Introduction

In novel pharmaceutical development, much attention is often given to breakthrough technologies like continuous manufacturing or digital twins. Yet, beneath these innovations lies a fundamental concept that determines whether a process can consistently deliver high-quality medicine: the control strategy.

The U.S. Pharmacopeia (USP) has highlighted this in recent publications, including their fourth technical guide, Control strategy for continuous manufacturing of drug substances, reinforcing that a robust control strategy should not bean afterthought - it is the framework that ensures product quality across the lifecycle. 

What Is a Control Strategy?

According to USP, a control strategy is a planned set of controls derived from product and process understanding that assures process performance and product quality. These controls span:

• Material attributes: specifications for APIs and excipients
• Process parameters: conditions under which unit operations run
• In-process monitoring: measurements during manufacturing
• Design space: the multidimensional region where process parameters yield acceptable quality

In practice, this means a control strategy integrates knowledge from early development through commercialization to establish a consistent approach to managing risk.

Why Control Strategy Matters Now

The pharmaceutical industry is under mounting pressure to improve agility, reduce costs and meet evolving regulatory expectations. The FDA’s recent release of >200 Complete Response Letters (CRLs) from 2020-2024 highlights the real-world struggle to balance these demands, given that 74% of the CRLs were related to quality and manufacturing, including in chemical, manufacturing and controls (CMC).The control strategy plays a pivotal role in the prevention of these missteps with the following core tenets:

• Quality by Design (QbD): A control strategy operationalizes QbD principles, ensuring product quality is built in rather than tested at the end.
• Regulatory alignment: Agencies such as the FDA and EMA increasingly expect to see clear justification of control strategies during submissions, particularly for advanced technologies like continuous manufacturing.
• Lifecycle management: With global supply chains and rapid shifts in demand, manufacturers need strategies that are flexible enough to adapt without compromising compliance.

The Role of Modeling & Simulation

A strong control strategy is only as good as the scientific understanding behind it. This is where first-principles models and data-driven simulations make a difference:

• Predicting unit operations: Mechanistic models grounded in physics and chemistry simulate how equipment, materials, and processes behave under varying conditions.
• Designing the design space: Simulations explore multidimensional operating ranges, helping define where a process runs reliably.
• Exploring the design space: The correlations between equipment performance, materials behavior, and outcome (yield, quality) are often very nonlinear. Exploring all possible combinations of parameters (full factorial DoE) with experiments is very costly (especially at manufacturing scale) and the lack of resources (such as material) makes it virtually impossible. In contrast, running simulations provides a comprehensive view of the entire design space in a relatively short time and with limited cost.
• Evaluating risks: Virtual experiments reveal how deviations or disturbances might impact critical quality attributes, guiding proactive mitigation plans.

Importantly, regulators increasingly recognize the value of mechanistic models, particularly when they are used to develop and justify design space definitions, risk analysis, sensitivity analysis, and defining control measures. This provides manufacturers with a scientifically rigorous pathway to regulatory confidence.

From Guidance to Implementation

The FDA issued the draft guidance, Considerations for Complying with 21 CFR 211.110which focuses on the adoption of advanced manufacturing technology, without compromising quality. Improvements in product and process development help pave the way for more effective control strategies, making them less a collection of documents and more of a living framework, evolving as new knowledge is gained. To implement this effectively, organizations must:

1. Integrate early: Build control considerations into development rather than bolting them on late in the process.
2. Build process and product understanding: With QbD as the foundation, use mechanistic models to explain the correlation between parameters and to identify condition sensitivities.
3. Leverage digital tools: Use modeling & simulation to reduce experimentation and accelerate knowledge building.
4. Align with regulators: Engage agencies early and often, especially when using innovative approaches to support control strategy claims.

Looking Ahead

As manufacturing technologies evolve, control strategies will become even more central. Whether in batch or continuous systems, the ability to design, justify and adapt controls will determine how successfully companies meet quality, efficiency and compliance goals.

At Procegence, we specialize in bridging the gap between cutting-edge modeling techniques and regulatory expectations. Our experts apply QbD principles using first-principles and data-driven models to help define design spaces, evaluate risks and build control strategies that regulators trust.

Are you confident your control strategy can withstand regulatory scrutiny while supporting innovation? Contact us today to see how our team can help you navigate requirements and build scientifically sound, future-ready strategies.