Applying Computational Predictions of Biorelevant Solubility Ratio Upon Self-Emulsifying Lipid-Based Formulations Dispersion to Predict Dose Number
| Autor: | Joseph P. O'Shea, Niklas J. Koehl, Harriet Bennett-Lenane, Karl J. Box, Patrick J O'Dwyer, Brendan T. Griffin |
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| Rok vydání: | 2020 |
| Předmět: |
Drug
media_common.quotation_subject Drug Compounding Pharmaceutical Science Biological Availability 02 engineering and technology 030226 pharmacology & pharmacy 03 medical and health sciences 0302 clinical medicine Drug Delivery Systems Lipid-based formulations Partial least squares Computational chemistry Molecular descriptor Linear regression Partial least squares regression Solubility Developability screening media_common In silico modelling Chemistry 021001 nanoscience & nanotechnology Lipids Drug delivery system(s) Physiochemical properties Partition coefficient Multivariate analysis Drug delivery Emulsions 0210 nano-technology Dispersion (chemistry) |
| Zdroj: | Journal of pharmaceutical sciences. 110(1) |
| ISSN: | 1520-6017 |
| Popis: | Computational approaches are increasingly utilised in development of bio-enabling formulations, including self-emulsifying drug delivery systems (SEDDS), facilitating early indicators of success. This study investigated if in silico predictions of drug solubility gain i.e. solubility ratios (SR), after dispersion of a SEDDS in biorelevant media could be predicted from drug properties. Apparent solubility upon dispersion of two SEDDS in FaSSIF was measured for 30 structurally diverse poorly water soluble drugs. Increased drug solubility upon SEDDS dispersion was observed in all cases, with higher SRs observed for cationic and neutral versus anionic drugs at pH 6.5. Molecular descriptors and solid-state properties were used as inputs during partial least squares (PLS) modelling resulting in predictive models for SRMC (r2 = 0.81) and SRLC (r2 = 0.77). Multiple linear regression (MLR) facilitated generation of simplified SR equations with high predictivity (SRMC r2 = 0.74; SRLC r2 = 0.69), requiring only three drug properties; partition coefficient at pH 6.5 (logD6.5), melting point (Tm) and aromatic bonds as fraction of total bonds (F-AromB). Through using the equations to inform developability classification system (DCS) classes for drugs that have already been licensed as lipid-based formulations, merits for development with SEDDS was predicted for 2/3 drugs. |
| Databáze: | OpenAIRE |
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