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References

📍 Where we are: The evidence base for the whole book, gathered in one place.

Every inline citation in this book — written as a bracketed marker like [1] — resolves here, grouped by chapter. The numbering on this page matches the [N] markers in each chapter.

Preface​

  1. Rathore AS, Winkle H. (2009). Quality by design for biopharmaceuticals. Nature Biotechnology 27(1):26-34. https://doi.org/10.1038/nbt0109-26
  2. ICH (International Council for Harmonisation). (2009). ICH Harmonised Tripartite Guideline Q8(R2): Pharmaceutical Development. ICH, Current Step 4 version, August 2009. https://database.ich.org/sites/default/files/Q8_R2_Guideline.pdf
  3. Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3:160018. https://doi.org/10.1038/sdata.2016.18
  4. Smith B, Ashburner M, Rosse C, et al. (2007). The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nature Biotechnology 25(11):1251-1255. https://doi.org/10.1038/nbt1346

Chapter 1 — The Upper Spine: Continuants, Occurrents, and Why Everyone Builds on BFO​

  1. ISO/IEC (International Organization for Standardization / International Electrotechnical Commission). (2021). ISO/IEC 21838-2:2021 — Information technology — Top-level ontologies (TLO) — Part 2: Basic Formal Ontology (BFO). ISO/IEC, Geneva. https://www.iso.org/standard/74572.html
  2. Arp R, Smith B, Spear AD. (2015). Building Ontologies with Basic Formal Ontology. The MIT Press, Cambridge, MA, 248 pp., ISBN 978-0-262-52781-1. https://doi.org/10.7551/mitpress/9780262527811.001.0001
  3. Smith B, Ashburner M, Rosse C, et al. (2007). The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nature Biotechnology 25(11):1251-1255. https://doi.org/10.1038/nbt1346
  4. Smith B, Ceusters W, Klagges B, Köhler J, Kumar A, Lomax J, Mungall C, Neuhaus F, Rector AL, Rosse C. (2005). Relations in biomedical ontologies. Genome Biology 6(5):R46. https://doi.org/10.1186/gb-2005-6-5-r46
  5. Kulvatunyou BS, Wallace E, Kiritsis D, Smith B, Will C. (2018). The Industrial Ontologies Foundry Proof-of-Concept Project. In: Moon I, et al. (eds), Advances in Production Management Systems (APMS 2018), IFIP AICT 536, Springer, pp. 402-409. https://doi.org/10.1007/978-3-319-99707-0_50

Chapter 2 — Classes, Relations, and Axioms: Building the Vocabulary​

  1. W3C OWL Working Group. (2012). OWL 2 Web Ontology Language Primer (Second Edition). W3C Recommendation, 11 December 2012. https://www.w3.org/TR/owl2-primer/
  2. Cyganiak R, Wood D, Lanthaler M (eds). (2014). RDF 1.1 Concepts and Abstract Syntax. W3C Recommendation, 25 February 2014. https://www.w3.org/TR/rdf11-concepts/
  3. Knublauch H, Kontokostas D (eds). (2017). Shapes Constraint Language (SHACL). W3C Recommendation, 20 July 2017. https://www.w3.org/TR/shacl/
  4. Musen MA. (2015). The Protégé project: A look back and a look forward. AI Matters 1(4):4-12. https://doi.org/10.1145/2757001.2757003
  5. Baader F, Calvanese D, McGuinness DL, Nardi D, Patel-Schneider PF (eds). (2003). The Description Logic Handbook: Theory, Implementation and Applications. Cambridge University Press, ISBN 978-0-521-78176-3. https://doi.org/10.1017/CBO9780511711787

Chapter 3 — Identifiers and Units: IRIs, QUDT, and the Typed Value​

  1. Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3:160018. https://doi.org/10.1038/sdata.2016.18
  2. Cyganiak R, Wood D, Lanthaler M (eds). (2014). RDF 1.1 Concepts and Abstract Syntax. W3C Recommendation, 25 February 2014. https://www.w3.org/TR/rdf11-concepts/
  3. QUDT.org. (2024). QUDT — Quantities, Units, Dimensions and Types Ontology (Version 2.1). QUDT.org. https://qudt.org/
  4. Schadow G, McDonald CJ. (2017). The Unified Code for Units of Measure (UCUM), Revision 2.1. Regenstrief Institute, Indianapolis, IN. https://ucum.org/
  5. Halpin H, Hayes PJ, McCusker JP, McGuinness DL, Thompson HS. (2010). When owl:sameAs Isn't the Same: An Analysis of Identity in Linked Data. In: The Semantic Web — ISWC 2010, LNCS 6496, Springer, pp. 305-320. https://doi.org/10.1007/978-3-642-17746-0_20

Chapter 4 — Modeling the Target and the Product Concept​

  1. Bandrowski A, Brinkman R, Brochhausen M, et al. (2016). The Ontology for Biomedical Investigations. PLoS ONE 11(4):e0154556. https://doi.org/10.1371/journal.pone.0154556
  2. Ashburner M, Ball CA, Blake JA, et al. (2000). Gene Ontology: tool for the unification of biology. Nature Genetics 25(1):25-29. https://doi.org/10.1038/75556
  3. Natale DA, Arighi CN, Blum M, et al. (2017). Protein Ontology (PRO): enhancing and scaling up the representation of protein entities. Nucleic Acids Research 45(D1):D339-D346. https://doi.org/10.1093/nar/gkw1075
  4. Schriml LM, Munro JB, Schor M, et al. (2022). The Human Disease Ontology 2022 update. Nucleic Acids Research 50(D1):D1255-D1261. https://doi.org/10.1093/nar/gkab1063
  5. Smith B, Ashburner M, Rosse C, et al. (2007). The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nature Biotechnology 25(11):1251-1255. https://doi.org/10.1038/nbt1346

Chapter 5 — Modeling the Molecule: Sequence, Modality, and Developability​

  1. Natale DA, Arighi CN, Blum M, et al. (2017). Protein Ontology (PRO): enhancing and scaling up the representation of protein entities. Nucleic Acids Research 45(D1):D339-D346. https://doi.org/10.1093/nar/gkw1075
  2. Jain T, Sun T, Durand S, et al. (2017). Biophysical properties of the clinical-stage antibody landscape. Proceedings of the National Academy of Sciences 114(5):944-949. https://doi.org/10.1073/pnas.1616408114
  3. Raybould MIJ, Marks C, Krawczyk K, et al. (2019). Five computational developability guidelines for therapeutic antibody profiling. Proceedings of the National Academy of Sciences 116(10):4025-4030. https://doi.org/10.1073/pnas.1810576116
  4. Bandrowski A, Brinkman R, Brochhausen M, et al. (2016). The Ontology for Biomedical Investigations. PLoS ONE 11(4):e0154556. https://doi.org/10.1371/journal.pone.0154556

Chapter 6 — Modeling the Cell Line and the Cell-Bank Genealogy​

  1. Federhen S. (2012). The NCBI Taxonomy database. Nucleic Acids Research 40(D1):D136-D143. https://doi.org/10.1093/nar/gkr1178
  2. Sarntivijai S, Lin Y, Xiang Z, et al. (2014). CLO: The Cell Line Ontology. Journal of Biomedical Semantics 5:37. https://doi.org/10.1186/2041-1480-5-37
  3. ICH (International Council for Harmonisation). (1997). ICH Harmonised Tripartite Guideline Q5D: Derivation and Characterisation of Cell Substrates Used for Production of Biotechnological/Biological Products. ICH, Current Step 4 version, July 1997. https://database.ich.org/sites/default/files/Q5D%20Guideline.pdf
  4. Wurm FM. (2004). Production of recombinant protein therapeutics in cultivated mammalian cells. Nature Biotechnology 22(11):1393-1398. https://doi.org/10.1038/nbt1026
  5. Xu X, Nagarajan H, Lewis NE, et al. (2011). The genomic sequence of the Chinese hamster ovary (CHO-K1) cell line. Nature Biotechnology 29(8):735-741. https://doi.org/10.1038/nbt.1932

Chapter 7 — Modeling the Design Space: CPPs, CQAs, and QbD as a Graph​

  1. ICH (International Council for Harmonisation). (2009). ICH Harmonised Tripartite Guideline Q8(R2): Pharmaceutical Development. ICH, Current Step 4 version, August 2009. https://database.ich.org/sites/default/files/Q8_R2_Guideline.pdf
  2. ICH (International Council for Harmonisation). (2023). ICH Harmonised Guideline Q9(R1): Quality Risk Management. ICH, Current Step 4 version, January 2023. https://database.ich.org/sites/default/files/ICH_Q9%28R1%29_Guideline_Step4_2023_0126_0.pdf
  3. ICH (International Council for Harmonisation). (2008). ICH Harmonised Tripartite Guideline Q10: Pharmaceutical Quality System. ICH, Current Step 4 version, June 2008. https://database.ich.org/sites/default/files/Q10%20Guideline.pdf

Chapter 8 — Modeling Analytical Methods and Results: Allotrope and OBI​

  1. Allotrope Foundation. (2024). Allotrope Framework: Allotrope Foundation Ontologies (AFO) and Allotrope Data Format (ADF). Allotrope Foundation. https://www.allotrope.org/
  2. Bandrowski A, Brinkman R, Brochhausen M, et al. (2016). The Ontology for Biomedical Investigations. PLoS ONE 11(4):e0154556. https://doi.org/10.1371/journal.pone.0154556
  3. ASTM International, Subcommittee E13.15. (2023). AnIML — Analytical Information Markup Language. ASTM International, West Conshohocken, PA. https://www.animl.org/

Chapter 9 — Modeling the Recipe and Tech Transfer: Portable Process Knowledge​

  1. IEC (International Electrotechnical Commission). (2010). IEC 61512-1: Batch control — Part 1: Models and terminology (ISA-88). IEC, Geneva. https://webstore.iec.ch/publication/5529
  2. IEC (International Electrotechnical Commission). (2013). IEC 62264-1: Enterprise-control system integration — Part 1: Models and terminology (ISA-95). IEC, Geneva. https://webstore.iec.ch/publication/21241
  3. MESA International. (2013). B2MML — Business To Manufacturing Markup Language. Manufacturing Enterprise Solutions Association (MESA). https://www.mesa.org/topics-resources/b2mml/
  4. ICH (International Council for Harmonisation). (2008). ICH Harmonised Tripartite Guideline Q10: Pharmaceutical Quality System. ICH, Current Step 4 version, June 2008. https://database.ich.org/sites/default/files/Q10%20Guideline.pdf
  5. ICH (International Council for Harmonisation). (2019). ICH Harmonised Guideline Q12: Technical and Regulatory Considerations for Pharmaceutical Product Lifecycle Management. ICH, Current Step 4 version, November 2019. https://database.ich.org/sites/default/files/Q12_Guideline_Step4_2019_1119.pdf

Chapter 10 — Modeling the Seed Train and the Start of Genealogy​

  1. Wurm FM. (2004). Production of recombinant protein therapeutics in cultivated mammalian cells. Nature Biotechnology 22(11):1393-1398. https://doi.org/10.1038/nbt1026
  2. ICH (International Council for Harmonisation). (1997). ICH Harmonised Tripartite Guideline Q5D: Derivation and Characterisation of Cell Substrates Used for Production of Biotechnological/Biological Products. ICH, Current Step 4 version, July 1997. https://database.ich.org/sites/default/files/Q5D%20Guideline.pdf

Chapter 11 — Modeling the Production Bioreactor: A Process, Its Phases, and Its Parameters​

  1. FDA (U.S. Food and Drug Administration). (2004). Guidance for Industry: PAT — A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance. FDA, CDER/CVM/ORA, September 2004. https://www.fda.gov/media/71012/download
  2. IEC (International Electrotechnical Commission). (2010). IEC 61512-1: Batch control — Part 1: Models and terminology (ISA-88). IEC, Geneva. https://webstore.iec.ch/publication/5529
  3. Abu-Absi NR, Kenty BM, Cuellar ME, Borys MC, Sakhamuri S, Strachan DJ, Hausladen MC, Li ZJ. (2011). Real time monitoring of multiple parameters in mammalian cell culture bioreactors using an in-line Raman spectroscopy probe. Biotechnology and Bioengineering 108(5):1215-1221. https://doi.org/10.1002/bit.23023
  4. Das S, Sundara S, Cyganiak R (eds). (2012). R2RML: RDB to RDF Mapping Language. W3C Recommendation, 27 September 2012 (with the RML extension, RDF Mapping Language, IDLab/Ghent University, https://rml.io/specs/rml/). https://www.w3.org/TR/r2rml/
  5. Haller A, Janowicz K, Cox S, Le Phuoc D, Taylor K, Lefrançois M (eds). (2017). Semantic Sensor Network Ontology (SOSA/SSN). W3C / OGC Recommendation, 19 October 2017. https://www.w3.org/TR/vocab-ssn/
  6. AVEVA (formerly OSIsoft). (2023). PI Web API Reference — RESTful interface to the AVEVA/OSIsoft PI System. AVEVA Group plc. https://docs.aveva.com/bundle/pi-web-api-reference/page/help.html

Chapter 12 — Modeling Harvest and Clarification: A Material Transformation​

  1. Shukla AA, Thömmes J. (2010). Recent advances in large-scale production of monoclonal antibodies and related proteins. Trends in Biotechnology 28(5):253-261. https://doi.org/10.1016/j.tibtech.2010.02.001
  2. Liu HF, Ma J, Winter C, Bayer R. (2010). Recovery and purification process development for monoclonal antibody production. mAbs 2(5):480-499. https://doi.org/10.4161/mabs.2.5.12645
  3. IEC (International Electrotechnical Commission). (2010). IEC 61512-1: Batch control — Part 1: Models and terminology (ISA-88). IEC, Geneva. https://webstore.iec.ch/publication/5529

Chapter 13 — Modeling Capture Chromatography and the Pooling Problem​

  1. Shukla AA, Thömmes J. (2010). Recent advances in large-scale production of monoclonal antibodies and related proteins. Trends in Biotechnology 28(5):253-261. https://doi.org/10.1016/j.tibtech.2010.02.001
  2. Liu HF, Ma J, Winter C, Bayer R. (2010). Recovery and purification process development for monoclonal antibody production. mAbs 2(5):480-499. https://doi.org/10.4161/mabs.2.5.12645
  3. Rathore AS, Parr L, Dermawan S, Lawson K, Lu Y. (2010). Large scale demonstration of a process analytical technology application in bioprocessing: use of on-line high performance liquid chromatography for making real time pooling decisions for process chromatography. Biotechnology Progress 26(2):448-457. https://doi.org/10.1002/btpr.320

Chapter 14 — Modeling Viral Safety: Inactivation and Filtration as Risk-Reducing Steps​

  1. ICH (International Council for Harmonisation). (2023). ICH Harmonised Guideline Q5A(R2): Viral Safety Evaluation of Biotechnology Products Derived from Cell Lines of Human or Animal Origin. ICH, Current Step 4 version, November 2023. https://database.ich.org/sites/default/files/ICH_Q5A%28R2%29_Guideline_2023_1101.pdf
  2. Shukla AA, Thömmes J. (2010). Recent advances in large-scale production of monoclonal antibodies and related proteins. Trends in Biotechnology 28(5):253-261. https://doi.org/10.1016/j.tibtech.2010.02.001
  3. Liu HF, Ma J, Winter C, Bayer R. (2010). Recovery and purification process development for monoclonal antibody production. mAbs 2(5):480-499. https://doi.org/10.4161/mabs.2.5.12645

Chapter 15 — Modeling Polishing: Multi-Step Purification and Quality Attributes​

  1. Liu HF, Ma J, Winter C, Bayer R. (2010). Recovery and purification process development for monoclonal antibody production. mAbs 2(5):480-499. https://doi.org/10.4161/mabs.2.5.12645
  2. Shukla AA, Thömmes J. (2010). Recent advances in large-scale production of monoclonal antibodies and related proteins. Trends in Biotechnology 28(5):253-261. https://doi.org/10.1016/j.tibtech.2010.02.001
  3. Fekete S, Beck A, Veuthey JL, Guillarme D. (2015). Ion-exchange chromatography for the characterization of biopharmaceuticals. Journal of Pharmaceutical and Biomedical Analysis 113:43-55. https://doi.org/10.1016/j.jpba.2015.02.037

Chapter 16 — Modeling the Drug Substance: The Lot That Anchors Release​

  1. ICH (International Council for Harmonisation). (1999). ICH Harmonised Tripartite Guideline Q6B: Specifications: Test Procedures and Acceptance Criteria for Biotechnological/Biological Products. ICH, Current Step 4 version, March 1999. https://database.ich.org/sites/default/files/Q6B%20Guideline.pdf
  2. Liu HF, Ma J, Winter C, Bayer R. (2010). Recovery and purification process development for monoclonal antibody production. mAbs 2(5):480-499. https://doi.org/10.4161/mabs.2.5.12645
  3. Shukla AA, Thömmes J. (2010). Recent advances in large-scale production of monoclonal antibodies and related proteins. Trends in Biotechnology 28(5):253-261. https://doi.org/10.1016/j.tibtech.2010.02.001

Chapter 17 — Modeling Formulation and Fill-Finish: From Substance to Product​

  1. Daugherty AL, Mrsny RJ. (2006). Formulation and delivery issues for monoclonal antibody therapeutics. Advanced Drug Delivery Reviews 58(5-6):686-706. https://doi.org/10.1016/j.addr.2006.03.011
  2. ICH (International Council for Harmonisation). (1999). ICH Harmonised Tripartite Guideline Q6B: Specifications: Test Procedures and Acceptance Criteria for Biotechnological/Biological Products. ICH, Current Step 4 version, March 1999. https://database.ich.org/sites/default/files/Q6B%20Guideline.pdf
  3. FDA (U.S. Food and Drug Administration). (1999). Guidance for Industry: Container Closure Systems for Packaging Human Drugs and Biologics. FDA, CDER/CBER, May 1999. https://www.fda.gov/media/70788/download

Chapter 18 — Modeling QC and the Release Gate: Specifications as SHACL​

  1. Knublauch H, Kontokostas D (eds). (2017). Shapes Constraint Language (SHACL). W3C Recommendation, 20 July 2017. https://www.w3.org/TR/shacl/
  2. ICH (International Council for Harmonisation). (1999). ICH Harmonised Tripartite Guideline Q6B: Specifications: Test Procedures and Acceptance Criteria for Biotechnological/Biological Products. ICH, Current Step 4 version, March 1999. https://database.ich.org/sites/default/files/Q6B%20Guideline.pdf
  3. FDA (U.S. Food and Drug Administration). (1997). 21 CFR Part 11 — Electronic Records; Electronic Signatures. U.S. Code of Federal Regulations, Title 21, Part 11 (with EU GMP Annex 11, Computerised Systems, as the European counterpart). https://www.ecfr.gov/current/title-21/chapter-I/subchapter-A/part-11

Chapter 19 — Modeling Packaging and Serialization: GS1 and Unit Identity​

  1. GS1. (2024). GS1 General Specifications (Release 24). GS1 AISBL, Brussels. https://www.gs1.org/standards/barcodes-epcrfid-id-keys/gs1-general-specifications
  2. GS1. (2022). EPCIS and Core Business Vocabulary (CBV) Standard, Release 2.0. GS1 AISBL, Brussels. https://www.gs1.org/standards/epcis
  3. FDA (U.S. Food and Drug Administration). (2013). Drug Supply Chain Security Act (DSCSA), Title II of the Drug Quality and Security Act. U.S. FDA (with EU Directive 2011/62/EU, the Falsified Medicines Directive, as the European counterpart). https://www.fda.gov/drugs/drug-supply-chain-integrity/drug-supply-chain-security-act-dscsa

Chapter 20 — Modeling Distribution and the Cold Chain to the Patient​

  1. European Commission. (2013). Guidelines of 5 November 2013 on Good Distribution Practice of medicinal products for human use (2013/C 343/01). Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52013XC1123%2801%29
  2. WHO (World Health Organization). (2011). Model guidance for the storage and transport of time- and temperature-sensitive pharmaceutical products. WHO Technical Report Series No. 961, Annex 9. https://www.who.int/publications/m/item/storage-transport-trs961-annex9
  3. USP (United States Pharmacopeia). (2023). General Chapter 1079: Good Storage and Distribution Practices for Drug Products. United States Pharmacopeia–National Formulary (USP–NF). https://www.usp.org/

Chapter 21 — Assembling the Digital Thread: Lineage, Impact, and the Whole-Lifecycle Query​

  1. Harris S, Seaborne A (eds). (2013). SPARQL 1.1 Query Language. W3C Recommendation, 21 March 2013. https://www.w3.org/TR/sparql11-query/
  2. Kritzinger W, Karner M, Traar G, Henjes J, Sihn W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine 51(11):1016-1022. https://doi.org/10.1016/j.ifacol.2018.08.474
  3. Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3:160018. https://doi.org/10.1038/sdata.2016.18

Chapter 22 — Governing the Model: Versioning, Change Control, and Ontology Stewardship​

  1. Moxon SAT, Solbrig H, Unni DR, et al. (2021). The Linked Data Modeling Language (LinkML): A General-Purpose Data Modeling Framework Grounded in Machine-Readable Semantics. Proceedings of the International Conference on Biomedical Ontologies (ICBO 2021), CEUR-WS Vol. 3073. https://ceur-ws.org/Vol-3073/
  2. Smith B, Ashburner M, Rosse C, et al. (2007). The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nature Biotechnology 25(11):1251-1255. https://doi.org/10.1038/nbt1346
  3. ISPE (International Society for Pharmaceutical Engineering). (2022). GAMP 5: A Risk-Based Approach to Compliant GxP Computerized Systems (Second Edition). ISPE, July 2022. https://guidance-docs.ispe.org/doi/book/10.1002/9781946964571

Chapter 23 — FAIR in Practice: Measuring Whether the Graph Actually Delivers​

  1. Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3:160018. https://doi.org/10.1038/sdata.2016.18
  2. Wilkinson MD, Sansone SA, Schultes E, Doorn P, Bonino da Silva Santos LO, Dumontier M. (2018). A design framework and exemplar metrics for FAIRness. Scientific Data 5:180118. https://doi.org/10.1038/sdata.2018.118
  3. Jacobsen A, de Miranda Azevedo R, Juty N, et al. (2020). FAIR Principles: Interpretations and Implementation Considerations. Data Intelligence 2(1-2):10-29. https://doi.org/10.1162/dint_r_00024

Chapter 24 — The Standards Bodies: Who Actually Builds Biopharma's Shared Vocabulary​

  1. Allotrope Foundation. (2024). About the Allotrope Foundation (founding history; Foundation Members; framework). Allotrope Foundation. https://www.allotrope.org/about-us
  2. Allotrope Foundation. (2024). Allotrope Foundation Ontologies (AFO). Allotrope Foundation. https://www.allotrope.org/ontologies
  3. Pistoia Alliance. (2024). Pistoia Alliance Launches Freely Available IDMP Ontology 1.0. Press release, 24 January 2024. https://pistoiaalliance.org/news/press-release-pistoia-alliance-launches-idmp-1-0/
  4. Pistoia Alliance. (2024). About the Pistoia Alliance (founders, founding year, membership). https://pistoiaalliance.org/membership/about/
  5. Pistoia Alliance. (2026). Pistoia Alliance Releases Version 1.0 of the Pharmaceutical CMC Process Ontology. https://pistoiaalliance.org/news/pistoia-alliance-releases-version-1-0-of-the-pharmaceutical-cmc-process-ontology/
  6. MESA International. (2020). B2MML — Business To Manufacturing Markup Language, Version 7 (V0700) (W3C XSD implementation of the ISA-95 / IEC 62264 family). Manufacturing Enterprise Solutions Association. https://mesa.org/topics-resources/b2mml/
  7. OPC Foundation. (2013). OPC 10030 — OPC UA for ISA-95 Common Object Model, Release 1.00. OPC Foundation. https://reference.opcfoundation.org/specs/OPC-10030
  8. PROFIBUS & PROFINET International (PI). (2024). MTP — Module Type Packages (VDI/VDE/NAMUR 2658; toward IEC 63280). https://www.profibus.com/technologies/mtp
  9. ISPE (International Society for Pharmaceutical Engineering). (2023). ISPE Baseline Guide Vol. 8: Pharma 4.0 (First Edition). ISPE, December 2023. https://ispe.org/publications/guidance-documents/baseline-guide-vol-8-pharma-40-1st-edition
  10. BioPhorum. (2023). Digital Plant Maturity Model 3.0. BioPhorum Operations Group, October 2023. https://www.biophorum.com/workstream/dpmm-v-3/
  11. GS1 US. (2024). Applying the GS1 System of Standards for DSCSA and Serialized Interoperable Traceability. GS1 US. https://www.gs1us.org/
  12. NIIMBL / Open Applications Group (OAGi). (2024). NIIMBL and OAGi Partner to Develop Open-Source Biopharmaceutical Manufacturing Ontologies. Press release, June 2024. https://www.prnewswire.com/news-releases/niimbl-and-open-applications-group-oagi-partner-to-develop-open-source-biopharmaceutical-manufacturing-ontologies-302172016.html

Chapter 25 — The Vocabularies in Use: From AFO to IDMP​

  1. Allotrope Foundation. (2024). Allotrope Foundation Ontologies (AFO). Allotrope Foundation. https://www.allotrope.org/ontologies
  2. Rise of the Allotrope Simple Model (ASM). (2024). Drug Discovery Today. https://www.sciencedirect.com/science/article/abs/pii/S1359644624000692
  3. EMA (European Medicines Agency). (2024). Data on medicines (ISO IDMP standards): Overview. https://www.ema.europa.eu/en/human-regulatory-overview/research-development/data-medicines-iso-idmp-standards-overview
  4. FDA (U.S. Food and Drug Administration). (2021). FDA's Global Substance Registration System (GSRS) / UNII. FDA. https://www.fda.gov/industry/fda-data-standards-advisory-board/fdas-global-substance-registration-system
  5. CDISC. (2024). CDISC Controlled Terminology (distributed via the NCI Thesaurus / NCI-EVS). https://www.cdisc.org/standards/terminology/controlled-terminology
  6. EMBL-EBI. (2024). ChEBI — Chemical Entities of Biological Interest. European Bioinformatics Institute. https://www.ebi.ac.uk/chebi/
  7. QUDT.org. (2024). QUDT — Quantities, Units, Dimensions and Types Ontology. QUDT.org. https://qudt.org/
  8. Schadow G, McDonald CJ. (2017). The Unified Code for Units of Measure (UCUM), Revision 2.1. Regenstrief Institute, Indianapolis, IN. https://ucum.org/
  9. Abeyruwan S, Vempati UD, Küçük-McGinty H, et al. (2014). Using the BioAssay Ontology for analyzing high-throughput screening data. Journal of Biomolecular Screening / SLAS Discovery 19(5):715-726. https://journals.sagepub.com/doi/10.1177/1087057114563493
  10. Batchelor C, et al. CHMO — the Chemical Methods Ontology. OBO Foundry. http://obofoundry.org/ontology/chmo.html
  11. OBO Foundry. PROCO — Process Chemistry Ontology. https://obofoundry.org/ontology/proco.html
  12. Natale DA, Arighi CN, Blum M, et al. (2017). Protein Ontology (PRO): enhancing and scaling up the representation of protein entities. Nucleic Acids Research 45(D1):D339-D346. https://doi.org/10.1093/nar/gkw1075
  13. Lebo T, Sahoo S, McGuinness D (eds). (2013). PROV-O: The PROV Ontology. W3C Recommendation, 30 April 2013. https://www.w3.org/TR/prov-o/

Chapter 26 — The Platforms: How Vendors Sell Semantics​

  1. TetraScience. (2024). Creating Data in the Allotrope Simple Model (ASM) at Scale. Factsheet. https://www.tetrascience.com/factsheet/creating-data-in-the-allotrope-simple-model
  2. SciBite (an Elsevier company). (2024). SciBite Brings Enterprise Ontologies to Benchling: Ontology-Backed Data Capture. https://scibite.com/knowledge-hub/news/benchling-ontology-backed-data-capture/
  3. Revvity Signals. (2024). Signals One — Ontology Support. https://revvitysignals.com/products/research/signals-one
  4. Körber. (2024). Werum PAS-X MES — MBR Design & Execution. Körber Pharma. https://www.koerber-pharma.com/en/solutions/software/werum-pas-x-mes-suite/werum-pas-x-mbr-design-execution
  5. AVEVA (formerly OSIsoft). (2023). What is PI Asset Framework?. AVEVA Group plc. https://www.aveva.com/en/perspectives/blog/easy-as-pi-asset-framework/
  6. Palantir Technologies. (2024). Foundry Ontology — Overview. https://www.palantir.com/docs/foundry/ontology/overview
  7. Stardog. (2024). Customer Story: Boehringer Ingelheim. https://www.stardog.com/company/customers/boehringer-ingelheim/
  8. Ontotext. (2023). Ontotext's New AI-Powered Target Discovery Solution. PR Newswire, 15 May 2023. https://www.prnewswire.com/news-releases/ontotexts-new-ai-powered-target-discovery-solution-enables-life-sciences-companies-to-achieve-10x-more-efficient-insight-discovery-and-4x-faster-information-retrieval-301824722.html
  9. Neo4j. (2025). GraphTalk Pharma & Life Sciences 2025 — A Recap. https://neo4j.com/blog/developer/graphtalk-pharma-life-sciences-2025/
  10. Das S, Sundara S, Cyganiak R (eds). (2012). R2RML: RDB to RDF Mapping Language. W3C Recommendation, 27 September 2012 (with the RML extension, https://rml.io/specs/rml/). https://www.w3.org/TR/r2rml/

Chapter 27 — Enterprise Knowledge Graphs at Big Pharma​

  1. Pistoia Alliance FAIR Toolkit. (2024). FAIR Data by Design — Roche. https://fairtoolkit.pistoiaalliance.org/use-cases/fair-data-by-design/
  2. metaphacts. (2024). Knowledge Democratization with an Enterprise Knowledge Graph at Boehringer Ingelheim. https://metaphacts.com/knowledge-democratization-with-an-enterprise-knowledge-graph-at-boehringer-ingelheim
  3. Phenome-wide identification of therapeutic genetic targets (Mantis-ML 2.0 with the AstraZeneca Biological Insights Knowledge Graph). (2024). Science Advances. https://pmc.ncbi.nlm.nih.gov/articles/PMC11078195/
  4. Novartis. (2024). The data42 Program and Ontology Designer — data42 (careers). https://www.novartis.com/stories/data42-program-shows-novartis-intent-go-big-data-and-digital
  5. Digital evolution: Novo Nordisk's shift to ontology-based data management. (2025). Journal of Biomedical Semantics 16. https://link.springer.com/article/10.1186/s13326-025-00327-4
  6. Pistoia Alliance. (2026). Pistoia Alliance Advances IDMP Ontology (J&J production product master). https://pistoiaalliance.org/news/pistoia-alliance-advances-idmp-ontology/
  7. BioProcess International. (2024). Adding Context: Data Mapping Key to Sanofi's Digitization Strategy. https://www.bioprocessintl.com/upstream-downstream-processing/adding-context-data-mapping-key-to-sanofi-s-digitization-strategy
  8. The Pistoia Alliance's methods database project: machine-readable HPLC-UV method transfer via the Allotrope Data Format. (2025). Journal of Pharmaceutical and Biomedical Analysis. https://pubmed.ncbi.nlm.nih.gov/40286673/

Chapter 28 — Regulatory Semantics: IDMP, SPL, KASA, and the Structured Submission​

  1. EMA (European Medicines Agency). (2024). Data on medicines (ISO IDMP standards); Substance, Product, Organisation and Referential (SPOR) master data; PMS go-live notices. https://www.ema.europa.eu/en/human-regulatory-overview/research-development/data-medicines-iso-idmp-standards-overview
  2. Galata SR, et al. (2021). The Global Substance Registration System (GSRS). Nucleic Acids Research 49(D1):D1179-D1185; FDA, GSRS / UNII. https://academic.oup.com/nar/article/49/D1/D1179/5952203
  3. FDA (U.S. Food and Drug Administration). (2024). Structured Product Labeling (SPL) Resources. https://www.fda.gov/industry/fda-data-standards-advisory-board/structured-product-labeling-resources
  4. CDISC; U.S. Federal Register. (2021). CDISC SEND (Standard for Exchange of Nonclinical Data); Technical Rejection Criteria for Study Data, Federal Register, 29 July 2021. https://www.cdisc.org/standards/foundational/send
  5. FDA's implementation of KASA (Knowledge-Aided Assessment and Structured Application) for manufacturing assessment of non-sterile solid oral dosage forms. (2025). AAPS Open. https://link.springer.com/article/10.1186/s41120-025-00141-3
  6. HL7 / FDA. (2024). PQ-CMC: Pharmaceutical Quality / Chemistry, Manufacturing and Controls FHIR Implementation Guide, v2.0.0. http://hl7.org/fhir/us/pq-cmc-fda/
  7. FDA (U.S. Food and Drug Administration). (2024). eCTD Submission Standards for eCTD v4.0 and Regional M1. https://www.fda.gov/drugs/electronic-regulatory-submission-and-review/ectd-submission-standards-ectd-v40-and-regional-m1
  8. ICH / ICMRA. (2025-2026). ICH M4Q(R2) draft guideline (Step 2); ICMRA Pharmaceutical Quality Knowledge Management — Unique Identifier Progress Report. https://www.federalregister.gov/documents/2026/01/21/2026-01073/ ; https://icmra.info/
  9. Pistoia Alliance. (2026). IDMP Ontology (IDMP-O) project; standardization via ISO/TS 21405. https://pistoiaalliance.org/project/idmp-o/
  10. Schadow G, McDonald CJ. (2017). The Unified Code for Units of Measure (UCUM), Revision 2.1. Regenstrief Institute, Indianapolis, IN. https://ucum.org/
  11. ISPE. (2022). GAMP 5: A Risk-Based Approach to Compliant GxP Computerized Systems (Second Edition); FDA, 21 CFR Part 11 — Electronic Records; Electronic Signatures (with EU GMP Annex 11). https://guidance-docs.ispe.org/doi/book/10.1002/9781946964571

Chapter 29 — The Shop Floor and the Digital Twin: Where Ontologies Are Still Arriving​

  1. Putting Together the Pieces (Genentech SAP-IDoc-to-B2MML integration). Pharmaceutical Technology; AVEVA, What is PI Asset Framework?. https://www.pharmtech.com/view/putting-together-pieces
  2. ISPE. (2023). A Simplified Integration of Qualified Laboratory Devices with the Asset Administration Shell as the Digital Twin. ISPE White Paper, May 2023. https://ispe.org/pharmaceutical-engineering/white-papers/simplified-integration-qualified-laboratory-devices-asset-administration
  3. SiLA Consortium / UniteLabs. (2024). UniteLabs Tecan FluentControl Connector (SiLA 2); The AC/DC Concept (Drug Discovery World). https://sila-standard.com/sila_device/unitelabs-tecan-fluentcontrol-connector/
  4. OPC Foundation. (2025). SPECTARIS LADS Showcases Integration of OPC UA with Allotrope Standards; OPC 30500 — LADS (Laboratory and Analytical Device Standard). https://opcfoundation.org/news/press-releases/breakthrough-in-smarter-labs-spectaris-lads-showcases-integration-of-opc-ua-with-allotrope-standards/
  5. Haller A, Janowicz K, Cox S, Le Phuoc D, Taylor K, Lefrançois M (eds). (2017). Semantic Sensor Network Ontology (SOSA/SSN). W3C / OGC Recommendation, 19 October 2017. https://www.w3.org/TR/vocab-ssn/
  6. BioPhorum. (2023). Big Data to Smart Data: Implementing an Ontology and Digital Data Capture to Improve Biomanufacturing. BioPhorum, 29 November 2023. https://www.biophorum.com/download/big-data-to-smart-data-implementing-an-ontology-and-digital-data-capture-to-improve-biomanufacturing/
  7. Digital Twins in Biopharmaceutical Manufacturing: Review and Perspective. (2025). arXiv preprint; with Samsung Biologics CFD twin (Pharma's Almanac) and GSK vaccine twin (Fierce Pharma). https://arxiv.org/pdf/2504.00286
  8. NIST (National Institute of Standards and Technology). (2023). Towards Ontologizing a Digital Twin Framework for Manufacturing (ISO 23247; BFO + IOF Core; bioreactor example). IFIP APMS 2023. https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936637
  9. MCBO — the Mammalian Cell Bioprocessing Ontology. (2026). bioRxiv preprint (not yet peer-reviewed). https://www.biorxiv.org/content/10.64898/2026.01.05.697007v1
  10. Industrial Ontologies Foundry / OAGi / NIIMBL. (2024-2025). Open-source biopharmaceutical-manufacturing (IOF Biopharma) reference ontologies. https://github.com/iofoundry/ontology/releases
  11. ISPE. (2020). Continued Process Verification in Stages 1-3; MilliporeSigma, Bio4C ProcessPad; Multivariate Data-Driven Modeling for Continued Process Verification, BioProcess International. https://ispe.org/pharmaceutical-engineering/july-august-2020/continued-process-verification-stages-1-3

Chapter 30 — The Frontier: Ontologies as the Ground Truth for AI​

  1. Pistoia Alliance. (2025). Pistoia Alliance Launches Third Phase of the CMC Process Ontology (making life-sciences data "AI-ready"). https://pistoiaalliance.org/news/pistoia-alliance-launches-third-phase-of-cmc-process-ontology/
  2. TetraScience. (2024-2025). TetraScience Collaborates with NVIDIA (BusinessWire, 12 November 2024); TetraScience Launches the Scientific AI Lighthouse (SAIL) Program with Takeda as Founding Partner (PR Newswire, 23 October 2025). https://www.businesswire.com/news/home/20241112651874/en/
  3. Neo4j. (2025). GraphTalk Pharma & Life Sciences 2025 — A Recap (Merck Group Synaptix; Bayer; Syngenta NOCTIS). https://neo4j.com/blog/developer/graphtalk-pharma-life-sciences-2025/
  4. Digital evolution: Novo Nordisk's shift to ontology-based data management. (2025). Journal of Biomedical Semantics 16. https://link.springer.com/article/10.1186/s13326-025-00327-4
  5. Rise of the Allotrope Simple Model (ASM). (2024). Drug Discovery Today. https://www.sciencedirect.com/science/article/abs/pii/S1359644624000692
  6. PROFIBUS & PROFINET International. (2025-2026). First MTP V2.0 Plugfest Successfully Completed; PI Publishes MTP Specification 2.0. https://www.profibus.com/newsroom/press-news/first-mtp-v20-plugfest-successfully-completed
  7. Digital Twins in Biopharmaceutical Manufacturing: Review and Perspective. (2025). arXiv preprint (the data-standardization bottleneck thesis). https://arxiv.org/pdf/2504.00286

Chapter 31 — An Honest Verdict: What Ontologies Solve, and What They Leave to People​

  1. Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3:160018. https://doi.org/10.1038/sdata.2016.18
  2. Smith B, Ashburner M, Rosse C, et al. (2007). The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nature Biotechnology 25(11):1251-1255. https://doi.org/10.1038/nbt1346
  3. ISO/IEC (International Organization for Standardization / International Electrotechnical Commission). (2021). ISO/IEC 21838-2:2021 — Information technology — Top-level ontologies (TLO) — Part 2: Basic Formal Ontology (BFO). ISO/IEC, Geneva. https://www.iso.org/standard/74572.html