COMBSecretomics

COMBSecretomics analyzes secretome-related higher-order drug combinations to identify how second- and higher-order multidrug treatments alter protein release patterns from inactive/unprovoked and active/provoked cell populations and to investigate resulting changes in cell-cell communication in disease and therapy.

Key Features:

• Higher-order combination analysis: Enables exhaustive analysis of second- and higher-order mixtures of candidate treatments to assess their effects on protein release.
• Cell-population contexts: Evaluates protein release patterns from inactive/unprovoked and active/provoked cell populations.
• Model-free combination methods: Implements two model-free methods including a generalization of the highest single agent principle and a data mining approach based on top-down hierarchical clustering.
• Protein concentration profile analysis: Analyzes protein concentration profiles released by cells to detect changes related to disease or pharmacological treatment.
• Quality control and statistics: Includes quality control procedures to eliminate outliers and applies non-parametric statistics to quantify uncertainty.
• Standardized reproducible format: Uses a standardized reproducible format compatible with any experimental platform that provides protein release data.
• Proof-of-principle application: Demonstrated in a pharmacological study of cartilage degradation.

Scientific Applications:

• Secretome research in complex disease: Systematic investigation of secretome alterations associated with complex diseases.
• Drug combination discovery: Identification of second- and higher-order drug combinations that modify or reverse malfunctioning secretomic patterns.
• Drug development projects: Support for candidate selection and mechanistic assessment in drug discovery and development.
• Clinical research and translational studies: Analysis to improve understanding of disease mechanisms and to inform potential therapeutic strategies.
• Cell-cell communication analysis: Assessment of how pharmacological treatments alter protein-mediated cell-cell communication.

Methodology:

Exhaustive search of second- and higher-order mixtures; model-free generalization of the highest single agent principle; data mining via top-down hierarchical clustering; quality control procedures to remove outliers; non-parametric statistics to quantify uncertainty; analysis of protein concentration profiles.