@article{ShityakovBroscheitFoerster2013,
  author    = {Shityakov, Sergey and Broscheit, Jens and F{\"o}rster, Carola},
  title     = {Multidrug resistance protein P-gp interaction with nanoparticles (fullerenes and carbon nanotube) to assess their drug delivery potential: a theoretical molecular docking study.},
  series = {International journal of computational biology and drug design},
  volume    = {6},
  journal   = {International journal of computational biology and drug design},
  number    = {4},
  doi       = {10.1504/IJCBDD.2013.056801},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-132089},
  pages     = {343-357},
  year      = {2013},
  abstract  = {P-glycoprotein (P-gp)-mediated efflux system plays an important role to maintain chemical balance in mammalian cells for endogenous and exogenous chemical compounds. However, despite the extensive characterisation of P-gp potential interaction with drug-like molecules, the interaction of carbon nanoparticles with this type of protein molecule is poorly understood. Thus, carbon nanoparticles were analysed, such as buckminsterfullerenes (C20, C60, C70), capped armchair single-walled carbon nanotube (SWCNT or C168), and P-gp interactions using different molecular docking techniques, such as gradient optimisation algorithm (ADVina), Lamarckian genetic algorithm (FastDock), and shape-based approach (PatchDock) to estimate the binding affinities between these structures. The theoretical results represented in this work show that fullerenes might be P-gp binders because of low levels of Gibbs free energy of binding (ΔG) and potential of mean force (PMF) values. Furthermore, the SWCNT binding is energetically unfavourable, leading to a total decrease in binding affinity by elevation of the residual area (Ares), which also affects the π-π stacking mechanisms. Further, the obtained data could potentially call experimental studies using carbon nanostructures, such as SWCNT for development of drug delivery vehicles, to administer and assess drug-like chemical compounds to the target cells since organisms probably did not develop molecular sensing elements to detect these types of carbon molecules.},
  language  = {en}
}
@article{ShityakovFoerster2014,
  author    = {Shityakov, Sergey and F{\"o}rster, Carola},
  title     = {In silico structure-based screening of versatile P-glycoprotein inhibitors using polynomial empirical scoring functions},
  series = {Advances and Applications in Bioinformatics and Chemistry},
  volume    = {7},
  journal   = {Advances and Applications in Bioinformatics and Chemistry},
  doi       = {10.2147/AABC.S56046},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-120214},
  pages     = {1-9},
  year      = {2014},
  abstract  = {P-glycoprotein (P-gp) is an ATP (adenosine triphosphate)-binding cassette transporter that causes multidrug resistance of various chemotherapeutic substances by active efflux from mammalian cells. P-gp plays a pivotal role in limiting drug absorption and distribution in different organs, including the intestines and brain. Thus, the prediction of P-gp-drug interactions is of vital importance in assessing drug pharmacokinetic and pharmacodynamic properties. To find the strongest P-gp blockers, we performed an in silico structure-based screening of P-gp inhibitor library (1,300 molecules) by the gradient optimization method, using polynomial empirical scoring (POLSCORE) functions. We report a strong correlation (r2=0.80, F=16.27, n=6, P<0.0157) of inhibition constants (Kiexp or pKiexp; experimental Ki or negative decimal logarithm of Kiexp) converted from experimental IC50 (half maximal inhibitory concentration) values with POLSCORE-predicted constants (KiPOLSCORE or pKiPOLSCORE), using a linear regression fitting technique. The hydrophobic interactions between P-gp and selected drug substances were detected as the main forces responsible for the inhibition effect. The results showed that this scoring technique might be useful in the virtual screening and filtering of databases of drug-like compounds at the early stage of drug development processes.},
  language  = {en}
}