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Modelling genetic diseases for drug development: Hypertrophic cardiomyopathy
Different tools for HCM disease modeling and pharmaceutical drug screening. This figure summarizes the different research platforms to model HCM and to screen novel drugs for this disease. [Display omitted] Hypertrophic cardiomyopathy (HCM) is the commonest genetic cardiac disease, with a prevalence...
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Published in: | Pharmacological research 2020-10, Vol.160, p.105176-105176, Article 105176 |
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Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Different tools for HCM disease modeling and pharmaceutical drug screening. This figure summarizes the different research platforms to model HCM and to screen novel drugs for this disease.
[Display omitted]
Hypertrophic cardiomyopathy (HCM) is the commonest genetic cardiac disease, with a prevalence of 1/500. It is caused by over 1400 different mutations, mainly involving the genes coding for sarcomere proteins. The main pathological features of HCM are left ventricular hypertrophy, diastolic dysfunction and the increased ventricular arrhythmogenesis. Predicting the risk of heart failure and lethal arrhythmias is the most challenging clinical task for HCM patient management. Moreover, there are no disease-modifying therapies that can prevent disease progression or sudden arrhythmic death in HCM patients. In this review, we will illustrate the most advanced research models and methods that have been employed for HCM studies, including preclinical tests of novel or existing drugs, along with visionary future development based on gene editing approaches. Acknowledging the advantages and limitations of the different models, and a critical consideration of the different, often conflicting result obtained using different approaches is essential for a deep understanding of HCM pathophysiology and for obtaining meaningful information on novel treatments, in order to improve patient risk stratification and therapeutic management. |
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ISSN: | 1043-6618 1096-1186 |
DOI: | 10.1016/j.phrs.2020.105176 |