Octanol-water Partition Research Articles

PICKAPEP: An application for parameter calculation and visualization of cyclized and modified peptidomimetics.

The interest in peptides and especially in peptidomimetic structures has risen enormously in the past few years. Novel modification strategies including nonnatural amino acids, sophisticated cyclization strategies, and side chain modifications to improve the pharmacokinetic properties of peptides are continuously arising. However, a calculator tool accompanying the current development in peptide sciences towards modified peptides is missing. Herein, we present the application PICKAPEP, enabling the virtual construction and visualization of peptidomimetics ranging from well-known cyclized and modified peptides such as ciclosporin A up to fully self-designed peptide-based structures with custom amino acids. Calculated parameters include the molecular weight, the water-octanol partition coefficient, the topological polar surface area, the number of rotatable bonds, and the peptide SMILES code. To our knowledge, PICKAPEP is the first tool allowing users to add custom amino acids as building blocks and also the only tool giving the possibility to process large peptide libraries and calculate parameters for multiple peptides at once. We believe that PICKAPEP will support peptide researchers in their work and will find wide application in current as well as future peptide drug development processes. PICKAPEP is available open source for Windows and Mac operating systems (https://www.research-collection.ethz.ch/handle/20.500.11850/681174).

Chemprop: A Machine Learning Package for Chemical Property Prediction.

Deep learning has become a powerful and frequently employed tool for the prediction of molecular properties, thus creating a need for open-source and versatile software solutions that can be operated by nonexperts. Among the current approaches, directed message-passing neural networks (D-MPNNs) have proven to perform well on a variety of property prediction tasks. The software package Chemprop implements the D-MPNN architecture and offers simple, easy, and fast access to machine-learned molecular properties. Compared to its initial version, we present a multitude of new Chemprop functionalities such as the support of multimolecule properties, reactions, atom/bond-level properties, and spectra. Further, we incorporate various uncertainty quantification and calibration methods along with related metrics as well as pretraining and transfer learning workflows, improved hyperparameter optimization, and other customization options concerning loss functions or atom/bond features. We benchmark D-MPNN models trained using Chemprop with the new reaction, atom-level, and spectra functionality on a variety of property prediction data sets, including MoleculeNet and SAMPL, and observe state-of-the-art performance on the prediction of water-octanol partition coefficients, reaction barrier heights, atomic partial charges, and absorption spectra. Chemprop enables out-of-the-box training of D-MPNN models for a variety of problem settings in fast, user-friendly, and open-source software.

Development of CHARMM compatible force field parameters and molecular dynamics simulations for the pesticide flupyradifurone.

Flupyradifurone (FLU) is a novel butenolide insecticide with partial agonist activity for insect nicotinic acetylcholine receptors. Its safety for non-target organisms has been questioned in the literature, despite initial claims of its harmlessness. Detailed understanding of its toxicity and related molecular mechanisms remain under discussion. Thus, in this work, an optimized set of CHARMM compatible parameters for FLU is presented. CHARMM General Force Field program was used as a starting point while the non-bonded and bonded parameters were adjusted and optimized to reproduce MP2/6-31G(d) accuracy level results. For the validity assessment of these parameters, infrared spectrum, water-octanol partition coefficient, and normal modes were computed and compared to experimental values found in the literature. Several MD simulations of FLU in water and FLU in complex with an acetylcholine-binding protein were performed to estimate the ability of the optimized parameters to correctly describe its torsional space and reproduce observed crystallographic trends respectively.

Neural Network Models for Predicting Solubility and Metabolism Class of Drugs in the Biopharmaceutics Drug Disposition Classification System (BDDCS).

The biopharmaceutics drug disposition classification system (BDDCS) categorizes drugs into four classes on the basis of their solubility and metabolism. This framework allows for the study of the pharmacokinetics of transporters and enzymatic metabolization on biopharmaceuticals, as well as drug-drug interactions in the body. The objective of the present study was to develop computational models by neural network models and structural parameters and physicochemical properties to estimate the class of a drug in the BDDCS system. In this study, deep learning methods were utilized to explore the potential of artificial and convolutional neural networks (ANNs and CNNs) in predicting the BDDCS class of 721 substances. The structural parameters and physicochemical properties [Abraham solvation parameters, octanol-water partition (log P) and over the pH range 1-7.5 (log D), number of rotatable bonds, hydrogen bond acceptor numbers, as well as hydrogen bond donor count] are calculated with various software. These compounds were then split into a training set consisting of 602 molecules and a test set of 119 compounds to validate the models. The results of this study showed that neural network models using applied parameters of the drug, i.e., log D and Abraham solvation parameters, are able to predict the class of solubility and metabolism in the BDDCS system with good accuracy. Neural network models are well equipped to deal with the relations between the structural parameters and physicochemical properties of drugs and BDDCS classes. In addition, log D is a more suitable parameter compared with log P in predicting BDDCS.

Revised descriptors for polycyclic aromatic and related hydrocarbons for the prediction of environmental properties using the solvation parameter model

Revised descriptors for twenty-five polycyclic aromatic and related hydrocarbons (PAHs) forming a component of the Wayne State University (WSU) descriptor database are provided for use with the solvation parameter model. The descriptors are determined by the Solver method using experimental data for calibrated gas-liquid and reversed-phase liquid chromatographic retention factors and liquid-liquid partition constants in totally organic biphasic systems. The characteristic solvation properties of the PAHs are accounted for mainly by the additional dispersion interactions (E descriptor) and dipole-type interactions (S descriptor) resulting from the availability of easily polarizable electrons that complement typical dispersion interactions for saturated hydrocarbons. The descriptors afford acceptable prediction of the water-air partition constant (average absolute deviation AAD = 0.17, n = 22), octanol-air partition constant (AAD = 0.12, n = 20), and water-octanol partition constant (AAD = 0.10, n = 23). A two-parameter model containing only the V and B descriptors provides an unbiased prediction of aqueous solubility for the PAHs with an AAD = 0.26 (n = 22). The descriptors estimated by convenient chromatographic and partition constant measurements are demonstrated to be a viable alternative to the experimental determination of environmental properties otherwise only available by tedious, expensive, and low data throughput experimental techniques.

Microwave Synthesis of Nanostructured Functionalized Polylactic Acid (nfPLA) for Incorporation Into a Drug Crystals to Enhance Their Dissolution

Active pharmaceutical ingredients that have low aqueous solubility pose a challenge in the field of drug delivery. In this paper we report for the first time the synthesis of nano-structured, hydrophilized polylactic acid (nfPLA) and its application in the delivery of low solubility drugs. Microwave induced acid oxidation was used to generate nfPLA where the oxygen concentration increased from 27.0 percent to 41.0 percent. Also, the original non dispersible PLA was converted to a relatively dispersible form with an average particle size of 131.4 nm and a zeta potential of -23.3 mV. Small quantities of the nfPLA were incorporated into the crystals (0.5 to 2.0 % by weight) of a highly hydrophobic, low solubility antifungal drug Griseofulvin (GF) to form a composite (GF-nfPLA). An antisolvent approach was used for the synthesis of the drug composite. SEM and Raman imaging showed non-uniform distribution of the nfPLA on the crystal surface. The solubility of GF increased from 8.89 µg/mL to as high as 49.67 µg/mL for the GF-nfPLA. At the same time zeta potential changed from -15.4 mV to -39.0 mV, therefore the latter was a relatively stable colloid. Octanol-water partitioning also showed a similar effect as logP reduced from 2.16 for pure GF to 0.55 for GF-nfPLA. In vitro dissolution testing showed six times higher aqueous solubility of GF-nfPLA compared to pure GF. The time for 50 (T50) and 80 % (T80) dissolution reduced significantly for the nfPLA composites; T50 reduced from 40.0 to 14.0 min and T80 reduced form unachievable to 47.0 min. Overall, the PLA which is an FDA approved, bioabsorbable polymer can be used to enhance the dissolution of hydrophobic pharmaceuticals and this can lead to higher efficacy and lower the required dosage for drugs.

Synthesis of Microwave Functionalized, Nanostructured Polylactic Co-Glycolic Acid (nfPLGA) for Incorporation into Hydrophobic Dexamethasone to Enhance Dissolution.

The low solubility and slow dissolution of hydrophobic drugs is a major challenge for the pharmaceutical industry. In this paper, we present the synthesis of surface-functionalized poly(lactic-co-glycolic acid) (PLGA) nanoparticles for incorporation into corticosteroid dexamethasone to improve its in vitro dissolution profile. The PLGA crystals were mixed with a strong acid mixture, and their microwave-assisted reaction led to a high degree of oxidation. The resulting nanostructured, functionalized PLGA (nfPLGA), was quite water-dispersible compared to the original PLGA, which was non-dispersible. SEM-EDS analysis showed 53% surface oxygen concentration in the nfPLGA compared to the original PLGA, which had only 25%. The nfPLGA was incorporated into dexamethasone (DXM) crystals via antisolvent precipitation. Based on SEM, RAMAN, XRD, TGA and DSC measurements, the nfPLGA-incorporated composites retained their original crystal structures and polymorphs. The solubility of DXM after nfPLGA incorporation (DXM-nfPLGA) increased from 6.21 mg/L to as high as 87.1 mg/L and formed a relatively stable suspension with a zeta potential of -44.3 mV. Octanol-water partitioning also showed a similar trend as the logP reduced from 1.96 for pure DXM to 0.24 for DXM-nfPLGA. In vitro dissolution testing showed 14.0 times higher aqueous dissolution of DXM-nfPLGA compared to pure DXM. The time for 50% (T50) and 80% (T80) of gastro medium dissolution decreased significantly for the nfPLGA composites; T50 reduced from 57.0 to 18.0 min and T80 reduced from unachievable to 35.0 min. Overall, the PLGA, which is an FDA-approved, bioabsorbable polymer, can be used to enhance the dissolution of hydrophobic pharmaceuticals and this can lead to higher efficacy and lower required dosage.

Phase Behavior of Alkyl Ethoxylate Surfactants in a Dissipative Particle Dynamics Model.

We present a dissipative particle dynamics (DPD) model capable of capturing the liquid state phase behavior of nonionic surfactants from the alkyl ethoxylate (CnEm) family. The model is based upon our recent work [Anderson et al. J. Chem. Phys. 2017, 147, 094503] but adopts tighter control of the molecular structure by setting the bond angles with guidance from molecular dynamics simulations. Changes to the geometry of the surfactants were shown to have little effect on the predicted micelle properties of sampled surfactants, or the water-octanol partition coefficients of small molecules, when compared to the original work. With these modifications the model is capable of reproducing the binary water-surfactant phase behavior of nine surfactants (C8E4, C8E5, C8E6, C10E4, C10E6, C10E8, C12E6, C12E8, and C12E12) with a good degree of accuracy.

Nα -Methylation of arginine: Implications for cell-penetrating peptides.

The field of cell-penetrating peptides is dominated by the use of oligomers of arginine residues. Octanol-water partitioning in the presence of an anionic lipid is a validated proxy for cell-penetrative efficacy. Here, we add one, two, or three N-methyl groups to Ac-Arg-NH2 and examine the effects on octanol-water partitioning. In the absence of an anionic lipid, none of these arginine derivatives can be detected in the octanol layer. In the presence of sodium dodecanoate, however, increasing N-methylation correlates with increasing partitioning into octanol, which is predictive of higher cell-penetrative ability. We then evaluated fully Nα -methylated oligoarginine peptides and observed an increase in their cellular penetration compared with canonical oligoarginine peptides in some contexts. These findings indicate that a simple modification, Nα -methylation, can enhance the performance of cell-penetrating peptides.

Visualization and metabolome for the migration and distribution behavior of pesticides residue in after-ripening of banana

Exploring the behavior of pesticide residues in fruits is important for effectively applying pesticides and minimizing the risk of pesticide exposure to humans. However, most studies do not consider in situ visual analysis of residues and migration patterns in fresh fruit samples. We investigated the migration patterns of thiram, propamocarb, imidacloprid and pyraclostrobin in fresh bananas based on ambient mass spectrometry imaging, metabolome and transcriptome analysis. The systemic pesticides entered via lateral penetration and vertical migration over time, which began to internally migrate to the inner core after 6 h. The non-systemic pesticide thiram did not enter the interior of the bananas, and remained only in the peel. The transportation rate of the pesticides increased with the decrease of water–octanol partition coefficient and the relative molecular mass. Moreover, the pesticide migrated fast with the increase of banana ripeness. The pesticides significantly enhanced pyruvate kinase, NADP-dependent malic enzyme, and malate synthase activities in the banana peels through carbohydrate metabolism. The banana pulp was also protected against the external toxicity of pesticides by the ascorbate-glutathione cycle. These results can provide guidelines for the appropriate application of pesticides and their safety evaluation.

Phenylcarbamate-Modified Paper for Paper Chromatographic Analysis of Hydrophobic Compounds

Paper chromatography is a low-cost and facile analytical method traditionally used to analyze hydrophilic substances. For the application to substances with lower polarity, we prepared a stationary phase based on filter paper modified with phenyl isocyanate (PI-FP), bearing phenyl carbamate moieties for hydrophobic, π-π, and electrostatic interactions. The preparation and chromatographic methods were established by selecting papers, comparing different chemical structures, optimizing the modification procedure, investigating eluents, and quantitatively parameterizing the separation behavior based on the character of the analytes. PI-FP exhibited better separation performance than esterified FPs and enabled chromatographic analysis of various dyes with both positive and negative clogP (calculated water-octanol partition coefficient). We also demonstrated an application of this system for a preparative separation of dyes using thread-like paper modified with PI.

Potential application of novel [formula omitted]-indices as molecular descriptors

A topological index or a descriptor is a graph invariant that describes the structure of a graph as a numerical value. This paper proposes eight novel indices called AL-indices, based on distance and degree, and analyzes their behavior to show that they can serve as potentially useful molecular descriptors. We find that among the proposed indices many have very good discriminative power when compared to the existing vertex-degree based indices and justifies the requirement of these new indices. Further, we propose a method to compute these indices and vertex-degree-based indices from a recently proposed graph matrix, referred to as neighborhood matrix.Computationally, we correlate the proposed indices’ efficiency against the octane isomers’ and polychlorobiphenyls’ physicochemical properties. We perform a comparative study of these indices with a few well-known vertex degree-based indices. Further, the proposed indices’ discriminative capacity is analyzed and shown to have higher discriminative power on the considered datasets. Among all the indices under study, four indices, namely AL4, AL5, AL7, and AL8 have shown the highest discriminative power on the set of octane isomers. While the indices AL4 and AL7 have the highest discriminative power on the set of PCB molecules compared to the other VDB indices.Among the proposed and considered indices, we show that the first index AL1 has a good correlation with the Acentric factor and entropy of octane isomer and with the total surface area, log-water-solubility, relative retention time, octanol–water-partition, and log-water-activity coefficient of PCBs.

In vitro and in silico evaluation of Ononis isoflavonoids as molecules targeting the central nervous system.

Isoflavonoids with various structural elements show a promising potential effect on central nervous system activities. Despite their favorable medicinal properties, the pharmacokinetic characteristics of this thoroughly investigated group of natural phenolics have only been described to a limited extent. Regarding the lack of information about the BBB permeability of isoflavones, isoflavanones, and pterocarpans found in Ononis species, the aim of our study was to investigate their physico-chemical properties influencing their absorption and distribution. Furthermore, we aimed to characterize the possible MAO-B inhibiting features of Ononis isoflavonoids in silico. Octanol-water partitioning and BBB-PAMPA permeability of formononetin, calycosin D, onogenin, sativanone, medicarpin and maackiain were assessed for the first time in our study. The log P values ranged from 2.21 to 3.03 and log D7.4 values from 2.48 to 3.03, respectively, indicating optimal polarity for BBB permeation. The results of PAMPA-BBB expressed as log Pe values fell between -5.60 and -4.45, predicting their good permeation capability as well. The effective permeability values showed structure-dependent differences, indicating that the pterocarpan type skeleton was the most preferred type, followed by isoflavanones, then isoflavones. The methoxy or methylenedioxy substitution of the same skeleton did not influence the permeability significantly, contrary to an additional hydroxyl group. Membrane retention showed a similar structure dependent pattern to that of effective permeability, ranging from 16% to 70%. For the identification of volumes of chemical space related to particular biological activities the ChemGPS-NP framework was used. The MAO-B inhibitory potency and selectivity were also predicted and validated. Based on our results, MAO-B inhibitory potency could be predicted with good precision, but in the case of selectivity, only the direction could be concluded (favors MAO-B or MAO-A), not the magnitude. Our finding reflects that Ononis isoflavonoid aglycones show an excellent fit with the suggested parameters for BBB permeability and this is the first study to confirm the highly favorable position of these natural products for MAO-B inhibition.

2D‐QSAR and molecular docking studies of carbamate derivatives to discover novel potent anti‐butyrylcholinesterase agents for Alzheimer's disease treatment

AbstractCurrently, anti‐butyrylcholinesterase (anti‐BuChE)is among the greatest therapeutic agents for the treatment of Alzheimer's disease. In this research, a series of 36 carbamate derivatives were subjected to a quantitative structure–activity relationships study using DFT and Lipinski's descriptors. Multiple linear regression (MLR) was used to explore the relationships between the structural features of these compounds and BuChE inhibitory activity. In order to generate results applicable in the experimental plan, the Organization of Economic Cooperation and Development guidelines were adopted. The quality of MLR model was evaluated by several statistical parameters including internal and external validation parameters (R2, R2adj, F, VIF, R2test, Q2CV, R2Rand, Q2CV [Rand], and cRp2). The built model displayed a high predictive power (R2test = 0.817). What is more, the internal validation parameters (Q2CV = 0.774; average R2Rand = 0.118; average Q2CV [Rand] = −0.438; cRp2 = 0.820) highlight the robustness of our built model. Besides, the obtained result revealed that anti‐BuChE activity is mainly attributed to the following molecular descriptors: octanol–water partition (log P), highest occupied molecular orbital energy (EHOMO), total energy (ET), and dipole moment (μ). Based on these findings, a series of newer synthesizable compounds with enhanced anti‐BuChE activities were designed and their ADMET and drug‐likeness properties were further predicted to filter out compounds likely to fail during drug development stages. Finally, molecular docking and molecular dynamics were performed to identify the binding types between the best designed compounds and BuChE enzyme.

A near-infrared, colorimetric and ratiometric fluorescent sensor with high sensitivity to hydrogen peroxide and viscosity for solutions detection and imaging living cells

Considering to the importance of hydrogen peroxide (H2O2) in a number of pathological processes and fluorescence sensor as a powerful tool for imaging and therapy of H2O2-related diseases, herein, a near-infrared fluorescent sensor for colorimetric and ratiometric detecting H2O2 and viscosity, named NCR, was constructed and reported. Its long emission wavelength (670 nm) avoids the interference of biological autofluorescence. When NCR interacted with 10 equiv. of H2O2, colorimetric characteristic provides a clear naked eye recognition (colourless turned to green under UV light and light purple turned to colorless under visible light). In addition, NCR shows high sensitivity to viscosity with or without the addition of H2O2. It was also found alkaline environment contribute to enhance fluorescence response to H2O2. A suitable water-octanol partition coefficient (logP, 0.521 ± 0.003) and low cytotoxicity displayed that NCR is very favorable to image living cells. Furthermore, exogenous and endogenous H2O2 was detected successfully by NCR in living Hela cells. These studies indicate that NCR has great potential to develop as a practical tool for monitoring H2O2 level in biological process.

The use of biomimetic chromatography to predict acute aquatic toxicity of pharmaceutical compounds

The potential of biomimetic chromatography to predict ecotoxicological endpoints of pharmaceutical compounds was investigated. For this purpose, a data set of previously and newly measured chromatographic retention data for 36 structurally diverse drugs was used. Standardized retention times were measured on the immobilized artificial membrane, human serum albumin, and alpha-1-acid glycoprotein stationary phases. As ecotoxicological endpoints, half-maximal lethal concentration values of fish and half-maximal effective concentration (immobilization) values of a water flea (Daphnia magna spp.) determined with a two-day static method were considered. Ecotoxicity values correlated with octanol-water partitioning and the positive charge of compounds contributed even more to the toxicity. Models based on membrane partition exhibited the best statistics and predictive performance, attributed to lipophilicity and membrane electrostatic interactions. Alpha-1-acid glycoprotein binding led to satisfactory models, owing to its function as a binder of neutral and basic lipophilic compounds. Albumin binding, however, did not result in sound models, as it is governed by lipophilicity and the negative charge of compounds, contrary to the mechanism of toxicity. Both membrane and alpha-1-acid glycoprotein models were superior statistically from those derived from the octanol-water system. Overall, membrane and alpha-1-acid glycoprotein retention can be suggested as promising indices to assess the ecotoxicological risk of drugs.

Prediction of partition coefficient in high-pressure carbon dioxide–water systems using machine learning

A method of predicting the partition coefficients (log K) of organic compounds in high-pressure carbon dioxide–water systems using machine learning was investigated. Using the collected literature data of log K, several linear and non-linear regression models were constructed. A cross-validation using these models indicated that log K can be approximately predicted with a root-mean-squared error of 0.6–1.1. Overall, the non-linear model predicted log K better, but linear models such as lasso regression exhibited comparable performances when features describing compounds were reduced. Parity plots indicated several outliers, most of which contain several polar functional groups. The analysis of feature importance revealed that the constructed model primarily consisted of the feature of log P (the 1-octanol–water partition coefficient) and was modified using process parameters and features related to the charges of compounds. The machine-learning approach yields a physicochemically reasonable model.

Precise force-field-based calculations of octanol-water partition coefficients for the SAMPL7 molecules.

We predicted water-octanol partition coefficients for the molecules in the SAMPL7 challenge with explicit solvent classical molecular dynamics (MD) simulations. Water hydration free energies and octanol solvation free energies were calculated with a windowed alchemical free energy approach. Three commonly used force fields (AMBER GAFF, CHARMM CGenFF, OPLS-AA) were tested. Special emphasis was placed on converging all simulations, using a criterion developed for the SAMPL6 challenge. In aggregate, over 1000[Formula: see text]s of simulations were performed, with some free energy windows remaining not fully converged even after 1[Formula: see text]s of simulation time. Nevertheless, the amount of sampling produced [Formula: see text] estimates with a precision of 0.1 log units or better for converged simulations. Despite being probably as fully sampled as can expected and is feasible, the agreement with experiment remained modest for all force fields, with no force field performing better than 1.6 in root mean squared error. Overall, our results indicate that a large amount of sampling is necessary to produce precise [Formula: see text] predictions for the SAMPL7 compounds and that high precision does not necessarily lead to high accuracy. Thus, fundamental problems remain to be solved for physics-based [Formula: see text] predictions.

Descriptors for adamantane and some of its derivatives

Literature data on solubilities of adamantane in organic solvents have been used to obtain properties, or descriptors, of adamantane. There is much less data on substituted adamantanes but we have been able to obtain descriptors for some 40 substituted adamantanes. These descriptors can then be used to estimate a wide range of physicochemical, environmental and other properties of the adamantanes. For the first time, the water-solvent partition coefficient and the gas-solvent partition coefficient into a large range of solvents, can be estimated, the latter being equivalent to Henry's Law constants. A variety of other important properties can also be estimated. These include vapor pressures, enthalpies of vaporization and sublimation, partitions from air and from blood into biological tissues, and skin permeability from water. The descriptors themselves are not exceptional. Adamantane itself has a rather low dipolarity, zero hydrogen bond acidity and a very low hydrogen bond basicity, in common with other multicyclic aliphatic compounds. These lead to adamantane being a very hydrophobic compound, as is evident from our estimated water-octanol partition coefficient.

A comparative study on the adsorption behavior of pesticides by pristine and aged microplastics from agricultural polyethylene soil films

Compared with pristine agricultural polyethylene (PE) soil films microplastics (MPs), aged agricultural polyethylene (APE) soil films MPs have a rougher surface, more cracks and have some oxygen-containing functional groups that makes them adsorb organic pollutants, such as pesticides more easily. This may be more harmful to human beings than marine MPs as the agricultural soil films are closer to our living environment. But few works focused on the adsorption of pesticides on pristine or aged agricultural polyethylene soil films MPs. In order to promote the risk assessment of co-exposure of pesticides and agricultural polyethylene soil films MPs, a comparative study on the adsorption behavior and mechanism of four pesticides (carbendazim, diflubenzuron, malathion, difenoconazole) by pristine PE MPs and APE MPs were carried out in this paper. The results showed microcracks and surface oxidation observed on APE MPs. The adsorption kinetics and isotherm models indicated that the adsorption capacity of APE MPs was higher than that of PE MPs, which attribute to the larger surface area of APE MPs. The adsorption capacities of pesticides on APE MPs were positively correlated with LogKow (Water octanol partition coefficient) values of these four pesticides, showed the hydrophobic partitioning played the most important part in the adsorption, but also some H-bonding between secondary amines in the molecular of diflubenzuron and polar O-containing functional groups on APE MPs may be formed. And electrostatic forces or interactions are not the determining factor for these pesticides adsorption behavior of PE MPs, and the effect of pH is mainly driven by changes in sorbate properties rather than changes in surface properties of MPs. The results presented herein show the APE MPs can be a better vector of most hydrophobic pesticides than pristine MPs in the agricultural field, and more attention should be paid to the problem of films and pesticides residue in farmland soil.