Prediction of pH-dependent aqueous solubility of Histone Deacetylase (HDAC) inhibitors

Recently we developed a model for prediction of pH-dependent aqueous solubility of drugs and drug like molecules. In the present work, the model was applied on a series of novel Histone Deacetylases (HDAC) inhibitors discovered at TopoTarget. The applicability of our model was evaluated on the series of HDAC inhibitors by use of Self-Organizing Maps (SOM) and 2D-projection of the HDAC inhibitors on the chemical space of the training data set of the artificial neural network (ANN) module. The model was refined for the particular chemical space of interest, which led to two modifications in the training data set of the ANN. The performance of the original and the two modified versions of the model were evaluated against the commercial software from Simulations-plus and pH-dependent solubility measurements for representative compounds of the series. The results of the evaluation indicate that one can develop models that are more accurate in predicting differences in the solubility of structurally very similar compounds than models that have been trained on structurally unbiased, diverse data sets. Such 'tailor-made' models have the potential to become trustworthy enough to replace time-consuming and expensive medium- and high-throughput solubility experiments by providing results of similar or even better quality.     

Docking,Binding Free Energy Calculations and In Vitro Characterization of Pyrazine Linked 2-Aminobenzamides as Novel Class I Histone Deacetylase (HDAC) Inhibitors

Class I histone deacetylases, HDAC1, HDAC2, and HDAC3, represent potential targets for cancer treatment. However, the development of isoform-selective drugs for these enzymes remains challenging due to their high sequence and structural similarity. In the current study, we applied a computational approach to predict the selectivity profile of developed inhibitors. Molecular docking followed by MD simulation and calculation of binding free energy was performed for a dataset of 2-aminobenzamides comprising 30 previously developed inhibitors. For each HDAC isoform, a significant correlation was found between the binding free energy values and in vitro inhibitory activities. The predictive accuracy and reliability of the best preforming models were assessed on an external test set of newly designed and synthesized inhibitors. The developed binding free-energy models are cost-effective methods and help to reduce the time required to prioritize compounds for further studies.     

Light‐Controlled Histone Deacetylase (HDAC) Inhibitors: Towards Photopharmacological Chemotherapy

Cancer treatment suffers from limitations that have a major impact on the patient’s quality of life and survival. In the case of chemotherapy, the systemic distribution of cytotoxic drugs reduces their efficacy and causes severe side effects due to nonselective toxicity. Photopharmacology allows a novel approach to address these problems because it employs external, local activation of chemotherapeutic agents by using light. The development of photoswitchable histone deacetylase (HDAC) inhibitors as potential antitumor agents is reported herein. Analogues of the clinically used chemotherapeutic agents vorinostat, panobinostat, and belinostat were designed with a photoswitchable azobenzene moiety incorporated into their structure. The most promising compound exhibits high inhibitory potency in the thermodynamically less stable cis form and a significantly lower activity for the trans form, both in terms of HDAC activity and proliferation of HeLa cells. This approach offers a clear prospect towards local photoactivation of HDAC inhibition to avoid severe side effects in chemotherapy.     

Prediction of aqueous solubility of drugs and pesticides with COSMO-RS

The COSMO-RS method, originally developed for the prediction of liquid-liquid and liquid-vapor equilibrium constants based on quantum chemical calculations, has been extended to solid compounds by addition of a heuristic expression for the Gibbs free energy of fusion. By this addition, COSMO-RS is now capable of a priori prediction of aqueous solubilities of a wide range of typical neutral drug and pesticide compounds. Only three parameters in the heuristic expression have been fitted on a data set of 150 drug-like compounds. On these data an rms deviation of 0.66 log-units was achieved. Later, the model was tested on a set of 107 pesticides, which have been critically selected based on two experimental data sources and by a crosscheck with an independent HQSAR model. On this data set an rms of 0.61 log-units was achieved, without any adjustments to the structurally extremely diverse pesticides. This result verifies the ability of this extended COSMO-RS to predict aqueous solubilities of drugs and pesticides of almost arbitrary structural classes. The new method is COSMO-RSol.     

Novel enhanced applications of QSPR models: Temperature dependence of aqueous solubility

A model developed to predict aqueous solubility at different temperatures has been proposed based on quantitative structure–property relationships (QSPR) methodology. The prediction consists of two steps. The first one predicts the value of k parameter in the linear equation , where S_w is the value of solubility and T is the value of temperature. The second step uses Random Forest technique to create high‐efficiency QSPR model. The performance of the model is assessed using cross‐validation and external test set prediction. Predictive capacity of developed model is compared with COSMO‐RS approximation, which has quantum chemical and thermodynamic foundations. The comparison shows slightly better prediction ability for the QSPR model presented in this publication. © 2016 Wiley Periodicals, Inc.     

Design,Synthesis and Biological Evaluation of Novel Selective Thiol-based Histone Deacetylase(HDAC) VI InhibitorsBearing Indeno[1,2-c]pyrazole or Benzoindazole Scaffold

A series of thiol-based indeno[1,2-c]pyrazoles and benzoindazole compounds was designed and synthesized according to the structural specificity of histone deacetylase VI(HDAC6) and the structural characteristics of HDAC inhibitors. The inhibitory activities of the target compounds against HDAC6 and HDAC1 were screened by fluorescence analysis. Most of the target compounds showed moderate inhibitory activity against HDAC6(IC₅₀=44—598 nmol/L). Among them, compound A-4 displayed the highest selectivity against HDAC6 and similar inhibitory activity(IC₅₀=44 nmol/L) to that of the positive drug SAHA(IC₅₀=41 nmol/L) against HDAC6.     

Prediction of aqueous solubility of organic compounds by the general solubility equation (GSE)

The revised general solubility equation (GSE) is used along with four different methods including Huuskonen's artificial neural network (ANN) and three multiple linear regression (MLR) methods to estimate the aqueous solubility of a test set of the 21 pharmaceutically and environmentally interesting compounds. For the selected test sets, it is clear that the GSE and ANN predictions are more accurate than MLR methods. The GSE has the advantages of being simple and thermodynamically sound. The only two inputs used in the GSE are the Celsius melting point (MP) and the octanol water partition coefficient (K(ow)). No fitted parameters and no training data are used in the GSE, whereas other methods utilize a large number of parameters and require a training set. The GSE is also applied to a test set of 413 organic nonelectrolytes that were studied by Huuskonen. Although the GSE uses only two parameters and no training set, its average absolute errors is only 0.1 log units larger than that of the ANN, which requires many parameters and a large training set. The average absolute error AAE is 0.54 log units using the GSE and 0.43 log units using Huuskonen's ANN modeling. This study provides evidence for the GSE being a convenient and reliable method to predict aqueous solubilities of organic compounds.     

New data on the solubility of amorphous silica in organic compound-water solutions and a new model for the thermodynamic behavior of aqueous silica in aqueous complex solutions

Experimental solubilities of amorphous silica in several aqueous electrolyte solutions and in aqueous solutions of organic compounds, and theoretical considerations concerning cavity formation, electrostriction collapse, ion solvation, and long- and short-range interaction of the solvated ions with one another⁽¹⁾ permit the calculation of the partial excess free energies and the activity coefficients of aqueous silica. It is shown that, in the case of non-dissociated aqueous organic solutions, the variation of log m (SiO₂) with the reciprocal of the dielectric constant of the solution is described by a single linear equation independent of the nature of the organic compound. For aqueous electrolyte solutions, a specific linear relationship between log m (SiO₂) and the reciprocal of the dielectric constant occurs for each electrolyte. The success of the equation in reproducing the experimental solubilities of amorphous silica in aqueous solutions of electrolytes and organic compounds supports previous evidence indicating a polar charge distribution in the solvated SiO₂ molecule. Our data permit the calculation of the effective local charge of dissolved SiO₂ molecules and of the short-range interaction parameters between SiO₂ and various ions. The proposed equation of state can be used to calculate the affinity of reactions among SiO₂ minerals and complex aqueous solutions.     

应用人工神经网络预测氢化可的松的溶解度

以量子化学方法在密度泛函B3LYP/6-31G(d)水平上计算得到含有电负性原子的溶剂水、醇类、酮类、酯类、氯代烷烃共17种溶剂的结构参数：最高占用轨道能(EHOMO)、分子最低空轨道能(ELUMO)、分子偶极矩(μ)、分子总能量(Etotal) 、最正原子净电荷(q+)、最负原子净电荷(q-)。采用误差反向传播(BP)算法的三层人工神经网络,确定隐含层节点数为7,建立了EHOMO、ELUMO、μ、Etotal、q+、q-、摩尔体积(VM)、介电常数(ε)、温度(T)共9个参数与氢化可的松在不同温度下不同溶剂中的溶解度之间关系的模型。运用此神经网络模型可预测不同分离条件下氢化可的松的溶解度,平均预测相对误差为7.0%。     

Histone Deacetylase 3-Directed PROTACs Have Anti-inflammatory Potential by Blocking Polarization of M0-like into M1-like Macrophages

Macrophage polarization plays a crucial role in inflammatory processes. The histone deacetylase 3 (HDAC3) has a deacetylase-independent function that can activate pro-inflammatory gene expression in lipopolysaccharide-stimulated M1-like macrophages and cannot be blocked by traditional small-molecule HDAC3 inhibitors. Here we employed the proteolysis targeting chimera (PROTAC) technology to target the deacetylase-independent function of HDAC3. We developed a potent and selective HDAC3-directed PROTAC, P7 , which induces nearly complete HDAC3 degradation at low micromolar concentrations in both THP-1 cells and human primary macrophages. P7 increases the anti-inflammatory cytokine secretion in THP-1-derived M1-like macrophages. Importantly, P7 decreases the secretion of pro-inflammatory cytokines in M1-like macrophages derived from human primary macrophages. This can be explained by the observed inhibition of macrophage polarization from M0-like into M1-like macrophage. In conclusion, we demonstrate that the HDAC3-directed PROTAC P7 has anti-inflammatory activity and blocks macrophage polarization, demonstrating that this molecular mechanism can be targeted with small molecule therapeutics.     

人工神经网络预测煤炭成浆浓度的研究

考虑煤炭的多种理化特性建立了成浆浓度的神经网络预测模型,对其数据预处理方法、学习率和中间层节点数等进行了深入讨论。水分、挥发分、分析基碳、灰分和氧等五个因子对于煤炭成浆性的预测起到主导作用。五因子、七因子和八因子神经网络模型对煤炭成浆浓度的预测误差分别为：0.53%、0.50%和0.74%,而现有回归分析方程的误差为1.15%,故神经网络模型比回归分析方程有更好的预测能力,尤以七因子模型最佳。     

The solubility of CaMoO4(c) and an aqueous thermodynamic model for Ca2+-MoO 4 2 -ion-interactions

The solubility of powellite [CaMoO₄(c)] was studied in aqueous Na₂MoO₄, CaCl₂ and Ca(NO₃)₂ solutions ranging in concentrations from 1×10^–4M to 1.0M and over equilibration times extending to 36 days. Our experimental data were interpreted using the aqueous ion-interaction model of Pitzer and coworkers. The Ca²⁺–MoO ₄ ^2– ion-interactions were found to be analogous to Ca²⁺–SO ₄ ^2– . The use of Ca²⁺–MoO ₄ ^2– ion-interactions parameters (⁽⁰⁾=0.2, ⁽¹⁾ = 3.1973 and ⁽²⁾) and a logK _sp of –7.93 gave excellent predictions of all of the experimental data. Commonion ternary interaction parameters such as MoO ₄ ^2– –Cl^– or MoO ₄ ^2– –NO ₃ ^– were not required.     

Design,Synthesis, Bioactivity Evaluation,Crystal Structures,and In Silico Studies of New α-Amino Amide Derivatives as Potential Histone Deacetylase 6 Inhibitors

Hydroxamate, as a zinc-binding group (ZBG), prevails in the design of histone deacetylase 6(HDAC6) inhibitors due to its remarkable zinc-chelating capability. However, hydroxamate-associated genotoxicity and mutagenicity have limited the widespread application of corresponding HDAC6 inhibitors in the treatment of human diseases. To avoid such side effects, researchers are searching for novel ZBGs that may be used for the synthesis of HDAC6 inhibitors. In this study, a series of stereoisomeric compounds were designed and synthesized to discover non-hydroxamate HDAC6 inhibitors using α-amino amide as zinc-ion-chelating groups, along with a pair of enantiomeric isomers with inverted L-shaped vertical structure as cap structures. The anti-proliferative activities were determined against HL-60, Hela, and RPMI 8226 cells, and 7a and its stereoisomer 13a exhibited excellent activities against Hela cells with IC₅₀ = 0.31 µM and IC₅₀ = 5.19 µM, respectively. Interestingly, there is a significant difference between the two stereoisomers. Moreover, an evaluation of cytotoxicity toward human normal liver cells HL-7702 indicated its safety for normal cells. X-ray single crystal diffraction was employed to increase insights into molecule structure and activities. It was found that the carbonyl of the amide bond is on the different side from the amino and pyridine nitrogen atoms. To identify possible protein targets to clarify the mechanism of action and biological activity of 7a, a small-scale virtual screen using reverse docking for HDAC isoforms (1–10) was performed and the results showed that HDAC6 was the best receptor for 7a, suggesting that HDAC6 may be a potential target for 7a. The interaction pattern analysis showed that the α-amino amide moiety of 7a coordinated with the zinc ion of HDAC6 in a bidentate chelate manner, which is similar to the chelation pattern of hydroxamic acid. Finally, the molecular dynamics simulation approaches were used to assess the docked complex’s conformational stability. In this work, we identified 7a as a potential HDAC6 inhibitor and provide some references for the discovery of non-hydroxamic acid HDAC6 inhibitors.     

Extended Hildebrand solubility approach applied to some sulphapyrimidines in some {methanol (1) + water (2)} mixtures

Extended Hildebrand solubility approach (EHSA) was applied to analyse the equilibrium solubility of sulphadiazine, sulphamerazine and sulphamethazine in some {methanol (1) + water (2)} mixtures at 298.15K. Reported experimental solubilities and some fusion properties of these drugs were used for EHSA calculations. A good predictive character of EHSA (with mean deviations lower than 4.0%) was found by using regular polynomials in order 4 when correlating the interaction parameter (W) and the Hildebrand solubility parameter of solvent mixtures free of drug (δ₁₊₂). Nevertheless, the predictive character of EHSA was almost the same as obtained when logarithmic drug solubilities (log x₃) were correlated with δ₁₊₂.     

Correlation of solubility data of ammonia in ionic liquids for gas separation processes using artificial neural networks

Artificial neural networks have been used for the correlation and prediction of solubility data of ammonia in ionic liquids. This solubility of ammonia is highly variable for different types of ionic liquids at the same temperature and pressure, its correlation and prediction is of special importance in the removal of ammonia from flue gases for which effective and efficient solvents are required. Nine binary ammonia + ionic liquids mixtures were considered in the study. Solubility data (P–T–x) of these systems were taken from the literature (208 data points for training and 50 data points for testing). The training variables are the temperature and the pressure of the binary systems (T, P), being the target variable the solubility of ammonia in the ionic liquid (x). The study shows that the neural network model is a good alternative method for the estimation of solubility for this type of mixtures. Absolute average deviations were below 5.6%, for each isothermal data set and overall absolute average deviations were below 3.0%. Only in the range of low solubility (below 0.2 in mole fraction) did predicted solubility give deviations higher than 10%.     

Copper(II) oxide solubility behavior in aqueous sodium phosphate solutions at elevated temperatures

A platinum-lined, flowing autoclave facility is used to investigate the solubility behavior of copper(II) oxide (CuO) in aqueous sodium phosphate solutions at temperatures between 19 and 262°C. Copper solubilities are observed to increase continuously with temperature and phosphate concentration. The measured solubility behavior is examined via a Cu(II) ion hydrolysis/complexing model and thermodynamic functions for the hydrolysis/complexing reactions are obtained from a leastsquares analysis of the data. Altogether, thermochemical properties are established for five anionic complexes: Cu(OH) ₃ ^– , Cu(OH) ₄ ^2– , Cu(OH) ₂ (HPO ₄ ) ^2– , Cu(OH) ₃ (H ₂ PO ₄ ) ^2– , and Cu(OH) ₂ (PO ₄ ) ^3– . Precise thermochemical parameters are also derived for the Cu(OH)⁺ hydroxocomplex based on CuO solubility behavior previously observed (Ref. 3) for pure water at elevated temperatures. The relative ease of Cu(II) ion hydrolysis is such that Cu(OH) ₃ ^– species become the preferred hydroxocomplex for pH9.4.Prepared for presentation at the Fourth International Symposium on solubility Phenomena, Rensselaer Polytechnic Institute, August 1990.