Targeting the cytoskeleton and extracellular matrix in cardiovascular disease drug discovery.
Targeting the cytoskeleton and extracellular matrix in cardiovascular disease drug discovery.
复制标题
靶向细胞骨架和细胞外基质在心血管疾病药物开发中的应用。
DOI：
10.1080/17460441.2022.2047645
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发表时间：
2022-05
影响因子：
6.3
通讯作者：
Davidson, Michael H.
中科院分区：
文献类型：
--
作者：
Khomtchouk, Bohdan B.;Lee, Yoon Seo;Khan, Maha L.;Sun, Patrick;Mero, Deniel;Davidson, Michael H.
Currently, cardiovascular disease (CVD) drug discovery has focused primarily on addressing the inflammation and immunopathology aspects inherent to various CVD phenotypes such as cardiac fibrosis and coronary artery disease. However, recent findings suggest new biological pathways for cytoskeletal and extracellular matrix (ECM) regulation across diverse CVDs, such as the roles of matricellular proteins (e.g., tenascin-C) in regulating the cellular microenvironment. The success of anti-inflammatory drugs like colchicine, which targets microtubule polymerization, further suggests that the cardiac cytoskeleton and ECM provide prospective therapeutic opportunities. Potential therapeutic targets include proteins such as gelsolin and calponin 2, which play pivotal roles in plaque development. This review focuses on the dynamic role that the cytoskeleton and ECM play in CVD pathophysiology, highlighting how novel target discovery in cytoskeletal and ECM-related genes may enable therapeutics development to alter the regulation of cellular architecture in plaque formation and rupture, cardiac contractility, and other molecular mechanisms. Further research into the cardiac cytoskeleton is an area ripe for novel target discovery. Furthermore, the structural connection between the cytoskeleton and the ECM provides an opportunity to evaluate both entities as sources of potential therapeutic targets for CVDs. Refining computational analytical techniques for drug discovery over the next five, ten or so years will increase the success probability of the novel targets determined and further de-risk drug development. The prevalence of cardiovascular disease (CVD) is rapidly rising, and it is predicted that by 2030 there will be more than 23.6 million CVD-related deaths per year, with approximately half of the United States adult population living with some form of CVD diagnosis by 2035. In the United States alone, CVDs are the cause of over $350 billion in annual spending just for management and treatment, with the most spending allocated to ischemic heart disease and hypertension health services. The growing burden of CVDs globally highlights the need for increased and sustained global prevention efforts and large-scale drug discovery approaches that can adequately address clinical unmet needs. The cytoskeletome, which we define as the complete set of cytoskeletal proteins, such as filaments and microtubules, and other associated material including the underlying extracellular matrix (ECM) that supports its architecture, present relatively new and underexplored avenues of CVD drug target discovery. The majority of currently approved drugs for heart disease target traditional risk factors such as high cholesterol or blood pressure. Here we highlight how targeting the cytoskeletome can inform more precise therapies by tailoring treatment to cell-specific factors intrinsic to the vessel wall. These novel avenues have the potential to significantly contribute to cardioinformatics and precision cardiology initiatives that advance the use of data science methods to fight heart disease with computation by creating better drugs -- for example, by building target deconvolution algorithms that systematically identify and prioritize novel drug targets, or by assisting with biomarker-guided drug repurposing efforts from existing pharmacogenomic data. With better drugs for various cardiovascular diseases, physicians will be able to rewrite treatment guidelines for patients with diseases that do not currently have effective therapies but rather only have lifestyle changes or treating other phenotypes as part of the treatment plan and clinical practice guidelines. Additionally, improving the safety and efficacy of CVD drugs will help slow and decrease the number of CVD cases every year, which in turn will prevent more deaths and also decrease the amount of money needed to manage these diseases from a population-level healthcare perspective. Further research into the cardiac cytoskeleton (and the underlying ECM that supports its architecture) is an area ripe for novel target discovery. There is mounting evidence suggesting that the cytoskeleton is an under-investigated and undervalued therapeutic target for CVD phenotypes and new computational advances provide an opportunity to discover the biological mechanisms driving changes in the cytoskeleton during CVD pathophysiology. Furthermore, the structural connection between the cytoskeleton and the ECM provides an opportunity to evaluate both entities as sources of potential therapeutic targets for CVDs. Incorporating data-driven computational methods to study the cytoskeleton and the ECM in the context of cardiovascular diseases will provide a more holistic perspective on the progression of CVDs such as cardiac fibrosis, hypertension, heart failure, and atherosclerosis. Additionally, further research into the cytoskeletome could lead to discoveries of related systems and pathways that drive the pathogenesis of CVDs that may lead to additional new therapeutic discoveries as well. Overall, advancing and shifting to computational drug discovery will assist future research in discovering new effective drugs for CVDs without the difficult, time-consuming, expensive, and risky process defining the current status quo of the traditional pharmaceutical industry. Drug discovery needs to become a much more rapidly moving field as an increasing number of people are developing cardiovascular diseases, amongst others, while our current drugs are showing to not be as effective as needed and the rates of novel therapeutic development is declining. Expediting future research into computational drug discovery will potentially combat these declining rates and discover new innovative therapies for diseases that currently do not have any. The cardioinformatics drug discovery field, with continued further research and support, will hopefully evolve into being one of the forefront tactics in novel drug development as we must rely more on synthetically designing novel therapeutics and utilizing the vast amounts of data already available. Refining computational analytical techniques for drug discovery over the next five, ten or so years will increase the success probability of the novel targets determined and further de-risk drug development by further optimizing this process (e.g., saving time, money, and other resources for all parties involved) by way of computationally-derived therapeutics.
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影响因子：
20.1
作者：
Afonso MS;Sharma M;Schlegel M;van Solingen C;Koelwyn GJ;Shanley LC;Beckett L;Peled D;Rahman K;Giannarelli C;Li H;Brown EJ;Khodadadi-Jamayran A;Fisher EA;Moore KJ
通讯作者：
Moore KJ
影响因子：
6
作者：
Belmadani, Souad;Bernal, Juan;Berecek, Kathleen H.
通讯作者：
Berecek, Kathleen H.
影响因子：
8.2
作者：
Gellen, Barnabas;Thorin-Trescases, Nathalie;Saulnier, Pierre-Jean
通讯作者：
Saulnier, Pierre-Jean
Transgelin是一种TGFβ诱导的基因，可通过肌动蛋白Cytoskeleston组织调节人骨骼干细胞的成骨细胞和成糖分化。
影响因子：
9
作者：
通讯作者：
--
影响因子：
64.8
作者：
Bassat E;Mutlak YE;Genzelinakh A;Shadrin IY;Baruch Umansky K;Yifa O;Kain D;Rajchman D;Leach J;Riabov Bassat D;Udi Y;Sarig R;Sagi I;Martin JF;Bursac N;Cohen S;Tzahor E
通讯作者：
Tzahor E