ACD/Labs Launches White Paper Series on AI-Digital-Physical Convergence in Drug Discovery

Exploring AI-Digital-Physical Convergence: ACD/Labs Releases New White Paper Series

The leading informatics company, ACD/Labs, which advertises and develops software to fuel digitalized R&D, has revealed a new two-part white paper series, titled "AI-Digital-Physical Form." It’s the next generation of DMTA in drug discovery and development. The series explores how the pharmaceutical industry can leverage innovation by aligning with the modern era, advancing the design-make-test-analyze (DMTA) cycle through scientific software and AI applications that integrate with physical experiments and the expertise of scientists.

The papers demonstrate the transitional ability of an ‘AI-digital-physical DMTA cycle’ that can support organizations in reducing the data preparation duration for AI/ML and predictive modeling applications directly from 80% to zero.

Part one aims to provide outlines and discovery, exploring how AI and digital twins can leverage the utilization of and lessen the stress of manual synthesis design, while enhancing decision-making throughout the confirmatory and discovery experimentation process. By integrating synthesis, testing, analysis, and design, researchers can minimize the time period to address the appropriate clinical candidates with scientific precision.

Part two demonstrates the execution of these principal theses in pharmaceutical development, focusing on chemistry, manufacturing, and controls (CMC). The innovation that elevates the pharmaceutical development can instantly cut the cost of establishing an API into a drug product. The AI-augmented DMTA cycles allow organizations to choose quality by design (QBD) principles more precisely and effectively.

 It can accelerate the design of Bayesian optimization and experiments (DOE) for strong and attractive designs. It can also implement digital twins for processes to enhance regulatory preparedness and achieve consistent optimization, as well as further enhance drug product formulation and drug substance characterization, resulting in increased compliance and reproducibility.

Views and Statements from the Company Leaders

White paper author and vice president of innovation and informatics strategy at ACD/Labs, Andrew Anderson, said,

“The scientific method is being reaffirmed. While most of the R&D industry is performing well in its digitalization sector, many have consistently preferred to work in fragmented surroundings that depend on manual data transfer between multiple systems. This leads to inefficiency and elevates the risk of slowdown and errors in the transformation from scientific insight to clinical reality.”

Further, he added,

“We’re significantly noticing that machine-readable data acts as a work product of experiments to minimize the project timelines. The pharmaceutical R&D leaders are seeking to allow partnership with the structured data by their scientists between machines and scientists, and machine-to-machine.”