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

点击翻译按钮获取中文摘要

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.