QSAR-based solubility model for drug-like compounds

Solubility plays a very important role in the selection of compounds for drug screening. In this context, a QSAR model was developed for predicting water solubility of drug-like compounds. First, a set of relevant parameters for establishing a drug-like chemical space was defined. The comparison of chemical structures from the FDAMDD and PHYSPROP databases allowed the selection of properties that were more efficient in discriminating drug-like compounds from other chemicals. These filters were later on applied to the PHYSPROP database and 1174 chemicals fulfilling these criteria and with experimental solubility information available at 25 °C were retained. Several QSAR solubility models were developed from this set of compounds, and the best one was selected based on the accuracy of correct classifications obtained for randomly chosen training and validation subsets. Further validation of the model was performed with a set of 102 drugs for which experimental solubility data have been recently reported. A good agreement between the predictions and the experimental values confirmed the reliability of the QSAR model.     

Biopartitioning micellar chromatography: an alternative high-throughput method for assessing the ecotoxicity of anilines and phenols

An investigation of the use of the chromatographic retention (log k) as an in vitro approach for modelling the toxicity to Fathead Minnows of anilines and phenols is developed. A data set of 65 compounds with available experimental toxicity data was used. Log k data at three pH values were used for the compounds classification and two groups or 'MODEs' were identified. For one 'MODE' a quantitative retention-activity relationship (QRAR) model was calculated. Finally, it was used to estimate the toxicity to Fathead minnows of anilines and phenols for which experimental data are not available. These estimations were compared to those obtained from another toxicity (to Tetrahymena pyriformis) data set and those estimated from a U.S. EPA QSAR approach (ECOSAR software) to decide on the toxicity level according to the Directive 3/21/EEC.     

Exploration of Novel Inhibitors for Class I Histone Deacetylase Isoforms by QSAR Modeling and Molecular Dynamics Simulation Assays

Histone deacetylases (HDAC) are metal-dependent enzymes and considered as important targets for cell functioning. Particularly, higher expression of class I HDACs is common in the onset of multiple malignancies which results in deregulation of many target genes involved in cell growth, differentiation and survival. Although substantial attempts have been made to control the irregular functioning of HDACs by employing various inhibitors with high sensitivity towards transformed cells, limited success has been achieved in epigenetic cancer therapy. Here in this study, we used ligand-based pharmacophore and 2-dimensional quantitative structure activity relationship (QSAR) modeling approaches for targeting class I HDAC isoforms. Pharmacophore models were generated by taking into account the known IC₅₀ values and experimental energy scores with extensive validations. The QSAR model having an external R² value of 0.93 was employed for virtual screening of compound libraries. 10 potential lead compounds (C1-C10) were short-listed having strong binding affinities for HDACs, out of which 2 compounds (C8 and C9) were able to interact with all members of class I HDACs. The potential binding modes of HDAC2 and HDAC8 to C8 were explored through molecular dynamics simulations. Overall, bioactivity and ligand efficiency (binding energy/non-hydrogen atoms) profiles suggested that proposed hits may be more effective inhibitors for cancer therapy.     

QSAR study on permeability of hydrophobic compounds with artificial membranes

We previously reported a classical quantitative structure-activity relationship (QSAR) equation for permeability coefficients (P(app-pampa)) by parallel artificial membrane permeation assay (PAMPA) of structurally diverse compounds with simple physicochemical parameters, hydrophobicity at a particular pH (logP(oct) and |pK(a)-pH|), hydrogen-accepting ability (SA(HA)), and hydrogen-donating ability (SA(HD)); however, desipramine, imipramine, and testosterone, which have high logP(oct) values, were excluded from the derived QSAR equation because their measured P(app-pampa) values were lower than calculated. In this study, for further investigation of PAMPA permeability of hydrophobic compounds, we experimentally measured the P(app-pampa) of more compounds with high hydrophobicity, including several pesticides, and compared the measured P(app-pampa) values with those calculated from the QSAR equation. As a result, compounds having a calculated logP(app-pampa)>-4.5 showed lower measured logP(app-pampa) than calculated because of the barrier of the unstirred water layer and the membrane retention of hydrophobic compounds. The bilinear QSAR model explained the PAMPA permeability of the whole dataset of compounds, whether hydrophilic or hydrophobic, with the same parameters as the equation in the previous study. In addition, PAMPA permeability coefficients correlated well with Caco-2 cell permeability coefficients. Since Caco-2 cell permeability is effective for the evaluation of human oral absorption of compounds, the proposed bilinear model for PAMPA permeability could be useful for not only effective screening for several drug candidates but also the risk assessment of chemicals and agrochemicals absorbed by humans.     

Computational modeling and design of renin inhibitors

The recently introduced field-based QSAR was employed to develop robust quantitative 3D QSAR models to comprehend the activity of several structurally diverse classes of small molecule renin inhibitors reported in literature. A reasonable predictive model with an r² (pred) of ～0.67 and rmse of 0.79 was achieved for an external validation set of ～150 compounds centered on the model developed using ～450 training set compounds. Based on the developed 3D QSAR models and additional insights gained from reported X-ray structures, opportunity for activity improvements in the [aza]indole scaffold was explored using a carefully designed virtual library of ～2300 compounds. The potential for success of such combined structure-guided and ligand-based approach was justified when the resulting prediction was compared against a representative with supporting experimental results.     

In silico Prediction of Human Oral Absorption Based on QSAR Analyses of PAMPA Permeability

The parallel artificial membrane permeation assay (PAMPA) was developed as a model for the prediction of transcellular permeation in the process of drug absorption. Our research group has measured the PAMPA permeability of peptide‐related compounds, diverse drugs, and agrochemicals. This work led to a classical quantitative structure–activity relationship (QSAR) equation for PAMPA permeability coefficients of structurally diverse compounds based on simple physicochemical parameters such as lipophilicity at a particular pH (log P_oct and |pK_a?pH|), H‐bond acceptor ability (SA_HA), and H‐bond donor ability (SA_HD). Since the PAMPA permeability of lipophilic compounds decreased with their apparent lipophilicity due to the unstirred water layer (UWL) barrier on membrane surfaces and to membrane retention, a bilinear QSAR model was introduced to explain the permeability of a broader set of compounds using the same physicochemical parameters as those used for the linear model. We also compared PAMPA and Caco‐2 cell permeability coefficients of compounds transported by various absorption mechanisms. The compounds were classified according to their absorption pathway (passively transported compounds, actively transported compounds, and compounds excreted by efflux systems) in the plot of Caco‐2 vs. PAMPA permeability. Finally, based on the QSAR analyses of PAMPA permeability, an in silico prediction model of human oral absorption for possibly transported compounds was proposed, and the usefulness of the model was examined.     

基于支持向量回归的酚类化合物QSAR建模

目的：采用定量构效关系（QSAR）方法探索酚类化合物的毒性与分子结构参数的关系。方法：基于支持向量回归（SVR）、依均方误差最小原则选择最优核函数,对酚类化合物及其衍生物进行了QSAR研究。结果：不同数据集选取的最优核函数有异,对小样本、非线性等问题,SVR具有较优的稳定性及预测能力,在酚类化合物及其衍生物的QSAR研究中得到了优于原文献方法的独立预测结果。结论：SVR模型具有较好的预测能力,在QSAR及相关研究中可得到更广泛应用。     

On the relationship of anthranilic derivatives structure and the FXR (Farnesoid X receptor) agonist activity

Farnesoid X receptor (FXR) is a nuclear receptor related to lipid and glucose homeostasis and is considered an important molecular target to treatment of metabolic diseases as diabetes, dyslipidemia, and liver cancer. Nowadays, there are several FXR agonists reported in the literature and some of it in clinical trials for liver disorders. Herein, a compound series was employed to generate QSAR models to better understand the structural basis for FXR activation by anthranilic acid derivatives (AADs). Furthermore, here we evaluate the inclusion of the standard deviation (SD) of EC₅₀ values in QSAR models quality. Comparison between the use of experimental variance plus average values in model construction with the standard method of model generation that considers only the average values was performed. 2D and 3D QSAR models based on the AAD data set including SD values showed similar molecular interpretation maps and quality (Q²_LOO, Q²_(F2), and Q²_(F3)), when compared to models based only on average values. SD-based models revealed more accurate predictions for the set of test compounds, with lower mean absolute error indices as well as more residuals near zero. Additionally, the visual interpretation of different QSAR approaches agrees with experimental data, highlighting key elements for understanding the biological activity of AADs. The approach using standard deviation values may offer new possibilities for generating more accurate QSAR models based on available experimental data.     

Removal of organic compounds by a biofilm supported on GAC: modelling of batch and column data

The performance of a biofilm of Arthrobacter viscosus supported on granular activated carbon on the retention of organic compounds was evaluated. The presence of functional groups on the cell wall surface of the biomass that may interact with the organic compounds was confirmed by Fourier transform infrared spectroscopy, to assess the applicability of this system to the removal of those compounds. The batch assays showed that the removal percentage decreases with the increasing initial concentration. The removal of phenol ranged from 99.5 to 93.4%, the chlorophenol removal ranged from 99.3 to 61.6% and the o-cresol removal ranged from 98.7 to 73.5%, for initial concentrations between 100 and 1,700 mg/L. The batch data were described by Freundlich, Langmuir, Redlich–Peterson, Dubinin-Radushkevich, Sips and Toth model isotherms and the best fit for the retention of phenol and for the retention of o-cresol was obtained with the Sips model, while for chlorophenol, the best fit was obtained with the Freundlich model. The column tests showed that the retention performance followed the order: phenol > chlorophenol > o-cresol, and increased with the increasing initial organic compound concentration. Data from column runs were described by Adams–Bohart, Wolborska and Yoon and Nelson models with good fitting for all the models.     

Opioid analgetics retention-pharmacologic activity models using biopartitioning micellar chromatography

Opioids are drugs used in medicine for pain control. In this paper, retention-pharmacokinetics and retention-pharmacodynamics relationships of opioids are proposed and statistically validated. These models are based on the compound retention in the biopartitioning micellar chromatography system (BMC), a new methodology which has successfully been used to develop QRAR models for many other families of compounds. The obtained results are compared to the traditional QSAR models using lipophilicity data. The adequacy of QRAR models is due to the fact that the characteristics of the compounds such as the hydrophobicity, electronic charge and steric effects determine both their retention in BMC and their pharmacokinetic and pharmacodynamic behavior.