Luka T GebelThe integration of artificial intelligence (AI) and machine learning (ML) with digital twins is leading to a revival in discussions about what innovation means and how it should be defined to capture value outcomes for patients. In the context of pharmaceutical medicine, innovation has traditionally been thought of as the development of new and improved drugs and treatments. However, this narrow definition has been challenged in recent years as technology has provided new opportunities for improving patient outcomes.
Institutions are struggling to define innovation in this context because it does not always mean higher value outcomes for patients. For example, in the case of rare diseases, traditional approaches to drug development may not be effective, and there is a need to explore new avenues for innovation. This highlights the importance of defining innovation in a way that captures the full range of opportunities for improving patient outcomes, including those that may be unconventional or outside of traditional drug development pathways.
A clear example of the importance of defining innovation well in the context of rare diseases can be seen in the development of treatments for Lysosomal Storage Disorders (LSDs). LSDs are a group of rare genetic disorders that result in the buildup of toxic substances in the body due to a deficiency in specific enzymes.
Traditionally, treatment for LSDs involved the replacement of the deficient enzyme through regular infusions. This treatment provided a significant improvement in patient health outcomes and was considered a major innovation in the field of rare diseases. However, in recent years, there have been several new innovations in LSD treatment, including the development of oral enzyme replacement therapies and the use of gene therapy. These new treatments have the potential to greatly improve patient outcomes, but they also raise important questions about their cost and accessibility.
This example highlights the importance of defining innovation well and using adjectives to group various innovations. In this case, the replacement of the deficient enzyme was a patient-centered innovation that directly improved patient health outcomes, while the development of oral enzyme replacement therapies and gene therapy may be considered process innovations or technological innovations. These new treatments may improve the efficiency and cost of treatment, but it is important to evaluate their impact on patient health outcomes and determine whether they truly represent an improvement in the overall treatment of LSDs.
To better capture the full range of innovations in pharmaceutical medicine, it is necessary to categorize different types of innovations. For example, technological innovation refers to the development of new technologies or tools to advance the field, such as AI and ML. Process innovation refers to improvements in the way processes are carried out, with the aim of increasing efficiency, reducing costs, and improving quality. In the context of pharmaceutical medicine, process innovation can refer to innovations in the way drugs are manufactured, developed, and distributed. Patient-centric innovation refers to innovations that focus on improving the experience of patients, such as through personalized medicine or the development of new delivery systems. This can help distinguish between innovations that have a direct impact on patients and those that improve internal processes or efficiency.
Similarly to gene therapy and oral enzyme replacement, AI and ML are transforming the way rare diseases are treated. The traditional definition of innovation in pharmaceutical medicine has been expanded due to advancements in technology that provide new opportunities to enhance patient outcomes. The challenge of defining innovation in this context arises from the fact that not all innovations lead to higher value outcomes for patients, especially in the case of rare diseases where conventional drug development may not always be effective. To better understand the impact of innovation, it is crucial to categorize different types of innovation, including technological, process, and patient-centric. AI and ML can play a critical role by analyzing large data sets to inform individualized treatment plans and improve the design of clinical trials. To fully capture the potential of AI and ML, it is essential to define innovation in a way that embraces the full range of opportunities to improve patient outcomes.