Recommending Drug Combinations Using Reinforcement Learning targeting Genes/proteins associated with Heterozygous Familial Hypercholesterolemia: A comprehensive Systematic Review and Net-work Meta-analysis

Author:

Kiaei Ali A.1,Boush Mahnaz2,Abadijou Sadegh3,Momeni Saeb4,Safaei Danial5,Bahadori Reza6,Salari Nader7,Mohammadi Masoud8

Affiliation:

1. Sharif University of Technology

2. Shahid Beheshti University of Medical Sciences

3. Shahid Bahonar University of Kerman

4. Zabol University of Medical Sciences

5. University of Kashan

6. Ilam University

7. Kermanshah University of Medical Sciences

8. Gerash University of Medical Sciences

Abstract

Abstract Background: Familial Hypercholesterolemia (FH) is a genetic disorder in lipoprotein metabolism caused by mutations that increase LDL and total cholesterol levels. High LDL and cholesterol levels increase atherosclerosis risk. FH mutations impact the LDL receptor (LDLR) gene, apolipoprotein B, and PCSK9. About 20% of FH cases have a polygenic basis that affects LDL levels. We decided to conduct a systematic review of the available research in this field to provide a thorough genes/proteins network meta-analysis on the impact of drug combinations on the management of heterozygous Familial Hypercholesterolemia (HeFH). This paper reviews and analyzes the literature on the effects of medication combinations on HeFH management. This study investigates articles that analyzed the management and adjuvants of HeFH to recommend forceful drug combinations. Methods: This systematic review and network meta-analysis analyzed the Science Direct, Embase, Scopus, PubMed, Web of Science (ISI), and Google Scholar databases without a lower time limit and up to July 2022. The current study consists of three fundamental stages. Firstly, drug combinations are recommended by reinforcement learning. In the second stage, we used a systematic review to analyze RL's outcomes in diverse populations (with a variety of ages, sex, etc.). Natural Language Processing (NLP) employs context to search these articles. We contrasted manual and NLP-based searches and discovered that NLP could find articles based on MeSH, not simply words. In stage three, we analyze RL outcomes using network meta-analysis. Results: This study uses the RAIN method to investigate the most effective medication combination for managing Heterozygous Familial Hypercholesterolemia (HeFH). Results from the method indicate that the best-recommended scenario is 2.7 times more efficient than the prescription of Ezetimibe as the initial scenario. Conclusion: Our systematic review and network meta-analysis review indicate that a drug combination of Ezetimibe, Pravastatin, and Simvastatin is highly effective. However, additional high-quality clinical trials are required to determine the efficacy and safety of other treatments.

Publisher

Research Square Platform LLC

Reference56 articles.

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2. Khera AV, Won HH, Peloso GM, Lawson KS, Bartz TM, Deng X et al. Diagnostic Yield and Clinical Utility of Sequencing Familial Hypercholesterolemia Genes in Patients With Severe Hypercholesterolemia.J Am Coll Cardiol. 2016 Jun7;67(22):2578–89.

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