Bespoke polymers for ASDs

开发新药需要精心的配方，并且由于新药分子往往具有较差的物理化学性质，水溶性和溶解性是一个关键挑战，因此开发新药变得越来越具有挑战性。一种典型的策略是开发非晶固体分散体（ASD），其中药物分子分散在聚合物基质中，整体产品处于非晶状态（与结晶状态相反）。然而，ASD的使用也面临着挑战，包括药物和聚合物之间的相容性，ASD在结晶方面的稳定性（这在热力学上是有利的，但却否定了ASD制剂的好处），以及聚合物的选择。虽然有一些经验指导，但制定新的asd药物仍然是一个挑战。这些药物总是新的定制化学品，而聚合物的选择仅限于一些现成的“常见嫌疑人”，它们主要用于传统而非科学驱动的原因。该项目将研究定制聚合物在ASD配方中的潜在用途，并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。其目的是创造一组聚合物，每一种聚合物都能稳定特定化学类别的asd。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）“极端小型化”机器人系统。诺丁汉机器人允许以（例如）1%的药物聚合物装载增量制备微阵列，这代表了我们解决复杂配方挑战的能力向前迈进了一大步。将生成大型数据集，团队将采用通常的机器学习（ML）方法来开发和完善模型，以预测药物-聚合物的相容性（通常的方法=分子信息学和多元线性回归来建模适当的数据）。

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).