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Design and development of a biorelevant simulated human lung fluid.
Hassoun, Mireille; Royall, Paul G; Parry, Mark; Harvey, Richard D; Forbes, Ben.
Afiliação
  • Hassoun M; King's College London, Institute of Pharmaceutical Science, London, SE1 9NH, UK.
  • Royall PG; King's College London, Institute of Pharmaceutical Science, London, SE1 9NH, UK.
  • Parry M; Intertek-Melbourn Scientific Limited, Melbourn, SG8 6DN, UK.
  • Harvey RD; Institute of Pharmacy, Martin-Luther-Universität Halle-Wittenberg, 06108, Halle (Saale), Germany.
  • Forbes B; King's College London, Institute of Pharmaceutical Science, London, SE1 9NH, UK.
J Drug Deliv Sci Technol ; 47: 485-491, 2018 Oct.
Article em En | MEDLINE | ID: mdl-30283501
ABSTRACT
Biorelevant fluids are required to enable meaningful in vitro experimental determinations of the biopharmaceutical properties of inhaled medicines, e.g. drug solubility, particle dissolution, cellular uptake. Our aim was to develop a biorelevant simulated lung fluid (SLF) with a well-defined composition and evidence-based directions for use. The SLF contained dipalmitoylphosphotidylcholine, dipalmitoylphosphatidylglycerol, cholesterol, albumin, IgG, transferrin and antioxidants. Freshly made SLF had pH 7.2, viscosity 1.138 × 10-3 Pa s, conductivity 14.5 mS/m, surface tension 54.9 mN/m and density 0.999 g/cm3. Colour, surface tension and conductivity were the most sensitive indicators of product deterioration. The simulant was stable for 24 h and 48 h at 37 °C and 21 °C, respectively, (in-use stability) and for 14 days when stored in a refrigerator (storage stability). To extend stability, the SLF was vacuum freeze-dried in batches to produce lyophilised powder that can be reconstituted readily when needed at the point of use. In conclusion, we have reported the composition and manufacture of a biorelevant, synthetic SLF, provided a detailed physico-chemical characterisation and recommendations for how to store and use a product that can be used to generate experimental data to provide inputs to computational models that predict drug bioavailability in the lungs.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2018 Tipo de documento: Article