Literature DB >> 28460183

High-Throughput Automation in Chemical Process Development.

Joshua A Selekman1, Jun Qiu1, Kristy Tran1, Jason Stevens1, Victor Rosso1, Eric Simmons1, Yi Xiao1, Jacob Janey1.   

Abstract

High-throughput (HT) techniques built upon laboratory automation technology and coupled to statistical experimental design and parallel experimentation have enabled the acceleration of chemical process development across multiple industries. HT technologies are often applied to interrogate wide, often multidimensional experimental spaces to inform the design and optimization of any number of unit operations that chemical engineers use in process development. In this review, we outline the evolution of HT technology and provide a comprehensive overview of how HT automation is used throughout different industries, with a particular focus on chemical and pharmaceutical process development. In addition, we highlight the common strategies of how HT automation is incorporated into routine development activities to maximize its impact in various academic and industrial settings.

Keywords:  design of experiments; laboratory automation; miniaturization; parallel experimentation; process optimization

Mesh：

Year:  2017        PMID： 28460183     DOI： 10.1146/annurev-chembioeng-060816-101411

Source DB:  PubMed          Journal:  Annu Rev Chem Biomol Eng        ISSN： 1947-5438            Impact factor:   11.059


  6 in total

1.  Automated high throughput pKa and distribution coefficient measurements of pharmaceutical compounds for the SAMPL8 blind prediction challenge.

Authors:  Matthew N Bahr; Aakankschit Nandkeolyar; John K Kenna; Neysa Nevins; Luigi Da Vià; Mehtap Işık; John D Chodera; David L Mobley
Journal:  J Comput Aided Mol Des       Date:  2021-10-29       Impact factor: 4.179

2.  Challenges in the Conversion of Manual Processes to Machine-Assisted Syntheses: Activation of Thioglycoside Donors with Aryl(trifluoroethyl)iodonium Triflimide.

Authors:  Regis C Saliba; Zachary J Wooke; Gabriel A Nieves; An-Hsiang Adam Chu; Clay S Bennett; Nicola L B Pohl
Journal:  Org Lett       Date:  2018-01-16       Impact factor: 6.005

Review 3.  Rethinking drug design in the artificial intelligence era.

Authors:  Petra Schneider; W Patrick Walters; Alleyn T Plowright; Norman Sieroka; Jennifer Listgarten; Robert A Goodnow; Jasmin Fisher; Johanna M Jansen; José S Duca; Thomas S Rush; Matthias Zentgraf; John Edward Hill; Elizabeth Krutoholow; Matthias Kohler; Jeff Blaney; Kimito Funatsu; Chris Luebkemann; Gisbert Schneider
Journal:  Nat Rev Drug Discov       Date:  2019-12-04       Impact factor: 84.694

4.  Automated solubility screening platform using computer vision.

Authors:  Parisa Shiri; Veronica Lai; Tara Zepel; Daniel Griffin; Jonathan Reifman; Sean Clark; Shad Grunert; Lars P E Yunker; Sebastian Steiner; Henry Situ; Fan Yang; Paloma L Prieto; Jason E Hein
Journal:  iScience       Date:  2021-02-12

Review 5.  Automation isn't automatic.

Authors:  Melodie Christensen; Lars P E Yunker; Parisa Shiri; Tara Zepel; Paloma L Prieto; Shad Grunert; Finn Bork; Jason E Hein
Journal:  Chem Sci       Date:  2021-10-27       Impact factor: 9.825

Review 6.  Automation and data-driven design of polymer therapeutics.

Authors:  Rahul Upadhya; Shashank Kosuri; Matthew Tamasi; Travis A Meyer; Supriya Atta; Michael A Webb; Adam J Gormley
Journal:  Adv Drug Deliv Rev       Date:  2020-11-24       Impact factor: 15.470
  6 in total

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