Andreas Krause

Health authorities worldwide require clinical studies in children to ensure scientifically rigorous and consistent dosing. Before pediatric data are available, model-based methods provide a scientific and reproducible way to determine doses for pediatric studies. A general introduction to pediatric scaling is provided, followed by a focus on population pharmacokinetic methods to scale from adults to children using exposure matching. Exposure matching involves two steps: first, optimal pediatric doses are derived using a fine grid of body sizes and doses; second, feasible doses are selected based on availability of dose strengths. Three strategies are employed and compared, best fit (similar exposure in adults and children across ages and body sizes), conservative (children’s exposure not exceeding adults’), and progressive (children’s exposure not less than adults’). Each recommendation is evaluated against the exposure metrics C_max, C_trough, and AUC at steady state for the optimal dosing scheme. Simulations assess interindividual variability in exposure. Visualizations enable risk assessment of exposure distributions and post-hoc refinement of dosing schemes. The formalized approach offers clinical teams a reproducible basis for pediatric dose selection. An implementation in R and Monolix is provided.

Lucerastat is an iminosugar with the potential to provide substrate reduction therapy for the treatment of Fabry disease (FD), an inherited X‐linked lysosomal storage disorder. The aims of this study were to develop a population pharmacokinetic (PK) model describing lucerastat plasma concentration over time, to investigate the relationships between subject‐specific characteristics and model parameters, and to assess the influence of these differences between subjects on lucerastat exposure via model‐based simulations. Longitudinal nonlinear mixed effects modeling was applied to develop a model based on data from 250 participants in six Phase 1 and two Phase 3 studies. Lucerastat pharmacokinetics were described by a two‐compartment model with linear first‐order absorption and elimination, including allometric scaling of body weight on clearance and volume parameters. Lucerastat clearance was reduced in subjects with lower estimated glomerular filtration rate (eGFR). Disease status (with/without FD) was found to impact clearance and volumes of distribution to a limited extent. The model described the data well across the dose range from 100 to 4000 mg. Body weight, disease status, and renal function were shown to influence exposure, with dose adaptation only required in patients with renal function impairment, as body weight and disease status had limited impact. Dose adaptation as applied in Phase 3 (i.e., eGFR [mL/min/1.73 m ² ] ≥60: 1000 mg; ≥45 and <60: 750 mg; ≥30 and <45: 500 mg; ≥15 and <30: 250 mg b.i.d.) resulted in achieving similar exposure in study participants with FD with different levels of renal function impairment.

This year marks the 20th edition of the Basel Modeling and Simulation (M&S) Seminar, an initiative rooted in a commitment to promoting the exchange of the latest advancements in pharmacometrics and related disciplines in the region of Basel, Switzerland. This article provides insight into the history of this event, its operations to the present date, and a glimpse at the future.

The white-paper regression model is the standard method for assessing QT liability of drugs. The quantity of interest, placebo-corrected QTc change from baseline (ΔΔQTc) with corresponding confidence interval (CI), is derived from the difference in model-estimated ΔQTc for active compound and placebo in a linear model. Model assumptions include linearity and no time delay between change in concentration and change in ΔQTc. Alternative models are commonly not considered unless there is a clear indication of inappropriateness of the assumptions. This work introduces several extensions for concentration-QT modeling in a pharmacometric context. The model is formulated as linear drug-effect model with treatment, nominal time, and centered baseline as covariates on the intercept. This approach enables straightforward use of other concentration-ΔQTc relationships, including loglinear, E_max, and indirect-effects models. In addition, the setup allows for the use of pharmacometric model assessments for ΔQTc and ΔΔQTc, including visual predictive checks and quantitative model comparison based on the Bayesian information criterion. The proposed approach is applied to several compounds from a previously published QTc study. The results suggest that a nonlinear mixed-effects model for ΔΔQTc and comparing a set of candidate models quantitatively can be a more powerful approach than fitting only the white-paper regression model. A semi-automated approach that compares nonlinear and hysteresis models to the linear model enables a reliable choice of the best model and determination of the degree of prolongation at the concentration of interest. Standard pharmacometric tools can assess the appropriateness of the models and the potential extent of hysteresis.

Aprocitentan is a novel, potent, dual endothelin receptor antagonist that recently demonstrated efficacy in the treatment of difficult-to-treat (resistant) hypertension. The aim of this study was to develop a population pharmacokinetic (PK) model describing aprocitentan plasma concentration over time, to investigate relationships between subject-specific factors (covariates) and model parameters, and to quantify the influence of the identified covariates on the exposure to aprocitentan via model-based simulations, enabling judgment about the clinical relevance of the covariates. PK data from 902 subjects in ten Phase 1, one Phase 2, and one Phase 3 study were pooled to develop a joint population PK model. The concentration-time course of aprocitentan was described by a two-compartment model with absorption lag time, first-order absorption and elimination, and reduced relative bioavailability following very high doses of 300 and 600 mg. The population PK model described the observed data well. Volume and clearance parameters were associated with body weight. Renal function as reflected by estimated glomerular filtration rate (eGFR), hepatic impairment, and sex were identified as relevant covariates on clearance. The subject-specific characteristics of body weight, eGFR, hepatic impairment, and sex were shown to influence exposure parameters area under the concentration-time curve and maximum concentration in steady state to a limited extent, i.e., not more than 25% different from a reference subject, and therefore do not warrant dose adjustments.

Background: The P2Y₁₂ receptor antagonist selatogrel is being developed for subcutaneous self-administration with a ready-to-use autoinjector at the onset of acute myocardial infarction (AMI) symptoms. The unique pharmacological profile of selatogrel (fast, potent, and short-acting) can bridge the time gap between the onset of AMI and first medical care. A clinical Phase 1 study showed a time-dependent pharmacodynamic interaction between selatogrel and loading doses of clopidogrel and prasugrel. As treatment switching is a common clinical practice, the assessment of subsequent switching from a clopidogrel loading dose to the first maintenance dose of oral P2Y₁₂ receptor antagonists is highly relevant. Objectives: Model-based predictions of inhibition of platelet aggregation (IPA) for the drugs triggering pharmacodynamic interactions were to be derived to support clinical guidance on the transition from selatogrel to oral P2Y₁₂ receptor antagonists. Methods: Scenarios with selatogrel 16 mg administration or placebo followed by a clopidogrel loading dose and, in turn, prasugrel or ticagrelor maintenance doses at different times of administration were studied. Population pharmacokinetic/pharmacodynamic modeling and simulations of different treatment scenarios were used to derive quantitative estimates for IPA over time. Results: Following selatogrel/placebo and a clopidogrel loading dose, maintenance treatment with ticagrelor or a prasugrel loading dose followed by maintenance treatment quickly achieved sustained IPA levels above 80%. Prior to maintenance treatment, a short time span from 18 to 24 h was identified where IPA levels were predicted to be lower with selatogrel than with placebo if clopidogrel was administered 12 h after selatogrel or placebo. Predicted IPA levels reached with placebo alone and a clopidogrel loading dose at 4 h were consistently lower than with selatogrel administration, followed by a clopidogrel loading dose at 12 h. If a clopidogrel loading dose is administered at 12 h, selatogrel maintains higher IPA levels up to 16 h. IPA levels are subsequently lower than on the placebo until the administration of the first maintenance dose. Conclusions: Model-based predictions informed the transition from selatogrel subcutaneous administration to oral P2Y₁₂ therapy. The application of modeling techniques illustrates the value of employing pharmacokinetic and pharmacodynamic modeling for the simulation of various clinical scenarios of switching therapies.

The novel oral complement factor 5a receptor 1 antagonist ACT‐1014‐6470 was well tolerated in single‐ and multiple‐ascending dose studies, including 24 h Holter electrocardiogram (ECG) recordings evaluating its cardiodynamics based on data from single doses of 30–200 mg and twice‐daily (b.i.d.) dosing of 30–120 mg for 4.5 days. By‐time point, categorical, and morphological analyses as well as concentration‐QT modeling and simulations were performed. No relevant effect of ACT‐1014‐6470 on ECG parameters was observed in the categorical and morphological analyses. After single‐dose administration, the by‐time point analysis indicated a delayed dose‐dependent increase in placebo‐corrected change from baseline in QT interval corrected with Fridericia's formula (ΔΔQTcF) at >6 h postdose. After b.i.d. dosing, ΔΔQTcF remained elevated during the 24‐h recording period, suggesting that the effect was not directly related to ACT‐1014‐6470 plasma concentration. The concentration‐QT model described change from baseline in QTcF (ΔQTcF)‐time profiles best with a 1‐oscillator model of 24 h for circadian rhythm, an effect compartment, and a sigmoidal maximum effect model. Model‐predicted ΔΔQTcF was derived for lower doses and less‐frequent dosing than assessed clinically. Median and 90% prediction intervals of ΔΔQTcF for once‐daily doses of 30 mg and b.i.d. doses of 10 mg did not exceed the regulatory threshold of 10 ms but would achieve ACT‐1014‐6470 plasma concentrations enabling adequate target engagement. Results from cardiodynamic assessments identified dose levels and dosing regimens that could be considered for future clinical trials, attempting to reduce QT liability.

The analysis aimed at identifying subject-specific characteristics (covariates) influencing exposure to daridorexant and quantification of covariate effects to determine clinical relevance. Data from 13 phase I, two phase II, and two phase III studies were pooled to develop a population pharmacokinetic model describing daridorexant concentration over time. Covariate effects were quantified based on model predictions. A two-compartment model with dose-dependent bioavailability, absorption lag time, linear absorption, and nonlinear elimination described the data best. Statistically significant covariates were food status on absorption (lag time and rate constant), time of drug administration (morning, bedtime) on absorption rate constant, lean body weight on central volume of distribution and elimination, fat mass on peripheral volume of distribution and intercompartmental drug transfer, and age and alkaline phosphatase on elimination. Age, lean body weight, fat mass, and alkaline phosphatase influence exposure (area under the curve, time of maximum concentration after dose administration, maximum plasma concentration, and next-morning concentration) to a limited extent, that is, less than 20% difference from a typical subject. Morning administration is not relevant for daridorexant use by insomnia patients. The food effect with simultaneous intake of a high-fat, high-calorie food is an extreme-case scenario unlikely to occur in clinical practice. Body composition, alkaline phosphatase, and age showed clinically negligible effects on exposure to daridorexant. Lean body weight and fat mass described the pharmacokinetics of daridorexant better than other body size descriptors (body weight, height, body mass index), suggesting a convenient physiological alternative to reduce the number of covariates in population pharmacokinetic models. The results indicate that differences between subjects do not require dose adjustments.

Selatogrel is a potent and reversible P2Y₁₂ receptor antagonist developed for subcutaneous self‐administration by patients with suspected acute myocardial infarction. After single‐dose emergency treatment with selatogrel, patients are switched to long‐term treatment with oral P2Y₁₂ receptor antagonists. Selatogrel shows rapid onset and offset of inhibition of platelet aggregation (IPA) to overcome the critical initial time after acute myocardial infarction. Long‐term benefit is provided by oral P2Y₁₂ receptor antagonists such as clopidogrel, prasugrel, and ticagrelor. A population pharmacokinetic (PK)/pharmacodynamic (PD) model based on data from 545 subjects in 4 phase I and 2 phase II studies well described the effect of selatogrel on IPA alone and in combination with clopidogrel, prasugrel, and ticagrelor. The PK of selatogrel were described by a three‐compartment model. The PD model included a receptor‐pool compartment to which all drugs can bind concurrently, reversibly or irreversibly, depending on their mode of action. Furthermore, ticagrelor and its active metabolite can bind to the selatogrel‐receptor complex allosterically, releasing selatogrel from the binding site. The model provided a framework for predicting the effect on IPA of selatogrel followed by reversibly and irreversibly binding oral P2Y₁₂ receptor antagonists for sustained effects. Determining the timepoint for switching from emergency to maintenance treatment is critical to achieve sufficient IPA at all times. Simulations based on the interaction model showed that loading doses of clopidogrel and prasugrel administered 15 h and 4.5 h after selatogrel, respectively, provide sustained IPA with clinically negligible drug interaction. Study Highlights: WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? Selatogrel is a potent reversible P2Y₁₂ receptor antagonist developed for subcutaneous self‐administration by patients in case of suspected acute myocardial infarction. Transition to oral P2Y₁₂ receptor antagonists without drug interaction and sufficient inhibition of platelet aggregation must be assured at all times. WHAT QUESTION DID THIS STUDY ADDRESS? The pharmacokinetic/pharmacodynamic model semimechanistically describes the effect of selatogrel on platelet inhibition alone and in combination with the oral P2Y₁₂ receptor antagonists clopidogrel, prasugrel, and ticagrelor. WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE? Model‐based simulations showed that loading doses of clopidogrel and prasugrel can be administered from 15 h and 4.5 h after selatogrel, respectively. HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE? These results support guiding the clinical transition from selatogrel emergency treatment to oral maintenance therapy in a safe and efficacious way.