Predicting liquid chromatography−electrospray ionization/mass spectrometry signal from the structure of model compounds and experimental factors; case study of aripiprazole and its impurities
Abstract
A priori estimation of analyte response is crucial for the efficient development of liquid chromatography–electrospray ionization/mass spectrometry (LC–ESI/MS) methods, but remains a demanding task given the lack of knowledge about the factors affecting the experimental outcome. In this research, we address the challenge of discovering the interactive relationship between signal response and structural properties, method parameters and solvent-related descriptors throughout an approach featuring quantitative structure–property relationship (QSPR) and design of experiments (DoE). To systematically investigate the experimental domain within which QSPR prediction should be undertaken, we varied LC and instrumental factors according to the Box-Behnken DoE scheme. Seven compounds, including aripiprazole and its impurities, were subjected to 57 different experimental conditions, resulting in 399 LC–ESI/MS data endpoints. To obtain a more standard distribution of the measured response, the pe...ak areas were log-transformed before modeling. QSPR predictions were made using features selected by Genetic Algorithm (GA) and providing Gradient Boosted Trees (GBT) with training data. Proposed model showed satisfactory performance on test data with a RMSEP of 1.57 % and a of 96.48 %. This is the first QSPR study in LC–ESI/MS that provided a holistic overview of the analyte’s response behavior across the experimental and chemical space. Since intramolecular electronic effects and molecular size were given great importance, the GA–GBT model improved the understanding of signal response generation of model compounds. It also highlighted the need to fine-tune the parameters affecting desolvation and droplet charging efficiency.
Keywords:
Liquid Chromatography–Mass Spectrometry / Quantitative Structure–Property Relationship / Genetic Algorithm / Gradient Boosted Trees / AripiprazoleSource:
Journal of Pharmaceutical and Biomedical Analysis, 2023, 233Publisher:
- Elsevier Inc.
Funding / projects:
- Razvoj i primena proizvoda na bazi mineralnih sirovina u proizvodnji bezbedne hrane (RS-MESTD-MPN2006-2010-20016)
- Ministry of Science, Technological Development and Innovation of the Republic of Serbia, institutional funding - 200135 (University of Belgrade, Faculty of Technology and Metallurgy) (RS-MESTD-inst-2020-200135)
- Ministry of Science, Technological Development and Innovation of the Republic of Serbia, institutional funding - 200287 (Innovation Center of the Faculty of Technology and Metallurgy) (RS-MESTD-inst-2020-200287)
Note:
- Related to dataset: https://farfar.pharmacy.bg.ac.rs/handle/123456789/4883
Related info:
- Referenced by
https://farfar.pharmacy.bg.ac.rs/handle/123456789/4883
DOI: 10.1016/j.jpba.2023.115422
ISSN: 0731-7085
PubMed: 37150055
WoS: 001044797200001
Scopus: 2-s2.0-85156106306
Collections
Institution/Community
PharmacyTY - JOUR AU - Krmar, Jovana AU - Tolić Stojadinović, Ljiljana AU - Đurkić, Tatjana AU - Protić, Ana AU - Otašević, Biljana PY - 2023 UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/4881 AB - A priori estimation of analyte response is crucial for the efficient development of liquid chromatography–electrospray ionization/mass spectrometry (LC–ESI/MS) methods, but remains a demanding task given the lack of knowledge about the factors affecting the experimental outcome. In this research, we address the challenge of discovering the interactive relationship between signal response and structural properties, method parameters and solvent-related descriptors throughout an approach featuring quantitative structure–property relationship (QSPR) and design of experiments (DoE). To systematically investigate the experimental domain within which QSPR prediction should be undertaken, we varied LC and instrumental factors according to the Box-Behnken DoE scheme. Seven compounds, including aripiprazole and its impurities, were subjected to 57 different experimental conditions, resulting in 399 LC–ESI/MS data endpoints. To obtain a more standard distribution of the measured response, the peak areas were log-transformed before modeling. QSPR predictions were made using features selected by Genetic Algorithm (GA) and providing Gradient Boosted Trees (GBT) with training data. Proposed model showed satisfactory performance on test data with a RMSEP of 1.57 % and a of 96.48 %. This is the first QSPR study in LC–ESI/MS that provided a holistic overview of the analyte’s response behavior across the experimental and chemical space. Since intramolecular electronic effects and molecular size were given great importance, the GA–GBT model improved the understanding of signal response generation of model compounds. It also highlighted the need to fine-tune the parameters affecting desolvation and droplet charging efficiency. PB - Elsevier Inc. T2 - Journal of Pharmaceutical and Biomedical Analysis T1 - Predicting liquid chromatography−electrospray ionization/mass spectrometry signal from the structure of model compounds and experimental factors; case study of aripiprazole and its impurities VL - 233 DO - 10.1016/j.jpba.2023.115422 ER -
@article{ author = "Krmar, Jovana and Tolić Stojadinović, Ljiljana and Đurkić, Tatjana and Protić, Ana and Otašević, Biljana", year = "2023", abstract = "A priori estimation of analyte response is crucial for the efficient development of liquid chromatography–electrospray ionization/mass spectrometry (LC–ESI/MS) methods, but remains a demanding task given the lack of knowledge about the factors affecting the experimental outcome. In this research, we address the challenge of discovering the interactive relationship between signal response and structural properties, method parameters and solvent-related descriptors throughout an approach featuring quantitative structure–property relationship (QSPR) and design of experiments (DoE). To systematically investigate the experimental domain within which QSPR prediction should be undertaken, we varied LC and instrumental factors according to the Box-Behnken DoE scheme. Seven compounds, including aripiprazole and its impurities, were subjected to 57 different experimental conditions, resulting in 399 LC–ESI/MS data endpoints. To obtain a more standard distribution of the measured response, the peak areas were log-transformed before modeling. QSPR predictions were made using features selected by Genetic Algorithm (GA) and providing Gradient Boosted Trees (GBT) with training data. Proposed model showed satisfactory performance on test data with a RMSEP of 1.57 % and a of 96.48 %. This is the first QSPR study in LC–ESI/MS that provided a holistic overview of the analyte’s response behavior across the experimental and chemical space. Since intramolecular electronic effects and molecular size were given great importance, the GA–GBT model improved the understanding of signal response generation of model compounds. It also highlighted the need to fine-tune the parameters affecting desolvation and droplet charging efficiency.", publisher = "Elsevier Inc.", journal = "Journal of Pharmaceutical and Biomedical Analysis", title = "Predicting liquid chromatography−electrospray ionization/mass spectrometry signal from the structure of model compounds and experimental factors; case study of aripiprazole and its impurities", volume = "233", doi = "10.1016/j.jpba.2023.115422" }
Krmar, J., Tolić Stojadinović, L., Đurkić, T., Protić, A.,& Otašević, B.. (2023). Predicting liquid chromatography−electrospray ionization/mass spectrometry signal from the structure of model compounds and experimental factors; case study of aripiprazole and its impurities. in Journal of Pharmaceutical and Biomedical Analysis Elsevier Inc.., 233. https://doi.org/10.1016/j.jpba.2023.115422
Krmar J, Tolić Stojadinović L, Đurkić T, Protić A, Otašević B. Predicting liquid chromatography−electrospray ionization/mass spectrometry signal from the structure of model compounds and experimental factors; case study of aripiprazole and its impurities. in Journal of Pharmaceutical and Biomedical Analysis. 2023;233. doi:10.1016/j.jpba.2023.115422 .
Krmar, Jovana, Tolić Stojadinović, Ljiljana, Đurkić, Tatjana, Protić, Ana, Otašević, Biljana, "Predicting liquid chromatography−electrospray ionization/mass spectrometry signal from the structure of model compounds and experimental factors; case study of aripiprazole and its impurities" in Journal of Pharmaceutical and Biomedical Analysis, 233 (2023), https://doi.org/10.1016/j.jpba.2023.115422 . .