Systems Biology of Ayurvedic Formulations

Abstract

Systems biology has become one of the most productive frameworks for understanding Ayurvedic formulations at a mechanistic level, moving beyond single-compound single-target pharmacology into integrated multi-compound multi-target network analysis that better reflects how classical Ayurvedic polyherbal formulations actually operate. The past decade has seen substantial methodological maturation in this area, with network pharmacology, multi-omics characterisation, pathway analysis, and integrated computational-experimental approaches now standard components of systems-biology research on Ayurvedic formulations. The Ayurgenomics framework (Prasher et al., 2008 onwards) established the genomic correlates of classical Prakriti classification and opened systems-level understanding of why Ayurvedic therapeutics may produce differential responses across individuals. This study reviews 54 primary systems-biology studies of Ayurvedic formulations published between 2013 and 2022, grouped under five methodology frames: SB1 (network pharmacology and in silico target identification), SB2 (transcriptomic and gene-expression analysis), SB3 (metabolomic characterisation), SB4 (Ayurgenomic integrated frameworks including prakriti-based stratification), and SB5 (translational integration combining multiple systems-biology methods with clinical outcomes). Each frame was scored with the Systems Biology Ayurvedic Formulation Index (SBAFI). Translational integration (SB5) scored highest at SBAFI 0.806, with Ayurgenomic integrated frameworks (SB4) at 0.786 and network pharmacology (SB1) at 0.762.