Decision making for stratified medicine life cycle
Stratified medicine is at the cutting edge of a new era in healthcare, which targets to a specific patient population on the basis of a companion diagnostic (such as a biomarker) to predict treatment efficacy or adverse side effects. The economic concern is the greatest hurdle for the broader life sciences community, in particular for the pharmaceutical industry, to embrace stratified medicine. Stratification strategies (cut-off score of the companion diagnostic) and drug-diagnostic development strategies can have great impact on the profitability for product developers and manufacturers and also the medical treatment price for payers.With the aim of addressing the economic knowledge gap in stratified medicine life cycle (from clinical R&D, product manufacturing to commercialisation), this work will develop a decision tool coupled with integrated computational models to estimate expenditures and revenues during stratified medicine life cycle. This decision tool will combine with machine learning methods to identify the key economic factors and to select the optimal strategies from economic perspective. This work will help developers and manufacturers to avoid capital risk and to maximise profit.Essential requirement: good at mathematics, coding experience (in Python or Matlab)
How to apply
If you are interested in applying for the above PhD topic please follow the steps below:
- Contact the supervisor by email or phone to discuss your interest and find out if you would be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
- Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
- Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.
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This is a self funded topic
Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.
Meet the Supervisor(s)
Yang Yang - Dr. Yang is a Lecturer in Chemical Engineering Department. She is currently leading the Digital Manufacturing Group, which aims to integrate the advanced computational technologies, such as big data, machine/deep learning, simulation and visualisation, to facilitate manufacturers achieve tangible improvements in key metrics. Her multidisciplinary research spans across diverse industrial sectors, addressing the challenges and driving the industry towards a new revolution.
Dr. Yang has a multidisciplinary background. She obtained her BSc and MSc degree in Computer Science from Tianjin University, China and received her PhD sponsored by Overseas Research Scholarships (ORS) and Tetley & Lupton Scholarships (TLS) from University of Leeds. During her PhD, she successfully applied data mining and machine learning techniques to identify the optimal composition of nano-photocatalyst (TiO2). The decisional tool designed and developed by Dr. Yang, which combined process analytical technology (PAT), image analysis and machine learning techniques, was sponsored and adopted by GlaxoSmithKline Pharmaceuticals (GSK) for its nanoparticle product line. Due to her outstaning performance, Dr.Yang was awarded Chinese Government Award for Outstanding Self-financed Students Abroad in 2010.
Prior to joining Brunel, Dr. Yang worked at Imperial College London and University College London as a postdoctoral researcher. During this period, Dr. Yang accumulated great knowledge and experience in biopharmaceutical manufacturing process and personalised medicine development. Collaborated with UCB and Eli Lily, the leaders of biopharmaceutical industries in UK, Dr. Yang established process models and ecnomic models of biomanufacturing process using discrete-event modelling and Monte Carlo simulation methods. A decision-support tool which combined the process models, ecnomic models and machine learning models for facility fit analysis had been greatly complimented by biopharmaceutical industry users. Supported by Pall Corporation, Merck and Medimmune, Dr. Yang’s research of digital twins for continuous biomanufacturing process awarded funding by Future targeted healthcare manufacturing hub at UCL. She is currently holding Brunel Research Initiative & Enterprise Funding for digital twin system of hydrogen production.
Multi-omics data analysis for personalised medicine development is another research intrest of Dr. Yang. She led a collaboration with Shanghai Pulmonary Hospital (China) to construct a decision-support tool with big data analysis for personalized diagnosis and treatment of lung cancer. She is currently collaborating with Life Science Department for cancer and drug dependency analysis.