Population pharmacokinetics and CD20 binding dynamics for mosunetuzumab in relapsed/refractory B-cell non-Hodgkin lymphoma.

Mosunetuzumab (Mosun) is a CD20xCD3 T-cell engaging bispecific antibody that redirects T cells to eliminate malignant B cells. The approved step-up dose regimen of 1/2/60/30 mg IV is designed to mitigate cytokine release syndrome (CRS) and maximize efficacy in early cycles. A population pharmacokinetic (popPK) model was developed from 439 patients with relapsed/refractory B-Cell Non-Hodgkin lymphoma receiving Mosun IV monotherapy, including fixed dosing (0.05-2.8 mg IV every 3 weeks (q3w)) and Cycle 1 step-up dosing groups (0.4/1/2.8-1/2/60/30 mg IV q3w). Prior to Mosun treatment, ~50% of patients had residual levels of anti-CD20 drugs (e.g., rituximab or obinutuzumab) from prior treatment. CD20 receptor binding dynamics and rituximab/obinutuzumab PK were incorporated into the model to calculate the Mosun CD20 receptor occupancy percentage (RO%) over time. A two-compartment model with time-dependent clearance (CL) best described the data. The typical patient had an initial CL of 1.08 L/day, transitioning to a steady-state CL of 0.584 L/day. Statistically relevant covariates on PK parameters included body weight, albumin, sex, tumor burden, and baseline anti-CD20 drug concentration; no covariate was found to have a clinically relevant impact on exposure at the approved dose. Mosun CD20 RO% was highly variable, attributed to the large variability in residual baseline anti-CD20 drug concentration (median = 10 µg/mL). The 60 mg loading doses increased Mosun CD20 RO% in Cycle 1, providing efficacious exposures in the presence of the competing anti-CD20 drugs. PopPK model simulations, investigating Mosun dose delays, informed treatment resumption protocols to ensure CRS mitigation.

Tumor growth inhibition modeling of individual lesion dynamics and interorgan variability in HER2-negative breast cancer patients treated with docetaxel.

Information on individual lesion dynamics and organ location are often ignored in pharmacometric modeling analyses of tumor response. Typically, the sum of their longest diameters is utilized. Herein, a tumor growth inhibition model was developed for describing the individual lesion time-course data from 183 patients with metastatic HER2-negative breast cancer receiving docetaxel. The interindividual variability (IIV), interlesion variability (ILV), and interorgan variability of parameters describing the lesion time-courses were evaluated. Additionally, a model describing the probability of new lesion appearance and a time-to-event model for overall survival (OS), were developed. Before treatment initiation, the lesions were largest in the soft tissues and smallest in the lungs, and associated with a significant IIV and ILV. The tumor growth rate was 2.6 times higher in the breasts and liver, compared with other metastatic sites. The docetaxel drug effect in the liver, breasts, and soft tissues was greater than or equal to 1.2 times higher compared with other organs. The time-course of the largest lesion, the presence of at least 3 liver lesions, and the time since study enrollment, increased the probability of new lesion appearance. New lesion appearance, along with the time to growth and time-course of the largest lesion at baseline, were identified as the best predictors of OS. This tumor modeling approach, incorporating individual lesion dynamics, provided a more complete understanding of heterogeneity in tumor growth and drug effect in different organs. Thus, there may be potential to tailor treatments based on lesion location, lesion size, and early lesion response to provide better clinical outcomes.

Population pharmacokinetic-pharmacodynamic modelling in oncology: a tool for predicting clinical response.

In oncology trials, overall survival (OS) is considered the most reliable and preferred endpoint to evaluate the benefit of drug treatment. Other relevant variables are also collected from patients for a given drug and its indication, and it is important to characterize the dynamic effects and links between these variables in order to improve the speed and efficiency of clinical oncology drug development. However, the drug-induced effects and causal relationships are often difficult to interpret because of temporal differences. To address this, population pharmacokinetic-pharmacodynamic (PKPD) modelling and parametric time-to-event (TTE) models are becoming more frequently applied. Population PKPD and TTE models allow for exploration towards describing the data, understanding the disease and drug action over time, investigating relevance of biomarkers, quantifying patient variability and in designing successful trials. In addition, development of models characterizing both desired and adverse effects in a modelling framework support exploration of risk-benefit of different dosing schedules. In this review, we have summarized population PKPD modelling analyses describing tumour, tumour marker and biomarker responses, as well as adverse effects, from anticancer drug treatment data. Various model-based metrics used to drive PD response and predict OS for oncology drugs and their indications are also discussed.

A mechanistic pharmacokinetic model elucidating the disposition of trastuzumab emtansine (T-DM1), an antibody-drug conjugate (ADC) for treatment of metastatic breast cancer.

Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate (ADC) therapeutic for treatment of human epidermal growth factor receptor 2 (HER2)-positive cancers. The T-DM1 dose product contains a mixture of drug-to-antibody ratio (DAR) moieties whereby the small molecule DM1 is chemically conjugated to trastuzumab antibody. The pharmacokinetics (PK) underlying this system and other ADCs are complex and have not been elucidated. Accordingly, we have developed two PK modeling approaches from preclinical data to conceptualize and understand T-DM1 PK, to quantify rates of DM1 deconjugation, and to elucidate the link between trastuzumab, T-DM1, and DAR measurements. Preclinical data included PK studies in rats (n = 34) and cynomolgus monkeys (n = 18) at doses ranging from 0.3 to 30 mg/kg and in vitro plasma stability. T-DM1 and total trastuzumab (TT) plasma concentrations were measured by enzyme-linked immunosorbent assay. Individual DAR moieties were measured by affinity capture liquid chromatography-mass spectrophotometry. Two PK modeling approaches were developed for T-DM1 using NONMEM 7.2 software: a mechanistic model fit simultaneously to TT and DAR concentrations and a reduced model fit simultaneously to TT and T-DM1 concentrations. DAR moieties were well described with a three-compartmental model and DM1 deconjugation in the central compartment. DM1 deconjugated fastest from the more highly loaded trastuzumab molecules (i.e., DAR moieties that are ≥3 DM1 per trastuzumab). T-DM1 clearance (CL) was 2-fold faster than TT CL due to deconjugation. The two modeling approaches provide flexibility based on available analytical measurements for T-DM1 and a framework for designing ADC studies and PK-pharmacodynamic modeling of ADC efficacy- and toxicity-related endpoints.

Onartuzumab (MetMAb): using nonclinical pharmacokinetic and concentration-effect data to support clinical development.

PURPOSE: We characterized the pharmacokinetics of onartuzumab (MetMAb) in animals and determined a concentration-effect relationship in tumor-bearing mice to enable estimation of clinical pharmacokinetics and target doses. EXPERIMENTAL DESIGN: A tumor growth inhibition model was used to estimate tumoristatic concentrations (TSC) in mice. Human pharmacokinetic parameters were projected from pharmacokinetics in cynomolgus monkeys by the species-invariant time method. Monte Carlo simulations predicted the percentage of patients achieving steady-state trough serum concentrations (Ctrough ss) ≥TSC for every 3-week (Q3W) dosing. RESULTS: Onartuzumab clearance (CL) in the linear dose range was 21.1 and 12.2 mL/d/kg in mice and cynomolgus monkeys with elimination half-life at 6.10 and 3.37 days, respectively. The estimated TSC in KP4 pancreatic xenograft tumor-bearing mice was 15 µg/mL. Projected CL for humans in the linear dose range was 5.74 to 9.36 mL/d/kg with scaling exponents of CL at 0.75 to 0.9. Monte Carlo simulations projected a Q3W dose of 10 to 30 mg/kg to achieve Ctrough ss of 15 µg/mL in 95% or more of patients. CONCLUSIONS: Onartuzumab pharmacokinetics differed from typical bivalent glycosylated monoclonal antibodies with approximately 2-times faster CL in the linear dose range. Despite this higher CL, xenograft efficacy data supported dose flexibility with Q1W to Q3W dose regimens in the clinical setting with a TSC of 15 µg/mL as the Ctrough ss target. The projected human efficacious dose of 10 to 30 mg/kg Q3W should achieve the target TSC of 15 µg/mL. These data show effective pharmacokinetic/pharmacodynamic modeling to project doses to be tested in the clinic.

A population pharmacokinetic/pharmacodynamic model of thrombocytopenia characterizing the effect of trastuzumab emtansine (T-DM1) on platelet counts in patients with HER2-positive metastatic breast cancer.

PURPOSE: Trastuzumab emtansine (T-DM1) is an antibody-drug conjugate in the development for the treatment of human epidermal growth factor receptor 2-positive cancers. Thrombocytopenia (TCP) is the dose-limiting toxicity of T-DM1. A semimechanistic population pharmacokinetic/pharmacodynamic (PK/PD) model was developed to characterize the effect of T-DM1 on patient platelet counts. METHODS: A PK/PD model with transit compartments that mimic platelet development and circulation was fit to concentration-platelet-time course data from two T-DM1 single-agent studies (TDM3569g; N = 52 and TDM4258g; N = 112). NONMEM(®) 7 software was used for model development. Data from a separate phase II study (TDM4374g; N = 110) were used for model evaluation. Patient baseline characteristics were evaluated as covariates of model PD parameters. RESULTS: The model described the platelet data well and predicted the incidence of grade ≥3 TCP. The model predicted that with T-DM1 3.6 mg/kg given every 3 weeks (q3w), the lowest platelet nadir would occur after the first dose. Also predicted was a patient subgroup (46 %) having variable degrees of downward drifting platelet-time profiles, which were predicted to stabilize by the eighth treatment cycle to platelet counts above grade 3 TCP. Baseline characteristics were not significant covariates of PD parameters in the model. CONCLUSIONS: This semimechanistic PK/PD model accurately captures the cycle 1 platelet nadir, the downward drift noted in some patient platelet-time profiles, and the ~8 % incidence of grade ≥3 TCP with T-DM1 3.6 mg/kg q3w. This model supports T-DM1 3.6 mg/kg q3w as a well-tolerated dose with minimal dose delays or reductions for TCP.

Population pharmacokinetics of cytarabine, etoposide, and daunorubicin in the treatment for acute myeloid leukemia.

PURPOSE: Interpatient variability in the pharmacokinetics (PK) of cytarabine, etoposide, and daunorubicin following body surface area-adjusted doses calls for studies that point to other covariates to explain this variability. The purpose of this study was to investigate such relationships and give insights into the PK of this combination treatment. METHODS: A prospective population PK study of twenty-three patients with acute myeloid leukemia was undertaken. Plasma concentrations of patients were determined by high-pressure liquid chromatography. PK models were developed with NONMEM; for daunorubicin, PK information from a prior study was utilized. RESULTS: Baseline white blood cell count (bWBC) influenced the PK for all drugs. A small, statistically insignificant improvement in model fit was achieved when a relationship between bWBC and daunorubicin central volume of distribution was included. The volume increased 1.9% for each increase in bWBC by 1 × 10(6) cells/mL. The clearances of etoposide and cytarabine were significantly increased and decreased, respectively, by increased bWBC. Tenfold changes in bWBC were needed for these relationships to have potential clinical relevance. A decrease in creatinine clearance of 60 mL/min resulted in a decrease in etoposide clearance of 32%. CONCLUSIONS: Population-based models characterized the PK for all three drugs. bWBC was a significant covariate for etoposide and cytarabine and showed a trend for daunorubicin. Linking the significant bWBC relationships and the relationship between kidney function and etoposide clearance to clinical end points would support dose individualization. Patients with above-normal creatinine clearances and high bWBC may receive sub-optimal treatment due to elevated etoposide clearances.