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)

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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 明星八卦, 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 明星八卦 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.