inhibitors and Efficacy in Rodent Hypertension Models

The observed structure-activity relationship of three distinct ATP noncompetitive With-No-Lysine (WNK) kinase inhibitor series, together with a crystal structure of a previously disclosed allosteric inhibitor bound to WNK1, led to an overlay hypothesis defining core and side-chain relationships across the different series. This in turn enabled an efficient optimization through scaffold morphing, resulting in compounds with a good balance of selectivity, cellular potency, and pharmacokinetic profile, which were suitable for in vivo proof-of-concept studies. When dosed orally, the optimized compound reduced blood pressure in mice overexpressing human WNK1, and induced diuresis, natriuresis and kaliuresis in spontaneously hypertensive rats (SHR), confirming that this mechanism of inhibition of WNK kinase activity is effective at regulating cardiovascular homeostasis.

Characterizing the time-course of antihypertensive activity and optimal dose range of fimasartan via mechanism-based population modeling

Fimasartan is a novel angiotensin II receptor blocker. Our aims were to characterize the time-course of the antihypertensive activity of fimasartan via a new population pharmacokinetic/pharmacodynamic model and to define its optimal dose range. We simultaneously modelled all fimasartan plasma concentrations and 24-h ambulatory blood pressure monitoring (ABPM) data from 39 patients with essential hypertension and 56 healthy volunteers. Patients received placebo, 20, 60, or 180mg fimasartan every 24h for 28days and healthy volunteers received placebo or 20 to 480mg as a single oral dose or as seven doses every 24h. External validation was performed using data on 560 patients from four phase II or III studies. One turnover model each was used to describe diastolic and systolic blood pressure. The input rates into these compartments followed a circadian rhythm and were inhibited by fimasartan. The average predicted (observed) diastolic blood pressure over 24-h in patients decreased by 10.1±7.5 (12.6±9.2; mean±SD)mmHg for 20mg, 14.2±7.0 (15.1±9.3) mmHg for 60mg, and 15.9±6.8 (11.5±9.9)mmHg for 180mg daily relative to placebo. The model explained the saturation of antihypertensive activity by counter-regulation at high fimasartan concentrations. Drug effect was maximal at approximately 23ng/mL fimasartan for diastolic and 12ng/mL for systolic blood pressure. The proposed mechanism-based population model characterized the circadian rhythm of ABPM data and the antihypertensive effect of fimasartan. After internal and external model validation, 30 to 60mg oral fimasartan given once daily was predicted as optimal dose range.

Quantitative pharmacokinetic-pharmacodynamic modelling of baclofen-mediated cardiovascular effects using BP and heart rate in rats

BACKGROUND AND PURPOSE

While the molecular pathways of baclofen toxicity are understood, the relationships between baclofen-mediated perturbation of individual target organs and systems involved in cardiovascular regulation are not clear. Our aim was to use an integrative approach to measure multiple cardiovascular-relevant parameters [CV: mean arterial pressure (MAP), systolic BP, diastolic BP, pulse pressure, heart rate (HR); CNS: EEG; renal: chemistries and biomarkers of injury] in tandem with the pharmacokinetic properties of baclofen to better elucidate the site(s) of baclofen activity.

EXPERIMENTAL APPROACH

Han-Wistar rats were administered vehicle or ascending doses of baclofen (3, 10 and 30 mg·kg(-1) , p.o.) at 4 h intervals and baclofen-mediated changes in parameters recorded. A pharmacokinetic-pharmacodynamic model was then built by implementing an existing mathematical model of BP in rats.

KEY RESULTS

Final model fits resulted in reasonable parameter estimates and showed that the drug acts on multiple homeostatic processes. In addition, the models testing a single effect on HR, total peripheral resistance or stroke volume alone did not describe the data. A final population model was constructed describing the magnitude and direction of the changes in MAP and HR.

CONCLUSIONS AND IMPLICATIONS

The systems pharmacology model developed fits baclofen-mediated changes in MAP and HR well. The findings correlate with known mechanisms of baclofen pharmacology and suggest that similar models using limited parameter sets may be useful to predict the cardiovascular effects of other pharmacologically active substances.

Systems pharmacology - Towards the modeling of network interactions

Mechanism-based pharmacokinetic and pharmacodynamics (PKPD) and disease system (DS) models have been introduced in drug discovery and development research, to predict in a quantitative manner the effect of drug treatment in vivo in health and disease. This requires consideration of several fundamental properties of biological systems behavior including: hysteresis, non-linearity, variability, interdependency, convergence, resilience, and multi-stationarity. Classical physiology-based PKPD models consider linear transduction pathways, connecting processes on the causal path between drug administration and effect, as the basis of drug action. Depending on the drug and its biological target, such models may contain expressions to characterize i) the disposition and the target site distribution kinetics of the drug under investigation, ii) the kinetics of target binding and activation and iii) the kinetics of transduction. When connected to physiology-based DS models, PKPD models can characterize the effect on disease progression in a mechanistic manner. These models have been found useful to characterize hysteresis and non-linearity, yet they fail to explain the effects of the other fundamental properties of biological systems behavior. Recently systems pharmacology has been introduced as novel approach to predict in vivo drug effects, in which biological networks rather than single transduction pathways are considered as the basis of drug action and disease progression. These models contain expressions to characterize the functional interactions within a biological network. Such interactions are relevant when drugs act at multiple targets in the network or when homeostatic feedback mechanisms are operative. As a result systems pharmacology models are particularly useful to describe complex patterns of drug action (i.e. synergy, oscillatory behavior) and disease progression (i.e. episodic disorders). In this contribution it is shown how physiology-based PKPD and disease models can be extended to account for internal systems interactions. It is demonstrated how SP models can be used to predict the effects of multi-target interactions and of homeostatic feedback on the pharmacological response. In addition it is shown how DS models may be used to distinguish symptomatic from disease modifying effects and to predict the long term effects on disease progression, from short term biomarker responses. It is concluded that incorporation of expressions to describe the interactions in biological network analysis opens new avenues to the understanding of the effects of drug treatment on the fundamental aspects of biological systems behavior.

Inter-study variability of preclinical in vivo safety studies and translational exposure-QTc relationships--a PKPD meta-analysis

BACKGROUND AND PURPOSE

Preclinical cardiovascular safety studies (CVS) have been compared between facilities with respect to their sensitivity to detect drug-induced QTc prolongation (ΔQTc). Little is known about the consistency of quantitative ΔQTc predictions that are relevant for translation to humans.

EXPERIMENTAL APPROACH

We derived typical ΔQTc predictions at therapeutic exposure (ΔQTcTHER ) with 95% confidence intervals (95%CI) for 3 Kv 11.1 (hERG) channel blockers (moxifloxacin, dofetilide and sotalol) from a total of 14 CVS with variable designs in the conscious dog. Population pharmacokinetic-pharmacodynamic (PKPD) analysis of each study was followed by a meta-analysis (pooling 2-6 studies including 10-32 dogs per compound) to derive meta-predictions of typical ΔQTcTHER . Meta-predictions were used as a reference to evaluate the consistency of study predictions and to relate results to those found in the clinical literature.

KEY RESULTS

The 95%CIs of study-predicted ΔQTcTHER comprised in 13 out of 14 cases the meta-prediction. Overall inter-study variability (mean deviation from meta-prediction at upper level of therapeutic exposure) was 30% (range: 1-69%). Meta-ΔQTcTHER predictions for moxifloxacin, dofetilide and sotalol overlapped with reported clinical QTc prolongation when expressed as %-prolongation from baseline.

CONCLUSIONS AND IMPLICATIONS

Consistent exposure-ΔQTc predictions were obtained from single preclinical dog studies of highly variable designs by systematic PKPD analysis, which is suitable for translational purposes. The good preclinical-clinical pharmacodynamic correlations obtained suggest that such an analysis should be more routinely applied to increase the informative and predictive value of results obtained from animal experiments.

Modeling and Simulation Approaches for Cardiovascular Function and Their Role in Safety Assessment

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.

Sensitivity of pharmacokinetic-pharmacodynamic analysis for detecting small magnitudes of QTc prolongation in preclinical safety testing

INTRODUCTION

Preclinical concentration-effect (pharmacokinetic-pharmacodynamic, PKPD) modeling has successfully quantified QT effects of several drugs known for significant QT prolongation. This study investigated its sensitivity for detecting small magnitudes of QT-prolongation in a typical preclinical cardiovascular (CV) safety study in the conscious telemetered dog (crossover study in 4-8 animals receiving a vehicle and three dose levels). Results were compared with conventional statistical analysis (analysis of covariance, ANCOVA).

METHODS

A PKPD model predicting individual QTc was first developed from vehicle arms of 28 typical CV studies and one positive control study (sotalol). The model quantified between-animal, inter-occasion and within-animal variability and described QTc over 24h as a function of circadian variation and drug concentration. This "true" model was used to repeatedly (n = 500) simulate studies with typical drug-induced QTc prolongation (∆QTc) of 1 to 12 ms at high-dose peak concentrations. Simulated studies were re-analyzed by both PKPD analysis (with varying complexity) and ANCOVA. Sensitivity (power) was calculated as the percentage of studies in which a significant (α = 0.05) drug effect was found. One simulation scenario did not include a concentration-effect relationship and served to investigate false-positive rates. Exposure-effect relationships were derived from both PKPD analysis (linear concentration-effect) and ANCOVA (linear trend test for dose) and compared.

RESULTS

PKPD analysis/ANCOVA had a sensitivity of 80% to detect the effects of 7/13 ms (n = 4), 5/10 ms (n = 6) and 4.5/8 ms (n = 8), respectively. The false-positive rate was much higher using ANCOVA (40%) compared to PKPD analysis (1%). Typical drug effects were more precisely predicted using estimated concentration-effect slopes (± 1.5-2.8 ms) than dose-effect slopes (± 3.3-3.7 ms).

DISCUSSION

Preclinical PKPD analysis can increase the confidence in the quantification of small QTc effects and potentially allow reducing the number of animals while maintaining the required study sensitivity. This underscores the value of PKPD modeling in preclinical safety testing.

The 24 h pattern of arterial pressure in mice is determined mainly by heart rate-driven variation in cardiac output

Few studies have systematically investigated whether daily patterns of arterial blood pressure over 24 h are mediated by changes in cardiac output, peripheral resistance, or both. Understanding the hemodynamic mechanisms that determine the 24 h patterns of blood pressure may lead to a better understanding of how such patterns become disturbed in hypertension and influence risk for cardiovascular events. In conscious, unrestrained C57BL/6J mice, we investigated whether the 24 h pattern of arterial blood pressure is determined by variation in cardiac output, systemic vascular resistance, or both and also whether variations in cardiac output are mediated by variations in heart rate and or stroke volume. As expected, arterial pressure and locomotor activity were significantly (P < 0.05) higher during the nighttime period compared with the daytime period when mice are typically sleeping (+12.5 ± 1.0 mmHg, [13%] and +7.7 ± 1.3 activity counts, [254%], respectively). The higher arterial pressure during the nighttime period was mediated by higher cardiac output (+2.6 ± 0.3 mL/min, [26%], P < 0.05) in association with lower peripheral resistance (-1.5 ± 0.3 mmHg/mL/min, [-13%] P < 0.05). The increased cardiac output during the nighttime was mainly mediated by increased heart rate (+80.0 ± 16.5 beats/min, [18%] P < 0.05), as stroke volume increased minimally at night (+1.6 ± 0.5 μL per beat, [6%] P < 0.05). These results indicate that in C57BL/6J mice, the 24 h pattern of blood pressure is hemodynamically mediated primarily by the 24 h pattern of cardiac output which is almost entirely determined by the 24 h pattern of heart rate. These findings suggest that the differences in blood pressure between nighttime and daytime are mainly driven by differences in heart rate which are strongly correlated with differences in locomotor activity.

Drug effects on the CVS in conscious rats: separating cardiac output into heart rate and stroke volume using PKPD modelling

BACKGROUND AND PURPOSE

Previously, a systems pharmacology model was developed characterizing drug effects on the interrelationship between mean arterial pressure (MAP), cardiac output (CO) and total peripheral resistance (TPR). The present investigation aims to (i) extend the previously developed model by parsing CO into heart rate (HR) and stroke volume (SV) and (ii) evaluate if the mechanism of action (MoA) of new compounds can be elucidated using only HR and MAP measurements.

EXPERIMENTAL APPROACH

Cardiovascular effects of eight drugs with diverse MoAs (amiloride, amlodipine, atropine, enalapril, fasudil, hydrochlorothiazide, prazosin and propranolol) were characterized in spontaneously hypertensive rats (SHR) and normotensive Wistar-Kyoto (WKY) rats following single administrations of a range of doses. Rats were instrumented with ascending aortic flow probes and aortic catheters/radiotransmitters for continuous recording of MAP, HR and CO throughout the experiments. Data were analysed in conjunction with independent information on the time course of the drug concentration following a mechanism-based pharmacokinetic-pharmacodynamic modelling approach.

KEY RESULTS

The extended model, which quantified changes in TPR, HR and SV with negative feedback through MAP, adequately described the cardiovascular effects of the drugs while accounting for circadian variations and handling effects.

CONCLUSIONS AND IMPLICATIONS

A systems pharmacology model characterizing the interrelationship between MAP, CO, HR, SV and TPR was obtained in hypertensive and normotensive rats. This extended model can quantify dynamic changes in the CVS and elucidate the MoA for novel compounds, with one site of action, using only HR and MAP measurements. Whether the model can be applied for compounds with a more complex MoA remains to be established.