CYTOCON DB Manager InSysBio UK Ltd Madison, Wisconsin, United States
Disclosure(s):
Vladislav Leonov, PhD: No financial relationships to disclose
Objectives: Reliable calibration of Quantitative Systems Pharmacology (QSP) models requires consistent in vivo baseline data (e.g., cytokine and cell concentrations). However, literature-derived data are often fragmented by inconsistent units (e.g., mg/mL vs. cells/mm²), variable demographics (e.g., age, disease stage), and nonuniform statistics (e.g., medians, ranges). To address these challenges, we developed CYTOCON DB to (1) unify literature data in a structured, searchable format, (2) enable standardized comparisons with model outputs, and (3) reduce manual effort in data processing.
Methods: CYTOCON DB (Cell and YTOkine CONcentrations Database) is a web application built on ASP.NET MVC with Microsoft SQL Server, Telerik Kendo UI, and Bootstrap. Key features include: 1. Unit Standardization: Cytokine concentrations (e.g., mg/mL) are converted to pM, and cell counts (e.g., cells/mm²) to kcell/L, using predefined formulas embedded in the database. 2. Data Consistency & Transformation: All types of reported averages and dispersion (e.g., median, interquartile range) are normalized to mean and standard deviation (SD). Additionally, weighted mean and SD calculations enable more accurate comparisons. 3. Quality Control: A two-step workflow involves annotators extracting data from literature and reviewers validating entries. Automated outlier detection algorithms help identify potential misprints or inconsistencies in source data and reduce human error. 4. Dynamic Updates: >1,000 new values added monthly from peer-reviewed sources. 5. API Access: R and Python scripts enable direct queries, visualizations, and modeling integration.
Results: Database now aggregate 98,755 concentration values for cytokines and cells, curated from 2,637 scientific papers and public sources. To illustrate the capabilities of CYTOCON DB, we present two use cases: 1. IL-1α, IL-1β, IL-6, and TNF-α concentrations in serum samples from healthy controls (HC) and SLE patients were retrieved, visualized, and statistically compared. Although mean concentrations of IL-1α, IL-1β, and IL-6 were not significantly different between groups, TNF-α levels were notably higher in SLE (1.972±4.418 pM) than in HC (0.928±3.305 pM). 2. Сell abundance data—often reported as percentages of a parental subset (e.g., PD1+ CD8+ CD3+ cells relative to CD8+ CD3+ cells)—were transformed into absolute counts by leveraging averaged values from similar entries in the database. When applied to melanoma and NSCLC data, recalculated absolute counts (kcells/L) revealed fewer myeloid dendritic cells and natural killer cells in melanoma tumors compared to NSCLC (e.g., mDC: 1.5×10^6±2.9×10^6 vs. 1.1×10^7±4.6×10^6).
Conclusions: CYTOCON DB is a standardized, user-friendly framework for integrating, transforming, and comparing baseline concentrations across diverse data sources, including both healthy and diseased states. By automating unit conversions, normalizing varied statistical measures, and providing rigorous quality control with dynamic updates, the platform significantly streamlines QSP model creation, calibration, and validation. Its robust API capabilities further promote seamless integration into computational workflows.
Citations: 1. Leonov, V., Mogilevskaya, E., Gerasimuk, E., Gizzatkulov, N. & Demin, O. CYTOCON: The manually curated database of human in vivo cell and molecule concentrations. CPT Pharmacometrics Syst Pharmacol (2022) doi:10.1002/psp4.12867 2. “CYTOCON DB Open.” Accessed April 23, 2025. https://www.cytocon-open.insysbio.com
