Identification of approved drugs with ALDH1A1 inhibitory potential aimed at enhancing chemotherapy sensitivity in cancer cells: an in-silico drug repurposing approach.

The aldehyde dehydrogenase 1A1 (ALDH1A1) also known as retinal dehydrogenase, is an enzyme normally involved in the cellular metabolism, development and detoxification processes in healthy cells. However, it's also considered a cancer stem cell marker and its high levels of expression in several cancers, including breast, lung, ovarian, and colon cancer have been associated with poor prognosis and resistance to chemotherapy. Given its crucial role in chemotherapy resistance by detoxification of chemotherapeutic drugs, ALDH1A1 has attracted significant research interest as a potential therapeutic target for cancer. Though a few synthetic inhibitors of ALDH1A1 have been synthesized and their efficacy has been proved in-vitro and in-vivo studies, none of them have passed clinical trials so far. In this scenario, we have performed an in-silico study to verify whether any of the already approved drugs used for various purposes has the ability to inhibit catalytic activity of ALDH1A1, so that they can be repurposed for cancer therapy. Keeping in mind the feasibility of repurposing in a larger population we have selected the approved drugs from five widely used drug categories such as antibiotic, antiviral, antifungal, anti diabetic and antihypertensive for screening. Computational techniques like molecular docking, molecular dynamics simulations and MM-PBSA binding energy calculation have been used in this study to screen the approved drugs. Based on the logical analysis of results, we propose that three drugs - telmisartan, irbesartan and maraviroc can inhibit the catalytic activity of ALDH1A1 and thus can be repurposed to increase chemotherapy sensitivity in cancer cells.Communicated by Ramaswamy H. Sarma.

A combined in silico approaches of 2D-QSAR, molecular docking, molecular dynamics and ADMET prediction of anti-cancer inhibitor activity for actinonin derivatives.

Inhibition of human mitochondrial peptide deformylase (HsPDF) plays a major role in reducing growth, proliferation, and cellular cancer survival. In this work, a series of 32 actinonin derivatives for HsPDF (PDB: 3G5K) inhibitor's anticancer activity was computationally analyzed for the first time, using an in silico study considering 2D-QSAR modeling, and molecular docking studies, and validated by molecular dynamics and ADMET properties. The results of multilinear regression (MLR) and artificial neural networks (ANN) statistical analysis reveal a good correlation between pIC50 activity and the seven (7) descriptors. The developed models were highly significant with cross-validation, the Y-randomization test and their applicability range. In addition, all considered data sets show that the AC30 compound, exhibits the best binding affinity (docking score = -212.074 kcal/mol and H-bonding energy = -15.879 kcal/mol). Furthermore, molecular dynamics simulations were performed at 500 ns, confirming the stability of the studied complexes under physiological conditions and validating the molecular docking results. Five selected actinonin derivatives (AC1, AC8, AC15, AC18 and AC30), exhibiting best docking score, were rationalized as potential leads for HsPDF inhibition, in well agreement with experimental outcomes. Furthermore, based on the in silico study, new six molecules (AC32, AC33, AC34, AC35, AC36 and AC37) were suggested as HsPDF inhibition candidates, which would be combined with in-vitro and in-vivo studies to perspective validation of their anticancer activity. Indeed, the ADMET predictions indicate that these six new ligands have demonstrated a fairly good drug-likeness profile.

Mesoporous silica synthesized from natural local kaolin as an effective adsorbent for removing of Acid Red 337 and its application in the treatment of real industrial textile effluent.

This paper presents a synthesis of mesoporous silica (MS) from natural clay as a silica source using Pluronic L35 (EO11PO16EO11) as a structure-directing agent. The prepared material was characterized by XRD, X-ray fluorescence, thermogravimetric analysis, SEM, TEM, and N2 adsorption-desorption analyses. Then, mesoporous material was used for the removal of Acid Red 337 (AR337) from aqueous solution, and the treatment of real textile effluent. The effect of pH, contact time, weight of adsorbent, and initial concentration was studied in batch adsorption. The synthesized mesoporous material showed good discoloration efficiency with a 62% percentage. Experiment with real textile wastewater showed that 39%, 40%, and 31.5% of the color, TOC, and chemical oxygen demand respectively were eliminated by using 1 g of MS per liter of wastewater.

Structure, electronic properties, and NBO and TD-DFT analyses of nickel(II), zinc(II), and palladium(II) complexes based on Schiff-base ligands.

In this work we studied the structural and electronic properties of the metal-Schiff base complexes Ni[Formula: see text] (1), Pd[Formula: see text] (2), Zn[Formula: see text] (3), and Ni[Formula: see text](4), where L1 and L2 are Schiff bases synthesized from salicylaldehyde and 2-hydroxy-5-methylbenzaldehyde, respectively. Natural bond analysis showed that in complexes 1 and 2, the metal ion coordinates to the ligands through electron donation from lone pairs on ligand nitrogen and oxygen atoms to s and d orbitals on the metal ion. In complex 3, metal-N and metal-O bonds are formed through charge transfer from the lone pairs on nitrogen and oxygen atoms to an s orbital of Zn. Dimethylation of the phenolate rings in the ligands decreases the energy gap and redshifts the spectrum of the nickel complex. The main absorptions observed were assigned on the basis of singlet-state transitions. The simulated spectra of the two complexes 1 and 2 are characterized by excited states with ligand-to-ligand charge-transfer (LLCT), metal-to-ligand charge-transfer (MLCT), ligand-to-metal charge-transfer (LMCT), and metal-centered (MC) character. Graphical abstract Geometric structure of the palladium complex.

DFT/TDDFT computational study of the structural, electronic and optical properties of rhodium (III) and iridium (III) complexes based on tris-picolinate bidentate ligands.

The electronic structures and spectroscopic properties of two complexes [M(pic)3] (M = Ir, Rh) containing picolinate as bidentate ligands have been calculated by means density functional theory (DFT) and time-dependent DFT/TD-DFT using three hybrid functionals B3LYP, PBE0 and mPW1PW91. The PBE0 and mPW1PW91 functionals, which have the same HF exchange fraction (25%), give similar results and do not differ drastically from B3LYP results. Calculated geometric parameters of the complexes are in good agreement with the available experimental data. The UV absorptions observed in acetonitrile were assigned on the basis of singlet state transitions. The most intense band observed in the UV-C region corresponds to ligand-to-ligand charge transfer states (LLCT) in both complexes. The theoretical spectrum of the rhodium complex is characterized by a large degree of mixing between metal-to-ligand-charge-transfer (MLCT), LLCT and metal centered (MC) states in the UV-A region. The presence of low-lying excited states with MC character affects the absorption spectrum under spin-orbit coupling (SOC) effects and play important roles in the photochemical properties. Graphical abstract Frontier molecular orbital diagram of mer-M(pic)3 (M=Ir, Rh).

Prediction of the melting points of fatty acids from computed molecular descriptors: a quantitative structure-property relationship study.

The aim of these works present in this paper consisted in the development and evaluation of quantitative structure property models (QSPR) for the prediction of the melting points of a series constituted by 62 fatty acids. The best multilinear regressions method (MLR) is used to develop models for the prediction of the melting points. The descriptors of the model are selected among an extended set of more than 500 descriptors (constitutional, topological, geometric, quantum chemical and thermodynamic descriptors). Applicability domains were defined and the predictive power was determined using a set of validations The QSPR models are established using the BMLR method implemented in CODESSA software, It turns out that the best QSPR model (R(2)=0.948, R(adj)(2)=0.936, SD=0.940 and F-test=190.90) is obtained with five molecular descriptors.