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

Population kinetic/pharmacodynamic modelling of the haemodynamic effects of cafedrine/theodrenaline (Akrinor) under general anaesthesia

AIMS

The 20:1 combination of cafedrine and theodrenaline (C/T) is widely used in Germany for the treatment of arterial hypotension. Since there is little knowledge about the impact of covariates on the effect, the aim was to develop a kinetic/pharmacodynamic covariate model describing mean arterial pressure (MAP), systolic (SBP) and diastolic blood pressure (DBP), and heart rate (HR) for 30 min after the administration of C/T.

METHODS

Data of patients receiving C/T from the HYPOTENS study (NCT02893241, DRKS00010740) were analysed using nonlinear mixed-effects modelling techniques.

RESULTS

Overall, 16 579 measurements from 315 patients were analysed. The combination of two kinetic compartments and a delayed effect model, coupled with distinct Emax models for HR, SBP and DBP, described the data best. The model included age, sex, body mass index (BMI), antihypertensive medication, American Society of Anaesthesiologists (ASA) physical status classification grade, baseline SBP at the time of hypotension and pre-surgery HR as covariates (all P < .001). A higher baseline SBP led to a lower absolute increase in MAP. Patients with higher age, higher BMI and lower ASA grade showed smaller increases in MAP. The initial increase was similar for male and female patients. The long-term effect was higher in women. Concomitant antihypertensive medication caused a delayed effect and a lower maximum MAP. The HR increased only slightly (median increase 2.6 bpm, P < .001).

CONCLUSIONS

Seven covariates with an impact on the effect of C/T could be identified. The results will enable physicians to optimize the dose with respect to individual patients.

A tutorial for model-based evaluation and translation of cardiovascular safety in preclinical trials

Assessment of drug-induced effects on the cardiovascular (CV) system remains a critical component of the drug discovery process enabling refinement of the therapeutic index. Predicting potential drug-related unintended CV effects in the preclinical stage is necessary for first-in-human dose selection and preclusion of adverse CV effects in the clinical stage. According to the current guidelines for small molecules, nonclinical CV safety assessment conducted via telemetry analyses should be included in the safety pharmacology core battery studies. However, the manual for quantitative evaluation of the CV safety signals in animals is available only for electrocardiogram parameters (i.e., QT interval assessment), not for hemodynamic parameters (i.e., heart rate, blood pressure, etc.). Various model-based approaches, including empirical pharmacokinetic-toxicodynamic analyses and systems pharmacology modeling could be used in the framework of telemetry data evaluation. In this tutorial, we provide a comprehensive workflow for the analysis of nonclinical CV safety on hemodynamic parameters with a sequential approach, highlight the challenges associated with the data, and propose respective solutions, complemented with a reproducible example. The work is aimed at helping researchers conduct model-based analyses of the CV safety in animals with subsequent translation of the effect to humans seamlessly and efficiently.

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.

Precision Cardio-Oncology: Use of Mechanistic Pharmacokinetic and Pharmacodynamic Modeling to Predict Cardiotoxicities of Anti-Cancer Drugs

The cardiotoxicity of anti-cancer drugs presents as a challenge to both clinicians and patients. Significant advances in cancer treatments have improved patient survival rates, but have also led to the chronic effects of anti-cancer therapies becoming more prominent. Additionally, it is difficult to clinically predict the occurrence of cardiovascular toxicities given that they can be transient or irreversible, with large between-subject variabilities. Further, cardiotoxicities present a range of different symptoms and pathophysiological mechanisms. These notwithstanding, mechanistic pharmacokinetic (PK) and pharmacodynamic (PD) modeling offers an important approach to predict cardiotoxicities and offering precise cardio-oncological care. Efforts have been made to integrate the structures of physiological and pharmacological networks into PK-PD modeling to the end of predicting cardiotoxicities based on clinical evaluation as well as individual variabilities, such as protein expression, and physiological changes under different disease states. Thus, this review aims to report recent progress in the use of PK-PD modeling to predict cardiovascular toxicities, as well as its application in anti-cancer therapies.

A quantitative systems pharmacology approach to predict the safe-equivalent dose of doxorubicin in patients with cardiovascular comorbidity

Patients with cardiovascular comorbidity are less tolerant to cardiotoxic drugs and should be treated with reduced doses to prevent cardiotoxicity. However, the safe‐equivalent dose of antitumor drugs in patients with cardiovascular disease/risk is difficult to predict because they are usually excluded from clinical trials as a result of ethical considerations. In this study, a translational quantitative system pharmacology‐pharmacokinetic‐pharmacodynamic (QSP‐PK‐PD) model was developed based on preclinical study to predict the safe‐equivalence dose of doxorubicin in patients with or without cardiovascular disease. Virtual clinical trials were conducted to validate the translational QSP‐PK‐PD model. The model replicated several experimental and clinical observations: the left ventricular ejection fraction (LVEF) was reduced and the left ventricular end‐diastolic volume (LVEDV) was elevated in systolic dysfunction rats, the LVEF was preserved and LVEDV reduced in diastolic dysfunction rats, and patients with preexisting cardiovascular disease were more vulnerable to doxorubicin‐induced cardiac dysfunction than cardiovascular healthy patients. A parameter sensitivity analysis showed that doxorubicin‐induced cardiovascular dysfunction was mainly determined by the sensitivity of cardiomyocytes to cardiotoxic drugs and the baseline value of LVEDV, reflected in LVEF change percentage from the baseline. Blood pressure was the least sensitive factor affecting doxorubicin‐induced cardiotoxicity.

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.

Applications of Quantitative Systems Pharmacology in Model-Informed Drug Discovery: Perspective on Impact and Opportunities

Quantitative systems pharmacology (QSP) approaches have been increasingly applied in the pharmaceutical since the landmark white paper published in 2011 by a National Institutes of Health working group brought attention to the discipline. In this perspective, we discuss QSP in the context of other modeling approaches and highlight the impact of QSP across various stages of drug development and therapeutic areas. We discuss challenges to the field as well as future opportunities.

Quantifying the relationship between inhibition of VEGF receptor 2, drug-induced blood pressure elevation and hypertension

BACKGROUND AND PURPOSE

Several anti-angiogenic cancer drugs that inhibit VEGF receptor (VEGFR) signalling for efficacy are associated with a 15-60% incidence of hypertension. Tyrosine kinase inhibitors (TKIs) that have off-target activity at VEGFR-2 may also cause blood pressure elevation as an undesirable side effect. Therefore, the ability to translate VEGFR-2 off-target potency into blood pressure elevation would be useful in development of novel TKIs. Here, we have sought to quantify the relationship between VEGFR-2 inhibition and blood pressure elevation for a range of kinase inhibitors.

EXPERIMENTAL APPROACH

Porcine aortic endothelial cells overexpressing VEGFR-2 (PAE) were used to determine IC₅₀ for VEGFR-2 phosphorylation. These IC₅₀ values were compared with published reports of exposure attained during clinical use and the corresponding incidence of all-grade hypertension. Unbound average plasma concentration (C_av,u ) was selected to be the most appropriate pharmacokinetic parameter. The pharmacokinetic-pharmacodynamic (PKPD) relationship for blood pressure elevation was investigated for selected kinase inhibitors, using data derived either from clinical papers or from rat telemetry experiments.

KEY RESULTS

All-grade hypertension was predominantly observed when the C_av,u was >0.1-fold of the VEGFR-2 (PAE) IC₅₀ . Furthermore, based on the PKPD analysis, an exposure-dependent blood pressure elevation >1 mmHg was observed only when the C_av,u was >0.1-fold of the VEGFR-2 (PAE) IC₅₀ .

CONCLUSIONS AND IMPLICATIONS

Taken together, these data show that the risk of blood pressure elevation is proportional to the amount of VEGFR-2 inhibition, and a margin of >10-fold between VEGFR-2 IC₅₀ and C_av,u appears to confer a minimal risk of hypertension.

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.

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.

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.