How AI and digital twins are revolutionizing pharma

Summary — scientific concepts, discoveries, methods, and applications

Key scientific concepts and technologies

• Artificial intelligence (AI) and machine learning (ML): mining literature and datasets, predicting molecular behavior, accelerating simulation, and automating engineering tasks.
• Digital twins: virtual, connected replicas of molecules, processes, equipment, plants, and potentially patients that mirror real-time data and enable simulation, hypothesis testing, and closed‑loop optimization.
• In silico modeling/simulation: computational prediction of molecular properties, formulation behavior, device performance, equipment flow (CFD), multiphysics, and whole-process flowsheets.
• Computational fluid dynamics (CFD) and multiphysics: applied for reactor design, scale-up, and airflow/contamination control in filling lines.
• Model‑based engineering and virtual commissioning: calibrated virtual process models and virtual controllers used to train staff and commission equipment before physical installation.
• Generative AI / industrial copilots: AI assistants that generate code, advise operators, automate issue resolution, and reduce engineering workload.
• Design space exploration and AI simulation predictors: software that runs many simulated experiments and uses historical simulation data to cut iterations and speed design.

Applications across the drug lifecycle

• Drug discovery: AI mines publications, patents, and datasets to find or repurpose targets (example: an existing drug identified for COVID‑19 use within 48 hours).
• Preclinical / pharmacokinetics and formulation: model-based environments combine models and data to design stable, bioactive formulations and simulate absorption and side effects.
• Device and delivery design: 3D scans (e.g., mouth and lungs) and digital twins used to optimize inhaler design and dosage for faster clinical progression.
• Process development and manufacturing design: virtual flowsheets and simulations create recipes and optimize cost, materials, and energy; thousands of simulated experiments replace many physical tests.
• Equipment design and scale-up: CFD and multiphysics used for reactor design and biologics scale-up (example: 50% reduction in scale-up time and $1M materials savings).
• Plant layout and operations: modelling full filling lines or continuous direct compression and deploying operational digital twins to collect live data and enable AI-driven continuous optimization.
• Regulatory and sustainability use: in silico approaches support reduced animal testing, seek FDA exemptions, and align with sustainability goals by reducing physical experiments and material use.

Methodologies / workflows

1. Collect high-quality real-time data from physical asset(s).
2. Build a digital twin that mirrors behavior and captures live telemetry.
3. Train AI/ML models on live, high‑quality digital twin data (often superior to static historical datasets).
4. Use AI to generate predictions, hypotheses, or optimized designs.
5. Test AI outputs inside the digital twin (in silico validation).
6. Deploy validated AI outputs to production/operations.
7. Continue the loop: operational data feeds back to retrain/update models for continuous optimization.

Reported benefits, metrics and case figures

• Industry research trends: reports (e.g., Stanford) show industry outpacing academia in AI research; AI can outperform a PhD student in some tasks.
• R&D / preclinical phase: claims of up to ~50% time and cost savings using rapid data analysis, digital twins, and AI.
• Simulation efficiency: AI simulation predictors can reduce iterations by ~40%.
• Bioreactor scale-up: one customer reported a 50% reduction in scale-up time and $1M saved in test materials.
• Equipment engineering: an example showing ~30% reduction in engineering effort on a vial filling machine.
• Regulatory uptake: FDA reported >100 drug/biological submissions in 2022 that used AI/ML components.

Regulatory, ethical and practical challenges

• Data silos and poor data quality hinder AI learning and value extraction; integration and governance are needed.
• Patient data privacy and broader ethical considerations must be addressed.
• FDA engagement: the agency recognizes AI’s potential, has an action plan for AI/data in the medical product lifecycle, and the FDA Modernization Act (2022) enables certain non‑animal testing alternatives.
• Organizational need: holistic, end‑to‑end digital transformation strategies are required across organizations to realize benefits.

Future directions

• Patient digital twins for personalized medicine and end‑to‑end connected twins spanning discovery, manufacturing, and care.
• Use of generative AI to create digital twins and accelerate engineering and clinical workflows.
• Continued movement toward responsible, sustainable pharma innovation combining AI and digital twins.

Sources, organizations and examples mentioned

• Siemens (presenter’s affiliation; referenced Siemens products and solutions such as Industrial Copilot)
• Stanford University (annual report on AI research trends)
• U.S. Food and Drug Administration (FDA) and FDA Modernization Act of 2022
• GSK (first digital twin of a vaccine manufacturing process cited)
• Manufacturing Innovation Centre (Scotland) — example site for a continuous direct compression filling line
• An unnamed pharmaceutical company (COVID‑19 drug repurposing example)
• Industrial Copilot (Siemens’ industrial AI assistant/product)