TY  - JOUR
AU  - Walther, Rasmus
AU  - Krmar, Jovana
AU  - Leistner, Adrian
AU  - Svrkota, Bojana
AU  - Otašević, Biljana
AU  - Malenović, Anđelija
AU  - Holzgrabe, Ulrike
AU  - Protić, Ana
PY  - 2023
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/4698
AB  - An alternative to the time-consuming and error-prone pharmacopoeial gas chromatography method for the analysis of fatty acids (FAs) is urgently needed. The objective was therefore to propose a robust liquid chromatography method with charged aerosol detection for the analysis of polysorbate 80 (PS80) and magnesium stearate. FAs with different numbers of carbon atoms in the chain necessitated the use of a gradient method with a Hypersil Gold C18 column and acetonitrile as organic modifier. The risk-based Analytical Quality by Design approach was applied to define the Method Operable Design Region (MODR). Formic acid concentration, initial and final percentages of acetonitrile, gradient elution time, column temperature, and mobile phase flow rate were identified as critical method parameters (CMPs). The initial and final percentages of acetonitrile were fixed while the remaining CMPs were fine-tuned using response surface methodology. Critical method attributes included the baseline separation of adjacent peaks (α-linolenic and myristic acid, and oleic and petroselinic acid) and the retention factor of the last compound eluted, stearic acid. The MODR was calculated by Monte Carlo simulations with a probability equal or greater than 90%. Finally, the column temperature was set at 33 °C, the flow rate was 0.575 mL/min, and acetonitrile linearly increased from 70 to 80% (v/v) within 14.2 min.
PB  - MDPI
T2  - Pharmaceuticals
T1  - Analytical Quality by Design: Achieving Robustness of an LC-CAD Method for the Analysis of Non-Volatile Fatty Acids
VL  - 16
IS  - 4
DO  - 10.3390/ph16040478
ER  - 

@article{
author = "Walther, Rasmus and Krmar, Jovana and Leistner, Adrian and Svrkota, Bojana and Otašević, Biljana and Malenović, Anđelija and Holzgrabe, Ulrike and Protić, Ana",
year = "2023",
abstract = "An alternative to the time-consuming and error-prone pharmacopoeial gas chromatography method for the analysis of fatty acids (FAs) is urgently needed. The objective was therefore to propose a robust liquid chromatography method with charged aerosol detection for the analysis of polysorbate 80 (PS80) and magnesium stearate. FAs with different numbers of carbon atoms in the chain necessitated the use of a gradient method with a Hypersil Gold C18 column and acetonitrile as organic modifier. The risk-based Analytical Quality by Design approach was applied to define the Method Operable Design Region (MODR). Formic acid concentration, initial and final percentages of acetonitrile, gradient elution time, column temperature, and mobile phase flow rate were identified as critical method parameters (CMPs). The initial and final percentages of acetonitrile were fixed while the remaining CMPs were fine-tuned using response surface methodology. Critical method attributes included the baseline separation of adjacent peaks (α-linolenic and myristic acid, and oleic and petroselinic acid) and the retention factor of the last compound eluted, stearic acid. The MODR was calculated by Monte Carlo simulations with a probability equal or greater than 90%. Finally, the column temperature was set at 33 °C, the flow rate was 0.575 mL/min, and acetonitrile linearly increased from 70 to 80% (v/v) within 14.2 min.",
publisher = "MDPI",
journal = "Pharmaceuticals",
title = "Analytical Quality by Design: Achieving Robustness of an LC-CAD Method for the Analysis of Non-Volatile Fatty Acids",
volume = "16",
number = "4",
doi = "10.3390/ph16040478"
}

Walther, R., Krmar, J., Leistner, A., Svrkota, B., Otašević, B., Malenović, A., Holzgrabe, U.,& Protić, A.. (2023). Analytical Quality by Design: Achieving Robustness of an LC-CAD Method for the Analysis of Non-Volatile Fatty Acids. in Pharmaceuticals
MDPI., 16(4).
https://doi.org/10.3390/ph16040478

Walther R, Krmar J, Leistner A, Svrkota B, Otašević B, Malenović A, Holzgrabe U, Protić A. Analytical Quality by Design: Achieving Robustness of an LC-CAD Method for the Analysis of Non-Volatile Fatty Acids. in Pharmaceuticals. 2023;16(4).
doi:10.3390/ph16040478 .

Walther, Rasmus, Krmar, Jovana, Leistner, Adrian, Svrkota, Bojana, Otašević, Biljana, Malenović, Anđelija, Holzgrabe, Ulrike, Protić, Ana, "Analytical Quality by Design: Achieving Robustness of an LC-CAD Method for the Analysis of Non-Volatile Fatty Acids" in Pharmaceuticals, 16, no. 4 (2023),
https://doi.org/10.3390/ph16040478 . .

TY  - JOUR
AU  - Pawellek, Ruben
AU  - Krmar, Jovana
AU  - Leistner, Adrian
AU  - Đajić, Nevena
AU  - Otašević, Biljana
AU  - Protić, Ana
AU  - Holzgrabe, Ulrike
PY  - 2021
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/3926
AB  - The charged aerosol detector (CAD) is the latest representative of aerosol-based detectors that generate a response  independent of the analytes’ chemical structure. This study was aimed at accurately predicting the CAD response  of homologous fatty acids under varying experimental conditions. Fatty acids from C12 to C18 were used as model  substances due to semivolatile characterics that caused non-uniform CAD behaviour. Considering both experimental  conditions and molecular descriptors, a mixed quantitative structure–property relationship (QSPR) modeling was  performed using Gradient Boosted Trees (GBT ). The ensemble of 10 decisions trees (learning rate set at 0.55, the  maximal depth set at 5, and the sample rate set at 1.0) was able to explain approximately 99%  (Q2: 0.987, RMSE: 0.051)  of the observed variance in CAD responses. Validation using an external test compound confirmed the high predic- tive ability of the model established  (R2: 0.990, RMSEP: 0.050). With respect to the intrinsic attribute selection strategy,  GBT used almost all independent variables during model building. Finally, it attributed the highest importance to  the power function value, the flow rate of the mobile phase, evaporation temperature, the content of the organic  solvent in the mobile phase and the molecular descriptors such as molecular weight (MW ), Radial Distribution Func- tion—080/weighted by mass (RDF080m) and average coefficient of the last eigenvector from distance/detour matrix  (Ve2_D/Dt). The identification of the factors most relevant to the CAD responsiveness has contributed to a better  understanding of the underlying mechanisms of signal generation. An increased CAD response that was obtained for  acetone as organic modifier demonstrated its potential to replace the more expensive and environmentally harmful  acetonitrile.
PB  - BioMed Central Ltd
T2  - Journal of Cheminformatics
T1  - Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach
VL  - 13
IS  - 1
DO  - 10.1186/s13321-021-00532-0
ER  - 

@article{
author = "Pawellek, Ruben and Krmar, Jovana and Leistner, Adrian and Đajić, Nevena and Otašević, Biljana and Protić, Ana and Holzgrabe, Ulrike",
year = "2021",
abstract = "The charged aerosol detector (CAD) is the latest representative of aerosol-based detectors that generate a response  independent of the analytes’ chemical structure. This study was aimed at accurately predicting the CAD response  of homologous fatty acids under varying experimental conditions. Fatty acids from C12 to C18 were used as model  substances due to semivolatile characterics that caused non-uniform CAD behaviour. Considering both experimental  conditions and molecular descriptors, a mixed quantitative structure–property relationship (QSPR) modeling was  performed using Gradient Boosted Trees (GBT ). The ensemble of 10 decisions trees (learning rate set at 0.55, the  maximal depth set at 5, and the sample rate set at 1.0) was able to explain approximately 99%  (Q2: 0.987, RMSE: 0.051)  of the observed variance in CAD responses. Validation using an external test compound confirmed the high predic- tive ability of the model established  (R2: 0.990, RMSEP: 0.050). With respect to the intrinsic attribute selection strategy,  GBT used almost all independent variables during model building. Finally, it attributed the highest importance to  the power function value, the flow rate of the mobile phase, evaporation temperature, the content of the organic  solvent in the mobile phase and the molecular descriptors such as molecular weight (MW ), Radial Distribution Func- tion—080/weighted by mass (RDF080m) and average coefficient of the last eigenvector from distance/detour matrix  (Ve2_D/Dt). The identification of the factors most relevant to the CAD responsiveness has contributed to a better  understanding of the underlying mechanisms of signal generation. An increased CAD response that was obtained for  acetone as organic modifier demonstrated its potential to replace the more expensive and environmentally harmful  acetonitrile.",
publisher = "BioMed Central Ltd",
journal = "Journal of Cheminformatics",
title = "Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach",
volume = "13",
number = "1",
doi = "10.1186/s13321-021-00532-0"
}

Pawellek, R., Krmar, J., Leistner, A., Đajić, N., Otašević, B., Protić, A.,& Holzgrabe, U.. (2021). Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach. in Journal of Cheminformatics
BioMed Central Ltd., 13(1).
https://doi.org/10.1186/s13321-021-00532-0

Pawellek R, Krmar J, Leistner A, Đajić N, Otašević B, Protić A, Holzgrabe U. Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach. in Journal of Cheminformatics. 2021;13(1).
doi:10.1186/s13321-021-00532-0 .

Pawellek, Ruben, Krmar, Jovana, Leistner, Adrian, Đajić, Nevena, Otašević, Biljana, Protić, Ana, Holzgrabe, Ulrike, "Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach" in Journal of Cheminformatics, 13, no. 1 (2021),
https://doi.org/10.1186/s13321-021-00532-0 . .