2021 - Online - In the cloud

PAGE 2021: Software Demonstration
Ekaterina Mogilevskaya

CYTOCON DB as storage of unified data on in vivo human concentrations of cells and molecules in health and disease

Ekaterina Mogilevskaya, Vlad Leonov, Elita Gerasimuk, Nikolay Pervushin, Nail Gizzatkulov, Oleg Demin

INSYSBIO LLC, Moscow, Russia

Objectives: Quantitative Systems Pharmacology (QSP) models require their validation via comparison of model simulations with in vivo baseline data measured in different tissues of patients. The information is available in scientific literature and represents cytokine and cell concentrations measured in patient tissues. The data is often presented in different units and for different patient groups with various demographic and disease-associated characteristics. These features make it difficult to direct use of the baseline in vivo data for calibration of a QSP model. To cope with the difficulties and facilitate access of QSP modelers to such types of in vivo data we developed an online database CYTOCON DB. The poster aims are to describe the database and demonstrate an example of its application to compare cell profiles measured in different tissues of melanoma/NSCLC patients and healthy subjects.

Methods: CYTOCON DB (Cell and cYTOkine CONcentrations DataBase) is a manually curated database developed as a web application [1,2] based on the ASP.NET MVC framework, Microsoft IIS web server, Microsoft SQL Server database, Telerik Kendo UI, and Bootstrap. Workflow for papers annotation was designed in such a way as to provide a satisfactory level of quality control. A group of annotators extracts information from papers to the DB and a group of reviewers verifies the accuracy and completeness of the data. CYTOCON DB is continuously extended with new data. Most of the baseline values found in papers were converted from original units (i.e., mg/mL, number of cells per mm2 of biopsy surface section) to unified units: “pM” for cytokine and “kcell/L” for cell concentration via formulas implemented in the database.

Results: Key features implemented in CYTOCON DB:

  • The search query may include both patient group attributes (age, gender, patient number, etc) and disease-specific clinically measured characteristics (asthma – FEV1 range, dermatitis – SCORAD range, etc)
  • Unification of measurement units
  • Both table and graphic representation of search results
  • Export of search results in csv format
  • Constant updates of DB information (about 1500 values per month).

One particular feature of the CYTOCON DB is the possibility to recalculate cell concentrations into the unified units when they are represented in articles as % of parental cell pool (% of T-cells[IFNg-, Il17-, IL22+] from T-cells[CD4+]). For the recalculation we use the average value of the paternal pool from the same paper. If it is unavailable, the average value is calculating from all concentrations available in the database for this parental cell pool in corresponding compartment and disease. This general average value is constantly updating when new data about its concentration is committed to the database. For example, with a recent update for unit “%__T-cells_;CD4+_Blood_Healthy control” the average concentration of T-cells (CD4+) in healthy control blood was changed from 453964 to 439880 kcell/L (averaged from 66 concentrations from articles committed to the database)

CYTOCON DB can be applied for the comparison of baseline values of cells and cytokines measured in tissues of different patient groups. For example, aggregated data from 20 articles on immune cell concentrations in patients suffering from Melanoma and NSCLC were converted to kcells/L and recalculated as Mean ? SD (mDC 1.5E+06?2.9E+06 vs. 1.1E+07?4.6E+06; NK: 4.1E+05?4.3E+05 vs. 6.7E+06?5.7E+06 in melanoma and NSCLC tumors respectively).

Conclusions: CYTOCON DB is a convenient source of in vivo baseline data measured in different tissues of patients suffering from various pathologies. Comparison of unified cell/cytokine concentrations of melanoma/NSCLC patients and healthy subjects allows us to come to several conclusions including the following one. The amount of myeloid dendritic cells and natural killer cells is lower in melanoma tumors than in NSCLC.

[1] “CYTOCON DB.” Accessed May 10, 2021. http://www.cytocon.insysbio.com/
[2] “CYTOCON DB Open.” Accessed May 10, 2021. http://www.cytocon-open.insysbio.com/

Reference: PAGE 29 (2021) Abstr 9773 [www.page-meeting.org/?abstract=9773]
Software Demonstration