How simulation accelerates drug development and reduces scale-up risk

Drug substance and drug product development leaders face tighter timelines, more complex therapies and higher costs when scale-up or late-stage processes fail. Traditional development, process design and technology transfer methods no longer match the speed and complexity required in today’s pharmaceutical landscape.

Simulation offers a strategic path forward by reducing physical trials, improving process understanding and accelerating knowledge transfer from lab to production.

What simulation enables in pharmaceutical development

Simulation in pharmaceutical development spans multiple scales, from molecular-level chemistry to full production processes.

At the molecular level, teams can model complex biological formulations and evaluate aggregation risk, viscosity behavior and stability before extensive lab work begins. This helps derisk formulation decisions earlier in the process. 

At the process level, simulation models fluid dynamics, heat and mass transfer, particle behavior and process kinetics across reactors, vessels and manufacturing equipment. Together, these capabilities connect formulation, process design, scale-up and operations in a Digital Twin, giving teams a more complete understanding of product and process behavior across the lifecycle.

Scale-up: where simulation delivers measurable impact

Scale-up remains one of the most expensive and unpredictable stages of pharmaceutical development. As scale increases, underlying physicochemical behavior can change significantly, making traditional approaches based on empirical correlations unreliable.

Simulation provides a more complete view of these interactions. Teams can reduce experiments, lower material waste and make more informed decisions earlier.

Real-world applications demonstrate the impact: 

• ABEC, a biopharmaceutical manufacturer, improved bioreactor performance across scales, identified better impeller designs and reduced blend time by 50 percent
• Takeda Pharmaceuticals optimized a hopper design, reducing drug use by 5 percent and saving up to $1 million annually 

These results show how simulation directly improves scale-up efficiency and cost control. 

Co-simulation: connecting physics for faster decisions

High-fidelity 3D simulations provide detailed insight into fluid flow and mixing behavior, but they can be computationally intensive. Co-simulation connects these detailed models with process-scale models to balance accuracy and speed. 

In this approach: 

• 3D simulations capture flow behavior, heat transfer and local process conditions 
• Process models represent kinetics, operating strategy and system-level performance 

Together, they enable faster and more informed decisions. 

For example: 

• In crystallization, co-simulation improves prediction of crystal size distribution, yield and robustness 
• In bioreactors, it helps balance oxygen transfer, shear sensitivity and productivity 

This combination strengthens scale-up decisions and improves confidence in process design. 

Where AI accelerates development

AI extends the value of simulation by accelerating insight generation and decision making.

In practice, teams use AI for: 

• Virtual screening to compare formulation and process options earlier 
• Surrogate and reduced-order models to explore design space faster 
• Simulation-trained models that improve predictive accuracy across standard and edge-case scenarios 

Simulation provides the physics-based foundation. AI builds on that foundation to help teams evaluate more scenarios, faster and with greater confidence.

Connecting development to manufacturing

Simulation has become a strategic capability because it reveals behavior that is difficult or costly to capture experimentally, including molecular interactions, fluid flow, particle dynamics and structural effects.

Its full value emerges when simulation capabilities operate together as part of a connected Digital Twin.

For development leaders, this enables: 

• Stronger continuity from research and development through clinical and commercial manufacturing
• Digital recipe transformation aligned with ISA-88 standards 
• More efficient knowledge transfer across the product lifecycle 

The result is more robust processes, more efficient scaling and better alignment between development and production.  

Read the white paper

Explore how simulation supports faster development, improved scale-up and stronger decision making across the pharmaceutical lifecycle. 

Frequently asked questions

Is simulation only relevant for large pharmaceutical companies? 
Simulation is a strategic capability across the pharmaceutical industry. Scalable computing options make it accessible to teams with different resource levels. 

Does simulation replace physical experiments? 
No. Simulation reduces reliance on physical trials and helps make them more targeted. Experimental validation remains essential.

What is the difference between CFD, DEM and FEA? 

• CFD (Computational Fluid Dynamics) models fluid flow, including heat and mass transfer 
• DEM (Discrete Element Method) models particle behavior in processes such as powder handling and blending  
• FEA (Finite Element Analysis) evaluates structural integrity under forces, pressure and thermal conditions 

These capabilities are complementary and used based on specific process needs.

How does AI fit into a simulation strategy? 
AI accelerates decision making by extending simulation insights through surrogate models, design-space exploration and simulation-trained predictions. 

Where does simulation add value for biologics development? 
Simulation supports biologics formulation by predicting aggregation, viscosity and stability under realistic conditions, helping teams identify risks earlier in development.