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2000
Volume 4, Issue 1
  • ISSN: 2950-3779
  • E-ISSN: 2950-3787

Abstract

The application of machine learning (ML) algorithms is causing a revolutionary change in the pharmaceutical production sector, providing previously unheard-of chances to improve productivity, precision, and creativity. From supply chain management and predictive maintenance to process optimization and quality assurance, machine learning approaches are transforming many aspects of drug manufacturing. These algorithms greatly lower production costs and minimize errors by utilizing large datasets to provide real-time analysis, anomaly detection, and predictive insights. The potential of these technologies to simplify intricate industrial processes is further enhanced by emerging trends like automation, sophisticated process control systems, and the integration of machine learning with digital twins. With an emphasis on important applications such as formulation optimization, adaptive process controls, and predictive modeling for regulatory compliance, this paper examines the changing field of machine learning in pharmaceutical production. It emphasizes how ML and Industry 4.0 technologies-like robotics and the Internet of Things (IoT)-work together to create smart industrial environments. The essay also discusses issues like algorithm openness, data privacy, and the requirement for a trained staff in order to fully utilize machine learning in this field. A key tool in addressing the rising demand for complicated biologics and tailored medications, machine learning (ML), is emerging as the pharmaceutical industry shifts to more flexible, economical, and sustainable production models. This review highlights the revolutionary impact of machine learning (ML)-driven production and offers a roadmap for its successful adoption in the pharmaceutical industry by analyzing its present status and potential future developments.

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References

  1. HumaT. PengZ. Introduction to regulatory affairs and different regulatory bodies for pharmaceutical products and impact of digitalization on regulatory affairs.Pharmacol. Pharm.2023141146347710.4236/pp.2023.1411030
     [Google Scholar]
  2. OrehekJ. TeslićD. LikozarB. Continuous crystallization processes in pharmaceutical manufacturing: A review.Org. Process Res. Dev.2021251164210.1021/acs.oprd.0c00398
     [Google Scholar]
  3. Bhuvanesh KumarM. SathiyaP. Methods and materials for additive manufacturing: A critical review on advancements and challenges.Thin-walled Struct.202115910722810.1016/j.tws.2020.107228
     [Google Scholar]
  4. JabbarS. LloydH. HammoudehM. AdebisiB. RazaU. Blockchain-enabled supply chain: analysis, challenges, and future directions.Multimedia Syst.202127478780610.1007/s00530‑020‑00687‑0
     [Google Scholar]
  5. Abdul RaheemA.K. DhannoonB.N. Automating drug discovery using machine learning.Curr. Drug Discov. Technol.2023206e07062321777610.2174/157016382066623060716331337287304
     [Google Scholar]
  6. DedeloudiA. WeaverE. LamprouD.A. Machine learning in additive manufacturing & Microfluidics for smarter and safer drug delivery systems.Int. J. Pharm.202363612281810.1016/j.ijpharm.2023.12281836907280
     [Google Scholar]
  7. MdA.Q. JhaK. HaneefS. SivaramanA.K. TeeK.F. A review on data-driven quality prediction in the production process with machine learning for industry 4.0.Processes 20221010196610.3390/pr10101966
     [Google Scholar]
  8. LakshmipathyA. VardhineediM. SekharamahanthiV.R.P. PatelD.D. SainiS. MohammedS. MediCaption: Integrating YOLO-driven computer vision and NLP for advanced pharmaceutical package recognition and annotation.Authorea Preprints202410.36227/techrxiv.171177556.62647395/v1
     [Google Scholar]
  9. AlexopoulosK. NikolakisN. ChryssolourisG. Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing.Int. J. Comput. Integrated Manuf.202033542943910.1080/0951192X.2020.1747642
     [Google Scholar]
  10. TasiyanaS.N. ChitophoP. MushiriT. YahyaA. A Machine Learning Approach to Improve the Pharmaceutical Industry Machinery: Case of Zimbabwe.Blockchain Technology in Healthcare Applications.CRC Press202211514110.1201/9781003224075‑6
     [Google Scholar]
  11. MoralesE.F. EscalanteH.J. A brief introduction to supervised, unsupervised, and reinforcement learning.Biosignal processing and classification using computational learning and intelligence.Academic Press202211112910.1016/B978‑0‑12‑820125‑1.00017‑8
     [Google Scholar]
  12. HiranK.K. JainR.K. LakhwaniK. DoshiR. Machine Learning: Master supervised and unsupervised learning algorithms with real examples (English Edition).BPB Publications2021
     [Google Scholar]
  13. DhanarajR. K. RajkumarK. HariharanU. Enterprise IoT modeling: Supervised, unsupervised, and reinforcement learning.Business Intelligence for Enterprise Internet of ThingsSpringerCham20205579
     [Google Scholar]
  14. SahuS.K. MokhadeA. BokdeN.D. An overview of machine learning, deep learning, and reinforcement learning-based techniques in quantitative finance: Recent progress and challenges.Appl. Sci.2023133195610.3390/app13031956
     [Google Scholar]
  15. YangS. KarS. Application of artificial intelligence and machine learning in early detection of adverse drug reactions (ADRs) and drug-induced toxicity.AIC202312100011
     [Google Scholar]
  16. MadugulaS.S. JohnL. NagamaniS. GaurA.S. PoroikovV.V. SastryG.N. Molecular descriptor analysis of approved drugs using unsupervised learning for drug repurposing.Comput. Biol. Med.202113810485610.1016/j.compbiomed.2021.10485634555571
     [Google Scholar]
  17. VadapalliS. AbdelhalimH. ZeeshanS. AhmedZ. Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine.Brief. Bioinform.2022235bbac19110.1093/bib/bbac19135595537
     [Google Scholar]
  18. ObaidoG. MienyeI.D. EgbelowoO.F. EmmanuelI.D. OgunleyeA. OgbuokiriB. MienyeP. ArulebaK. Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects.MLWA20241710057610.1016/j.mlwa.2024.100576
     [Google Scholar]
  19. YangC.Y. ShiranthikaC. WangC.Y. ChenK.W. SumathipalaS. Reinforcement learning strategies in cancer chemotherapy treatments: A review.Comput. Methods Programs Biomed.202322910728010.1016/j.cmpb.2022.10728036529000
     [Google Scholar]
  20. SelvarajC. ChandraI. SinghS.K. Artificial intelligence and machine learning approaches for drug design: Challenges and opportunities for the pharmaceutical industries.Mol. Divers.202112134686947
     [Google Scholar]
  21. HarrisonC.J. Sidey-GibbonsC.J. Machine learning in medicine: A practical introduction to natural language processing.BMC Med. Res. Methodol.202121115810.1186/s12874‑021‑01347‑134332525
     [Google Scholar]
  22. KompaB. HakimJ.B. PalepuA. KompaK.G. SmithM. BainP.A. WoloszynekS. PainterJ.L. BateA. BeamA.L. Artificial intelligence based on machine learning in pharmacovigilance: A scoping review.Drug Saf.202245547749110.1007/s40264‑022‑01176‑135579812
     [Google Scholar]
  23. KolluriS. LinJ. LiuR. ZhangY. ZhangW. Machine learning and artificial intelligence in pharmaceutical research and development: A review.AAPS J.20222411910.1208/s12248‑021‑00644‑334984579
     [Google Scholar]
  24. ReskaD. CzajkowskiM. JurczukK. BoldakC. KwedloW. BauerW. KretowskiM. Integration of solutions and services for multi-omics data analysis towards personalized medicine.BBE202141416461663
     [Google Scholar]
  25. AtitallahS.B. RabahC.B. DrissM. BoulilaW. KoubaaA. 2024Self-supervised learning for graph-structured data in healthcare applications: A comprehensive review.2412.05312
     [Google Scholar]
  26. ChengY. GongY. LiuY. SongB. ZouQ. Molecular design in drug discovery: A comprehensive review of deep generative models.Brief. Bioinform.2021226bbab34410.1093/bib/bbab34434415297
     [Google Scholar]
  27. ZhouJ. GandomiA.H. ChenF. HolzingerA. Evaluating the quality of machine learning explanations: A survey on methods and metrics.Electronics 202110559310.3390/electronics10050593
     [Google Scholar]
  28. YacoubyR. AxmanD. 2020Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models.Proceedings of the first workshop on evaluation and comparison of NLP systemsNov 2020, pp. 79-9110.18653/v1/2020.eval4nlp‑1.9
     [Google Scholar]
  29. EricksonB.J. KitamuraF. Magician’s corner: 9. Performance metrics for machine learning models.Radiol. Artif. Intell.202133e20012610.1148/ryai.202120012634136815
     [Google Scholar]
  30. OladimejiO.O. OladimejiA. OlayanjuO. Machine learning models for diagnostic classification of hepatitis C tests.Frontiers in Health Informatics20211017010.30699/fhi.v10i1.274
     [Google Scholar]
  31. ChiccoD. WarrensM.J. JurmanG. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation.PeerJ Comput. Sci.20217e62310.7717/peerj‑cs.62334307865
     [Google Scholar]
  32. GengS. Analysis of the different statistical metrics in Machine Learning.HSET202488350356
     [Google Scholar]
  33. VaroquauxG. ColliotO. Evaluating Machine Learning models and their diagnostic valueMachine Learning for Brain DisordersHumanaNew York, NY2023
     [Google Scholar]
  34. YatesL.A. AandahlZ. RichardsS.A. BrookB.W. Cross validation for model selection: A review with examples from ecology.Ecol. Monogr.2023931e155710.1002/ecm.1557
     [Google Scholar]
  35. ZhuX. NinhA. ZhaoH. LiuZ. Demand forecasting with supply‐chain information and machine learning: Evidence in the pharmaceutical industry.Prod. Oper. Manag.20213093231325210.1111/poms.13426
     [Google Scholar]
  36. RathoreA.S. NikitaS. ThakurG. MishraS. Artificial intelligence and machine learning applications in biopharmaceutical manufacturing.Trends Biotechnol.202341449751010.1016/j.tibtech.2022.08.00736117026
     [Google Scholar]
  37. KumarS.A. Ananda KumarT.D. BeerakaN.M. PujarG.V. SinghM. Narayana AkshathaH.S. BhagyalalithaM. Machine learning and deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry.Future Med. Chem.202214424527010.4155/fmc‑2021‑024334939433
     [Google Scholar]
  38. DenglerS. LahririS. TrunzerE. Vogel-HeuserB. Applied machine learning for a zero defect tolerance system in the automated assembly of pharmaceutical devices.Decis. Support Syst.202114611354010.1016/j.dss.2021.113540
     [Google Scholar]
  39. ÇınarZ.M. Abdussalam NuhuA. ZeeshanQ. KorhanO. AsmaelM. SafaeiB. Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0.Sustainability20201219821110.3390/su12198211
     [Google Scholar]
  40. ShahbaziZ. ByunY.C. Smart manufacturing real-time analysis based on blockchain and machine learning approaches.Appl. Sci.2021118353510.3390/app11083535
     [Google Scholar]
  41. VoraL.K. GholapA.D. JethaK. ThakurR.R.S. SolankiH.K. ChavdaV.P. Artificial intelligence in pharmaceutical technology and drug delivery design.Pharmaceutics2023157191610.3390/pharmaceutics1507191637514102
     [Google Scholar]
  42. MeenakshiD. U. NandakumarS. FrancisA. P. SweetyP. FuloriaS. FuloriaN. K. KhanS. A. Deep Learning and site‐specific drug delivery: The future and intelligent decision support for pharmaceutical manufacturing science.Deep Learning for Targeted Treatments: Transformation in HealthcareWiley2022138
     [Google Scholar]
  43. ChenY. YangO. SampatC. BhalodeP. RamachandranR. IerapetritouM. Digital twins in pharmaceutical and biopharmaceutical manufacturing: A literature review.Processes 202089108810.3390/pr8091088
     [Google Scholar]
  44. GoodarzianF. NavaeiA. EhsaniB. GhasemiP. MuñuzuriJ. Designing an integrated responsive-green-cold vaccine supply chain network using Internet-of-Things: artificial intelligence-based solutions.Ann. Oper. Res.2022328114535540307
     [Google Scholar]
  45. SinghM. SachanS. SinghA. SinghK. K. Internet of Things in pharma industry: Possibilities and challengesEmergence of Pharmaceutical Industry Growth with Industrial IoT ApproachAcademic Press2020195216
     [Google Scholar]
  46. SharmaA. KaurJ. SinghI. Internet of things (IoT) in pharmaceutical manufacturing, warehousing, and supply chain management.SN Computer Science20201423210.1007/s42979‑020‑00248‑2
     [Google Scholar]
  47. KumarS. RautR.D. NarkhedeB.E. A proposed collaborative framework by using artificial intelligence-internet of things (AI-IoT) in COVID-19 pandemic situation for healthcare workers.Int. J. Healthc. Manag.202013433734510.1080/20479700.2020.1810453
     [Google Scholar]
  48. GerogiorgisD.I. Castro-RodriguezD. A digital twin for process optimisation in pharmaceutical manufacturing.Computer-Aided Chem. Eng.202150253258[). Elsevier.].10.1016/B978‑0‑323‑88506‑5.50041‑3
     [Google Scholar]
  49. CoitoT. MartinsM.S.E. FirmeB. FigueiredoJ. VieiraS.M. SousaJ.M.C. Assessing the impact of automation in pharmaceutical quality control labs using a digital twin.J. Manuf. Syst.20226227028510.1016/j.jmsy.2021.11.014
     [Google Scholar]
  50. FischerR.P. VolpertA. AntoninoP. AhrensT.D. Digital patient twins for personalized therapeutics and pharmaceutical manufacturing.Frontiers in Digital Health20245130233810.3389/fdgth.2023.130233838250053
     [Google Scholar]
  51. LealF. ChisA.E. CatonS. González-VélezH. García-GómezJ.M. DuráM. Sánchez-GarcíaA. SáezC. KarageorgosA. GerogiannisV.C. XenakisA. LallasE. NtounasT. VasileiouE. MountzourisG. OttiB. PucciP. PapiniR. CerraiD. MierM. Smart pharmaceutical manufacturing: Ensuring end-to-end traceability and data integrity in medicine production.Big Data Research20212410017210.1016/j.bdr.2020.100172
     [Google Scholar]
  52. UddinM. SalahK. JayaramanR. PesicS. EllahhamS. Blockchain for drug traceability: Architectures and open challenges.Health Informatics J.20212721460458221101122810.1177/1460458221101122833899576
     [Google Scholar]
  53. UddinM. Blockchain Medledger: Hyperledger fabric enabled drug traceability system for counterfeit drugs in pharmaceutical industry.Int. J. Pharm.202159712023510.1016/j.ijpharm.2021.12023533549813
     [Google Scholar]
  54. MusamihA. SalahK. JayaramanR. ArshadJ. DebeM. Al-HammadiY. EllahhamS. A blockchain-based approach for drug traceability in healthcare supply chain.IEEE Access202199728974310.1109/ACCESS.2021.3049920
     [Google Scholar]
  55. NunavathR.S. NagappanK. Future of pharmaceutical industry: Role of Artificial Intelligence, automation and robotics.J. Pharmacol. Pharmacother.2024152
     [Google Scholar]
  56. SooriM. ArezooB. DastresR. Artificial intelligence, machine learning and deep learning in advanced robotics, a review.Cognitive Robotics20233547010.1016/j.cogr.2023.04.001
     [Google Scholar]
  57. PuranikA. DandekarP. JainR. Exploring the potential of machine learning for more efficient development and production of biopharmaceuticals.Biotechnol. Prog.2022386e329110.1002/btpr.329135918873
     [Google Scholar]
  58. SuoN. Watson for the cloud: How IBM is leading the way in medical AI research and developmentai-powered mental health monitoring: Transforming healthcareISAIMS '23: Proceedings of the 2023 4th International Symposium on Artificial Intelligence for Medicine ScienceChengdu, China, 2024, pp. 811–817
     [Google Scholar]
  59. MuthaR.E. BagulV.S. TadeR.S. VinchurkarK. An overview of Artificial Intelligence (AI) in drug delivery and development.AI Innovations in Drug Delivery and Pharmaceutical Sciences; Advancing Therapy through TechnologyBentham Science2024127
     [Google Scholar]
  60. MunyaloE. Quality management system implementation and continuous improvement in Glaxosmithkline Kenya Limited.Research Project Submitted In Partial Fulfillment Of The Requirements For The Award Of Degree Of Business Administration, School Of Business, University Of Nairobi.2020
     [Google Scholar]
  61. MehtaA. ChoudharyV. COVID-19 as a catalyst for innovation: Pharmaceutical industry manufacturing techniques and management of Endemic diseases.IJMSA202324242251
     [Google Scholar]
  62. LiW. GlickJ. HatsisP. FuY. ChinH. MoenchP. FlarakosJ. An integrated outsourcing practice of nonclinical LC-MS bioanalysis and toxicokinetics at Novartis small molecule drug development.Bioanalysis202113121001101010.4155/bio‑2021‑007234081547
     [Google Scholar]
  63. AchouchM. DimitrovaM. ZianeK. Sattarpanah KarganroudiS. DhouibR. IbrahimH. AddaM. On predictive maintenance in industry 4.0: Overview, models, and challenges.Appl. Sci. 20221216808110.3390/app12168081
     [Google Scholar]
  64. SinghT.P. NatrajB.H. Next-generation probiotics: A promising approach towards designing personalized medicine.Crit. Rev. Microbiol.202147447949810.1080/1040841X.2021.190294033822669
     [Google Scholar]
  65. IjigaA. C. PeaceA. E. IdokoI. P. AgboD. O. HarryK. D. EzebukaC. I. UkatuI. E. Ethical considerations in implementing generative AI for healthcare supply chain optimization: A cross-country analysis across India, the United Kingdom, and the United States of America.IJBPSA2023701048063
     [Google Scholar]
  66. FennellyO. CunninghamC. GroganL. CroninH. O’SheaC. RocheM. LawlorF. O’HareN. Successfully implementing a national electronic health record: A rapid umbrella review.Int. J. Med. Inform.202014410428110.1016/j.ijmedinf.2020.10428133017724
     [Google Scholar]
  67. MaguireA. O'MahonyM. Gaining organisational acceptance of a data analytics programme in Eli Lilly.Level 320211614
     [Google Scholar]
  68. MagnussonF. BohmanM. BellgranM. Green design: Introducing a new methodology to increase environmental sustainability in capital investments at AstraZeneca.IFIP International Conference on Advances in Production Management SystemsSpringer, Cham, 2023, vol. 692, pp. 367-38110.1007/978‑3‑031‑43688‑8_26
     [Google Scholar]
  69. ThomasM. GrubenmannF. What is the “right” speed for post-acquisition integration within the pharmaceutical and biotechnology industries?Mergers and Acquisitions.Routledge202418419910.4324/9781003245438‑16
     [Google Scholar]
  70. EricksonJ. BakerJ. BarrettS. BradyC. BrowerM. CarbonellR. CharleboisT. CoffmanJ. Connell-CrowleyL. CoolbaughM. FallonE. GarrE. GillespieC. HartR. HaugA. NybergG. PhillipsM. PollardD. QadanM. RamosI. RogersK. SchaeferG. WaltherJ. LeeK. End‐to‐end collaboration to transform biopharmaceutical development and manufacturing.Biotechnol. Bioeng.202111893302331210.1002/bit.2768833480041
     [Google Scholar]
  71. BergerM.L. GanzP.A. ZouK.H. GreenfieldS. When will real-world data fulfill its promise to provide timely insights in oncology?JCO Clin. Cancer Inform.202488e240003910.1200/CCI.24.0003938950323
     [Google Scholar]
  72. ReddyB.M. Machine learning for drug discovery and manufacturing.AI and Blockchain in Healthcare.Springer NatureSingapore202333010.1007/978‑981‑99‑0377‑1_1
     [Google Scholar]
  73. Aguilar-GallardoC. Bonora-CentellesA. Integrating Artificial Intelligence for academic advanced therapy medicinal products: Challenges and opportunities.Appl. Sci. 2024143130310.3390/app14031303
     [Google Scholar]
  74. PadmanabanH. Navigating the complexity of regulations: Harnessing AI/ML for precise reporting.JAIGS2024314961
     [Google Scholar]
  75. ElbadawiM. GaisfordS. BasitA.W. Advanced machine-learning techniques in drug discovery.Drug Discov. Today202126376977710.1016/j.drudis.2020.12.00333290820
     [Google Scholar]
  76. TerayamaK. SumitaM. TamuraR. TsudaK. Black-box optimization for automated discovery.Acc. Chem. Res.20215461334134610.1021/acs.accounts.0c0071333635621
     [Google Scholar]
  77. TaharaK. Pharmaceutical formulation and manufacturing using particle/powder technology for personalized medicines.Adv. Powder Technol.202031138739210.1016/j.apt.2019.10.031
     [Google Scholar]
  78. AvramescuA. AllmendingerR. López-IbáñezM. Managing manufacturing and delivery of personalised medicine: Current and future models.arXiv:2105.126992021
     [Google Scholar]
  79. AbbasiN. NizamullahF. N. U. ZebS. AI in healthcare: Using cutting-edge technologies to revolutionize vaccine development and distribution.JIH2023111729
     [Google Scholar]
  80. HusnainA. RasoolS. SaeedA. HussainH.K. Revolutionizing pharmaceutical research: Harnessing machine learning for a paradigm shift in drug discovery.IJMSA20232214915710.47709/ijmdsa.v2i2.2897
     [Google Scholar]
  81. BasuK. SinhaR. OngA. BasuT. Artificial intelligence: How is it changing medical sciences and its future?Indian J. Dermatol.202065536537010.4103/ijd.IJD_421_2033165420
     [Google Scholar]
  82. SmithG.L. BanegasM.P. AcquatiC. ChangS. ChinoF. ContiR.M. GreenupR.A. KrollJ.L. LiangM.I. PisuM. PrimmK.M. RothM.E. ShankaranV. YabroffK.R. Navigating financial toxicity in patients with cancer: A multidisciplinary management approach.CA Cancer J. Clin.202272543745310.3322/caac.2173035584404
     [Google Scholar]
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Transforming Pharmaceutical Manufacturing: The Role of Machine Learning Algorithms and Emerging Trends
Curr. Comp. Sci. 4, E29503779365887 (2025); https://doi.org/10.2174/0129503779365887250312072050
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  • Article Type:     Review Article
Keyword(s): algorithms; artificial intelligence; machine learning; Pharmaceutical manufacturing; real-time monitoring; robotics
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