Literature DB >> 31677899

Drug-Drug Interactions in Prostate Cancer Treatment.

Doris Hebenstreit1, Renate Pichler1, Isabel Heidegger2.   

Abstract

Polypharmacy is associated with an increased risk of drug-drug interactions (DDIs), which can cause serious and debilitating drug-induced adverse events. With a steadily aging population and associated increasing multimorbidity and polypharmacy, the potential for DDIs becomes considerably important. Prostate cancer (PCa) is the most common cancer in men and occurs mostly in elderly men in the Western world. Therefore, the aim of this review is to give an overview of DDIs in PCa therapy to better understand pharmacodynamic and pharm kinetic side effects as well as their interactions with other medications. Last, we explore potential future strategies, which might help to optimize treatment and reduce adverse events patients with polypharmacy and PCa.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Adverse events; Drug-drug interaction; Pharmacodynamic; Polypharmacy; Prostate cancer

Year:  2019        PMID: 31677899     DOI: 10.1016/j.clgc.2019.05.016

Source DB:  PubMed          Journal:  Clin Genitourin Cancer        ISSN: 1558-7673            Impact factor:   2.872


  3 in total

Review 1.  Next-Generation Androgen Receptor-Signaling Inhibitors for Prostate Cancer: Considerations for Older Patients.

Authors:  Zizhen Feng; Julie N Graff
Journal:  Drugs Aging       Date:  2021-02-09       Impact factor: 3.923

2.  Apalutamide, enzalutamide, and darolutamide for non-metastatic castration-resistant prostate cancer: a systematic review and network meta-analysis.

Authors:  Keiichiro Mori; Hadi Mostafaei; Benjamin Pradere; Reza Sari Motlagh; Fahad Quhal; Ekaterina Laukhtina; Victor M Schuettfort; Mohammad Abufaraj; Pierre I Karakiewicz; Takahiro Kimura; Shin Egawa; Shahrokh F Shariat
Journal:  Int J Clin Oncol       Date:  2020-09-14       Impact factor: 3.402

3.  Novel deep learning-based transcriptome data analysis for drug-drug interaction prediction with an application in diabetes.

Authors:  Qichao Luo; Shenglong Mo; Yunfei Xue; Xiangzhou Zhang; Yuliang Gu; Lijuan Wu; Jia Zhang; Linyan Sun; Mei Liu; Yong Hu
Journal:  BMC Bioinformatics       Date:  2021-06-11       Impact factor: 3.169

  3 in total

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