Test-Driven Setpoint Adjustment — Letting Lab Data Move the Target Inside a Validated Box

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

Updated November 2025 • In-Process Assay Gate, Dynamic Recipe Scaling, Concentration-Adjusted Charge, Recipe Management • IPC, Setpoints, MES, QbD

Test-driven setpoint adjustment is the controlled practice of recalculating process setpoints (targets for weights, volumes, times, temperatures, speeds and similar parameters) in response to laboratory or inline test results. Instead of treating setpoints as static, the MES evaluates current data – assay, potency, % solids, titer, pH, viscosity, density, temperature – and adjusts targets within predefined limits so the process stays “on recipe” in functional terms even when materials or conditions vary. In regulated manufacturing, test-driven setpoint adjustment is one of the core mechanisms for implementing a modern, data-driven control strategy without sacrificing GMP discipline or validation.

“The test result doesn’t just go in a logbook. It moves the target, inside a box that QA and validation have already agreed.”

1) Core Concept and Definition

Test-driven setpoint adjustment means that one or more process targets are not fixed numbers but are calculated from current test data. Examples include:

• adjusting an addition quantity based on assay or % solids;
• modifying a mixing time based on viscosity or particle-size data;
• changing a hold time or temperature based on microbiological or bioburden tests;
• adjusting a dilution volume to reach a defined concentration.

The “test-driven” part is key: setpoints move because of specific test results, not arbitrary choice. The logic connecting test to target is defined in recipe design and validated under CSV. The MES or control system then uses those rules at runtime to compute setpoints, with appropriate audit trails and role-based access control.

In a V5-style environment, test-driven setpoint adjustment is often implemented as a combination of recipe formulas, rule engines and in-process gates that ensure setpoints are only recalculated when valid data is available and the result remains within the defined design space.

2) Relationship to In-Process Assay Gates and Dynamic Recipe Scaling

Test-driven setpoint adjustment is tightly coupled with:

• In-Process Assay Gates — which ensure that relevant test results (assay, titer, solids, pH) exist, are approved and are in range before the next step proceeds.
• Dynamic Recipe Scaling — which changes total batch size or unit count and then cascades new setpoints throughout the recipe.

In simple cases, test-driven setpoint adjustment just changes one or two targets at the current step. In more advanced scenarios, the same test results can trigger both a recalculation of the immediate setpoint (e.g. how much concentrate to add) and a dynamic scaling decision (e.g. how many units to produce from the available active). The assay gate is the control point where tests are interpreted and authorised; the test-driven adjustment is the mechanism that turns that interpretation into actionable setpoints for execution.

Conceptually: the gate decides whether you may proceed; test-driven adjustment decides how you proceed while still meeting dose and CQA requirements.

3) Typical Test Inputs for Setpoint Adjustment

Test-driven setpoint adjustment can be based on a wide range of test parameters, such as:

• Potency / assay: e.g. % w/w, mg/g, mg/mL, IU/mL; drives potency adjustment factors and concentration-adjusted charges.
• Solids and moisture: via LOD adjustment and % solids basis, affecting dilution and charge volumes.
• Density / specific gravity: changing volume targets when dosing by mass or vice versa.
• pH and conductivity: adjusting neutralisation charges, titrants or buffer additions.
• Viscosity / rheology: tuning mixing speeds, times or temperatures.
• Titer and activity: for bioreactors and enzymes, adjusting harvest volume or dose of functional units.
• Microbiological / bioburden: altering hold times, sterilisation steps or diversion decisions.

In all cases, the core pattern is the same: test → rule/formula → updated setpoint, enforced automatically by MES and documented in the eBMR. The rules are the “brain”; the tests are the “sensors”; the setpoints are the “muscles” that move in response.

4) Examples of Test-Driven Setpoint Adjustment

A few illustrative examples:

• Assay-based API charge: an API is 97.5 % potent on an anhydrous basis instead of nominal 100 %. MES calculates a potency adjustment factor and increases the target weight proportionally to maintain 100 mg active per unit.
• Solids-based concentrate charge: a vitamin concentrate is 58 % solids instead of 60 %. MES recalculates the volume of concentrate to add, so that the same mg of active per unit enters the batch.
• pH-controlled titration: in a neutralisation step, inline pH data is used to determine when to stop adding base, rather than using a fixed charge amount.
• Viscosity-driven mixing time: an in-process viscosity test indicates higher-than-normal viscosity; mixing time is extended within a defined window to ensure proper dispersion before proceeding.
• Titer-driven harvest volume: a bioreactor harvest has higher titer than expected; the volume transferred to formulation is reduced to keep dose per vial consistent, or the batch size is increased via dynamic scaling.

Importantly, all of these adjustments are governed by predefined formulas, ranges and roles; operators are not expected to improvise the maths. The system does the calculation; operators confirm and execute with clear visibility and controls.

5) Protecting Dose, CQAs and Design Space

Test-driven setpoint adjustment must operate inside a validated box defined by:

• dose per unit limits (label claim and acceptable potency range);
• CQA limits (e.g. particle size, viscosity, pH, osmolality);
• CPP ranges (e.g. mixing speeds, temperatures, times);
• equipment capability and scale constraints.

When designing rules, engineers and QA should define:

• the normal adjustment range (e.g. ±10 % change in a charge);
• guardbands and limits (e.g. charge cannot exceed a level that would violate dose limits even at worst-case assay);
• conditions under which adjustments are not allowed and a deviation is required instead.

The aim is to use test results to keep the process inside a “good” part of the design space, not to stretch or explore it during routine manufacture. MES enforces these constraints; if the calculated setpoint would exceed them, the system should stop, flag an exception and require formal review rather than quietly proceeding with an unvalidated configuration.

6) Implementation in Master Recipes and Control Logic

In practice, test-driven setpoint adjustment is implemented in the master recipe and, where appropriate, in underlying control logic (e.g. SCADA/DCS function blocks). Recipe design should:

• identify which steps use test-driven targets (weigh, add, mix, heat, hold, cool, fill);
• define the input tests and data sources (LIMS, inline instruments, manual entry with dual verification);
• specify the formulas for setpoint calculation, including potency, solids, density and any applicable factors (e.g. stability-driven overage);
• declare permitted adjustment ranges and safe defaults if data is missing or invalid;
• define which roles can review and approve calculated setpoints.

The recipe then orchestrates the sequence:

• collect or receive test data;
• perform calculation;
• display proposed setpoint and rationale (optional);
• require confirmation or e-signature where appropriate;
• apply setpoint to the execution step; and
• record all inputs and outputs in the eBMR.

From a CSV perspective, the formulas and limits become part of the system configuration that must be tested under IQ/OQ/PQ and controlled under change control.

7) Data Integrity and Analytical Lot Links

Because test-driven setpoint adjustment directly affects dose and CQAs, it is inherently GxP-critical and must meet data-integrity expectations:

• test results should be captured in LIMS or validated inline systems, not manually keyed from paper where avoidable;
• results used for calculations must be linked to lots via an analytical lot link;
• any manual data entry should be minimised and supported by dual verification and audit trails for changes;
• calculation results and any overrides must be attributable to specific users with timestamps and reasons.

When an inspector asks, “Why was this batch charged with 10.2 kg instead of 10.0 kg?”, the organisation should be able to show:

• the underlying lab result (e.g. 98 % potency with LOD of 3 %);
• the calculation formula and parameters used;
• the system-generated setpoint and operator confirmation; and
• validation evidence that the formula behaves correctly across the permitted range.

This is only feasible if test-driven logic and analytical data are integrated and traceable rather than being implemented via opaque spreadsheets and unrecorded conversations between lab and production.

8) Benefits for Yield, COPQ and Flexibility

When done properly, test-driven setpoint adjustment directly improves:

• Yield and resource utilisation: by matching doses and process conditions to actual material properties, avoiding under- or over-dosing and unnecessary rework.
• Cost of poor quality (COPQ): by reducing failed batches, deviations, OOS events and scrap caused by “fixed” setpoints that ignore variability.
• Flexibility: by allowing the process to adapt to new lots, minor upstream changes and modest variability without constant recipe re-authorisation.
• Investigations: by providing clear, quantitative links between test results, process decisions and batch outcomes.

When combined with potency-normalised yield and active-equivalent consumption, test-driven adjustments allow technical and finance teams to see how much active was conserved or wasted under different material conditions and control strategies, in the same units that matter for dose and cost.

9) Use Cases Across Industries

Test-driven setpoint adjustment is relevant across regulated and quality-critical sectors:

• Pharmaceuticals and biologics: potency- and titer-based adjustments to API charges, bioreactor harvest volumes, buffer additions, neutralisation charges and drying times.
• Dietary supplements: assay- and solids-driven adjustments to vitamin premix additions, botanical extracts and fortification syrups.
• Food and beverage: enzyme activity and solids-based adjustments to enzyme dosing, syrup concentrations and flavour additions.
• Cosmetics and personal care: active concentration and viscosity-driven adjustments to active concentrates, thickeners and emulsifiers to hit both efficacy and sensory targets.
• Chemicals and speciality materials: activity- and composition-driven adjustments to catalysts, initiators and performance additives to meet specification and functional performance targets.

In each case, the same pattern holds: measured variation in materials or intermediates is used to recalibrate targets so that the final material behaves as designed, rather than forcing materials into a static, nominal recipe that ignores reality.

10) Common Pitfalls and Anti-Patterns

When organisations attempt “test-driven control” informally, several problems typically appear:

• Spreadsheet drift: setpoints are recalculated in local spreadsheets that are not validated or integrated, with formulas that differ between sites and engineers.
• Manual retyping: lab results are copied by hand into calculators and then into MES, increasing error risk and eroding data integrity.
• Hidden logic: rules live in tribal knowledge (“Jim always adds 5 % if solids are low”) rather than in controlled recipes.
• Unbounded adjustment: setpoints are changed beyond validated ranges with no system-level checks, slowly drifting the process outside design space.
• Poor documentation: eBMRs show what was done, but not why parameters differed or which tests drove the changes.

Formal test-driven setpoint adjustment addresses these issues by embedding logic in MES recipes and by making test–setpoint relationships explicit, validated and auditable. The goal is to keep human decision-making at the level of risk assessment and exception handling, not routine recalculation of targets that a system can perform more consistently and traceably.

11) Practical Implementation Steps

To introduce test-driven setpoint adjustment in a controlled way, organisations typically:

• identify candidates where fixed setpoints are clearly inadequate (e.g. potency-variable raw materials, variable solids, known viscosity swings);
• document existing “manual adjustment” rules already used by experienced operators and technical staff;
• formalise those rules into equations and boundaries, referencing underlying process understanding and validation studies;
• configure those rules in master recipes, integrated with in-process assay gates and analytical interfaces;
• validate the calculation logic and limits under CSV, including boundary conditions and error handling;
• update SOPs, training and review checklists so that QA and operations know how to interpret and challenge calculated setpoints where appropriate;
• monitor initial batches with test-driven adjustments and compare performance (yield, deviations, review effort) to historical fixed-setpoint operation.

Starting with a small set of high-impact steps allows teams to build confidence, refine change-control practices and fine-tune user interfaces before rolling out test-driven adjustments more broadly across the plant or network.

12) Interaction with QRM, QbD and Lifecycle Management

Test-driven setpoint adjustment fits naturally into modern quality frameworks:

• Quality Risk Management (QRM): risk assessments identify where variability in inputs (potency, solids, titer) poses risk to CQAs; test-driven rules become risk controls, with residual risk evaluated and documented.
• Quality by Design (QbD): design space and control strategy definitions explicitly describe how the process will respond to variability. Test-driven adjustments are one way of implementing that control strategy in practice.
• Lifecycle management: as more data is gathered, adjustment rules can be refined, tightened or even relaxed based on CPV and PQR evidence, with appropriate regulatory engagement where necessary.

Over time, organisations can move from simple, linear adjustment rules to more sophisticated models (for example, multivariate or model-predictive control) while still operating within the same conceptual framework: tests drive setpoints inside a validated box, and MES/eBMR provide the traceability required for GMP compliance and scientific lifecycle management.

13) FAQ

Q1. How is test-driven setpoint adjustment different from automatic control loops (PID etc.)?
Classic control loops adjust parameters continuously based on feedback (e.g. temperature vs setpoint). Test-driven setpoint adjustment recalculates the setpoint itself based on discrete test results (e.g. assay, solids), usually at defined decision points rather than continuously.

Q2. Does test-driven setpoint adjustment always require a laboratory?
No. Some adjustments are based on inline or at-line sensors (pH, conductivity, NIR, titer). Others use offline lab data. The common feature is that measured values, not assumptions, drive the setpoints.

Q3. Can operators override test-driven setpoints?
Only under controlled conditions. Many organisations allow overrides in exceptional cases, but require appropriate roles, electronic signatures, reasons and often a linked deviation. Routine use of overrides indicates either that the rules are wrong or that they are not trusted and should be reviewed.

Q4. Does test-driven setpoint adjustment increase validation effort?
There is additional up-front validation for formulas and limits, but over time it usually reduces effort by decreasing deviations, investigations and rework caused by rigid, nominal setpoints that do not reflect actual material behaviour.

Q5. What is a practical first step to adopt test-driven setpoint adjustment?
Identify one step where experienced operators already “do the maths” based on assay or solids before entering a target. Capture their logic, formalise it into a simple formula with limits, configure it in MES, validate it and then compare outcome metrics before and after. Use that success to justify expanding the pattern across other steps.

Related Reading
• Potency & Batching: Batch-Specific Potency | Potency Basis | Potency Adjustment Factor | Concentration-Adjusted Charge
• Gates, Scaling & Tests: In-Process Assay Gate | Dynamic Recipe Scaling | Tests & Laboratory Analyses
• Yield, Economics & Records: Potency-Normalised Yield | Active-Equivalent Consumption | Mass Balance | Electronic Batch Record (eBMR) | Computer System Validation (CSV)