Bespoke polymers for ASDs

用于 ASD 的定制聚合物

• 批准号: 2739113
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2739113
• 关键词: Bespoke; polymers; ASDs

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).

开发一种新药需要仔细的配方，由于新药分子往往具有较差的物理化学性质，而水的溶解性和溶出性是一个关键挑战，因此开发新药正变得越来越具有挑战性。一种典型的策略是开发无定形固体分散体(ASD)，其中药物以分子形式分散在聚合物基质中，整个产品处于非晶态(而不是结晶态)。然而，ASD的使用确实带来了挑战，包括药物和聚合物之间的兼容性，ASD在结晶方面的稳定性(这在热力学上是有利的，但这否定了ASD配方的好处)，以及聚合物的选择。虽然已经有了一些经验指导，但将新药作为自闭症制定仍然是一个挑战。这些药物总是新的定制化学品，而聚合物的选择仅限于几个现成的“常见嫌疑人”，这些药物主要用于传统原因，而不是科学驱动的原因。该项目将研究定制聚合物在ASD配方中的潜在用途，正在设计和开发新的聚合物，以增加药物和聚合物之间的氢键相互作用。其目的是创造一系列聚合物，每一种聚合物都将稳定特定化学类别的自闭症。聚合物合成将在伦敦大学学院进行(如果需要，也可以在诺丁汉进行)，配方使用诺丁汉的高通量(HT)“极端微型化”机器人系统。诺丁汉机器人允许以(例如)1%的药物聚合物负载增量即可制造微阵列，这代表着我们解决复杂配方挑战的能力向前迈进了一大步。将产生大量数据集，该团队将使用常见的机器学习(ML)方法来开发和改进模型，以预测药物-聚合物的兼容性(通常的方法=分子信息学和多元线性回归来对适当的数据进行建模)。