SUMMARY & CONCLUSION

The credit of pyridine discovery goes to Anderson who first obtained it from bone oil. Derivatives of Pyridine have been used in different applications like medicine, used as anticancer, anti-hypertension, antifungal etc.

Different Derivatives of pyridine have been tested for various biological activities. New derivatives are being synthesized by researchers across the world. Success of a particular derivative for one activity can never be predetermined let alone for multiple activities. QSAR can be a possible approach to rationally obtain leads for synthetic chemists. These compounds can be checked for suitability against different activities through extrapolation of QSAR models on already synthesized derivatives.

QSAR is acronym for Quantitative Structure Activity Relationship (as distinguished from QPAR and QSPR) is an attempt to mathematically relate Biological Activity of certain compound to different quantitative properties of the molecule. These may include easily calculated ones like molecular weight, number of particular atom, groups etc to complex properties like electrostatic, steric fields etc. These properties which are calculated are known as descriptors. The increase in computing power over the last decade has led to a growth in number of descriptors.

Developing a QSAR model is essentially deriving a mathematical equation to relate these descriptors to property under consideration. A group of analogous compounds called as dataset is used as input for developing this equation.

These QSAR equations can be used as basis to predict activities of similar compound. Descriptors being properties of the molecule can be calculated for novel compounds also. By calculating the descriptors these novel compounds can be checked for their probable activities via a vis multiple models representing different activities.

Synthesis of any new compound without any direction is not very convenient way because synthesis of any new compound requires a lot of time and lesser amount of outcome. So, that with help of softwares predicted a new compound leads to the QSAR study. In these study through various QSAR models and approaches predicted 1,4 dihyropyridine derivatives .after the synthesis we evaluate their biological activity and compare with the predicted activity.

This thesis divided into eight chapters.

Chapter-1

This chapter describes introduction of QSAR, history of QSAR, list of descriptors, methologies, software as well as basic understanding of different statistical parameter used in QSAR model development. This chapter also represent various part of QSAR, how to develop a model, descriptors with their different type, feature selection, statistics, molecular modeling etc.

The whole overview on Quantitative Structure Activity Relationship in briefly takes in this chapter.

Chapter-2

This chapter represent a review of literature consist of different QSAR studies on different derivatives of 1,4 dihyropyridine. It also includes various historical developments on QSAR studies. This chapter also presents different authors views on these studies. A survey of different synthetic methods of Hantzsch reaction as well as other methods has to be taken. Step by step progresses reported in QSAR field are described. Many types of software are used for QSAR were integrated.

Chapter-3

This chapter describes the QSAR modeling on 31 compound of 3-hyroxypyridine -4-one and 3-hyroxypyrane-4-one dataset with their known minimum inhibitory concentration (MIC) biological activity determined on s.aureus and c.albicans microorganism. These 31 compounds of dataset were used for QSAR model development and developed around 50 models using various different approaches like multiple linear regression, principal component analysis, simulated annealing etc. around 299 physiochemical descriptors were used for these study. After that to find out the best QSAR equation (correlation coefficient values) through the V-life software, this equation is used for prediction of new compounds. For the prediction of new compounds we substitute different alkyl, alkynes and functional groups on different position of parent compound.

The predicted compounds were proposed for prospect study.

Chapter-4

A dataset of 1, 4 -dihyropyridine of 40 compounds and their calcium channel antagonist activity determined on guinea pig ileum smooth muscles taken from literature for QSAR modeling in this chapter. Various descriptors show different contribution for new QSAR equation. Between 50 models finally discussed 4 models in this chapter.65 substitution was done on different position of substituted phenyl ring .those compounds shows higher prediction as compare to actual values were projected for further study.

Chapter-5

In this chapter qsar modeling was done on 31 compounds of given calcium channel antagonist activity (IC50) .in this chapter substitution has to be done on 2, 3 and 4rth position of substituted phenyl ring of 1,4 dihyropyridine. Predicted values were used for prediction of new compounds for the direction of non-synthesized compounds.

Chapter-6

This chapter describe the methodology of synthesis of 1,4-dihyropyridine derivatives named as A1 to A4. Synthesis was done through Hantzsch single step reaction. Synthesis of derivatives of 1,4 dihyropyridine using reaction mixture of aldehyde,acetoacetate and ammonium hydroxide by condensation method .

Chapter-7

In this chapter mentioned the spectral analysis of all synthesized derivatives of 1,4-dihyropyridine for their structure elucidation. it includes IR ,NMR and Mass spectral analysis along with CHN analysis of all derivatives. The IR spectra of compounds A1-A4 showed absorbtion band at 3418-3341 cm-1 due to the presence of N-H stretching. The 1667-1690 cm-1, showed absorption band due to the presence of keto group in the ester groups.

The 1HNMR spectra of compounds from A1 to A4 showed a singlet at 7.2 to 8.5 ppm trait to NH protons present in 1, 4-dihyropyridine ring. Another important singlet at 3.8 to 5.7 ppm, which was attributable to the C4-H protons present in the 1, 4 dihyropyridine ring. Mass spectral analysis of all the compounds showed molecular ion peaks, which confirmed the molecular mass of those compounds.

Chapter-8

In this chapter mentioned the experimental minimum inhibitory concentration values of synthesized compounds. Values are evaluated against different kind of micro-oraganism such as S.aureus, E.coli, P.aeroginosa and E.facelis. The antibacterial assay was carried out through broth dilution method. All the compounds experimental biological (MIC) activities were measured in microgram/ml.

The predicted values (discussed in chapter 3) and experimental values were compared for the development of new QSAR model. A new QSAR equation were developed included with their descriptor contribution ,data fitness plot and test set training set predicted vs experimental values graph.

The new QSAR equation showed better results as compare to past results.

CONCLUSION

In the present Quantitative structure activity relationship study developed more than 100 models on different large data sets for 1,4 dihyropyridine derivatives. After this study we concluded that

1) QSAR methodology is useful for pyridine derivatives. With the help of qsar study on different large data sets gave more exploration and applicability to pyridine derivatives.

2) These study is very reliable for the prediction of new lead compounds. They provide us a new way for non-synthesized compounds but at the same time it is always required same biological activity data sets for the prediction.

3) It is not necessary that always QSAR model gives us same predicted and experimental activity but if the trial and error process is continuously further proceed. The new model shows very good prediction.

4) Present study represents a new effective QSAR equation for advance study of different pyridine derivatives.

5) QSAR models were generated through different approaches shows the various comparable effectiveness on used data sets.

6) Different software made qsar study very fewer time consuming with less cost and more productive with greater outcome as well as reduced the usage of animal testing.

In short QSAR is the excellent tool for new drug discovery.

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