To date, the scientific progress of drug discovery has largely evolved through three paradigms: serendipitous or empirical drug discovery (SDD) (from ancient times to the 19th century), phenotypic drug discovery (PDD) (mid-19th century to the 1950–1960s), and target-based drug discovery (TDD) (from the molecular biology revolution of the 1980s to the present) [1], [2]. Despite increasing investments in drug research and development over the years, the field continues to face the “Eroom’s law” phenomenon-where research and development (R&D) costs double approximately every nine years, while the number of new drugs produced per billion dollars of investment halves over the same period [3]. This highlights a significant translational gap between current drug discovery approaches, which often reduce complex human systems into TDD-based investigational models (reductionist approach), and the eventual need to validate efficacy within the complex phenotypic systems of the human body (systems approach) [4]. This challenge is particularly evident in addressing modern complex diseases such as Alzheimer’s disease, Parkinson’s disease, glucolipid metabolic disorders, and malignant tumors, where both TDD and PDD show notable limitations [5], [6], [7]. To better meet the demands of drug development and therapy for complex diseases, new strategies centered on multi-target therapeutics (MTTs) have emerged and have achieved breakthroughs in clinical treatment [8], [9]. These mainly include drug combination therapies (also referred to as cocktail therapy), fixed-dose combination (FDC), and multi-target drug design (MTDD; also known as designed multiple ligands, DMLs). These approaches aim to simultaneously modulate multiple biomolecules or pathways, thereby offering potentially improved efficacy and safety profiles compared with single-target agents [10], [11]. Furthermore, the development of artificial intelligence-driven drug discovery platforms that integrate PDD and TDD has become a growing trend, accelerating the pace of innovative drug development [12], [13], [14].
It is noteworthy and significant that there exist considerable differences between traditional Chinese medicine (TCM) and Western medical theoretical systems in understanding diseases and drug intervention strategies, particularly in the approaches and methodologies for translational research in drug discovery and evaluation.TCM formulas, as multi-component combinations used in clinical practice for over 3000 years, follow unique diagnostic and therapeutic principles of “Li-Fa-Fang-Yao” and composition theories of “Jun-Chen-Zuo-Shi” They employ personalized combination therapies to meet the need for systemic, multi-target regulation of human diseases, demonstrating clinical value in treating complex diseases, chronic conditions, and emerging infectious diseases, thus garnering considerable attention [11]. Over the past two decades, emerging nanotechnologies have made significant breakthroughs in elucidating the complex mechanisms of TCM. It has been discovered that the active components of TCM formulas and their constituent herbs generally do not exist as free monomers. Instead, they tend to form self‑assembled nanoparticles (SANs) or exosome‑like nanoparticles (ELNs) to exert combined pharmacological effects [15], [16]. We propose that the nanoparticles derived from TCM formulas and their component drugs, referred to as formula‑derived nanoparticles of TCM (FDN), encompass various supramolecular nano‑aggregates. These include TCM exosomes (TCM‑Exo), TCM decoctosomes (TCM‑Deco), TCM pillosomes (TCM‑Pillo), TCM carbon quantum dots (TCM‑CDs), and TCM component bencosomes (TCM‑Benc). At the nanoscale, these FDN acquire novel physical, chemical, and biological properties, serving as ideal carriers or forms for delivering multi-component active substances from TCM. This enables the comprehensive delivery of all or multiple active components from TCM formulas and facilitates cross-species regulation. Consequently, FDN can exert multi-tiered and multi-dimensional targeting modulation on the dynamic network of complex diseases, operating from “points” (key targets) to “lines” (signaling pathways), “planes” (hub nodes), and ultimately the entire “volume” (disease network), thereby achieving synergistic multi-component, multi-target therapeutic effects [2], [17]. This study integrates modern nanotechnology with TCM formula theory, focusing on FDN as the core concept. It aims to systematically analyze and elucidate the scientific connotation, core strategies, key technologies, and translational pathways of formula‑derived nanoparticle drug discovery (FDD). FDD not only integrates the advantages of PDD and TDD to address the bottlenecks in multi‑component delivery and unclear mechanisms of action of TCM, but also overcomes the technical challenges associated with selecting multiple active molecules and combining multiple targets in multi‑target compound drugs of chemical origin. This approach provides a novel and efficient solution for developing urgently needed multi‑target therapeutics, better aligning with the demands of treating complex diseases. Furthermore, we address the technical challenges in translating the new FDD‑based paradigm for multi‑target drug development into application, proposing regulatory measures for benefit‑risk identification and assessment. This work aims to offer theoretical guidance and practical references to advance this innovative technology from the laboratory to clinical application.