JOURNAL CLUB PRESENTATION (20L81S0704-PA ) Presented by: M. MALARVANNAN (Department of pharmaceutical analysis ).RIPER, anantapur

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

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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

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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

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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

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• 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

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• 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

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• 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

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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

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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

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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

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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.

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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)

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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)

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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)

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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)

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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

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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)

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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

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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)

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Results and discussion
Plot of the fitted values logk (predicted) vs. logk (experimental) for three subsets (train, test
and validation) of investigated compounds

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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

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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

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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

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Results and discussion
Further investigation of separation mechanisms with graphical assessment
PCA biplots for four studied chromatographic systems (YMC)

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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

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• 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

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• 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

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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
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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
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Reference