Clinical Pharmacokinetics and Pharmacodynamics of Immune Checkpoint Inhibitors

Abstract

Immune checkpoint inhibitors (ICIs) have demonstrated significant clinical impact in improving overall survival of several malignancies associated with poor outcomes; however, only 20-40% of patients will show long-lasting survival. Further clarification of factors related to treatment response can support improvements in clinical outcome and guide the development of novel immune checkpoint therapies. In this article, we have provided an overview of the pharmacokinetic (PK) aspects related to current ICIs, which include target-mediated drug disposition and time-varying drug clearance. In response to the variation in treatment exposure of ICIs and the significant healthcare costs associated with these agents, arguments for both dose individualization and generalization are provided. We address important issues related to the efficacy and safety, the pharmacodynamics (PD), of ICIs, including exposure-response relationships related to clinical outcome. The unique PK and PD aspects of ICIs give rise to issues of confounding and suboptimal surrogate endpoints that complicate interpretation of exposure-response analysis. Biomarkers to identify patients benefiting from treatment with ICIs have been brought forward. However, validated biomarkers to monitor treatment response are currently lacking.

Economics of alternative dosing strategies for pembrolizumab and nivolumab at a single academic cancer center

“…Weight-based doses of nivolumab as low as 0.3 mg/kg have been shown to saturate peripheral PD-1 receptors, and the exposure-response curve flattens between 1 and 3 mg/kg, suggesting no increase in efficacy beyond this dose range. 28,29 Increased exposure without therapeutic benefit is "wasted" drug spending.…”

Crosstalk between HER2 and PD-1/PD-L1 in Breast Cancer: From Clinical Applications to Mathematical Models

“…As per available literature, potential predictive biomarkers that can be used to select patients who may benefit from combined treatment using HER2-targeted and PD-1/PD-L1 axis based therapeutic agents are (1) HER2 amplification/overexpression, (2) PD-1/PD-L1 expression, (3) presence of a greater number of TILs and fewer Tregs, (4) higher TMB (tumor mutation burden), (5) PTEN expression, and (6) expression of CD5, CD74, CD96, and CD226, to name only few [38,86,121,[131][132][133][134][135][136][137][138]. However, it is still not clear which combination of clinicopathological factors are most reliable predictive biomarkers to implement effective treatment protocols using anti-HER2 and/or PD-1/PD-L1 pathways [39,139]. Moreover, since blocking immune checkpoints can have side effects such as organ damage, careful analysis using multiple biomarkers is required while developing combination therapy protocols.…”

“…It is apparent that mathematical models of tumor-immune interaction with respect to HER2-targeted therapy and/or immune checkpoint blockade can be used to explore tumor dynamics in detail and to answer questions that are difficult to answer by clinical analysis [54,71,145]. Since 1954, mathematical model-based analysis has contributed heavily to various areas of cancer research such as drug scheduling, estimating drug response in terms of desired effect, testing research hypothesis, and to study interdependence and sensitivity of various parameters involved in cancer dynamics [39,40,70,[154][155][156][157]. At this point in time, it is worth noting that nuclear physics, neuroscience, epidemiology, and physical chemistry are fundamental areas of research that witnessed a big leap forward due to the integration of empirical and theoretical works.…”

“…On the other hand, the results obtained from theoretical analysis using mathematical models (e.g., tumor doubling time, optimal drug dose, predicted tumor volume, estimated time for relapse of disease) are used to optimize the clinical experiment and proposed therapeutic strategy [31,[35][36][37][38]. Specifically, mathematical models can be used to analyze drug distribution (pharmacokinetics), drug response (pharmacodynamics) to monotherapy and combination therapy, development of drug resistance, and effect of drug toxicity related to cancer treatment [39,40]. Even though substantial efforts have been dedicated to the development of mathematical modeling of various types of cancers and their treatments, these contributions have been isolated from the clinical framework of cancer care and management.…”

Neurotoxicity and Immunotherapy

“…Interval, kdy je ještě možné očekávat účinek plazmaferézy, je závislý na farmakokinetických vlastnostech použitého checkpoint inhibitoru. Pro nivolumab je terminální bio logický poločas kolem 25 dnů, pro ipilimumab 14 dnů, pro atezulizumab 27 dnů a pro avelumab 6 dnů, pro pembrolizumab se interval pohybuje mezi 14 až 27 dny [12].…”