Protodioscin is a naturally occurring steroidal saponin found in several medicinal plants, particularly Tribulus terrestris, Dioscorea species, and fenugreek (Trigonella foenum-graecum). Growing interest in protodioscin has led to increasing research on its chemical properties, biological activities, analytical characterization, and possible therapeutic applications. This review summarizes the available information on the botanical sources, chemical characteristics, biosynthetic aspects, analytical methods, pharmacological activities, safety, and future prospects of protodioscin. The review discusses commonly used techniques for the isolation, identification, characterization, and quantification of protodioscin, including TLC, HPTLC, HPLC, UV–Visible spectroscopy, FT-IR, NMR, LC–MS/MS, GC–MS, and ion-exchange chromatography. The advantages and limitations of these analytical approaches are also considered in relation to the standardization and quality assessment of protodioscin-containing plant materials. Reported biological activities, including antioxidant, anti-inflammatory, antimicrobial, cytotoxic, and reproductive-health-related effects, are summarized, while the need for further pharmacokinetic and long-term safety studies is emphasized. In addition, the emerging role of artificial intelligence, machine learning, and chemometric approaches in protodioscin research is highlighted. These technologies may assist in chromatographic optimization, spectral interpretation, peak recognition, structural characterization, impurity detection, authentication, and quality control. The integration of AI with conventional analytical techniques could make protodioscin analysis more efficient and reproducible. Greater attention is also needed to improve method validation, batch consistency, extract authentication, and translation of laboratory findings into practical applications. Overall, further experimental and clinical studies are needed to establish its pharmacological potential, safety, mechanisms of action, and reliable analytical methods, while validating the practical application of AI-assisted approaches in future protodioscin research.
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Bhavana et al. (2026) studied this question.
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