Challenges in collecting pharmacokinetic and pharmacodynamic information in an intensive care setting: PK/PD modelling of clazosentan in patients with aneurysmal subarachnoid haemorrhage

Clinical Pharmacology of Clazosentan, a Selective Endothelin A Receptor Antagonist for the Prevention and Treatment of aSAH-Related Cerebral Vasospasm

Aneurysmal subarachnoid hemorrhage (aSAH) may lead to cerebral vasospasm and is associated with significant morbidity and mortality. It represents a major unmet medical need due to few treatment options with limited efficacy. The role of endothelin-1 (ET-1) and its receptor ET_A in the pathogenesis of aSAH-induced vasospasm suggests antagonism of this receptor as promising asset for pharmacological treatment. Clazosentan is a potent ET_A receptor antagonist for intravenous use currently under development for the prevention of aSAH-induced cerebral vasospasm. The pharmacokinetics of clazosentan are characterized by an intermediate clearance, a volume of distribution similar to that of the extracellular fluid volume, dose-proportional exposure, an elimination independent of drug-metabolizing enzymes, and a disposition mainly dependent on the hepatic uptake transporter organic anion transport polypeptide 1B1/1B3. In healthy subjects, clazosentan leads to an increase in ET-1 concentration and prevents the cardiac and renal effects mediated by infusion of ET-1. In patients, it significantly reduced the incidence of moderate or severe vasospasm as well as post-aSAH vasospasm-related morbidity and mortality. Clazosentan is well tolerated up to the expected therapeutic dose of 15 mg/h and, in aSAH patients, lung complications, hypotension, and anemia were adverse events more commonly reported following clazosentan than placebo. In summary, clazosentan has a pharmacokinetic, pharmacodynamic, and safety profile suitable to become a valuable asset in the armamentarium of therapeutic modalities to prevent aSAH-induced cerebral vasospasm.

PK/PD modeling of a clazosentan thorough QT study with hysteresis in concentration-QT and RR-QT

Clazosentan's potential QT liability was investigated in a thorough QT study in which clazosentan was administered intravenously as a continuous infusion of 20 mg/h immediately followed by 60 mg/h. Clazosentan prolonged the placebo-corrected change-from-baseline QT interval corrected for RR with Fridericia's formula (ΔΔQTcF) with the maximum QT effect occurring 4 h after the maximum drug concentration, apparently associated with vomiting. The delayed effect precluded the standard linear modeling approach. This analysis aimed at characterizing the concentration-QT relationship in consideration of RR-QT hysteresis, concentration-ΔΔQTcF hysteresis, and the influence of vomiting. Nonlinear mixed-effects modeling was applied to characterize pharmacokinetics and pharmacodynamics, i.e., ΔΔQTcF. Simulations were used to predict ΔΔQTcF for expected therapeutic dose used in Phase 3 clinical development. Correction for RR-QT hysteresis did not influence ΔΔQTcF to a relevant extent. Pharmacokinetics of clazosentan were best described by a linear two-compartment model. The delayed QT prolongation was characterized by an indirect-response model with loglinear drug effect. Vomiting had no statistically significant influence on QT prolongation despite apparent differences between subjects vomiting and not vomiting, probably since vomiting occurred mostly after the main QT prolongation. Following a simulated 3-h infusion of 15 mg/h of clazosentan, the upper bound of the predicted 90% CI for mean ΔΔQTcF was expected to exceed the 10-ms regulatory threshold of concern with maximum effect 3.5 h after end of infusion. TRN: NCT03657446, 05 Sep 2018.

Population Modeling of Selexipag Pharmacokinetics and Clinical Response Parameters in Patients With Pulmonary Arterial Hypertension

Selexipag (Uptravi) is an oral selective IP prostacyclin receptor agonist approved for the treatment of pulmonary arterial hypertension (PAH). The pivotal GRIPHON study was the largest clinical study ever conducted in PAH patients, providing long‐term data from 1,156 patients. PAH comedication did not affect exposure to selexipag, while exposure to its active metabolite ACT‐333679 was reduced by 30% when taken in combination, clinically not relevant in the context of individual dose up‐titration. Using log‐linear regression models linking model‐predicted steady‐state exposure to pharmacodynamics (PD), exposure to selexipag and ACT‐333679 showed some statistically significant, albeit not clinically relevant, effects on exercise capacity, laboratory values, and the occurrence of prostacyclin‐related adverse events, but not on vital signs or adverse events denoting hemorrhage. Using suitable modeling techniques, the GRIPHON study yielded clinically relevant data with limited burden of pharmacokinetics (PK) blood sampling, demonstrating that PK/PD modeling enables firm conclusions even with sparse PK and PD sampling.

Modeling for critically ill patients: An introduction for beginners

Models are mathematical tools used to describe real-world features. Therapeutic interventions in the field of critical care medicine may easily be translated into such models. Indeed, numerous variables influencing drug pharmacokinetics and pharmacodynamics are systematically documented in the intensive care unit over time. Organ failure, fluid shifts, other drug administration, and renal replacement therapy may cause changes in physiological values, such as body weight and composition, temperature, serum protein levels, arterial pH, and renal or hepatic function. Trials assessing the efficacy and safety of novel drugs usually exclude critically ill patients, and guidelines regarding drug dosage rarely apply to such patients. Modeling in the critically ill may allow physicians to inform decisions related to therapeutic interventions, particularly relating to infectious diseases. However, few clinicians are familiar with these methods. Here, we present a current overview of population pharmacokinetic and pharmacodynamic models applicable in critically ill patients aimed at nonspecialists and then emphazize recent potential of modeling for optimizing treatments and care in the intensive care unit.

Dosage assessment of valnemulin in pigs based on population pharmacokinetic and Monte Carlo simulation

To estimate the valnemulin pharmacokinetic profile in a swine population and to assess a dosage regimen for increasing the likelihood of optimization. This study was, respectively, performed in 22 sows culled by p.o. administration and in 80 growing-finishing pigs by i.v. administration at a single dose of 10 mg/kg to develop a population pharmacokinetic model and Monte Carlo simulation. The relationships among the plasma concentration, dose, and time of valnemulin in pigs were illustrated as C(i,v) = X(0 )(8.4191 × 10(-4) × e(-0.2371t) + 1.2788 × 10(-5) × e(-0.0069t)) after i.v. and C(p.o) = X(0) (-8.4964 × 10(-4) × e(-0.5840t) + 8.4195 × e(-0.2371t) + 7.6869 × 10(-6) × e(-0.0069t)) after p.o. Monte Carlo simulation showed that T(>MIC) was more than 24 h when a single daily dosage at 13.5 mg/kg BW in pigs was administrated by p.o., and MIC was 0.031 mg/L. It was concluded that the current dosage regimen at 10-12 mg/kg BW led to valnemulin underexposure if the MIC was more than 0.031 mg/L and could increase the risk of treatment failure and/or drug resistance.