Investigation of fragment based quantitative regression on a series of substituted chromen-2-one derivatives as FXa inhibitors

Santosh Sahadeo Kumbhar; Prafulla Balkrishna Choudhari; Manish Sudesh Bhatia

doi:10.12991/marupj.323292

Research Article

Investigation of fragment based quantitative regression on a series of substituted chromen-2-one derivatives as FXa inhibitors

Year 2017, Volume: 21 Issue: 3, 620 - 630, 23.06.2017

Santosh Sahadeo Kumbhar Prafulla Balkrishna Choudhari Manish Sudesh Bhatia

https://doi.org/10.12991/marupj.323292

Abstract

Factor Xa (FXa), a trypsin-like serine protease, is wellestablished

target for the development of the anticoagulants.

Number of molecules were reported as Factor Xa inhibitors

but most of them have pharmacokinetic issues. In this present

communication, we report development and validation of

the group based quantitative structure activity relationship

(GQSAR) studies on 48 chromen-2-one derivatives as effective

inhibitors of FXa. All the molecules were fragmented into eleven

functional fragments (R1, R2, R3, R4, R5, R6, R7, R8, R9, R10

and R11). All the developed GQSAR models were generated

using multiple linear regression analysis (MLR). The generated

GQSAR models were selected on the basis of statistical data that

models having r2 should be above 0.6 were used to check the

external predictivity while the significance of the model was

decided on the basis of F value. Developed GQSAR models

reveled presence of lipophilic groups on fragment R6 will

diminish the bio-activity while at R2 it will lead to increase

in bioactivity of molecules. Additionally, minimum number

of rotatable bonds at fragments R1 was fruitful for better FXa

inhibition activity. The results of GQSAR models may lead to

better understanding of design and development of novel FXa

inhibitors.

Keywords

FXa, Vlife MDS, Anticoagulant, Quantitative Structure Activity Relationship, GQSAR, Slogp

References

1. Bhatia MS, Ingale KB, Choudhari PB, Bhatia NM, Sawant RL. Application quantum and physico chemical molecular descriptors utilizing principal components to study mode of anticoagulant activity of pyridyl chromen-2-one derivatives. Bioorg Med Chem 2009; 17: 1654-62.
2. Bohm M, Sturzebecher J, Klebe GJ. Three-Dimensional quantitative structure−activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa. Med Chem 1999; 42: 458-77.
3. Hirsh J, Anand SS, Halperin JL, Fuster V. Guide to anticoagulant therapy: Heparin. Circulation 2001; 103: 2994-3018.
4. Hogg PJ, Jackson CM. Fibrin monomer protects thrombin from inactivation by heparin-antithrombin III: Implications for heparin efficacy. P Natl Acad Sci USA 1989; 86: 3619-23.
5. Weitz JI, Hudoba M, Massel D, Maraganore J, Hirsh J. Clotbound thrombin is protected from inhibition by heparinantithrombin III but is susceptible to inactivation by antithrombin III-independent inhibitors. J Clin Invest 1990; 86: 385-91.
6. Walenga JM. An overview of the direct thrombin inhibitor argatroban. Pathophysiol Haemost Thromb 2002; 32: 9-14.
7. Riva N, Lip GY. A new era for anticoagulation in atrial fibrillation. Which anticoagulant should we choose for long term prevention of thromboembolic complications in patients with atrial fibrillation? Pol Arch Med Wewn 2012; 122: 45–53.
8. Hart RG, Pearce LA, Aquilar MI. Meta-analysis: Antithrombotic therapy to prevent stroke in patients who have nonvalvular atrial fibrillation. Ann Intern Med 2007;146: 857–67.
9. Perez A, Eraso L, Merli G. Implications of new anticoagulants in primary practice. Int J Clin Pract 2013; 67: 139–56.
10. Vílchez JA, Gallego P, Lip GY. Safety of new oral anticoagulant drugs: A perspective. Ther Adv Drug Saf 2014, 5: 8-20.
11. Stewart S, Hart CL, Hole DJ, McMurray JJ. Population prevalence, incidence, and predictors of atrial fibrillation in the Renfrew/Paisley study. Heart 2001; 86: 516–21.
12. Colilla S, Crow A, Petkun W, Singer DE, Simon T, Liu X. Estimates of current and future incidence and prevalence of Atrial Fibrillation in the U.S. adult population. Am J Cardiol 2013; 112: 1142–7.
13. Hagens VE, Ranchor AV, Van Sonderen E, Bosker HA, Kamp O, Tijssen JG, Kingma JH, Crijns HJ, Van Gelder IC,RACE Study Group. Effect of rate or rhythm control on quality of life in persistent atrial fibrillation. J Am Coll Cardiol 2004; 43: 241–7.
14. Heeringa J, van der Kuip DA, Hofman A, Kors JA, van Herpen G, Stricker BH, Stijnen T, Lip GY, Witteman JC. Prevalence, incidence and lifetime risk of atrial fibrillation: the Rotterdam study. Eur Heart J 2006; 27: 949–53.
15. Hadjipavlou LD. Review, reevaluation, and new results in quantitative structure-activity studies of anticonvulsants. Med Res Rev 1998; 18: 91-119.
16. Singh Bhadoriya K, Sharma MC, Sharma S, Jain SV, Avchar MA. An approach to design potent anti-Alzheimer’s agents by 3D-QSAR studies on fused 5,6-bicyclic heterocycles as γ-secretase modulators using kNN–MFA methodology. Arab J Chem 2014; 7:924-35.
17. Patel HM, Noolvi MN, Sharma P, Jaiswal V, Bansal S, Lohan S, Kumar SS, Abbot V, Dhiman S, Bhardwaj V. Quantitative structure–activity relationship (QSAR) studies as strategic approach in drug discovery. Med Chem Res 2014;12: 4991– 5007.
18. Ajmani S, Jadhav K, Kulkarni SA. Group-Based QSAR (G-QSAR): Mitigating interpretation challenges in QSAR. QSAR & Comb Sci 2009; 28:36-51.
19. Ajmani S, Kulkarni SA. Application of GQSAR for scaffold hopping and lead optimization in multitarget inhibitors. Mol Inform 2012;31:473-90.
20. VlifeMDS: Molecular Design Suite 4.4. In., 3.0 edn: Vlife Sciences Technologies Pvt. Ltd., Pune, India. 2015.
21. Choudhari P, Kumbhar S, Phalle S, Choudhari S, Desai S, Khare S, Jadhav S. Application of group-based QSAR on 2-thioxo-4 thiazolidinone for development of potent antidiabetic compounds. J Mol Struct 2017; 1128: 355-60.
22. Halgren TA. Molecular geometries and vibrational frequencies for MMFF94. J Comput Chem 1996; 17: 553–86.
23. Baumann K. An alignment-independent versatile structure descriptor for QSAR and QSPR based on the distribution of molecular features. J Chem Inf Comput Sci 2002; 42: 26–35.
24. Veerasamy R, Rajak H, Jain A, Sivadasan S, Varghese CP, Agrawal RK. Validation of QSAR models-strategies and importance. Int J Drug Des Disc 2011; 2: 511-9.
25. Cramer RD, Patterson DE, Bunce JD. Comparative molecular field analysis (CoMFA) 1: Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 1988; 110: 5959–67.
26. Ajmani S, Agrawal A, Kulkarni SA. A comprehensive structure–activity analysis of protein kinase B-alpha (Akt1) inhibitors. J Mol Graph Model 2010; 28: 683-94.

Fxa inhibitörü etkili sübstitüe kromen-2-on bileşiklerinin fragment temelli kantitatif regresyon özelliklerinin incelenmesi

Year 2017, Volume: 21 Issue: 3, 620 - 630, 23.06.2017

Santosh Sahadeo Kumbhar Prafulla Balkrishna Choudhari Manish Sudesh Bhatia

https://doi.org/10.12991/marupj.323292

Abstract

Faktör Xa (FXa), tripsin-benzeri bir serin proteaz olup

antikoagülan etkili bileşiklerin tasarlanmasında temel alınan

önemli bir hedeftir. Literatürde, Faktör Xa inhibitörü olduğu

bildirilen birçok bileşiğin farmakokinetik açıdan sorunlu

olduğu bildirilmektedir. Bu çalışmada, FXa’yı önemli ölçüde

inhibe eden 48 kromen-2-on türevinin grup temelli kantitatif

yapı etki ilişkisi (GQSAR) üzerinde çalışılmış ve elde edilen

bulgular doğrulanmıştır. Bütün bileşikler onbir fonksiyonel

fragmente (R1, R2, R3, R4, R5, R6, R7, R8, R9, R10 ve R11)

ayrılarak incelenmiştir. Geliştirilen tüm GQSAR modelleri çoklu

lineer regresyon analizi (MLR) kullanılarak oluşturulmuştur.

Geliştirilen GQSAR modellerinden r2 değerleri 0.6’nın üzerinde

olanlar istatistiksel yöntemler doğrultusunda seçilmiş ve dış

tahmine dayalı analiz yönteminin sağlamasını yapmak amacıyla

kullanılmış, oluşturulan modelin anlamlılığı F değeri dikkate

alınarak tanımlanmıştır. Geliştirilen GQSAR modelleri; lipofilik

grupların fragment R6 üzerindeki varlığının biyoaktiviteyi

azalttığını, fragment R2 üzerinde bulunmaları durumunda ise

biyoaktivitenin arttığını göstermiştir. Buna ek olarak, fragment

R1’de minimum sayıda dönebilen bağ olması durumunun FXa

inhibisyonunu arttırdığı tespit edilmiştir. GQSAR modellerinin

incelenmesi ile elde edilen sonuçların yeni FXa inhibitörlerinin

tasarımı ve geliştirilmesi için yararlı olduğu düşünülmektedir.

Keywords

FXa, Vlife MDS, Antikoagülan, Kantitatif Yapı Etki İlişkisi, GQSAR, Slogp

References

1. Bhatia MS, Ingale KB, Choudhari PB, Bhatia NM, Sawant RL. Application quantum and physico chemical molecular descriptors utilizing principal components to study mode of anticoagulant activity of pyridyl chromen-2-one derivatives. Bioorg Med Chem 2009; 17: 1654-62.
2. Bohm M, Sturzebecher J, Klebe GJ. Three-Dimensional quantitative structure−activity relationship analyses using comparative molecular field analysis and comparative molecular similarity indices analysis to elucidate selectivity differences of inhibitors binding to trypsin, thrombin, and factor Xa. Med Chem 1999; 42: 458-77.
3. Hirsh J, Anand SS, Halperin JL, Fuster V. Guide to anticoagulant therapy: Heparin. Circulation 2001; 103: 2994-3018.
4. Hogg PJ, Jackson CM. Fibrin monomer protects thrombin from inactivation by heparin-antithrombin III: Implications for heparin efficacy. P Natl Acad Sci USA 1989; 86: 3619-23.
5. Weitz JI, Hudoba M, Massel D, Maraganore J, Hirsh J. Clotbound thrombin is protected from inhibition by heparinantithrombin III but is susceptible to inactivation by antithrombin III-independent inhibitors. J Clin Invest 1990; 86: 385-91.
6. Walenga JM. An overview of the direct thrombin inhibitor argatroban. Pathophysiol Haemost Thromb 2002; 32: 9-14.
7. Riva N, Lip GY. A new era for anticoagulation in atrial fibrillation. Which anticoagulant should we choose for long term prevention of thromboembolic complications in patients with atrial fibrillation? Pol Arch Med Wewn 2012; 122: 45–53.
8. Hart RG, Pearce LA, Aquilar MI. Meta-analysis: Antithrombotic therapy to prevent stroke in patients who have nonvalvular atrial fibrillation. Ann Intern Med 2007;146: 857–67.
9. Perez A, Eraso L, Merli G. Implications of new anticoagulants in primary practice. Int J Clin Pract 2013; 67: 139–56.
10. Vílchez JA, Gallego P, Lip GY. Safety of new oral anticoagulant drugs: A perspective. Ther Adv Drug Saf 2014, 5: 8-20.
11. Stewart S, Hart CL, Hole DJ, McMurray JJ. Population prevalence, incidence, and predictors of atrial fibrillation in the Renfrew/Paisley study. Heart 2001; 86: 516–21.
12. Colilla S, Crow A, Petkun W, Singer DE, Simon T, Liu X. Estimates of current and future incidence and prevalence of Atrial Fibrillation in the U.S. adult population. Am J Cardiol 2013; 112: 1142–7.
13. Hagens VE, Ranchor AV, Van Sonderen E, Bosker HA, Kamp O, Tijssen JG, Kingma JH, Crijns HJ, Van Gelder IC,RACE Study Group. Effect of rate or rhythm control on quality of life in persistent atrial fibrillation. J Am Coll Cardiol 2004; 43: 241–7.
14. Heeringa J, van der Kuip DA, Hofman A, Kors JA, van Herpen G, Stricker BH, Stijnen T, Lip GY, Witteman JC. Prevalence, incidence and lifetime risk of atrial fibrillation: the Rotterdam study. Eur Heart J 2006; 27: 949–53.
15. Hadjipavlou LD. Review, reevaluation, and new results in quantitative structure-activity studies of anticonvulsants. Med Res Rev 1998; 18: 91-119.
16. Singh Bhadoriya K, Sharma MC, Sharma S, Jain SV, Avchar MA. An approach to design potent anti-Alzheimer’s agents by 3D-QSAR studies on fused 5,6-bicyclic heterocycles as γ-secretase modulators using kNN–MFA methodology. Arab J Chem 2014; 7:924-35.
17. Patel HM, Noolvi MN, Sharma P, Jaiswal V, Bansal S, Lohan S, Kumar SS, Abbot V, Dhiman S, Bhardwaj V. Quantitative structure–activity relationship (QSAR) studies as strategic approach in drug discovery. Med Chem Res 2014;12: 4991– 5007.
18. Ajmani S, Jadhav K, Kulkarni SA. Group-Based QSAR (G-QSAR): Mitigating interpretation challenges in QSAR. QSAR & Comb Sci 2009; 28:36-51.
19. Ajmani S, Kulkarni SA. Application of GQSAR for scaffold hopping and lead optimization in multitarget inhibitors. Mol Inform 2012;31:473-90.
20. VlifeMDS: Molecular Design Suite 4.4. In., 3.0 edn: Vlife Sciences Technologies Pvt. Ltd., Pune, India. 2015.
21. Choudhari P, Kumbhar S, Phalle S, Choudhari S, Desai S, Khare S, Jadhav S. Application of group-based QSAR on 2-thioxo-4 thiazolidinone for development of potent antidiabetic compounds. J Mol Struct 2017; 1128: 355-60.
22. Halgren TA. Molecular geometries and vibrational frequencies for MMFF94. J Comput Chem 1996; 17: 553–86.
23. Baumann K. An alignment-independent versatile structure descriptor for QSAR and QSPR based on the distribution of molecular features. J Chem Inf Comput Sci 2002; 42: 26–35.
24. Veerasamy R, Rajak H, Jain A, Sivadasan S, Varghese CP, Agrawal RK. Validation of QSAR models-strategies and importance. Int J Drug Des Disc 2011; 2: 511-9.
25. Cramer RD, Patterson DE, Bunce JD. Comparative molecular field analysis (CoMFA) 1: Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 1988; 110: 5959–67.
26. Ajmani S, Agrawal A, Kulkarni SA. A comprehensive structure–activity analysis of protein kinase B-alpha (Akt1) inhibitors. J Mol Graph Model 2010; 28: 683-94.

There are 26 citations in total.

Details

Subjects	Health Care Administration
Journal Section	Articles
Authors	Santosh Sahadeo Kumbhar This is me Prafulla Balkrishna Choudhari This is me Manish Sudesh Bhatia This is me
Publication Date	June 23, 2017
Published in Issue	Year 2017 Volume: 21 Issue: 3

Cite

APA	Sahadeo Kumbhar, S., Balkrishna Choudhari, P., & Sudesh Bhatia, M. (2017). Investigation of fragment based quantitative regression on a series of substituted chromen-2-one derivatives as FXa inhibitors. Marmara Pharmaceutical Journal, 21(3), 620-630. https://doi.org/10.12991/marupj.323292
AMA	Sahadeo Kumbhar S, Balkrishna Choudhari P, Sudesh Bhatia M. Investigation of fragment based quantitative regression on a series of substituted chromen-2-one derivatives as FXa inhibitors. J Res Pharm. June 2017;21(3):620-630. doi:10.12991/marupj.323292
Chicago	Sahadeo Kumbhar, Santosh, Prafulla Balkrishna Choudhari, and Manish Sudesh Bhatia. “Investigation of Fragment Based Quantitative Regression on a Series of Substituted Chromen-2-One Derivatives As FXa Inhibitors”. Marmara Pharmaceutical Journal 21, no. 3 (June 2017): 620-30. https://doi.org/10.12991/marupj.323292.
EndNote	Sahadeo Kumbhar S, Balkrishna Choudhari P, Sudesh Bhatia M (June 1, 2017) Investigation of fragment based quantitative regression on a series of substituted chromen-2-one derivatives as FXa inhibitors. Marmara Pharmaceutical Journal 21 3 620–630.
IEEE	S. Sahadeo Kumbhar, P. Balkrishna Choudhari, and M. Sudesh Bhatia, “Investigation of fragment based quantitative regression on a series of substituted chromen-2-one derivatives as FXa inhibitors”, J Res Pharm, vol. 21, no. 3, pp. 620–630, 2017, doi: 10.12991/marupj.323292.
ISNAD	Sahadeo Kumbhar, Santosh et al. “Investigation of Fragment Based Quantitative Regression on a Series of Substituted Chromen-2-One Derivatives As FXa Inhibitors”. Marmara Pharmaceutical Journal 21/3 (June 2017), 620-630. https://doi.org/10.12991/marupj.323292.
JAMA	Sahadeo Kumbhar S, Balkrishna Choudhari P, Sudesh Bhatia M. Investigation of fragment based quantitative regression on a series of substituted chromen-2-one derivatives as FXa inhibitors. J Res Pharm. 2017;21:620–630.
MLA	Sahadeo Kumbhar, Santosh et al. “Investigation of Fragment Based Quantitative Regression on a Series of Substituted Chromen-2-One Derivatives As FXa Inhibitors”. Marmara Pharmaceutical Journal, vol. 21, no. 3, 2017, pp. 620-3, doi:10.12991/marupj.323292.
Vancouver	Sahadeo Kumbhar S, Balkrishna Choudhari P, Sudesh Bhatia M. Investigation of fragment based quantitative regression on a series of substituted chromen-2-one derivatives as FXa inhibitors. J Res Pharm. 2017;21(3):620-3.

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