PURPOSE: To assess the physicochemical properties of hyaluronic acid (HA)-based artificial tears. METHODS: The average molecular weight (MW) and polydispersion index (PDI) of HA in 18 commercially available artificial tears were determined by light scattering/high-performance liquid chromatography. Osmolality, pH, viscosity, and sodium concentration were determined using an osmometer, pH meter, rheometer, and inductively coupled plasma mass spectrometer, respectively. RESULTS: The MW of HA varied considerably between formulations. The PDI was >2.0 in two formulations (2.28 and 4.94), suggesting the presence of a copolymer and/or HA size variability. Three formulations exhibited viscosity exceeding the blur threshold at different shear rates. Viscosity at low shear rates was generally highest in formulations containing high-MW HA. Correlations were found between observed viscosity and a predictive/calculated value, except for four copolymer-containing formulations, and osmolality (range, 154-335 mOsm/kg) and sodium concentration (range, 22-183 mM), with two exceptions. Compared with organic osmolytes, adding sodium decreased viscosity, particularly at lower shear rates. CONCLUSIONS: In the context of the literature, our findings suggest that for most patients with dry eye disease, the ideal HA-based artificial tear should include high-MW HA with a low PDI and exhibit enhanced viscosity at low shear rate (without exceeding the blur threshold). The inclusion of synergistic copolymers and a low sodium concentration may increase viscosity, but whether any of these physicochemical properties or correlations can predict clinical efficacy will require further investigation. TRANSLATIONAL RELEVANCE: Understanding the properties of HA-based artificial tears will support the development of unique formulations that target specific ocular surface conditions.
Physicochemical Properties of Hyaluronic Acid-Based Lubricant Eye Drops.
阅读:11
作者:Aragona Pasquale, Simmons Peter A, Wang Hongpeng, Wang Tao
| 期刊: | Translational Vision Science & Technology | 影响因子: | 2.600 |
| 时间: | 2019 | 起止号: | 2019 Nov 1; 8(6):2 |
| doi: | 10.1167/tvst.8.6.2 | ||
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