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Opinion - (2022) Volume 14, Issue 12

Precision treatment for cardiovascular disease is made possible by an in silico model of atherosclerosis with individual patient calibration

Sharon Zhang*
Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai, China
*Correspondence: Sharon Zhang, Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai, China, Email:

Received: 01-Dec-2022, Manuscript No. ipaom-23-13361; Editor assigned: 03-Dec-2022, Pre QC No. P-13361; Reviewed: 15-Dec-2022, QC No. Q-13361; Revised: 20-Dec-2022, Manuscript No. R-13361; Published: 27-Dec-2022


An unmet need exists for advice on how to tailor treatment for individual patients with atherosclerosis to prevent myocardial infarction and ischemic stroke. Computational modeling might make it possible for this kind of development. Given the multifactorial science of atherosclerosis, demonstrating should be founded on complete natural organizations that catch protein communications assessed to drive illness movement. Here, we intended to foster a clinically important scale model of atherosclerosis, align it with individual patient information, and use it to recreate improved pharmacotherapy for individual patients [1].


Myocardial infarction (MI) and ischemic stroke (IS), major outcomes of unstable atherosclerotic lesions, are the most common causes of death worldwide. Atherosclerosis Precision medicine Systems biology Proteomics Polypharmacy At the moment, treatment efficacy at the group level serves as the basis for guidance regarding the prevention of MI and IS. In any case, it is progressively revealed that particular sickness aggregates exist in atherosclerotic cardiovascular illness (CVD), recommending contrasting ideal treatments. It would be very useful to be able to predict whether or not a given patient will respond to a drug in advance. However, there are no practical ways to tailor treatment to each patient. Although circulating biomarkers have been shown to be effective in some instances, tissue examination is required for the most sensitive and specific assessment. On the other hand, information from tissue biopsies has been used to develop personalized cancer treatment plans. For CVD, such approaches have not been possible [2].

We hypothesize that individualized therapeutics, similar to personalized cancer care but applied to atherosclerosis, could make it easier for patients to access promising treatments before the disease progresses to their full potential. The inclusion of molecular pathway analysis, which addresses fundamental complexities required for many clinical scenarios in which tissue biopsies are not feasible, has recently been reported as a means for noninvasive assessment. The following stage could be its utilization in making separately aligned frameworks science models for recreating drug reaction. To create specialized in silico systems biology models, biobanks that already exist and contain detailed disease-specific information at various morphological and molecular scales can be used. The capacity to use in silico reenactment to foresee drug reaction has been a significant area of improvement for a long time, with applications including assessment of secondary effects, thought of medication mixes, evaluation of medication system, and displaying of medication reusing [3].

For a long time, the ability to use in-silico simulation to predict drug response has been regarded as a crucial area of development. Models have been created for side effect evaluation, drug combination consideration, drug mechanism comprehension, and drug re-purposing. A natural application that has not yet been realized is the capacity to anticipate whether a particular patient will respond to a drug. Although circulating biomarkers have been shown to be effective in some situations, including pathway analysis may add value by addressing fundamental complexities that are required for many clinical scenarios in which it is not possible to measure molecular species in plasma or tissue biopsies [4].

Consolidating sub-atomic pathway examination in an in-silico setting requires an enthusiasm for the various underlying and natural elements portraying the shaky atheroma where a wide range of pathways interleave in complex connections. For instance, perivascular adipose tissue has been suggested to increase plaque inflammation that results in atherothrombosis, MI, or IS. On the other hand, reductions in atherogenic lipoproteins resulting from phospholipid and cholesterol efflux improve stability. Endothelial to mesenchymal transition may influence tissue structure with both stabilizing and destabilizing effects.

Disease modeling necessitates taking into account more extensive biological networks than have been previously reported because of atherosclerosis's complexity and multifactorial nature. One type of model uses idealized or patient-specific vessel geometries without the characteristics of the individual patient's vessel wall to simulate the physical behavior and interaction between tissue and blood through the use of biomechanical computations. The use of biomechanical and biological descriptors, also known as "agent-based modeling," as an alternative to combinatory modeling, has also been looked into. However, biological processes represented by pathway networks of molecular interactions essential for disease progression must be included in order to sufficiently capture information, including the prediction of disease-critical biological responses to various drugs. Models have primarily focused on one or two scales up until now. Nonetheless, recent findings have demonstrated that multiple scales, from the organismal to the molecular, can be used to make predictions that are more comprehensive. In particular, limited-scope models have been created by combining data from gene expression and structural anatomy in a rabbit model that allowed for tissue collection.

In this work, we created a systems biology model of atherosclerotic plaque instability that is applicable to clinical practice and may facilitate precision medicine for cardiovascular disease. Utilizing proteomic information from carotid plaque examples combined with freely organized organizations of atomic pathways, we consolidated illness explicit pathways across various cell types and exhibited implies for individual patient adjustment. By simulating the effects of various pharmacological treatments on molecular processes relevant to stabilizing atherosclerotic lesions, the model's potential was evaluated. The study indicates that it may be possible to predict individual pharmacological effects, highlighting the potential for individual therapy as part of a strategy to prevent ischemic stroke and heart attack in the future [5].

To create a more comprehensive systems biology disease model than has been previously reported, the study made use of a plaque proteomic dataset that was only made up of plaques and represented patients who had either stable or unstable atherosclerosis. The model was then used to simulate individual patient responses to various drug categories. An in-silico systems biology model of atherosclerosis with integrated intima, topologically accurate plasma interfaces, and networks of proteinprotein interactions for relevant cell types was developed for simulating individual patient-specific disease responses to intensive medical therapies. The following is a nitty gritty depiction of each featured step.

There were three levels of integration of the resulting set of pathways into cell networks: utilizing a program to divide into "core," "mid," and "full." kgml files by type of cell. Core" networks contained pathways specific to each type of cell. Mid" included pathways that were shared by one other type of cell. These and other human cell types share pathways that are typically linked to the function of mammalian cells. Full" BioNSi was used to combine the selected pathways for each cell type at each scope into a Cytoscape representation, but the edge weights were overridden to make room for a more extensive set of relations than BioNSi normally supports. After that, we compared the generated node lists to the plaque protein measurements from our cohort that were readily available. Proteins with no direct exploratory estimation and no approaching edges were pruned.

Once created, marks from all subjects and plaque tissue areas were put away in a data set of models. A weighted measure that was calculated as the product of a cosine similarity-defined rank and the prevalence weighted index per phenotype was used to create a look-up table. After perturbing the networks to simulate drug response, it was utilized in subsequent signature matching. The exemplars were arranged so that an objective comparison could be made between the treated and baseline signatures. We came to the conclusion that a patient would likely benefit from the treatment if it reduced instability levels.


According to the findings, each selected drug category's effect was simulated using the individually calibrated networks. Using the same iterative method for interpolating unmeasured protein levels, signatures that represented the drug's effect on each patient were determined. Based on the drug's mechanism of action, the nodes with the greatest impact are locked in this instance. In order to replicate the effect of treatment on plaque instability, these "perturbed" networks were matched back into the database of exemplars after being re-scored in the same manner as the baseline instability.


  1. Leopold JA, Loscalzo J. Emerging role of precision medicine in cardiovascular disease. Circ Res. 2018 Apr 27;122(9):1302-15.
  2. Google Scholar, Crossref, Indexed at

  3. Lambin P, Leijenaar RT, Deist TM, et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749-62.
  4. Google Scholar, Crossref, Indexed at

  5. Lee, Geewon, Ho Yun Lee, Eun Sook Ko, et al. "Radiomics and imaging genomics in precision medicine." Precis Future Med. 2017;(1): 10-31.
  6. Google Scholar, Crossref 

  7. Buckler AJ, Karlöf E, Lengquist M, et al. Virtual transcriptomics: Noninvasive phenotyping of atherosclerosis by decoding plaque biology from computed tomography angiography imaging. Arterioscler Thromb Vasc Biol. 2021;41(5):1738-50.
  8. Google Scholar, Crossref, Indexed at

  9. Pai S, Hui S, Isserlin R, et al. Netdx: Interpretable patient classification using integrated patient similarity networks. Mol Syst Biol. 2019 Mar;15(3):e8497.
  10. Google Scholar, Crossref, Indexed at