Biopharmaceutical Manufacturing Industry Council Projects

If you are interested in contributing to this work, register your interest at:

Submitting the form will lead to an OAGi staff member contacting you via email to schedule a meeting.

BMIC Overview

The Biopharmaceutical Manufacturing Industry Council (BMIC) is an organizational unit of OAGi's Industrial Ontology Foundries (IOF). It was established in 2023 in coordination with the National Institute for Innovation in Manufacturing Biopharmaceuticals (NIIMBL). The projects described here are made possible by NIIMBL sponsorship.

Biopharmaceutical manufacturing generates vast amounts of data across ELNs, LIMS, QMS platforms, and supplier systems. This data is fragmented, semantically inconsistent, and difficult to correlate—especially when attempting to link raw material variability to process performance and product quality. To address this, OAGi’s Biopharmaceutical Manufacturing Industry Council (BMIC) is leading the development of a modular suite of open and interoperable ontologies. These ontologies will provide a shared semantic foundation for describing materials, processes, and quality attributes in a consistent, machine-readable manner. The ontologies produced by this initiative will not replacing existing systems but enhance them with ontological context, enabling more effective querying, correlation, and analysis of manufacturing data.

The ontology-development is occurring in under the guidance of OAGi’s Industrial Ontologies Foundry (IOF). IOF has been developing high-quality ontologies for the past nine years.

The Problem: Semantic Fragmentation

Biopharmaceutical engineers and scientists often spend weeks aligning experimental data, process records, and supplier specifications. This is because:

Field names and concepts differ across ELNs, LIMS, and QMS platforms
Media composition is not digitally structured or queryable
Supplier data uses inconsistent naming and formats
There's no formal connection between media lots, process parameters, and CQAs

These gaps make deviation investigations laborious and delay actionable insights.

The Vision: Ontology-Enhanced Biomanufacturing

The BMIC Ontology Suite will enable:

Semantically consistent descriptions of materials, parameters, and quality attributes
Automated correlation of experimental runs, process conditions, and supplier lots
Reuse of statistical models across experiments and systems
Standardized structure for integrating supplier metadata into internal systems

All ontologies will be developed in alignment with the IOF’s modular architecture, annotated with clear semantics, and released as open-source under the MIT license.

Foundational Design Principles

Modularity
Ontologies will be modularized to ensure scalability, reusability, and clear domain boundaries.
IOF Compliance
All ontology development will follow IOF technical standards, annotation guidelines, and publication infrastructure.
Domain and Industry Alignment
Domain knowledge will be captured through engagement with manufacturers, suppliers, and SMEs. Industry-agnostic concepts will be reused from IOF reference ontologies where applicable.
Conceptual Interoperability
The BMIC ontology suite is designed to align with established industrial standards, including ISA-88 and ISA-95, to ensure compatibility with existing process and enterprise modeling practices.
Data Integration and Querying
Ontologies will support instantiation in knowledge graphs and be tested with sample queries that span materials, processes, and quality data.

Project Structure

The BMIC initiative is structured around three core ontology development tracks, each led by a dedicated Working Group. These Working Groups will incrementally build a modular suite of interoperable ontologies to support end-to-end semantic enrichment of biopharmaceutical manufacturing data.

The Year 1 use case - correlating media composition with process performance and product quality - serves as a launching point to validate the foundational ontology modules and working structures. Over time, the scope of each Working Group will expand to encompass deeper domain knowledge, cross-enterprise integration, and advanced analytical capabilities.

BMIC Process Working Group

Models planned and executed biomanufacturing processes, starting with cell culture and expanding to downstream operations.
Defines concepts for equipment, control strategies, process parameters, and performance indicators.
Supports process modeling at both the execution and trend-analysis level.
Lays the groundwork for semantic representation of process knowledge used in internal operations and potential supplier interactions.

BMIC Quality Management Working Group

Represents measurement processes, quality attributes, and statistical constructs relevant to monitoring and control.
Captures plans and execution records for quality-related measurements across the product lifecycle.
Provides structure for integrated control strategies and batch record metadata.
Evolves toward supporting root-cause analysis, multi-run investigations, and traceable linkage to material and process variability.

BMIC Materials Working Group

Structures media and raw material entities, including component identity, properties, lot metadata, and variation history.
Expands over time to include a wider range of material types such as excipients, consumables, and filters.
Encodes material-specific behaviors (e.g., dissolution, stability, granulation) relevant to process performance.
Enables standardized, canonical mapping of external supplier data into enterprise knowledge systems.

Each Working Group will follow IOF-compliant practices for ontology development, testing, and integration, with a focus on semantic clarity, modular reuse, and support for real-world use cases. The ultimate goal is to provide an extensible semantic infrastructure for digital transformation across biomanufacturing operations.

Collaboration with IOF Mid-Level Ontology Working Groups

BMIC WGs are tightly integrated with the IOF ecosystem through:

Push/pull mechanisms for shared concepts: BMIC may propose terms to IOF reference ontologies (push), and IOF may incorporate BMIC-originated constructs (pull).
Participation reciprocity: BIC members may join IOF WGs, and IOF participants may join BMIC WGs, fostering bi-directional knowledge exchange.
Shared policies and review procedures: Ontology changes with cross-domain implications are coordinated between IOF-TOB and BIC-TOB to ensure consistency, reuse, and conflict resolution.

This collaboration ensures that BMIC ontologies are not only domain-relevant but also structurally aligned with broader industrial semantics. It maximizes reuse of established modeling patterns, reduces duplication of effort, and ensures that biomanufacturing terms integrate cleanly with upstream and downstream systems, including those used by suppliers, partners, and regulators. By working within IOF, BMIC benefits from robust infrastructure, open licensing, and a governance model designed to ensure long-term maintainability, interoperability, and cross-sector impact.

Use Case: Media Composition and Product Quality Correlation

The BMIC Ontology Suite is being launched with a high-impact, real-world use case focused on enabling engineers to understand how raw material variability—particularly in media composition—affects process performance and product quality.

Context

In today’s biopharmaceutical manufacturing environments, experimental data, quality measurements, and supplier records are stored across multiple siloed systems (e.g., ELN, LIMS, QMS). These systems use inconsistent formats and lack semantic alignment, making it difficult to correlate inputs (like media lots) with outcomes (such as glycosylation or pH control). The process of tracing quality deviations back to potential raw material or process causes is manual, time-consuming, and increasingly complex as experimentation and supplier diversity grow.

This foundational use case focuses on overcoming those barriers.

Objective

To demonstrate that the BMIC ontologies can support:

Semantically consistent representation of media composition, including trace components and lot-level metadata
Modeling of process parameters and performance indicators, including control actions like media addition
Representation of product quality attributes (CQAs), analytical methods, and measurement results
Querying and analysis of correlations between inputs (e.g., glucose or zinc levels), process dynamics (e.g., pH transitions), and outputs (e.g., glycosylation patterns)
Deviation analysis: identifying when a KPI or CQA moves outside control limits and linking that deviation to potential variation in upstream inputs

Why This Use Case Matters

This use case addresses a core pain point in biopharma: the inability to seamlessly trace variability across materials, processes, and product outcomes due to lack of a shared semantic structure.

It serves as a launch point to validate the initial ontology modules and build working group practices, tooling, and collaboration mechanisms.

Key features demonstrated in this use case will include:

Ontology-enhanced data alignment across ELN, LIMS, and QMS systems
Structured representation of historical media variation and supplier metadata
Statistical model support for variation correlation and control limit setting
Semantic traceability between material properties, process events, and product attributes

Example Competency Questions

What is the impact of glucose addition rate on ammonia accumulation and pH transitions?
Which media lots exhibit the highest variation in trace metals, and how does this correlate with glycosylation outcomes?
What is the normal operating range of a media addition parameter that ensures CQA consistency?
How do vitamin degradation rates in stored media relate to expansion performance KPIs?
What upstream material factors are statistically associated with deviations outside control limits?

Ontology Development Roadmap

The BMIC initiative is currently operating under a structured three-year development roadmap, designed to incrementally expand the scope and maturity of its ontology suite. This roadmap provides a strategic plan for developing core capabilities across materials, processes, and quality domains.

Importantly, the roadmap does not represent the full lifecycle of the initiative. The BMIC ontology suite is expected to evolve well beyond Year 3, with future phases focusing on detailed modeling of tech transfer activities, lifecycle transitions, digital comparability, and integration into regulatory and supply chain ecosystems.

Year 1: Foundation Building

The initial project phase focuses on validating core ontology structures through a representative use case: correlating media composition with process performance indicators and product quality attributes. Key goals include:

Establishing the BMIC Process, Quality, and Materials Working Groups
Developing initial ontology modules for:
- Cell culture process planning and execution
- Measurement methods and CQAs
- Media composition and raw material properties
Validating ontologies with real or simulated example data
Demonstrating ontology-enhanced support for statistical correlation and deviation analysis
Aligning modular components with IOF mid-level reference ontologies

This year also establishes tooling, collaboration workflows, and governance patterns that will support longer-term sustainability and scaling.

Year 2: Lifecycle and System Integration

Building on the Year 1 foundation, Year 2 expands the ontology suite to model broader biomanufacturing workflows and integrate deeper quality oversight. Key areas of focus:

Extend process ontologies to represent downstream drug substance operations
Introduce support for batch production records and integrated control strategies in the quality ontology
Expand material coverage to include excipients, consumables, and filters
Strengthen modeling of variability over time, including historical trending and out-of-spec context
Enable linkage between process context and quality excursions across multiple batches

This phase begins to unify data semantics across larger segments of the product lifecycle.

Year 3: Specialized Knowledge and Supplier Interoperability

In Year 3, the ontology suite matures to support specialty knowledge, richer relationships between materials and processes, and standardized external data exchange.

Key goals:

Capture domain-specific process knowledge, such as:
- Granulation and dissolution properties
- Turbidity, stability, and degradation dynamics
Support canonical semantic mapping of supplier specifications and certificates of analysis (CoAs)
Enable two-way supplier-manufacturer integration by structuring data in a format that supports automated ingestion and interpretation
Provide semantic infrastructure for advanced root-cause analysis, risk-based investigations, and cross-organizational alignment

By the end of Year 3, the suite will be positioned for broad adoption across digital quality systems, manufacturing execution platforms, and advanced analytics environments.

Beyond Year 3: Toward Lifecycle Continuity and Tech Transfer Modeling

While the current roadmap defines the first three years of structured development, the BMIC ontology suite is designed for long-term evolution.

In future phases, priorities will include:

Modeling technology transfer processes, including knowledge handoff from development to manufacturing, and between sites or contract partners
Representing comparability protocols and lifecycle transitions for materials and processes
Integrating with digital regulatory submissions and global standards for knowledge traceability
Supporting cross-product alignment, change control, and post-approval variation tracking

These extensions will reinforce the suite’s role as a foundational asset in digital biomanufacturing transformation and global data interoperability.

Impact and Benefits

Faster investigations
Ontology-enhanced traceability enables rapid root-cause analysis across materials, processes, and quality data.

Improved data reuse
Structured, semantically aligned data supports reusable queries, models, and analytics across experiments and sites.

Supplier interoperability
Canonical representations facilitate two-way integration of specifications, test results, and material metadata.

Lifecycle traceability
Machine-readable semantics improve transparency across development, manufacturing, and tech transfer stages.

Regulatory and audit readiness
Rich metadata supports compliant documentation, comparability assessments, and knowledge retention.

AI/ML enablement
Clean, typed knowledge graphs provide a robust foundation for predictive modeling and automation.

Benefits of Working Within the IOF Framework

BMIC ontology development is embedded within the Industrial Ontologies Foundry (IOF), ensuring modularity, long-term sustainability, and alignment with broader manufacturing standards. Key benefits include:

Cross-industry interoperability
BMIC ontologies align with IOF reference ontologies, enabling integration with adjacent domains such as discrete manufacturing, logistics, and automation.
Reuse of mid-level constructs
BMIC Working Groups leverage shared IOF structures (e.g., material role, control action, process execution), reducing redundancy and ensuring semantic consistency.
Push–pull contribution model
BMIC can contribute domain-neutral terms to IOF (push) and adopt IOF-supported constructs (pull), maximizing visibility and reuse of biopharma innovations.
Governed development and quality assurance
All modules follow IOF’s peer-reviewed, version-controlled release process, ensuring clarity, stability, and technical rigor.
Shared infrastructure and tooling
BMIC benefits from IOF-managed infrastructure (e.g., GitHub repositories, RDF tooling, Confluence documentation), accelerating development and collaboration.
Open licensing for broad adoption
Ontologies are released under permissive MIT and CC BY 4.0 licenses, removing adoption barriers and supporting integration across industry platforms.
Strategic positioning for the future
IOF alignment ensures that BMIC ontologies are well-positioned for regulatory integration, supplier collaboration, and future data standardization efforts.

Biopharmaceutical Manufacturing Industry Council (BMIC)