Development of UPLC method using quality by design (QbD) approach for determination of impurities of azithromycin

Cilj ovog diplomskog rada bio je razviti UPLC metodu za određivanje onečišćenja azitromicina kako bi se olakšala i ubrzala analiza, te pomoću načela kvalitete ugrađene u dizajn, QbD, pokazati produktivnost takvog pristupa u razvoju metode. QbD pristup u razvoju metode omogućuje variranje više varijabli u vremenu te koristi statistički dizajn eksperimenata za razvoj robusne metode koja definira eksperimentalno područje u kojem promjena parametara metode neće značajno utjecati na rezultat. Na temelju različitog relativnog faktora odgovora pojedinog onečišćenja, pripremljena je selektivna otopina koja je sadržavala različite koncentracije pojedinog onečišćenja i API: 0,01 mg/ml onečišćenja F, 0,004 mg/ml onečišćenja H, 0,1 mg/ml onečišćenja L i onečišćenja I, a API u koncentraciji 5 mg/ml. Optimalni pH za njihovo odjeljivanje uspješno je predviđen pomoću programskog paketa Marvin Beans, dok je metoda dobivena korištenjem programskog paketa DryLab koji na temelju unesenih podataka prikazuje virtualne kromatograme i učinkovitost odjeljivanja ovisno o različitim parametrima kao što su vrijeme eluacije, temperatura kolone, sastav mobilne faze B, pH koji su prilikom provođenja eksperimenata bili varirani. Metoda je razvijena na koloni Acquity UPLC BEH C18 (1,7 μm, 2,1 x 100mm) na tekućinskom kromatografu ultra visoke djelotvornosti. Kao pokretne faze korištene su fosfatni pufer pH 8,5 i acetonitril, dok je protok pokretne faze bio 0,5 ml/min, a temperatura kolone 35 ⁰C. Metoda za određivanje onečišćenja traje 15 minuta, pri čemu je prvih 3,7 min eluacija izokratna s 42 % pokretne faze B, a zatim do kraja gradijentna s gradijentom 42–80 % pokretne faze B. Korištenjem programskog paketa DryLab omogućen je razvoj robusne metode. Programski paket DryLab pokazao se kao odličan alat kojim je moguće skratiti vrijeme potrebno za razvoj metode. Pokazao se kao izrazito efikasan i produktivan alat jer korištenjem stvarnih podataka simulira tisuće eksperimenata i na taj način pruža informacije o tome koji eksperimenti su vrijedni daljnjeg provođenja, a koji su utrošak vremena.The aim of this study was to develop UPLC method for determination of impurities of azithromycin in order to facilitate and speed up analysis and also to show QbD approach in development of analytical method and its efficiency. QbD approach uses a systemathic screening to evaluate more variables at time, uses statistical design of experiments to develop a robust method design space, which defines the experimental region in which changes of method parametars will not significantly affect the result. Because of the different relative response factor of each impurity, the selectivity solution was prepared, that contained impurities and API in different concentrations. The concentration of impurity F was 0,01 mg/ml, of impurity H 0,004 mg/ml, of impurities L and I 0,1 mg/ml and of API 5 mg/ml. The optimal pH for their successful separation is provided by Marvin Beans software, while the method was developed using DryLab software. Based on the entered data, DryLab shows virtual chromatograms and separation efficiency depending on various parameters such as the time of elution, the column temperature, composition of mobile phase B, pH, which were varied during the experiments. The method was developed using Acquity UPLC BEH C18 column (1,7 mm, 2,1 x 100 mm) on ultra performance liquid chromatograpf. Mobile phases, which were used are phosphate buffer pH 8,5 and acetonitrile, while the flow rate of mobile phase was 0,5 ml/min and column temperature was 35 ⁰C. The method for determination of impurities lasts 15 minutes, wherein the first 3,7 min was isocratic elution with 42% mobile phase B, and then starts a gradient elution with the gradient 42-80% of mobile phase B. DryLab software enabled the develoment of robust method. DryLab software proved to be a great tool that can shorten the time required to develop methods. It proved to be highly efficient and productive tool because it simulates thousands of experiments, based on the input data and on that way provides information about which experiments are worthy of further implementation, and which are time-consuming