A Review of Artificial Intelligence and Machine Learning in Product Life Cycle Management

References

1. 1.↵
   1. McCulloch W. S.,
   2. Pitts W.

   A Logical Calculus of the Ideas Immanent in Nervous Activity. Bull. Math. Biophys. 1943, 5 (4), 115–133. https://doi.org/10.1007/BF02478259.

   OpenUrlCrossRef
2. 2.↵
   1. Turing A.

   I.—Computing Machinery and Intelligence. Mind 1950, LIX (236), 433–460.
3. 3.↵
   1. Goodfellow I.,
   2. Bengio Y.,
   3. Courville A.

   Deep Learning, MIT Press, 2016.
4. 4.↵
   1. Flam S.

   Machine Learning in Healthcare. Foresee Medical Web site. https://www.foreseemed.com/blog/machine-learning-in-healthcare (accessed August 8, 2023).
5. 5.↵
   1. Liu Q.,
   2. Huang R.,
   3. Hsieh J.,
   4. Zhu H.,
   5. Tiwari M.,
   6. Liu G.,
   7. Jean D.,
   8. ElZarrad M. K.,
   9. Fakhouri T.,
   10. Berman S.,
   11. Dunn B.,
   12. Diamond M. C.,
   13. Huang S.-M.

   Landscape Analysis of the Application of Artificial Intelligence and Machine Learning in Regulatory Submissions for Drug Development From 2016 to 2021. Clin. Pharmacol. Ther. 2023, 113 (4), 771–774. https://doi.org/10.1002/cpt.2668.

   OpenUrlPubMed
6. 6.↵
   1. Manzano T.,
   2. Langer G.

   Getting Ready for Pharma 4.0. Data Integrity in Cloud and Big Data Applications. Pharm. Eng. 2018, 72–79.
7. 7.↵
   1. Herwig C.,
   2. Woelbeling C.,
   3. Zimmer T.

   A Holistic Approach to Production Control from Industry 4.0 to Pharma 4.0. Pharm. Eng. 2017, 37 (3), 44–49.

   OpenUrl
8. 8.↵
   U.S. Food and Drug Administration. Data Integrity and Compliance with CGMP Guidance for Industry. 2016. https://www.fda.gov/files/drugs/published/Data-Integrity-and-Compliance-With-Current-Good-Manufacturing-Practice-Guidance-for-Industry.pdf (accessed August 8, 2023).
9. 9.↵
   U.S. Food and Drug Administration. Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices (accessed August 1, 2023).
10. 10.↵
    1. Muehlemann N.

    ML-Based Population Selection and Enrichment in Drug Development. Biopharm. Rep. 2023, 30 (I), 16–22.

    OpenUrl
11. 11.↵
    U.S. Food and Drug Administration. Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD). 2019. https://www.fda.gov/media/122535/download (accessed August 2, 2023).
12. 12.↵
    U.S. Food and Drug Administration. Good Machine Learning Practice for Medical Device Development: Guiding Principles. 2021. https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles (accessed August 3, 2023).
13. 13.↵
    U.S. Food and Drug Administration. Using Artificial Intelligence & Machine Learning in the Development of Drug and Biological Products - Discussion Paper and Request for Feedback. 2023. https://www.fda.gov/media/167973/download?attachment (accessed August 2, 2023).
14. 14.↵
    1. Liu Q.,
    2. Zhu H.,
    3. Liu C.,
    4. Jean D.,
    5. Huang S.-M.,
    6. ElZarrad M. K.,
    7. Blumenthal G.,
    8. Wang Y.

    Application of Machine Learning in Drug Development and Regulation: Current Status and Future Potential. Clin. Pharmacol. Ther. 2020, 107 (4), 726–729. https://doi.org/10.1002/cpt.1771.

    OpenUrlPubMed
15. 15.↵
    1. Samson Y.

    ALCOA+ - What Does It Mean? https://www.gmp-compliance.org/gmp-news/alcoa-what-does-it-mean (accessed November 9, 2023).
16. 16.↵
    Ensuring Safe, Secure, and Trustworthy AI. 2023. https://www.whitehouse.gov/wp-content/uploads/2023/07/Ensuring-Safe-Secure-and-Trustworthy-AI.pdf (accessed August 1, 2023).
17. 17.↵
    The White House. FACT SHEET: Biden-Harris Administration Secures Voluntary Commitments from Leading Artificial Intelligence Companies to Manage the Risks Posed by AI. https://www.whitehouse.gov/briefing-room/statements-releases/2023/07/21/fact-sheet-biden-harris-administration-secures-voluntary-commitments-from-leading-artificial-intelligence-companies-to-manage-the-risks-posed-by-ai/ (accessed August 1, 2023).
18. 18.↵
    1. Jumper J.,
    2. Evans R.,
    3. Pritzel A.,
    4. Green T.,
    5. Figurnov M.,
    6. Ronneberger O.,
    7. Tunyasuvunakool K.,
    8. Bates R.,
    9. Žídek A.,
    10. Potapenko A.,
    11. Bridgland A.,
    12. Meyer C.,
    13. Kohl S. A. A.,
    14. Ballard A. J.,
    15. Cowie A.,
    16. Romera-Paredes B.,
    17. Nikolov S.,
    18. Jain R.,
    19. Adler J.,
    20. Back T.,
    21. Petersen S.,
    22. Reiman D.,
    23. Clancy E.,
    24. Zielinski M.,
    25. Steinegger M.,
    26. Pacholska M.,
    27. Berghammer T.,
    28. Bodenstein S.,
    29. Silver D.,
    30. Vinyals O.,
    31. Senior A. W.,
    32. Kavukcuoglu K.,
    33. Kohli P.,
    34. Hassabis D.

    Highly Accurate Protein Structure Prediction with AlphaFold. Nature 2021, 596 (7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2.

    OpenUrlCrossRefPubMed
19. 19.↵
    1. Zhavoronkov A.,
    2. Vanhaelen Q.,
    3. Oprea T. I.

    Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology? Clin. Pharmacol. Ther. 2020, 107 (4), 780–785. https://doi.org/10.1002/cpt.1795.

    OpenUrlPubMed
20. 20.↵
    1. Kadurin A.,
    2. Aliper A.,
    3. Kazennov A.,
    4. Mamoshina P.,
    5. Vanhaelen Q.,
    6. Khrabrov K.,
    7. Zhavoronkov A.

    The Cornucopia of Meaningful Leads: Applying Deep Adversarial Autoencoders for New Molecule Development in Oncology. Oncotarget 2017, 8 (7), 10883–10890. https://doi.org/10.18632/oncotarget.14073.

    OpenUrlPubMed
21. 21.↵
    1. Bailly M.,
    2. Mieczkowski C.,
    3. Juan V.,
    4. Metwally E.,
    5. Tomazela D.,
    6. Baker J.,
    7. Uchida M.,
    8. Kofman E.,
    9. Raoufi F.,
    10. Motlagh S.,
    11. Yu Y.,
    12. Park J.,
    13. Raghava S.,
    14. Welsh J.,
    15. Rauscher M.,
    16. Raghunathan G.,
    17. Hsieh M.,
    18. Chen Y.-L.,
    19. Nguyen H. T.,
    20. Nguyen N.,
    21. Cipriano D.,
    22. Fayadat-Dilman L.

    Predicting Antibody Developability Profiles Through Early Stage Discovery Screening. mAbs 2020, 12 (1), https://doi.org/10.1080/19420862.2020.1743053.
22. 22.↵
    1. Pais D. A. M.,
    2. Mayer J.-P. A.,
    3. Felderer K.,
    4. Batalha M. B.,
    5. Eichner T.,
    6. Santos S. T.,
    7. Kumar R.,
    8. Silva S. D.,
    9. Kaufmann H.

    Holistic in Silico Developability Assessment of Novel Classes of Small Proteins Using Publicly Available Sequence-Based Predictors. 2023, 11.10.566515. bioRXiv. https://doi.org/10.1101/2023.11.10.566515.
23. 23.↵
    1. Zhavoronkov A.,
    2. Ivanenkov Y. A.,
    3. Aliper A.,
    4. Veselov M. S.,
    5. Aladinskiy V. A.,
    6. Aladinskaya A. V.,
    7. Terentiev V. A.,
    8. Polykovskiy D. A.,
    9. Kuznetsov M. D.,
    10. Asadulaev A.,
    11. Volkov Y.,
    12. Zholus A.,
    13. Shayakhmetov R. R.,
    14. Zhebrak A.,
    15. Minaeva L. I.,
    16. Zagribelnyy B. A.,
    17. Lee L. H.,
    18. Soll R.,
    19. Madge D.,
    20. Xing L.,
    21. Guo T.,
    22. Aspuru-Guzik A.

    Deep Learning Enables Rapid Identification of Potent DDR1 Kinase Inhibitors. Nat. Biotechnol. 2019, 37 (9), 1038–1040. https://doi.org/10.1038/s41587-019-0224-x.

    OpenUrlCrossRefPubMed
24. 24.↵
    1. Bader J.,
    2. Narayanan H.,
    3. Arosio P.,
    4. Leroux J.-C.

    Improving Extracellular Vesicles Production through a Bayesian Optimization-Based Experimental Design. Eur. J. Pharm. Biopharm. 2023, 182, 103–114. https://doi.org/10.1016/j.ejpb.2022.12.004.

    OpenUrlPubMed
25. 25.↵
    1. Narayanan H.,
    2. Dingfelder F.,
    3. Condado Morales I.,
    4. Patel B.,
    5. Heding K. E.,
    6. Bjelke J. R.,
    7. Egebjerg T.,
    8. Butté A.,
    9. Sokolov M.,
    10. Lorenzen N.,
    11. Arosio P.

    Design of Biopharmaceutical Formulations Accelerated by Machine Learning. Mol. Pharmaceutics 2021, 18 (10), 3843–3853. https://doi.org/10.1021/acs.molpharmaceut.1c00469.

    OpenUrl
26. 26.↵
    1. Bayer B.,
    2. Striedner G.,
    3. Duerkop M.

    Hybrid Modeling and Intensified DoE: An Approach to Accelerate Upstream Process Characterization. Biotechnol. J. 2020, 15 (9), 2000121.

    OpenUrl
27. 27.↵
    U.S. Food and Drug Administration. Process Validation: General Principles and Practices. 2011. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/process-validation-general-principles-and-practices (accessed August 7, 2023).
28. 28.↵
    1. Marschall L.,
    2. Taylor C.,
    3. Zahel T.,
    4. Kunzelmann M.,
    5. Wiedenmann A.,
    6. Presser B.,
    7. Studts J.,
    8. Herwig C.

    Specification-Driven Acceptance Criteria for Validation of Biopharmaceutical Processes. Front. Bioeng. Biotechnol. 2022, 10. https://doi.org/10.3389/fbioe.2022.1010583.
29. 29.↵
    International Council for Harmonisation. Quality Guideline Q8(R2) on Pharmaceutical Development. 2009. https://www.ema.europa.eu/en/documents/scientific-guideline/international-conference-harmonisation-technical-requirements-registration-pharmaceuticals-human-use_en-11.pdf (accessed August 8, 2023).
30. 30.↵
    International Council for Harmonisation. Harmonised Guideline Q9(R1): Quality Risk Management. 2023. https://database.ich.org/sites/default/files/ICH_Q9%28R1%29_Guideline_Step4_2023_0126_0.pdf (accessed August 8, 2023)
31. 31.↵
    1. Vuolo M.,
    2. Margetts D.

    Breaking with Tradition: Laying the Foundation for Validation 4.0. Pharm. Eng. 2021.
32. 32.↵
    1. Adhibhatta S.,
    2. DiMartino M.,
    3. Falcon R.,
    4. Haman E.,
    5. Legg K.,
    6. Payne R.,
    7. Pipkins K.,
    8. Zamamiri A.

    Continued Process Verification (CPV) Signal Responses in Biopharma. Pharm. Eng. 2017.
33. 33.↵
    1. Huang J.,
    2. O’Connor T.,
    3. Ahmed K.,
    4. Chatterjee S.,
    5. Garvin C.,
    6. Ghosh K.,
    7. Ierapetritou M.,
    8. Jeffers M.,
    9. Pla D. L.,
    10. Lee S. L.,
    11. Lovett D.,
    12. Lyngberg O.,
    13. Mack J.,
    14. McManus E.,
    15. Romero‐Torres S.,
    16. Undey C.,
    17. Venkatasubramanian V.,
    18. Warman M.

    AIChE PD2M Advanced Process Control Workshop ‐ Moving APC Forward in the Pharmaceutical Industry. J. Adv. Manuf. Process. 2021, 3 (1).
34. 34.↵
    1. Ondracka A.,
    2. Gasset A.,
    3. García-Ortega X.,
    4. Hubmayr D.,
    5. van Wijngaarden J.,
    6. Montesinos-Seguí J. L.,
    7. Valero F.,
    8. Manzano T.

    CPV of the Future: AI-Powered Continued Process Verification for Bioreactor Processes. PDA J. Pharm. Sci. Technol. 2023, 77 (3), 146–165. https://doi.org/10.5731/pdajpst.2021.012665.

    OpenUrlAbstract/FREE Full Text
35. 35.↵
    1. Chen Y.,
    2. Yang O.,
    3. Sampat C.,
    4. Bhalode P.,
    5. Ramachandran R.,
    6. Ierapetritou M.

    Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature Review. Processes 2020, 8 (9), 1088. https://doi.org/10.3390/pr8091088.

    OpenUrl
36. 36.↵
    1. Singh M.,
    2. Fuenmayor E.,
    3. Hinchy E.,
    4. Qiao Y.,
    5. Murray N.,
    6. Devine D.

    Digital Twin: Origin to Future. Appl. Syst. Innovation 2021, 4 (2), 36. https://doi.org/10.3390/asi4020036.

    OpenUrl
37. 37.↵
    1. Zhao B.,
    2. Li X.,
    3. Sun W.,
    4. Qian J.,
    5. Liu J.,
    6. Gao M.,
    7. Guan X.,
    8. Ma Z.,
    9. Li J.

    BioDT: An Integrated Digital-Twin-Based Framework for Intelligent Biomanufacturing. Processes 2023, 11 (4), 1213. https://doi.org/10.3390/pr11041213.

    OpenUrl
38. 38.↵
    1. Ghatti S.,
    2. Yurish L. A.,
    3. Shen H.,
    4. Rheuban K.,
    5. B. Enfield K.,
    6. Facteau N. R.,
    7. Engel G.,
    8. Dowdell K.

    Digital Twins in Healthcare: A Survey of Current Methods. Arch. Clin. Biomed. Res. 2023, 7 (3), 365–381. https://doi.org/10.26502/acbr.50170352.

    OpenUrl
39. 39.↵
    1. Kaplan S.,
    2. Garrick B. J.

    On The Quantitative Definition of Risk. Risk Anal. 1981, 1 (1), 11–27. https://doi.org/10.1111/j.1539-6924.1981.tb01350.x.

    OpenUrlCrossRef
40. 40.↵
    1. Aven T.,
    2. Krohn B. S.

    A New Perspective on How to Understand, Assess and Manage Risk and the Unforeseen. Reliab. Eng. Syst. Saf. 2014, 121, 1–10. https://doi.org/10.1016/j.ress.2013.07.005.

    OpenUrl
41. 41.↵
    1. Niesen T.,
    2. Houy C.,
    3. Fettke P.,
    4. Loos P.

    Towards an Integrative Big Data Analysis Framework for Data-Driven Risk Management in Industry 4.0. 49th Hawaii International Conference on System Sciences (HICSS), Koloa, HI, 2016, pp 5065–5074. https://doi.org/10.1109/HICSS.2016.627.
42. 42.↵
    1. Silva R. C.,
    2. Almeida R.,
    3. Ferreira P.,
    4. Menezes J.,
    5. Martinho Â.

    Agile, Data-Driven Life Cycle Management for Continuous Manufacturing. Pharm. Eng. 2023.
43. 43.↵
    1. Paltrinieri N.,
    2. Comfort L.,
    3. Reniers G.

    Learning about Risk: Machine Learning for Risk Assessment. Saf. Sci. 2019, 118, 475–486. https://doi.org/10.1016/j.ssci.2019.06.001.

    OpenUrl