Applying Computational Predictions of Biorelevant Solubility Ratio Upon Self-Emulsifying Lipid-Based Formulations Dispersion to Predict Dose Number
Copyright © 2020 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:110 |
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| Enthalten in: |
Journal of pharmaceutical sciences - 110(2021), 1 vom: 01. Jan., Seite 164-175 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Bennett-Lenane, Harriet [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 17.06.2021 Date Revised 17.06.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.xphs.2020.10.055 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM317124951 |
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| 520 | |a Copyright © 2020 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a Developability screening | |
| 650 | 4 | |a Drug delivery system(s) | |
| 650 | 4 | |a In silico modelling | |
| 650 | 4 | |a Lipid-based formulations | |
| 650 | 4 | |a Multivariate analysis | |
| 650 | 4 | |a Partial least squares | |
| 650 | 4 | |a Physiochemical properties | |
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| 650 | 7 | |a Lipids |2 NLM | |
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| 700 | 1 | |a O'Dwyer, Patrick J |e verfasserin |4 aut | |
| 700 | 1 | |a Box, Karl J |e verfasserin |4 aut | |
| 700 | 1 | |a O'Shea, Joseph P |e verfasserin |4 aut | |
| 700 | 1 | |a Griffin, Brendan T |e verfasserin |4 aut | |
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