Development of PK/PD Model Platforms to Support the Optimal Deployment of New Drug Combinations for the Treatment of Malaria

2016年，全世界估计有2.16亿疟疾病例，44.5万人死于疟疾。虽然目前存在许多治疗这种疾病的方法，但对其中许多治疗方法的耐药性正在上升。在临床研究中进行试验之前，对新提出的给药方案（特别是那些使用药物组合的方案）的潜在临床活性进行准确预测变得越来越重要。这种准确的预测将有助于节省资源，加快药物开发进程。虽然过去预测单一药物对疟疾的活性相当成功，但预测抗疟疾药物组合的总体活性却极其复杂，并没有取得同样的成功。不同的抗疟疾药物作用于寄生虫生命周期的不同阶段；这引入了一定程度的复杂性，使得当前标准PKPD模型不太可能准确预测不同药物组合的整体临床活性。此外，存在大量抗疟疾化合物的药物活性测定；这些方法包括具有不同药理学输出的体外试验，以及在具有或不具有活跃免疫系统的受感染动物中测试药物的体内试验。本项目中提议的活动预计将产生一个新的数学框架，该框架将整合用抗疟疾化合物进行的不同药物分析所得数据的复杂性。这将导致同时将不同实验室的不同产出转化为对药物组合潜在临床活性的切实预测。将使用最先进的数学建模来解决这个问题（例如机器学习和人工神经网络）。使用该数学框架生成的预测将与在该领域进行的临床研究结果进行验证。如果该模型能够成功地预测临床活性，那么它将成为一个强有力的工具，可以选择新的药物组合，从而在该领域实现最大的活性。最终，这个数学工具将有能力评估目前正在使用的不同药物组合和临床研究建议的组合的潜力。这将有助于临床试验中的决策制定，并有可能改变在该领域应用这种组合的政策。这些预测将进一步评估药物组合的总体暴露情况，以评估产生耐药性的可能性。

In 2016, there were an estimated 216 million malaria cases and 445,000 deaths from the disease worldwide. While many therapies to the disease currently exist, resistance to many of these treatments are on the rise. It is becoming increasingly important to have accurate predictions about the potential clinical activity of newly proposed dosing regimens (especially those utilising drug combinations) before testing them in clinical studies. Such accurate predictions will help save resources and accelerate the drug development process. While the prediction of the activity of single drugs in malaria has been fairly successful in the past, the prediction of the overall activity of drug combinations against malaria is extremely more complicated and has not been equally successful. Different anti-malarial drugs act on different stages of the parasite life cycle; this introduces a level of complexity that makes current standard PKPD models less likely to accurately predict the overall clinical activity of different drug combinations. Additionally, a large number of drug activity assays exist for anti-malarial compounds; these include in-vitro assays with different pharmacological outputs as well as in-vivo assays where the drug is tested in infected animals with or without an active immune system. The activities proposed in this project are expected to result in developing a new mathematical framework that will consolidate the complexity of data derived from different drug assays that have been performed with anti-malarial compounds. This would result in simultaneously translating diverse outputs from different labs into tangible predictions about potential clinical activity of drug combinations. State of the art mathematical modelling will be used to address the issue (e.g. machine learning and artificial neural networks). The predictions generated using this mathematical framework will be validated against results from clinical studies performed on the field. If the model was successful in predicting clinical activity then it will become a powerful tool that can select for new drug combinations that can achieve maximal activity on the field. Ultimately, this mathematical tool will have the power to assess the potential of different drug combinations that are currently in use and combinations proposed for clinical studies. This will help with decision making in clinical trials and will have the potential of altering the policy in which such combinations are applied in the field. The predictions will further assess the overall exposure of drug combinations to assess the potential of development of resistance.