Multi-metric greenness, performance and applicability evaluation of a green HPLC-ELSD method for polysorbate 80 determination in various pharmaceuticals compared with AI-assisted scoring systems


SEZGİN B., ARLİ G., SOYSEVEN M.

Microchemical Journal, cilt.218, 2025 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 218
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.microc.2025.115710
  • Dergi Adı: Microchemical Journal
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, Chemical Abstracts Core, Chimica, Food Science & Technology Abstracts, Index Islamicus, Veterinary Science Database
  • Anahtar Kelimeler: artificial intelligence, Green analytical chemistry, HPLC-ELSD, pharmaceutical analysis, Polysorbate 80
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

Polysorbate 80 (PS80) is a widely used non-ionic surfactant in pharmaceutical formulations due to its emulsifying and stabilizing properties, especially for protein-based therapeutics. Its amphiphilic nature and susceptibility to degradation necessitate precise, and sensitive analytical methods for quality control. In this study, a novel high-performance liquid chromatography method coupled with evaporative light scattering detection (HPLC-ELSD) was developed and validated for the quantification of PS80 in pharmaceutical products. The method utilized a Nanologica SVEA C18 column (250 mm × 4.6 mm, 5 μm) maintained at 30 °C, with ethanol–water as the mobile phase under a linear gradient (EtOH:water from 10:90 to 50:50 v/v) over 10 min. The flow rate was set to 1.0 mL min−1 with an injection volume of 10 μL. The method was rigorously validated per ICH Q2(R2) guidelines, showing excellent linearity (R2 = 0.9967), precision, trueness, and robustness. Greenness and sustainability were assessed using multi-metric tools, supported by artificial intelligence (AI) to compare evaluation efficiency. The method achieved high scores across environmental, analytical, and applicability indices, indicating strong performance and low ecological impact. Application to commercial samples demonstrated its practical utility. The developed HPLC-ELSD method offers improved simplicity, greenness, and analytical efficiency compared to previously reported approaches.