[3H5-OS-10c] OS-10

[3H5-OS-10c-02] Taking on the Challenge: Precision Medicine Powered by Deep Learning

〇Shinichi Tsuchiwata¹ (1. Pfizer R&D Japan)

Keywords:Precision Medicine, GWAS, Deep Learning

With the popularization of the model-informed drug development (MIDD) approach, most newly approved drugs in recent years have included mathematical models that describe the relationship between blood drug concentration/exposure and efficacy/safety in their regulatory submission documents. These documents include not only summaries of the data used to build the models but also information on the covariates influencing the outcomes. Additionally, because these models are defined as stochastic, the likelihood of efficacy or safety is indicated as an estimated probability, such as "the expected response rate following administration of x mg of the drug is y%." Most of these probabilities fall within the range of 30% to 70%. However, there is potential for more efficient treatment choices if these probabilities could be significantly increased (e.g. to over 80%).Recently, “precision medicine” has attracted significant attention for its potential to enhance the accuracy of therapeutic outcome predictions by integrating AI technology with a diverse array of data, including genetic information and life logs. In this presentation, we will show a case study that attempts to realize precision medicine by combining genetic data obtained from genome-wide association studies (GWAS) with deep learning models.