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Therapeutic Optimization of Anti-Tumor Drugs Based on Pharmacokinetics

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Drug Development".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1646

Special Issue Editors


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Guest Editor
Laboratory of Pharmacology, Department of Drug Sciences, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
Interests: cancer; anti-tumor drugs; pharmacokinetics; therapeutic drug monitoring; pharmacodynamics

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Co-Guest Editor
Laboratory of Pharmacology, Department of Drug Sciences, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
Interests: cancer; anti-tumor drugs; pharmacokinetics; therapeutic drug monitoring; pharmacodynamics

Special Issue Information

Dear Colleagues,

The pharmacological therapy indicated for the treatment of malignant pathologies has seen a complex evolution, both in terms of the quantity and diversity of therapeutic targets and strategies available. From traditional cytotoxic chemotherapy to cell therapy and immunotherapy, the variety of alternatives must be accompanied by the development of strategies to individualize the treatments chosen for each patient.

Pharmacokinetics makes it possible to predict the time course of the drug in the body and various methodologies for individualizing therapy based on pharmacokinetic principles have been developed. Via therapeutic drug monitoring and applying Bayesian estimation methodologies to determine individual pharmacokinetic parameters, it is possible to establish an individualized dosage regimen that minimizes the risk of toxicity and increases therapeutic effectiveness.

This Special Issue aims to organize a set of original research and review articles, to guide future guidelines for the therapeutic optimization of drugs used in the treatment of malignant pathologies. This special edition also aims to suggest future lines of research to obtain more sustainable evidence on the above subject.

Dr. Paula Fresco
Dr. Joaquim António Faria Monteiro
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • anti-cancer drugs
  • pharmacokinetics
  • therapeutic drug monitoring
  • cancer
  • populational pharmacokinetics models

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Published Papers (1 paper)

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Review

26 pages, 1116 KB  
Review
Optimizing Anti-PD1 Immunotherapy: An Overview of Pharmacokinetics, Biomarkers, and Therapeutic Drug Monitoring
by Joaquim Faria Monteiro, Alexandrina Fernandes, Diogo Gavina Tato, Elias Moreira, Ricardo Ribeiro, Henrique Reguengo, Jorge Gonçalves and Paula Fresco
Cancers 2025, 17(19), 3262; https://doi.org/10.3390/cancers17193262 - 8 Oct 2025
Viewed by 1306
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 [...] Read more.
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. Full article
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