In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach.
ABSTRACT: Oral administration is the preferred and predominant route of choice for medication. As such, drug absorption is one of critical drug metabolism and pharmacokinetics (DM/PK) parameters that should be taken into consideration in the process of drug discovery and development. The cell-free in vitro parallel artificial membrane permeability assay (PAMPA) has been adopted as the primary screening to assess the passive diffusion of compounds in the practical applications. A classical quantitative structure-activity relationship (QSAR) model and a machine learning (ML)-based QSAR model were derived using the partial least square (PLS) scheme and hierarchical support vector regression (HSVR) scheme to elucidate the underlying passive diffusion mechanism and to predict the PAMPA effective permeability, respectively, in this study. It was observed that HSVR executed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical assessments. When applied to the mock test, which was designated to mimic real challenges, HSVR also showed better predictive performance. PLS, conversely, cannot cover some mechanistically interpretable relationships between descriptors and permeability. Accordingly, the synergy of predictive HSVR and interpretable PLS models can be greatly useful in facilitating drug discovery and development by predicting passive diffusion.
Project description:Recent controversial publications, citing studies purporting to show that P-gp mediates the transport of propranolol, proposed that passive biological membrane transport is negligible. Based on the BDDCS, the extensively metabolized-highly permeable-highly soluble BDDCS class 1 drug, propranolol, shows a high passive permeability at concentrations unrestricted by solubility that can overwhelm any potential transporter effects. Here we reinvestigate the effects of passive diffusion and carrier-mediated transport on S-propranolol.Bidirectional permeability and inhibition of efflux transport studies were carried out in MDCK, MDCK-MDR1 and Caco-2 cell lines at different concentrations. Transcellular permeability studies were conducted at different apical pHs in the rat jejunum Ussing chamber model and PAMPA system.S-propranolol exhibited efflux ratios lower than 1 in MDCK, MDCK-MDR1 and Caco-2 cells. No significant differences of Papp, B->A in the presence and absence of the efflux inhibitor GG918 were observed. However, an efflux ratio of 3.63 was found at apical pH 6.5 with significant decrease in Papp, A->B and increase in Papp, B->A compared to apical pH 7.4 in Caco-2 cell lines. The pH dependent permeability was confirmed in the Ussing chamber model. S-propranolol flux was unchanged during inhibition by verapamil and rifampin. Furthermore, pH dependent permeability was also observed in the PAMPA system.S-propranolol does not exhibit active transport as proposed previously. The "false" positive efflux ratio can be explained by the pH partition theory. As expected, passive diffusion, but not active transport, plays the primary role in the permeability of the BDDCS class 1 drug propranolol.
Project description: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.
Project description:In this study, we used the adult zebrafish model of ACR acute neurotoxicity for determining the suitability of NAC to protect the brain against ACR toxicity. First, the potential protective effects of NAC on the ACR neurotoxicity were evaluated at the behavioral and molecular (transcriptome and proteome) levels. Then, in order to understand the mild to negligible protective effect of NAC, the ability of this drug to cross BBB by passive diffusion was evaluated by different approaches. Whereas the BBB parallel artificial membrane permeability assay (BBB-PAMPA) was used to determine the ability of NAC to cross BBB-like phospholipid membranes by passive diffusion, the determination of the NAC content in the brain provides direct evidences of the BBB permeability in vivo. Finally, we evaluated the ability of drug to recover the depleted GSx stores and to scavenge ACR in the brain of ACR-exposed fish.
Project description:Drug absorption is one of the critical factors that should be taken into account in the process of drug discovery and development. The human colon carcinoma cell layer (Caco-2) model has been frequently used as a surrogate to preliminarily investigate the intestinal absorption. In this study, a quantitative structure-activity relationship (QSAR) model was generated using the innovative machine learning-based hierarchical support vector regression (HSVR) scheme to depict the exceedingly confounding passive diffusion and transporter-mediated active transport. The HSVR model displayed good agreement with the experimental values of the training samples, test samples, and outlier samples. The predictivity of HSVR was further validated by a mock test and verified by various stringent statistical criteria. Consequently, this HSVR model can be employed to forecast the Caco-2 permeability to assist drug discovery and development.
Project description:Endothelial cells forming the blood-brain barrier limit drug access into the brain, due to tight junctions, membrane drug transporters, and unique lipid composition. Passive permeability, thought to mediate drug access, is typically tested using porcine whole-brain lipid. However, human endothelial cell lipid composition differs. This investigation evaluated the influence of lipid composition on passive permeability across artificial membranes. Permeability of CNS-active drugs across an immobilized lipid membrane was determined using three lipid models: crude extract from whole pig brain, human brain microvessel lipid, and microvessel lipid plus cholesterol. Lipids were immobilized on polyvinylidene difluoride, forming donor and receiver chambers, in which drug concentrations were measured after 2 h. The log of effective permeability was then calculated using the measured concentrations. Permeability of small, neutral compounds was unaffected by lipid composition. Several structurally diverse drugs were highly permeable in porcine whole-brain lipid but one to two orders of magnitude less permeable across human brain endothelial cell lipid. Inclusion of cholesterol had the greatest influence on bulky amphipathic compounds such as glucuronide conjugates. Lipid composition markedly influences passive permeability. This was most apparent for charged or bulky compounds. These results demonstrate the importance of using species-specific lipid models in passive permeability assays.
Project description:The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure-activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 53, r2 = 0.93, q CV 2 = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q2 = 0.75-0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q2 = 0.78-0.92, RMSE = 0.19, s = 0.09). The built HSVR model consistently met the most stringent criteria when subjected to various statistical assessments. A mock test also assured the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.
Project description:Parallel artificial membrane permeability assay (PAMPA) is a screening tool for the evaluation of drug permeability across various biological membrane systems in a microplate format. In PAMPA, a drug candidate is allowed to pass through the lipid layer of a particular well during an incubation period of, typically, 10-16 h. In a second step, the samples of each well are transferred to a UV-Vis-compatible microplate and optically measured (applicable only to analytes with sufficient absorbance) or sampled by mass-spectrometric analysis. The required incubation period, sample transfer, and detection methods jointly limit the scalability of PAMPA to high-throughput screening format. We introduce a modification of the PAMPA method that allows direct fluorescence detection, without sample transfer, in real time (RT-PAMPA). The method employs the use of a fluorescent artificial receptor (FAR), composed of a macrocycle in combination with an encapsulated fluorescent dye, administered in the acceptor chamber of conventional PAMPA microplates. Because the detection principle relies on the molecular recognition of an analyte by the receptor and the associated fluorescence response, concentration changes of any analyte that binds to the receptor can be monitored (molecules with aromatic residues in the present example), regardless of the spectroscopic properties of the analyte itself. Moreover, because the fluorescence of the (upper) acceptor well can be read out directly by fluorescence in a microplate reader, the permeation of the drug through the planar lipid layer can be monitored in real time. Compared with the traditional assay, RT-PAMPA allows not only quantification of the permeability characteristics but also rapid differentiation between fast and slow diffusion events.
Project description:In this study, we genetically characterized the Uruguayan pig breed Pampa Rocha. Genetic variability was assessed by analyzing a panel of 25 microsatellite markers from a sample of 39 individuals. Pampa Rocha pigs showed high genetic variability with observed and expected heterozygosities of 0.583 and 0.603, respectively. The mean number of alleles was 5.72. Twenty-four markers were polymorphic, with 95.8% of them in Hardy Weinberg equilibrium. The level of endogamy was low (FIS = 0.0475). A factorial analysis of correspondence was used to assess the genetic differences between Pampa Rocha and other pig breeds; genetic distances were calculated, and a tree was designed to reflect the distance matrix. Individuals were also allocated into clusters. This analysis showed that the Pampa Rocha breed was separated from the other breeds along the first and second axes. The neighbour-joining tree generated by the genetic distances DA showed clustering of Pampa Rocha with the Meishan breed. The allocation of individuals to clusters showed a clear separation of Pampa Rocha pigs. These results provide insights into the genetic variability of Pampa Rocha pigs and indicate that this breed is a well-defined genetic entity.
Project description:PPAR agonists represent a new therapeutic opportunity for the prevention and treatment of neurodegenerative disorders, but their pharmacological success depends on favourable pharmacokinetic properties and capability to cross the BBB. In this study, we assayed some PPAR agonists previously synthesized by us for their physicochemical properties, with particular references to lipophilicity, solubility and permeability profiles, using the PAMPA. Although tested compounds showed high lipophilicity and low aqueous solubility, the results revealed a good overall druggability profile, encouraging further studies in the field of neurodegenerative diseases.
Project description:The biophysical basis of passive membrane permeability is well-understood, but most methods for predicting membrane permeability in the context of drug design are based on statistical relationships that indirectly capture the key physical aspects. Here, we investigate molecular mechanics-based models of passive membrane permeability and evaluate their performance against different types of experimental data, including parallel artificial membrane permeability assays (PAMPA), cell-based assays, in vivo measurements, and other in silico predictions. The experimental data sets we use in these tests are diverse, including peptidomimetics, congeneric series, and diverse FDA approved drugs. The physical models are not specifically trained for any of these data sets; rather, input parameters are based on standard molecular mechanics force fields, such as partial charges, and an implicit solvent model. A systematic approach is taken to analyze the contribution from each component in the physics-based permeability model. A primary factor in determining rates of passive membrane permeation is the conformation-dependent free energy of desolvating the molecule, and this measure alone provides good agreement with experimental permeability measurements in many cases. Other factors that improve agreement with experimental data include deionization and estimates of entropy losses of the ligand and the membrane, which lead to size-dependence of the permeation rate.