Dynamic Recipe Scaling — Adjusting Batch Size and Setpoints While Protecting Dose and Design Space

This topic is part of the SG Systems Global regulatory & operations glossary.

Updated November 2025 • Test-Driven Setpoint Adjustment, Concentration-Adjusted Charge, Potency-Normalised Yield, Recipe Management • Scaling, Potency, MES, QbD

Dynamic recipe scaling is the controlled adjustment of batch size, unit count or key setpoints in response to real-world constraints or test results, while preserving the intended dose, quality and validated design space. Instead of treating batch size as a fixed value, the MES can scale recipes up or down within predefined rules based on potency, % solids, inventory availability, equipment capacity or in-process assay. Done correctly, dynamic scaling allows plants to use materials more efficiently, respond to variability and minimise waste without compromising GMP, validation or label claim.

“Dynamic recipe scaling says: you may change how big the batch is, but not how much active each patient receives.”

1) What Is Dynamic Recipe Scaling?

Dynamic recipe scaling allows certain batch parameters to be calculated at run time instead of hard-coded. The recipe defines relationships such as:

• “If potency is higher than X, reduce charge volume and/or increase batch size within this range.”
• “If available inventory is limited, reduce target units and scale intermediate additions accordingly.”
• “If % solids is outside the target window, adjust diluent or concentrate additions while keeping dose constant.”

Rather than simply using static “theoretical” quantities, MES uses rules and formulas to recalculate the actual batch size, fill volume or component targets before or during execution. Crucially, dynamic scaling is pre-designed and validated, not a last-minute operator choice. It is part of the control strategy that defines how the process can adapt to variation while remaining compliant and predictable.

2) Relationship to Test-Driven Setpoint Adjustment

Dynamic recipe scaling is closely related to test-driven setpoint adjustment, but they focus on different levels:

• Test-driven setpoint adjustment changes individual setpoints (e.g. charge quantity, temperature, time) in response to lab or inline results.
• Dynamic recipe scaling changes the overall batch size or unit count and then cascades that change into many setpoints.

For example, an in-process assay might show that a concentrate is twice as strong as expected. Test-driven setpoint adjustment could simply halve the charge volume into the next step. Dynamic recipe scaling might instead keep the charge volume and double the number of units produced from that batch, maintaining dose per unit while increasing yield. The two concepts frequently work together: tests determine how large a batch should be and which setpoints must change, and dynamic scaling implements those decisions consistently across the recipe.

3) Core Design Principle: Protecting Dose and CQAs

The key principle in dynamic recipe scaling is that dose and critical quality attributes (CQAs) must remain within design limits regardless of scaling decisions. This typically means:

• dose per unit (e.g. mg per tablet, IU per mL) is fixed within narrow tolerances;
• ratio of critical excipients to active remains within defined ranges;
• CPPs (critical process parameters) stay within the QbD design space;
• volume, time and mixing limits stay within validated equipment capacities.

Scaling logic therefore focuses on adjusting non-critical aspects such as total batch size, number of units or mass of some non-critical excipients, while ensuring that actives and critical excipients maintain their required relationships and process parameters remain within validated ranges. MES should enforce these relationships via recipe constraints so that scaling cannot push the process outside its design space without generating a deviation or change-control requirement.

4) Inputs That Drive Dynamic Scaling

Dynamic recipe scaling may be triggered by several types of inputs:

• Potency and assay: changes in batch-specific potency of actives or concentrates.
• % solids and density: changes in % solids basis and density for syrups, slurries and pastes.
• Inventory constraints: limited availability of a key raw material, leading to smaller batch sizes while keeping dose per unit constant.
• Equipment capacity: vessel volume or mixer power limiting maximum batch size, prompting scaling to the largest feasible size or to a smaller size that fits new constraints.
• Market or order requirements: demand changes leading to planned half-batches or double batches within validated scaling rules.

In all cases, the dynamic scaling rules must be transparent and documented: which inputs are allowed to affect scaling, what ranges are permitted and what happens when those inputs fall outside the actionable range (e.g. force a deviation rather than scaling further).

5) Interaction with Concentration-Adjusted Charges

Dynamic recipe scaling and concentration-adjusted charges often appear together. Typical patterns include:

• Scaling driven by concentration: a stronger-than-expected concentrate leads either to smaller charges at fixed batch size or to the same charges at a larger batch size, as long as the scaled batch stays within validated limits.
• Scaling driven by volume limits: if a concentrate charge would exceed vessel volume, the system may reduce batch size and recalculate charges proportionally.
• Multi-material coordination: when active and key excipients are both impacted, dynamic scaling ensures that all affected charges change coherently, keeping formulation ratios within defined bounds.

In a potency-aware MES, the combination looks like this: concentration and potency define batch-specific potency; potency feeds concentration-adjusted charges; scaling rules decide whether to alter batch size or simply adjust charges; and the execution layer enforces those decisions and records them in the eBMR with full traceability.

6) Validated Scaling Ranges and Design Space

Dynamic scaling must operate within validated scaling ranges. In many processes, development and validation define a safe scale window – for example, 0.5× to 2.0× the nominal batch size – within which mixing, heat transfer, reaction kinetics and other factors remain acceptable. Outside that window, scale-up/scale-down effects may change CQAs enough to require new validation.

Dynamic recipe scaling should therefore be constrained by:

• minimum and maximum batch sizes per product;
• minimum and maximum fill volumes, line speeds and holding times;
• limits on how far formulation ratios may deviate;
• upper limits on dose and total active content per batch.

These constraints form part of the process’s design space. MES should enforce them as hard boundaries: if a proposed scaling decision would push parameters outside these ranges, the system should block automatic scaling and require formal deviation or change control rather than silently proceeding with unvalidated operation.

7) Role in Potency-Normalised Yield and Resource Utilisation

Dynamic recipe scaling can materially improve potency-normalised yield and resource utilisation by:

• allowing more units to be produced from high-potency lots while maintaining dose, improving active utilisation;
• avoiding under-filled vessels or “half-batches” where that would waste capacity, by scaling up to the maximum validated batch size;
• matching batch size to available inventory, reducing partially filled batches or costly pre-production material transfers.

When combined with active-equivalent consumption metrics, dynamic scaling makes it possible to measure “active consumed per released unit” under different scaling decisions and to choose strategies that minimise active waste without adding process complexity. Over time, data from dynamically scaled batches can inform refinements to design space and economic models, especially for high-cost actives and concentrates.

8) Integration with Recipe Management and Version Control

Dynamic recipe scaling depends on clear recipe structures in recipe management. Typical requirements include:

• distinguishing between scale-dependent and scale-independent parameters (e.g. times vs quantities);
• defining calculation rules for each component (fixed quantity, per-batch, per-unit, scaled with batch size, scaled within limits);
• capturing scaling logic as part of the master recipe, not local work instructions;
• version controlling any changes to scaling logic under change control.

For example, some cleaning or purge steps may be scale-independent (fixed volumes or times), while others scale with batch size. Some excipients may scale fully, others partially or not at all. These decisions must be intentional, documented and validated. MES then uses these rules at runtime to recalculate setpoints when batch size is changed, ensuring that scaling behaviour remains consistent and reproducible from batch to batch and across sites.

9) Data Integrity, eBMR and Regulatory Expectations

From a regulatory and data-integrity perspective, dynamic recipe scaling must be as transparent and auditable as any other critical calculation. Inspectors may ask:

• “What was the planned batch size? What size did you actually run?”
• “Why was this batch scaled down/up? Which parameters changed and based on what data?”
• “How do you know this scaled batch is still within your validated design space?”

The eBMR should therefore show:

• original recipe scale and scaling limits;
• final scale and rationale (inventory, potency, test result, planned partial batch);
• key recalculated setpoints and which ones changed due to scaling;
• relevant test results, such as in-process assays and % solids, linked via analytical lot links;
• audit trail entries for any manual scaling overrides or parameter changes.

Dynamic scaling logic should be validated under CSV, with documented testing of boundary conditions (minimum and maximum scales, edge-case potency and solids values) to show that the system behaves safely and predictably across the permitted range.

10) Use Cases Across Industries

Dynamic recipe scaling is applicable wherever batch size, potency and equipment constraints interact:

• Pharmaceuticals and biologics: scaling bioreactor harvests into formulation batches based on titer; resizing tablet batches to match available API while maintaining mg per unit; adapting fill volumes to assay results for injection solutions.
• Dietary supplements: scaling vitamin or botanical blends to available premix potency and stock; running half-batches or double-batches within validated limits to match seasonal demand.
• Food and beverage: adjusting batch size of enzyme-treated products based on enzyme activity and tank capacity; scaling fortification levels to match concentrate potency.
• Cosmetics and personal care: scaling emulsions or gels based on active concentrate potency and available active volume, while keeping percentage actives per unit constant.
• Chemicals and speciality materials: scaling catalyst or additive charges and batch sizes based on activity tests, available inventory and reactor limits.

In all cases, the goal is the same: use available active and capacity intelligently while ensuring that every unit meets its specification and the process stays within its validated operating envelope.

11) Common Pitfalls and Anti-Patterns

When dynamic scaling is handled informally, problems arise quickly:

• Spreadsheet scaling: operators or planners calculate new setpoints in ad-hoc spreadsheets and then hand-key them into MES or DCS, bypassing validation and audit trails.
• Hidden rules: scaling rules live in local work instructions or tribal knowledge, not in master recipes, making behaviour inconsistent across shifts and sites.
• Unbounded scaling: batch sizes are adjusted outside validated ranges, with no system-level checks on process capability or CPPs.
• Broken ratios: some ingredients are scaled and others are not, unintentionally moving the formulation outside its intended design space.
• Poor documentation: eBMRs show only what was done, not why; reviewers cannot see which tests or constraints drove scaling decisions.

Formal dynamic recipe scaling solves these issues by codifying scaling logic in recipes, enforcing limits in MES and ensuring that all resulting parameter changes are traceable and reviewable. It turns scaling from an improvisation into a controlled, validated feature of the process design.

12) Practical Implementation Steps

To adopt dynamic recipe scaling in a robust way, organisations typically:

• map which products and processes would benefit most (e.g. constrained APIs, variable concentrates, equipment-limited steps);
• define minimum and maximum batch sizes and scaling ranges as part of the control strategy and validation plan;
• identify which parameters should scale with batch size and which should remain fixed;
• configure scaling formulas and constraints in master recipes, integrating with potency, solids and test-driven logic where relevant;
• validate scaling logic under CSV, including tests at scaling boundaries and with representative potency/solids values;
• update SOPs, training and PQR/CPV reviews to include dynamic scaling behaviour and its impact on yield, cost and CQAs.

Starting with one or two pilot products allows teams to refine scaling rules, gather data and demonstrate benefits before extending the pattern across a larger portfolio. Over time, dynamic recipe scaling can become a standard tool for managing potency variability, resource constraints and demand shifts in a way that is both economically rational and fully compliant.

FAQ

Q1. How is dynamic recipe scaling different from simply running a half-batch?
A half-batch is a fixed alternative recipe size. Dynamic recipe scaling calculates batch size and setpoints at runtime based on potency, inventory or other inputs, within validated limits, and documents the rationale and calculations in the eBMR.

Q2. Does dynamic recipe scaling change the dose per unit?
No, not if implemented correctly. The core principle is that dose per unit and CQAs stay within specification. Scaling changes total batch size and related setpoints, not the target strength of each unit.

Q3. Can operators decide scaling values manually?
Operators may be allowed to choose from predefined scale options (e.g. 0.5×, 1.0×, 1.5×) within limits, but calculation rules and constraints should be embedded and enforced by MES. Manual recalculation of setpoints outside the system is a data-integrity risk.

Q4. Does dynamic recipe scaling always require in-process lab tests?
Not always. Scaling can be driven by planned order size or inventory alone. However, the strongest use cases involve tests (assay, % solids, titer) that inform how much active is available, enabling both test-driven setpoint adjustment and dynamic scaling.

Q5. What is a practical first step to implement dynamic recipe scaling?
Start with a product where batch size is frequently adjusted manually today. Document the rules and constraints that operators currently apply, formalise them in the master recipe and MES, validate the behaviour, and compare performance (yield, deviations, review effort) before and after introducing controlled scaling.

Related Reading
• Potency & Batching: Batch-Specific Potency | Potency Basis | Potency Adjustment Factor | Concentration-Adjusted Charge
• Test-Driven Control: Test-Driven Setpoint Adjustment | In-Process Assay Gate | Recipe Management
• Yield, Economics & Records: Potency-Normalised Yield | Active-Equivalent Consumption | Mass Balance | Electronic Batch Record (eBMR)