Parallel Artificial Membrane Permeability Assay Permeability Research Articles

Extending the limitations in the prediction of PAMPA permeability with machine learning algorithms

Gastrointestinal absorption is a key factor amongst the ADME-related (absorption, distribution, metabolism and excretion) pharmacokinetic properties; therefore, it has a major role in drug discovery and drug safety determinations. The Parallel Artificial Membrane Permeability Assay (PAMPA) can be considered as the most popular and well-known screening assay for the measurement of gastrointestinal absorption. Our study provides quantitative structure-property relationship (QSPR) models based on experimental PAMPA permeability data for almost four hundred diverse molecules, which is a great extension of the applicability of the models in the chemical space. Two- and three-dimensional molecular descriptors were applied for the model building in every case. We have compared the performance of a classical partial least squares regression (PLS) model with two major machine learning algorithms: artificial neural networks (ANN) and support vector machine (SVM). Due to the applied gradient pH in the experiments, we have calculated the descriptors for the model building at pH values of 7.4 and 6.5, and compared the effect of pH on the performance of the models. After a complex validation protocol, the best model had an R2=0.91 for the training set, and R2= 0.84 for the external test set. The developed models are capable for the robust and fast prediction of new compounds with an excellent accuracy compared to the previous QSPR models.

Predicting PAMPA permeability using the 3D-RISM-KH theory: are we there yet?

The parallel artificial membrane permeability assay (PAMPA), a non-cellular lab-based assay, is extensively used to measure the permeability of pharmaceutical compounds. PAMPA experiments provide a working mimic of a molecule passing through cells and PAMPA values are widely used to estimate drug absorption parameters. There is an increased interest in developing computational methods to predict PAMPA permeability values. We developed an in silico model to predict the permeability of compounds based on the PAMPA assay. We used the three-dimensional reference interaction site model (3D-RISM) theory with the Kovalenko-Hirata (KH) closure to calculate the excess chemical potentials of a large set of compounds and predicted their apparent permeability with good accuracy (mean absolute error or MAE = 0.69 units) when compared to a published experimental data set. Furthermore, our in silico PAMPA protocol performed very well in the binary prediction of 288 compounds as being permeable or impermeable (precision = 94%, accuracy = 93%). This suggests that our in silico protocol can mimic the PAMPA assay and could aid in the rapid discovery or screening of potentially therapeutic drug leads that can be delivered to a desired tissue.

In vitro investigation of permeability and metabolism of licoricidin

AimLicoricidin has multiple pharmacological activities. The present study was designed to investigate the permeability and pharmacokinetic behavior of licoricidin using in vitro models. Material and methodsFirst, human liver microsomes and recombinant human supersomes were used to investigate the interactions between metabolic enzymes and licoricidin. In addition, rat, minipig, rabbit, dog, monkey, and human liver microsomes were used to determine metabolic differences among species. The parallel artificial membrane permeability assay (PAMPA) was used to explore licoricidin permeability behavior. Key findingsAt 100 μM, licoricidin strongly inhibited CYP2C9, CYP2C19, CYP3A4, UGT1A3, UGT1A6, UGT1A7, UGT1A8, UGT1A9, UGT2B4, UGT2B7, UGT2B15, and UGT2B17. Licoricidin metabolism exhibited considerable differences among species; dog, pig, and rat liver microsomes showed higher metabolic capacity than the other species. Seven licoricidin metabolites were identified by liquid chromatography-tandem mass spectrometry, and hydration and hydroxylation were the major metabolic pathways. The PAMPA permeability of licoricidin was moderate at both pH 4.0 and 7.4. SignificanceThe present study will support further pharmacological or toxicological research on licoricidin.

Advances in the prediction of gastrointestinal absorption: Quantitative Structure-Activity Relationship (QSAR) modelling of PAMPA permeability

Gastrointestinal absorption (GI absorption) is a key absorption, distribution, metabolism, and excretion (ADME) property when the biological effects of substances are evaluated. The Parallel Artificial Membrane Permeability Assay (PAMPA) has emerged as a primary screen for determining passive transcellular permeability, the dominant GI absorption mechanism for many drugs, thus helping with the prioritisation of the most promising lead compounds for pharmacokinetic studies. Recently the PAMPA assay has attracted increasing interest from various other industrial sectors, including cosmetics, where such non-animal models may provide a crucial source of information for in vitro – in vivo extrapolation. This method is also a reliable source of experimental data for Quantitative Structure-Activity Relationship (QSAR) modelling of GI absorption. In this investigation, published QSAR models for PAMPA were reviewed with the aim to summarise and assess critically the current state of the art. The review indicates a relatively small number of QSARs compared to some endpoints, but much consistency within the models. PAMPA permeability increases with hydrophobicity and decreases with the surface area occupied by hydrogen bond acceptor/donor atoms. The models can be applied to screening for bioactive compounds with the potential to pass the gastrointestinal barrier as well as to design new structures with increased PAMPA permeability, thus with better expectations towards improved in vivo GI absorption.

In vitro and in silico studies of the membrane permeability of natural flavonoids from Silybum marianum (L.) Gaertn. and their derivatives

BackgroundIn recent years the number of natural products used as pharmaceuticals, components of dietary supplements and cosmetics has increased tremendously requiring more extensive evaluation of their pharmacokinetic properties. PurposeThis study aims at combining in vitro and in silico methods to evaluate the gastrointestinal absorption (GIA) of natural flavonolignans from milk thistle (Silybum marianum (L.) Gaertn.) and their derivatives. MethodsA parallel artificial membrane permeability assay (PAMPA) was used to evaluate the transcellular permeability of the plant main components. A dataset of 269 compounds with measured PAMPA values and specialized software tools for calculating molecular descriptors were utilized to develop a quantitative structure-activity relationship (QSAR) model to predict PAMPA permeability. ResultsThe PAMPA permeabilities of 7 compounds constituting the main components of the milk thistle were measured and their GIA was evaluated. A freely-available and easy to use QSAR model predicting PAMPA permeability from calculated physico-chemical molecular descriptors was derived and validated on an external dataset of 783 compounds with known GIA. The predicted permeability values correlated well with obtained in vitro results. The QSAR model was further applied to predict the GIA of 31 experimentally untested flavonolignans. ConclusionsAccording to both in vitro and in silico results most flavonolignans are highly permeable in the gastrointestinal tract, which is a prerequisite for sufficient bioavailability and use as lead structures in drug development. The combined in vitro/in silico approach can be used for the preliminary evaluation of GIA and to guide further laboratory experiments on pharmacokinetic characterization of bioactive compounds, including natural products.

Predicting a Drug's Membrane Permeability: A Computational Model Validated With in Vitro Permeability Assay Data.

Membrane permeability is a key property to consider during the drug design process, and particularly vital when dealing with small molecules that have intracellular targets as their efficacy highly depends on their ability to cross the membrane. In this work, we describe the use of umbrella sampling molecular dynamics (MD) computational modeling to comprehensively assess the passive permeability profile of a range of compounds through a lipid bilayer. The model was initially calibrated through in vitro validation studies employing a parallel artificial membrane permeability assay (PAMPA). The model was subsequently evaluated for its quantitative prediction of permeability profiles for a series of custom synthesized and closely related compounds. The results exhibited substantially improved agreement with the PAMPA data, relative to alternative existing methods. Our work introduces a computational model that underwent progressive molding and fine-tuning as a result of its synergistic collaboration with numerous in vitro PAMPA permeability assays. The presented computational model introduces itself as a useful, predictive tool for permeability prediction.

Development of Cassette PAMPA for Permeability Screening.

The parallel artificial membrane permeability assay (PAMPA) is widely used in early-stage drug discovery to discriminate compounds by intestinal permeability. The purpose of the current study was to establish a cassette (n-in-1) PAMPA to enable permeability screening of lipophilic compounds. A double-sink PAMPA consisting of a pH gradient (i.e., pH 6.5 and 7.4 for the donor and receiver compartments, respectively) and a lipophilic sink (i.e., a surfactant in the receiver solution) was utilized with cassette incubation of 10 reference compounds. Sample analysis was conducted using selected reaction monitoring (SRM) with a triple quadrupole LC-MS/MS system. Correlation between PAMPA permeability and human intestinal absorption (HIA) of the reference compounds yielded two false negatives, namely propranolol (PPN) and verapamil (VER); these two compounds showed a substantially lower recovery (ca. 10%) than other reference compounds (>69%). This cassette PAMPA was repeated subsequently with polysorbate 80 added to the donor compartments, which resulted in a significant increase in both the recovery and the permeability of the false negatives. Accordingly, the permeability class of all reference compounds could be unambiguously differentiated using this cassette PAMPA. Also, a strong linear correlation (r=0.9845) was observed between the cassette and discrete permeability of all reference compounds.

Highly predictive and interpretable models for PAMPA permeability

Cell membrane permeability is an important determinant for oral absorption and bioavailability of a drug molecule. An in silico model predicting drug permeability is described, which is built based on a large permeability dataset of 7488 compound entries or 5435 structurally unique molecules measured by the same lab using parallel artificial membrane permeability assay (PAMPA). On the basis of customized molecular descriptors, the support vector regression (SVR) model trained with 4071 compounds with quantitative data is able to predict the remaining 1364 compounds with the qualitative data with an area under the curve of receiver operating characteristic (AUC-ROC) of 0.90. The support vector classification (SVC) model trained with half of the whole dataset comprised of both the quantitative and the qualitative data produced accurate predictions to the remaining data with the AUC-ROC of 0.88. The results suggest that the developed SVR model is highly predictive and provides medicinal chemists a useful in silico tool to facilitate design and synthesis of novel compounds with optimal drug-like properties, and thus accelerate the lead optimization in drug discovery.

Predicting the exposure and antibacterial activity of fluoroquinolones based on physicochemical properties

The purposes of this study are to evaluate if the PAMPA (Parallel Artificial Membrane Permeability Assay) permeability and the true partition coefficient could be useful for predicting AUC and MIC data of a group of antibacterial fluoroquinolones (FQs). The protonation macro- and microconstants, the n-octanol/water partition coefficients at isoelectric pHs, and the PAMPA permeability of 11 selected FQs were determined, and used to calculate the true partition coefficient, the interactivity parameter between the acidic and basic group, and the apparent intrinsic permeability. It has been shown that the apparent intrinsic permeability correlates well with the AUC in human, whereas the true partition coefficient and the interactivity parameter correlate with 1/MIC values on two Gram-positive bacteria, namely Streptococcus pneumonia and Staphylococcus aureus (methicillin-susceptible). The AUC/MIC ratios predicted from these correlations have shown to be in good agreement with the literature values. It is envisaged that the models described in this study could be useful in the development of new FQs by enabling an early prediction of AUC/MIC ratios based on physicochemical properties.

Sotalol permeability in cultured-cell, rat intestine, and PAMPA system.

To clarify sotalol's classification in the BCS versus BDDCS systems through cellular, rat everted sac and PAMPA permeability studies. Studies were carried out in Madin Darby canine kidney (MDCK) and MDR1-transfected MDCK (MDCK-MDR1) cell lines, rat everted gut sacs and the Parallel Artificial Membrane Permeability Assay (PAMPA) system. Three-hour transport studies were conducted in MDCK cell lines (with apical pH changes) and MDCK-MDR1 cells (with and without the P-glycoprotein inhibitor GG918); male Sprague-Dawley rats (300~350g) were used to prepare everted sacs. In the PAMPA studies, drug solutions at different pH's were dosed in each well and incubated for 5h. Samples were measured by LC-MS/MS, or liquid scintillation counting and apparent permeability (P(app)) was calculated. Sotalol showed low permeability in all of the cultured-cell lines, everted sacs and PAMPA systems. It might be a border line P-glycoprotein substrate. The PAMPA study showed that sotalol's permeability increased with a higher apical pH, while much less change was found in MDCK cells. The low permeability rate for sotalol correlates with its Class 3 BDDCS assignment and lack of in vivo metabolism.

PAMPA permeability, plasma protein binding, blood partition, pharmacokinetics and metabolism of formononetin, a methoxylated isoflavone

Formononetin (FMN) is a methoxylated isoflavone which is the major constituent in red clover and in commercially available extracts of this plant. In this study, we investigated the parallel artificial membrane permeability assay (PAMPA) permeability, protein binding, blood uptake characteristics, pharmacokinetics and metabolism of FMN. The permeability study samples were analyzed by HPLC–PDA method; whereas the pharmacokinetic study, protein binding and whole blood partitioning samples were analyzed by LC–MS/MS method. The PAMPA permeability of FMN was found to be high at pH 4.0 and 7.0. Plasma protein binding of FMN was found to be 93.61±0.44% and 96.14±0.15% at the tested concentration of 50 and 150ng/mL, respectively. FMN reached equilibrium fast between red blood cells (RBCs) and plasma, and the partition coefficients between RBCs and plasma (KRBC/PL) were independent of the initial rat blood concentrations of FMN. The bioavailability of unchanged/free FMN was found to be poor, i.e. approximately 3%. FMN was found to have a high clearance (5.13L/h/kg) and a large apparent volume of distribution (14.16L/kg). Circulating conjugates (glucuronides/sulfates) of FMN and daidzein (DZN) were quantified using enzymatic hydrolysis of plasma samples. The levels of isoflavone glucuronides/sulfates were found to be much greater than that of the corresponding aglycones.

Importance of Physicochemical Properties for the Design of New Pesticides

The physicochemical properties of candidate compounds play important roles in the design of new pesticides. Pesticides must be absorbed by pests, be transported to the target site, and then interact with proteins. Hydrophobicity is very important for these processes. Log P, where P is the partition coefficient in the 1-octanol/water system, is commonly used as a hydrophobic descriptor and correlates with membrane permeation and transport. It was recently reported that permeability by the parallel artificial membrane permeation assay (PAMPA) could be used to predict human oral absorption of passively transported compounds. PAMPA, which is a rapid high-throughput screening system, may be useful to predict pesticide absorption because PAMPA permeability can be calculated using log P and other parameters. Electronic and structural properties as well as hydrophobicity are important factors for protein-ligand interaction. To show the importance of physicochemical properties, the classic QSAR and CoMFA of neonicotinoids and prediction of bioavailability of pesticides in terms of membrane permeability in comparison with drugs are described.

In silico Prediction of Human Oral Absorption Based on QSAR Analyses of PAMPA Permeability

The parallel artificial membrane permeation assay (PAMPA) was developed as a model for the prediction of transcellular permeation in the process of drug absorption. Our research group has measured the PAMPA permeability of peptide-related compounds, diverse drugs, and agrochemicals. This work led to a classical quantitative structure-activity relationship (QSAR) equation for PAMPA permeability coefficients of structurally diverse compounds based on simple physicochemical parameters such as lipophilicity at a particular pH (log P(oct) and |pKa-pH|), H-bond acceptor ability (SA(HA)), and H-bond donor ability (SA(HD)). Since the PAMPA permeability of lipophilic compounds decreased with their apparent lipophilicity due to the unstirred water layer (UWL) barrier on membrane surfaces and to membrane retention, a bilinear QSAR model was introduced to explain the permeability of a broader set of compounds using the same physicochemical parameters as those used for the linear model. We also compared PAMPA and Caco-2 cell permeability coefficients of compounds transported by various absorption mechanisms. The compounds were classified according to their absorption pathway (passively transported compounds, actively transported compounds, and compounds excreted by efflux systems) in the plot of Caco-2 vs. PAMPA permeability. Finally, based on the QSAR analyses of PAMPA permeability, an in silico prediction model of human oral absorption for possibly transported compounds was proposed, and the usefulness of the model was examined.

QSAR application for the prediction of compound permeability with in silico descriptors in practical use

The parallel artificial membrane permeation assay (PAMPA) was developed as a model for the prediction of transcellular permeation in the process of drug absorption. In our previous report, it was revealed that PAMPA permeability is governed by log P, pK (a), and the hydrogen-bonding ability of compounds. In order to construct a new filtering method for selecting informative compounds from the whole combinatorial library, this study tried to predict PAMPA permeability with in silico descriptors. Log P, pK(a), and polar surface areas (PSA) as a hydrogen-bonding descriptor were calculated by commercially available or free-accessible web programs. Five-fold cross-validations and conventional regression analyses were examined with the training set for the entire 81 combinations with nine log P, three pK(a) and three PSA descriptors. By comparison of statistical indices, four equations were selected and then the model with the best combination of in silico descriptors was determined based on the external validation. The PAMPA prediction equation obtained in this report could be applied for the prediction of both Caco-2 cell permeability and human intestinal absorption of mainly passively-transported drugs.

The usefulness of an artificial membrane accumulation index for estimation of the bioconcentration factor of organophosphorus pesticides

The bioconcentration factor ( BCF) is an important ecotoxicological parameter that describes the accumulation of chemicals in organisms. As many studies have reported, log BCF has good correlation with the logarithm of the 1-octanol/water partition coefficient, log P oct, for chemicals that have log P oct values below about 6 and are not significantly metabolized in organisms. In this study we measured a membrane accumulation index, that is, the membrane partition ( P M) of organophosphorus pesticides using the parallel artificial membrane permeation assay (PAMPA) to clarify whether this index is useful for the estimation of BCF. As a result, log P M had good correlation with log P oct except for iprobenfos and edifenphos. Furthermore, log BCF in five kinds of fish, especially male guppy correlated better with log P M than log P oct. The results indicate that log P M is a better index for predicting log BCF than log P oct. We have already reported that PAMPA permeability coefficients could predict human oral absorption of compounds, including hydrophobic chemicals and agrochemicals. Since it is expected that both human oral absorption and BCF can be estimated simultaneously using PAMPA, PAMPA is useful for exposure estimation to humans of chemicals in the environment.

Correlation between PAMPA permeability and cellular activities of hepatitis C virus protease inhibitors

Parallel artificial membrane permeability assay (PAMPA) and Caco-2 cells have been frequently used for the evaluation of in vitro permeability of new chemical entities. In this study we evaluated the correlation between permeability, assessed by both methods, and the cellular potency of 34 novel hepatitis C virus (HCV) protease inhibitors. Two types of assays were used to determine the potency of HCV protease inhibitors: a cell-free assay that evaluates the intrinsic affinity ( K i) between the protease and the inhibitor and a cell-based replicon assay that determines the inhibitors’ IC 90. When the K i/IC 90 ratios were compared with the PAMPA permeability and the Caco-2 permeability by linear regression analysis, a reasonable correlation was found between the K i/IC 90 ratio and PAMPA permeability ( r 2 = 0.76) but not with Caco-2 permeability ( r 2 = 0.29). Correlations were also assessed between K i/IC 90 ratios and the following physico-chemical properties: log P ( r 2 = 0.41), log D ( r 2 = 0.58), c log P ( r 2 = 0.13), and m log P ( r 2 = 0.30). These results suggest that passive permeability may play a role in the uptake and cellular activity of these HCV protease inhibitors, and that PAMPA was more predictive of cellular activity than physico-chemical properties or Caco-2 permeability.

QSAR study on permeability of hydrophobic compounds with artificial membranes

We previously reported a classical quantitative structure–activity relationship (QSAR) equation for permeability coefficients ( P app-pampa) by parallel artificial membrane permeation assay (PAMPA) of structurally diverse compounds with simple physicochemical parameters, hydrophobicity at a particular pH (log P oct and |p K a − pH|), hydrogen-accepting ability ( SA HA), and hydrogen-donating ability ( SA HD); however, desipramine, imipramine, and testosterone, which have high log P oct values, were excluded from the derived QSAR equation because their measured P app-pampa values were lower than calculated. In this study, for further investigation of PAMPA permeability of hydrophobic compounds, we experimentally measured the P app-pampa of more compounds with high hydrophobicity, including several pesticides, and compared the measured P app-pampa values with those calculated from the QSAR equation. As a result, compounds having a calculated log P app-pampa > −4.5 showed lower measured log P app-pampa than calculated because of the barrier of the unstirred water layer and the membrane retention of hydrophobic compounds. The bilinear QSAR model explained the PAMPA permeability of the whole dataset of compounds, whether hydrophilic or hydrophobic, with the same parameters as the equation in the previous study. In addition, PAMPA permeability coefficients correlated well with Caco-2 cell permeability coefficients. Since Caco-2 cell permeability is effective for the evaluation of human oral absorption of compounds, the proposed bilinear model for PAMPA permeability could be useful for not only effective screening for several drug candidates but also the risk assessment of chemicals and agrochemicals absorbed by humans.

Conformational Flexibility, Internal Hydrogen Bonding, and Passive Membrane Permeability: Successful in Silico Prediction of the Relative Permeabilities of Cyclic Peptides

We report an atomistic physical model for the passive membrane permeability of cyclic peptides. The computational modeling was performed in advance of the experiments and did not involve the use of "training data". The model explicitly treats the conformational flexibility of the peptides by extensive conformational sampling in low (membrane) and high (water) dielectric environments. The passive membrane permeabilities of 11 cyclic peptides were obtained experimentally using a parallel artificial membrane permeability assay (PAMPA) and showed a linear correlation with the computational results with R(2) = 0.96. In general, the results support the hypothesis, already well established in the literature, that the ability to form internal hydrogen bonds is critical for passive membrane permeability and can be the distinguishing factor among closely related compounds, such as those studied here. However, we have found that the number of internal hydrogen bonds that can form in the membrane and the solvent-exposed polar surface area correlate more poorly with PAMPA permeability than our model, which quantitatively estimates the solvation free energy losses upon moving from high-dielectric water to the low-dielectric interior of a membrane.

Lipid composition effect on permeability across PAMPA

The parallel artificial membrane permeability assay (PAMPA) system has promise to rapidly screen drug candidate passive permeability, but has been poorly described in terms of its lipid membrane structure and function. The objective was to investigate the role of PAMPA lipid composition on the permeability of five model compounds. PAMPA was used and employed individual phospholipids that varied in phosphate head group and acyl chain unsaturation. Transport of benzoic acid, taurocholic acid, metoprolol, sucrose, and mannitol was measured. Membrane fluidity was assessed by 1,3-diphenylhexatriene fluorescence anisotropy. Results indicate that compound permeability across PAMPA differed in their sensitivity to membrane lipid composition, where compounds with appreciable permeability (i.e. at least 0.2×10−6cm/s) were possibly sensitive to membrane fluidity and apparent ion pair effects. Benzoic acid permeability ranged 51-fold across membrane types, suggesting acyl chain effect on membrane fluidity. Mannitol, sucrose, and taurocholic acid permeabilities were low and independent of lipid composition. Metoprolol permeability ranged 17-fold and exhibited a markedly high permeability across 1,2-dioleoyl-sn-glycero-3-[phospho-l-serine] due to apparent ion pair-facilitated transport. Compound permeability was lowest across the phosphatidylcholines, which is consistent with phosphatidylcholine exhibiting relatively high membrane rigidity. In contrast to results from phosphatidylethanolamines and phosphatidylserines, acyl chain unsaturation had no effect on permeability across phosphatidylcholines. In conclusion, while much remains unknown about PAMPA structure and subsequent PAMPA permeability, results here from five solutes suggest that, for solutes with appreciable permeability, lipid composition modulated drug permeability through possible membrane fluidity and apparent ion pair influences.

A novel high-throughput automated chip-based nanoelectrospray tandem mass spectrometric method for PAMPA sample analysis

Parallel artificial membrane permeability assay (PAMPA) has recently gained popularity as a novel, high-throughput assay capable of rapidly screening compounds for their permeability characteristics in early drug discovery. The analytical techniques typically used for PAMPA sample analysis are HPLC-UV, LC/MS or more recently UV-plate reader. The LC techniques, though sturdy and accurate, are often labor and time intensive and are not ideal for high-throughput. On the other hand, UV-plate reader technique is amenable to high-throughput but is not sensitive enough to detect the lower concentrations that are often encountered in early drug discovery work. This article investigates a novel analytical method, a chip-based automated nanoelectrospray mass spectrometric method for its ability to rapidly analyze PAMPA permeability samples. The utility and advantages of this novel analytical method is demonstrated by comparing PAMPA permeability values obtained from nanoelectrospray to those from conventional analytical methods. Ten marketed drugs having a broad range of structural space, physico-chemical properties and extent of intestinal absorption were selected as test compounds for this investigation. PAMPA permeability and recovery experiments were conducted with model compounds followed by analysis by UV-plate reader, UV-HPLC as well as the automated nanoelectrospray technique (nanoESI-MS/MS). There was a very good correlation ( r 2 > 0.9) between the results obtained using nanoelectrospray and the other analytical techniques tested. Moreover, the nanoelectrospray approach presented several advantages over the standard techniques such as higher sensitivity and ability to detect individual compounds in cassette studies, making it an attractive high-throughput analytical technique. Thus, it has been demonstrated that nanoelectrospray analysis provides a highly efficient and accurate analytical methodology to analyze PAMPA samples generated in early drug discovery.