TY  - JOUR
A1  - Leistner, Adrian
A1  - Holzgrabe, Ulrike
T1  - Impurity Profiling of Baclofen Using Gradient HPLC–UV Method
JF  - Chromatographia
N2  - The GABA\(_{B}\) receptor agonist baclofen is a medication commonly used for the treatment of muscle spasticity. It is an amino acid and related to the neurotransmitter GABA. In this study, we developed a new, gradient high-performance liquid chromatography (HPLC) method for the impurity assessment of baclofen, which is appropriate for pharmacopoeial purposes. Since the impurities related to the synthesis pathway are acids, zwitterionic, or neutral, the method development is challenging. However, the separation of all components was achieved on a C18 stationary phase using a water–acetonitrile–trifluoroacetic acid gradient. A limit of detection (LOD) of at least 0.02% was registered for all specified impurities. Additionally, CAD detection was performed to detect potential impurities lacking off a chromophore. The baclofen batches analyzed are far more pure than expected. All impurities were found below the specification limit, and thus, they can be regarded as unspecified. Moreover, the required runtime could be significantly reduced compared to the current USP or Ph. Eur. method.
KW  - GABA\(_{B}\)
KW  - impurity profiling
KW  - baclofen
KW  - gradient HPLC–UV
KW  - CAD detection
KW  - pharmaceutical analysis
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-268921
SN  - 1612-1112
VL  - 84
IS  - 10
ER  - 
TY  - JOUR
A1  - Pawellek, Ruben
A1  - Krmar, Jovana
A1  - Leistner, Adrian
A1  - Djajić, Nevena
A1  - Otašević, Biljana
A1  - Protić, Ana
A1  - Holzgrabe, Ulrike
T1  - Charged aerosol detector response modeling for fatty acids based on experimental settings and molecular features: a machine learning approach
JF  - Journal of Cheminformatics
N2  - 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% (Q\(^2\): 0.987, RMSE: 0.051) of the observed variance in CAD responses. Validation using an external test compound confirmed the high predictive ability of the model established (R-2: 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 Function-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.
KW  - High-performance liquid chromatography (HPLC)
KW  - Charged aerosol detector (CAD)
KW  - Gradient boosted trees (GBT)
KW  - Quantitative structure-property relationship modeling (QSPR)
KW  - Fatty acids
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-261618
VL  - 13
IS  - 1
ER  -