Badr Sara - Program search result - SCEJ 88th Annual Meeting (Tokyo, 2023)

`SCEJ 88th Annual Meeting (Tokyo, 2023)`

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Program search result : Badr Sara : 5 programs

The preprints(abstracts) are now open (Mar. 1st). These can be viewed by clicking the Paper IDs. The ID/PW sent to the Registered participants in Period I/II and invited persons are required.

Authors and Chairs (J) field exact matches “Badr Sara”; 5 programs are found.
The search results are sorted by the start time.

Time	Paper ID	Title / Authors	Keywords	Topic code	Ack. number
Day 1 14:00– 14:20	D116	[Invited lecture] The role of data-driven and hybrid modeling in biopharmaceutical manufacturing (U. Tokyo) (Reg)Badr Sara	PSE	K-2	612
Day 2 9:00– 9:20	I201	Modeling cell metabolic shifts in cultivation processes for monoclonal antibody production (UTokyo) *(Stu)Okamura K., (Int)Badr S., (Reg·SPCE)Yamada A., (Reg)Sugiyama H.	Biopharmaceuticals Lactate consumption Hybrid modeling	6-b	394
Day 2 10:40– 11:00	I206	A comprehensive design approach for injectable manufacturing processes considering new technologies and operational aspects (U. Tokyo) *(Stu)Yamada Masahiro, (Reg)Hayashi Yusuke, (Int)Badr Sara, (Shionogi Pharma) (Cor)Zenitani Kenichi, (Cor)Kubota Kokichi, (Reg)Nakanishi Hayao, (U. Tokyo) (Reg)Sugiyama Hirokazu	Process design Multiobjective evaluation Pareto frontier	6-b	171
Day 2 11:00– 11:20	F207	Hybrid modelling of active pharmaceutical ingredient flow synthesis in ring-opening reaction of an epoxide with a Grignard reagent (U. Tokyo) *(Stu)Kim Junu, (Reg)Hayashi Yusuke, (Int)Badr Sara, (Pharmira) Okamoto Kazuya, Hakogi Toshikazu, (Reg)Furukawa Haruo, (Shionogi Pharma) (Reg)Yoshikawa Satoshi, (Reg)Nakanishi Hayao, (U. Tokyo) (Reg)Sugiyama Hirokazu	Flow chemistry Machine learning Random forest regression	5-i	167
Day 2 13:20– 14:20	PC229	Simulation and design of integrated upstream and downstream monoclonal antibody production processes (UTokyo) *(Stu)Shigeyama A., (Int)Badr S., (Reg)Hayashi Y., (Reg)Sugiyama H.	Surrogate model Bayesian optimization Machine learning	6-b	496

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SCEJ 88th Annual Meeting (Tokyo, 2023)

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