Molecular Dynamics Simulation of Small Molecules Interacting with Biological Membranes

Cell membranes protect and compartmentalise cells and their organelles. The semi-permeable nature of these membranes controls the exchange of solutes across their structure. Characterising the interaction of small molecules with biological membranes is critical to understanding of physiological processes, drug action and permeation, and many biotechnological applications. This review provides an overview of how molecular simulations are used to study the interaction of small molecules with biological membranes, with a particular focus on the interactions of water, organic compounds, drugs and short peptides with models of plasma cell membrane and stratum corneum lipid bilayers. This review will not delve on other types of membranes which might have different composition and arrangement, such as thylakoid or mitochondrial membranes. The application of unbiased molecular dynamics simulations and enhanced sampling methods such as umbrella sampling, metadynamics and replica exchange are described using key examples. This review demonstrates how state-of-the-art molecular simulations have been used successfully to describe the mechanism of binding and permeation of small molecules with biological membranes, as well as associated changes to the structure and dynamics of these membranes. The review concludes with an outlook on future directions in this field.     

Tomocomd-Cardd, a novel approach for computer-aided ‘ rational’ drug design: I. Theoretical and experimental assessment of a promising method for computational screening and in silico design of new anthelmintic compounds

Summary In this work, the TOMOCOMD-CARDD approach has been applied to estimate the anthelmintic activity. Total and local (both atom and atom-type) quadratic indices and linear discriminant analysis were used to obtain a quantitative model that discriminates between anthelmintic and non-anthelmintic drug-like compounds. The obtained model correctly classified 90.37% of compounds in the training set. External validation processes to assess the robustness and predictive power of the obtained model were carried out. The QSAR model correctly classified 88.18% of compounds in this external prediction set. A second model was performed to outline some conclusions about the possible modes of action of anthelmintic drugs. This model permits the correct classification of 94.52% of compounds in the training set, and 80.00% of good global classification in the external prediction set. After that, the developed model was used in virtual in silicoscreening and several compounds from the Merck Index, Negwers handbook and Goodman and Gilman were identified by models as anthelmintic. Finally, the experimental assay of one organic chemical (G-1) by an in vivo test coincides fairly well (100) with model predictions. These results suggest that the proposed method will be a good tool for studying the biological properties of drug candidates during the early state of the drug-development process.     

Computational models to predict blood–brain barrier permeation and CNS activity

The blood-brain permeation of a structurally diverse set of 281 compounds was modeled using linear regression and a multivariate genetic partial least squares (G/PLS) approach. Key structural features affecting the logarithm of blood-brain partitioning (logBB) were captured through statistically significant quantitative structure-activity relationship (QSAR) models. These relationships reveal the importance of logP, polar surface area, and a variety of electrotopological indices for accurate predictions of logBB. The best models reveal an excellent correlation (r > 0.9) for a training set of 58 compounds. Likewise, the comparison of the average logBB values obtained from an ensemble of QSAR models with experimental values also verifies the statistical quality of the models (r > 0.9). The models provide good agreement (r approximately 0.7) between the predicted logBB values for 34 molecules in the external validation set and the experimental values. To further validate the models for use during the drug discovery process, a prediction set of 181 drugs with reported CNS penetration data was used. A >70% success rate is obtained by using any of the QSAR models in the qualitative prediction for CNS permeable (active) drugs. A lower success rate (approximately 60%) was obtained for the best model for CNS impermeable (inactive) drugs. Combining the predictions obtained from all the models (consensus) did not significantly improve the discrimination of CNS active and CNS inactive molecules. Finally, using the therapeutic classification as a guiding tool, the CNS penetration capability of over 2000 compounds in the Synthline database was estimated. The results were very similar to the smaller set of 181 compounds.     

High-throughput caco-2 cell permeability screening by cassette dosing and sample pooling approaches using direct injection/on-line guard cartridge extraction/tandem mass spectrometry

A method for high-throughput Caco-2 permeability screening of drug candidates has been developed using thirteen generic drugs as test compounds. The high throughput was achieved by either a sample pooling or a cassette dosing approach, along with the use of a rapid, simple and sensitive direct injection/on-line guard cartridge extraction/tandem mass spectrometric assay that was also developed in this study. It was of concern that possible drug-drug interactions (e.g., inhibition of P-glycoprotein-mediated transport of a drug by another, and/or competition of the drugs for transport pathways), when the cassette dosing regimen was implemented, may give rise to inconsistent results compared with those attained by a traditional single-drug dosing approach. However, the apparent permeability coefficients of the test drugs across Caco-2 monolayers measured by the sample pooling or cassette dosing (up to five drugs co-administered in this study) strategy were in good conformity with the data obtained by single-drug dosing followed by discrete sample analysis.     

Permeability of the Perindopril Arginine under In Vitro Conditions across Caco-2 Monolayer and Biomimetic Phospholipid Membrane

Perindopril arginine (PA) as an angiotensin-converting enzyme (ACE) inhibitor is widely used in cardiovascular diseases, especially in systemic hypertension and heart failure. Although the pharmacokinetics of PA are well documented, there is no available detailed data on its permeation in in vitro conditions. The present study aimed to assess the transport of PA across both biological membranes and artificial biomimetic ones. For the determination of PA transport, the Caco-2 cell line was selected as a reliable in vitro model of gastrointestinal biological barriers. Additionally, a novel 96-well plate with phospholipid membrane PermeaPad was used to evaluate the transport of PA by passive diffusion. We confirmed that PA is relatively poorly permeable across the Caco-2 monolayer. The permeability results obtained from the non-cell-based model demonstrated higher transport of PA as compared to that of Caco-2. Thus, PA transport across the biological membranes might be suggested to be regulated by the membrane transporters.     

Neural network studies : Part 3. Prediction of partition coefficients

In a previous paper (N. Bodor, A. Harget and M.-J. Huang, J. Am. Chem. Soc., 113 (1991) 9480) we demonstrated the utility of a neural network approach in the estimation of the aqueous solubility of organic compounds. This approach has now been extended to the prediction of partition coefficients. A training set of AM1 calculated properties and experimental values for 302 compounds was used and, after training, the neural network was tested for its ability to predict the partition coefficients of 21 compounds not included in the training set. We also tested six more compounds with molecular properties out of the training set property range. A comparison was made with the results obtained from a previous study which had used a regression analysis approach (N. Bodor and M.-J. Huang, J. Pharm. Sci., 81 (1992) 272). The neural network results compared favorably with those given by the regression analysis approach, both for the training set and for the new compounds.     

Development of an in-cell SPME method to determine the chemical resistance of polymeric membranes to permeation by organic solvents

A stainless steel cell with an in-cell solid-phase microextraction (SPME) sampling device is proposed to investigate the permeation of dichloromethane, 1,2-dichloroethane, and benzene through a high-density polyethylene (HDPE) membrane. The advantage of using SPME as a direct sampling device in the collection chamber is that it is a simple and sensitive means to monitor the concentrations of organic compounds in the collection medium for a closed-loop test system. Compared with the permeation results for an ASTM F739 cell, the standardized breakthrough times were shorter and the permeability coefficients were greater using the alternative cell. Although the optimum SPME sampling parameters should be obtained in advance, the in-cell SPME method can be an appropriate approach to determine the resistance of polymeric membranes to permeation by organic solvents.