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Dose individualization potential for checkpoint inhibitors
Stijn Koolen
Hospital pharmacist – Clinical pharmacologist
Department of Medical Oncology and Department of Pharmacy
Erasmus MC Cancer Institute
ICPAD
22 November 2019
Working mechanism
Immune system modulating antibodies (checkpoint inhibitors)
▪ Targeting and blocking the PD-1 receptor
PD-1 binds PD-L1 and PD-L2
▪ Inhibition of T-cell activation
Tumor cells expressing PD-L1 → escape mechanism
Problems
Ribas et al. Science 2018
• Development of immune related toxicity
• Checkpoint inhibitor monotherapy results in response in a minority of cancer patients
• Expensive
Group Indication Objective response rate (%)
High response rate Melanoma 35-70%
RCC 25%
NSCLC 20%
Intermediate response rate Bladder and urinary tract 15%
Can we use pharmacokinetic data to:
• Increase efficacy?
• Reduce side effects?
• Reduce costs?
Nivolumab
Nivolumab:
• PD-1 immune checkpoint inhibitor
• Monoclonal antibodies (IgG4; human)
• High molecular mass
• Slow distribution over tissues
• Elimination into peptides and aminoacids
• Very much alike endogeneous immunoglobulins
• T1/2 ~25 days
Factors influencing mAbPharmacokinetics
Oude Munnink et al 2016 CPT
Multomab study
Observational real-life study in patients treated with immunotherapy to:
• Explore PK in relation with effectiveness and toxicity
• Explore periperal blood immune cell characeteristics in relation with PK and outcome
• Explore effect of somatic genetic alterations on outcome after immunotherapy
Inclusion criteria
• > 18 years
• Treated with a monoclonal antibody
• Informed consent
Design
• Treatment according to standard of care
• At baseline, prior every cycle and at end of treatment a blood sample for:
• Serum → PK and biomarkers
• PBMC;
• ctDNA
Results
Between April 2016 – Sep 2019: 657 pts
Correlation between nivolumab exposure and treatment outocme in NSCLC
Basak et al. 2019 EJC - Bins presentation ICPAD 2018
A Prospective cohort study on the PK of nivolumab
• Advanced NSCLC, melanoma and RCC patients who started nivolumab treatment between April 2016 - October 2018
• Trough concentrations of nivolumab were meausered with an ELISA*
• PK data were analyzed using nonlinear mixed effect modeling
Hurkmans et al. 2019 JITC; *Basak et al. 2018 TDM
Results
• Patient characteristics
Results
1. The effect of patient factors on nivolumab pharmacokinetics
Age
Gender
Tumor type
Prior weight loss
Tumor burden
Laboratory results
Results
Model development → Initial model → Covariate incorparation → Final model
Results
• Women had 22% lower clearance than men
• The critical threshold that led to an estimated >20% increase of nivolumab clearance:
• For BSA > 2.2 m2
• For baseline serum albumin < 37.5 g/L
Hurkmans JITC 2019
1. The effect of patient factors on nivolumab pharmacokinetics
2. Relationship of nivolumab pharmacokinetics and outcome measures
Age
Gender
Tumor type
Prior weight loss
Tumor burden
Laboratory results
Radiological response
Toxicity
Results
• Pharmacokinetics vs. clinical outcome (RECIST v1.1) / toxicity (CTCAE 4.03)
Results
• Drug clearance was 42% higher in NSCLC patients with PD compared to PR/CR
• PD: mean 0.24; 95%CI: 0.22-0.27 L/day
• PR/CR: 0.17; 0.15-0.19 L/day
Hurkmans et al. JITC 2019
Results
• NSCLC patients stratified into quartiles based on drug clearance
• Low clearance (quartile 1) associated with better PFS and OS compared to patients with higher clearance (quartile 4)
Hurkmans et al. JITC 2019
Discussion
• Translational relevance?
• Dose adjustment based on patient characteristics?
• Different dosing strategy for NSCLC?
• True causal E-R relationship or reflecting the metabolic state of patient?
• Pembrolizumab at 2 and 10 mg/kg doses and also observed E-R trends within each dose level1
Turner et al., CCR 2019
• Modest E-R relationships within 1-mg/kg or the 10-mg/kg dose levels of nivolumab
Agrawal JITC 2016
Results
• Inverse clearance-response relationship nivolumab for NSCLC
• Gender, baseline BSA and serum albumin had a significant effect on nivolumab PK
• Translational relevance → individualized dosing strategy might be beneficial in the case of NSCLC - in contrast to melanoma.
Hurkmans 2019 JITC
Dose individualisation to reducecosts?
Ratain et al. 2019 JAMA oncol
Target saturation
• 90% target occupancy is reached at concentrations above 10 mg/L in an ex vivo model
• This concentration is already reached after the first cycle.*
• Median Ctrough at steady state: 60 mg/L.
• Median Ctrough after cycle 1: 19 mg/L
• Potential role for TDM to reduce dose or increase dosing interval in patients with high Ctrough levels.
→ Which cut-off level should be selected?
*Ogungbenbro et al 2018 CPT
• Example of two patients:
• with and without dose delays
Can we use pharmacokinetic data to:
• Increase efficacy? →/
• Reduce side effects? →
• Reduce costs? →
Acknowledgments
Dept of Medical Oncology
• Daan Hurkmans
• Edwin Basak
• Karlijn de Joode
• Sander Bins
• Tanja van Dijk
• Kersten Landa
• Nina Schepers
• Esther Oomen-de Hoop
• Reno Debets
• Astrid van der Veldt
• Ron Mathijssen
Dept of Pulmonology
• Darlene Mercieca
• Joachim Aerts
Dept of Immunology
• Annemarie Wijkhuis
• Marco Schreurs
Dept of Radiology and Nuclear Medicine
• Arlette Odink
Ampia Hosptial Breda, NL
Cor van der Leest
Cantonal Hospital St Gallen, Switzerland
• Marcus Joerger