2022
[Advanced Healthcare Materials] High-Yield Production of Extracellular Vesicle Subpopulations with Constant Quality Using Batch-Refeed Cultures
Congratulations to Carolina! In this paper (https://doi.org/10.1002/adhm.202202232) perfusion cultures are simulated with batch-refeed systems and their productivity is compared with that achieved using batch cultures. It is shown that a shift from batch to batch-refeed system can increase the space-time yields of a target EV subpopulation.
[Journal of Pharmaceutical Sciences] Surface-Induced Protein Aggregation and Particle Formation in Biologics: Current Understanding of Mechanisms, Detection and Mitigation Strategies
Congratulations to Marie Kopp, Fulvio Grigolato and Dominik Zürcher for the pulication of their review!
[Small] High-Yield Separation of Extracellular Vesicles Using Programmable Zwitterionic Coacervates
Congratulations to Carolina! In this paper (doi.org/10.1002/smll.202204736) programmable zwitterionic coacervates were designed to purify extracellular vesicles in high yields. Our system combines several advantages of different purification methods, including the scalability of precipitation, the gentle phase of aqueous two-phase systems, and the programmability of chromatography. This work was developed in the context of the European projects VES4US and BOW.
The work led by Harini on Artificial Intelligence for development and delivery of biologics is in the top read articles of Molecular Pharmaceutics ("Design of Biopharmaceutical Formulations accelerated by machine learning", Mol. Pharm., 2021 [https://doi.org/10.1021/acs.molpharmaceut.1c00469]
In this great collaboration with NovoNordisk we show an example of the potential of modern computational tools to assist and accelerate the development of safe and active biologics. See also Narayanan et al., Trends in Pharmacological Sciences, 42, 3, 151, 2021 for a more comprehensive review on machine learning for biologics. The work has been featured also by GEN: https://www.genengnews.com/insights/optimizing-biodrug-formulations-with-machine-learning/