Quantitative Structure-activity Relationship Equation Research Articles

Photodegradation of polychlorinated biphenyls in water/nitrogen-doped silica and air/nitrogen-doped silica systems: Kinetics, mechanism and quantitative structure activity relationship (QSAR) analysis

Developing efficient and low-cost photocatalytic materials is essential for removing polychlorinated biphenyls (PCBs). In this work, the photodegradation process of fourteen representative polychlorinated biphenyls (PCBs) in both water/nitrogen-doped SiO2 (N-SiO2) and air/N-SiO2 systems was studied. The photodegradation kinetics of PCBs is consistent with the pseudo-first-order kinetic equation. The variation in the degradation effects of different PCBs in the two systems is primarily related to the position of the Cl substituent and the effective absorption wavelength range of PCBs. A total of fourteen intermediates for 4′-Dichlorobiphenyl (PCB-15), 2,2′,4,4′,6,6′-Hexachlorobiphenyl (PCB-155), and 2,2′,3,3′,4,4′,5,5′,6,6′-Decachlorobiphenyl (PCB-209) generated from four reaction pathways were identified based on both mass spectrometry analysis and theoretical calculations. Using the values of lnk (k denotes pseudo-first-order kinetic constants) for the 11 PCBs in the training set and the calculated molecular and structural parameters, quantitative structure-activity relationship (QSAR) models for the two systems were constructed by using multiple linear regression (MLR) method to better understand the factors affecting the photodegradation rate of PCBs. The QSAR equations were obtained with Cl atom substitution at position 3 (N3) as the main parameter, which were lnk = −1.98 - 0.19 N3 for the water/N-SiO2 system and lnk = −1.56 - 0.34 N3 for the air/N-SiO2 system, with the correlation coefficient (R2) of 0.66 and 0.73, leave-one-out cross-validation (Q2LOO) of 0.51 and 0.59, respectively, and bootstrapping validation coefficients (Q2BOOT) values of both 0.74, confirming that the models were well fitted and showed high robustness and prediction ability. This study provides valuable insights into photocatalytic degradation studies of PCBs.

Revealing the Potency of 1,3,5-Trisubstituted Pyrazoline as Antimalaria Through Combination of in Silico Studies

The potency of 1,3,5-trisubstituted pyrazoline as an antimalarial agent has been studied through quantitative structure-activity relationship, molecular docking, and molecular dynamics simulation as a combination of in silicostudies. The study commenced by applying quantitative structure-activity relationship (QSAR) to 25 derivative compounds using 3D-descriptor. The genetic algorithm and multiple linear regression analysis were used to construct the QSAR model, which resulting an equation that has Rtraining as 0.8100 and Rtest set as 0.9222. Descriptors involved in the QSAR equation are TDB4 m, TDB8s, RDF30e, and RDF552, all of which belong to the group of 3D autocorrelation and RDF. This result is in line with the principal component analysis, which shows that both group descriptors represent whole 3D descriptors. Molecular docking analysis is conducted to study the interaction between pyrazoline derivatives and the falcipain-2 enzyme. Interactions between compound 14 and falcipain-2 is describing by hydrogen bond against Glu14 amino acid residue, more pi-stacking interaction, and van der Waals. Chloroquine as a positive control also presented one hydrogen bond with Gly83, pi-sulfur against Cys42, and van der Waals. The stability of the ligand–enzyme interaction is evaluated by molecular dynamics simulation, and after 100 ns simulations, the root mean square deviation results show that compound 14 and chloroquine have a stable interaction with the falcipain-2 enzyme. Overall, this research provides the insight of 1,3,5-trisubstitued pyrazoline compounds as antimalaria by giving a QSAR equation and used to design a better falcipain-2 inhibitors.

Quantitative structure activity relationship (QSAR) modeling study of some novel thiazolidine 4-one derivatives as potent anti-tubercular agents

This study aims to develop a QSAR model for Antitubercular activity. The quantitative structure-activity relationship (QSAR) approach predicted the thiazolidine-4-ones derivative’s Antitubercular activity. For the QSAR study, 53 molecules with Antitubercular activity on H37Rv were collected from the literature. Compound structures were drawn by ACD/Labs ChemSketch. The energy minimization of the 2D structure was done using the MM2 force field in Chem3D pro. PaDEL Descriptor software was used to construct the molecular descriptors. QSARINS software was used in this work to develop the 2D QSAR model. A series of thiazolidine 4-one with MIC data were taken from the literature to develop the QSAR model. These compounds were split into a training set (43 compounds) and a test set (10 compounds). The PaDEL software calculated 2300 descriptors for this series of thiazolidine 4-one derivatives. The best predictive Model 4, which has R 2 of 0.9092, R 2adj of 0.8950 and LOF parameter of 0.0289 identify a preferred fit. The QSAR study resulted in a stable, predictive, and robust model representing the original dataset. In the QSAR equation, the molecular descriptor of MLFER_S, GATSe2, Shal, and EstateVSA 6 positively correlated with Antitubercular activity. While the SpMAD_Dzs 6 is negatively correlated with Antitubercular activity. The high polarizability, Electronegativities, Surface area contributions and number of Halogen atoms in the thiazolidine 4-one derivatives will increase the Antitubercular activity.

Quantitative chirality in the binding of androgens to their receptor

Chirality is a binary yes/no molecular property that nevertheless can be described by a continuous chirality measure function. Chirality measure values can be understood as sensitive descriptors of molecular shape. In this study, we evaluated the role of quantitative chirality (measures) in binding of androgens to their receptor. We demonstrate that a simple Quantitative Structure-Activity Relationship equation correlating the binding affinity with the partial charges of pharmacophoric oxygen atoms is significantly improved upon introducing quantitative chirality descriptors as additional variables. In such models, the charge descriptors account for the strength of the formed hydrogen bonds. However, the picture is completed by the chirality measures that indirectly contain information on the geometries of the hydrogen bond networks and subtle differences in the van der Waals contacts connected with different local or global shapes of the molecules. The model case studied here (11 simple and very similar steroids) proves that both global and local chirality is important in the androgen binding to their receptors.

An Empirical Quantitative Structure-Activity Relationship Equation Assists the Discovery of High-Affinity Phosphodiesterase 4D Inhibitors as Leads to PET Radioligands.

A positron emission tomography (PET) radioligand for imaging phosphodiesterase 4D (PDE4D) would benefit drug discovery and the investigation of neuropsychiatric disorders. The most promising radioligand to date, namely, [11C]T1650, has shown unstable quantification in humans. Structural elaboration of [11C]T1650 was therefore deemed necessary. High target affinity in the low nM range is usually required for successful PET radioligands. In our PDE4D PET radioligand development, we formulated and optimized an empirical equation (log[IC50 (nM)] = P1 + P2 + P3 + P4) that well described the relationship between binding affinity and empirically derived values (P1-P4) for the individual fragments in four subregions commonly composing each inhibitor (R2 = 0.988, n = 62). This equation was used to predict compounds that would have high inhibitory potency. Fourteen new compounds were obtained with IC50 of 0.3-10 nM. Finally, eight compounds were judged to be worthy of future radiolabeling and evaluation as PDE4D PET radioligands.

Extensive Study of DFT-Quantum Calculations Based QSAR Modeling of Fused 1,2,4-Triazine Derivatives Revealed Potent CYP1A1 Inhibitors

In a continuing effort for our research group, we identify new active 1,2,4-triazine derivatives as novel cancer preventive agents that target CYP1A1 activity, through the application of quantitative structure–activity relationship (QSAR) theory. For this purpose, novel fused 1,2,4-triazine derivatives, namely, 4-Amino-6-substituted benzyl-3-(2-substituted benzylidenehydrazinyl)-1,2,4-triazin-5(4H)-ones and acetylated 6-substituted benzyl-7-oxo-3-substituted phenyl-2,3-dihydro-7[Formula: see text]-[1,2,4]triazolo[4,3-[Formula: see text]][1,2,4]triazine analogs were utilized for the prediction of the QSAR model. Furthermore, the application of this QSAR equation served successfully as a rational guide for predicting the activities of newly synthesized analogs. The DFT calculation was applied to recognize the physicochemical parameters at B3LYP/6- 311G* level to gain a clear view of global and local molecular reactivity. Besides, the molecular features of the investigated compounds were identified through HOMO, LUMO and molecular electrostatic potential (MEP) which were plotted to determine the charge transfer within the molecules. Promising two novel 1,2,4-triazine compounds were identified with observed significant inhibitions (83% and 67%) and CYP1A1 activity was predicted. The chemical configuration of the two actives showed good alignment of [Formula: see text]-methoxy benzyl aminotriazinone fragments, hydrazino and carbonyl linker in structure–activity relationship and interaction. Molecular modeling analysis supported with MD simulations was undertaken to investigate different binding interactions with the target binding site. These projects offered a good application of a promising QSAR model for prediction of highly active compounds for further lead optimization.

Toward Novel Antioxidant Drugs: Quantitative Structure-Activity Relationship Study of Eugenol Derivatives

The study of the Quantitative Structure-Activity Relationship (QSAR) of eugenol compound and its derivatives towards antioxidant activities was conducted using electronic and molecular descriptors. These descriptors were generated from semi-empirical chemical computation with PM3 level of theory. The QSAR model in this research could be used to predict novel antioxidant compounds which are more potent. The activity of the compound determined based on the IC50 value (Inhibition Concentration 50%) was linked with the descriptor results that had been calculated in a QSAR equation. The data showed that the descriptors that had a significant effect were a net charge of C-6, C-7, O-12, and HOMO energy and hydration energy. The best QSAR equations obtained with these descriptors and their parameters are shown as follows.log1/IC50 = - 3,3026 (± 0,4066) qC6 - 4,7450 (± 0,7224) qC7 + 3,2746 (± 0,6752) qO12 + 0,6326 (± 0,0645) HOMO - 0,0086 (± 0,0011) hydration energy + 4,8053 (± 0,6336)(n = 8 ; R = 1,000 ; s = 0,004 ; F = 3655,537 ; p = 0,0003 ; Q2 = 0,988 ; SPress = 0,039 ; SDEP = 0,021)

Design of New Chlorochalcone Derivatives as Potential Breast Anticancer Compound Based on QSAR Analysis and Molecular Docking Study

Quantitative structure-activity relationships (QSAR) proposes a model that relates the biological activities of drugs to their chemical structures, and the interaction between the drug and its target enzyme is revealed by molecular docking research. These studies were conducted on chalcone to produce a model that could design highly potent breast anticancer MCF7 cells. The compounds were optimized using ab initio using a basis set 6-31G, then their descriptors calculated using this method. Genetic Function Algorithm (GFA) was used to select descriptors and build the model. One of the six models generated was found to be the best with internal and external squared correlation coefficient (R2) of 0.743 and 0.744, respectively, adjusted squared correlation coefficient (adjusted R2) of 0.700, Standard estimate of error (SEE) of 0.198, Fcalc/Ftable of 6.423, and Predicted residual sum of squares (PRESS) of 1.177. The QSAR equation is pIC50 = 3.869 + (1.427 x qC1) + (4. 027 x qC10) + (0.856 x qC15) - (35.900 x ELUMO) + (0.208 x Log P). Hence, it can predict the breast anticancer activities of new chlorochalcones A-F. The compound with the best prediction was chlorochalcone A with pIC50 2.65 and IC50 value of 2.26 μM. The chlorochalcones A-F were able to bind to the main amino acid residues, namely Arg120 and Tyr355, on the active site of the COX-2 enzyme. These results could serve as a model for designing novel chlorochalcone as inhibitors of COX-2 with higher breast anticancer activities. Keywords: Chlorochalcone, COX-2, QSAR, MCF-7, Molecular docking

The use of minimal topological differences to inspire the design of novel tetrahydroisoquinoline analogues with antimalarial activity

A quantitative structure-activity relationship (QSAR) study was conducted using nineteen previously synthesized, and tested 1-aryl-6-hydroxy-1,2,3,4-tetrahydroisoquinolines with proven in vitro activities against Plasmodium falciparum. In order to computationally design and screen potent antimalarial agents, these compounds with known biological activity ranging from 0.697 to 35.978 μM were geometry optimized at the B3LYP/6-311 + G(d,p) level of theory, using the Gaussian 09W software. To calculate the topological differences, the series of the nineteen compounds was superimposed and a hypermolecule obtained with s¯ = 17 and 20 vertices. Other molecular descriptors were considered in order to build a highly predictive QSAR model. These include the minimal topological differences (MTD), LogP, two dimensional polarity surface area (TDPSA), dipole moment (μ), chemical hardness (η), electrophilicity (ω), potential energy (Ep), electrostatic energy (Eele) and number of rotatable bonds (NRB). By using a training set composed of 15 randomly selected compounds from this series, several QSAR equations were derived. The QSAR equations obtained were then used to attempt to predict the IC50 values of 4 remaining compounds in a test (or validation) set. Ten analogues were proposed by a fragment search of a fragment library containing the pharmacophore model of the active compounds contained in the training set. The most active proposed analogue showed a predicted activity within the lower micromolar range.

Computational Estimation of the Aqueous Acidities of Alcohols, Hydrates, and Enols.

Alcohols are ubiquitous chemical species, and their acid dissociation constants are critical properties in many applications. It is useful to have a reliable and convenient computational procedure for estimating these acidities. Here we describe a quantitative structure-activity relationship (QSAR) for the pKas of alcohols in aqueous solution employing density functional theory computations at the B3LYP/6-31+G(d,p) level with the CPCM implicit solvent model for the aqueous solutions. For the pKas, the energy difference in solution between the parent compound and the anion, ΔE(H2O), was used as a single descriptor. High quality QSAR equations are obtained both for the gas-phase Gibbs energy changes (ΔGr0s, R2 = 0.9764) and the aqueous pKas (R2 = 0.9594). It is shown that the aqueous equation can also be used to estimate the pKas of fluorinated alcohols, hydrates formed by carbonyl compounds in aqueous solution, and enols that appear as minor components in keto-enol equilibria.

Predicting Pharmacokinetic Properties of Potential Anticancer Agents via Their Chromatographic Behavior on Different Reversed Phase Materials.

The Quantitative Structure-Activity Relationship (QSAR) methodology was used to predict biological properties, i.e., the blood–brain distribution (log BB), fraction unbounded in the brain (fu,brain), water-skin permeation (log Kp), binding to human plasma proteins (log Ka,HSA), and intestinal permeability (Caco-2), for three classes of fused azaisocytosine-containing congeners that were considered and tested as promising drug candidates. The compounds were characterized by lipophilic, structural, and electronic descriptors, i.e., chromatographic retention, topological polar surface area, polarizability, and molecular weight. Different reversed-phase liquid chromatography techniques were used to determine the chromatographic lipophilicity of the compounds that were tested, i.e., micellar liquid chromatography (MLC) with the ODS-2 column and polyoxyethylene lauryl ether (Brij 35) as the effluent component, an immobilized artificial membrane (IAM) chromatography with phosphatidylcholine column (IAM.PC.DD2) and chromatography with end-capped octadecylsilyl (ODS) column using aqueous solutions of acetonitrile as the mobile phases. Using multiple linear regression, we derived the statistically significant quantitative structure-activity relationships. All these QSAR equations were validated and were found to be very good. The investigations highlight the significance and possibilities of liquid chromatographic techniques with three different reversed-phase materials and QSARs methods in predicting the pharmacokinetic properties of our important organic compounds and reducing unethical animal testing.

Evaluation of QSAR Equations for Virtual Screening.

Quantitative Structure Activity Relationship (QSAR) models can inform on the correlation between activities and structure-based molecular descriptors. This information is important for the understanding of the factors that govern molecular properties and for designing new compounds with favorable properties. Due to the large number of calculate-able descriptors and consequently, the much larger number of descriptors combinations, the derivation of QSAR models could be treated as an optimization problem. For continuous responses, metrics which are typically being optimized in this process are related to model performances on the training set, for example, and . Similar metrics, calculated on an external set of data (e.g., ), are used to evaluate the performances of the final models. A common theme of these metrics is that they are context -” ignorant”. In this work we propose that QSAR models should be evaluated based on their intended usage. More specifically, we argue that QSAR models developed for Virtual Screening (VS) should be derived and evaluated using a virtual screening-aware metric, e.g., an enrichment-based metric. To demonstrate this point, we have developed 21 Multiple Linear Regression (MLR) models for seven targets (three models per target), evaluated them first on validation sets and subsequently tested their performances on two additional test sets constructed to mimic small-scale virtual screening campaigns. As expected, we found no correlation between model performances evaluated by “classical” metrics, e.g., and and the number of active compounds picked by the models from within a pool of random compounds. In particular, in some cases models with favorable and/or values were unable to pick a single active compound from within the pool whereas in other cases, models with poor and/or values performed well in the context of virtual screening. We also found no significant correlation between the number of active compounds correctly identified by the models in the training, validation and test sets. Next, we have developed a new algorithm for the derivation of MLR models by optimizing an enrichment-based metric and tested its performances on the same datasets. We found that the best models derived in this manner showed, in most cases, much more consistent results across the training, validation and test sets and outperformed the corresponding MLR models in most virtual screening tests. Finally, we demonstrated that when tested as binary classifiers, models derived for the same targets by the new algorithm outperformed Random Forest (RF) and Support Vector Machine (SVM)-based models across training/validation/test sets, in most cases. We attribute the better performances of the Enrichment Optimizer Algorithm (EOA) models in VS to better handling of inactive random compounds. Optimizing an enrichment-based metric is therefore a promising strategy for the derivation of QSAR models for classification and virtual screening.

Quantitative Structure-Activity Relationship Studies Of Series Of Chalcones Derivatives as Inhibitors Of Tumor Necrosis Factor-Alpha

Quantitative structure-activity relationship (QSAR) teqnique was used to predict the biological activity of a series of chalcones compounds as anti-inflammatory. 26 physicochemical descriptors are tested in QSAR equations configuration to predict biological effectiveness of compounds under study. The values of R2 in Eqs (1–3) ranged from 0.794–0.873, the F values ranged from 14.161–26.206 and the S values ranged from 0.262–0.334. The results demonstrated excellent models based on Eq.3, along with high of R2, F and minimum S by employing three parameters r(C3-C5), (LUMO+1) and (LUMO+2). This signifies that these parameters play a significant role in determining anti-inflammatory characteristics.

Design of New 2,4-Substituted Furo [3,2-B] Indole Derivatives as Anticancer Compounds Using Quantitative Structure-Activity Relationship (QSAR) and Molecular Docking

This study aimed to propose new indole derivatives as anticancer through Quantitative Structure-Activity Relationship (QSAR) and molecular docking method. The best predicted anticancer activity of indole derivatives was recommended based on the QSAR equation. A data set consist of 18 indole derivatives from literature with anticancer activity against the A498 cell line was used to generate a QSAR model equation. The data set was divided randomly into training (14) and test (4) set compounds. The structure of indole compound was optimized first using AM1 semi-empirical methods, and the descriptors involved were analyzed using Multiple Linear Regression (MLR). The best QSAR equation obtained was Log IC50 = 65.596 (qC2) + 366.764 (qC6) – 92.742 (qC11) + 503.297 (HOMO) – 492.550 (LUMO) – 76.966. Based on the QSAR model, varying electron-withdrawing groups in C2 and C6 atom, as well as adding electron-donating groups in C11 were proposed could increase the anticancer activity of the indole derivatives. The QSAR analysis showed that compound 15 has the best predicted anticancer activity, supported by molecular docking results that showed hydrogen bond interaction with essential amino acids to build anticancer activity such as MET769, THR830, and THR766 residues.

QSAR and pharmacophore analysis on pyridazinone derivatives as acetylcholinesterase inhibitors

Aim: The aim of this study is to provide a qualitative and quantitative explanation of the structure activity relationships with pharmacophore analysis and Quantitative Structure Activity Relationship (QSAR) studies of the compounds synthesized as acetylcholinesterase enzyme inhibitor by our research group. Material and Methods: Maestro 11.9 (Schrodinger, New York) was used for pharmacophore model studies. Pharmacophore analysis was performed for all compounds showing acetylcholine esterase inhibitory effect. For QSAR studies, various physicochemical parameters of these compounds were calculated using GaussView 5.0 and ChemDraw 15.0 programs. Regression analysis was performed by using these parameters and QSAR equation was obtained. Results: All compounds overlapped and hypotheses generated. The most appropriate pharmacophore model was created by comparing the hypothesis and activity results of the compounds. The analyses were performed using 8 different parameters for all compounds. R2 value of equation was found 1. Conclusion: The pharmacophore analysis and the QSAR equation are applicable for all compounds synthesized as acetylcholinasterase inhibitory and containing pyridazinon-2-ylacetohydrazide structure. Also, the estimated IC50 values can be calculated before the compounds are synthesized using the QSAR equation.

A non-conformational QSAR study for plant-derived larvicides against Zika Aedes aegypti L. vector

A set of 263 plant-derived compounds with larvicidal activity against Aedes aegypti L. (Diptera: Culicidae) vector is collected from the literature, and is studied by means of a non-conformational quantitative structure-activity relationships (QSAR) approach. The balanced subsets method (BSM) is employed to split the complete dataset into training, validation and test sets. From 26,775 freely available molecular descriptors, the most relevant structural features of compounds affecting the bioactivity are taken. The molecular descriptors are calculated through four different freewares, such as PaDEL, Mold2, EPI Suite and QuBiLs-MAS. The replacement method (RM) variable subset selection technique leads to the best linear regression models. A successful QSAR equation involves 7-conformation-independent molecular descriptors, fulfiling the evaluated internal (loo, l30%o, VIF and Y-randomization) and external (test set with Ntest = 65 compounds) validation criteria. The practical application of this QSAR model reveals promising predicted values for some natural compounds with unknown experimental larvicidal activity. Therefore, the present model constitutes the first one based on a large molecular set, being a useful computational tool for identifying and guiding the synthesis of new active molecules inspired by natural products.

Quantitative structure-activity relationship (QSAR) investigation on 2-arylideneaminobenzimidazole derivatives as anti-proliferative activity against mv4-11 human leukaemia cells

This undertaking involves QSAR investigations on the use of 28 2-arylideneaminobenzimidazole derivatives, for curbing the proliferative activity of cells in the mv4-11 human leukaemia cell line. The geometries of the compounds under investigation were initially optimized at level (PM3) in accordance to the semi-empirical theory, and subsequently through the B3LYP procedure at the 6-31G(d) basis set in accordance to the DFT theory. The multiple regression procedure was employed for the construction of two QSAR equations, to assess the anti-proliferative activity of these compounds (Equations 1 and 2). The values of R2 extended from 0.792 to 0.812, those of S from 0.187 to 0.193, and those of F from 24.897 to 30.429. According to the results attained, the use of four parameters in Equation 2[(N5), (N10), r(C1=N2) andLUMO+4] led to raised R2 values and minimized S values. This suggests that these parameters are significant for identifying the anti-proliferative effectiveness of the compounds, in the context of mv4-11 human leukaemia cell line cells. This revelation is also an indication that QSAR can be successfully applied for a broad range of compounds.Â

Studi Potensi Pirazolin Tersubstitusi 1-N dari Thiosemicarbazone sebagai Agen Antiamuba melalui Uji In Silico

This Research aims to study Quantitative Structure-Activity Relationship (QSAR) of pyrazoline analogues, designing the new potential compounds as antiamoebic and study the interactions between the new compunds and the drugs target by molecular docking approach. This research was a theoritical research using computational chemistry method. The object of research was 21 novel of 1-N-substituted pyrazoline analogues of thiosemicarbazones with their antiamoebic biological activity. The data of research was obtained from quantum chemistry calculation and statistically analysis using Multiple Linear Regression (MLR). The resulting QSAR equation was Log IC50 = 0.869 + (0.081 x TPSA) + (0.018 x HF) + (0.527 x E-HOMO) + (3.378 x E-LUMO) + (-16.938 x Glob) + (0.234 x LogP), with statistic parameters of n = 21; R2 = 0.933; SEE = 0.14558; FHitung/FTabel = 8.607; PRESS = 0.491. This equation was used as a basic for designing and predicting the new antiamoebic compounds of pyrazoline analogues. The design of new compound of two lead compounds with the Topliss resulted 5 of 18 new compounds having theoretical better activity than the lead compound. Molecular docking study indicated that all of the best compounds have ability to bind to drug target macromolecule.

Baseline Toxicity and Volatility Cutoff in Reporter Gene Assays Used for High-Throughput Screening.

Most studies using high-throughput in vitro cell-based bioassays tested chemicals up to a certain fixed concentration. It would be more appropriate to test up to concentrations predicted to elicit baseline toxicity because this is the minimal toxicity of every chemical. Baseline toxicity is also called narcosis and refers to nonspecific intercalation of chemicals in biological membranes, leading to loss of membrane structure and impaired functioning of membrane-related processes such as mitochondrial respiration. In cells, baseline toxicity manifests as cytotoxicity, which was quantified by a robust live-cell imaging method. Inhibitory concentrations for baseline toxicity varied by orders of magnitude between chemicals and were described by a simple quantitative structure activity relationship (QSAR) with the liposome-water partition constant as a sole descriptor. The QSAR equations were remarkably similar for eight reporter gene cell lines of different cellular origin, six of which were used in Tox21. Mass-balance models indicated constant critical membrane concentrations for all cells and all chemicals with a mean of 69 mmol·kglip-1(95% CI: 49-89), which is in the same range as for bacteria and aquatic organisms and consistent with the theory of critical membrane burden of narcosis. The challenge of developing baseline QSARs for cell lines is that many confirmed baseline toxicants are rather volatile. We deduced from cytotoxicity experiments with semi-volatile chemicals that only chemicals with medium-air partition constants >10,000 L/L can be tested in standard robotic setups without appreciable loss of effect. Chemicals just below that cutoff showed crossover effects in neighboring wells, whereas the effects of chemicals with lower medium-air partition constants were plainly lost. Applying the "volatility cut-off" to >8000 chemicals tested in Tox21 indicated that approximately 20% of Tox21 chemicals could have partially been lost during the experiments. We recommend applying the baseline QSARs together with volatility cut-offs for experimental planning of reporter gene assays, that is, to dose only chemicals with medium-air partition constants >10,000 at concentrations up to the baseline toxicity level.

Prediction of Xanton Derivatives as Anti Heart Cancer using In Silico Quantitative Structure-Property Relationships

Quantitative Structure-Activity Relationship (QSAR) study have been performed on Xanthone derivatives as anti-cancer activity. The objectives of this research is to design a new Xanthone derivatives from the best QSAR equation model. The data set were taken from the previous study, involving 41 Xanthone derivatives and their biology activities in Inhibitor Concentration 50 % (IC50). The parameters (descriptors) were calculated by semiempirical PM3 method. The selection of the best QSAR equation models was determined by multilinear regression analysis. The best linear equation resulted from that analysis is: Log 1/IC50 = 13,099 + 2,837 qC1 + 0,098 qC2 + 11,214 qC10 + 2,065 qC13 – 1,236 qC14 + 35,356 qO15 + 0,001 (vol) – 0,025 (log P) + 0,283 (dipole) n = 41; r = 0.735; adjusted r2 = 0.360; Fhit/Ftab = 1.2911; PRESS = 5.0089. Based on that model, a new Xanthon derivatives has been design which show better predicted biology activity (log 1/IC50= 15,0863), new derivatives have the log 1/IC50 higher than the old one (log 1/IC50= 9). This result indicated that new Xanthone derivatives has potential to developed as new anti-cancer drug