DataSheet1_Mathematical modeling of antihypertensive therapy.PDF

Hypertension is a multifactorial disease arising from complex pathophysiological pathways. Individual characteristics of patients result in different responses to various classes of antihypertensive medications. Therefore, evaluating the efficacy of therapy based on in silico predictions is an important task. This study is a continuation of research on the modular agent-based model of the cardiovascular and renal systems (presented in the previously published article). In the current work, we included in the model equations simulating the response to antihypertensive therapies with different mechanisms of action. For this, we used the pharmacodynamic effects of the angiotensin II receptor blocker losartan, the calcium channel blocker amlodipine, the angiotensin-converting enzyme inhibitor enalapril, the direct renin inhibitor aliskiren, the thiazide diuretic hydrochlorothiazide, and the β-blocker bisoprolol. We fitted therapy parameters based on known clinical trials for all considered medications, and then tested the model’s ability to show reasonable dynamics (expected by clinical observations) after treatment with individual drugs and their dual combinations in a group of virtual patients with hypertension. The extended model paves the way for the next step in personalized medicine that is adapting the model parameters to a real patient and predicting his response to antihypertensive therapy. The model is implemented in the BioUML software and is available at https://gitlab.sirius-web.org/virtual-patient/antihypertensive-treatment-modeling.</p

An example of cell cycle model visualization and simulation by the BioUML workbench (the diagram DGR0068a of the Cyclonet database)

<p><b>Copyright information:</b></p><p>Taken from "CYCLONET—an integrated database on cell cycle regulation and carcinogenesis"</p><p>Nucleic Acids Research 2007;35(Database issue):D550-D556.</p><p>Published online Jan 2007</p><p>PMCID:PMC1899094.</p><p>© 2006 The Author(s)</p

Web interface of the Cyclonet database generated by BeanExplorer technology

<p><b>Copyright information:</b></p><p>Taken from "CYCLONET—an integrated database on cell cycle regulation and carcinogenesis"</p><p>Nucleic Acids Research 2007;35(Database issue):D550-D556.</p><p>Published online Jan 2007</p><p>PMCID:PMC1899094.</p><p>© 2006 The Author(s)</p> Top screen displays fragment of microarray series classification in the Cyclonet database, bottom left screen demonstrates a fragment of the list of pharmacological activities for anticancer therapy, bottom right—examples of chemical structure of two tubulin antagonists

Diagrams and models of carcinogenesis related processes as the basis for information integration in the Cyclonet database

<p><b>Copyright information:</b></p><p>Taken from "CYCLONET—an integrated database on cell cycle regulation and carcinogenesis"</p><p>Nucleic Acids Research 2007;35(Database issue):D550-D556.</p><p>Published online Jan 2007</p><p>PMCID:PMC1899094.</p><p>© 2006 The Author(s)</p

Additional file 2: Table S1. of Computational master-regulator search reveals mTOR and PI3K pathways responsible for low sensitivity of NCI-H292 and A427 lung cancer cell lines to cytotoxic action of p53 activator Nutlin-3

Normalized expression values of all genes with detected expression in the studies conditions and mapped to Ensembl. In the tab “Nsen vs Sen” we give the results of Limma analysis of the LogFC between Nutlin-3 insensitive (Nsen) and sensitive cell lines. (XLSX 3291 kb

Additional file 6: Table S5. of Computational master-regulator search reveals mTOR and PI3K pathways responsible for low sensitivity of NCI-H292 and A427 lung cancer cell lines to cytotoxic action of p53 activator Nutlin-3

List of 62 genes up-regulated in Nutlin-3 insensitive cell lines and matching the disease category “Causal Lung Neoplasms”. This list is used for the promoter analysis. (XLSX 61 kb

Additional file 3: Table S2. of Computational master-regulator search reveals mTOR and PI3K pathways responsible for low sensitivity of NCI-H292 and A427 lung cancer cell lines to cytotoxic action of p53 activator Nutlin-3

GO analysis of all 7 sets of genes - Up- and Down- regulated genes upon treatment by Nutlin-3 in two concentrations 5 μM and 30 μM of Nutlin-3 (p-value< 0.05, LogFC> 0.58 (which corresponds to FC > 1.5) for up-regulated and LogFC<− 0.58 for down-regulated genes). Parameter Sum_Logpval sums up logarithms of p-values for one GO term in different conditions of treatment. It allows to sort GO terms according to their total significance in all conditions. (XLSX 334 kb

Additional file 8: Table S7. of Computational master-regulator search reveals mTOR and PI3K pathways responsible for low sensitivity of NCI-H292 and A427 lung cancer cell lines to cytotoxic action of p53 activator Nutlin-3

Results of correlation analysis of gene expression in 52 cancer cell lines and their sensitivity (IC50) value towards Mdm2 inhibitor AMGMDS3. We found 168 genes positively correlated with IC50 (insensitivity to the Mdm2 inhibitor) and 227 genes negatively correlated (p-value < 0.01). (XLSX 2438 kb

Additional file 4: Table S3. of Computational master-regulator search reveals mTOR and PI3K pathways responsible for low sensitivity of NCI-H292 and A427 lung cancer cell lines to cytotoxic action of p53 activator Nutlin-3

Results of gene set enrichment analysis (GSEA) of the obtained three gene expression profiles of differences between sensitive and insensitive lung cancer cell lines. For that we used geneXplain platform and applied the pathways ontology of TRANSPATHŽ database. (XLSX 147 kb

Additional file 9: Table S8. of Computational master-regulator search reveals mTOR and PI3K pathways responsible for low sensitivity of NCI-H292 and A427 lung cancer cell lines to cytotoxic action of p53 activator Nutlin-3

Pathway analysis of the gene expression correlations using GSEA method and TRANSPATH pathway ontology. (XLSX 103 kb