RESUMEN
Niosomes are vesicles formed mostly by nonionic surfactant and cholesterol incorporation as an excipient. The drug entrapment efficiency of niosomal vesicles is particularly important and depends on many parameters. Changing the effective parameters to have maximum entrapment efficiency in the laboratory is time-consuming and costly. In this study, a machine learning framework was proposed to address these problems. In order to find the most critical parameter affecting the entrapment efficiency and its optimal value in a specific experiment, data were first extracted from articles of the last decade using keywords of niosome and thin-film hydration method. Then, deep neural network (DNN), linear regression, and polynomial regression models were trained with four cost functions. Afterward, the most influential parameter on entrapment efficiency was determined using the sensitivity experiment. Finally, the optimal point of the most influential parameter was found by keeping the other parameters constant and changing the most influential parameter. The veracity of this test was evaluated by entrapment efficiency results of 7 niosomal formulations containing doxycycline hyclate prepared in the laboratory. The best model was DNN, which yielded root mean square error (RMSE) of 13.587 ± 2.61, mean absolute error (MAE) of 10.17 ± 1.421, and R-squared (R2) of 0.763 ± 0.1 evaluated by 5-fold cross-validation. The hydrophilic-lipophilic balance (HLB) was identified as the most influential parameter, and the entrapment efficiency change curve was plotted versus the HLB value. This study uses machine learning methods to synthesize niosomal systems with optimal entrapment efficiency at a lower cost and time.
Asunto(s)
Excipientes , Liposomas , Doxiciclina , Colesterol , Tensoactivos , Aprendizaje AutomáticoRESUMEN
BACKGROUND: Acne is the pilosebaceous units' disorder. The most important cause of acne is the colonization of bacteria in the follicles. Among antibiotics, doxycycline hyclate kills a wide range of bacteria. OBJECTIVES: The study aims to prevent oral administration's side effects, overcome the barriers of conventional topical treatment, and improve the therapeutic effectiveness; this drug was loaded into niosomal nanocarriers for topical application. METHODS: Doxycycline hyclate was loaded into four niosomal formulations prepared by the thinfilm hydration method with different percentages of constituents. Drug-containing niosomal systems were evaluated for morphological properties via scanning electron microscopy, particle size, drug entrapment efficiency, zeta potential, in vitro drug release, physical stability after 60 days, in vitro drug permeation through rat skin, in vitro drug deposition in rat skin, toxicity on human dermal fibroblasts (HDF) by MTT method after 72 hours, and antibacterial properties against the main acne-causing bacteria via antibiogram test. RESULTS: The best formulation had the appropriate particle size of 362.88 ± 13.05 nm to target follicles, entrapment efficiency of 56.3 ± 2.1%, the zeta potential of - 24.46±1.39 mV, in vitro drug release of 54.93 ± 1.99% after 32 hours, and the lowest permeation of the drug through the rat skin among all other formulations. Improved cell viability, increased antibacterial activity, and an approximately three-fold increase in drug deposition were the optimal niosomal formulation features relative to the free drug. CONCLUSION: This study demonstrated the ability of nano-niosomes containing doxycycline hyclate to treat skin acne compared with the free drug.