Implementation of a computational medical assemblage heuristic to improve rational design of phytomedicines for safety, efficacy and regulatory approval.

Phytomedicines are key to global healthcare and underutilized in Western medical systems. The inherent complexity of multi-species polypharmaceutical formulations presents barriers (standardization, reproducibility and regulatory approval) that can relegate potentially safe, effective and lower cost medication to nutraceutical settings in countries such as the US. The identification of Minimal Essential Effective Formulations (MEEFs) is a strategy to rationally simplify phytomedicines while preserving efficacy, but it requires computational innovation to de-risk and prioritize compounds due to the time and resource burden of conventional screening. We developed a computational framework that operationalizes a Chief-Deputy-Assistant-Envoy (CDAE) Asian medicine heuristic used to assemble ingredient organisms in formulations but translates it to the compound level. We deployed a novel high-content, multi-ontology data platform (PhAROS™, Phytomedical Analytics for Research Optimization at Scale) that aggregates open-source data from 8 global medical systems comprising ∼6B multiwise linkages across organisms, indications, formulations, compounds and targets, with additional data layers providing decision support based on druggability indices and absorption-distribution-metabolism-excretion (ADME). Using PhAROS™, we defined and computationally classified "chief" compounds using pain formulations as a test case. Kernel density estimation and network centrality analyses revealed that Chief compounds exhibit favorable pharmacokinetic profiles and occupy highly connected positions in formulation-target networks. Cross-system similarity analyses further demonstrated non-random convergence in species, compound, and target usage across disparate medical systems, suggesting that biogeographically and culturally independent practices have arrived at biomedically robust solutions. This work demonstrates a proof of concept for enabling rational complexity reduction for phytomedicines, bridging traditional formulation logic with pharmacological informatics.