Quantitative Structure-activity Relationship Equation Research Articles

Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review

A comprehensive review of quantitative structure-activity relationships (QSAR) allowing the prediction of the fate of organic compounds in the environment from their molecular properties was done. The considered processes were water dissolution, dissociation, volatilization, retention on soils and sediments (mainly adsorption and desorption), degradation (biotic and abiotic), and absorption by plants. A total of 790 equations involving 686 structural molecular descriptors are reported to estimate 90 environmental parameters related to these processes. A significant number of equations was found for dissociation process (pKa), water dissolution or hydrophobic behavior (especially through the KOW parameter), adsorption to soils and biodegradation. A lack of QSAR was observed to estimate desorption or potential of transfer to water. Among the 686 molecular descriptors, five were found to be dominant in the 790 collected equations and the most generic ones: four quantum-chemical descriptors, the energy of the highest occupied molecular orbital (EHOMO) and the energy of the lowest unoccupied molecular orbital (ELUMO), polarizability (α) and dipole moment (μ), and one constitutional descriptor, the molecular weight. Keeping in mind that the combination of descriptors belonging to different categories (constitutional, topological, quantum-chemical) led to improve QSAR performances, these descriptors should be considered for the development of new QSAR, for further predictions of environmental parameters. This review also allows finding of the relevant QSAR equations to predict the fate of a wide diversity of compounds in the environment.

In silico molecular modeling and prediction of activity of substituted tetrahydropyrans as COX-2 inhibitor

Series of mono-, di-, and triaryl substituted tetrahydropyrans were taken for quantitative structure–activity relationship (QSAR) study using density functional theory-based (DFT-based) quantum chemical and empirical descriptors. Several QSAR equations were formulated through regression analysis, which were able to explain 93–98 % of the variance in the data. The best equations were selected from the various statistically significant equations by following established statistical procedures. The model equations revealed that the presence of 4-cholorobenzene at R1 position is favorable for the activity and the presence of 4-bromo benzene at R1 position seems to be unfavorable for the activity, however, DFT-based descriptors demonstrate that the high values of HOMO and softness whereas low values of LUMO, hardness, and absence of –CH3 group at R1 position enhances the activity. The predictive ability of equations were cross validated by evaluating of the residual activity and R 2 values and also by leave one out (LOO) technique.

2D-QSAR Study of Indolylpyrimidines Derivative as Antibacterial against Pseudomonas aeruginosa and Staphylococcus aureus: A Comparative Approach

A set of 15 indolylpyrimidine derivatives with their antibacterial activities in terms of minimum inhibitory concentration against the gram-negative bacteria Pseudomonas aeruginosa and gram-positive Staphylococcus aureus were selected for 2D quantitative structure activity relationship (QSAR) analysis. QSAR was performed using a combination of various descriptors such as steric, electronic and topological. Stepwise regression method was used to derive the most significant QSAR equation for predicting the inhibitory activity of this class of molecules. The best QSAR model was further validated by a leave one out technique as well as by the random trials. A high correlation between experimental and predicted inhibitory values was observed. A comparative picture of behavior of indolylpyrimidines against both of the microorganisms is discussed.

How good are ensembles in improving QSAR models? The case with eCoRIA

A conceptually new idea in quantitative structure–activity relationships (QSAR) which makes use of ensembles from molecular dynamics (MD) trajectories and information retrieved from enzyme–inhibitor binding thermodynamics is presented in this study. This new methodology, termed ensemble comparative residue interaction analysis (eCoRIA), attempts to overcome the current one chemical–one structure–one parameter value dogma in computational chemistry by modeling the biological activity as a function of molecular descriptors derived from an ensemble of conformers of enzyme–inhibitor complexes. The approach is distinctly different from the standard QSAR methodology which uses a single low-energy conformation or the properties averaged over a set of conformers to correlate with the activity. Each conformational ensemble derived from MD simulations is analyzed for the distribution of the non-bonded interaction energies (steric, electrostatic, and hydrophobic) along with solvation, strain, and entropic energy of the inhibitors with the individual amino acid residues in the receptor and these are correlated to the activity through a QSAR model. The scope of the new method is demonstrated with three diverse enzyme–inhibitor data-sets – glycogen phosphorylase b, human immunodeficiency virus-1 protease and cyclin-dependent kinase 2. The QSAR equations derived from the methodology have revealed all the structure activity relationships previously reported for these classes of molecules as well as uncovered some features that were hitherto unknown and may have a hidden role in driving the ligand–receptor-binding process. Impressive improvements in the predictions of affinity have been achieved compared to other QSAR formalisms namely CoMFA, CoMSIA (receptor-independent QSAR techniques), and CoRIA (a receptor-dependent QSAR technique). eCoRIA could provide an understanding of the thermodynamic properties influencing the ligand–receptor binding over a time scale as sampled by the MD simulation. The advantage of analyzing enzyme–inhibitor interaction energies in a statistical domain is that the noise due to inaccuracies in the potential energy functions can be reduced and mechanistically important interaction terms related to protein–ligand binding specificity can be identified which can assist the medicinal chemists in designing new molecules and biologists in studying the influence of position-specific mutations in the receptor on ligand binding.

Predicting the bioconcentration factor of highly hydrophobic organic chemicals

Bioconcentration refers to the process of uptake and buildup of chemicals in living organisms. Experimental measurement of bioconcentration factor (BCF) is time-consuming and expensive, and is not feasible for a large number of chemicals of regulatory concern. Quantitative structure–activity relationship (QSAR) models are used for estimating BCF values to help in risk assessment of a chemical. This paper presents the results of a QSAR study conducted to address an important problem encountered in the prediction of the BCF of highly hydrophobic chemicals. A new QSAR model is derived using a dataset of diverse organic chemicals previously tested in a United States Environmental Protection Agency laboratory. It is noted that the linear relationship between the BCF and hydrophobic parameter, i.e., calculated octanol–water partition coefficient (ClogP), breaks down for highly hydrophobic chemicals. The parabolic QSAR equation, log BCF=3.036 ClogP−0.197 ClogP2−0.808 MgVol (n=28, r2=0.817, q2=0.761, s=0.558) (experimental log BCF range=0.44–5.29, ClogP range=3.16–11.27), suggests that a non-linear relationship between BCF and the hydrophobic parameter, along with inclusion of additional molecular size, weight and/or volume parameters, should be considered while developing a QSAR model for more reliable prediction of the BCF of highly hydrophobic chemicals.

Elaborate ligand-based modeling reveals new human neutrophil elastase inhibitors

Human neutrophil elastase inhibitors (HNE-Is) have been recently implicated in inflammatory diseases. Accordingly, we applied a drug discovery workflow to unveil novel inhibitory HNE leads via combining pharmacophore modeling, quantitative structure–activity relationship (QSAR) analysis, and in silico screening. We employed the pharmacophoric models and associated QSAR equation to screen the National Cancer Institute (NCI) list of compounds. Virtual screening identified 14 novel leads from NCI compounds. The most potent hit 126 exhibited 93 % inhibition at 10 μM.

Discovery of novel urokinase plasminogen activator (uPA) inhibitors using ligand-based modeling and virtual screening followed by in vitro analysis

Urokinase plasminogen activator (uPA)-a serine protease-is thought to play a central role in tumor metastasis and angiogenesis and, therefore, inhibition of this enzyme could be beneficial in treating cancer. Toward this end, we explored the pharmacophoric space of 202 uPA inhibitors using seven diverse sets of inhibitors to identify high-quality pharmacophores. Subsequently, we employed genetic algorithm-based quantitative structure-activity relationship (QSAR) analysis as a competition arena to select the best possible combination of pharmacophoric models and physicochemical descriptors that can explain bioactivity variation within the training inhibitors (r (2) 162 = 0.74, F-statistic = 64.30, r (2) LOO = 0.71, r (2) PRESS against 40 test inhibitors = 0.79). Three orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least three binding modes accessible to ligands within the uPA binding pocket. This conclusion was supported by receiver operating characteristic (ROC) curve analyses of the QSAR-selected pharmacophores. Moreover, the three pharmacophores were comparable with binding interactions seen in crystallographic structures of bound ligands within the uPA binding pocket. We employed the resulting pharmacophoric models and associated QSAR equation to screen the national cancer institute (NCI) list of compounds. The captured hits were tested in vitro. Overall, our modeling workflow identified new low micromolar anti-uPA hits.

Elaborate ligand-based modeling coupled with QSAR analysis and in silico screening reveal new potent acetylcholinesterase inhibitors

Inhibition of the enzyme acetylcholinesterase (AChE) has been shown to alleviate neurodegenerative diseases prompting several attempts to discover and optimize new AChE inhibitors. In this direction, we explored the pharmacophoric space of 85 AChE inhibitors to identify high quality pharmacophores. Subsequently, we implemented genetic algorithm-based quantitative structure-activity relationship (QSAR) modeling to select optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of explaining bioactivity variation among training compounds (r2(68)=0.94, F-statistic=125.8, r2 LOO=0.92, r2 PRESS against 17 external test inhibitors = 0.84). Two orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least two binding modes accessible to ligands within AChE binding pocket. The successful pharmacophores were comparable with crystallographically resolved AChE binding pocket. We employed the pharmacophoric models and associated QSAR equation to screen the national cancer institute list of compounds. Twenty-four low micromolar AChE inhibitors were identified. The most potent gave IC50 value of 1.0 μM.

Does tautomerism influence the outcome of QSAR modeling?

Tautomerism is an important aspect associated with a variety of pharmacologically and biologically active compounds. It is a challenge to account for tautomerism in computer-aided drug designing (CADD). The estimations and calculations of many physico-chemical properties and theoretical descriptors of the molecules are sensitive to tautomerism. In this study, we have attempted to analyze the effect of tautomerism on feature selection and statistical performance/characteristics of conventional quantitative structure–activity relationship (QSAR) equations. These equations are developed using 2D and 3D-descriptors employing two different statistical methods, i.e., genetic algorithm (GA) and stepwise regression (SR). Five datasets of moderate sizes viz. (1) anti-malarial activity of synthetic prodiginines against multi-drug resistant strain (N = 43), (2) anti-malarial activity of bisaryl quinolones (N = 37), (3) anti-malarial activity of phosphoramidate and phosphorothioamidate analogs of amiprophos methyl (N = 36), (4) anti-proliferative activity of substituted N-phenyl ureidobenzenesulfonate derivatives (N = 44), and (5) anti-HIV activity of indolylarylsulfones as HIV-1 non-nucleoside reverse transcriptase inhibitors (N = 36) showing different types of tautomerism were used in the study. In each case, the developed model and the selected descriptors derived using one tautomer were applied on other tautomeric forms to understand the influence of tautomerism on QSAR equations. Different parameters like R, R 2, R adj 2 , R cv 2 , F, S and Y-randomization were used for thorough validation of all the models. The results revealed that tautomerism has significant influence on feature selection. In addition, it was found that tautomerism has a great influence on the performance of QSAR models of the second and the third datasets. However, no significant influence was observed on the statistical characteristics of QSAR models for datasets 1, 4, and 5. Therefore, it is suggested that separate models need to be developed for different tautomeric forms of a dataset.

Aquatic toxicity of cationic surfactants to Daphnia magna

Quantitative structure–activity relationship (QSAR) modelling of aquatic toxicity for cationic surfactants has received limited attention despite the fact that surfactants of this type are generally more toxic than predicted by general narcosis or polar narcosis equations. Here we report measurement of log P for three types of aromatic quaternary ammonium halides at sub-micellar concentrations, refinement of earlier rules for log P calculation, and development of a hydrophobicity based QSAR, using both calculated and measured log P values, for the aquatic toxicity of quaternary ammonium halides to Daphnia magna. The QSAR for cationics has a substantially larger intercept than the log P-based QSARs for nonionic and anionic surfactants. This is rationalised in terms of the head group interactions with membrane phospholipid in a two-dimensional partitioning model. The effect of the positive nitrogen on the log P contributions of methylene groups along alkyl chains varies, depending on the other groups bonded to the positive nitrogen. We propose a mechanistic explanation, but until these effects can be put on a more predictable quantitative basis it is recommended that, for quaternaries other than the three types discussed here, calculated log P values should not be relied on and experimental values should be determined, e.g. for prediction of toxicity by the QSAR equation reported here.

Quantitative Structure-Activity Analyses of Nitrobenzene Toxicity to <i>Paramecium caudatum</i>

In this study IGC50 (50% inhibitory growth concentration) values of 26 nitrobenzenes were determined for population growth endpoint of Paramecium caudatum. Quantitative structure activity relationships (QSARs) were developed using log of the inverse of the IGC50 (logIGC50-1) in mole liter as the dependent variable and 6 molecular descriptors (logP, 1XⅤ, I, 1Ka, ∑σ- and ELUMO) as the independent variables. Through multiplicate regression analysis, one best QSAR equation was obtained. The equation was used to estimate IGC50 for 7 analogues.

Quantitative Structure-Activity Relationships Predicting the Antioxidant Potency of 17β-Estradiol-Related Polycyclic Phenols to Inhibit Lipid Peroxidation

The antioxidant potency of 17β-estradiol and related polycyclic phenols has been well established. This property is an important component of the complex events by which these types of agents are capable to protect neurons against the detrimental consequences of oxidative stress. In order to relate their molecular structure and properties with their capacity to inhibit lipid peroxidation, a marker of oxidative stress, quantitative structure-activity relationship (QSAR) studies were conducted. The inhibition of Fe3+-induced lipid peroxidation in rat brain homogenate, measured through an assay detecting thiobarbituric acid reactive substances for about seventy compounds were correlated with various molecular descriptors. We found that lipophilicity (modeled by the logarithm of the n-octanol/water partition coefficient, logP) was the property that influenced most profoundly the potency of these compounds to inhibit lipid peroxidation in the biological medium studied. Additionally, the important contribution of the bond dissociation enthalpy of the phenolic O–H group, a shape index, the solvent-accessible surface area and the energy required to remove an electron from the highest occupied molecular orbital were also confirmed. Several QSAR equations were validated as potentially useful exploratory tools for identifying or designing novel phenolic antioxidants incorporating the structural backbone of 17β-estradiol to assist therapy development against oxidative stress-associated neurodegeneration.

Towards predictable transmembrane transport: QSAR analysis of anion binding and transport

The transport of anions across biological membranes by small molecules is a growing research field due to the potential therapeutic benefits of these compounds. However, little is known about the exact mechanism by which these drug-like molecules work and which molecular features make a good transporter. An extended series of 1-hexyl-3-phenylthioureas were synthesized, fully characterized (NMR, mass spectrometry, IR and single crystal diffraction) and their anion binding and anion transport properties were assessed using 1H NMR titration techniques and a variety of vesicle-based experiments. Quantitative structure–activity relationship (QSAR) analysis revealed that the anion binding abilities of the mono-thioureas are dominated by the (hydrogen bond) acidity of the thiourea NH function. Furthermore, mathematical models show that the experimental transmembrane anion transport ability is mainly dependent on the lipophilicity of the transporter (partitioning into the membrane), but smaller contributions of molecular size (diffusion) and hydrogen bond acidity (anion binding) were also present. Finally, we provide the first step towards predictable anion transport by employing the QSAR equations to estimate the transmembrane transport ability of four new compounds.

Ligand-based pharmacophore exploration and QSAR analysis of transition state analogues followed by in silico screening guide the discovery of new sub-micromolar β-secreatase inhibitors

Transition state analogues (TSA)-based inhibitors have particularly potent inhibitory profiles. The fact that β-secretase (BACE) inhibitors have potential as anti-Alzheimer’s treatments prompted us to explore the pharmacophoric space of 68 known TSA BACE inhibitors. Subsequently, quantitative structure–activity relationship (QSAR) analysis was employed to select optimal combination of binding model(s) and 2D physicochemical descriptors capable of explaining bioactivity variation. One pharmacophoric model emerged in the successful QSAR equation. However, to closely mimic the sterically demanding transition state of BACE we were obliged to complement the successful pharmacophore with strict shape-based query decorated with carefully positioned hydroxyl fragment that simulates the statin hydroxyl of known TSA BACE inhibitors. Both models, i.e., the hybrid shape–OH and optimal pharmacophore, were validated via receiver-operating characteristic curve analysis and were found to exhibit excellent abilities in discerning active compounds from decoys. Subsequent in silico screening against the National Cancer Institute structural database using the two models yielded two novel inhibitors of nanomolar and low micromolar IC50 values.

Combined QM/MM (ONIOM) and QSAR Approach to the Study of Complex Formation of Matrix Metalloproteinase-9 with a Series of Biphenylsulfonamides–LERE-QSAR Analysis (V)

We previously proposed a novel QSAR (quantitative structure-activity relationship) procedure called LERE (linear expression by representative energy terms)-QSAR involving molecular calculations such as an ab initio fragment molecular orbital ones. In the present work, we applied LERE-QSAR to complex formation of matrix metalloproteinase-9 (MMP-9) with a series of substituted biphenylsulfonamides. The results shows that the overall free-energy change accompanying complex formation is due to predominantly the contribution from the electrostatic interaction with the zinc atom in the active site of MMP-9. Carbonic anhydrase (CA) belongs to the zinc-containing protease family. In contrast to the current case of MMP-9, the overall free-energy change during complex formation of CA with a series of benzenesulfonamides is due to the contributions from the solvation and dissociation free-energy changes, as previously reported. Comparison of the two sets of results indicates quantitative differences in the relative contributions of free-energy components to the overall free-energy change between the two data sets, corresponding with those in the respective classical QSAR equations. The LERE-QSAR procedure was demonstrated to quantitatively reveal differences in the binding mechanisms between the two cases involving similar but different zinc-containing proteins at the electronic and atomic levels.

QSAR, QSPR and QSRR in Terms of 3-D-MoRSE Descriptors for In Silico Screening of Clofibric Acid Analogues.

A series of 27 analogues of clofibric acid, mostly heteroarylalkanoic derivatives, have been analyzed by a novel high-throughput reversed-phase HPLC method employing combined gradient of eluent's pH and organic modifier content. The such determined hydrophobicity (lipophilicity) parameters, log kw , and acidity constants, pKa , were subjected to multiple regression analysis to get a QSRR (Quantitative StructureRetention Relationships) and a QSPR (Quantitative Structure-Property Relationships) equation, respectively, describing these pharmacokinetics-determining physicochemical parameters in terms of the calculation chemistry derived structural descriptors. The previously determined in vitro log EC50 values - transactivation activity towards PPARα (human Peroxisome Proliferator-Activated Receptor α) - have also been described in a QSAR (Quantitative StructureActivity Relationships) equation in terms of the 3-D-MoRSE descriptors (3D-Molecule Representation of Structures based on Electron diffraction descriptors). The QSAR model derived can serve for an a priori prediction of bioactivity in vitro of any designed analogue, whereas the QSRR and the QSPR models can be used to evaluate lipophilicity and acidity, respectively, of the compounds, and hence to rational guide selection of structures of proper pharmacokinetics.

Three-dimensional quantitative structure activity relationship (QSAR) of cytotoxic active 3,5-diaryl-4,5-dihydropyrazole analogs: a comparative molecular field analysis (CoMFA) revisited study

In vitro antitumor evaluation of the synthesized 46 compounds of 3,5-diaryl-4,5-dihydropyrazoles against EAC cell lines and 3D QSAR study using pharmacophore and Comparative Molecular Field Analysis (CoMFA) methods were described. CoMFA derived QSAR model shows a good conventional squared correlation coefficient r2 and cross validated correlation coefficient r2cv 0.896 and 0.568 respectively. In this analysis steric and electrostatic field contribute to the QSAR equation by 70% and 30% respectively, suggesting that variation in biological activity of the compounds is dominated by differences in steric (van der Waals) interactions. To visualize the CoMFA steric and electrostatic field from partial least squares (PLS) analysis, contour maps are plotted as percentage contribution to the QSAR equation and are associated with the differences in biological activity.BackgroundPyrazole derivatives exhibit a wide range of biological properties including promising antitumor activity. Furthermore, Aldol condensation assisted organic synthesis has delivered rapid routes to N-containing heterocycles, including pyrazoles. Combining these features, the use of chalconisation-assisted processes will provide rapid access to a targeted dihydropyrazoles library bearing a hydrazino 3D QSAR study using pharmacophore and Comparative Molecular Field Analysis (CoMFA) methods were described for evaluation of antioxidant properties.ResultsChalcones promoted 1 of the 2 steps in a rapid, convergent synthesis of a small library of hydrazinyl pyrazole derivatives, all of which exhibited significant antitumor activity against Ehrlich Ascites Carcinoma (EAC) human tumor cell line comparable to that of the natural anticancer doxorubicin, as a reference standard during this study. In order to understand the observed pharmacological properties, quantitative structure-activity relationship (3D QSAR) study was initiated.ConclusionsChalcones heating provides a rapid and expedient route to a series of pyrazoles to investigate their chracterization scavenging properties. Given their favorable properties, in comparison with known anticancer, these pyrazole derivatives are promising leads for further development and optimization.

Extensive ligand-based modeling and in silico screening reveal nanomolar inducible nitric oxide synthase (iNOS) inhibitors

Inducible nitric oxide synthase (iNOS) has been implicated in a variety of diseases prompting several attempts to discover and optimize new iNOS inhibitors. Accordingly, we explored the pharmacophoric space of 143 iNOS inhibitors. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and 2D physicochemical descriptors to produce self-consistent quantitative structure–activity relationship (QSAR) of optimal predictive potential (correlation coefficient r115=0.83, F=23.92, rLOO2=0.61, rPRESS2 against 28 external test inhibitors=0.51). Two orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least two binding modes accessible to ligands within iNOS binding pocket. The pharmacophores were validated by comparison with crystallographic complexes of active iNOS inhibitors and receiver operating characteristic (ROC) curves analysis. We employed the pharmacophoric models and associated QSAR equation to screen the national cancer institute list of compounds (NCI). Three low nanomolar inhibitors were identified. The most potent hit exhibited irreversible inhibition of iNOS with IC50 value of 1.4nM.

Structure-Based and Multiple Potential Three-Dimensional Quantitative Structure–Activity Relationship (SB-MP-3D-QSAR) for Inhibitor Design

The inhibitions of enzymes (proteins) are determined by the binding interactions between ligands and targeting proteins. However, traditional QSAR (quantitative structure-activity relationship) is a one-side technique, only considering the structures and physicochemical properties of inhibitors. In this study, the structure-based and multiple potential three-dimensional quantitative structure-activity relationship (SB-MP-3D-QSAR) is presented, in which the structural information of host protein is involved in the QSAR calculations. The SB-MP-3D-QSAR actually is a combinational method of docking approach and QSAR technique. Multiple docking calculations are performed first between the host protein and ligand molecules in a training set. In the targeting protein, the functional residues are selected, which make the major contribution to the binding free energy. The binding free energy between ligand and targeting protein is the summation of multiple potential energies, including van der Waals energy, electrostatic energy, hydrophobic energy, and hydrogen-bond energy, and may include nonthermodynamic factors. In the foundational QSAR equation, two sets of weighting coefficients {aj} and {bp} are assigned to the potential energy terms and to the functional residues, respectively. The two coefficient sets are solved by using iterative double least-squares (IDLS) technique in the training set. Then, the two sets of weighting coefficients are used to predict the bioactivities of inquired ligands. In an application example, the new developed method obtained much better results than that of docking calculations.

PENGGUNAAN FRONTIER ORBITAL MOLEKUL SEBAGAI DESKRIPTOR PADA ANALISIS HUBUNGAN KUANTITATIF STRUKTUR AKTIVITAS (HKSA) TOKSIK SENYAWA KHLOROFENOL

Quantitative structure-activity relationship (QSAR) methods have been applied to prediction of the toxicity of certain chemical compound. In this research a QSAR descriptor used frontier molecular orbital (LUMO energy (EL), HOMO energy (EH), and band gap (ΔE) and its derivatives were obtained from density functional theory (DFT) (chemical hardness (η) chemical potential (μ) or absolute electronegativity (χ) and global electrophicility indeks (ω)). Frontier molecular orbital (EL and EH) was calculated by ab initio quantum methods. This research found the correlation between the experimental ecotoxicological data of chlorophenols and toxicity prediction were calculated based on the best QSAR equation model of all equation model which have been studied. The best QSAR equation model using parameter LUMO energy (EL), and global electrophilicity index (ω) as descriptor on QSAR toxic of chlorophenol compounds against Bacilus sp TL81 is - log IC50 = 11,022 - 1,767 EL - 5,687 ω, and it has the coefficient of determination (R2) = 0,581 and standard deviation (SD) = 0,6111.