Kamalesh Mistry,
Dhananjay Mistry,
Tusar Ranjan Patil,
Sangram Keshari Panda,
Dillip Kumar Brahma,
Manas Ranjan Nayak,
- Assistant Professor, NSHM Institute of Pharmaceutical Technology, Shibtala Rd, Muchipara, Arrah Kalinagar, Arra, Durgapur, West Bengal, India
- Research Scholar, Department of Pharmacognosy, Jeypore College of Pharmacy, Bana Vihar, Randapali, Jeypore, Odisha, India
- Assistant Professor, Department of Pharmaceutical Analysis, Nityananda College of Pharmacy, Balasore, Odisha, India
- Professor, Department of Pharmacognosy, Jeypore College of Pharmacy, Bana Vihar, Randapali, Jeypore, Odisha, India
- Professor & HOD, NSHM Institute of Pharmaceutical Technology, Shibtala Rd, Muchipara, Arrah Kalinagar, Arra, Durgapur, West Bengal, India
- Associate Professor, NSHM Institute of Pharmaceutical Technology, Shibtala Rd, Muchipara, Arrah Kalinagar, Arra, Durgapur, West Bengal, India
Abstract
It is feasible to conclude that RP-HPLC still holds relevance in pharmaceutical quality control Where and when interdisciplinary research is being completed for the estimation of drugs within dosage forms, HPLC techniques provide high sensitivity, specificity as well as reliability. However, conventional RP-HPLC techniques have drawbacks like longer time of analysis, poor peak shapes, higher use of solvent, and poor resolution, which prompts enhanced modifications to achieve better performance, reliability, and efficiency. This review focuses on newly developed advance techniques in RP-HPLC along with the new strategy for method development such as QbD, AI optimization, green chemistry, Nano-RP-HPLC, LC-MS and LC-NMR. A critical review of new trends in RP-HPLC analysis includes the mobile phase selection, gradient and isocratic elution method, suitable column, and use of AI for chromatographic simulation. Some important performance characteristics namely, retention factor (k’), resolution (Rs/N), selectivity (α), and peak asymmetry (Af) are concisely discussed. In addition, there are discussed tendencies in risk based validation and PAT for real time continuous quality monitoring. The past year findings show that, the artificial intelligence based chromatographic modeling has been found efficient to minimize method development time 30-50% or more and the use of green chromatography, new environment friendly solvent has reduced the solvent consumption up to 40% or more. Advanced techniques like machine learning helped method development, MFs, and green chromatography gave better accuracy, solvent, and better peak separations to analyze the samples. Furthermore, by using the blockchain and cloud chromatographic data management, it improves the regulation of ICH, USP, and FDA, as well as data security. RP-HPLC is now gradually transitioning to a more automated system, high throughout and rapid analysis, and RTRT; moreover, AI-based modeling, innovative chemistries of HPLC column, and big data analysis scattered throughout pharmaceuticals with a more effective, acceptable method of reproducibility, regulatory compliance, and operational performance.
Keywords: RP-HPLC, AI-driven optimization, green chromatography, QbD, LC-MS, PAT, Real-Time Release Testing.
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Research & Reviews: A Journal of Pharmacognosy
| Volume | 17 | |
| 03 | ||
| Received | 09/09/2026 | |
| Accepted | 02/11/2026 | |
| Published | 09/11/2026 | |
| Publication Time | 61 Days |