MOLECULAR AND REGENERATIVE APPROACHES TO THE TREATMENT OF ISCHEMIC HEART DISEASE
Abstract
Highlights
- Contemporary approaches to the treatment of ischemic heart disease (IHD) extend beyond conventional invasive techniques such as percutaneous coronary intervention and coronary artery bypass grafting. Although these procedures remain effective, they are often associated with significant risks, particularly in patients with multiple comorbidities. This underscores the growing need for safer and more personalized therapeutic strategies. Increasing attention is being directed toward molecular and regenerative technologies aimed at targeting the underlying pathogenic mechanisms of the disease.
- Nanotechnology, RNA-based therapy, and cell-based platforms have demonstrated promising potential to transform clinical practice. The use of polymeric, lipid-based, and metallic nanoparticles enables targeted drug delivery, attenuation of inflammation, and stabilization of atherosclerotic plaques. Small interfering RNAs (siRNAs) and microRNAs (miRNAs) regulate gene expression involved in lipid metabolism and inflammatory responses, while stem cells promote myocardial regeneration and immunomodulation. These innovations are forming the foundation of a new paradigm in precision IHD therapy.
- The integration of artificial intelligence (AI) into cardiology practice enhances the analytical potential of diagnosis and prognosis. AI algorithms (e.g., systems for coronary CT angiography analysis, automatic calcium index calculation, and ECG analysis, such as HeartFlow and KardiaAI) are successfully applied to medical image evaluation, risk stratification, biomarker analysis, and complication prediction. Taken together, this comprehensive approach, combining advances in invasive cardiology, molecular biology, regenerative medicine, and digital technologies, paves the way for personalized and pathogenetically based treatment of coronary artery disease.
Abstract
Coronary artery disease (CAD) remains a leading cause of death, despite significant progress in cardiology. This article presents a systematic review of current approaches to CAD treatment, including traditional invasive methods, nanomedicine, gene therapy, and cell technologies. Percutaneous coronary intervention and coronary artery bypass grafting remain the mainstays of treatment for critical stenoses. However, in some patients, these methods are associated with a high risk of complications, such as nephropathy, atrial fibrillation, or graft failure. Therefore, the attention of researchers is increasingly shifting to high-tech molecular strategies. Experimental studies in animal models (in vivo) have demonstrated that pitavastatin-loaded nanoparticles based on the copolymer of lactic and glycolic acids (PLGA) have the ability to reduce inflammation and stabilize atherosclerotic plaques. Preclinical studies indicate that the use of liposomal forms of prednisolone can provide targeted delivery of the drug to sites of restenosis. However, the clinical translation of nanotechnology faces challenges related to safety, toxicity, biodistribution, high production costs, and regulatory barriers. Photoacoustic and fluorescence imaging using functionalized nanoparticles opens up new diagnostic possibilities. Delivery of small interfering RNA (siRNA), particularly siPCSK9, via lipid carriers leads to a significant reduction in low-density lipoprotein levels in experimental models. Experimental data, primarily obtained in animal models and in vitro, link the use of microRNAs such as miR-124 and miR-34 to the modulation of inflammation and apoptosis in atherosclerosis, but their therapeutic potential in humans requires further study. Stem cells (MSCs, UCSCs) demonstrate potential for myocardial regeneration and immune response regulation. The role of artificial intelligence (e.g., deep learning algorithms for plaque segmentation, radiomics, and machine-learning-based prognostic models) in imaging data analysis, intervention planning, and outcome prediction is also discussed. Overall, the article highlights the transformation of coronary heart disease treatment toward precision, targeted, and bioengineered therapies, opening new horizons in cardiology practice.
About the Authors
Dmitriy A. SavenkovRussian Federation
student, N.I. Pirogov Russian National Research Medical University (Pirogov University), Moscow, Russian Federation
Dinara R. Mambetova
Russian Federation
student, Astrakhan State Medical University, Astrakhan, Russian Federation
Marina U. Saidova
Russian Federation
student, Astrakhan State Medical University, Astrakhan, Russian Federation
Pyatimar M. Ersinoeva
Russian Federation
student, Astrakhan State Medical University, Astrakhan, Russian Federation
Diana A. Piiter
Russian Federation
student, Astrakhan State Medical University, Astrakhan, Russian Federation
Maria D. Popova
Russian Federation
student, Astrakhan State Medical University, Astrakhan, Russian Federation
Iana A. Kovtun
Russian Federation
student, Pacific State Medical University, Vladivostok, Russian Federation
Emiliya R. Kantemirova
Russian Federation
student, V.I. Vernadsky Crimean Federal University, Simferopol, Russian Federation
Vladislav A. Mishenin
Russian Federation
student, V.I. Vernadsky Crimean Federal University, Simferopol, Russian Federation
Zinira F. Bikkuzhina
Russian Federation
student, Bashkir State Medical University, Ufa, Russian Federation
Anaida A. Agaronyan
Russian Federation
student, Kuban State Medical University, Krasnodar, Russian Federation
Asker A. Shakhaliev
Russian Federation
student, H.M. Berbekov Kabardino-Balkarian State University; Nalchik, Russian Federation
Zainab S. Aidemirova
Russian Federation
student, H.M. Berbekov Kabardino-Balkarian State University; Nalchik, Russian Federation
Yulduz R. Ruzimuratova
Russian Federation
student, Bashkir State Medical University, Ufa, Russian Federation
Kristina S. Asryan
Russian Federation
student, Tver State Medical University, Tver, Russian Federation
References
1. Vaisman D.Sh., Enina E.N. Сoronary artery disease mortality rates in the Russian Federation and a number of regions: dynamics and structure specifics. Cardiovascular Therapy and Prevention. 2024;23(7):3975. (In Russ.) https://doi.org/10.15829/1728-8800-2024-3975.
2. Boytsov SA, Provatorov SI. Possibilities of dispensary observation in reducing mortality from coronary heart disease. Terapevticheskii Arkhiv (Ter. Arkh.). 2023;95(1):5–10. https://doi.org/10.26442/00403660.2023.01.202038
3. Shepel R.N., Samorodskaya I.V., Kakorina E.P., Drapkina O.M. Mortality from chronic ischaemic heart disease in the Russian Federation: are there enough data for analysis and decision-making? Cardiovascular Therapy and Prevention. 2024;23(12):4293. (In Russ.) https://doi.org/10.15829/1728-88002024-4293.
4. Kidenko V.A., Metova M.M., Gabrielyan E.Yu., et al. Nanoparticles for targeted drug delivery in modern cardiology. Clinical Medicine (Russian Journal). 2023;101(9-10):454-466. (In Russ.). https://doi.org/10.30629/0023-2149-2023-101-9-10-454-466
5. Yusuf A, Almotairy ARZ, Henidi H, et al. Nanoparticles as Drug Delivery Systems: A Review of the Implication of Nanoparticles' Physicochemical Properties on Responses in Biological Systems. Polymers (Basel). 2023;15(7):1596. doi: 10.3390/polym15071596.
6. Musaeva FT, Sumenova ER, Islamgulov AK, et al. Role of artificial intelligence and novel visualization techniques in the early diagnosis of pancreatic cancer: a review. Digital Diagnostics. 2025;6(2):317-330. (In Russ.) doi: 10.17816/DD670193
7. Silva AK, Letourneur D, Chauvierre C. Polysaccharide nanosystems for future progress in cardiovascular pathologies. Theranostics. 2014;4(6):579-91. doi: 10.7150/thno.7688
8. Soumya RS, Raghu KG. Recent advances on nanoparticle-based therapies for cardiovascular diseases. J Cardiol. 2023;81(1):10-18. doi: 10.1016/j.jjcc.2022.02.009.
9. Li Y, Pan Y, Wu X, et al. Dual-modality imaging of atherosclerotic plaques using ultrasmall superparamagnetic iron oxide labeled with rhodamine. Nanomedicine (Lond). 2019;14(15):1935-1944. doi: 10.2217/nnm-2019-0062.
10. Samreen N, Bhatt AA, Glockner J, Lee CU. A Case of Ferumoxytol (Feraheme®) Prompting Critical Modification to Our Patient Prebreast Magnetic Resonance Imaging Questionnaire. J Clin Imaging Sci. 2019;9:6. Published 2019 Mar 28. doi:10.25259/JCIS-9-6
11. Hoffman HT, Quets J, Toshiaki T, et al. Functional magnetic resonance imaging using iron oxide particles in characterizing head and neck adenopathy. Laryngoscope. 2000;110(9):1425-1430. doi:10.1097/00005537-200009000-00002
12. DiStasio N, Lehoux S, Khademhosseini A, Tabrizian M. The Multifaceted Uses and Therapeutic Advantages of Nanoparticles for Atherosclerosis Research. Materials (Basel). 2018;11(5):754. doi: 10.3390/ma11050754.
13. Kwon SP, Jeon S, Lee SH, et al. Thrombin-activatable fluorescent peptide incorporated gold nanoparticles for dual optical/computed tomography thrombus imaging. Biomaterials. 2018;150:125-136. doi: 10.1016/j.biomaterials.2017.10.017
14. Ambesh P, Campia U, Obiagwu C, et al. Nanomedicine in coronary artery disease. Indian Heart J. 2017;69(2):244-251. doi: 10.1016/j.ihj.2017.02.007.
15. Ikeda H, Ishii A, Sano K, et al. Activatable fluorescence imaging of macrophages in atherosclerotic plaques using iron oxide nanoparticles conjugated with indocyanine green. Atherosclerosis. 2018;275:1-10. doi: 10.1016/j.atherosclerosis.2018.05.028.
16. Karimi M, Zare H, Bakhshian Nik A, et al. Nanotechnology in diagnosis and treatment of coronary artery disease. Nanomedicine (Lond). 2016;11(5):513-30. doi: 10.2217/nnm.16.3.
17. Beldman TJ, Senders ML, Alaarg A, et al. Hyaluronan Nanoparticles Selectively Target Plaque-Associated Macrophages and Improve Plaque Stability in Atherosclerosis. ACS Nano. 2017;11(6):5785-5799. doi: 10.1021/acsnano.7b01385.
18. Distasio N, Dierick F, Ebrahimian T, et al. Design and development of Branched Poly(ß-aminoester) nanoparticles for Interleukin-10 gene delivery in a mouse model of atherosclerosis. Acta Biomater. 2022;143:356-371. doi: 10.1016/j.actbio.2022.02.043.
19. Xu C, Yin L, Teng Z, et al. of Obesity Related Diseases through Laminarin-induced targeted delivery of Bindarit. Theranostics. 2020;10(21):9544-9560. doi: 10.7150/thno.45788.
20. Ozcan G, Ozpolat B, Coleman RL, et al. Preclinical and clinical development of siRNA-based therapeutics. Adv Drug Deliv Rev. 2015;87:108-19. doi: 10.1016/j.addr.2015.01.007.
21. Leung AK, Tam YY, Cullis PR. Lipid nanoparticles for short interfering RNA delivery. Adv Genet. 2014;88:71-110. doi: 10.1016/B978-0-12-800148-6.00004-3.
22. Zhao Y, Gao H, He J, et al. Co-delivery of LOX-1 siRNA and statin to endothelial cells and macrophages in the atherosclerotic lesions by a dual-targeting core-shell nanoplatform: A dual cell therapy to regress plaques. J Control Release. 2018;283:241-260. doi: 10.1016/j.jconrel.2018.05.041.
23. Civeira F, Martín C, Cenarro A. APOE and familial hypercholesterolemia. Curr Opin Lipidol. 2024;35(4):195-199. doi: 10.1097/MOL.0000000000000937.
24. Leiter LA, Raal FJ, Schwartz GG, et al. Inclisiran in individuals with diabetes or obesity: Post hoc pooled analyses of the ORION-9, ORION-10 and ORION-11 Phase 3 randomized trials. Diabetes Obes Metab. 2024;26(8):3223-3237. doi: 10.1111/dom.15650.
25. Fitzgerald K, Frank-Kamenetsky M, Shulga-Morskaya S, et al. Effect of an RNA interference drug on the synthesis of proprotein convertase subtilisin/kexin type 9 (PCSK9) and the concentration of serum LDL cholesterol in healthy volunteers: a randomised, single-blind, placebo-controlled, phase 1 trial. Lancet. 2014;383(9911):60-68. doi: 10.1016/S0140-6736(13)61914-5.
26. Ali Sheikh MS, Alduraywish A, Almaeen A, et al. Therapeutic Value of miRNAs in Coronary Artery Disease. Oxid Med Cell Longev. 2021;2021:8853748. doi: 10.1155/2021/8853748.
27. Парфенова е.В., Дергилев К.В. Клеточная терапия в кардиологии: время надежд. Кардиологический вестник. 2023;18(4):7‑18 [Parfyonova YeV, Dergilev KV. Cell therapy in cardiology: a time for a hope. Russian Cardiology Bulletin. 2023;18(4):7‑18. (In Russ.)] https://doi.org/10.17116/Cardiobulletin2023180417
28. Kandaswamy E, Zuo L. Recent Advances in Treatment of Coronary Artery Disease: Role of Science and Technology. Int J Mol Sci. 2018;19(2):424. doi: 10.3390/ijms19020424.
29. Selvakumar D, Clayton ZE, Chong JJH. Robust Cardiac Regeneration: Fulfilling the Promise of Cardiac Cell Therapy. Clin Ther. 2020;42(10):1857-1879. doi: 10.1016/j.clinthera.2020.08.008.
30. Ding Y, Su J, Shan B, et al. Brown adipose tissue-derived FGF21 mediates the cardioprotection of dexmedetomidine in myocardial ischemia/reperfusion injury. Sci Rep. 2024 Aug 7;14(1):18292. doi: 10.1038/s41598-024-69356-w.
31. Leri A. Human cardiac stem cells: the heart of a truth. Circulation. 2009;120(25):2515-8. doi: 10.1161/CIRCULATIONAHA.109.911107.
32. Fisher SA, Doree C, Mathur A, et al. Stem cell therapy for chronic ischaemic heart disease and congestive heart failure. Cochrane Database Syst Rev. 2016;12(12):CD007888. doi:10.1002/14651858
33. Malliaras K, Makkar RR, Smith RR, et al. Intracoronary cardiosphere-derived cells after myocardial infarction: evidence of therapeutic regeneration in the final 1-year results of the CADUCEUS trial (CArdiosphere-Derived aUtologous stem CElls to reverse ventricUlar dySfunction). J Am Coll Cardiol. 2014;63(2):110-122. doi:10.1016/j.jacc.2013.08.724
34. Giacca M. Cardiac Regeneration After Myocardial Infarction: an Approachable Goal. Curr Cardiol Rep. 2020;22(10):122. doi: 10.1007/s11886-020-01361-7.
35. Li Y, Shi G, Han Y, et al. Therapeutic potential of human umbilical cord mesenchymal stem cells on aortic atherosclerotic plaque in a high-fat diet rabbit model. Stem Cell Res Ther. 2021;12(1):407. doi: 10.1186/s13287-021-02490-8.
36. Singh RB, Mojto V, Fedacko J, et al. New Technologies for Treatment of Coronary Artery Disease. Biomed J Sci & Tech Res 13(3):2019. DOI: 10.26717/ BJSTR.2019.13.002411
37. Islamgulov AK, Bogdanova AS, Sufiiarov DI, et al. Modern capabilities of artificial intelligence technologies in cardiovascular imaging. Digital Diagnostics. 2025;6(1):116-129. (In Russ.). doi: 10.17816/DD640895
38. Parsa S, Shah P, Doijad R, Rodriguez F. Artificial Intelligence in Ischemic Heart Disease Prevention. Curr Cardiol Rep. 2025;27(1):44. doi: 10.1007/s11886-025-02203-0.
39. Khan SS, Matsushita K, Sang Y, et al. Development and Validation of the American Heart Association's PREVENT Equations. Circulation. 2024 Feb 6;149(6):430-449. doi: 10.1161/CIRCULATIONAHA.123.067626.
40. Ward A, Sarraju A, Chung S, et al. Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population. NPJ Digit Med. 2020;3:125. doi: 10.1038/s41746-020-00331-1.
41. Sarraju A, Ward A, Chung S, et al. Machine learning approaches improve risk stratification for secondary cardiovascular disease prevention in multiethnic patients. Open Heart. 2021;8(2):e001802. doi: 10.1136/openhrt-2021-001802.
42. Patel AP, Wang M, Ruan Y, et al. A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease. Nat Med. 2023;29(7):1793-1803. doi: 10.1038/s41591-023-02429-x.
43. Nurmohamed NS, Belo Pereira JP, Hoogeveen RM, et al. Targeted proteomics improves cardiovascular risk prediction in secondary prevention. Eur Heart J. 2022; 43(16):1569-1577. doi: 10.1093/eurheartj/ehac055.
44. Wu J, Giles C, Dakic A, et al. Lipidomic Risk Score to Enhance Cardiovascular Risk Stratification for Primary Prevention. J Am Coll Cardiol. 2024;84(5):434-446. doi: 10.1016/j.jacc.2024.04.060.
45. Stehlik J, Schmalfuss C, Bozkurt B, et al. Continuous Wearable Monitoring Analytics Predict Heart Failure Hospitalization: The LINK-HF Multicenter Study. Circ Heart Fail. 2020;13(3):e006513. doi: 10.1161/CIRCHEARTFAILURE.119.006513
46. Shufelt CL, Kim A, Joung S, et al. Biometric and Psychometric Remote Monitoring and Cardiovascular Risk Biomarkers in Ischemic Heart Disease. J Am Heart Assoc. 2020;9(18):e016023. doi: 10.1161/JAHA.120.016023.
47. Zhang N, Yang G, Zhang W, et al. Fully automatic framework for comprehensive coronary artery calcium scores analysis on non-contrast cardiac-gated CT scan: Total and vessel-specific quantifications. Eur J Radiol. 2021;134:109420. doi: 10.1016/j.ejrad.2020.109420.
48. Naghavi M, Reeves AP, Atlas K, et al. Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improves cardiovascular events prediction. NPJ Digit Med. 2024 Nov 5;7(1):309. doi: 10.1038/s41746-024-01308-0.
49. Eslami P, Parmar C, Foldyna B, et al. Radiomics of Coronary Artery Calcium in the Framingham Heart Study. Radiol Cardiothorac Imaging. 2020;2(1):e190119. doi: 10.1148/ryct.2020190119.
50. Choi AD, Marques H, Kumar V, et al. CT Evaluation by Artificial Intelligence for Atherosclerosis, Stenosis and Vascular Morphology (CLARIFY): A Multi-center, international study. J Cardiovasc Comput Tomogr. 2021;15(6):470-476. doi: 10.1016/j.jcct.2021.05.004.
51. Lin A, Manral N, McElhinney P, et al. Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study. Lancet Digit Health. 2022;4(4):e256-e265. doi: 10.1016/S2589-7500(22)00022-X.
52. Hughes JW, Tooley J, Torres Soto J, et al. A deep learning-based electrocardiogram risk score for long term cardiovascular death and disease. NPJ Digit Med. 2023;6(1):169. doi: 10.1038/s41746-023-00916-6.
Review
For citations:
Savenkov D.A., Mambetova D.R., Saidova M.U., Ersinoeva P.M., Piiter D.A., Popova M.D., Kovtun I.A., Kantemirova E.R., Mishenin V.A., Bikkuzhina Z.F., Agaronyan A.A., Shakhaliev A.A., Aidemirova Z.S., Ruzimuratova Yu.R., Asryan K.S. MOLECULAR AND REGENERATIVE APPROACHES TO THE TREATMENT OF ISCHEMIC HEART DISEASE. Complex Issues of Cardiovascular Diseases. 2026;15(3):105-123. (In Russ.)
JATS XML

































