Development of new computational and experimental methods for fragment based drug discovery by NMR

许多处方药会产生不良反应，缺乏疗效或依从性差。精准医学概念的核心是最大化药物疗效和最大限度地减少副作用。由于“孟德尔随机化”，观察性研究可以对遗传变异对治疗反应的影响提供明确的估计。英国初级保健数据为此类观察性药物遗传学研究提供了独特的机会。学生将利用电子健康、环境和DNA(超越)队列的本地扩展研究的数据，开发和验证研究方法，这些方法可以识别英国初级保健使用的电子医疗记录中的不良反应或缺乏疗效的特征。然后，这些将被用来进行强大的药物遗传学研究，使用大型电子医疗记录研究中的初级保健数据，包括英国生物库。这名学生将在一个国际领先的遗传流行病学小组工作，该小组有培训获奖学生的记录。该项目将成为遗传流行病学高级培训的极好工具。申请者应具有统计学、计算生物学、生物信息学或密切相关领域的背景，并具有流行病学知识和/或经验以及良好的计算机编程技能。

Many prescribed drugs generate adverse effects, lack efficacy or are poorly adhered to. Central to the concept of precision medicine is the maximisation of drug efficacy and the minimisation of side effects. Due to "Mendelian randomisation" observational studies can provide unconfounded estimates of the effects of genetic variants on treatment response. UK primary care data provides unique opportunities for such observational pharmacogenetic studies. Working with data from the local Extended Study of E-health, Environment and DNA (EXCEED) cohort, the student will develop and validate research methods which can identify signatures of adverse reactions or lack of efficacy in electronic medical records employed in UK primary care. These will then be utilised to undertake well powered pharmacogenetic studies using primary care data in large electronic medical record based studies, including UK Biobank. The student will be based in an internationally leading genetic epidemiology group with a track record of training award winning students. The project will be an excellent vehicle for advanced training in genetic epidemiology. Applicants should have a background in statistics, computational biology, bioinformatics or closely related area coupled with knowledge and/or experience of epidemiology and good computer programming skills.