Prediction of nanoparticle stability using advanced physico-chemical techniques.
Lead Research Organisation:
University of Manchester
Department Name: School of Health Sciences
Abstract
Lipid nanoparticles, in their various forms - including solid lipid nanoparticles, nanostructured lipid carriers, lipid drug conjugate and polymer-lipid hybrid nanoparticles - have attracted considerable interest as delivery vehicles for a range of therapeutic candidates. However, regardless of their precise structure, these colloidal particles share common stability issues such as agglomeration/aggregation and sedimentation. Whilst electrostatic, steric or electrosteric stabilization of the nanoparticles can overcome problems with agglomeration/aggregation as well as controlling sedimentation, such strategies frequently take time to develop and contribute to the attrition of the candidates during the development process. To date little or no work has systematically determined nanoparticle stability as a function of nanoparticle composition, stress, time after production, nanoparticle concentration.
The main aim of the current project is to establish which physical determinants (e.g. storage time, nanoparticle concentration as well as the application of external stresses (e.g. temperature, pH, ionic stress, excipients, plasma components)) destabilise lipid nanoparticles by the use of an unique combination of low volume sampling techniques and thereby propose rules to facilitate the development process.
Nanoparticles are increasingly being used to formulate biopharmaceutical molecules such as nucleic acids. By systematically understanding the physical determinants of nanoparticle stability, including stress to accelerate the stability studies, it will be possible to define rules to produce therapeutic-agent containing formulations of the required stability. The required understanding will be gained by the use of a novel combination of a range of advanced physico-chemical techniques that can either be used 'in-line' and/or use very low sample volumes. Such detailed understanding and product characterisation is increasingly required by the regulator.
The specific aims of this project are:
- Quantification of the interactions between lipid nanoparticles as a function of external factors (pH, ionic strength, temperature, excipients) with nanoparticle composition using a range of advanced physico-chemical techniques.
- Characterisation of the integrity of the nanoparticles (including their payload) as well as the absence of aggregates using RICS (and SANS) under microfluidic flow.
The main aim of the current project is to establish which physical determinants (e.g. storage time, nanoparticle concentration as well as the application of external stresses (e.g. temperature, pH, ionic stress, excipients, plasma components)) destabilise lipid nanoparticles by the use of an unique combination of low volume sampling techniques and thereby propose rules to facilitate the development process.
Nanoparticles are increasingly being used to formulate biopharmaceutical molecules such as nucleic acids. By systematically understanding the physical determinants of nanoparticle stability, including stress to accelerate the stability studies, it will be possible to define rules to produce therapeutic-agent containing formulations of the required stability. The required understanding will be gained by the use of a novel combination of a range of advanced physico-chemical techniques that can either be used 'in-line' and/or use very low sample volumes. Such detailed understanding and product characterisation is increasingly required by the regulator.
The specific aims of this project are:
- Quantification of the interactions between lipid nanoparticles as a function of external factors (pH, ionic strength, temperature, excipients) with nanoparticle composition using a range of advanced physico-chemical techniques.
- Characterisation of the integrity of the nanoparticles (including their payload) as well as the absence of aggregates using RICS (and SANS) under microfluidic flow.
People |
ORCID iD |
| Alain Pluen (Primary Supervisor) | |
| Lauren Daniels (Student) |