[89Zr]-Pertuzumab PET Imaging Reveals Paclitaxel Treatment Efficacy Is Positively Correlated with HER2 Expression in Human Breast Cancer Xenograft Mouse Models

Paclitaxel (PTX) treatment efficacy varies in breast cancer, yet the underlying mechanism for variable response remains unclear. This study evaluates whether human epidermal growth factor receptor 2 (HER2) expression level utilizing advanced molecular positron emission tomography (PET) imaging is correlated with PTX treatment efficacy in preclinical mouse models of HER2+ breast cancer. HER2 positive (BT474, MDA-MB-361), or HER2 negative (MDA-MB-231) breast cancer cells were subcutaneously injected into athymic nude mice and PTX (15 mg/kg) was administrated. In vivo HER2 expression was quantified through [⁸⁹Zr]-pertuzumab PET/CT imaging. PTX treatment response was quantified by [¹⁸F]-fluorodeoxyglucose ([¹⁸F]-FDG) PET/CT imaging. Spearman’s correlation, Kendall’s tau, Kolmogorov–Smirnov test, and ANOVA were used for statistical analysis. [⁸⁹Zr]-pertuzumab mean standard uptake values (SUV_mean) of BT474 tumors were 4.9 ± 1.5, MDA-MB-361 tumors were 1.4 ± 0.2, and MDA-MB-231 (HER2−) tumors were 1.1 ± 0.4. [¹⁸F]-FDG SUV_mean changes were negatively correlated with [⁸⁹Zr]-pertuzumab SUV_mean (r = −0.5887, p = 0.0030). The baseline [¹⁸F]-FDG SUV_mean was negatively correlated with initial [⁸⁹Zr]-pertuzumab SUV_mean (r = −0.6852, p = 0.0002). This study shows PTX treatment efficacy is positively correlated with HER2 expression level in human breast cancer mouse models. Molecular imaging provides a non-invasive approach to quantify biological interactions, which will help in identifying chemotherapy responders and potentially enhance clinical decision-making.     

Tris-(2-pyridyl)-pyrazolyl Borate Zinc(II) Complexes: Synthesis,DNA/Protein Binding and In Vitro Cytotoxicity Studies

Zn(II) complexes bearing tris[3-(2-pyridyl)-pyrazolyl] borate (Tp^py) ligand (1–3) was synthesized and examined by spectroscopic and analytical tools. Mononuclear [Tp^pyZnCl] (1) has a Zn(II) centre with one arm (pyrazolyl-pyridyl) dangling outside the coordination sphere which is a novel finding in Tp^pyZn(II) chemistry. In complex [Tp^pyZn(H₂O)][BF₄] (2) hydrogen bonding interaction of aqua moiety stabilizes the dangling arm. In addition, solution state behaviour of complex 1 confirms the tridentate binding mode and reactivity studies show the exogenous axial substituents used to form the [Tp^pyZnN₃] (3). The complexes (1–3) were tested for their ability to bind with Calf thymus (CT) DNA and Bovine serum albumin (BSA) wherein they revealed to exhibit good binding constant values with both the biomolecules in the order of 10⁴–10⁵ M⁻¹. The intercalative binding mode with CT DNA was confirmed from the UV-Visible absorption, viscosity, and ethidium bromide (EB) DNA displacement studies. Further, the complexes were tested for in vitro cytotoxic ability on four triple-negative breast cancer (TNBC) cell lines (MDA-MB-231, MDA-MB-468, HCC1937, and Hs 578T). All three complexes (1–3) exhibited good IC₅₀ values (6.81 to 16.87 μM for 24 h as seen from the MTS assay) results which indicated that these complexes were found to be potential anticancer agents against the TNBC cells.     

Cytotoxicity and 2D-QSAR study of some heterocyclic compounds

Herein we have studied the cytotoxicity and quantitative structure–activity relationship (QSAR) of heterocyclic compounds containing cyclic urea and thiourea nuclei. A set of 22 hydantoin and thiohydantoin related heterocyclic compounds were investigated with respect to their LC₅₀ values (Log of LC₅₀) against brine shrimp lethality bioassay in order to derive the 2D-QSAR models using MLR, PLS and ANN methods. The best predictive models by MLR, PLS and ANN methods gave highly significant square correlation coefficient (R²) values of 0.83, 0.81 and 0.91 respectively. The model also exhibited good predictive power confirmed by the high value of cross validated correlation coefficient Q² (0.74).     

Monte Carlo method based QSAR modelling of natural lipase inhibitors using hybrid optimal descriptors

Obesity is one of the most provoking health burdens in the developed countries. One of the strategies to prevent obesity is the inhibition of pancreatic lipase enzyme. The aim of this study was to build QSAR models for natural lipase inhibitors by using the Monte Carlo method. The molecular structures were represented by the simplified molecular input line entry system (SMILES) notation and molecular graphs. Three sets – training, calibration and test set of three splits – were examined and validated. Statistical quality of all the described models was very good. The best QSAR model showed the following statistical parameters: r² = 0.864 and Q² = 0.836 for the test set and r² = 0.824 and Q² = 0.819 for the validation set. Structural attributes for increasing and decreasing the activity (expressed as pIC₅₀) were also defined. Using defined structural attributes, the design of new potential lipase inhibitors is also presented. Additionally, a molecular docking study was performed for the determination of binding modes of designed molecules.     

Predictive models for tyrosinase inhibitors: Challenges from heterogeneous activity data determined by different experimental protocols

Quantitative Structure-Activity Relationship (QSAR) models of tyrosinase inhibitors were built using Random Forest (RF) algorithm and evaluated by the out-of-bag estimation (R²_OOB) and 10-fold cross validation (Q²_CV). We found that the performances of QSAR models were closely correlated with the systematic errors of inhibitory activities of tyrosinase inhibitors arising from the different measuring protocols. By defining ERR_sys, outliers with larger errors can be efficiently identified and removed from heterogeneous activity data. A reasonable QSAR model (R²_OOB of 0.74 and Q²_CV of 0.80) was obtained by the exclusion of 13 outliers with larger systematic errors. It is a clear example of the challenge for QSAR model that can overwhelm heterogeneous data from different experimental protocols.     

Synthesis of Novel Methyl 3-(hetero)arylthieno[3,2-b]pyridine-2-carboxylates and Antitumor Activity Evaluation: Studies In Vitro and In Ovo Grafts of Chick Chorioallantoic Membrane (CAM) with a Triple Negative Breast Cancer Cell Line

A series of novel functionalized methyl 3-(hetero)arylthieno[3,2-b]pyridine-2-carboxylates 2a–2h were synthesized by C-C Pd-catalyzed Suzuki-Miyaura cross-coupling of methyl 3-bromothieno[3,2-b]pyridine-2-carboxylate with (hetero)aryl pinacol boranes, trifluoro potassium boronate salts or boronic acids. Their antitumoral potential was evaluated in two triple negative breast cancer (TNBC) cell lines—MDA-MB-231 and MDA-MB-468, by sulforhodamine B assay. Their effects on the non-tumorigenic MCF-12A cells were also evaluated. The results demonstrated that three compounds caused growth inhibition in both TNBC cell lines, with little or no effect against the non-tumorigenic cells. The most promising compound was further studied concerning possible effects on cell viability (by trypan blue exclusion assay), cell proliferation (by bromodeoxyuridine assay) and cell cycle profile (by flow cytometry). The results demonstrated that the GI₅₀ concentration of compound 2e (13 μM) caused a decreased in MDA-MB-231 cell number, which was correlated with a decreased in the % of proliferating cells. Moreover, this compound increased G0/G1 phase and decreased S phases, when compared to control cells (although was not statistic significant). Interestingly, compound 2e also reduced tumor size using an in ovo CAM (chick chorioallantoic membrane) model. This work highlights the potential antitumor effect of a novel methyl 3-arylthieno[3,2-b]pyridine-2-carboxylate derivative.     

Molecular structural investigations of quinoxaline derivatives through 3D-QSAR,molecular docking,ADME prediction and pharmacophore modeling studies for the search of novel antimalarial agent

In the present article, a dataset of 63 quinoxaline derivatives were taken for antimalarial activity and pharmacophore were developed. Atom based method was used to develop a three dimensional quantitative structure activity relationship (3D-QSAR) model. On comparison of all statistical parameters, model AHRRR23 was found to be the most effective and predictive QSAR model as it satisfied all statistical parameters of a good model. The model AHRRR23 showed an adequate R² value for the training set 0.9446, good predictive power with Q² of 0.6409, good F- value, low SD 0.1218 value and outstanding Pearson-R values and low RMSE 0.2779 values of the model. The docking studies also gives very good results with good RMSD values. 3D QSAR, docking and ADME studies exhibits that the developed model could be employed as a potential lead for further study as antimalarial drug.     

Quantitative structure–activity relationship model for prediction study of corrosion inhibition efficiency using two‐stage sparse multiple linear regression

A new quantitative structure–activity relationship (QSAR) of the inhibition of mild steel corrosion in 1 M hydrochloric acid using furan derivatives was developed by proposing two‐stage sparse multiple linear regression. The sparse multiple linear regression using ridge penalty and sparse multiple linear regression using elastic net (SMLRE) were used to develop the QSAR model. The results show that the SMLRE‐based model possesses high predictive power compared with sparse multiple linear regression using ridge penalty‐based model according to the mean‐squared errors for both training and test datasets, leave‐one‐out internal validation (Q²_int = 0.98), and external validation (Q²_ext = 0.95). In addition, the results of applicability domain assessment using the leverage approach reveal a reliable and robust SMLRE‐based model. In conclusion, the developed QSAR model using SMLRE can be efficiently used in the studies of corrosion inhibition efficiency. Copyright © 2016 John Wiley & Sons, Ltd.