Development of a Mechanistic Amino Acid Activation Reaction Kinetics Model for Safe and Efficient Solid Phase Peptide Synthesis

Wang, Jingyao; Agrawal, Paridhi; Buser, Jonas Y.; Embry, Matthew C.; McFarland, Adam D.; Berglund, Mark R.; McClary Groh, Jennifer; Viswanath, Shekhar K.

doi:10.1021/acs.iecr.3c02145

Cited by 4 publications

(2 citation statements)

References 38 publications

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“…22 During the revision of this manuscript, Wang and co-workers released a study demonstrating the value of NMR kinetics in elucidating mechanistic aspects of amide coupling. 23 For monitoring of liquid phase peptide synthesis, Livingston's team have reported a powerful advance in UHPLC-MS. 24 Gómez-Bombarelli and Pentelute have demonstrated the value of UV-vis analysis of in-ow Fmoc deprotection reactions to generate data-rich input to build predictive deep learning insights for Fmoc deprotection efficiency. 15,25 Otake and co-workers have demonstrated the value of in-line near infrared (NIR) ow cells as a means of tracking liquid phase peptide synthesis in ow.…”

Section: Monitoring Methods For Solid Phase Peptide Synthesis (Spps)mentioning

confidence: 99%

Computer vision as a new paradigm for monitoring of solution and solid phase peptide synthesis

Yan,

Fyfe,

Minty

et al. 2023

Chem. Sci.

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Section: Monitoring Methods For Solid Phase Peptide Synthesis (Spps)mentioning

confidence: 99%

Computer vision as a new paradigm for monitoring of solution and solid phase peptide synthesis

Yan,

Fyfe,

Minty

et al. 2023

Chem. Sci.

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“…Reaction kinetics models are divided into detailed models and lumped models . Detailed kinetics models focus on all reaction products and intermediates, describing all elementary reaction pathways in detail . Mihail et al.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Modeling of Methanol to Olefin Reaction Kinetics Based on the Artificial Neural Network

Wang,

Sun

et al. 2024

Ind. Eng. Chem. Res.

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Methanol to olefin (MTO) is an important coalbased process to ensure olefin yield. The current reaction kinetics model as a crucial tool of process regulation lacks real-time, accuracy, and simplicity in describing the catalyst deactivation. To fill in the gap, a reaction kinetics hybrid model of MTO was developed by a machine learning approach. Specifically, a catalyst deactivation model was first built by an artificial neural network (ANN) and then integrated into a lump-based reaction kinetics model over a fresh SAPO-34 catalyst. Reaction kinetics parameters were estimated by the genetic algorithm. A priori knowledge was extracted by analyzing the deactivation experimental data and used in the training of ANN to enhance its extrapolability. The developed model of MTO agreed well with the kinetics experimental data considering the catalyst deactivation. The model provided a satisfactory predictive performance. Results show that a higher reaction temperature, longer reaction space time, and shorter catalyst residence time are beneficial to the yield of ethylene and propylene. The hybrid model developed in this work is expected to apply to the process control of MTO. The proposed hybrid modeling approach could be used as theoretical guidance for other catalytic reaction kinetics modeling.

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Quality by design of solid‐phase peptide/protein coupling reaction via mechanistic reaction kinetics modeling approach

Wang,

Agrawal,

Berglund

et al. 2024

AIChE Journal

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The peptide coupling reaction is one of the most critical steps in the solid phase synthesis of therapeutic peptides/proteins. Improper reaction conditions can result in several common impurities such as single amino acid deletions, additions, N‐terminus modifications, and D‐isomers, all while potentially impacting the active pharmaceutical ingredient critical quality attributes. In this work, we developed a first‐principle mechanistic reaction kinetics model for the solid‐phase peptide/protein coupling reaction based on well‐established reaction mechanisms and experimental data from literature. Utilizing the reaction kinetics model, we present a systematic, quality by design approach for the coupling reaction control strategy. Critical process parameters are identified via univariate analysis and the design space is designated via multivariate risk assessment. The presented approach provides a novel solution for designing solid‐phase peptide/protein synthesis control strategies and identifying normal operating ranges for each process parameter, as well as the associated design space.

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Development of a Mechanistic Amino Acid Activation Reaction Kinetics Model for Safe and Efficient Solid Phase Peptide Synthesis

Cited by 4 publications

References 38 publications

Computer vision as a new paradigm for monitoring of solution and solid phase peptide synthesis

Computer vision as a new paradigm for monitoring of solution and solid phase peptide synthesis

Hybrid Modeling of Methanol to Olefin Reaction Kinetics Based on the Artificial Neural Network

Quality by design of solid‐phase peptide/protein coupling reaction via mechanistic reaction kinetics modeling approach

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