Evaluation of the potential impact on pharmacokinetics of various cytochrome P450 substrates of increasing IL-6 levels following administration of the T-cell bispecific engager glofitamab.

Glofitamab is a novel T cell bispecific antibody developed for treatment of relapsed-refractory diffuse large B cell lymphoma and other non-Hodgkin's lymphoma indications. By simultaneously binding human CD20-expressing tumor cells and CD3 on T cells, glofitamab induces tumor cell lysis, in addition to T-cell activation, proliferation, and cytokine release. Here, we describe physiologically-based pharmacokinetic (PBPK) modeling performed to assess the impact of glofitamab-associated transient increases in interleukin 6 (IL-6) on the pharmacokinetics of several cytochrome P450 (CYP) substrates. By refinement of a previously described IL-6 model and inclusion of in vitro CYP suppression data for CYP3A4, CYP1A2, and 2C9, a PBPK model was established in Simcyp to capture the induced IL-6 levels seen when glofitamab is administered at the intended dose and dosing regimen. Following model qualification, the PBPK model was used to predict the potential impact of CYP suppression on exposures of various CYP probe substrates. PBPK analysis predicted that, in the worst-case, the transient elevation of IL-6 would increase exposures of CYP3A4, CYP2C9, and CYP1A2 substrates by less than or equal to twofold. Increases for CYP3A4, CYP2C9, and CYP1A2 substrates were projected to be 1.75, 1.19, and 1.09-fold following the first administration and 2.08, 1.28, and 1.49-fold following repeated administrations. It is recommended that there are no restrictions on concomitant treatment with any other drugs. Consideration may be given for potential drug-drug interaction during the first cycle in patients who are receiving concomitant CYP substrates with a narrow therapeutic index via monitoring for toxicity or for drug concentrations.

Population Pharmacokinetics and Pharmacodynamics of Meropenem in Critically Ill Patients: How to Achieve Best Dosage Regimen According to the Clinical Situation.

BACKGROUND AND OBJECTIVES: Meropenem is frequently used for the treatment of severe bacterial infections in critically ill patients. Because critically ill patients are more prone to pharmacokinetic variability than other patients, ensuring an effective blood concentration can be complex. Therefore, describing this variability to ensure a proper use of this antibiotic drug limits the rise and dissemination of antimicrobial resistance, and helps preserve the current antibiotic arsenal. The aims of this study were to describe the pharmacokinetics of meropenem in critically ill patients, to identify and quantify the patients' characteristics responsible for the observed pharmacokinetic variability, and to perform different dosing simulations in order to determine optimal individually adapted dosing regimens. METHODS: A total of 58 patients hospitalized in the medical intensive care unit and receiving meropenem were enrolled, including 26 patients with renal replacement therapy. A population pharmacokinetic model was developed (using NONMEM software) and Monte Carlo simulations were performed with different dosing scenarios (bolus-like, extended, and continuous infusion) exploring the impact of clinical categories of residual diuresis (anuria, oliguria, and preserved diuresis) on the probability of target attainment (MIC: 1-45 mg/L). RESULTS: The population pharmacokinetic model included five covariates with a significant impact on clearance: glomerular filtration rate, dialysis (continuous and semi-continuous), renal function status, and volume of residual diuresis. The clearance for a typical patient in our population is 4.20 L/h and volume of distribution approximately 44 L. Performed dosing regimen simulations suggested that, for equivalent doses, the continuous infusion mode (with loading dose) allowed the obtaining of the pharmacokinetic/pharmacodynamic target for a larger number of patients (100% for MIC ≤ 20 mg/L). Nevertheless, for the treatment of susceptible bacteria (MIC ≤ 2 mg/L), differences in the probability of target attainment between bolus-like, extended, and continuous infusions were negligible. CONCLUSIONS: Identified covariates in the model are easily accessible information in patient health records. The model highlighted the importance of considering the patient's overall condition (renal function and dialysis) and the pathogen's characteristics (MIC target) during the establishment of a patient's dosing regimen.

Model-Informed Therapeutic Drug Monitoring of Meropenem in Critically Ill Patients: Improvement of the Predictive Ability of Literature Models with the PRIOR Approach.

BACKGROUND AND OBJECTIVE: To improve the predictive ability of literature models for model-informed therapeutic drug monitoring (TDM) of meropenem in intensive care units, we propose to tweak the literature models with the "prior approach" using a subset of the data. This study compares the predictive ability of both literature and tweaked models on TDM concentrations of meropenem in critically ill patients. METHODS: Blood samples were collected from patients of an intensive care unit treated with intravenous meropenem. Data were split six times into an "estimation" and a "prediction" datasets. Population pharmacokinetic (popPK) models of meropenem were selected from literature. These models were run on the "estimation" dataset with the $PRIOR subroutine in NONMEM to obtain tweaked models. The literature and tweaked models were used a priori (with covariate only) and with Bayesian fitting to predict each individual concentration from the previous concentration(s). Their respective predictive abilities were compared using median relative prediction error (MDPE%) and median absolute relative prediction error (MDAPE%). RESULTS: The total dataset was composed of 115 concentrations from 58 patients. For each of the six splits, the "estimation" and the "prediction" datasets were respectively composed of 44 and 14 patients or 45 and 13 patients. Six popPK models were selected in the literature. MDPE% and MDAPE% were globally lower for the tweaked than for the literature models, especially for a priori predictions. CONCLUSION: The "prior approach" could be a valuable tool to improve the predictive ability of literature models, especially for a priori predictions, which are important to optimize dosing in emergency situations.