![RIPER
AUTONOMOUS
NAAC &
NBA (UG)
SIRO- DSIR
Raghavendra Institute of Pharmaceutical Education and Research - Autonomous
K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 21
Results and discussion
Compound HPLC system tR (experimental) tR (predicted)
Relative error
[%]
DPCA1 YMC/MeOH 2.6 2.76 6.1
DPCA4 YMC/MeOH 3.76 3.71 1.2
DPCA6 YMC/MeOH 2.96 2.91 1.8
DPCA14 YMC/MeOH 11.38 11.44 0.5
DPCA15 YMC/MeOH 9.43 9.28 1.6
PPCA10 YMC/MeOH 7.67 8.29 8.1
PPCA21 YMC/MeOH 26.01 24.8 4.7
DPCA8 YMC/AcN 2.17 2.22 2.6
DPCA20 YMC/AcN 5.54 5.65 2
PPCA12 YMC/AcN 2.22 2.24 1.2
PPCA21 YMC/AcN 4.4 4.37 0.8
DPCA10 NUC/MeOH 7.02 7.54 7.5
DPCA13 NUC/MeOH 10.86 11.17 2.9
DPCA19 NUC/MeOH 27.56 26.42 4.1
DPCA28 NUC/MeOH 105.62 112.95 6.9
PPCA5 NUC/MeOH 2.85 2.82 1.1
PPCA10 NUC/MeOH 6.34 6.42 1.2
PPCA19 NUC/MeOH 20.44 22.24 8.8
PPCA24 NUC/MeOH 18.93 22.03 16.4
DPCA10 NUC/AcN 2.86 2.82 1.2
DPCA12 NUC/AcN 2.36 2.37 0.5
DPCA25 NUC/AcN 8.94 9.15 2.3
PPCA7 NUC/AcN 2.57 2.55 0.7](https://image.slidesharecdn.com/journalclub04-220603150451-db0835ce/75/JOURNAL-CLUB-PRESENTATION-20L81S0704-PA-21-2048.jpg)
JOURNAL CLUB PRESENTATION (20L81S0704-PA )
This document presents a journal club presentation related to a study on Quantitative Structure-Retention Relationship (QSRR) modeling for the retention behavior of specific alkoxy phenylcarbamic acid esters. The research highlights the application of multivariate data analysis techniques to predict retention data and elucidate separation mechanisms in HPLC systems. The findings are based on a series of chromatographic analysis involving various compounds and their retention factors.
In this document
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Introduction to the presentation by M.Malarvannan, including guidance from Dr. K.Vinod Kumar. Highlights research structure including aim, hypothesis, and methodology.
Introduction to Quantitative Structure–Retention Relationship (QSRR). Emphasizes its role in predicting retention data and literature on computational techniques for separation science.
Details on research aims focusing on QSRR modeling of specific compounds. Description of materials, methods, and equipment used including HPLC conditions.
Results discussing elution patterns, retention factor calculations for DPCA and PPCA compounds across different HPLC systems.
Continued discussion on retention factors and results from artificial neural networks for modeling retention data, showing high model accuracy.
Visualization of fitted values for predicted vs. experimental results, sensitivity analysis of descriptors, and interpretation of retention mechanisms.
Conclusions drawn from the study on QSRR modeling effectiveness and references supporting the research.
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JOURNAL CLUB PRESENTATION (20L81S0704-PA )
- 1. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 1 As a part of curricular requirement for M. Pharm III semester Presented by M.MALARVANNAN. (20L81S0704). M.PHARM Department of Pharmaceutical Analysis. Under the guidance/Mentorship of Dr. K.Vinod Kumar., Ph.D. Associate professor & HOD, Department of Pharmaceutical analysis Journal Club Presentation
- 2. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 2 Publisher Springer Journal Chemical papers (2021) Impact factor 2.097 (2020), 1.831 (five year) DOI https://doi.org/10.1007/s11696-020-01470-1 Title, Author & Affiliations
- 3. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 3 Introduction Literature Hypothesis Aim & Objectives Material & Method Results and discussion Author conclusion My conclusion Reference Contents
- 4. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 4 • Quantitative Structure–Retention Relationship (QSRR) is an important approach for assessing and interpreting retention data in relation to the chemical structure of investigated substances which is numerically expressed by molecular descriptors. • Retention factor in the form of logk is usually well correlated with the lipophilicity descriptor logP (n-octanol/water partition coefficient) and in many cases can be used as an alternative lipophilicity descriptor in Quantitative Structure–Activity Relationship (QSAR) studies. • Another aim of QSRR is possibility of retention data prediction of novel, not yet synthesized compounds, only from their molecular descriptors. The QSRR approach has been therefore spread out and applied in many pharmaceutical studies Introduction
- 5. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 5 • The above research paper was selected as the recent trends in computational machine learning approaches in the separation science • Literature search were done from, Elsevier journals, RSC, Springer, wiley and Scopus indexed journals etc… the journal was screened based on the impact factor and science indexed (SCI), well peer reviewed. • Recent research articles from 2018-2021 were screened. The following article is chosen after the above justifications. Literature
- 6. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 6 • This paper is oriented on the application of several techniques of multivariate data analysis for development of efficient QSRR models which could be utilized for prediction of retention data of the promising compounds. • In addition, presented chemometrical approach can be also utilized to elucidate the separation mechanisms of studied compounds in the particular HPLC systems. Hypothesis
- 7. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 7 • The research work is aimed on the QSRR modelling of retention behavior of 2-(dimethylamino)ethyl esters (DPCA) and 2-pyrrolidine-1-yl-ethyl esters of alkoxy phenylcarbamic acid (PPCA). • The present study deals with experimental retention time and predicted results were compared and calculating the % error as well as similarity between the particular system. Aim & Objectives
- 8. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 8 Chromatographic conditions Material & Method Equipment AGILENT series of 1200 HPLC system with binary pump Column YMC-Triart C18 (150 × 4.6 mm; 5 μm) and Nucleodur Sphinx C18-phenyl (150 × 4.6 mm; 5 μm). Mobile phase Organic phase: Acetonitrile and methanol. Aqueous phase: ammonium acetate (8.0 g/L; pH~7.1) Separation systems YMC/MeOH (1), YMC/AcN(2), NUC/MeOH (3), NUC/AcN (4). Mobile phase ration 65:35 (v/v) isocratic elution. Flow rate 1.0 mL/min Wavelength 215, 235 and 278 nm. Injection volume 20 μL. Column temperature 40 °C
- 9. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 9 Standard sample • Standard samples for HPLC analysis were prepared in mass concentration of 100 μg/mL in methanol. Studied compounds and molecular descriptors • Two different homologous series of alkoxyphenylcarbamic acid esters were investigated in this work: 2-(dimethylamino) ethyl esters (DPCA) and 2- pyrrolidine-1-yl-ethyl esters (PPCA) of alkoxy phenylcarbamic acid • Molecular surface (MS), solvent accessible surface area (SASA), molecular weight (MW), molecular volume (MV), hydration energy (HE), refractivity (Ref), polarizability (Plr), chain length in A (CL) and partial charges labeled C1, C2, C3, C4 and C5 Material & Method
- 10. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 10 Software used • HyperChem 8.06 (optimization of structures) • ALOGPS 2.1 (Solubility in water (logS) and lipophilicity(logP) calculations). • JMP-11 software (Multiple linear regression and PCA). • Statistica Neural Networks 8.0 (ANNs calculations) • SPSS Statistics 22 (correlation analysis (CA)) Material & Method
- 11. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 11 Results and discussion • Positional isomers with same length of alkoxychain in the all studied HPLC systems eluted in the following order: para, meta, ortho. • The first eluted isomers (para) may exist in a quinoid form with the disruption of aromaticity and increased compound polarity. • The elution order of meta-isomers is probably affected by free alkoxy chain rotation around R–O bond which may act in the column as a steric blocker. • in the case of ortho-derivatives may cause certain fixation which increased interaction intensity with C18 chains in column. • These deductions are based on the isolated ortho-peak position compared to other isomers which was observed in all chromatographic systems, irrespective of used mobile or stationary phase.
- 12. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 12 Results and discussion Compound n P R logk1 YMC/MeOH N = 45 logk2 YMC/AcN N = 39 logk3 NUC/MeOH N = 44 logk4 NUC/AcN N = 39 DPCA 1 1 o CH3 − 0.114 NM NM NM DPCA 2 1 m CH3 − 0.114 NM NM NM DPCA 3 1 p CH3 − 0.218 NM NM NM DPCA 4 2 o C2H5 0.164 NM 0.130 NM DPCA 5 2 m C2H5 0.073 NM 0.024 NM DPCA 6 2 p C2H5 − 0.029 NM − 0.079 NM DPCA 7 3 o C3H7 0.372 − 0.034 0.366 − 0.059 DPCA 8 3 m C3H7 0.297 − 0.235 0.271 − 0.160 DPCA 9 3 p C3H7 0.195 − 0.331 0.177 − 0.222 DPCA 10 4 o C4H9 0.641 0.133 0.588 0.089 DPCA 11 4 m C4H9 0.568 − 0.021 0.488 − 0.023 DPCA 12 4 p C4H9 0.472 − 0.117 0.397 − 0.078 DPCA 13 5 o C5H11 0.874 0.309 0.815 0.231 DPCA 14 5 m C5H11 0.796 0.149 0.707 0.117 DPCA 15 5 p C5H11 0.700 0.074 0.617 0.064 Summarization of retention factors (logk) in all studied chromatographic systems expressed by means of the three repeated measurements (DPCA-1 to 15)
- 13. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 13 Results and discussion Compound n P R logk1 YMC/MeOH N = 45 logk2 YMC/AcN N = 39 logk3 NUC/MeOH N = 44 logk4 NUC/AcN N = 39 DPCA 16 6 o C6H13 1.108 0.477 1.035 0.367 DPCA 17 6 m C6H13 1.027 0.310 0.921 0.251 DPCA 18 6 p C6H13 0.933 0.239 0.830 0.199 DPCA 19 7 o C7H15 1.365 0.651 1.257 0.502 DPCA 20 7 m C7H15 1.282 0.480 1.136 0.386 DPCA 21 7 p C7H15 NA NA NA NA DPCA 22 8 o C8H17 1.637 0.816 1.479 0.638 DPCA 23 8 m C8H17 1.550 0.642 1.350 0.518 DPCA 24 8 p C8H17 1.454 0.573 1.259 0.469 DPCA 25 9 o C9H19 1.862 0.988 1.653 0.776 DPCA 26 9 m C9H19 1.759 0.810 1.515 0.653 DPCA 27 9 p C9H19 NA NA NA NA DPCA 28 10 o C10H21 2.111 1.163 1.858 0.912 DPCA 29 10 m C10H21 NA NA NA NA DPCA 30 10 p C10H21 1.912 0.914 1.626 0.737 Summarization of retention factors (logk) in all studied chromatographic systems expressed by means of the three repeated measurements (DPCA-16 to 30)
- 14. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 14 Results and discussion Compound n P R logk1 YMC/MeOH N = 45 logk2 YMC/AcN N = 39 logk3 NUC/MeOH N = 44 logk4 NUC/AcN N = 39 PPCA 1 1 o CH3 NM NM NM NM PPCA 2 1 m CH3 NM NM NM NM PPCA 3 1 p CH3 NM NM NM NM PPCA 4 2 o C2H5 NM NM NM NM PPCA 5 2 m C2H5 NM NM − 0.014 NM PPCA 6 2 p C2H5 NM NM − 0.116 NM PPCA 7 3 o C3H7 0.445 − 0.155 0.314 − 0.002 PPCA 8 3 m C3H7 0.300 − 0.325 0.223 − 0.100 PPCA 9 3 p C3H7 0.192 − 0.394 0.130 − 0.150 PPCA 10 4 o C4H9 0.612 0.062 0.528 0.137 PPCA 11 4 m C4H9 0.546 − 0.097 0.433 0.038 PPCA 12 4 p C4H9 0.366 − 0.202 0.342 − 0.014 PPCA 13 5 o C5H11 NA NA NA NA PPCA 14 5 m C5H11 0.757 0.055 0.611 0.151 PPCA 15 5 p C5H11 0.703 0.010 0.556 0.124 Summarization of retention factors (logk) in all studied chromatographic systems expressed by means of the three repeated measurements (PPCA-1 to 15)
- 15. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 15 Results and discussion Compound n P R logk1 YMC/MeOH N = 45 logk2 YMC/AcN N = 39 logk3 NUC/MeOH N = 44 logk4 NUC/AcN N = 39 PPCA 16 6 o C6H13 NA NA NA NA PPCA 17 6 m C6H13 1.052 0.243 0.854 0.306 PPCA 18 6 p C6H13 0.955 0.175 0.763 0.257 PPCA 19 7 o C7H15 1.464 0.576 1.118 0.539 PPCA 20 7 m C7H15 1.303 0.412 1.004 0.440 PPCA 21 7 p C7H15 1.211 0.346 0.921 0.391 PPCA 22 8 o C8H17 NA NA NA NA PPCA 23 8 m C8H17 NA NA NA NA PPCA 24 8 p C8H17 1.390 0.509 1.081 0.523 PPCA 25 9 o C9H19 NA NA NA NA PPCA 26 9 m C9H19 1.809 0.741 1.370 0.706 PPCA 27 9 p C9H19 1.712 0.677 1.280 0.658 PPCA 28 10 o C10H21 NA NA NA NA PPCA 29 10 m C10H21 2.060 0.909 1.735 0.840 DPCA 30 10 p C10H21 1.961 0.844 1.638 0.790 Summarization of retention factors (logk) in all studied chromatographic systems expressed by means of the three repeated measurements (PPCA-16 to 30)
- 16. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 16 Results and discussion 3D chromatogram of positional isomers (ortho, meta and para) with the same length of alkoxychain (DPCA 22, DPCA 23 and DPCA 24) in the system YMC/AcN
- 17. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 17 Results and discussion System MLR model N RMSE R2 YMC/MeOH logk1 = 0.456CL−0.009SASA−0.030Ref + 4.989 45 0.051 0.994 YMC/AcN logk2 = 0.004SASA + 0.300HE−0.006MW + 0.312 39 0.014 0.998 NUC/MeOH logk3 = 0.005SASA + 0.257HE−0.016Ref −0.396 44 0.044 0.994 NUC/AcN logk4 = 0.198HE + 0.003SASA−0.027CL−0.831 39 0.013 0.998 N number of objects measured in particular system; RMSE root mean square error; R2 coefficient of determination Summary of developed MLR models for individual chromatographic systems, supplemented by basic statistical characteristics (MLR was performed using JMP software)
- 18. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 18 Results and discussion • MLR technique does not allow utilization of categorical variables (such as system, mobile phase, column or positional isomer); therefore, the development of one complex model for all separation systems was impossible. • This was the main reason for utilization of artificial neural networks (ANNs) as more robust technique in the next step of QSRR modelling. (software-Statistica Neural Networks 8.0). • All objects, measured logk values, (N = 167) were randomly divided by software into three subsets (training, testing and validation) in the percentage ratio 70:15:15. The validation of the acquired model can be either internal or external. • Quality of QSRR model can be characterized by the coefficient of determination R2 (common way) but also by the Q2F3 parameter, which is more accurate equivalent for external validation
- 19. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 19 Results and discussion Number of neurons Type of activation function Q2 F3 Input Hidden Output Hidden layer Output layer 0.998 20 4 1 Logarithmic Identity Topological and external validation (Q2 F3) parameters of the best ANN model, containing information about numbers of neurons in the individual layers and their types of activation functions Input neurons: logS, logP, MS, SASA, MW, MV, HE, Ref, Plr, CL, C1, C2, C3, C4, C5 (continuous descriptors); Etype, Ptype, System, Column, MobPhase (categorical descriptors)
- 20. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 20 Results and discussion Plot of the fitted values logk (predicted) vs. logk (experimental) for three subsets (train, test and validation) of investigated compounds
- 21. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 21 Results and discussion Compound HPLC system tR (experimental) tR (predicted) Relative error [%] DPCA1 YMC/MeOH 2.6 2.76 6.1 DPCA4 YMC/MeOH 3.76 3.71 1.2 DPCA6 YMC/MeOH 2.96 2.91 1.8 DPCA14 YMC/MeOH 11.38 11.44 0.5 DPCA15 YMC/MeOH 9.43 9.28 1.6 PPCA10 YMC/MeOH 7.67 8.29 8.1 PPCA21 YMC/MeOH 26.01 24.8 4.7 DPCA8 YMC/AcN 2.17 2.22 2.6 DPCA20 YMC/AcN 5.54 5.65 2 PPCA12 YMC/AcN 2.22 2.24 1.2 PPCA21 YMC/AcN 4.4 4.37 0.8 DPCA10 NUC/MeOH 7.02 7.54 7.5 DPCA13 NUC/MeOH 10.86 11.17 2.9 DPCA19 NUC/MeOH 27.56 26.42 4.1 DPCA28 NUC/MeOH 105.62 112.95 6.9 PPCA5 NUC/MeOH 2.85 2.82 1.1 PPCA10 NUC/MeOH 6.34 6.42 1.2 PPCA19 NUC/MeOH 20.44 22.24 8.8 PPCA24 NUC/MeOH 18.93 22.03 16.4 DPCA10 NUC/AcN 2.86 2.82 1.2 DPCA12 NUC/AcN 2.36 2.37 0.5 DPCA25 NUC/AcN 8.94 9.15 2.3 PPCA7 NUC/AcN 2.57 2.55 0.7
- 22. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 22 Results and discussion Variable Sensitivity ratio Variable Sensitivity ratio MobPhase 358.834 Etype 20.150 System 88.824 C4 17.135 SASA 62.748 Ptype 16.264 Column 60.999 C3 7.498 logP 41.463 C2 4.396 Plr (polarizability) 28.116 C1 3.286 Results of sensitivity analysis for variables (descriptors) used in the best ANN model
- 23. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 23 Results and discussion Interpretation of separation mechanisms: • Correlations between retention factors (logk1–logk4) and molecular descriptors can provide parts of information about principles of separation mechanisms in studied systems. CA performed with SPSS Statistics software (SPSS Statistics 22, IBM Corp.) Descriptor logk1 logk2 logk3 logk4 YMC/ MeOH (N=45) YMC/acn (N=39) NUC/ MeOH (N=44) NUC/acn (N=39) logP R 0.975 0.929 0.943 0.973 p 8.4E-22 6.7E-15 2.2E-16 3.3E-21 logS R − 0.919 − 0.834 − 0.859 − 0.920 p 4.7E-14 1.7E-09 1.5E-10 3.6E-14 SASA R 0.963 0.945 0.963 0.933 p 3.9E-19 1.5E-16 3.4E-19 2.6E-15 MS R 0.913 0.844 0.852 0.935 p 1.2E-13 7.1E-10 3.1E-10 1.6E-15 MV R 0.908 0.831 0.846 0.923 p 3.1E-13 2.2E-09 5.5E-10 2.1E-14 HE R 0.903 0.892 0.863 0.950 p 6.2E-13 3.2E-12 1.1E-10 3.7E-17 Ref R 0.915 0.844 0.853 0.936 p 8.8E-14 7.1E-10 2.8E-10 1.4E-15 Plr R 0.915 0.844 0.853 0.936 p 8.8E-14 7.1E-10 2.8E-10 1.4E-15 MW R 0.915 0.844 0.853 0.936 p 8.8E-14 7.1E-10 2.8E-10 1.4E-15 CL R 0.984 0.971 0.972 0.987 p 1.1E-24 9.9E-21 4.8E-21 1.2E-22
- 24. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 24 Results and discussion Further investigation of separation mechanisms with graphical assessment PCA biplots for four studied chromatographic systems (YMC)
- 25. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 25 Results and discussion PCA biplots for four studied chromatographic systems (NUC) The PCA biplot displays relationships between individual variables (both descriptors and target variables) and objects (investigated compounds) Advantage: Reduction of multidimensional space into the 2 or 3 dimensions without the information dropout
- 26. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 26 • Focusing on the development of single QSRR model that would reliably predict the retention factor in all studied HPLC systems. • MLR models developed for each system separately provided overall satisfying results. • ANNs are more advisable. External validation proved high prediction accuracy of developed ANN model ( Q2F3 = 0.998) for the retention factor of two studied derivative series • combination of utilized column with organic solvent in mobile phase significantly influenced the strength of analyte–sorbent interactions applied in the particular system. • Combination of acetonitrile and C18-phenyl column caused suppression of π − π interactions and resulted in lower resolutions of positional isomers. Author conclusion
- 27. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 27 • The present utilization of Chemometrics and QSRR modelling was useful to the understanding the chromatography separation of novel drugs having identical descriptors. • Overall, It is the right platform to learn and gain computational chemistry knowledge. My conclusion
- 28. RIPER AUTONOMOUS NAAC & NBA (UG) SIRO-DSIR Raghavendra Institute of Pharmaceutical Education and Research - Autonomous K.R.Palli Cross, Chiyyedu, Anantapuramu, A. P- 515721 28 1. Bak A, Kozik V, Malik I, Jampilek J, Smolinski A (2018) Probabilitydriven 3D pharmacophore mapping of antimycobacterial potential of hybrid molecules combining phenylcarbamoyloxy and N-arylpiperazine fragments. SAR QSAR Environ Res 29(10):801–821. https ://doi.org/10.1080/10629 36X.2018.15172 78 2. Consonni V, Ballabio D, Todeschini R (2010) Evaluation of model predictive ability by external validation techniques. J Chemom 24(3–4):194–201. https ://doi.org/10.1002/cem.1290 3. StatSoft Inc. (2009) Statistica Neural Networks v80 (software). StatSoft Inc., Tulsa 4. Heberger K (2007) Quantitative structure-(chromatographic) retention relationships. J Chromatogr A 1158(1–2):273–305. https ://doi.org/10.1016/j.chrom a.2007.03.108 5. Waisser K, Čižmarik J (2012) Derivatives of phenylcarbamic acid as potential antituberculotics. Ceska Slov Far 61(1–2):17–20 (PMID: 22536648) 6. Studzińska S, Molikova M, Kosobucki P, Jandera P, Buszewski B (2011) Study of the interactions of ionic liquids in IC by QSRR. Chromatographia 73(Suppl 1):35–44. https ://doi.org/10.1007/s1033 7-011-1960-3 Reference