The development of a multiple linear regression model for aiding formulation development of solid dispersions

As poor solubility continues to be problem for new chemical entities (NCEs) in medicines development the use and interest in solid dispersions as a formulation-based solution has grown. Solid dispersions, where a drug is typically dispersed in a molecular state within an amorphous water-soluble poly...

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Main Author: Fridgeirsdottir, Gudrun A.
Format: Thesis (University of Nottingham only)
Language:English
Published: 2018
Subjects:
Online Access:https://eprints.nottingham.ac.uk/52176/
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author Fridgeirsdottir, Gudrun A.
author_facet Fridgeirsdottir, Gudrun A.
author_sort Fridgeirsdottir, Gudrun A.
building Nottingham Research Data Repository
collection Online Access
description As poor solubility continues to be problem for new chemical entities (NCEs) in medicines development the use and interest in solid dispersions as a formulation-based solution has grown. Solid dispersions, where a drug is typically dispersed in a molecular state within an amorphous water-soluble polymer, present a good strategy to significantly enhance the effective drug solubility and hence bioavailability of drugs. The main drawback of this formulation strategy is the inherent instability of the amorphous form. With the right choice of polymer and manufacturing method, sufficient stability can be accomplished. However, finding the right combination of carrier and manufacturing method can be challenging, being labour, time and material costly. Therefore, a knowledge based support tool based upon a statistically significant data set to help with the formulation process would be of great value in the pharmaceutical industry. Here, 60 solid dispersion formulations were produced using ten, poorly soluble, chemically diverse APIs, three commonly used polymers and two manufacturing methods (spray drying and hot-melt extrusion). A long term stability study, up to one year, was performed on all formulations at accelerated conditions. Samples were regularly checked for the onset of crystallisation during the period, using mainly, polarised light microscopy. The stability data showed a large variance in stability between, methods, polymers and APIs. No obvious trends could be observed. Using statistical modelling, the experimental data in combination with calculated and predicted physicochemical properties of the APIs, several multiple linear regression (MLR) models were built. These had a good adjusted R2 and most showed good predictability in leave-one-out cross validations. Additionally, a validation on half of the models (eg. those based on spray-drying models) using an external dataset showed excellent predictability, with the correct ranking of formulations and accurate prediction of stability. In conclusion, this work has provided important insight into the complex correlations between the physical stability of amorphous solid dispersions and factors such as manufacturing method, carrier and properties of the API. Due to the expansive number of formulations studied here, which is far greater than previously published in the literature in a single study, more general conclusions can be drawn about these correlations than has previously been possible. This thesis has shown the potential of using well-founded statistical models in the formulation development of solid dispersion and given more insight into the complexity of these systems and how stability of these is dependent on multiple factors.
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spelling nottingham-521762025-02-28T14:09:06Z https://eprints.nottingham.ac.uk/52176/ The development of a multiple linear regression model for aiding formulation development of solid dispersions Fridgeirsdottir, Gudrun A. As poor solubility continues to be problem for new chemical entities (NCEs) in medicines development the use and interest in solid dispersions as a formulation-based solution has grown. Solid dispersions, where a drug is typically dispersed in a molecular state within an amorphous water-soluble polymer, present a good strategy to significantly enhance the effective drug solubility and hence bioavailability of drugs. The main drawback of this formulation strategy is the inherent instability of the amorphous form. With the right choice of polymer and manufacturing method, sufficient stability can be accomplished. However, finding the right combination of carrier and manufacturing method can be challenging, being labour, time and material costly. Therefore, a knowledge based support tool based upon a statistically significant data set to help with the formulation process would be of great value in the pharmaceutical industry. Here, 60 solid dispersion formulations were produced using ten, poorly soluble, chemically diverse APIs, three commonly used polymers and two manufacturing methods (spray drying and hot-melt extrusion). A long term stability study, up to one year, was performed on all formulations at accelerated conditions. Samples were regularly checked for the onset of crystallisation during the period, using mainly, polarised light microscopy. The stability data showed a large variance in stability between, methods, polymers and APIs. No obvious trends could be observed. Using statistical modelling, the experimental data in combination with calculated and predicted physicochemical properties of the APIs, several multiple linear regression (MLR) models were built. These had a good adjusted R2 and most showed good predictability in leave-one-out cross validations. Additionally, a validation on half of the models (eg. those based on spray-drying models) using an external dataset showed excellent predictability, with the correct ranking of formulations and accurate prediction of stability. In conclusion, this work has provided important insight into the complex correlations between the physical stability of amorphous solid dispersions and factors such as manufacturing method, carrier and properties of the API. Due to the expansive number of formulations studied here, which is far greater than previously published in the literature in a single study, more general conclusions can be drawn about these correlations than has previously been possible. This thesis has shown the potential of using well-founded statistical models in the formulation development of solid dispersion and given more insight into the complexity of these systems and how stability of these is dependent on multiple factors. 2018-07-20 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/52176/1/Fridgeirsdottir.GA_corr.pdf Fridgeirsdottir, Gudrun A. (2018) The development of a multiple linear regression model for aiding formulation development of solid dispersions. PhD thesis, University of Nottingham. solid dispersion multiple linear regression
spellingShingle solid dispersion
multiple linear regression
Fridgeirsdottir, Gudrun A.
The development of a multiple linear regression model for aiding formulation development of solid dispersions
title The development of a multiple linear regression model for aiding formulation development of solid dispersions
title_full The development of a multiple linear regression model for aiding formulation development of solid dispersions
title_fullStr The development of a multiple linear regression model for aiding formulation development of solid dispersions
title_full_unstemmed The development of a multiple linear regression model for aiding formulation development of solid dispersions
title_short The development of a multiple linear regression model for aiding formulation development of solid dispersions
title_sort development of a multiple linear regression model for aiding formulation development of solid dispersions
topic solid dispersion
multiple linear regression
url https://eprints.nottingham.ac.uk/52176/