Dose Optimization of Pembrolizumab: Less May Be More

Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development

Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence and with the mindset of model-informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.

Extending the dosing intervals of nivolumab: model-based simulations in unselected cancer patients

BACKGROUND

Reducing nivolumab dose intensity could increase patients' life quality and decrease the financial burden while maintaining efficacy. The aims of this study were to develop a population PK model of nivolumab based on data from unselected metastatic cancer patients and to simulate extended-interval regimens allowing to maintain minimal effective plasma concentrations (MEPC).

METHODS

Concentration-time data (992 plasma nivolumab concentrations, 364 patients) were modeled using a two-compartment model with linear elimination clearance in Monolix software. Extended-interval regimens allowing to maintain steady-state trough concentrations (Cmin,ss) above the MEPC of 2.5 mg/L or 1.5 mg/L in >90% of patients were simulated.

RESULTS

Increasing 3-times the dosing interval from 240 mg every two weeks (Q2W) to Q6W and 2-times from 480 mg Q4W to Q8W resulted in Cmin,ss above 2.5 mg/L in 95.8% and 95.4% of patients, respectively. 240 mg Q8W and 480 mg Q10W resulted in Cmin,ss above 1.5 mg/L in 91.0% and 91.8% of patients, respectively. Selection of a 240 mg Q6W regimen would decrease by 3-fold the annual treatment costs compared to standard regimen of 240 mg Q2W (from 78,744€ to 26,248€ in France).

CONCLUSIONS

Clinical trials are warranted to confirm the non-inferiority of extended-interval compared to standard regimen.

Utility and impact of quantitative pharmacology on dose selection and clinical development of immuno-oncology therapy

Immuno-oncology (IO) therapies have changed the cancer treatment landscape. Immune checkpoint inhibitors (ICIs) have improved overall survival in 20-40% of patients with malignancies that were previously refractory. Due to the uniqueness in biology, modalities and patient responses, drug development strategies for IO differed from that traditionally used for cytotoxic and target therapies in oncology, and quantitative pharmacology utilizing modeling approach can be applied in all phases of the development process. In this review, we used case studies to showcase how various modeling methodologies were applied from translational science and dose selection through to label change, using examples that included anti-programmed-death-1 (anti-PD-1), anti-programmed-death ligand-1 (anti-PD-L1), anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA-4), and anti-glucocorticoid-induced tumor necrosis factor receptor-related protein (anti-GITR) antibodies. How these approaches were utilized to support phase I-III dose selection, the design of phase III trials, and regulatory decisions on label change are discussed to illustrate development strategies. Model-based quantitative approaches have positively impacted IO drug development, and a better understanding of the biology and exposure-response relationship may benefit the development and optimization of new IO therapies.

Killing a fly with a sledgehammer: Atezolizumab exposure in real-world lung cancer patients

Atezolizumab is an anti-PDL1 approved for treating lung cancer. A threshold of 6 μg/mL in plasma has been associated with target engagement. The extent to which patients could be overexposed with the standard 1200 mg q3w dosing remains unknown. Here, we monitored atezolizumab peak and trough levels in 27 real-world patients with lung cancer as part of routine therapeutic drug monitoring. Individual pharmacokinetic (PK) parameters were calculated using a population approach and optimal dosing-intervals were simulated with respect to the target trough levels. No patient had plasma levels below 6 μg/mL. The results showed that the mean trough level after the first treatment was 78.3 ± 17 μg/mL, that is, 13 times above the target concentration. The overall response rate was 55.5%. Low-grade immune-related adverse events was observed in 37% of patients. No relationship was found between exposure metrics of atezolizumab (i.e., minimum plasma concentration, maximum plasma concentration, and area under the curve) and pharmacodynamic end points (i.e., efficacy and toxicity). Further simulations suggest that the dosing interval could be extended from 21 days to 49 up to 136 days (mean: 85.7 days, i.e., q12w), while ensuring plasma levels still above the 6 μg/mL target threshold. This observational, real-world study suggests that the standard 1200 mg q3w fixed-dose regimen of atezolizumab results in significant overexposure in all the patients. This was not associated with increased side effects. As plasma levels largely exceed pharmacologically active concentrations, interindividual variability in PK parameters did not impact efficacy. Our data suggest that dosing intervals could be markedly extended with respect to the target threshold associated with efficacy.