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Related Concept Videos

Drug Toxicity: Risk factors01:24

Drug Toxicity: Risk factors

113
Adverse Drug Reactions (ADRs) are potential complications that arise during pharmacotherapy, influenced by multiple risk factors. Age plays a significant role; both neonates and the elderly are at heightened risk due to their respective immature and diminished metabolic and elimination processes. Gender also impacts ADRs, with females experiencing a 1.5 to 1.7-fold greater risk than males, which may be linked to pharmacokinetic, pharmacodynamic, and hormonal differences. Notably, neonates, the...
113
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

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PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure...
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Pharmacokinetic–Pharmacodynamic Relationship: Exposure, Response and Effect01:26

Pharmacokinetic–Pharmacodynamic Relationship: Exposure, Response and Effect

171
The pharmacokinetic-pharmacodynamic (PK-PD) relationship describes the intricate link between drug exposure, efficacy, and toxicity, forming the foundation for optimal dosing regimens. This relationship uses mathematical modeling to characterize drug concentration-effect dynamics, ensuring precise therapeutic outcomes.Exposure represents the pharmacokinetic aspect of the PK-PD relationship, denoting the drug amount that elicits a biological response. It is typically quantified by administered...
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Therapeutic Drug Monitoring: Affecting Factors01:29

Therapeutic Drug Monitoring: Affecting Factors

316
Therapeutic Drug Monitoring (TDM) is the clinical practice of measuring specific drug levels in a patient's blood or body tissues to manage and optimize therapy. TDM is crucial for drugs with narrow therapeutic windows, like warfarin and phenytoin, where incorrect doses can lead to treatment failure or severe side effects. This monitoring ensures the dosage administered is within a safe and effective range. The factors affecting therapeutic drug monitoring include:Patient-Specific Factors:a.
316

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Related Experiment Video

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Predicting cardiovascular toxicity in anti-PD-1/PD-L1 therapy: a risk factor analysis and model development.

Zhihui Yan1,2, Juan Wang1, Jianxiu Sun3

  • 1Department of Cardiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.

Frontiers in Cardiovascular Medicine
|March 19, 2026
PubMed
Summary

This study identified key risk factors for cardiovascular toxicity in patients treated with anti-PD-1/PD-L1 therapy. A predictive model was developed, showing good accuracy for identifying patients at risk.

Keywords:
cardiovascular toxicityimmune examination point inhibitors (ICIs)nomogramprogrammatic cell death protein-1 (PD-1)programming death ligand-1 (PD-L1)risk factors

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Area of Science:

  • Oncology
  • Cardiology
  • Immunotherapy

Background:

  • Immune checkpoint inhibitors (ICIs) like anti-PD-1/PD-L1 therapies have revolutionized cancer treatment.
  • Cardiovascular toxicity is a significant concern associated with ICI therapy, impacting patient outcomes.
  • Identifying predictive factors for cardiovascular toxicity is crucial for risk stratification and management.

Purpose of the Study:

  • To investigate risk factors associated with cardiovascular toxicity in patients undergoing anti-PD-1/PD-L1 therapy.
  • To develop and validate a predictive model (nomogram) for cardiovascular toxicity risk.
  • To enhance clinical decision-making for patients receiving immunotherapy.

Main Methods:

  • Retrospective analysis of 2,665 patients with solid tumors treated with anti-PD-1/PD-L1 therapy.
  • Univariate and multivariate logistic regression to identify predictors of cardiovascular toxicity.
  • Development and internal validation of a nomogram using ROC, DCA, and calibration curves.

Main Results:

  • Systemic Inflammatory Response Index (SIRI), Eastern Cooperative Oncology Group performance status (ECOG), hypertension, diabetes, tumor metastasis, tumor stage, and sex were significant predictors in univariate analysis.
  • ECOG performance status emerged as an independent risk factor, while tumor metastasis was an independent protective factor in multivariate analysis.
  • The developed nomogram demonstrated good accuracy (AUC 0.77) and discrimination for predicting cardiovascular toxicity.

Conclusions:

  • ECOG performance status and tumor metastasis are key independent predictors of cardiovascular toxicity in patients receiving anti-PD-1/PD-L1 therapy.
  • The developed nomogram provides a valuable tool for assessing cardiovascular toxicity risk in this patient population.
  • Clinical utility of the nomogram can aid in proactive management and personalized treatment strategies for immunotherapy patients.