ReviewCancers2025
Optimizing Anti-PD1 Immunotherapy: An Overview of Pharmacokinetics, Biomarkers, and Therapeutic Drug Monitoring.
Review in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- The Immune Checkpoint Inhibitors Journey: From Early Promise to Lasting Impact.Journal of immunotherapy and precision oncology · 2026Review
- Development of a simplified population pharmacokinetic-pharmacodynamic framework for exposure-informed risk stratification of neutropenia during amrubicin treatment.Cancer chemotherapy and pharmacology · 2026Article
- Inflammatory Memory of Adipose Tissue Macrophages: From CD68 Footprint to Cardiometabolic and Cancer Risk During Weight Cycling.International journal of molecular sciences · 2026Review
- Toward adaptive therapeutic timing: integration of mechanistic pharmacology and artificial intelligence in precision dosing.Frontiers in pharmacology · 2026Review
- PD-L1-stratified health-related quality of life in solid tumors: pembrolizumab versus chemotherapy-a narrative review.Frontiers in medicine · 2026Review
- Association between HLA-DRB1*04:05 and the efficacy of immune checkpoint inhibitors for patients with advanced cancer.Frontiers in endocrinology · 2026Article
- Pre-treatment endocrine-nutritional signatures predict clinical benefit from PD-1/PD-L1 blockade in hematologic malignancies.Frontiers in nutrition · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anti-PD-1 therapies have transformed cancer treatment by restoring antitumor T cell activity. Despite their broad clinical use, variability in treatment response and immune-related adverse events underscore the need for therapeutic optimization. This article provides an integrative overview of the pharmacokinetics (PKs) of anti-PD-1 antibodies-such as nivolumab, pembrolizumab, and cemiplimab-and examines pharmacokinetic-pharmacodynamic (PK-PD) relationships, highlighting the impact of clearance variability on drug exposure, efficacy, and safety. Baseline clearance and its reduction during therapy, together with interindividual variability, emerge as important dynamic biomarkers with potential applicability across different cancer types for guiding individualized dosing strategies. The review also discusses established biomarkers for anti-PD-1 therapies, including tumor PD-L1 expression and immune cell signatures, and their relevance for patient stratification. The evidence supports a shift from traditional weight-based dosing toward adaptive dosing and therapeutic drug monitoring (TDM), especially in long-term responders and cost-containment contexts. Notably, the inclusion of clearance-based biomarkers-such as baseline clearance and its reduction-into therapeutic models represents a key step toward individualized, dynamic immunotherapy. In conclusion, optimizing anti-PD-1 therapy through PK-PD insights and biomarker integration holds promise for improving outcomes and reducing toxicity. Future research should focus on validating PK-based approaches and developing robust algorithms (machine learning models incorporating clearance, tumor burden, and other validated biomarkers) for tailored cancer treatment.
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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.