Efficient Computational Analysis Of 2- Pyridyl Pyrimidine Derivetives For Design Of Potent Anti-Leishmanial Agents.

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1 International Journal of PharmTech Research CODE (USA): IJPRIF ISS : Vol.4, o.4, pp , Oct-Dec 2012 Efficient Computational Analysis Of 2- Pyridyl Pyrimidine Derivetives For Design Of Potent Anti-Leishmanial Agents. Ankur Kothari*, Priyanka Joshi, Pankaj Bahrani. Department of Pharmaceutical chemistry, Swami Vivekanand College of Pharmacy, Indore (M.P.) ,India. *Corres.author: ankur.kothari3@gmail.com Tel.: Abstract: QSAR plays an important role in the drug discovery and development involving data preparation, data analysis, and test & training sets.the development of predictive QSAR models depends not only on the Statistical Analysis method but also on the algorithm used for the selection of training and test sets. Leishmania has created a major concern worldwide and an urgent need for newer Anti- Leishmanial agents. The best quantitative structure activity relationship models were further validated by LOO method of cross-validation. The study of best model shows that the Thermodynamic property Like Henry's Law Constant (HLC), Torsion Energy (TOE) contributed negatively and Partition coefficient (PC) contributed positively. The equation obtained were validated by using the Test & Training Analysis method resulting data study that shows the Thermodynamic property like Partition Coefficient contributed positively and Steric Property like Sum of Degrees (SOD) contributed negatively and Total connectivity (TC) contributed positively. The study suggested that substitution at R, R 1 & R 2 R 3 on 2- Pyridyl Pyrimidine ring by certain functional groups which increase the Partition Coefficient and Total Connectivity may lead to enhancement of the Anti-Leishmanial activity. Attempts are made to minimize Henry s Law Constant and Torsion Energy and Sum of Degrees for better biological activity. The current quantitative structure activity relationship study provides important structural insights in designing of potent Anti-Leishmanial agents. Key Words: Anti-Leishmanial, Physiochemical descriptors, QSAR Analysis, Test and Training set. ITRODUCTIO 1-17 Leishmaniasis is a group of tropical diseases caused by a number of species of protozoan parasites belonging to the genus Leishmania, and it is an ailment that affects around 12 million people in 88 countries. It is estimated that there are about two to three million new leishmaniasis cases each year, and that some 350 million people are at risk of infection. Over the last 20 years there has been an increase in the number of cases of all Leishmaniasis forms worldwide. Historically, the chemotherapy of Leishmaniasis has been based on the use of toxic heavy metals, particularly antimony compounds. When this kind of treatment is not effective, other medications are utilized, including pentamidine and amphotericin B. All these pharmaceuticals require administration by injection and clinical supervision or hospitalization during treatment, due to the severity of the possible side effects. Thus, there is absolute need for safer and more effective treatments against the various Leishmaniasis forms. Leishmania is a genus of Trypanosomatid protozoa, and is the parasite responsible for the disease Leishmaniasis. It is spread through sand flies of the genus Phlebotomies in the Old World,

2 Ankur Kothari et al /Int.J.PharmTech Res.2012,4(4) 1440 and of the genus Lutzomyia in the ew World. Their primary hosts are vertebrates; Leishmania commonly infects hyraxes, canids, rodents, and humans. The parasite was named in 1903 after the Scottish pathologist William Boog Leishman. Pentavalent antimonial compounds such as sodium stibogluconate and meglumine antimoniate are the traditional treatments for Leishmaniasis. Among all of the computationally screened compounds, pentamidine, 1, 3-dinitroadamantane, acyclovir and analogs of acyclovir had higher binding affinities than the real substrate ( guano sine monophosphate). It is predicted that patients suffering from both HIV and visceral Leishmaniasis (VL) may benefit if they are treated with acyclovir or pentamidine in conjunction with first-line antileishmanial therapies such as miltefosine. Paromomycin is an inexpensive alternative with fewer side effects than amphotericin that The Institute for One World Health has funded for production as an orphan drug for use in treatment of Leishmaniasis, starting in India. ew research in the etherlands on a cancer drug called Miltefosine has shown promising results in treating Leishmaniasis. Miltefosine is the first effective oral treatment for Leishmaniasis and is currently undergoing human trials. On its own it has an 88.2% success rate in curing Leishmania but when paired with antimonial the success rate jumps to near 100%. Pyrimidine are regarded as a promising class of bioactive heterocyclic compounds that exhibit a wide range of biological activities. Infections caused by Leishmania can cause debilitating effects and can be difficult to treat with available antileishmanial agents. Consequently, the recent research area highly concerns the development of newer anti- Leishmanial agents against opportunistic infections. In the present work, we describe the QSAR studies from multiple linear regression analysis (MRA) in order to investigate the quantitative effect between the various physicochemical parameters of 2- Pyridyl Pyrimidine (Fig.1) on their antileishmanial activity against Leishmania donovani parasites. Fig. 1 R R 2 R 3 R 1 QSARs are being applied in many disciplines for example risk assessment, toxicity prediction, and regulatory decisions in addition to drug discovery and lead optimization. Obtaining a good quality QSAR model depends on many factors, such as the quality of biological data, the choice of descriptors and statistical methods. Any QSAR modeling should ultimately lead to statistically robust models capable of making accurate and reliable predictions of biological activities of new compounds. For validation of QSAR models usually four strategies are adopted: 1. Internal validation or cross-validation. 2. Validation by dividing the data set into training and test compounds. 3. True external validation by application of model on external data. 4. Data randomization or Y-scrambling. MATERIALS AD METHODS 1-17 The Table 1 shows the structural features of given derivatives along with their biological activities (MIC μg/ml). The biological activity data MIC (minimum inhibitory concentration in μg/ml) were converted to negative logarithmic dose in moles (pmic) for QSAR analysis. The correlations were sought between inhibitory activity and various substituent constants at position R, R1 & R2R3 of the molecule. The series was subjected to molecular modeling using CS Chem-Office 8.0. Structures of all the compounds were sketched using builder module of the programmed. These structures were then subjected to energy minimization using force field molecular mechanics-2 (MM2) until the root mean square (RMS) gradient value became smaller than 0.1 kcal/mol. Å. Minimized molecules were subjected to optimization by MOPAC method until the RMS gradient attained a value smaller than kcal/mol. Å. The descriptor values for all the molecules were calculated using software. Statistical analysis was performed on the observed descriptors using VALSTAT. Multiple linear regression analysis method was used to perform QSAR analysis. The best model was selected on the basis of various statistical parameters such as correlation coefficient (r), standard error of estimation (std), sequential Fischer test (F). Quality of the each model was estimated from the cross-validated squared correlation coefficient (Q 2 ). Calculated root mean square error (SDEP), chance statistics evaluated as the ratio of the equivalent regression equations to the total number of randomized sets; a chance

3 Ankur Kothari et al /Int.J.PharmTech Res.2012,4(4) 1441 value of corresponds to 0.1% chance of fortuitous correlation and boot-strapping square correlation coefficient (r 2 bs), which confirm the robustness and applicability of QSAR equation. RESULT The model 2 shows that thermodynamic parameter (partition coefficient) shows positive contribution and parameters (Henry s law constant and Torsion Energy) show negative contribution towards the activity. The model has correlation coefficient (r) of It shows significance level more than 95.0% against tabulated value F=33.8, with a low standard deviation of estimation , demonstrating accuracy of the model. The robustness of model was shown by magnitude of the bootstrapping r 2, which was near to conventional r 2. The internal predictivity of model (Q 2 = ) was also good. The model once again favored by the least SPRESS and SDEP values. After using internal and external validation data was obtained. The model 5 shows that thermodynamic parameter (partition coefficient) shows positive contribution and steric parameters (sum of degrees) show negative contribution while the parameter (total connectivity) shows positive contribution towards the activity. The model has correlation coefficient (r) of It shows significance level more than 95.0% against tabulated value F=42.8, with a low standard deviation of estimation , demonstrating accuracy of the model. The robustness of model was shown by magnitude of the bootstrapping r 2, which was near to conventional r 2. The internal predictivity of model (Q 2 = ) was also good. The model once again favored by the least SPRESS and SDEP values. After using internal and external validation data was obtained. Figure I: Leishmania donovani in bone marrow cell. TABLE 1 Model:- R R 1 R 2 R 3 S.O. COMP. O. R R1 R 2 R Me Cl H Me Cl Me Me Cl Et 2

4 Ankur Kothari et al /Int.J.PharmTech Res.2012,4(4) Me Cl 5 8 Me Cl 6 9 Me Cl 7 10 Me Cl 8 11 Me Cl 9 12 Me Cl Me Cl Me Cl O Me Cl SO 2 Me SO 2 Me Me Cl Cl Me Cl

5 Ankur Kothari et al /Int.J.PharmTech Res.2012,4(4) 1443 Cl Me Cl Cl Me Cl OMe Me Cl Me Cl Me Cl Me Cl Me Cl Me Cl Me Cl Me Cl

6 Ankur Kothari et al /Int.J.PharmTech Res.2012,4(4) 1444 O Me Cl S O O Me Cl S O Me Cl H CF Me Cl OH Me H CF 3 H Figure II

7 Ankur Kothari et al /Int.J.PharmTech Res.2012,4(4) 1445 Figure III TABLE 2 PHYSIOCHEMICAL DESCRIPTORS FOR GIVE COMPOUD. [METHOD: - STATISTICAL AALYSIS] S.O. COMP. O. Henry's Law Constant (HLC) Partition Coefficient (PC)(Octanol/Water) Torsion Energy (TOE)

8 Ankur Kothari et al /Int.J.PharmTech Res.2012,4(4) 1446 TABLE 3 PHYSIOCHEMICAL DESCRIPTORS FOR GIVE COMPOUD. [METHOD: - TEST AD TRAIIG AALYSIS] S.O. COMP. O. Partition Coefficient (PC) (Octanol/Water) Sum of Degrees (SOD) Total Connectivity (TC) Model no. 2 results are (Statistical analysis)... BA = [ (± )] - HLC [ (± )] +PC [ (± )] -ToE [ (± )] TABLE 4 Shows the Values Related Model no. 2 S.O. PARAMETERS VALUES R r Variance Std F FIT Bootstrapping r Q S press SDEP

9 Ankur Kothari et al /Int.J.PharmTech Res.2012,4(4) 1447 Model no. 5 results are (Test and Training analysis)... BA = [7.4614(± )] +PC [ (± )] -SOD [ (± )]+TC [ (± )] TABLE 5 Shows the Values Related Model no. 5 S.O. PARAMETERS VALUES R r Variance Std F FIT Bootstrapping r Q S press SDEP TABLE 6 containing the comparative data of statistical set activity and Training set activity (pmic) [statistical set (model 2: observed & calculated activity) and training set (model 5: predicted activity)] o. of Compound (Test Set): 10 o. of Compound (Training set): 20 S. O. Compound o. Observed Activity (pmic) Calculated Activity (pmic) Predicted activity (pmic)

10 Ankur Kothari et al /Int.J.PharmTech Res.2012,4(4) 1448 DISCUSSIO AD COCLUSIO In order to develop QSAR between anti- Leishmanial activity as dependent variables and substituent constants as independent variables. Multiple linear regression analysis of data was done & several equations were obtained. The statistically significant equations were considered as best model. The challenge in QSAR studies is not only constructing a model that is statistically able to predict the activity within the validation but also developing a model with the capacity to accurately predict the activity of untested chemical. The model testing and training is essential and critical but often neglected component of QSAR development. ot only proper validation but also proper testing process is required in QSAR analysis. The present study is aimed to conclude method of test and training used in QSAR analysis and their importance to develop an effective and better accurate QSAR models. The figure II shows plot of observed versus calculated pmic values for training set molecules And figure III is plot of calculated versus predicted pmic values. In figure II the line graph of blue line shows data related to the Observed Activity and red line shows data related to the Calculated Activity. In figure III the line graph of blue line shows data related to the Calculated Activity and red line shows data related to the Predicted Activity. In conclusion, the compounds no. 4 to 33 were found effective so, we may synthesize using several substituted 2- Pyridyl Pyrimidine. On the basis of model no.2 equation, the Partition Coefficient which is Thermodynamic properties contributes positively. This suggests that by increasing oil soluble content, the Partition Coefficient (PC) of the compound increases hence this enhance the antileishmanial activity. The Henry's Law Constant (HLC), and Torsion Energy (TOE) thermodynamic properties, contributes negatively. This suggests that by decreasing the Energy, the activity increases. On the basis of test and training model no.5 equation, the Partition Coefficient which is Thermodynamic property contributes positively. This suggests that by increasing oil soluble content, the Partition Coefficient (PC) of the compound is increases hence this enhance the antileishmanial activity. And the Total Connectivity (TC) which is steric property also contributes positively and helps to enhance the antileishmanial activity. The Sum of Degrees (SD) which steric property contributes negatively, SD is the Sum-of-Squares deviation for each activity from the mean. This suggests that by evaluate or improve the fitting process the activity increases. REFERECES 1. Chitalu C, Gavin A, Whitlock B, Michael J, Witty B, Brun R, Marcel Kaiser, Bioorganic & Medicinal Chemistry: 2009, 19, Verma A, Srivastava1 S, Sane1 SA, Marrapu VK, Srinivas, Yadav M, Bhandari K, Gupta1 S. Acta Tropica: 117(2), 11, Dawn A, Delfín, Bhattacharjee KA, Yakovich Adam J, Karl A. Journal of Medicinal Chemistry: 2006, 49 (14), Tempone AG, Pompeu da Silva CM, Brandt CA, Martinez FS, Treiger Borborema SE, Barata da Silveira MA, Andrade Franco de H. Journal of Antimicrobial Agents Chemotherapy: 2005, 49(3) Davis AJ, Kedzierski L. Current Opinion in Investigational Drugs: 2005, 6(2) Mondal SK, Mondal B, Banerjee S, Mazumder UK. Indian Journal of Pharmacology: 2009, 41(4), Foroumadi A, Emami S, Pournourmohammadi S, Kharazmi A, Shafiee A. European Journal of Medicinal Chemistry. 2005, 40 (12), Chan C, Yin H, Garforth J, McKie JH, Jaouhari R, Speers P, Douglas KT, Rock PJ, Yardley V, Croft SL, Fairlamb AH. Journal of Medicinal Chemistry.1998, 41 (2), Gamage SA, Figgitt DP, Wojcik SJ, Ralph RK, Ransijn A, Mauel J, YardleyV, Snowdon D, Croft SL, Denny WA. Journal of Medicinal Chemistry. 1997, 40 (16), Ryan KJ, Ray CG. Sherris Medical Micro biology. 2004, Myler P, Fasel. Leishmania: After the Genome. Caister Academic Press Momen H, Cupolillo E. PMID , 95 (4), Majer V, Svoboda V. Blackwell Scientific Publications. 1985, ISB

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