CPT: Pharmacometrics & Systems Pharmacology最新文献

Cotadutide is a dual glucagon-like peptide-1 (GLP-1)/glucagon receptor agonist. Gastrointestinal adverse effects are known to be associated with GLP-1 receptor agonism and can be mitigated through tolerance development via a gradual up-titration. This analysis aimed to characterize the relationship between exposure and nausea incidence and to optimize titration schemes. The model was developed with pooled data from cotadutide-administrated studies. Three different modeling approaches, proportional odds (PO), discrete-time Markov, and two-stage discrete-time Markov models, were employed to characterize the exposure–nausea relationship. The severity of nausea was modeled as different states (non-nausea, mild, and moderate/severe). The most appropriate model was selected to perform the covariate analysis, and the final covariate model was used to simulate the nausea event rates for various titration scenarios. The two Markov models demonstrated comparable performance and were better than the PO model. The covariate analysis was conducted with the standard Markov model for operational simplification and identified disease indications (NASH, obesity) and sex as covariates on Markov parameters. The simulations indicated that the biweekly titration with twofold dose escalation is superior to other titration schemes with a relatively low predicted nausea event rate at 600 μg (25%) and a shorter titration interval (8 weeks) to reach the therapeutic dose. The model can be utilized to optimize starting dose and titration schemes for other therapeutics in clinical trials to achieve an optimal risk–benefit balance and reach the therapeutic dose with minimal titration steps.

Extracorporeal membrane oxygenation (ECMO) is a cardiopulmonary bypass device commonly used to treat cardiac arrest in children. The American Heart Association guidelines for cardiopulmonary resuscitation (CPR) and emergency cardiovascular care recommend using amiodarone as a first-line agent to treat ventricular arrhythmias in children with cardiac arrest. However, there are no dosing recommendations for amiodarone to treat ventricular arrhythmias in pediatric patients on ECMO. Amiodarone has a high propensity for adsorption to the ECMO components due to its physicochemical properties leading to altered pharmacokinetics (PK) in ECMO patients. The change in amiodarone PK due to interaction with ECMO components may result in a difference in optimal dosing in patients on ECMO when compared with non-ECMO patients. To address this clinical knowledge gap, a physiologically-based pharmacokinetic model of amiodarone was developed in adults and scaled to children, followed by the addition of an ECMO compartment. The pediatric model included ontogeny functions of cytochrome P450 (CYP450) enzyme maturation across various age groups. The ECMO compartment was parameterized using the adsorption data of amiodarone obtained from ex vivo studies. Model predictions captured observed concentrations of amiodarone in pediatric patients with ECMO well with an average fold error between 0.5 and 2. Model simulations support an amiodarone intravenous (i.v) bolus dose of 22 mg/kg (neonates), 13 mg/kg (infants), 8 mg/kg (children), and 6 mg/kg (adolescents). This PBPK modeling approach can be applied to explore the dosing of other drugs used in children on ECMO.

The Simcyp Simulator is a software platform widely used in the pharmaceutical industry to conduct stochastic physiologically-based pharmacokinetic (PBPK) modeling. This approach has the advantage of combining routinely generated in vitro data on drugs and drug products with knowledge of biology and physiology parameters to predict a priori potential pharmacokinetic changes in absorption, distribution, metabolism, and excretion for populations of interest. Combining such information with pharmacodynamic knowledge of drugs enables planning for potential dosage adjustment when clinical studies are feasible. Although the conduct of dedicated clinical studies in some patient groups (e.g., with hepatic or renal diseases) is part of the regulatory path for drug development, clinical studies for all permutations of covariates potentially affecting pharmacokinetics are impossible to perform. The role of PBPK in filling the latter gap is becoming more appreciated. This tutorial describes the different input parameters required for the creation of a virtual population giving robust predictions of likely changes in pharmacokinetics. It also highlights the considerations needed to qualify the models for such contexts of use. Two case studies showing the step-by-step development and application of population files for obese or morbidly obese patients and individuals with Crohn's disease are provided as the backbone of our tutorial to give some hands-on and real-world examples.

Overadjustment bias is a term used in epidemiology to refer to the situation where bias is introduced when controlling for a variable in the analysis, for example, by including the variable as a covariate in a model.^{1, 2} There are several situations in which inclusion of covariates can cause overadjustment bias, two of which are graphically illustrated in Figure 1. In the first example, the drug exposure has a certain causal effect on the outcome and part of this causal effect is mediated by an intermediate. Because part of the causal effect of exposure on outcome is explained by the effect of exposure on the intermediate, including the intermediate as a covariate in the model will result in a biased (i.e., under-estimated) estimate of the total effect of exposure on the outcome. In the second example, in Figure 1, the potential covariate is a descendant or consequence of the outcome.

The risk of overadjustment bias lies particularly in those covariates that are included in the model in a time-varying fashion or when using a time-constant, but post-baseline value as a covariate (e.g., the value observed at 3-months after treatment start in each subject). In contrast, the covariate values at baseline are not affected by treatment (assuming treatment starts at baseline) and one would therefore not expect overadjustment bias when including baseline covariates in a model.

Although overadjustment bias is rarely discussed within the context of PK/PD modeling, it is by no means less important for this setting. In a PK/PD model, the scenarios above would result in an under-estimated treatment effect. This bias could negatively impact real-life decisions. It may lead one to incorrectly conclude that the treatment is not effective (enough) or that a higher concentration may be needed to reach the target efficacy. Overadjustment bias could also result in overly conservative sample size calculations for a future clinical study.

To demonstrate the importance of overadjustment bias, we performed several illustrative simulations of a hypothetical placebo-controlled study (R code included in the Supplemental Material S1). Simulations were conducted for three scenarios involving a time-varying covariate influencing a numerical outcome variable (Figure 2a). Each scenario was simulated 1000 times, with each dataset containing 100 subjects that each have six observations for both covariate and outcome variables.

Scenario 1 assumes a direct treatment effect on the outcome, which is not mediated by the time-varying covariate (i.e., treatment does not have any effect on the covariate in this scenario). In Scenario 2, the treatment effect on the outcome is entirely mediated by the time-varying covariate: drug treatment lowers the covariate, which lowers the outcome value. Scenario 3 represents a hybrid scenario with 50% direct and 50% mediated treatment effects. The total treatment effect for a specific drug concentrati

The advent of machine learning has led to innovative approaches in dealing with clinical data. Among these, Neural Ordinary Differential Equations (Neural ODEs), hybrid models merging mechanistic with deep learning models have shown promise in accurately modeling continuous dynamical systems. Although initial applications of Neural ODEs in the field of model-informed drug development and clinical pharmacology are becoming evident, applying these models to actual clinical trial datasets—characterized by sparse and irregularly timed measurements—poses several challenges. Traditional models often have limitations with sparse data, highlighting the urgent need to address this issue, potentially through the use of assumptions. This review examines the fundamentals of Neural ODEs, their ability to handle sparse and irregular data, and their applications in model-informed drug development.

Despite the benefits of breastfeeding, lactating mothers who take prescribed medications may discontinue breastfeeding due to concerns associated with infant drug exposure in breastmilk. Consolidating the current knowledge of drug transport to breastmilk may inform understanding of the safety of medication use during lactation. This literature review summarizes the mechanisms of drug transport to breastmilk, details the physicochemical drug properties that may alter the extent of passive transport, and describes the expressional changes in mammary drug transporters that may affect active transport. During the period of 20 July 2023 to 11 August 2023, PubMed® was searched to identify journal articles pertinent to the mechanisms of drug transport from maternal plasma to breastmilk and the expression of mammary drug transporters during lactation. From the 28 studies included in this review, four mechanisms were identified for transporting drugs from maternal plasma to breastmilk: passive transport, active transport, lipid co-transport, and transcytosis. The lactational expression of 20 drug transporters was further summarized, with 9 transporters demonstrating downregulated expression during lactation and 11 transporters demonstrating upregulated expression during lactation. Understanding the mechanisms of drug transport to breastmilk may aid in estimating infant drug exposure, developing physiologically based pharmacokinetic (PBPK) models that describe drug transfer, and initiating clinical drug development programs in the lactating population.

Cefazolin is an antibiotic used to prevent surgical site infections. During cardiac surgery with cardiopulmonary bypass (CPB), its efficacy target could be underachieved. We aimed to develop a population pharmacokinetic model for cefazolin in children and optimize the prophylactic dosing regimen. Children under 25 kg undergoing cardiac surgery with CPB and receiving cefazolin at standard doses (50 mg/kg IV every 4–6 h) were included in this analysis. A population pharmacokinetic model and Monte Carlo simulations were used to evaluate the probability of target attainment (PTA) for efficacy and toxicity with the standard regimen and an alternative regimen of continuous infusion, where loading and maintenance doses were calculated from model-derived individual parameters. Twenty-two patients were included, with median (range) age, body weight, and eGFR of 19.5 (1–94) months, 8.7 (2–21) kg, and 116 (48–159) mL/min, respectively. Six patients received an additional dose in the CPB circuit. A two-compartment disposition model with an additional compartment for the CPB was developed, including weight-based allometric scaling and eGFR. For a 10 kg patient with eGFR of 120 mL/min/1.73 m², clearance was estimated as 0.856 L/h. Simulations indicated that the standard dosing regimen fell short of achieving the efficacy target >40% of the time within a dosing duration and in patients with good renal function, PTA ranged from <20% to 70% for the smallest to the largest patients, respectively, at high MICs. In contrast, the alternative regimen consistently maintained target concentrations throughout the procedure for all patients while using a lower overall dose.

Clinical trials seeking to delay or prevent the onset of type 1 diabetes (T1D) face a series of pragmatic challenges. Despite more than 100 years since the discovery of insulin, teplizumab remains the only FDA-approved therapy to delay progression from Stage 2 to Stage 3 T1D. To increase the efficiency of clinical trials seeking this goal, our project sought to inform T1D clinical trial designs by developing a disease progression model-based clinical trial simulation tool. Using individual-level data collected from the TrialNet Pathway to Prevention and The Environmental Determinants of Diabetes in the Young natural history studies, we previously developed a quantitative joint model to predict the time to T1D onset. We then applied trial-specific inclusion/exclusion criteria, sample sizes in treatment and placebo arms, trial duration, assessment interval, and dropout rate. We implemented a function for presumed drug effects. To increase the size of the population pool, we generated virtual populations using multivariate normal distribution and ctree machine learning algorithms. As an output, power was calculated, which summarizes the probability of success, showing a statistically significant difference in the time distribution until the T1D diagnosis between the two arms. Using this tool, power curves can also be generated through iterations. The web-based tool is publicly available: https://app.cop.ufl.edu/t1d/. Herein, we briefly describe the tool and provide instructions for simulating a planned clinical trial with two case studies. This tool will allow for improved clinical trial designs and accelerate efforts seeking to prevent or delay the onset of T1D.

Chiang, C. W., Zhang, P., Donneyong, M., Chen, Y., Su, Y., & Li, L. (2021). Random control selection for conducting high-throughput adverse drug events screening using large-scale longitudinal health data. CPT: Pharmacometrics & Systems Pharmacology 10(9):1032-1042. https://doi.org/10.1002/psp4.12673

In the published version of this article, the co-first author's name Pengyue Zhang was misspelled as Penyue Zhang.

The published article has also been corrected to reflect this change.

We apologize for this error.

The full random-effects model (FREM) is an innovative and relatively novel covariate modeling technique. It differs from other covariate modeling approaches in that it treats covariates as observations and captures their impact on model parameters using their covariances. These unique characteristics mean that FREM is insensitive to correlations between covariates and implicitly handles missing covariate data. In practice, this implies that covariates are less likely to be excluded from the modeling scope in light of the observed data. FREM has been shown to be a useful modeling method for small datasets, but its pre-specification properties make it a very compelling modeling choice for late-stage phases of drug development. The present tutorial aims to explain what FREM models are and how they can be used in practice.