Established Quantitative Structure-activity Relationship Model Research Articles

From molecular descriptors to the developmental toxicity prediction of pesticides/veterinary drugs/bio-pesticides against zebrafish embryo: Dual computational toxicological approaches for prioritization

The escalating introduction of pesticides/veterinary drugs into the environment has necessitated a rapid evaluation of their potential risks to ecosystems and human health. The developmental toxicity of pesticides/veterinary drugs was less explored, and much less the large-scale predictions for untested pesticides, veterinary drugs and bio-pesticides. Alternative methods like quantitative structure-activity relationship (QSAR) are promising because their potential to ensure the sustainable and safe use of these chemicals. We collected 133 pesticides and veterinary drugs with half-maximal active concentration (AC50) as the zebrafish embryo developmental toxicity endpoint. The QSAR model development adhered to rigorous OECD principles, ensuring that the model possessed good internal robustness (R2 > 0.6 and QLOO2 > 0.6) and external predictivity (Rtest2 > 0.7, QFn2 >0.7, and CCCtest > 0.85). To further enhance the predictive performance of the model, a quantitative read-across structure-activity relationship (q-RASAR) model was established using the combined set of RASAR and 2D descriptors. Mechanistic interpretation revealed that dipole moment, the presence of C-O fragment at 10 topological distance, molecular size, lipophilicity, and Euclidean distance (ED)-based RA function were main factors influencing toxicity. For the first time, the established QSAR and q-RASAR models were combined to prioritize the developmental toxicity of a vast array of true external compounds (pesticides/veterinary drugs/bio-pesticides) lacking experimental values. The prediction reliability of each query molecule was evaluated by leverage approach and prediction reliability indicator. Overall, the dual computational toxicology models can inform decision-making and guide the design of new pesticides/veterinary drugs with improved safety profiles.

New insight into biodegradation mechanism of phenylurea herbicides by cytochrome P450 enzymes: Successive N-demethylation mechanism

Phenylurea herbicides (PUHs) present one of the most important herbicides, which have cause serious effects on ecological environment and humans. Nowadays enzyme strategy shows great advantages in degradation of PUHs. Here density functional theory (DFT), quantitative structure − activity relationship (QSAR) and quantum mechanics/molecular mechanics (QM/MM) approaches are used to investigate the degradation mechanism of PUHs catalyzed by P450 enzymes. Two successive N-demethylation pathways are identified and two hydrogen abstraction (H-abstraction) reaction pathways are identified as the rate-determining step through high-throughput DFT calculations. The Boltzmann-weighted average energy barrier of the second H-abstraction pathway (19.95 kcal/mol) is higher than that of the first H-abstraction pathway (16.80 kcal/mol). Two QSAR models are established to predict the energy barriers of the two H-abstraction pathways based on the quantum chemical descriptors and mordred molecular descriptors. The determination coefficient (R2) values of QSAR models are > 0.9, which reveal that the established QSAR models have great predictive capability. QM/MM calculations indicate that human P450 enzymes are more efficient in degradation of PUHs than crop and weed P450 enzymes. Correlations between energy barriers and key structural/charge parameters are revealed and key parameters that have influence on degradation efficiency of PUHs are identified. This study provides lateral insights into the biodegradation strategy and removal method of PUHs and valuable information for designing or engineering of highly efficient degradation enzymes and genetically modified crops.

Local QSAR modeling of cytotoxic activity of newly designed androstane 3-oximes towards malignant melanoma cells

As one of the deadliest forms of skin cancers, malignant melanoma is the most common cause of death from this type of cancer. Malignant melanoma has a steadily increasing incidence and the medical treatment options are still quite limited. One of the possible options for malignant melanoma treatment is medication therapy. The present study is focused on the development of new compounds that can be possibly used for malignant melanoma treatment. A newly synthesized series of alkylaminoethyl derivatives of androstane 3-oximes expressed significant cytotoxic activity towards malignant melanoma cells. This was an excellent basis for the development of quantitative structure-activity relationship (QSAR) models for the prediction of cytotoxic activity of not yet synthesized compounds. Also, on the basis of the cytotoxic activity data the molecular docking and molecular dynamics analysis were carried out. This local QSAR modeling, which is based on a limited set of structurally similar compounds, resulted in one univariate linear regression model, four multiple linear regression models and five support vector machines models. All of the models were confirmed to be statistically reliable with quite good prediction ability. The results of comparative molecular docking and molecular dynamics analysis indicated a high binding potential of novel compounds in regards to cisplatin as well-known chemotherapy drug. The established QSAR models and the results of molecular docking and molecular dynamics can be considered to be the guidelines for the design of new compounds worth synthesizing as potential lead compounds for malignant melanoma treatment.

Density Functional Theory and Machine Learning-Based Quantitative Structure-Activity Relationship Models Enabling Prediction of Contaminant Degradation Performance with Heterogeneous Peroxymonosulfate Treatments.

Heterogeneous peroxymonosulfate (PMS) treatment is recognized as an effective advanced oxidation process (AOP) for the treatment of organic contaminants. Quantitative structure-activity relationship (QSAR) models have been applied to predict the oxidation reaction rates of contaminants in homogeneous PMS treatment systems but are seldom applied in heterogeneous treatment systems. Herein, we established QSAR models updated with density functional theory (DFT) and machine learning approaches to predict the degradation performance for a series of contaminants in heterogeneous PMS systems. We imported the characteristics of organic molecules calculated using constrained DFT as input descriptors and predicted the apparent degradation rate constants of contaminants as the output. The genetic algorithm and deep neural networks were used to improve the predictive accuracy. The qualitative and quantitative results from the QSAR model for the degradation of contaminants can be used to select the most appropriate treatment system. A strategy for selection of the optimum catalyst for PMS treatment of specific contaminants was also established according to the QSAR models. This work not only increases our understanding of contaminant degradation in PMS treatment systems but also highlights a novel QSAR model to predict the degradation performance in complicated heterogeneous AOPs.

Qualitative and quantitative prediction of food allergen epitopes based on machine learning combined with in vitro experimental validation

An allergen epitope is a part of molecules that can specifically bind to immunoglobulin E (IgE), causing an allergic reactions. To predict protein epitopes and their binding ability to IgE, quantitative structure–activity relationship (QSAR) models were established using four algorithms combined with the selected chemical descriptors. The model predicted the binding capabilities of the epitopes to IgE with the R2 and root mean squared error (RMSE) as 0.7494 and 0.2375, respectively. The model’s performance was validated using an enzyme-linked immunosorbent assay (ELISA). The results showed that the established QSAR model could efficiently and accurately predict the allergic reaction of food protein epitopes. The prediction results of the model and the experimental results were consistent, with a Pearson correlation coefficient of 0.8956. The results from both the QSAR model and in vitro experiments indicated that amino acid sequence 116–130 was a novel IgE-binding epitope of β-LG.

A New Method for Abrin Detection Based on the Interaction between Target Molecules and Fluorescently Labeled Aptamers on Magnetic Microspheres.

A quantitative structure–activity relationship (QSAR) model for the structure and affinity of abrin aptamers was established. A higher affinity abrin aptamer based on the established QSAR model was screened by site-directed mutagenesis. The fluorescence quenching effect between magnetic microspheres and fluorescent molecules was studied for the first time. A new method for abrin detection based on the interaction between target molecules and fluorescently labeled aptamers on magnetic microspheres was developed, with the detection limit of 5 ng mL−1. This method can overcome the influence of complex environmental interferents in abrin detection and can meet the analysis requirements for simulated samples such as water, soil, and food.

Ricin aptamer screening based on the QSAR model and construction of piezoresistive micro-cantilever aptasensor

Currently, many aptamers of different target molecules have been screened by systematic evolution of ligands by exponential enrichment (SELEX) method. However, since the crystal structure of aptamers is complicated to obtain, few studies focus on the quantitative structure activity relationship (QSAR) of aptamers. It is significant to develop a virtual screening method for aptamers based on the QSAR model. In this study, a piezoresistive micro-cantilever aptasensor was fabricated to quantificationally detect ricin based on a new aptamer obtained via a virtual screening method. The advanced screening method based on the established QSAR model can screen aptamers formed by site-directed mutation of bases. The results of intermolecular interactions and molecular docking displayed that the new aptamer was mainly bound to ricin A-chain, and its combination of ricin with ricin B-chain aptamer has a significant synergistic effect. Due to the aptamer with excellent performance and the good linear relationship between the relative resistance change and the vertical deformation of the micro-cantilever, the piezoresistive micro-cantilever aptasensor emerged a linear detection range from 7.5 pg mL−1 to 75 ng mL−1 (R2 = 0.949) with the limit of detection 750 fg mL−1 (S/N ≥ 3) for ricin. Furthermore, the proposed piezoresistive micro-cantilever sensor displayed excellent analytical performance with good reproducibility and high specificity.

Structure-guided discovery of food-derived GABA-T inhibitors as hunters for anti-anxiety compounds.

With the acceleration of the pace of life, people may face all kinds of pressure, and anxiety has become a common mental issue that is seriously affecting human life. Safe and effective food-derived compounds may be used as anti-anxiety compounds. In this study, anti-anxiety compounds were collected and curated for database construction. Quantitative structure-activity relationship (QSAR) models were developed using a combination of various machine-learning approaches and chemical descriptors to predict natural compounds in food with anti-anxiety effects. High-throughput molecular docking was used to screen out compounds that could function as anti-anxiety molecules by inhibiting γ-aminobutyrate transaminase (GABA-T) enzyme, and 7 compounds were screened for in vitro activity verification. Pharmacokinetic analysis revealed three compounds (quercetin, lithocholic acid, and ferulic acid) that met Lipinski's Rule of Five and inhibited the GABA-T enzyme to alleviate anxiety in vitro. The established QSAR model combined with molecular docking and molecular dynamics was proved by the synthesis and discovery of novel food-derived anti-anxiety compounds.

Ensemble machine learning to evaluate the in vivo acute oral toxicity and in vitro human acetylcholinesterase inhibitory activity of organophosphates.

Organophosphates (OPs) are hazardous chemicals widely used in industry and agriculture. Distribution of their residues in nature causes serious risks to humans, animals, and plants. To reduce hazards from OPs, quantitative structure-activity relationship (QSAR) models for predicting their acute oral toxicity in rats and mice and inhibition constants concerning human acetylcholinesterase were developed according to the bioactivity data of 456 unique OPs. Based on robust, two-dimensional molecular descriptors and quantum chemical descriptors, which accurately reflect OP electronic structures and reactivities, the influences of eight machine-learning algorithms on the prediction performance of the QSAR models were explored, and consensus QSAR models were constructed. Several strict model validation indices and the results of applicability domain evaluations show that the established consensus QSAR models exhibit good robustness, practical prediction abilities, and wide application scopes. Poor correlation was observed between acute oral toxicity at the mammalian level and the inhibition constants at the molecular level, indicating that the acute toxicity of OPs cannot be evaluated only by the experimental data of enzyme inhibitory activity, their toxicokinetic characteristics must also be considered. The constructed QSAR models described herein provide rapid, theoretical assessment of the bioactivity of unstudied or unknown OPs, as well as guidance for making decisions regarding their regulation.

Quantitative Structure-Activity Relationship Models for Predicting Inflammatory Potential of Metal Oxide Nanoparticles.

Background:Although substantial concerns about the inflammatory effects of engineered nanomaterial (ENM) have been raised, experimentally assessing toxicity of various ENMs is challenging and time-consuming. Alternatively, quantitative structure–activity relationship (QSAR) models have been employed to assess nanosafety. However, no previous attempt has been made to predict the inflammatory potential of ENMs.Objectives:By employing metal oxide nanoparticles (MeONPs) as a model ENM, we aimed to develop QSAR models for prediction of the inflammatory potential by their physicochemical properties.Methods:We built a comprehensive data set of 30 MeONPs to screen a proinflammatory cytokine interleukin (IL)-1 beta () release in THP-1 cell line. The in vitro hazard ranking was validated in mouse lungs by oropharyngeal instillation of six randomly selected MeONPs. We established QSAR models for prediction of MeONP-induced inflammatory potential via machine learning. The models were further validated against seven new MeONPs. Density functional theory (DFT) computations were exploited to decipher the key mechanisms driving inflammatory responses of MeONPs.Results:Seventeen out of 30 MeONPs induced excess production in THP-1 cells. In vivo disease outcomes were highly relevant to the in vitro data. QSAR models were developed for inflammatory potential, with predictive accuracy (ACC) exceeding 90%. The models were further validated experimentally against seven independent MeONPs (). DFT computations and experimental results further revealed the underlying mechanisms: MeONPs with metal electronegativity lower than 1.55 and positive were more likely to cause lysosomal damage and inflammation.Conclusions: released in THP-1 cells can be an index to rank the inflammatory potential of MeONPs. QSAR models based on were able to predict the inflammatory potential of MeONPs. Our approach overcame the challenge of time- and labor-consuming biological experiments and allowed for computational assessment of MeONP inflammatory potential by characterization of their physicochemical properties. https://doi.org/10.1289/EHP6508

Molecular docking and QSAR studies for modeling the antimalarial activity of hybrids 4-anilinoquinoline-triazines derivatives with the wild-type and mutant receptor pf-DHFR

Plasmodium falciparum dihydrofolate reductase (pf-DHFR) is one of the several targets in the treatment of malaria. Double and quadruple mutations at residues 51, 59, 108, and 164 of pf-DHFR have been linked to antifolate resistance. Several efforts are underway to overcome this drug resistance and to produce potential inhibitors. In this regard, the quantitative structure-activity relationship (QSAR) and docking studies were performed for previously reported 4-anilinoquinoline and 1,3,5-triazines based molecular hybrids. The generated model showed good correlation coefficients (R2 = 0.70) and test set prediction coefficient (R2 = 0.74). These outcomes showed the good predictive competence of the established QSAR model. Based on these results we docked into active site of pf-DHFR protein with the most active (4) and the less active (5) compounds. The docking results revealed that these molecules interact specifically with SER108 and ILE164 in the pf-DHFR binding pocket as that of best active compound but also showed additional interactions with LEU40 and GLY44.

Image-based QSAR Model for the Prediction of P-gp Inhibitory Activity of Epigallocatechin and Gallocatechin Derivatives.

Permeability glycoprotein (P-gp) is one of the cell membrane proteins that can push some drugs out of the cell causing drug tolerance and its inhibition can prevent drug resistance. In this study, we used image-based Quantitative Structure-Activity Relationship (QSAR) models to predict the P-gp inhibitory activity of epigallocatechin and gallocatechin derivatives. The 2D-chemical structures and their P-gp inhibitory activity were taken from literature. The pixels of images and their Principal Components (PCs) were calculated using MATLAB software. Principle Component Regression (PCR), Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches were used to develop QSAR models. Statistical parameters included the leave one out cross-validated correlation coefficient (q2) for internal validation of the models and R2 of test set, Root Mean Square Error (RMSE) and Concordance Correlation Coefficient (CCC) were applied for external validation. Six PCs from image analysis method were selected by stepwise regression for developing linear and non-linear models. Non-linear models i.e. ANN (with the R2 of 0.80 for test set) were chosen as the best for the established QSAR models. According to the result of the external validation, ANN model based on image analysis method can predict the P-gp inhibitory activity of epigallocatechin and gallocatechin derivatives better than the PCR and SVM models.

Effect of dissolved organic matter on pre-equilibrium passive sampling: A predictive QSAR modeling study

Pre-equilibrium passive sampling is a simple and promising technique for studying sampling kinetics, which is crucial to determine the distribution, transfer and fate of hydrophobic organic compounds (HOCs) in environmental water and organisms. Environmental water samples contain complex matrices that complicate the traditional calibration process for obtaining the accurate rate constants. This study proposed a QSAR model to predict the sampling rate constants of HOCs (polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and pesticides) in aqueous systems containing complex matrices. A homemade flow-through system was established to simulate an actual aqueous environment containing dissolved organic matter (DOM) i.e. humic acid (HA) and (2-Hydroxypropyl)-β-cyclodextrin (β-HPCD)), and to obtain the experimental rate constants. Then, a quantitative structure-activity relationship (QSAR) model using Genetic Algorithm-Multiple Linear Regression (GA-MLR) was found to correlate the experimental rate constants to the system state including physicochemical parameters of the HOCs and DOM which were calculated and selected as descriptors by Density Functional Theory (DFT) and Chem 3D. The experimental results showed that the rate constants significantly increased as the concentration of DOM increased, and the enhancement factors of 70-fold and 34-fold were observed for the HOCs in HA and β-HPCD, respectively. The established QSAR model was validated as credible (RAdj.2=0.862) and predictable (Q2=0.835) in estimating the rate constants of HOCs for complex aqueous sampling, and a probable mechanism was developed by comparison to the reported theoretical study. Main finding of the workThe present study established a QSAR model of passive sampling rate constants and calibrated the effect of DOM on the sampling kinetics.

Prediction of Log P of Halogenated Alkanes by Their ELUMO and Number of Chlorine and Carbon

Hydrophobicity, as measured by Log P, is an important molecular property in assessing toxicity and carcinogenicity of disinfection by-products (DBPs). With increasing public health concerns of DPBs, there are considerable benefits in developing Quantitative Structure Activity Relationship (QSAR) models, capable of accurately predicting Log P, which could be used in health risk assessment of DBPs. In this research, Log P values of 46 halogenated alkanes, as one class of DBP compounds, were used to develop QSAR models for this class. Three molecular descriptors, namely, Energy of the Lowest Unoccupied Molecular Orbital (ELUMO), Number of Chlorine (NCl) and Number of Carbon (NC) were used in Multiple Linear Regression (MLR) analysis. The QSAR models developed were validated according to the principles set up by the Organization for Economic Co-operation and Development (OECD). Model Applicability Domain (AD) of the developed QSAR models was defined and mechanisms were interpreted. Considering large number of halogenated alkane compounds, the established QSAR models performed very well in terms of goodness-of-fit, robustness and predictability. The predicted values of Log P of DBPs by the QSAR models have correlation coefficient R2 from 81 % to 98 % with the observed Log P. The leverage approach by Williams plot was applied to detect and remove outliers. As a result, the correlation coefficient, R2, of the QSAR models increased by approximately 2 % to 13 %, before and after removing the outliers, respectively. The developed QSAR model was statistically validated for its predictive power of Log P by the Leave-One-Out (LOO) and Leave-Many-Out (LMO) cross validation methods.

Correlating the effects of the N-substituent sizes of chiral 1,2-amino phosphinamide ligands on enantioselectivities in catalytic asymmetric Henry reaction using physical steric parameters.

In this study, a series of mono- and dialkylated chiral 1,2-amino phosphinamide ligands derived from modular (1R,2R)-diphenylethylenediamine were successfully applied in the chiral 1,2-amino phosphinamide-Zn(II) catalyzed asymmetric Henry reaction between benzaldehyde and nitromethane. Although the chiral N-monosubstituted and N,N-disubstituted 1,2-amino phosphinamide ligands gave the main alcohol products with opposite configurations, a validated quantitative structure-activity relationship (QSAR) mathematical model could be constructed between the physical Sterimol steric parameters of the N-substituents of the chiral ligands and the enantiomeric ratios of the alcohol products produced in the asymmetric Henry reaction. Since two sets of N-substituents are involved in the QSAR model construction, the key factor to succesfully construct a highly correlative and predictive model is to appropriately assign the N-substitutents. Ligand optimization based on the established QSAR model led to chiral 1,2-amino phosphinamide ligand 2r, which produced (R)-β-nitroalcohol in excellent yield and enantioselectivity (99% yield and 92% ee). In addition, a quantitative correlation could also be established with the use of subtractive Sterimol parameters.

Quantum chemical and molecular dynamics studies of imidazoline derivatives as corrosion inhibitor and quantitative structure–activity relationship (QSAR) analysis using the support vector machine (SVM) method

The inhibition performance of 10 imidazoline molecules with number of carbon from 15 to 21 of hydrocarbon straight-chain was studied by weight-loss method and theoretical approaches. The main purpose was to build a quantitative structure–activity relationship (QSAR) between the structural properties and the inhibition efficiencies, and then to predict efficiencies of new corrosion inhibitors. The quantum chemical calculation suggested that the active region of imidazoline molecules was located on the imidazoline ring and hydrophilic group, and active sites were concentrated on the nitrogen atoms of the molecules and carbon atoms of hydrophilic group. A model in accordance with the real experimental solution was built in the molecular dynamics, and the equilibrium configuration indicated that the imidazoline molecules were adsorbed on Fe (110) surface in parallel manner. Descriptors for QSAR model building were selected by principal component analysis (PCA) and the model was built by the support vector machine (SVM) approach, which shows good performance since the value of correlation coefficient (R) was 0.99 and the root mean square error (RMSE) was 0.94. Additionally, six new imidazoline molecules were theoretically designed and the inhibition efficiencies of three molecules were predicted to be more than 86% by the established QSAR model.

Quantitative Structure-Activity Relationship and Virtual Screening of ω-Conotoxins

ω-Conotoxins are active peptides composed of 24-31 amino acids isolated from venomous marine predatory cone snails. ω-Conotoxins selectively inhibit voltage-gated calcium channels (VGCCs) in nociceptors, so are considered attractive molecules for drug design. In this study, based on a set of new amino acid structure descriptors (c-scales) and genetic partial least squares (G/PLS) regression method, quantitative structure- activity relationship (QSAR) models for N-type and P/Q-type VGCC antagonists of ω-conotoxins were developed. Two virtual polypeptide libraries with 2244 peptides were designed and established for N-type and P/Q-type VGCC antagonists, respectively. Then, based on the biological activities predicted from the constructed QSAR models and chemical similarities to the probes MVIIAand MVIIC, the polypeptide libraries were virtually screened. As a result, the established QSAR models had good predictability (cross-validated correlation coefficient CV-r 2 >0.89). The structural diversity of the libraries was validated using principal component analysis (PCA) and hierarchical cluster analysis (HCA) approaches. Six N-type and nineteen P/Q-type VGCC antagonists with high selectivity and activity were identified by virtual screening. The results of this study will be valuable for finding highly active polypeptide and non-peptide mimetics. Furthermore, the established polypeptide QSAR models and virtual screening strategy can also be applied to other peptide systems.

Quantitative structure–activity relationship studies on ironporphyrin-catalyzed cyclohexane oxidation with PhIO

Quantitative structure–activity relationship (QSAR) studies were performed on the ironporphyrin-catalyzed biomimetic oxidation of cyclohexane. Through quantum chemical calculations, the molecular structures of nine different ironporphyrin catalysts have been optimized and their respective quantum chemical descriptors (FMO energies E HOMO, E LUMO, FMO energy gap DEHL, partition coefficient log P) have been obtained. The ironporphyrin-catalyzed cyclohexane hydroxylation with PhIO was chosen as the model reaction. The yield of cyclohexanol (yield (%)) and the reaction rate constant (lg k) were obtained experimentally, and the reaction kinetics was studied accordingly. 2D-QSAR studies for ironporphyrin catalysts were performed by using multiple linear regression (MLR) analysis. From the established QSAR model equations of lg k, yield (%) and the quantum chemical descriptors (lg k = −1.433 + 0.009 log P − 0.406 E LUMO-b R = 0.968 and yield (%) = 9.556 + 0.500 log P − 8.997 E LUMO-b R = 0.821), we conclude that it is the frontier molecular orbital (FMO) energy level E LUMO-b which has the most significant effect on the catalytic activity of the ironporphyrins. Further molecular graphics studies and Mulliken's electron population analysis indicated that the energy level of E LUMO-b can be altered by introducing peripheral substituting groups on the meso-phenyl ring. Since the electron withdrawing substituents could lower E LUMO-b and disperse the electron density of around the centro-metal core of porphyrin better, they can facilitate ironporphyrin's binding with the oxidant and, consequently increase the catalytic activity of ironporphyrin. We also notice that the partition coefficient log P of ironporphyrin molecule affects the reaction rate and the yield of the cyclohexane hydroxylation reaction as well. Our study may be beneficial for future catalyst design for the metalloporphyrin-catalyzed hydrocarbon oxidations.

QSAR AND ACTION MECHANISM OF TROXACITABINE PRODRUGS WITH ANTITUMOR ACTIVITY

The quantitative structure–activity relationship (QSAR) of troxacitabine prodrugs with antitumor activity has been studied by using the density functional theory (DFT), molecular mechanics (MM2), and statistical methods. The established QSAR model shows not only significant statistical quality, but also predictive ability, with the square of adjusted correlation coefficient [Formula: see text] and the square of the cross-validation coefficient (q2 = 0.807). The antitumor activity is expressed as p IC50, which is defined as the negative value of the logarithm of necessary molar concentration of a compound to cause 50% growth inhibition against the human non-small-cell lung cancer cell line SW1573. It appears to be mainly governed by two factors (or original variables), i.e. the calculated hydrophobic coefficient (C log P) of whole molecule and the net charges of the first atom of substituent R (Q FR ), although three descriptors, i.e. C log P, (C log P)2, and Q FR , were selected in our multiple linear regression model. The factor C log P shows parabolic relation to p IC50 and its suitable range is around 5.6, and the other factor Q FR shows a significant negative correlation with p IC50. In this paper, a detailed discussion on these two factors was carried out, and their close correlation with the action mechanism of these prodrugs was reasonably revealed. Such results can offer some useful theoretical references for understanding the action mechanism and directing the molecular design of this kind of compound with antitumor activity.

Approach on quantitative structure–activity relationship for design of a pH neutral carrier containing tertiary amino group

The quantitative structure–activity relationship (QSAR) for neutral carriers used to prepare hydrogen ion sensors has been studied. A series of synthesized carrier compounds were taken as the training set. Five molecular structure parameters of the compounds were calculated by using CNDO/2 algorithm and used as feature variables in constructing QSAR model. The lower and upper limits of the linear pH response range were taken as the activity measure. The corresponding model equations were derived from the stepwise regression procedure. With the established QSAR model, a new pH carrier, (4-hydroxybenzyl) didodecylamine ( XIII) was proposed and synthesized. The PVC membrane pH electrode based on carrier XIII with a wide pH linear response range of 2.0–12.5 was prepared. Having a theoretical Nernstian response slope of 57.2 ± 0.3 mV/pH ( n = 5 at 25 °C) without a super-Nernstian phenomenon, the sensor had low resistance, short response time, high selectivity and good reproducibility. Moreover, the sensor was successfully applied to detecting the pH value of serum samples.