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.

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.