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3. Drug development

We develop in-silico approach to support drug development, especially antivirals.

Multiple effective COVID-19 vaccines were developed at an unprecedented speed during the pandemic and have resulted in more than 11 billion administered doses around the world as of May 2022. However, reaching herd immunity based on currently available vaccines is unlikely due to waning of protection and low efficacy against infection. As such, effective antiviral therapies are a high priority but currently only a handful of antiviral therapies have been approved. One factor that hinders the development and use of antiviral treatments is that their impact on clinical outcomes is complex to evaluate. For example, it is known that the late initiation of the treatment is not effective even if the efficacy is high, which has rarely been considered in the design of clinical trials for COVID-19 treatments. Therefore, the development of new therapeutic approaches requires a better understanding of the impact of the therapy on clinical outcome measures.

In this project, we plan to develop viral dynamics models that describe patient-level temporal dynamics of viral load with and without treatment. The models will be used to simulate clinical trials for antiviral therapies and provide “in silico” evidence for their best design (e.g., sample size calculation, inclusion/exclusion criteria). Using viral dynamics models for the development of antiviral therapies is a new and potentially transformative concept in the field of clinical sciences beyond infectious diseases. For example, our modelling approach would be further applicable to non-communicable diseases if we could develop mathematical models that describe dynamical disease condition (e.g., biomarkers for cancer). If successful, the envisioned approach has the potential to reduce both the individual and societal impact of various diseases.

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