QSAR study of dipeptidyl peptidase-4 inhibitors based on the Monte Carlo method

The Monte Carlo method was used for quantitative structure–activity relationships modeling of 36 quinoline/isoquinoline derivatives acting as dipeptidyl peptidase-4 inhibitors. Quantitative structure–activity relationships models were calculated with the representation of the molecular structure by the simplified molecular input-line entry system with one random split into the training and the test set. The statistical quality of the developed model was good. The best calculated quantitative structure–activity relationships model had the following statistical parameters: r2 = 0.9573 for the training set and r2 = 0.9079 for the test set. Structural indicators defined as molecular fragments responsible for increases and decreases in the inhibition activity were calculated. The computer-aided design of new compounds as potential dipeptidyl peptidase-4 inhibitors with the application of defined structural alerts was presented.

[1]  David Weininger,et al.  SMILES, 3. DEPICT. Graphical depiction of chemical structures , 1990, J. Chem. Inf. Comput. Sci..

[2]  Raghuvir R. S. Pissurlenkar,et al.  3D-QSAR studies of Dipeptidyl peptidase IV inhibitors using a docking based alignment , 2007, Journal of molecular modeling.

[3]  Alan Talevi,et al.  An integrated drug development approach applying topological descriptors. , 2012, Current computer-aided drug design.

[4]  J. Dearden,et al.  QSAR modeling: where have you been? Where are you going to? , 2014, Journal of medicinal chemistry.

[5]  Alexander Golbraikh,et al.  Predictive QSAR modeling workflow, model applicability domains, and virtual screening. , 2007, Current pharmaceutical design.

[6]  Kunal Roy,et al.  Comparative QSARs for antimalarial endochins: Importance of descriptor-thinning and noise reduction prior to feature selection , 2011 .

[7]  A. Woods,et al.  Dipeptidyl peptidase IV inhibition for the treatment of type 2 diabetes: potential importance of selectivity over dipeptidyl peptidases 8 and 9. , 2005, Diabetes.

[8]  G. McCaughan,et al.  CD26: A Multifunctional Integral Membrane and Secreted Protein of Activated Lymphocytes , 2001, Scandinavian journal of immunology.

[9]  Hualiang Jiang,et al.  Synthesis, Structure–Activity Relationship, and Pharmacophore Modeling Studies of Pyrazole‐3‐Carbohydrazone Derivatives as Dipeptidyl Peptidase IV Inhibitors , 2012, Chemical biology & drug design.

[10]  K. Roy,et al.  Further exploring rm2 metrics for validation of QSPR models , 2011 .

[11]  M. Taha,et al.  Discovery of DPP IV Inhibitors by Pharmacophore Modeling and QSAR Analysis followed by in silico Screening , 2008, ChemMedChem.

[12]  J. Kozarich,et al.  Prolyl peptidases: a serine protease subfamily with high potential for drug discovery. , 2003, Current opinion in chemical biology.

[13]  Aleksandar M Veselinović,et al.  Application of SMILES Notation Based Optimal Descriptors in Drug Discovery and Design. , 2015, Current topics in medicinal chemistry.

[14]  B. Fernhall,et al.  Exercise and type 2 diabetes: American College of Sports Medicine and the American Diabetes Association: joint position statement. Exercise and type 2 diabetes. , 2010, Medicine and science in sports and exercise.

[15]  Pablo R Duchowicz,et al.  QSAR study for carcinogenicity in a large set of organic compounds. , 2012, Current drug safety.

[16]  U. Saqib,et al.  3D-QSAR studies on triazolopiperazine amide inhibitors of dipeptidyl peptidase-IV as anti-diabetic agents , 2009, SAR and QSAR in environmental research.

[17]  Bernd Kuhn,et al.  Molecular recognition of ligands in dipeptidyl peptidase IV. , 2007, Current topics in medicinal chemistry.

[18]  M. Ghate,et al.  Recent approaches to medicinal chemistry and therapeutic potential of dipeptidyl peptidase-4 (DPP-4) inhibitors. , 2014, European journal of medicinal chemistry.

[19]  Yun Tang,et al.  3D-QSAR studies on fluoropyrrolidine amides as dipeptidyl peptidase IV inhibitors by CoMFA and CoMSIA , 2007, Journal of molecular modeling.

[20]  P. Roy,et al.  Exploring the impact of size of training sets for the development of predictive QSAR models , 2008 .

[21]  Yan-Ke Jiang,et al.  Molecular docking and 3D-QSAR studies on β-phenylalanine derivatives as dipeptidyl peptidase IV inhibitors , 2010, Journal of molecular modeling.

[22]  L. Goodyear,et al.  Diabetes: Exercise and type 2 diabetes mellitus—good for body and mind? , 2010, Nature Reviews Endocrinology.

[23]  Wencong Lu,et al.  QSAR studies on pyrrolidine amides derivatives as DPP-IV inhibitors for type 2 diabetes , 2013, Medicinal Chemistry Research.

[24]  C. Jiang,et al.  3D-QSAR and docking studies of arylmethylamine-based DPP IV inhibitors , 2012 .

[25]  M. Fowler.  Diabetes Treatment, Part 1: Diet and Exercise , 2007 .

[26]  E Benfenati,et al.  CORAL: building up the model for bioconcentration factor and defining it's applicability domain. , 2011, European journal of medicinal chemistry.

[27]  J. Yano,et al.  Discovery of potent, selective, and orally bioavailable quinoline-based dipeptidyl peptidase IV inhibitors targeting Lys554. , 2011, Bioorganic & medicinal chemistry.

[28]  Y. S. Prabhakar,et al.  CoMFA and CoMSIA of diverse pyrrolidine analogues as dipeptidyl peptidase IV inhibitors: active site requirements , 2011, Molecular Diversity.

[29]  A. Rosenbloom,et al.  Emerging epidemic of type 2 diabetes in youth. , 1999, Diabetes care.

[30]  B. Thorens.  Glucagon-like peptide-1 and control of insulin secretion. , 1995, Diabete & metabolisme.

[31]  Humayun Kabir,et al.  Comparative Studies on Some Metrics for External Validation of QSPR Models , 2012, J. Chem. Inf. Model..

[32]  Corwin Hansch,et al.  Comparative QSAR: Toward a Deeper Understanding of Chemicobiological Interactions. , 1996, Chemical reviews.

[33]  K. Roy,et al.  Be aware of error measures. Further studies on validation of predictive QSAR models , 2016 .

[34]  David Weininger,et al.  SMILES. 2. Algorithm for generation of unique SMILES notation , 1989, J. Chem. Inf. Comput. Sci..

[35]  Ji Zhang,et al.  Cationic Lipids Containing Cyclen and Ammonium Moieties as Gene Delivery Vectors , 2012, Chemical biology & drug design.

[36]  D. Drucker.  Glucagon-like peptide-1 and the islet beta-cell: augmentation of cell proliferation and inhibition of apoptosis. , 2003, Endocrinology.

[37]  I. Lu,et al.  A three-dimensional pharmacophore model for dipeptidyl peptidase IV inhibitors. , 2008, European journal of medicinal chemistry.

[38]  R. Pederson,et al.  Long-term treatment with the dipeptidyl peptidase IV inhibitor P32/98 causes sustained improvements in glucose tolerance, insulin sensitivity, hyperinsulinemia, and beta-cell glucose responsiveness in VDF (fa/fa) Zucker rats. , 2002, Diabetes.

[39]  Bhumika D. Patel,et al.  3D-QSAR studies of dipeptidyl peptidase-4 inhibitors using various alignment methods , 2014, Medicinal Chemistry Research.

[40]  Glen P. Kenny,et al.  Physical Activity/Exercise and Type 2 Diabetes , 2006, Diabetes Care.

[41]  Jerzy Leszczynski,et al.  QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells. , 2013, Chemosphere.

[42]  Kunal Roy,et al.  On some aspects of validation of predictive quantitative structure–activity relationship models , 2007, Expert opinion on drug discovery.

[43]  David Weininger,et al.  SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..

[44]  S. Bloom,et al.  Gut peptides in the regulation of food intake and energy homeostasis. , 2006, Endocrine reviews.

[45]  Paola Gramatica,et al.  Principles of QSAR models validation: internal and external , 2007 .

[46]  B. Fernhall,et al.  Exercise and Type 2 Diabetes , 2010, Diabetes Care.

[47]  M. Feinglos,et al.  Update on type 2 diabetes mellitus: understanding changes in the diabetes treatment paradigm , 2007, International journal of clinical practice. Supplement.