Publications

@article{Oetomo2025,

title = {Controlling tangential flow filtration in biomanufacturing processes via machine learning: A literature review},

author = {Bastian Oetomo and Ling Luo and Yiran Qu and Michele Discepola and Sandra E. Kentish and Sally L. Gras},

doi = {10.1016/j.dche.2024.100211},

issn = {2772-5081},

year  = {2025},

date = {2025-03-00},

journal = {Digital Chemical Engineering},

volume = {14},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Nik-Khorasani2025,

title = {Hyperbox Mixture Regression for process performance prediction in antibody production},

author = {Ali Nik-Khorasani and Thanh Tung Khuat and Bogdan Gabrys},

doi = {10.1016/j.dche.2025.100221},

issn = {2772-5081},

year  = {2025},

date = {2025-02-00},

journal = {Digital Chemical Engineering},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Qu2023,

title = {The Transition from Resin Chromatography to Membrane Adsorbers for Protein Separations at Industrial Scale*},

author = {Yiran Qu and Innocent Bekard and Ben Hunt and Jamie Black and Louis Fabri and Sally L. Gras and Sandra E. Kentish},

doi = {10.1080/15422119.2023.2226128},

issn = {1542-2127},

year  = {2024},

date = {2024-10-00},

urldate = {2024-10-00},

journal = {Separation & Purification Reviews},

volume = {53},

number = {4},

pages = {351--371},

publisher = {Informa UK Limited},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{KarimiAlavijeh2024,

title = {A perspective‐driven and technical evaluation of machine learning in bioreactor scale‐up: A case‐study for potential model developments*},

author = {Masih Karimi Alavijeh and Yih Yean Lee and Sally L. Gras},

doi = {10.1002/elsc.202400023},

issn = {1618-2863},

year  = {2024},

date = {2024-07-00},

urldate = {2024-07-00},

journal = {Engineering in Life Sciences},

volume = {24},

number = {7},

publisher = {Wiley},

abstract = {<jats:title>Abstract</jats:title><jats:p>Bioreactor scale‐up and scale‐down have always been a topical issue for the biopharmaceutical industry and despite considerable effort, the identification of a fail‐safe strategy for bioprocess development across scales remains a challenge. With the ubiquitous growth of digital transformation technologies, new scaling methods based on computer models may enable more effective scaling. This study aimed to evaluate the potential application of machine learning (ML) algorithms for bioreactor scale‐up, with a specific focus on the prediction of scaling parameters. Factors critical to the development of such models were identified and data for bioreactor scale‐up studies involving CHO cell‐generated mAb products collated from the literature and public sources for the development of unsupervised and supervised ML models. Comparison of bioreactor performance across scales identified similarities between the different processes and primary differences between small‐ and large‐scale bioreactors. A series of three case studies were developed to assess the relationship between cell growth and scale‐sensitive bioreactor features. An embedding layer improved the capability of artificial neural network models to predict cell growth at a large‐scale, as this approach captured similarities between the processes. Further models constructed to predict scaling parameters demonstrated how ML models may be applied to assist the scaling process. The development of data sets that include more characterization data with greater variability under different gassing and agitation regimes will also assist the future development of ML tools for bioreactor scaling.</jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yatipanthalawa2024b,

title = {Development and validation of a hybrid model for prediction of viable cell density, titer and cumulative glucose consumption in a mammalian cell culture system*},

author = {Bhagya S. Yatipanthalawa and Shauna E. Wallace Fitzsimons and Tizita Horning and Yih Yean Lee and Sally L. Gras},

doi = {10.1016/j.compchemeng.2024.108648},

issn = {0098-1354},

year  = {2024},

date = {2024-05-00},

urldate = {2024-05-00},

journal = {Computers & Chemical Engineering},

volume = {184},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Khuat2024,

title = {Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities},

author = {Thanh Tung Khuat and Robert Bassett and Ellen Otte and Alistair Grevis-James and Bogdan Gabrys},

doi = {10.1016/j.compchemeng.2024.108585},

issn = {0098-1354},

year  = {2024},

date = {2024-03-00},

journal = {Computers & Chemical Engineering},

volume = {182},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Yatipanthalawa2024,

title = {Predictive models for upstream mammalian cell culture development - A review*},

author = {Bhagya S. Yatipanthalawa and Sally L. Gras},

doi = {10.1016/j.dche.2023.100137},

issn = {2772-5081},

year  = {2024},

date = {2024-03-00},

urldate = {2024-03-00},

journal = {Digital Chemical Engineering},

volume = {10},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Qu2024b,

title = {Application of mechanistic modelling in membrane and fiber chromatography for purification of biotherapeutics — A review},

author = {Yiran Qu and Irene Baker and Jamie Black and Louis Fabri and Sally L. Gras and Abraham M. Lenhoff and Sandra E. Kentish},

doi = {10.1016/j.chroma.2023.464588},

issn = {0021-9673},

year  = {2024},

date = {2024-02-00},

urldate = {2024-02-00},

journal = {Journal of Chromatography A},

volume = {1716},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Qu2024,

title = {Economic optimization of antibody capture through Protein A affinity nanofiber chromatography*},

author = {Yiran Qu and Innocent Bekard and Ben Hunt and Jamie Black and Louis Fabri and Sally L. Gras and Sandra.E. Kentish},

doi = {10.1016/j.bej.2023.109141},

issn = {1369-703X},

year  = {2024},

date = {2024-01-00},

urldate = {2024-01-00},

journal = {Biochemical Engineering Journal},

volume = {201},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pham2023,

title = {A scoping review of supervised learning modelling and data-driven optimisation in monoclonal antibody process development*},

author = {Tien Dung Pham and Chaitanya Manapragada and Yuan Sun and Robert Bassett and Uwe Aickelin},

doi = {10.1016/j.dche.2022.100080},

issn = {2772-5081},

year  = {2023},

date = {2023-06-00},

urldate = {2023-06-00},

journal = {Digital Chemical Engineering},

volume = {7},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Manapragada2023,

title = {Pharmaceutical process optimisation: Decision support under high uncertainty*},

author = {Chaitanya Manapragada and Tien Dung Pham and Nikitaa Rajan and Uwe Aickelin},

doi = {10.1016/j.compchemeng.2022.108100},

issn = {0098-1354},

year  = {2023},

date = {2023-02-00},

urldate = {2023-02-00},

journal = {Computers & Chemical Engineering},

volume = {170},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{KarimiAlavijeh2022,

title = {Digitally enabled approaches for the scale up of mammalian cell bioreactors*},

author = {Masih Karimi Alavijeh and Irene Baker and Yih Yean Lee and Sally L. Gras},

doi = {10.1016/j.dche.2022.100040},

issn = {2772-5081},

year  = {2022},

date = {2022-09-00},

urldate = {2022-09-00},

journal = {Digital Chemical Engineering},

volume = {4},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}