The objective of the current study was to develop a specific, precise, accurate and robust gradient stability indicating reversed-phase ultra performance liquid chromatography (RP-UPLC-PDA) assay method and validated for determination of edoxaban tosylate in API. Gradient separation was achieved on an acquity UPLC BEH C18 column (50 mm, 2.1 mm and 1.7 μm) column using mobile phase of acetoitrile:20 mM potassium dihydrogen phosphate, pH 3.0 ± 0.05 adjust with OPA at flow rate of 0.6 mL/min, the injection volume was 1 μL and the detection was carried out of 289 nm by using photodiode array detector. The drug was subjected to oxidation, hydrolysis, photolysis, and heat to apply stress condition. The method was linear in the drug concentration range of 100-300 μg/mL with correlation coefficient of 0.999. Degradation products produced as a result of stress studies did not interfere with detection of edoxaban tosylate and the assay, thus developed stability indicating method can be used for routine analysis in pharmaceutical industry.

The present study examines simultaneous multiple response optimization using desirability function for the development of an HPTLC method to detect esomeprazole magnesium trihydrate and levosulpiride in pharmaceutical dosage form. HPTLC separation was performed on aluminium plates pre-coated with silica gel 60 F254 as the stationary phase using ethyl acetate:methanol:toluene:ammonia (7:1.5:1.5:0.1% v/v/v) as the mobile phase. Full factorial design applied for the optimization of degradation condition. Esomeprazole magnesium trihydrate and levosulpiride were subjected to acid, alkali hydrolysis, oxidation and photodegradation. Experimental full factorial design has been used during forced degradation to determine significant factors responsible for degradation and to optimize degradation conditions reaching maximum degradation. 32 and 23 full factorial design has been used for optimization of chromatographic condition in acid and base degradation study, respectively. Quantification was achieved based on a densitometric analysis of esomeprazole magnesium trihydrate and levosulpiride over the concentration range of 800-4000 ng/band and 1500-7500 ng/band, respectively at 254 nm. The method yielded compact and well-resolved bands at Rf of 0.70 ± 0.02 and 0.32 ± 0.02 for esomeprazole magnesium trihydrate and levosulpiride, respectively. The linear regression analysis for the calibration plots produced r2 = 0.9967 and r2 = 0.9981 for esomeprazole magnesium trihydrate and levosulpiride, respectively. Method is validated as per ICH (Q2)R1 guideline.

RP-UPLC method was developed and validated for the determination of chlorpheniramine maleate and dextromethorphan hydrobromide in tablet dosage form. Reverse phase waters acquity UPLC BEH C18 (50 mm × 2.1 mm, 1.7 μm) column using isocratic mobile phase of 0.5 mL 0.1% TFA (trifluroacetic acid) in H2O:CH3CN (70:30 %v/v). The flow rate was 0.2 mL/min and 252 nm wavelength use for detection on PDA detector. The retention time of chlorpheniramine maleate was 1.2 min and 2.2 min for dextromethorphan hydrobromide. Chlorpheniramine maleate and dextromethorphan hydrobromide was subjected to stress conditions including acidic, alkaline, oxidation, photolysis and thermal degradation. The method was validated as per ICH guideline with respect to samples to specificity, precision, accuracy, linearity and robustness.

Artificial intelligence (AI) is a broad word that refers to the theory and development of computer systems that can do tasks that would ordinarily require human cognition, such as perception, comprehension, reasoning, learning, planning, and problem solving. Understanding the terminology and methodologies used in AI can help you communicate more effectively with data scientists to work together to design models that will improve patient care. The healthcare and pharmaceutical industries have long been early adopters of technological developments, reaping major benefits as a result. AI is being applied in a range of health-related sectors, including the discovery of novel medications, the invention of new medical treatments, and the management of patient data and records. This review identifies and examines the fundamentals and applications of artificial intelligence in medicine and pharmacy.