QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental
2.2. Molecular Descriptors and Statistical Procedures
3. Data Analysis and Discussion
3.1. QSRR Models for 94 Metabolites Standards
3.2. QSRR Models for Each Chemical Group of 94 Metabolites Standards
3.3. QSRR Models for Tryptophan and Its Major Metabolites
4. Conclusions
Supplementary Materials
Acknowledgments
Author Contributions
Conflicts of Interest
References
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MDs (Amide) | Adjustable Parameters (Amide) | MDs (Bare Silica) | Adjustable Parameters (Bare Silica) |
---|---|---|---|
tpsaEfficiency | 9.64 ± 0.85 | tpsaEfficiency | 8.02 ± 0.88 |
XLogP | −0.84 ± 0.14 | nA | 3.73 ± 0.57 |
nBase | 3.37 ± 0.42 | nHBAcc | 0.35 ± 0.10 |
MDEC.33 | 0.25 ± 0.08 | fr_C_O_noCOO | 1.48 ± 0.31 |
nR | −8.77 ± 2.64 | fr_NH1 | −1.49 ± 0.50 |
C2SP3 | 0.24 ± 0.10 | khs.sNH2 | 1.02 ± 0.41 |
1.8/7.8/2.3 1 | 2.0/6.7/2.5 1 |
MDs/tR(R) | Adjustable Parameters |
---|---|
tpsaEfficiency | 5.62 ± 0.81 |
XLogP | −0.53 ± 0.11 |
nBase | 0.67 ± 0.46 |
MDEC.33 | 0.24 ± 0.60 |
nR | −1.49 ± 2.19 |
C2SP3 | 0.03 ± 0.08 |
tR(R) | 0.60 ± 0.07 |
1.3/4.2/1.7 1 |
Metabolites Chemical Group | MDs | Adjustable Parameters of Equation (2) | MDs/tR(R) | Adjustable Parameters of Equation (1) |
---|---|---|---|---|
Sugars | tpsaEfficiency | 13.07 ± 0.91 | tpsaEfficiency | 13.51 ± 0.94 |
nHBAcc | 0.53 ± 0.06 | nHBAcc | 0.24 ± 0.23 | |
tR(R) | 0.36 ± 0.17 | |||
0.7/2.1/1.0 1 | 0.6/1.9/0.9 1 | |||
Amino acids | tpsaEfficiency | 6.86 ± 1.79 | tpsaEfficiency | 2.63 ± 0.47 |
MinPartialCharge | −13.36 ± 1.87 | MinPartialCharge | 0.72 ± 0.77 | |
nHBAcc | 0.76 ± 0.21 | nHBAcc | 0.11 ± 0.06 | |
tR(R) | 0.95 ± 0.04 | |||
1.1/4.3/1.5 1 | 0.3/0.8/0.4 1 | |||
MDEC.33 | 0.80 ± 0.25 | MDEC.33 | 0.39 ± 0.10 | |
XLogP | −1.76 ± 0.33 | XLogP | −0.42 ± 0.17 | |
khs.sNH2 | 5.47 ± 1.42 | khs.sNH2 | 0.26 ± 0.70 | |
nHBAcc | 0.88 ± 0.26 | nHBAcc | −0.33 ± 0.15 | |
tR(R) | 1.50 ± 0.10 | |||
2.8/8.4/1.8 1 | 0.4/1.5/0.6 1 | |||
Nucleonic bases-nucleosides | tpsaEfficiency | 14.93 ± 1.21 | tpsaEfficiency | 7.24 ± 3.9 |
tR(R) | 0.92 ± 0.45 | |||
2/4.2/2.4 1 | 1.7/3.1/2.1 1 |
Three-Parameter QSRR Model | ||||||||
---|---|---|---|---|---|---|---|---|
EVO | Gemini | |||||||
MDs | G1 | G2 | G3 | G4 | G1 | G2 | G3 | G4 |
pKa1 | −0.82 ± 0.30 | −0.60 ± 0.32 | −0.61 ± 0.24 | −0.41 ± 0.50 | −1.08 ± 0.30 | −0.74 ± 0.37 | −0.71 ± 0.15 | −0.56 ± 0.65 |
pKa2 | 1.17 ± 0.13 | 0.86 ± 0.14 | 1.01 ± 0.11 | 0.73 ± 0.22 | 1.52 ± 0.13 | 1.09 ± 0.18 | 1.27 ± 0.07 | 0.93 ± 0.29 |
logP | 2.30 ± 0.57 | 1.96 ± 0.62 | 1.63 ± 0.47 | 2.36 ± 0.96 | 2.91 ± 0.58 | 2.04 ± 0.76 | 2.24 ± 0.29 | 2.22 ± 1.05 |
1.2/2.8/1.9 1 | 1.3/3.1/2.1 1 | 1.0/2.4/1.6 1 | 1.8/5.8/3.2 1 | 1.1/2.9/1.9 1 | 1.6/3.6/2.5 1 | 0.6/1.4/1.0 1 | 2.5/6.9/4.2 1 |
Three-Parameter QSRR Model | ||||||||
---|---|---|---|---|---|---|---|---|
EVO | Gemini | |||||||
MDs/tR(R) | G1 | G2 | G3 | G4 | G1 | G2 | G3 | G4 |
pKa1 | 0.15 ± 0.27 | −0.07 ± 0.21 | 0.42 ± 0.28 | −0.03 ± 0.26 | −0.33 ± 0.23 | −0.08 ± 0.26 | −0.37 ± 0.11 | −0.08 ± 0.34 |
pKa2 | −0.20 ± 0.33 | 0.08 ± 0.21 | −0.82 ± 0.45 | 0.08 ± 0.19 | 0.46 ± 0.26 | 0.13 ± 0.26 | 0.70 ± 0.14 | 0.08 ± 0.25 |
logP | −0.31 ± 0.68 | 0.50 ± 0.49 | −1.61 ± 0.82 | 0.82 ± 0.60 | 0.82 ± 0.57 | −0.13 ± 0.68 | 1.33 ± 0.26 | −0.54 ± 0.91 |
tR(R) | 0.90 ± 0.21 | 0.72 ± 0.18 | 1.44 ± 0.35 | 0.69 ± 0.17 | 0.91 ± 0.22 | 1.11 ± 0.28 | 0.56 ± 0.14 | 1.17 ± 0.28 |
0.6/0.9/0.9 1 | 0.6/1.2/1.0 1 | 0.5/0.7/0.7 1 | 1.0/1.5/1.6 1 | 0.6/1.0/0.9 1 | 0.7/1.6/1.3 1 | 0.3/0.6/0.5 1 | 1.2/2.4/2.0 1 |
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Zisi, C.; Sampsonidis, I.; Fasoula, S.; Papachristos, K.; Witting, M.; Gika, H.G.; Nikitas, P.; Pappa-Louisi, A. QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression. Metabolites 2017, 7, 7. https://doi.org/10.3390/metabo7010007
Zisi C, Sampsonidis I, Fasoula S, Papachristos K, Witting M, Gika HG, Nikitas P, Pappa-Louisi A. QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression. Metabolites. 2017; 7(1):7. https://doi.org/10.3390/metabo7010007
Chicago/Turabian StyleZisi, Chrysostomi, Ioannis Sampsonidis, Stella Fasoula, Konstantinos Papachristos, Michael Witting, Helen G. Gika, Panagiotis Nikitas, and Adriani Pappa-Louisi. 2017. "QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression" Metabolites 7, no. 1: 7. https://doi.org/10.3390/metabo7010007