ReviewBritish journal of clinical pharmacology2020
Opportunities for using in silico-based extended dosing regimens for monoclonal antibody immune checkpoint inhibitors.
Review in British journal of clinical pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- Pharmacokinetic Simulation Analysis of Less Frequent Nivolumab and Pembrolizumab Dosing: Pharmacoeconomic Rationale for Dose Deescalation.Journal of clinical pharmacology · 2022Trial
- Article
- Eco Friendly and Budget Smart: An Economic and Environmental Evaluation of Alternative PD-1 and PD-L1 Inhibitor Dosing Regimens.PharmacoEconomics · 2025Article
- Optimizing Anti-PD1 Immunotherapy: An Overview of Pharmacokinetics, Biomarkers, and Therapeutic Drug Monitoring.Cancers · 2025Review
- TDM-Based Tailored Dosing of Durvalumab in Lung Cancer Patients: A Comprehensive Population Pharmacokinetic-Pharmacoeconomic Evaluation.Clinical pharmacokinetics · 2025Article
- Development of type 1 diabetes mellitus after nivolumab dose escalation: A case report.Medicine · 2025Article
- High-Throughput Antigen Microarray Identifies Longitudinal Prognostic Autoantibody for Chemoimmunotherapy in Advanced Non-Small Cell Lung Cancer.Molecular & cellular proteomics : MCP · 2024Article
- Killing a fly with a sledgehammer: Atezolizumab exposure in real-world lung cancer patients.CPT: pharmacometrics & systems pharmacology · 2023Observational
- Interventional pharmacoeconomics for immune checkpoint inhibitors through alternative dosing strategies.British journal of cancer · 2023Review
- Adopting Weight-Based Dosing With Pharmacy-Level Stewardship Strategies Could Reduce Cancer Drug Spending By Millions.Health affairs (Project Hope) · 2023Article
- In Silico Re-Optimization of Atezolizumab Dosing Using Population Pharmacokinetic Simulation and Exposure-Response Simulation.Journal of clinical pharmacology · 2023Article
- Article
- Optimizing dose-schedule regimens with bayesian adaptive designs: opportunities and challenges.Frontiers in pharmacology · 2023Review
- When Less May Be Enough: Dose Selection Strategies for Immune Checkpoint Inhibitors Focusing on AntiPD-(L)1 Agents.Targeted oncology · 2022Review
- Immune checkpoint-targeted antibodies: a room for dose and schedule optimization?Journal of hematology & oncology · 2022Review
- Dosing Regimens of Immune Checkpoint Inhibitors: Attempts at Lower Dose, Less Frequency, Shorter Course.Frontiers in oncology · 2022Review
- Cross-Validation of a Multiplex LC-MS/MS Method for Assaying mAbs Plasma Levels in Patients with Cancer: A GPCO-UNICANCER Study.Pharmaceuticals (Basel, Switzerland) · 2021Article
- Dosage of anti-PD-1 monoclonal antibodies: a cardinal open question.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2021Review
- Model-based simulation to support the extended dosing regimens of atezolizumab.European journal of clinical pharmacology · 2021Article
- Taking the brake off the immune system: Hypotheses, trials, tribulations, and the evolving discipline of clinical immunopharmacology.British journal of clinical pharmacology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Therapeutic drug monitoring (TDM) involves frequent measurements of drug concentrations to ensure levels remain within a therapeutic window, and it is especially useful for drugs with narrow therapeutic indices or extensive interindividual pharmacokinetic variability. This technique has never been applied to immuno-oncology drugs, but, given recent examinations of clinical data (both exposure and response) on a number of these drugs, further investigations into TDM may be justified to reduce costs as well as potentially reducing the severity and/or duration of immune-related adverse events. Specifically, all but one of the approved PD-1 and PD-L1 inhibitors (pembrolizumab, nivolumab, cemiplimab-rwlc, atezolizumab, avelumab, durvalumab) have been shown to exhibit a plateaued exposure-response (E-R) curve at doses evaluated extensively to date, as well as time-dependent changes in drug exposure. Furthermore, responders have a greater decrease in drug clearance over time and would, therefore, have supratherapeutic serum concentrations. With frequent trough measurements, it is possible to use pharmacokinetic modelling and simulation to estimate drug clearance via Bayesian methods. Based on patient-specific estimates for clearance, optimal alternative dosing strategies can be simulated to lower drug and cost burden yet maintain therapeutic levels, especially as the clearance of the drug decreases over time. This review will comprehensively discuss each of the FDA approved PD-1, PD-L1/2 and CTLA-4 inhibitors regarding their indications and current recommended dosing, with evidence supporting the investigation of these types of TDM strategies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.