Current - Issue

1. Elagamy SH, Elattar RH. Modern optimization strategies in high-performance liquid chromatography analysis. J Sep Sci. 2026;49:e70487.
doi:10.1002/jssc.70487.
2. Elagamy SH, Chanduluru HK, Obaydo RH, Lotfy HM. The role of artificial intelligence in modern analytical chemistry:current trends
and future directions. Int J Anal Chem. 2026;2026:2645726.doi:10.1155/ianc/2645726.
3. Xie J, Chen S, Zhao L, Dong X. Application of artificial intelligence to quantitative structure-retention relationship calculations in
chromatography. J Pharm Anal. 2025;15:101155. doi:10.1016/j.jpha.2024.101155.
4. Jiang J, Ma X, Ouyang D, Williams RO III. Emerging artificial intelligence (AI) technologies used in the development of solid dosage
forms. Pharmaceutics. 2022;14:2257. doi:10.3390/pharmaceutics14112257.
5. Agarwal R, Ruilope LM, Ruiz-Hurtado G, Haller H, Schmieder RE, Anker SD, et al. Effect of finerenone on ambulatory blood pressure
in chronic kidney disease in type 2 diabetes. J Hypertens. 2023;41:295-302. doi:10.1097/HJH.0000000000003330
6. Alonso Salinas GL, Martínez León A, Aguiar Cano D, Esteban-Fernández A, Viéitez Flórez JM, del Prado Díaz S, et al. Clinical utility
and safety of finerenone in patients with heart failure: rationale and design of FINE registry. ESC Heart Fail. 2025;12:3163-3172.
doi:10.1002/ehf2.15260.
7. Heinig R, Eissing T. The pharmacokinetics of the nonsteroidal mineralocorticoid receptor antagonist finerenone. Clin Pharmaco kinet.
2023;62:1673-1693. doi:10.1007/s40262-023-01312-9.
8. Marie AA, Yassin MG, Elshenawy EA. Stability indicating RP-HPLC method for estimation of finerenone and its related substances in
new dosage form. Sci Rep. 2025;15:20229. doi:10.1038/s41598-025-07166-4.9. Myakala K, Wang XX, Shults N, Hughes EP, de Carvalho Ribeiro P, Penjweini R, et al. The non-steroidal MR antagonist finerenone
reverses Western diet-induced kidney disease by regulating mitochondrial and lipid metabolism and inflammation. Am J Physiol Renal
Physiol. 2025;329(5):F724-F743. doi:10.1152/ajprenal.00136.2025.
10. Kolkhof P, Bärfacker L. Mineralocorticoid receptor antagonists: 60 years of research and development. J Endocrinol. 2017;234(1):T125-
T140. doi:10.1530/JOE-16-0600.
11. Ali MB, Abdel-Raoof AM, Elbardisy HM, Omran GA, Ragab MA, Morshedy S. Green “turn-off” luminescent nanosensor for the sensitive
analysis of finerenone in various matrices: application of recent greenness assessment techniques. RSC Adv. 2025;15:46207-46217.
doi:10.1039/D5RA07857A.
12. Pardo-Cortina C, Escuder-Gilabert L, Medina-Hernández MJ, Sagrado S, Martín-Biosca Y. Toward AI-assisted greener chiral HPLC:
predicting efficient enantioseparation-mobile phase (EES-MP) profiles for MP selection—A Lux Cellulose-1 case study. Anal Chem.
2026;98:927-933. doi:10.1021/acs.analchem.5c06117.
13. Peraman R, Bhadraya K, Reddy YP. Analytical quality by design: a tool for regulatory flexibility and robust analytics. Int J Anal Chem.
2015;2015:868727. doi:10.1155/2015/868727.
14. Patil SB, Sutar SB, Bhimanwar R. Analytical Quality by Design-Based Reverse-Phase High-Performance Liquid Chromatography
Method Development for Biomarker: A Comprehensive Review of Quality Assessment and Analytical Trends. Biomed Chromatogr.
2026;40:e70573. doi:10.1002/bmc.70573.
15. Yang S, Hu X, Zhu J, Zheng B, Bi W, Wang X, et al. Aspects and Implementation of Pharmaceutical Quality by Design from Conceptual
Frameworks to Industrial Applications. Pharmaceutics. 2025;17:623. doi:10.3390/pharmaceutics17050623.
16. Ravisankar P, Naga Navya C, Pravallika D, Navya Sri D. A review on step-by-step analytical method validation. IOSR J Pharm.
2015;5(10):7-19.
17. Quijano Velasco P, Hippalgaonkar K, Ramalingam B. Emerging trends in the optimization of organic synthesis through high-throughput
tools and machine learning. Beilstein J Org Chem. 2025;21:10-38. doi:10.3762/bjoc.21.3.
18. Nithyanantham D, Nair A, Nayak UY. Leveraging artificial intelligence for advancements in liquid dosage formulations in the
pharmaceutical industry. Ther Innov Regul Sci. 2025;59:1004-1031. doi:10.1007/s43441-025-00823-w.
19. McDonald MA, Koscher BA, Canty RB, Jensen KF. Calibration-free reaction yield quantification by HPLC with a machine-learning
model of extinction coefficients. Chem Sci. 2024;15:10092-10100. doi:10.1039/D4SC01881H.
20. Taylor CJ, Pomberger A, Felton KC, Grainger R, Barecka M, Chamberlain TW, et al. A brief introduction to chemical reaction
optimization. Chem Rev. 2023;123:3089-3126. doi:10.1021/acs.chemrev.2c00798.
21. Haas CP, Lübbesmeyer M, Jin EH, McDonald MA, Koscher BA, Guimond N, et al. Open-source chromatographic data analysis for
reaction optimization and screening. ACS Cent Sci. 2023;9:307-317. doi:10.1021/acscentsci.2c01042.
22. den Uijl MJ, Schoenmakers PJ, Pirok BWJ, van Bommel MR. Recent applications of retention modelling in liquid chromatography. J Sep
Sci. 2021;44:88-114. doi:10.1002/jssc.202000905.
23. Bos TS, Knol WC, Molenaar SRA, Niezen LE, Schoenmakers PJ, Somsen GW, et al. Recent applications of chemometrics in one- and
two-dimensional chromatography. J Sep Sci. 2020;43:1678-1727. doi:10.1002/jssc.202000011.
24. Nambiar AMK, Breen CP, Hart T, Kulesza T, Jamison TF, Jensen KF. Bayesian optimization of computer-proposed multistep synthetic
routes on an automated robotic flow platform. ACS Cent Sci. 2022;8:825-836. doi:10.1021/acscentsci.2c00207.
25. Gisperg F, Klausser R, Elshazly M, Kopp J, Přáda Brichtová E, Spadiut O. Bayesian optimization in bioprocess engineering—where do
we stand today? Biotechnol Bioeng. 2025;122:1313-1325. doi:10.1002/bit.28960.
26. Taylor CJ, Felton KC, Wigh D, Jeraal MI, Grainger R, Chessari G, et al. Accelerated chemical reaction optimization using multi-task
learning. ACS Cent Sci. 2023;9:957-968. doi:10.1021/acscentsci.3c00050.
27. Furukawa S, Uchida H, Kishimoto T. Artificial intelligence in drug discovery and development: raising quality per decision.
Pharmacopsychiatry. 2026;59:103-116. doi:10.1055/a-2810-8972.
28. Kokudeva M, Vichev M, Naseva E, Miteva DG, Velikova T. Artificial intelligence as a tool in drug discovery and development. World J
Exp Med. 2024;14(3):96042. doi:10.5493/wjem.v14.i3.96042.
29. Lotfy HM, Erk N, Genc AA, Obaydo RH, Tiris G. Artificial intelligence in chromatography: Greenness and performance evaluation of
AI-predicted and in-lab optimized HPLC methods for simultaneous separation of amlodipine, hydrochlorothiazide, and candesartan.
Talanta Open. 2025;11:100473.doi:10.1016/j.talo.2025.100473.
30. Rahman MS, Khan MKH, Kawakami J, Wang K, Diaz A, Riley F. Recent applications of liquid chromatography-based QSRR models
for pharmaceutically relevant small molecules: A review. Journal of Pharmaceutical Sciences. 2026;115:104047.
doi:10.1016/j.xphs.2025.104047.
31. Park G, Kim MK, Go SH, Choi M, Jang YP. Analytical Quality by Design (AQbD) approach to the development of analytical procedures
for medicinal plants. Plants. 2022;11:2960. doi:10.3390/plants11212960.
32. Mishra AS, Vasanthan M. Design and validation of a robust stability-indicating reversed-phase HPLC method for quantification of
mesalamine in formulated drug products. BMC Chemistry. 2025;19:303. doi:10.1186/s13065-025-01666-0.
33. Mali C, Sonawane B, Mali R. Recent advances in analytical method validation as per ICH Q2(R2): a comparative review with ICH
Q2(R1). Int J Pharm Sci. 2025;3(7):1560-1567. doi:10.5281/zenodo.15863307.
34. Sri Ranjani CA, Sultana SF, Bhavyasri K. Artificial intelligence in pharmaceutical analysis: a review. Int J Pharm Sci. 2025;3(11):732-
742. doi:10.5281/zenodo.17532705.
35. Mulla T, Sonavane S, Tambe A, Hange M, Sable PN. Artificial intelligence in pharmaceutical process validation: a review. Int J Sci Res
Technol. 2026;3(7):823-834.
36. Shrivastava A. Chromatography and artificial intelligence. Int J Newgen Res Pharm Healthc. 2025;3(2):98-103.
doi:10.61554/ijnrph.v3i2.2025.196.