Evidence map›Paper›PMID 36797504›Full record

ArticlePharmaceutical research2023

Learning Relationships Between Chemical and Physical Stability for Peptide Drug Development.

Jonathan Fine, Prageeth R Wijewardhane, Sheik Dawood Beer Mohideen, Katelyn Smith, Jameson R Bothe, Yogita Krishnamachari, Alexandra Andrews, Yong Liu, Gaurav Chopra

Abstract read
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In one paragraph

Article in Pharmaceutical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jonathan Fine *Department of Chemistry, Purdue University, West Lafayette, IN, USA.
Prageeth R Wijewardhane *Department of Chemistry, Purdue University, West Lafayette, IN, USA.
Sheik Dawood Beer MohideenDepartment of Chemistry, Purdue University, West Lafayette, IN, USA.
Katelyn SmithAnalytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ, USA.
Jameson R BotheAnalytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ, USA.
Yogita KrishnamachariSterile and Specialty Products, Pharmaceutical Sciences, MRL, Merck & Co., Inc., Rahway, NJ, USA.
Alexandra AndrewsAnalytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ, USA.
Yong LiuTango Therapeutics, Boston, MA, USA.
Gaurav ChopraDepartment of Chemistry, Purdue University, West Lafayette, IN, USA. gchopra@purdue.edu.ORCID http://orcid.org/0000-0003-0942-7898

Funding

Merck-Purdue Center for Measurement Science 40002619
6 · The paper itself

Abstract

PURPOSE OR

objectiveChemical and physical stabilities are two key features considered in pharmaceutical development. Chemical stability is typically reported as a combination of potency and degradation product. Moreover, fluorescent reporter Thioflavin-T is commonly used to measure physical stability. 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 drug product, we introduce a machine learning-based model for predicting the chemical stability over time using both formulation conditions as well as aggregation curves.

methodsIn this work, we develop the relationships between the formulation, stability timepoint, and the chemical stability measurements and evaluated the performance on a random test set. We have developed a multilayer perceptron (MLP) for total degradation prediction and a random forest (RF) model for potency.

resultsThe coefficient of determination (R

conclusionsWe 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.

Indexed as

Machine LearningNeural Networks, ComputerRandom ForestSupport Vector Machinechemical degradationdrug developmentdrug formulationmachine learningphysical stability

Identifiers

PMID36797504

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.