CYTOCON DB: A versatile database of human cell and molecule concentrations for accelerating model development

In this issue of CPT: Pharmacometrics and Systems Pharmacology, Leonov et al.1 present a powerful tool for quantitative systems pharmacology (QSP) practitioners and immune modelers: “CYTOCON DB,” a comprehensive and manually curated database of publicly available concentrations of cells, cytokines, chemokines, and other molecules in various human organs in both healthy and diseased states. Organized information in the form of searchable databases can empower and spur innovation and even drive the progress of entire fields of research. The Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB)2 is a prime example; serving as the first open access digital repository of extensive sequence and structural information about proteins. Today, it serves the learning and experimental needs of structural biologists, computational scientists, students, and educators while catalyzing the creation of other protein structural databases, such as the “Membranome”3 and “AlphaFold DB,”4 collectively accelerating the field of protein structure and folding. Analogously, CYTOCON DB could potentially be a gamechanger for QSP immune modelers across therapeutic areas. QSP is a cuttingedge modeling tool that facilitates modelinformed drug discovery and development (MID3)5– 7 and informs decision making by combining extensive mechanistic knowledge of biological pathways and physiology with multiple streams of data ranging from omics to preclinical experiments and clinical biomarkers. Immune responses are a core component of QSP models across therapeutic areas, including infectious diseases, vaccines, oncology, autoimmune disorders, and rare diseases (Figure 1). A major obstacle for developing physiologically realistic immune models is a lack of organized information about baseline (pretreatment) concentrations of various cells involved in innate and adaptive immunity (e.g., dendritic cells, macrophages, neutrophils, lymphocytes – B cells, T cells, natural killer cells), and immuneregulatory molecules like cytokines and chemokines that serve as major factors to inform disease progression. For example, in oncology, tumor concentrations of inflammatory and regulatory cells and cytokines render the tumor as either “hot” or “cold,” which results in different progression patterns and response to therapies, and, therefore, prognosis.8 Similarly, with autoimmune disorders, optimizing the dose for immunomodulatory therapies necessitates the use and development of QSP models that account for differences in inflammatory cytokine levels and autoreactive immune responses between healthy individuals and patients. QSP models typically consist of tenstohundreds of state variables, parameters, equations, and initial conditions. Parameterization of such models is a laborious and ratelimiting process that often requires extensive mining of literature followed by data extraction.9,10 A key issue for accessing human data from literature is the enormous heterogeneity in experimental protocols and assay conditions across studies that necessitate development of domain expertise and timeintensive manual conversion of measurements and units by QSP modelers to ensure consistency. CYTOCON DB provides a solution to this issue via an expert manual curation process that results in readily available, annotated, and organized data in standard units. Subject matter experts associated with CYTOCON DB manually mark a paper, extract data, annotate, and standardize it for inclusion into the database (see Leonov et al.1 for further details). Impressively, an independent expert reviews the data and corrects errors. This twostep expert verification promises a highquality database. At the time of publication, CYTOCON DB covers 280 disease types, 183 tissues/physiological fluids, 452 molecules (cytokines/chemokines/growth factors), and 156 cell types and phenotypic states, all mined from nearly a thousand papers and other public sources (Figure 1). The database