dose individualization potential for checkpoint...
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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
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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
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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%
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Can we use pharmacokinetic data to:
• Increase efficacy?
• Reduce side effects?
• Reduce costs?
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Nivolumab
Nivolumab:
• PD-1 immune checkpoint inhibitor
• Monoclonal antibodies (IgG4; human)
• High molecular mass
• Slow distribution over tissues
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• Elimination into peptides and aminoacids
• Very much alike endogeneous immunoglobulins
• T1/2 ~25 days
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Factors influencing mAbPharmacokinetics
Oude Munnink et al 2016 CPT
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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
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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
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Results
Between April 2016 – Sep 2019: 657 pts
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Correlation between nivolumab exposure and treatment outocme in NSCLC
Basak et al. 2019 EJC - Bins presentation ICPAD 2018
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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
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Results
• Patient characteristics
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Results
1. The effect of patient factors on nivolumab pharmacokinetics
Age
Gender
Tumor type
Prior weight loss
Tumor burden
Laboratory results
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Results
Model development → Initial model → Covariate incorparation → Final model
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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
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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
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Results
• Pharmacokinetics vs. clinical outcome (RECIST v1.1) / toxicity (CTCAE 4.03)
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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
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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
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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?
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• Pembrolizumab at 2 and 10 mg/kg doses and also observed E-R trends within each dose level1
Turner et al., CCR 2019
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• Modest E-R relationships within 1-mg/kg or the 10-mg/kg dose levels of nivolumab
Agrawal JITC 2016
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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
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Dose individualisation to reducecosts?
Ratain et al. 2019 JAMA oncol
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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
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• Example of two patients:
• with and without dose delays
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Can we use pharmacokinetic data to:
• Increase efficacy? →/
• Reduce side effects? →
• Reduce costs? →
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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