RP-HPLC-DAD Method Development and Validation for Simultaneous Determination of Lansoprazole, Tinidazole, Amoxicillin, and Naproxen in Their Raw Materials and Combined Dosage Form: DOE Approach for Optimization of the Proposed Method.

BACKGROUND: Helicobacter pylori infection is a common cause of peptic ulcer disease and dyspepsia. In addition, it may result in gastric cancer and gastric mucosa associated lymphoid tissue lymphoma. First-line therapy usually consists of triple therapy containing clarithromycin or amoxicillin, one of the proton pump inhibitors, and metronidazole or tinidazole. In addition to the triple therapy, an analgesic is required to relieve pain such as naproxen. OBJECTIVE: A sensitive and selective method needs to be developed and validated for simultaneous determination of four drugs (amoxacillin, tinidazole, naproxen and lansoprazole), used for treating Helicobacter pylori infection, in their combined dosage forms. METHODS: With the aid of experimental design, the cited drugs were separated and quantified. HPLC with a diode array detector was used and metronidazole, one of the drugs also used for treatment, was the internal standard (IS). A Thermo Scientific BDS Hypersil C18 column (5 µm, 250 mm x 4.6 mm) with mobile phase composed of acetonitrile-water (40 + 60, by volume), pH 5 adjusted with phosphoric acid, at 30°C was used for the separation of the cited drugs. RESULTS: The method was linear over the concentration ranges 10-500 µg/mL for amoxacillin, 10-350 µg/mL for tinidazole, 10-250 µg/mL for naproxen, and 2-20 µg/mL for lansoprazole. The proposed method was fully validated according to International Conference of Harmonization (ICH) guidelines. Statistical analysis revealed no significant difference between the results obtained and the four reference methods for the investigated drugs. CONCLUSION: The method can be easily implemented in QC studies of the cited drugs in their dosage forms. HIGHLIGHTS: Experimental design was applied using Plackett-Burman design for preliminary screening of factors followed by Box-Behnken design for chromatographic method optimization.