Collaboration on rebuilding a clinical trial model to achieve drug development that meets patients' needs
Along with the transition from "one-size-fits-all" medical treatment to precision or personalized medicine, there is an attempt to optimize medical treatment for individual patient. In addition, patient participation in medical treatment is also proceeded with, in which patients are actively involved in decision making on their own treatment plans. The similar is happening in the drug development stage. In addition to drug safety and efficacy evaluated by doctors and investigators, drug effectiveness from patient perspective is also considered important, including patient's needs and priorities, the expected improvement on quality of life and burdens such as side effects by dosing, and suitability of individual patients. Consequently, patients are getting more involved in drug development than ever before.
However, traditional clinical trial models are designed with a focus on the process of collecting only necessary information from medical institutions. Methods to collect objective, continuous data simultaneously with minimizing burdens on patients as trial participants have not been established. Also, as information collected from patients is enormous, a large amount of time and labor is necessary for pharmaceutical companies to analyze the information.
NTT Data would like to rebuild patient-oriented/involved clinical trial models as a solution for the current situation by leveraging rapidly progressing wearable devices and AI technology, and establishing the optimal methods for data collection and analysis to incorporate the patient perspective.
- Related keywords
- Wearable device
- Big data
- Personalized medicine
- Social challenges to be addressed through collaboration
- When predicting drug efficacy becomes more precise by using objective, continuous data, it will contribute to improvement the success rate and efficiency of new drug development. This will in turn reduce costs, shorten development periods, and eliminate dosing through trial-and-error. As a result, a variety of drugs, as well as unique drugs, will be supplied to the market quickly, and patients will be able to select treatment that suits their own needs, lifestyles, and priorities, under the patient-centered medical treatment. Therefore, we can expect an improvement of patients' quality of life and optimization of health care costs.
- Market size of collaboration business or business scale
- As of 2017, total revenue of the market was 110 billion yen globally and 35 billion yen in North America. The market is expected to grow at exponential CAGR of about 15% through 2025 and reach at least 330 billion yen globally and 20 billion yen in Japan.
- Assets and opportunities to be offerred
- ・Platforms to collect, structure, standardize and manage medical data
- ・AI technology platform for giving data meaning and analyzing the data
- ・A large amount of data necessary for AI algorithm learning
- ・Smartphone applications for collecting and transferring patient reported data by connecting with wearable devices
- ・Relationships with pharmaceutical companies, partnerships with clinical research organizations, and proposal opportunities