QSAR Study of N-((3-Benzamido-4-oxo-3,4-Dihydroquinazolin 2-yl) methyl)-N-(Substituted) Phenyl Benzamide as Antiulcer Agents

Suppression of gastric acid secretion by use of proton pump inhibitors is an efficient way to control hyperacidity complications. An inhibitory activity of N-((3-Benzamido-4-oxo-3, 4 dihydro quinazolin -2-yl) methyl)-N-(substituted phenyl) benzamides on H+/K+-ATPase was established and reported earlier. Thus, it is significant to develop more promising agents by quantitative structure-activity relationship (QSAR) study of 37 ligands by multi-linear regression method to link the structures of molecules with inhibitory activity on H+/K+-ATPase (pIc50). QSAR model was built using genetic function approximation protocol of the software Discovery Studio Version 2.1 using training set carrying 23 compounds. The remaining 14 compounds were used as a test set. The generated model was showing satisfying statistical qualities, r2=0.84 and predicted correlation coefficient r2pred=0.88. The theoretical approach indicates that an increase in Log D, Shadow_XZ and SC 2, and reduction of Shadow_Z length causes more inhibition of enzyme by molecule.


INTRoDUCTIoN
Maximum people worldwide experience acidity occasionally.The prevalence of hyperacidity is increasing day by day due to multiple factors like, frequent use of Nonsteroidal anti-inflammatory drugs, by H-pylori infection, life style and daily habits of the people, which include eating high amount of meal and lying down after taking meal, food with high fat amount, types of food that can tend to increases acidity in stomach, family history of GERD, drinks like alcohol, smoking, high body mass index (BMI), less physical activity and age (Matsuura et al., 2013;Ter et al., 1998).The continuous experience of acidity symptoms on a regular basis can produce countable effects on quality of life (Dean et al., 2004;Tack et al., 2012;Pilotto et al., 2016;Maekawa et al., 1998).Additionally Gastric hyperacidity eventually may precipitates into Gastroesophageal reflux disease (GERD) (Craven et al., 2018, Johnson et al., 2004).It is a state of gastric hyperacidity where acid content from the stomach reverse back into the esophagus (Ness-Jensen et al., 2012).If GERD is left untreated, it may lead to life-threatening complications, like peptic ulcer, perforation and bleeding of GIT due to ulcer, Failure of esophageal peristalsis (Achem. Et al, 2003) and laryngopharyngeal carcinoma (Jarosz et al., 2014;).The worldwide prevalence of GERD is about 8.8-25.9% in Europe,11.6% in Australia, in the Middle East, 2.5-7.8% in East Asia and 23.0% in South America (El-Serag et al., 2014, Mahadeva et al., 2005;Eusebi et al., 2018).Simultaneously, there is also an increase in economic burden of health care system by rise in prevalence of the GERD and other complications (Becher et al., 2011).In most of such cases of gastric hyperacidity, people are not consulting with health care provider, but there are the cases were people are needed to be hospitalized as well as have to go though invasive surgeries when there are complications due to high GIT (Gastrointestinal system) damage (Thukkani et al., 2010;Sonnenberg et al., 1994).Despite of high research and discovery of different class of new drugs till date in this area, there is no promising agent to deal with the chronic hyper gastric acidity, GERD and Gastric ulcer (Vaezi et al., 2017;Fass et al., 2001).The drugs like antacids and other present antisecretory agents can deal with Hyperacidity and neutralize it or decrease the acid secretion.But even though people are getting temporary relief from the symptoms on taking available drugs and relapse of acidity is frequently seen in many cases.Therefore the permanent solution is needed to be searched to address this situation.In addition, many patients are required to take medicines for longer time to deal with gastric disturbance generated by treatment of different types of cancers or while undergoing long term treatment of some infections like Tuberculosis.So, drug induced Hyperacidity is also the matter of concern.
As a part of our affords to improve the quality of life of people suffering from gasric hyperacidity and to prevent other complications, in our earlier work, We have synthesized and reported the Antisecretory activity of N-((3-Benzamido-4-oxo-3, 4 dihydro quinazolin -2-yl) methyl)-N-(substituted phenyl) benzamides by inhibition of H + /K + ATPase.The activity was measured by an in-vitro method using an isolated Hog gastric H + /K + -ATPase enzyme.All the compounds were found to be potent inhibitors of Isolated Hog stomach H + / K + -ATPase enzyme with variant efficacy (parmar, 2014;Parmar & suhagia, 2021).It is significant to discover new molecules of the same series with high inhibitory action on H + /K + -ATPase enzyme with the help of QSAR (Quantitative Structure Activity Relationship) (Kenard et al., 1969;Bhadoriya et al., 2015;Hansch et al., 2004).
In continuation of our affords, in this present work we are proposing QSAR model which can be used to get more efficient agents of the series of N-((3-Benzamido-4-oxo-3, 4 dihydro quinazolin -2-yl) methyl)-N-(substituted phenyl) benzamides.QSAR remains an efficient method for building mathematical models to search out a statistically significant correlation between the chemical structure and continuous (pIC 50 , pEC 50 , Ki, etc.) or categorical/binary (active, inactive, toxic, nontoxic, etc.) toxicological/biological property using classification and regression techniques, respectively (Eriksson et al., 2003;Hernandez et al., 2009;Worachartcheewan et al., 2014, Hanch et al., 1995).QSAR methods are important tool for prediction of biological effect of chemical compounds based on mathematical and statistical relations (Hansch et., 1964;Hansch et., 2004;Chtita et al., 2016, Abraham et al., 2000).QSAR being one of the Computer added drug design (CADD) method which can help to find out more active and novel agent of known series of molecules that can be synthesized and screened subsequently (Sabet et al., 2010;Chen et al., 2015;Zhang et al., 2011).Here, we present a quantitative structure-activity relationship (QSAR) study of 37 legands to rationalize the relationship between the structural and physicochemical features of a series of N-((3-Benzamido-4oxo-3, 4 dihydro quinazolin-2-yl) methyl)-N-(substituted phenyl) benzamide with biological activity, which would help to discover more efficient and promising Antiulcer agents.(Talele et al., 2010) Moreover, it was reported earlier in QSAR study of schiff bases of quinazolinones as H + /K + ATPase inhibitors, it was proposed that compounds must have high value of polar surface area, hydrophobic constant, and polarizablity.These properties was playing crucial role in the activity of the designed Quinazoline derivatives compounds.(Jaiswal et al., 2021) In Quantitative structure-activity relationship and molecular modeling study on a series of Heteroaryl-and heterocyclyl-substituted imidazo[1,2-a] pyridine derivatives acting as acid pump antagonists, It was proposed that the derivatives may inhibit the enzyme through some electronic interaction with the enzyme and some of their small substituents may participate in hydrophobic interaction as well as steric interactions.(Agrawal et al., 2018).The quantitative structure-affinity relationship (QSAR) of other 30 quinazolinone derivatives as H + /K + -ATPase inhibitors showed that polarizablity and stearic properties of molecues are important for activity.(Mahmmad et al., 2018).These QSAR studies are not showing the role of these descriptors in mechanism of Potassium competitive acid blocker and how can it accelerates inhibition of enzyme.In proposed QSAR we also correlate the role of influencing descriptor in mechanism of biological action of N-((3-Benzamido-4-oxo-3, 4 dihydro quinazolin-2-yl) methyl)-N-(substituted phenyl) benzamide derivatives.

Selection of Training and Test Sets
To construct QSAR model of N-((3-Benzamido-4-oxo-3,4-dihydroquinolin-2-yl)methyl)-N-(substituted) phenyl benzamide analogs, out of 37 molecules, 23 representative molecules were sorted as a training set (Golbraikh et al., 2003;Shahlaei et al., 2013).The remaining 14 compounds were used as a test set molecules.The structures of 23 compounds of training set and their antiulcer activity are shown in Table 1.For every compound, the experimental values of biological activity were used in the negative logarithmic scale (pIC 50 ) to achieve normal distribution.For QSAR study, all compounds' structures were sketched by using Visualizer module of Discovery studio 2.1 software (Accelrys Inc., USA).To calculate potential energy CHARMM force field was used (Platts et al., 1999).All the compounds were energy minimized by using Smart Minimizer method until the root mean square gradient value becomes smaller than 0.001 kcal/mol Å, followed by geometry optimization by semi empirical MOPAC-AM1 method (Astin Method-1) (Parac et al., 2003).

Descriptors Selection
Numerous physicochemical descriptors involving structural, thermodynamic, steric, electronic and quantum mechanical descriptors, were calculated by calculate molecular properties protocol of the software Discovery Studio ver.2.1.The descriptors which are showing intra correlation value of 0.9 or above in correlation matrix were highly correlated descriptors and were removed from the study.The remaining descriptors were used for QSAR models.QSAR models were built using descriptors showing no inter correlated along with lesser degree of multi-co-linearity (Tropsha et al., 2010).VIF (Variance of Inflation) value was obtained by equation 1/1-r 2 and r 2 was the multiple correlation coefficient of one descriptor's effect regressed on the remaining molecular descriptors and it is indicated in Table 3.Values of Variance inflation factor (VIF) were also less than 10 which describes that the descriptors were not inter-correlated (Veerasamy et al., 2011).The correlation values of selected descriptors are given in Table 2.The value of selected descriptors for each molecule is given in Table 4.

Generation of QSAR models
QSAR model were constructed using Genetic Function Approximation protocol of the software Discovery Studio Version 2.1.To judge Statistical qualities of the generated models, the validation parameters (Cramer et al., 1988, Friedman et al., 1991) such as regression coefficient (r 2 ), adjusted r 2 (r 2 adj), cross-validated r 2 (r 2 cv), F-value, and Friedman's LOF were calculated and embedded already in the software.The equation's length was fixed up to five terms, more over the size of population was set as 100, the probability of mutation was framed as 0.1 and simple fully quadratic terms and linear polynomial equation term was adjusted (Jitender et al., 2010).Initially, 100 QSAR equations were generated that consist of 4 descriptors among QSAR random models.The best selected equation with the satisfactory value of statistical parameters is given below and statistical values are described in Table 5.

Validation of Model
To validate the model is one of the most crucial aspect of QSAR analysis.It is the process by which the predictive capacity of a QSAR and the mechanistic basis can be assessed in favour of practical purpose (Wold et al., 1991).Two methods of Validation can be used to determine integrity of the generated models, internal validation and external validation.(Roy et al., 2016, Tropsha et al., 2003)

Internal Validation
For internal validation the dataset used was from which the model was generated (Veerasamy et al., 2011).Here, Cross-Validation methods were used as internal validation method which includes Leaveone-out, Leave-Many-Out and Leave-Some-Out.The correlation coefficient of the cross-validation procedure, r 2 cv (Cross validated R square) was determined to check quality of the model which was found to be 0.77.The generally accepted value for an adequate QSAR model is r 2 cv > 0.5 (Tropsha et al., 2010, Hernandez et al., 2009).In another way validation was carried out by determining residuals of observed and predicted biological activity of training set.It was seen that the predicted activity  6 and the graphical presentation showing linear relationship actual and predicted activity as shown in Figure 1.

External Cross-Validation
Even if model is with excellent statistical characteristics (like r 2 , r 2 cv, F-value) and having satisfactory predictions, it is seen that sometimes there is a lack of true relationship between molecular descriptors and target property (Konovalov et al., 2008).Therefore, a reliable validation procedure must be carried out to avoid chance correlation.The validation of the model by external validation and determination of validation parameter like predictive r 2 (r 2 pred) value of test set compounds (Roy, 2016)  activity of test set compounds was appear to be very near to their actual activity as shown, which is showing the high robustness of model.But the residual observation is not only the optimum criteria for validation of the model.Hence, additionally the external predictability of the model was assessed by deriving r 2 pred value for the given model (Roy et al., 2009).r 2 pred is the predicted correlation coefficient and it is calculated from the predicted activity of all the test set compounds by equation Σ (YPred(test) -YObs(test)) 2 r 2 pred = 1-Σ (YObs(test) -Y(ӯtraining) 2 Where, YPred(test) and YObs(test) were the predicted and observed activity values, respectively, of the test set compounds and Ȳtraining was the mean activity value of the training set.Again the value of r 2 pred was came out as greater than 0.5 (0.88) and so indicating good external predictability (Hernandez et al., 2009).Further, the value of rm 2 (Roy, et al., 2009;Roy, et al., 2012) is also a promising measure of evaluation of the predictive power of the QSAR model which may be determined by equation Where r 2 is the squared correlation coefficient between predicted and observed values and rm 2 was the squared correlation coefficient between predicted and observed values without intercept.As the r 2 m value was found to be greater than 0.5 (0.75), good external predictability can be achieved.The Model is observed to be the best based on internal and external predictability, by values of r 2 , LOF, r 2 cv, F-value, r 2 pred, r 2 m values 0.9146, 0.100, 0.865, 41.09, 0.88, 0.75 respectively (Roy et al., 2012).

Discussion of Qsar Study
QSAR techniques was successfully applied on N-((3-Benzamido-4-oxo-3, 4 dihydro quinazolin -2-yl) methyl)-N-(substituted phenyl) benzamides as an inhibitor of H + /K + ATPase in order to produce a model that relates the chemical structures of the molecules with their inhibitory activity on enzyme.A reliable, predictable and robust model was generated by Genetic Function Approximation (GFA) technique in Discovery Studio software version 2.1.In this work, we have screened 21 preselected  value of validation coefficient of internal and external validation R 2 was found to be 0.9553 and 0.9894 respectively.The models displayed satisfactory r 2 pred and rm 2 values also .The generated model shows that the H + /K + ATPase inhibitory activity of N-((3-Benzamido-4-oxo-3, 4 dihydro quinazolin -2-yl) methyl)-N-(substituted phenyl) benzamides is influence by descriptors like Log D, Shadow_Z length, Shadow_X Z and SC 2 descriptors with greatest extend.The theoretical approach suggests that an increase in distribution coefficient (logD) has positive influence on the biological activity.Log D is simply being calculated from predicted Log P and predicted pKa of singly ionized species at certain pH.Incresed in LogD value can cause increase in biological activity.It reveals that the drug should be ionized at gastric pH in enough amount and also shld carry lipophilicity to avail at the site of action to bind with enzyme in luminal area of GIT (Gastrointestinal tract).Beyond this, the geometric characteristic also plays major role in augmentation of biological activity of molecules of described series.Geometric descriptor like Shadow XZ length is important for biological action, incrase the value of shadow XZ length give rise to biological action.Shadow XZ length is important for shape analysis of molecules.There is Positive contribution of Shadow XZ length towards biological activity.Shadow indices are set of geometric descriptors to characterize the shape of the molecules (Rohrbaugh et al., 1987).These descritors are calculated by projecting the model surface of molecule on three mutually per pendicular planes: xy, yz and xz.The molecules are first rotate to align the principle moment of intertia with x, y and z axes.They are not only depended on conformation of molecule but also on orientation of molecule.According to equation, principal moment of Shadow_Zlength is principal descriptor contributing negatively on biological activity of the compounds.So low value of Shadow_Zlength ause improvement in biogical action.Additionally increase in topological descriptor SC_2 also gives beneficial effect in biological response.(Chtita et al., 2014).These geometric descriptors allows three dimension binding of drug with enzyme in proper way to produce desired biological action.The Acid pump antagonists' mechanism of action suggest that these agents undergoes protonation in gastric pH and the protonated form of molecule bind with H+/K+ ATPase reversibly instead of Potassium and inhibits the enzyme for a period of time.The QSAR study indicates that if lipophilic and electronic factor is increased that may increase inhibition of enzyme.More electronic property by adding electronic releasing groups facilitates the protonation of molecule and more lipophilicity by alkyl or aromatic substitution will avail the molecule at luminal site of stomach for its action.The geometric parameter also facilitates the binding of molecule with enzyme.

Proposed Novel Compounds
Based on QSAR model correlation between biological activity and Molecular property new molecules can be designed and suggested which are supposed to be more promising compare to existing molecules.As indicated by model, the novel molecule should carry functional group (amines) that ionized at acidic pH of gastric juice with satisfactory pKa value and simultaneously enough lipophilic to gain desired log D value.The role of Geometric descriptors and topological descriptors suggest that molecular geometry is equally important for biological action.Increasing topological descriptor in particular bond and ring may have promising effect on activity

CoNCLUSIoN
In developed QSAR model, a strong correlation was observed between the experimental and predicted values of the biological activities and It is showcasing the validity and quality of the QSAR model.

Table 5 . Statistical values of generated QSAR Model
of training was very near and residuals value was very less.The actual activity, predicted activity and residuals for training set compounds are indicated in Table is ultimate method to establish integrity of any QSAR model.In external validation, the quality of QSAR model is mostly checked by determining its ability to perform predictions of compounds' activity those are not included in the training sets.In this regard,the activity of Test set compounds were predicted and the real validation of QSAR model was accomplished by determining and examining residuals of actual and predicted activity of Test compounds.The actual activity, predicted activity and residuals for test set compounds are tabulated in Table7and represented as Figure2.It is showing that the predicted