School of Pharmacy and Pharmaceutical Sciences, Cardiff University, Redwood Building, King Edward VII Avenue, Cardiff, CF10 3NB, UK.
Sci Rep. 2022 Aug 20;12(1):14215. doi: 10.1038/s41598-022-18332-3.
Despite the large prevalence of diseases affecting cartilage (e.g. knee osteoarthritis affecting 16% of population globally), no curative treatments are available because of the limited capacity of drugs to localise in such tissue caused by low vascularisation and electrostatic repulsion. While an effective delivery system is sought, the only option is using high drug doses that can lead to systemic side effects. We introduced poly-beta-amino-esters (PBAEs) to effectively deliver drugs into cartilage tissues. PBAEs are copolymer of amines and di-acrylates further end-capped with other amine; therefore encompassing a very large research space for the identification of optimal candidates. In order to accelerate the screening of all possible PBAEs, the results of a small pool of polymers (n = 90) were used to train a variety of machine learning (ML) methods using only polymers properties available in public libraries or estimated from the chemical structure. Bagged multivariate adaptive regression splines (MARS) returned the best predictive performance and was used on the remaining (n = 3915) possible PBAEs resulting in the recognition of pivotal features; a further round of screening was carried out on PBAEs (n = 150) with small variations of structure of the main candidates from the first round. The refinements of such characteristics enabled the identification of a leading candidate predicted to improve drug uptake > 20 folds over conventional clinical treatment; this uptake improvement was also experimentally confirmed. This work highlights the potential of ML to accelerate biomaterials development by efficiently extracting information from a limited experimental dataset thus allowing patients to benefit earlier from a new technology and at a lower price. Such roadmap could also be applied for other drug/materials development where optimisation would normally be approached through combinatorial chemistry.
尽管影响软骨的疾病（例如全球范围内有 16%的人患有膝骨关节炎）患病率很高，但由于药物向这种组织的局部定位能力有限，血管化程度低和静电排斥，因此无法治愈。虽然正在寻找有效的给药系统，但唯一的选择是使用高剂量的药物，这可能会导致全身副作用。我们引入了聚-β-氨基酯（PBAE），以将药物有效递送到软骨组织中。PBAE 是胺和二丙烯酸酯的共聚物，进一步用其他胺封端；因此，对于识别最佳候选物，有很大的研究空间。为了加速所有可能的 PBAE 的筛选，使用了一小部分聚合物（n = 90）的结果来训练各种机器学习（ML）方法，仅使用公共库中可用的聚合物特性或根据化学结构估计的聚合物特性。袋装多元自适应回归样条（MARS）的预测性能最好，并用于剩余的（n = 3915）可能的 PBAE，从而识别关键特征；对第一轮中主要候选物结构略有变化的 PBAE（n = 150）进行了进一步筛选。对这些特征的改进使我们能够确定一个领先的候选物，预计其药物摄取量将比传统临床治疗提高 20 倍以上；这种摄取量的提高也在实验中得到了证实。这项工作强调了机器学习通过从有限的实验数据集高效提取信息来加速生物材料开发的潜力，从而使患者更早地从新技术中受益，并且价格更低。这种路线图也可以应用于其他药物/材料的开发，在这种开发中，通常通过组合化学来优化。
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