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Precision Cardio-Oncology: Use of Mechanistic Pharmacokinetic and Pharmacodynamic Modeling to Predict Cardiotoxicities of Anti-Cancer Drugs.
Wen, Hai-Ni; Wang, Chen-Yu; Li, Jin-Meng; Jiao, Zheng.
Afiliação
  • Wen HN; Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Wang CY; Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Li JM; Department of Pharmacy, Affiliated Hangzhou Chest Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Jiao Z; Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
Front Oncol ; 11: 814699, 2021.
Article em En | MEDLINE | ID: mdl-35083161
ABSTRACT
The cardiotoxicity of anti-cancer drugs presents as a challenge to both clinicians and patients. Significant advances in cancer treatments have improved patient survival rates, but have also led to the chronic effects of anti-cancer therapies becoming more prominent. Additionally, it is difficult to clinically predict the occurrence of cardiovascular toxicities given that they can be transient or irreversible, with large between-subject variabilities. Further, cardiotoxicities present a range of different symptoms and pathophysiological mechanisms. These notwithstanding, mechanistic pharmacokinetic (PK) and pharmacodynamic (PD) modeling offers an important approach to predict cardiotoxicities and offering precise cardio-oncological care. Efforts have been made to integrate the structures of physiological and pharmacological networks into PK-PD modeling to the end of predicting cardiotoxicities based on clinical evaluation as well as individual variabilities, such as protein expression, and physiological changes under different disease states. Thus, this review aims to report recent progress in the use of PK-PD modeling to predict cardiovascular toxicities, as well as its application in anti-cancer therapies.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Oncol Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Oncol Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China