Journal article
Jonathan A Fine, Prageeth R Wijewardhane, Sheik Dawood Beer Mohideen, Katelyn J. Smith, J. Bothe, Y. Krishnamachari, Alexandra Andrews, P. Wuelfing, Y. Liu, G. Chopra
2022
2022
Semantic Scholar
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APA
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Fine, J. A., Wijewardhane, P. R., Mohideen, S. D. B., Smith, K. J., Bothe, J., Krishnamachari, Y., … Chopra, G. (2022). Learning relationships between chemical and physical stability for drug development.
Chicago/Turabian
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Fine, Jonathan A, Prageeth R Wijewardhane, Sheik Dawood Beer Mohideen, Katelyn J. Smith, J. Bothe, Y. Krishnamachari, Alexandra Andrews, P. Wuelfing, Y. Liu, and G. Chopra. “Learning Relationships between Chemical and Physical Stability for Drug Development” (2022).
MLA
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Fine, Jonathan A., et al. Learning Relationships between Chemical and Physical Stability for Drug Development. 2022.
BibTeX Click to copy
@article{jonathan2022a,
title = {Learning relationships between chemical and physical stability for drug development},
year = {2022},
author = {Fine, Jonathan A and Wijewardhane, Prageeth R and Mohideen, Sheik Dawood Beer and Smith, Katelyn J. and Bothe, J. and Krishnamachari, Y. and Andrews, Alexandra and Wuelfing, P. and Liu, Y. and Chopra, G.}
}
Abstract
Chemical and physical stabilities are two key features considered in pharmaceutical development. Chemical stability is typically reported as a combination of potency and degradation product. For peptide products, it is common to measure physical stability via aggregation or fibrillation using the fluorescent reporter Thioflavin T. Executing stability studies is a lengthy process and requires extensive resources. To reduce the resources and shorten the process for stability studies during the development of a product, we introduce a machine learning based model for predicting the chemical stability over time using both the formulation conditions as well as the aggregation curve. In this work, we explore the relationships between the formulation, stability time point, and the measurements of chemical stability and achieve a coefficient of determination on a random test set of 0.945 and a mean absolute error (MAE) of 0.421 when using a multilayer perceptron (MLP) neural network for total degradation. Similarly, we achieve a coefficient of determination of 0.908 and an MAE of 1.435 when predicting the potency using a random forest model. When measurements of physical stability are included into the model, the MAE in the prediction of TD decreases to 0.148 for the MLP model. Using a similar strategy for the prediction of potency, the MAE decreases to 0.705 for the random forest model. Therefore, we can conclude two important points: first, chemical stability can be modeled using machine learning techniques and second there is a relationship between the physical stability of a peptide and its chemical stability.
