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

2000-2023 over two decades of ICH S7A: has the time come for a revamp?

The ICH S7A guideline on safety pharmacology studies released over 20 years ago largely achieved its objective "to help protect clinical trial participants and patients receiving marketed products from potential adverse effects of pharmaceuticals". Although, Phase I clinical trials are generally very safe, the incidence and severity of adverse events, the safety related attrition and product withdrawal remain elevated during late-stage clinical development and post approval, a proportion of which can be attributed at least in part to safety pharmacology related issues. Considering the latest scientific and technological advancements in drug safety science, the paradigm shift of the drug discovery and development process and the continuously evolving regulatory landscape, we recommend revisiting, adapting and evolving the ICH S7A guideline. This might offer opportunities i) to select and progress optimized drugs with increased confidence in success, ii) to refine and adapt the clinical monitoring at all stages of clinical development resulting in an optimized benefit/risk assessment, iii) to increase likelihood of regulatory acceptance in a way compatible with an expedited and streamlined drug discovery and development process to benefit patients and iv) to avoid the unnecessary use of animals in 'tick-the-box' studies and encourage alternative approaches. As presented in the article, several options could be envisioned to revisit and adapt the ICH S7A taking into consideration several key features.

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).

An Integrative Approach for Improved Assessment of Cardiovascular Safety Data

Cardiovascular adverse effects in drug development are a major source of compound attrition. Characterization of blood pressure (BP), heart rate (HR), stroke volume (SV), and QT-interval prolongation are therefore necessary in early discovery. It is, however, common practice to analyze these effects independently of each other. High-resolution time courses are collected via telemetric techniques, but only low-resolution data are analyzed and reported. This ignores codependencies among responses (HR, BP, SV, and QT-interval) and separation of system (turnover properties) and drug-specific properties (potencies, efficacies). An analysis of drug exposure-time and high-resolution response-time data of HR and mean arterial blood pressure was performed after acute oral dosing of ivabradine, sildenafil, dofetilide, and pimobendan in Han-Wistar rats. All data were modeled jointly, including different compounds and exposure and response time courses, using a nonlinear mixed-effects approach. Estimated fractional turnover rates [h-1, relative standard error (%RSE) within parentheses] were 9.45 (15), 30.7 (7.8), 3.8 (13), and 0.115 (1.7) for QT, HR, total peripheral resistance, and SV, respectively. Potencies (nM, %RSE within parentheses) were IC 50 = 475 (11), IC 50 = 4.01 (5.4), EC 50 = 50.6 (93), and IC 50 = 47.8 (16), and efficacies (%RSE within parentheses) were I max = 0.944 (1.7), Imax = 1.00 (1.3), E max = 0.195 (9.9), and Imax = 0.745 (4.6) for ivabradine, sildenafil, dofetilide, and pimobendan. Hill parameters were estimated with good precision and below unity, indicating a shallow concentration-response relationship. An equilibrium concentration-biomarker response relationship was predicted and displayed graphically. This analysis demonstrates the utility of a model-based approach integrating data from different studies and compounds for refined preclinical safety margin assessment. SIGNIFICANCE STATEMENT: A model-based approach was proposed utilizing biomarker data on heart rate, blood pressure, and QT-interval. A pharmacodynamic model was developed to improve assessment of high-resolution telemetric cardiovascular safety data driven by different drugs (ivabradine, sildenafil, dofetilide, and pimobondan), wherein system- (turnover rates) and drug-specific parameters (e.g., potencies and efficacies) were sought. The model-predicted equilibrium concentration-biomarker response relationships and was used for safety assessment (predictions of 20% effective concentration, for example) of heart rate, blood pressure, and QT-interval.

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.

Current and future approaches to nonclinical cardiovascular safety assessment

Our goal is to accurately predict all types of cardiovascular events in patients utilising nonclinical cardiovascular safety data. In the past two decades, cardiovascular safety science has primarily focused on events associated with the electrocardiogram. Broadening out to other cardiovascular parameters, we share real-life case studies that highlight our progress towards improved and better-informed project progression based upon use of disease models, mechanism-based translation and structure-function relationships. To fulfil this goal, further advances in patient-relevant humanised models will be required to enable cardiovascular safety science to keep pace with the ever-changing landscape of novel therapeutic paradigms.

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.

Reduced Rank Least Squares for Real-Time Short Term Estimation of Mean Arterial Blood Pressure in Septic Patients Receiving Norepinephrine

Norepinephrine (NE), an endogenous catecholamine, is a mainstay treatment for septic shock, which is a life-threatening manifestation of severe infection. NE counteracts the loss in blood pressure associated with septic shock. However, an NE infusion that is too low fails to counteract the blood pressure drop, and an NE infusion that is too high can cause a hypertensive crisis and heart attack. Ideally, the NE infusion rate should maintain a patient’s mean arterial blood pressure (MAP) above 65 mmHg. There are a few data-driven, quantitative models to predict the MAP, and incorporate NE effects. This paper presents a model, driven by intensive care unit (ICU) measurable data and known NE inputs, to predict the future MAP of an ICU patient. We derive a least square estimation model for MAP based on available ICU data, including heart period, NE infusion rate, and respiration wave. We learn the parameters of our model from initial patient data and then use this information to predict future MAP data. We assess our model with data from 12 septic patients. Our model successfully predicts and tracks MAP when the NE infusion rate changes. Specifically, we predict MAP 3 to 20 min in the future with the mean error of less than 4 to 7 mmHg over 12 patients. Conclusion: this new approach creates the potential to advance methods for predicting NE infusion rate in septic patients. Significance: successfully predicted patients’ MAP could reduce catastrophic human error and lessen clinicians’ workload.

Biobehavioral effects of baclofen in anxious alcohol-dependent individuals: a randomized, double-blind, placebo-controlled, laboratory study

Baclofen has been suggested as a potential pharmacotherapy for alcohol use disorder, but the clinical data are conflicting. Here we investigated the biobehavioral effects of baclofen in a sample of anxious alcohol-dependent individuals. This was a randomized, double-blind, placebo-controlled, human laboratory study in non-treatment seeking alcohol-dependent individuals with high trait anxiety (N=34). Participants received baclofen (30 mg per day) or placebo for at least 8 days, then performed an experimental session consisting of alcohol cue-reactivity followed by alcohol administration procedure (alcohol priming, then alcohol self-administration). Total amount of alcohol self-administered was the primary outcome; alcohol craving, subjective/physiological responses and mood/anxiety symptoms were also evaluated. There was no significant medication effect on the total amount of alcohol consumed during the alcohol self-administration (P=0.76). Baclofen blunted the positive association between maximum breath alcohol concentration during priming and the amount of alcohol consumption (significant interaction, P=0.03). Ratings of feeling intoxicated were significantly higher in the baclofen group after consuming the priming drink (P=0.006). During the self-administration session, baclofen significantly increased ratings of feeling high (P=0.01) and intoxicated (P=0.01). A significant reduction in heart rate (P<0.001) and a trend-level increase in diastolic blood pressure (P=0.06) were also detected in the baclofen group during the alcohol laboratory session. In conclusion, baclofen was shown to affect subjective and physiological responses to alcohol drinking in anxious alcohol-dependent individuals. These results do not support an anti-craving or anti-reinforcing effect of baclofen, but rather suggest that baclofen may act as a substitution medication for alcohol use disorder.

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.