Digital Transformation in Drug Discovery and Development
Traditional techniques for clinical development process are coming to an end as technology and global digital transformation are driving clinical trials. The process of drug discovery and development is due for a major change, which would improve its key issues of lengthy processing time, the criticality of datapoints, and the high risk of failure.
Digital technologies can transform the approach taken by clinical research organizations and bio-pharmaceutical companies for drug discovery and development by incorporating multiple data sources, increasing clinical trial productivity, and improving the quality of clinical trial outcomes. At the same time, pharma companies will need to deal with several challenges to realize the potential of digital transformation in drug discovery and development, such as critical regulatory considerations, weak data infrastructure and poor governance.
New technologies, such as machine learning-based AI, are starting to be used more extensively for drug discovery. Various machine learning techniques, such as support vector machines, naive Bayesian and deep neural networks are proving their utility in the drug discovery process by helping with time optimization and making the overall process more efficient.
CRISPR technology is another powerful tool that’s being used to identify target sites for new drugs. CRISPR is an innovative method that enables genome editing and repair by identifying the fundamental genes and proteins that cause a disease.
The use of animals in medical testing is unpleasant but necessary, and efforts are underway to reduce the total number of animals used in pre-clinical studies. Leading pharmaceutical companies are now encouraging the development of alternatives to animal testing through digital transformation. The active implementation of the “3Rs” protocol, i.e., replacement, reduction and refinement of animal testing, in pre-clinical research is being led by new technologies such as computational model-based simulations, in-vitro molecular biology techniques, and organ-on-a-chip technology. Innovative drug manufacturing technologies such as 3D-printing can help control toxicity in new drugs in a way that helps patients better and benefits the entire drug development process.
Clinical trials can take over a decade to complete, cost millions of dollars, and come with a high risk of study failure at each stage of the trial. Advanced technologies such as big data, robotics and electronic data capture allow for the efficient recruitment and retention of trial participants throughout every phase and enable better overall clinical trial management.
Many leading pharmaceutical companies are investing in AI platforms for their drug discovery and development processes. In 2019, Iktos announced a research collaboration with Janssen Pharmaceutica that’s aimed toward developing new applications in drug discovery utilizing Iktos’ expertise in deep generative models applied to chemistry and Janssen's know-how of AI-enabled small molecule activity prediction. Meanwhile, in 2018 Alibaba and AstraZeneca signed a deal that aims to deliver smart health care services in China using AI and Internet of Things (IoT) technology. The partnership hopes to provide over a million patients with access to health care information by scanning the “AliHealth traceability code” on drug packages.
Clinical research organizations and bio-pharmaceutical companies are investing significant amounts of capital in building advanced, technology-driven drug discovery and development platforms. Advanced analytics and machine learning can optimize chemical or biological drug design, thereby increasing the chances for success. Digital technologies can be leveraged to automate clinical trial-grade material production and reduce the number of ingredients and process steps required in the drug development process. Combining the information-driven approach of technologies such as AI with innovations in clinical research and development can lead to a transformation in clinical study outcomes.