Commercially available pesticides were examined as Mus musculus and Homo sapiens acetylcholinesterase (mAChE and hAChE) inhibitors by means of ligand-based (LB) and structure-based (SB) in silico approaches. Initially, the crystal structures of simazine, monocrotophos, dimethoate, and acetamiprid were reproduced using various force fields. Subsequently, LB alignment rules were assessed and applied to determine the inter synaptic conformations of atrazine, propazine, carbofuran, carbaryl, tebufenozide, imidacloprid, diuron, monuron, and linuron. Afterwards, molecular docking and dynamics SB studies were performed on either mAChE or hAChE, to predict the listed pesticides’ binding modes. Calculated energies of global minima (Eglob_min) and free energies of binding (∆Gbinding) were correlated with the pesticides’ acute toxicities (i.e., the LD50 values) against mice, as well to generate the model that could predict the LD50s against humans. Although for most of the pesticides the low Eglob_min correlates with the high acute toxicity, it is the ∆Gbinding that conditions the LD50 values for all the evaluated pesticides. Derived pLD50 = f(∆Gbinding) mAChE model may predict the pLD50 against hAChE, too. The hAChE inhibition by atrazine, propazine, and simazine (the most toxic pesticides) was elucidated by SB quantum mechanics (QM) DFT mechanistic and concentration-dependent kinetic studies, enriching the knowledge for design of less toxic pesticides.
The
estrogen receptor α (ERα) represents a 17β-estradiol-inducible
transcriptional regulator that initiates the RNA polymerase II-dependent
transcriptional machinery, pointed for breast cancer (BC) development via either genomic direct or genomic indirect (i.e., tethered) pathway. To develop innovative ligands, structure-based
(SB) three-dimensional (3-D) quantitative structure–activity
relationship (QSAR) studies have been undertaken from structural data
taken from partial agonists, mixed agonists/antagonists (selective
estrogen receptor modulators (SERMs)), and full antagonists (selective
ERα downregulators (SERDs)) correlated with either wild-type
or mutated ERα receptors. SB and ligand-based (LB) alignments
allow us to rule out guidelines for the SB/LB alignment of untested
compounds. 3-D QSAR models for ERα ligands, coupled with SB/LB
alignment, were revealed to be useful tools to dissect the chemical
determinants for ERα-based anticancer activity as well as to
predict their potency. The herein developed protocol procedure was
verified through the design and potency prediction of 12 new coumarin-based
SERMs, namely, 3DQ-1a to 3DQ-1e, that upon
synthesis turned to be potent ERα antagonists by means of either in vitro or in vivo assays (described in
the second part of this study).
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