Bespoke polymers for ASDs
Bespoke polymers for ASDs
批准号:
2739113
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2022
资助国家:
英国
项目状态:
未结题
起止时间:
2022 至 --
中文摘要
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英文摘要
Developing a new medicines requires careful formulation, and is becoming increasingly challenging as new drug molecules tend to have poor physico-chemical properties, with aqueous solubility and dissolution properties being a key challenge. A typical strategy is to develop an Amorphous Solid Dispersion (ASD) in which the drug is molecularly dispersed within a polymer matrix, with the overall product being in the amorphous (as opposed to crystalline state). Use of ASDs does however come with challenges, including compatibility between drug and polymer, stability of the ASD with respect to crystallisation (which is thermodynamically favoured, but which negates the benefits of the ASD formulation), and choice of polymer. While some rule of thumb guidance is available it remains a challenge to formulate new drugs as ASDs. The drugs are always new bespoke chemicals, while choice of polymer is limited to a few off-the-shelf "usual suspects" which are used largely for traditional rather than science-driven reasons. The project will investigate the potential use of bespoke polymers for ASD formulation, with new polymers being designed and developed to increase hydrogen-bonding interactions between drug and polymer. The intention is to create a set of polymers, each of which will stabilise a particular chemical class of ASDs. Polymer synthesis will be undertaken at UCL (or Nottingham if required), formulation using a high-throughput (HT) "extreme miniaturised" robotic system at Nottingham. The Nottingham robot allows the ready fabrication of micro-arrays at (for example) 1% drug-polymer loading increments and this represents a giant stride forward in our ability to address complex formulation challenges. Large data sets will be generated and the team will employ the usual machine-learning (ML) methods to develop and refine models to predict drug-polymer compatibility (usual methods = molecular informatics and multiple linear regression to model appropriate data).
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