Research Projects
Long-Term Safety, Effectiveness and Cost-Effectiveness of Biologics: application of Machine Learning, Subphenotyping and Predictive Analytics

Autoimmune diseases are disorders caused by dysregulation of various aspects of normal immunity and inflammation. The introduction of biologics has significantly improved the control and prognosis of autoimmune diseases. There is a tendency that biologics and biosimilars will become the first-line treatment of autoimmune diseases, given the booming evidence from trial and real-world settings. All of these agents target cytokines or cells, which are also key components of normal immune homeostasis. Therefore, blocking particular cytokines or cells might result in adverse events, particularly increased the risk of severe and opportunistic infection, and this aroused the health careers and pharmacy advisors’ attention. It’s essential to regulate the efficacy and safety of biological treatments.

Open-label extension and post-marketing observation in a real-world setting are vital to evaluate effectiveness, safety and uptake of biologics in real practice settings. Some observational studies showed somewhat different patterns than that of RCTs suggesting retention of initial biologic decreased during long-term treatment among patients. Patients who failed in the first biologic may benefit from the second line but inferior in the retention rate. The retention rate of biologics is influenced by the tolerance of patients to the treatment, effectiveness, adverse effects or disease remission due to the usage of biologics. Few if any studies have ever investigated the overall pattern of biological treatments or treatment trajectories, including subsequent therapy changes and dose reductions, on an individual patient level.

The project aims to draw an overall pattern to better understanding the therapy utilisation and changing situations of biological treatments on an individual patient level from real-world big-data. The project will use the territory-wide electronic medical records (EMR) to evaluate the biological treatment utilisation in Hong Kong and provide vital evidence for further pharmacovigilance and clinical guidance among autoimmune diseases. Beyond the local data, the research program will also include the application of global big-data (UK, Taiwan, Korea and Mainland China) and machine learning, subphenotypes and predictive-analytics to develop explainable machine learning models, cluster patient profiles,  predict unfavorable and landscape the effectiveness, safety, cost and cost-effectiveness of biologics under the dynamic evolving market.

Professor SX Li, Department of Medicine

With over 10 years of experience in real-world and outcome research (HEOR) and decision analytics, I am currently working as an Assistant Professor at HKUMed. My research passion lies in bridging real-world evidence, disease simulation modelling and decision analytics for transparent and evidence-based health policymaking for cutting-edge innovative medicine (e.g. biologics, precision medicine, vaccine) and healthcare interventions (e.g. AI tools for screening and clinical decision assistance). 

My research often involves dynamic interactions with local and international academic collaborators, local government, industry partners, NGOs and other key opinion leaders. As the principal investigator of several HEOR projects funded by the Hong Kong government, I have led research projects covering the therapeutic areas of mental health, cardiovascular diseases, autoimmune diseases, oncology, rare genetic disorders and vaccinology, and published in world-leading medical journals such as Annals of Internal Medicine, JAMA Pediatrics, Annals of the Rheumatic Disease, eClinicalMedicine and Lancet Regional Health Western Pacific. I have co-authored ~120 peer-reviewed articles (total citation ~2800; H-index 28 as of Feb 2024) and have been selected as the Top 2% most cited scientists by Stanford University. As a Project Coordinator, I also lead the first Horizon Scanning project funded by the RGC Research Impact Fund to launch the HEOR training and root the Health Technology Assessment ecosystem for early innovative adoption in Hong Kong and the Greater Bay Area.

My current team (Xue’s lab) includes 12 full-time Postdoc/PhD/MPhil students and research fellows. With a team spirit of Proactive – Empathy – Resilience – Teamwork, and Self-motivation (X-PERTS), we embrace and enjoy the open research environment, international collaboration, and breakthrough methodologies in health science. Please email sxueli@hku.hk for full-time or part-time Postdoc/PhD/MPhil/RA opportunities. 

Biography
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ORCID
sxueli@hku.hk

For more information or to express interest for this project, please email the supervisor or the specified contact point in the project description.  Interested candidates are advised to enclose with your email:

1. your CV,
2. a brief description of your research interest and experience, and
3. two reference letters (not required for HKUMed UG students seeking MRes[Med]/URIS projects).

Information on the research programme, funding support and admission documentations could be referenced online at the Research Postgraduate Admissions website. General admission enquiries should be directed to rpgmed@hku.hk.

HKUMed MBBS students interested in the Master of Research in Medicine (MRes[Med]) programme may visit the programme website for more information.  

HKUMed UG students interested in the Undergraduate Research Internship Scheme (URIS) may visit the scheme’s website for more information.