The U.S. drug development process for novel therapeutics targeting difficult-to-treat diseases takes an average of 10 to 12 years to complete. This timeline leaves most patients battling severe illnesses without a lifeline. The regulatory process heavily scrutinizes the design to test link for predictable success in manufacturing, clinical trials. and mass production. The promise of new drug development can be thwarted by disruptive test results, contamination, as well as inadequate testing, and sparse documentation, that does not hold up under the necessarily stringent glare of the FDA.
Today, simulation and modeling is used for risk management in discrete instances of drug design and testing. For example, AI-powered virtual screening tools can analyze the three-dimensional structure of target proteins and predict how drug molecules would potentially interact with them. While this use of a "discrete digital twin" can certainly accelerate the process of drug design, it does not necessarily inspire confidence in compliance for future stages of drug development, testing, manufacturing planning, and full -blown production.
Inserting AI into classic techniques like traditional statistical modeling to which regulators are accustomed but have limitations, adds value to and can meaningfully impact the translation of drugs to clinical success. The benefit of a intelligent simulation is compounded when hybrid AI fuels a process digital twin leveraged for new product development. The traditional AI model is based on classic discrete simulation with a formulaic pattern of inputs that generate predictable outputs. Today’s process digital twin model can significantly reduce risk and improve drug development and testing timelines with an AI accelerator, that derives from discrete design simulation and generates, intricate neural networks, leveraging the context data of digital threads, and the reliability and flexibility of composable platforms.
From Design to Production, the drug development digital twin can embed simulation in the manufacturing process and use data from smart IOT devices and sensors perched on the brink of intelligent Edge networks. Drug Manufacturing Optimization is provided by an AI-based control layer for simulation optimization and offline analysis for predictive capability that can be used for design enhancements and for abbreviating the timeline for the design control and validation process. The scope of an intelligent digital twin extends into the post production timeline since AI can also determine how drug combinations will impact individual patients or groups of patients before a drug candidate is ever used in a clinical trial. If a drug’s efficacy and potential for adverse events can be tested based on AI’s broad understanding of human biology and chemistry before launching human clinical trials, there is a possibility that more potentially helpful drug candidates can be saved from unnecessary failure. Using the bill of materials data captured during the lifecycle from design to production, the design control digital thread in an intelligent digital twin, can serve as an AI model for the availability of critical materials and APIs.
Thus, this predictive function of the AI enriched pharmaceutical process twin removes one of the biggest barriers to successful, cost-effective, and timely drug development: risk.
