Quantification of uncertainty within systems pharmacology to optimise personalised therapy

拟议的研究将证明如何量化理论药理学模型中的不确定性可以用来优化个性化治疗。目前的单一剂量放之四海而皆准的观点、针对“平均”人群的剂量和剂量方案的经验推导显然不足以有效地治疗不同的人群。根据遗传因素和毒性病史确定治疗计划的个性化医疗是朝着改善临床结果迈出的一步。然而，当一个人根据药物遗传学被认为不适合使用标准药物/剂量进行治疗时，必须找到可能更昂贵、效果更差或有潜在毒性的替代药物。如果对亚种群的毒性机制有更深入的了解，并对风险和疗效进行适当的量化，就可以改善这种情况。数学和统计工具可以帮助提高对毒性机制的理解，优化治疗（例如，替代给药方案、多药给药、评估替代药物策略），并根据模型模拟中的不确定性传达建议治疗的风险。为了在研究期间开发和测试这一拟议的定量框架，将根据抗凝治疗中的不良反应研究两种范例药物。华法林将用于开发和验证框架，而较新的直接抗凝剂（如达比加群，利伐沙班），其代谢和毒性途径的特征较少，将用于更多的预测性研究。统计技术将用于定义和量化模型输出中的不确定性，并在转化为包含与药物引起的不良反应变异性相关的不同遗传和非遗传因素的优化治疗策略时定义风险。应用数学和统计学的整合构成了一个潜在的令人兴奋的新领域，将两种建模方法结合在一起，以更好地量化不确定性，并为工业药物开发和治疗提供信息。

The proposed research will demonstrate how the quantification of uncertainty within theoretical pharmacology models can be used to optimise personalised therapy. The current one-dose-fits-all viewpoint, dosing for the 'average' human and empirical derivations of dosing regimens are clearly not sufficient to effectively treat a diverse population. Personalised medicine, with the determination of treatment plans influenced by genetic factors and historical cases of toxicity, represents a step towards improving clinical outcomes. However, when an individual is deemed not suitable for treatment with the standard medicine/dose based on pharmacogenetics, an alternative must be found which is likely to either be costlier, less effective or potentially toxic. This scenario can be improved by having a greater understanding of both the mechanisms of toxicity in subpopulations and a proper quantification of risk and efficacy. Mathematical and statistical tools can help to improve the understanding of the mechanisms of toxicity, optimise treatment (e.g. alternative dosing regimens, multidrug administration, assess alternative medicine strategies) and communicate the risk of suggested therapy based on uncertainty in the model simulations. To develop and test this proposed quantitative framework during the fellowship, two exemplar drugs will be studied based on adverse reactions in anticoagulation therapy. Warfarin will be used to develop and validate the framework, while the newer direct anticoagulants (e.g. dabigatran, rivaroxaban), whose metabolism and toxicity pathways are less well characterised, will be used for more predictive research. Statistical techniques will be applied to define and quantify uncertainties within model output and define risk when translating to optimised treatment strategies incorporating different genetic and non-genetic factors associated with variability in drug-induced adverse reactions. The integration of applied mathematics and statistics comprises a potentially exciting new field bringing two modelling approaches together to better quantify uncertainty and inform industrial drug development and therapy.