610 Medizin und Gesundheit
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The process of viral integration into the host genome is an essential step of the HIV-1 life cycle. The viral integrase (IN) enzyme catalyzes integration. IN is an ideal therapeutic enzyme targeted by several drugs; raltegravir (RAL), elvitegravir (EVG), dolutegravir (DTG), and bictegravir (BIC) having been approved by the USA Food and Drug Administration (FDA). Due to high HIV-1 diversity, it is not well understood how specific naturally occurring polymorphisms (NOPs) in IN may affect the structure/function and binding affinity of integrase strand transfer inhibitors (INSTIs). We applied computational methods of molecular modelling and docking to analyze the effect of NOPs on the full-length IN structure and INSTI binding. We identified 13 NOPs within the Cameroonian-derived CRF02_AG IN sequences and further identified 17 NOPs within HIV-1C South African sequences. The NOPs in the IN structures did not show any differences in INSTI binding affinity. However, linear regression analysis revealed a positive correlation between the Ki and EC50 values for DTG and BIC as strong inhibitors of HIV-1 IN subtypes. All INSTIs are clinically effective against diverse HIV-1 strains from INSTI treatment-naïve populations. This study supports the use of second-generation INSTIs such as DTG and BIC as part of first-line combination antiretroviral therapy (cART) regimens, due to a stronger genetic barrier to the emergence of drug resistance.
The objective of the present investigation was to study the ability of sulfobutylether-\(\beta\)-cyclodextrin (SBECD) to form an inclusion complex with sevoflurane (SEV), a volatile anesthetic with poor water solubility. The inclusion complex was prepared, characterized and its cellular toxicity and blood-brain barrier (BBB) permeation potential of the formulated SEV have also been examined for the purpose of controlled drug delivery. The SEV-SBE\(\beta\)CD complex was nontoxic to the primary brain microvascular endothelial (pEND) cells at a clinically relevant concentration of sevoflurane. The inclusion complex exhibited significantly higher BBB permeation profiles as compared with the reference substance (propranolol) concerning calculated apparent permeability values (P\(_{app}\)). In addition, SEV binding affinity to SBE\(\beta\)CD was confirmed by a minimal Gibbs free energy of binding (ΔG\(_{bind}\)) value of -1.727 ± 0.042 kcal・mol\(^{-1}\) and an average binding constant (K\(_{b}\)) of 53.66 ± 9.24 mM indicating rapid drug liberation from the cyclodextrin amphiphilic cavity.
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.
The cytochrome P450 (CYP)3A4 enzyme affects the metabolism of most drug-like substances, and its inhibition may influence drug safety. Modulation of CYP3A4 by flavonoids, such as anthocyanins, has been shown to inhibit the mutagenic activity of mammalian cells. Considering the previous investigations addressing CYP3A4 inhibition by these substances, we studied the three-dimensional quantitative structure-activity relationship (3D-QSAR) in a series of anthocyanin derivatives as CYP3A4 inhibitors. For the training dataset (n=12), comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) yielded crossvalidated and non-crossvalidated models with a q (2) of 0.795 (0.687) and r (2) of 0.962 (0.948), respectively. The models were also validated by an external test set of four compounds with r (2) of 0.821 (CoMFA) and r (2) of 0.812 (CoMSIA). The binding affinity modes associated with experimentally derived IC50 (half maximal inhibitory concentration) values were confirmed by molecular docking into the CYP3A4 active site with r (2) of 0.66. The results obtained from this study are useful for a better understanding of the effects of anthocyanin derivatives on inhibition of carcinogen activation and cellular DNA damage.
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.