Current Status of Computer-Aided Drug Design for Type 2 Diabetes

BACKGROUND Diabetes is a metabolic disorder that requires multiple therapeutic approaches. The pancreas loses its functionality to properly produce the insulin hormone in patients with diabetes mellitus. In 2012, more than one million people worldwide died as a result of diabetes, which was the eighth leading cause of death. OBJECTIVE Most drugs currently available and approved by the U.S. Food and Drug Administration cannot reach an adequate level of glycemic control in diabetic patients, and have many side effects; thus, new classes of compounds are required. Efforts based on computer-aided drug design (CADD) can mine a large number of databases to produce new and potent hits and minimize the requirement of time and dollars for new discoveries. METHODS Pharmaceutical sciences have made progress with advances in drug design concepts. Virtual screening of large databases is most compatible with different computational methods such as molecular docking, pharmacophore, quantitative structure-activity relationship, and molecular dynamic simulation. Contribution of these methods in selection of antidiabetic compounds has been discussed. RESULTS The computer-aided drug design (CADD) approach has contributed to successful discovery of novel anti-diabetic agents. This mini-review focuses on CADD approach on currently approved drugs and new therapeutic agents-indevelopment that may achieve suitable glucose levels and decrease the risk of hypoglycemia, which is a major obstacle to glucose control and a special concern for therapies that increase insulin levels. CONCLUSION Drug design and development for type 2 diabetes have been actively studied. However, a large number of antidiabetic drugs are still in early stages of development. The conventional target- and structure-based approaches can be regarded as part of the efforts toward therapeutic mechanism-based drug design for treatment of type 2 diabetes. It is expected that further improvement in CADD approach will enhance the new discoveries.

[1]  D. Barford,et al.  Molecular basis for the dephosphorylation of the activation segment of the insulin receptor by protein tyrosine phosphatase 1B. , 2000, Molecular cell.

[2]  Matthew P. Repasky,et al.  Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. , 2004, Journal of medicinal chemistry.

[3]  A. Saxena,et al.  Identification of novel PTP1B inhibitors by pharmacophore based virtual screening, scaffold hopping and docking. , 2014, European journal of medicinal chemistry.

[4]  Thierry Langer,et al.  Efficient overlay of small organic molecules using 3D pharmacophores , 2007, J. Comput. Aided Mol. Des..

[5]  Computationally motivated synthesis and enzyme kinetic evaluation of N-(β-d-glucopyranosyl)-1,2,4-triazolecarboxamides as glycogen phosphorylase inhibitors , 2015 .

[6]  I. Kuntz,et al.  DOCK 6: combining techniques to model RNA-small molecule complexes. , 2009, RNA.

[7]  Gerard J Kleywegt,et al.  Application and limitations of X-ray crystallographic data in structure-based ligand and drug design. , 2003, Angewandte Chemie.

[8]  Omprakash Tanwar,et al.  Structure based virtual screening of MDPI database: discovery of structurally diverse and novel DPP-IV inhibitors. , 2014, Bioorganic & medicinal chemistry letters.

[9]  Santosh A. Khedkar,et al.  Successful applications of computer aided drug discovery: moving drugs from concept to the clinic. , 2010, Current topics in medicinal chemistry.

[10]  Allen B Reitz,et al.  Linear interaction energy models for beta-secretase (BACE) inhibitors: Role of van der Waals, electrostatic, and continuum-solvation terms. , 2006, Journal of molecular graphics & modelling.

[11]  Markus Christen,et al.  The GROMOS software for biomolecular simulation: GROMOS05 , 2005, J. Comput. Chem..

[12]  M. Taha,et al.  Ligand-based modeling followed by in vitro bioassay yielded new potent glucokinase activators. , 2015, Journal of molecular graphics & modelling.

[13]  D. Popov Novel protein tyrosine phosphatase 1B inhibitors: interaction requirements for improved intracellular efficacy in type 2 diabetes mellitus and obesity control. , 2011, Biochemical and biophysical research communications.

[14]  J. A. Grant,et al.  Gaussian docking functions. , 2003, Biopolymers.

[15]  Manuela Pavan,et al.  DRAGON SOFTWARE: AN EASY APPROACH TO MOLECULAR DESCRIPTOR CALCULATIONS , 2006 .

[16]  Thierry Langer,et al.  LigandScout: 3-D Pharmacophores Derived from Protein-Bound Ligands and Their Use as Virtual Screening Filters , 2005, J. Chem. Inf. Model..

[17]  Charles C. Persinger,et al.  How to improve R&D productivity: the pharmaceutical industry's grand challenge , 2010, Nature Reviews Drug Discovery.

[18]  D. E. Clark What has computer-aided molecular design ever done for drug discovery? , 2006, Expert opinion on drug discovery.

[19]  G. Klebe,et al.  Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. , 1994, Journal of medicinal chemistry.

[20]  René Thomsen,et al.  MolDock: a new technique for high-accuracy molecular docking. , 2006, Journal of medicinal chemistry.

[21]  Thomas Lengauer,et al.  Evaluation of the FLEXX incremental construction algorithm for protein–ligand docking , 1999, Proteins.

[22]  I D Kuntz,et al.  Structure-based design and combinatorial chemistry yield low nanomolar inhibitors of cathepsin D. , 1997, Chemistry & biology.

[23]  P. Sanseau,et al.  Computational Drug Repositioning: From Data to Therapeutics , 2013, Clinical pharmacology and therapeutics.

[24]  H. Kubinyi Comparative Molecular Field Analysis (CoMFA) , 2002 .

[25]  Richard D. Taylor,et al.  Improved protein–ligand docking using GOLD , 2003, Proteins.

[26]  D. Vanderwall,et al.  Inhibitors of dihydrodipicolinate reductase, a key enzyme of the diaminopimelate pathway of Mycobacterium tuberculosis. , 2001, Biochimica et biophysica acta.

[27]  R. Cramer,et al.  Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. , 1988, Journal of the American Chemical Society.

[28]  Thierry Langer,et al.  Molecule-pharmacophore superpositioning and pattern matching in computational drug design. , 2008, Drug discovery today.

[29]  J. Bajorath,et al.  Quo vadis, virtual screening? A comprehensive survey of prospective applications. , 2010, Journal of medicinal chemistry.

[30]  Holger Gohlke,et al.  The Amber biomolecular simulation programs , 2005, J. Comput. Chem..

[31]  David E. Shaw,et al.  PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results , 2006, J. Comput. Aided Mol. Des..

[32]  B. Shoichet,et al.  Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. , 2002, Journal of medicinal chemistry.

[33]  P. Sasikumar,et al.  Antidiabetic effect of Merremia emarginata Burm. F. in streptozotocin induced diabetic rats. , 2012, Asian Pacific journal of tropical biomedicine.

[34]  R. Friesner,et al.  Novel procedure for modeling ligand/receptor induced fit effects. , 2006, Journal of medicinal chemistry.

[35]  Silja Weber,et al.  Aminomethylpyrimidines as novel DPP-IV inhibitors: a 10(5)-fold activity increase by optimization of aromatic substituents. , 2004, Bioorganic & medicinal chemistry letters.

[36]  Martin Stahl,et al.  Novel dihydrofolate reductase inhibitors. Structure-based versus diversity-based library design and high-throughput synthesis and screening. , 2003, Journal of medicinal chemistry.

[37]  Hege S. Beard,et al.  Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. , 2004, Journal of medicinal chemistry.

[38]  D. Porte,et al.  Glucokinase activators (GKAs) promise a new pharmacotherapy for diabetics , 2010, F1000 medicine reports.

[39]  Florian Nigsch,et al.  Computational toxicology: an overview of the sources of data and of modelling methods. , 2009, Expert opinion on drug metabolism & toxicology.

[40]  E. Bradley,et al.  Performance of 3D-database molecular docking studies into homology models. , 2004, Journal of medicinal chemistry.

[41]  M. Karplus,et al.  CHARMM: A program for macromolecular energy, minimization, and dynamics calculations , 1983 .

[42]  N. de Kimpe,et al.  Natural medicines used in the traditional Chinese medical system for therapy of diabetes mellitus. , 2004, Journal of ethnopharmacology.

[43]  C. Bailey,et al.  Inhibition of dipeptidylpeptidase IV activity as a therapy of type 2 diabetes. , 2006, Expert opinion on emerging drugs.

[44]  David S. Goodsell,et al.  AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility , 2009, J. Comput. Chem..

[45]  M. Taha,et al.  Discovery of new potent human protein tyrosine phosphatase inhibitors via pharmacophore and QSAR analysis followed by in silico screening. , 2007, Journal of molecular graphics & modelling.

[46]  A. Combs Recent advances in the discovery of competitive protein tyrosine phosphatase 1B inhibitors for the treatment of diabetes, obesity, and cancer. , 2010, Journal of medicinal chemistry.