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Soft-Sensor Bioprocess Monitoring for Continuous Manufacturing

Infers hard-to-measure process variables in near real time for tighter process control Evidence basis: Recent bioprocess studies including AutoML soft sensors report feasibility for real-time nutrient and metabolite estimation; review evidence emphasizes lifecycle monitoring needs and alignment with continuous manufacturing guidance

The Problem

“Near-real-time soft-sensor monitoring for continuous bioprocess manufacturing”

Organizations face these key challenges:

1

Critical variables are hard to measure online or have long assay turnaround times

2

Fragmented process data across historians, MES, LIMS, and equipment systems

3

Nonlinear process behavior makes rule-based estimation unreliable

4

Model drift from raw material variability, fouling, scale changes, and equipment aging

5

Regulated environments require explainability, validation, auditability, and change control

6

Smaller firms often lack in-house expertise for continuous manufacturing design and control strategy development

7

Post-approval change assessments are document-heavy and slow

Impact When Solved

Infer nutrient, metabolite, biomass, and moisture-related variables in near real time without waiting for offline assaysEnable tighter closed-loop control in continuous operations such as spray drying and upstream/downstream bioprocessingReduce process variability, off-spec material, and operator reaction delaysSupport post-approval stability impact assessments with AI-assisted classification and recommendation workflowsProvide expert decision support for small and mid-size manufacturers adopting continuous manufacturingImprove lifecycle monitoring, model-based release readiness, and continued process verification

The Shift

Before AI~85% Manual

Human Does

  • •Review process data manually across batches and unit operations
  • •Coordinate quality and process checks in spreadsheets and reports
  • •Investigate deviations after results show out-of-range conditions
  • •Decide corrective actions based on retrospective trend review

Automation

  • •No AI-driven monitoring or prediction in the current workflow
  • •No automated prioritization of process risks or opportunities
  • •No real-time inference of hard-to-measure process variables
With AI~75% Automated

Human Does

  • •Review AI-flagged process risks and inferred variable trends
  • •Approve process adjustments and quality-related interventions
  • •Handle exceptions when model outputs conflict with operating context

AI Handles

  • •Infer hard-to-measure process variables in near real time
  • •Monitor process conditions continuously for emerging deviations
  • •Prioritize high-impact risks and opportunities for operator review
  • •Surface actionable alerts and trend summaries for tighter process control

Operating Intelligence

How Soft-Sensor Bioprocess Monitoring for Continuous Manufacturing runs once it is live

AI watches every signal continuously.

Humans investigate what it flags.

False positives train the next watch cycle.

Confidence89%
ArchetypeMonitor & Flag
Shape6-step linear
Human gates1
Autonomy
67%AI controls 4 of 6 steps

Who is in control at each step

Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

Loop shapelinear

Step 1

Observe

Step 2

Classify

Step 3

Route

Step 4

Exception Review

Step 5

Record

Step 6

Feedback

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI observes and classifies continuously. Humans only engage on flagged exceptions. Corrections sharpen future detection.

The Loop

6 steps

1 operating angles mapped

Operational Depth

Technologies

Technologies commonly used in Soft-Sensor Bioprocess Monitoring for Continuous Manufacturing implementations:

Continuous solution preparationOther
3 mentions
Continuous spray drying lineOther
3 mentions
OEE improvement workflowOther
2 mentions
Secondary drying integrationOther
2 mentions

Key Players

Companies actively working on Soft-Sensor Bioprocess Monitoring for Continuous Manufacturing solutions:

IQVIAMasterControlVeevaAlternative continuous manufacturing platforms

Real-World Use Cases

AI decision support for post-approval stability impact assessment

When a company changes something about a medicine after approval, AI can help estimate whether the change could affect stability and what extra testing is needed.

classification + recommendationproposed; the source supports the workflow context but does not mention deployed ai systems.
10.0

Integrated continuous process control for pharmaceutical spray drying

Instead of making medicine in stop-start steps, the project links preparation, drying, and testing into one smarter continuous line that can run more smoothly.

Process optimization and closed-loop operational coordinationproposed integrated manufacturing workflow under development in pharmsd 3.0.
10.0

Expert decision support for small and mid-size firms adopting pharmaceutical continuous manufacturing

An AI copilot can turn expert manufacturing guidance into step-by-step advice for teams that do not already know how to run continuous drug production.

Knowledge-guided recommendation and workflow orchestrationproposed workflow strongly supported by the source’s emphasis on practical guidance for organizations lacking pcm expertise.
10.0

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