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Current Pharmaceutical Design

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ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

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Meta-Analysis

Which Drugs are More Effective in Preventing Familial Adenomatous Polyposis Progression based on Network Meta-analysis?

Author(s): Pei Luo, Wenjun Shi, Xianshuo Cheng, Jun Yang, Gen Pei and Jian Dong*

Volume 30, Issue 20, 2024

Published on: 02 May, 2024

Page: [1548 - 1563] Pages: 16

DOI: 10.2174/0113816128289465240422074745

Price: $65

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Abstract

Background: Familial adenomatous polyposis (FAP) is an inherited disorder. At present, an increasing number of medications are being employed to treat FAP; however, only a few have been assessed for their efficacy and safety. Therefore, this study aimed to conduct a network meta-analysis to compare the therapeutic outcomes and adverse drug reactions of all FAP-associated medications.

Methods: Six relevant databases were searched to identify pertinent randomized controlled trials (RCTs), and information on the dosage and frequency of various drugs was extracted. Additionally, data on changes in polyp counts and dimensions, as well as treatment-related adverse reactions for different medications were collected. The Bayesian method was employed to directly or indirectly compare the impact of different treatment regimens on changes in polyp numbers and diameters, and the safety of the drugs was investigated.

Results: CXB at 16 mg/kg/day significantly reduced polyp numbers. Celecoxib at 8 mg/kg/day and sulindac (150 mg twice daily) plus erlotinib (75 mg/day) were effective for tolerant FAP patients. Additionally, EPAFFA 2 g daily and sulindac (150 mg twice daily) plus erlotinib (75 mg/day) emerged as the most effective for reducing polyp size.

Conclusion: The most effective treatment for reducing the number of colorectal polyps is celecoxib 16 mg/kg/day. On the other hand, a daily dosage of 2 g EPA-FFA demonstrates the best results in terms of decreasing colorectal polyp diameter.

Keywords: Familial adenomatous polyposis, drug treatment, inherited disorder, network meta-analysis, polyp counts, Bayesian method.

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