Predicting liquid chromatography−electrospray ionization/mass spectrometry signal from the structure of model compounds and experimental factors; case study of aripiprazole and its impurities
Само за регистроване кориснике
2023
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
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.
Кључне речи:
Aripiprazole / Genetic Algorithm / Gradient Boosted Trees / Liquid Chromatography–Mass Spectrometry / Quantitative Structure–Property RelationshipИзвор:
Journal of Pharmaceutical and Biomedical Analysis, 2023, 233, 115422-Издавач:
- Elsevier B.V.
Финансирање / пројекти:
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200161 (Универзитет у Београду, Фармацеутски факултет) (RS-MESTD-inst-2020-200161)
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200135 (Универзитет у Београду, Технолошко-металуршки факултет) (RS-MESTD-inst-2020-200135)
- Министарство науке, технолошког развоја и иновација Републике Србије, институционално финансирање - 200287 (Иновациони центар Технолошко-металуршког факултета у Београду доо) (RS-MESTD-inst-2020-200287)
Колекције
Институција/група
Inovacioni centarTY - JOUR AU - Krmar, Jovana AU - Tolić Stojadinović, Ljiljana AU - Đurkić, Tatjana AU - Protić, Ana AU - Otašević, Biljana PY - 2023 UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/6373 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 B.V. 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 SP - 115422 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 B.V.", 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", pages = "115422", 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 B.V.., 233, 115422. 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;233:115422. 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):115422, https://doi.org/10.1016/j.jpba.2023.115422 . .