AMMOS: A Software Platform to Assist in silico Screening

Three software packages based on the common platform of AMMOS (Automated Molecular Mechanics Optimization tool for in silico Screening) for assisting virtual ligand screening purposes have been recently developed. DG-AMMOS allows generation of 3D conformations of small molecules using distance geometry and molecular mechanics optimization. AMMOS_SmallMol is a package for structural refinement of compound collections that can be used prior to docking experiments. AMMOS_ProtLig is a package for energy minimization of protein-ligand complexes. It performs an automatic procedure for molecular mechanics minimization at different levels of flexibility - from rigid to fully flexible structures of both the ligand and the receptor. The packages have been tested on small molecules with a high structural diversity and proteins binding sites of completely different geometries and physicochemical properties. The platform is developed as an open source software and can be used in a broad range of in silico drug design studies

Improvement of conventional anti-cancer drugs as new tools against multidrug resistant tumors

Multidrug resistance (MDR) is the dominant cause of the failure of cancer chemotherapy. The design of antitumor drugs that are able to evade MDR is rapidly evolving, showing that this area of biomedical research attracts great interest in the scientific community. The current review explores promising recent approaches that have been developed with the aim of circumventing or overcoming MDR. Encouraging results have been obtained in the investigation of the MDR-modulating properties of various classes of natural compounds and their analogues. Inhibition of P-gp or downregulation of its expression have proven to be the main mechanisms by which MDR can be surmounted. The use of hybrid molecules that are able to simultaneously interact with two or more cancer cell targets is currently being explored as a means to circumvent drug resistance. This strategy is based on the design of hybrid compounds that are obtained either by merging the structural features of separate drugs, or by conjugating two drugs or pharmacophores via cleavable/non-cleavable linkers. The approach is highly promising due to the pharmacokinetic and pharmacodynamic advantages that can be achieved over the independent administration of the two individual components. However, it should be stressed that the task of obtaining successful multivalent drugs is a very challenging one. The conjugation of anticancer agents with nitric oxide (NO) donors has recently been developed, creating a particular class of hybrid that can combat tumor drug resistance. Appropriate NO donors have been shown to reverse drug resistance via nitration of ABC transporters and by interfering with a number of metabolic enzymes and signaling pathways. In fact, hybrid compounds that are produced by covalently attaching NO-donors and antitumor drugs have been shown to elicit a synergistic cytotoxic effect in a variety of drug resistant cancer cell lines. Another strategy to circumvent MDR is based on nanocarrier-mediated transport and the controlled release of chemotherapeutic drugs and P-gp inhibitors. Their pharmacokinetics are governed by the nanoparticle or polymer carrier and make use of the enhanced permeation and retention (EPR) effect, which can increase selective delivery to cancer cells. These systems are usually internalized by cancer cells via endocytosis and accumulate in endosomes and lysosomes, thus preventing rapid efflux. Other modalities to combat MDR are described in this review, including the pharmaco-modulation of acridine, which is a well-known scaffold in the development of bioactive compounds, the use of natural compounds as means to reverse MDR, and the conjugation of anticancer drugs with carriers that target specific tumor-cell components. Finally, the outstanding potential of in silico structure-based methods as a means to evaluate the ability of antitumor drugs to interact with drug transporters is also highlighted in this review. Structure-based design methods, which utilize 3D structural data of proteins and their complexes with ligands, are the most effective of the in silico methods available, as they provide a prediction regarding the interaction between transport proteins and their substrates and inhibitors. The recently resolved X-ray structure of human P-gp can help predict the interaction sites of designed compounds, providing insight into their binding mode and directing possible rational modifications to prevent them from becoming P-gp drug substrates. In summary, although major efforts were invested in the search for new tools to combat drug resistant tumors, they all require further implementation and methodological development. Further investigation and progress in the abovementioned strategies will provide significant advances in the rational combat against cancer MDR

Quantitative Structure - Skin permeability Relationships

This paper reviews in silico models currently available for the prediction of skin permeability with the main focus on the quantitative structure-permeability relationship (QSPR) models. A comprehensive analysis of the main achievements in the field in the last decade is provided. In addition, the mechanistic models are discussed and comparative studies that analyse different models are discussed

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

Background: In 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. Purpose: This 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. Methods: A 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. Results: The 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. Conclusions: According 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

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

Prediction of Promiscuous P-Glycoprotein Inhibition Using a Novel Machine Learning Scheme

BACKGROUND: P-glycoprotein (P-gp) is an ATP-dependent membrane transporter that plays a pivotal role in eliminating xenobiotics by active extrusion of xenobiotics from the cell. Multidrug resistance (MDR) is highly associated with the over-expression of P-gp by cells, resulting in increased efflux of chemotherapeutical agents and reduction of intracellular drug accumulation. It is of clinical importance to develop a P-gp inhibition predictive model in the process of drug discovery and development. METHODOLOGY/PRINCIPAL FINDINGS: An in silico model was derived to predict the inhibition of P-gp using the newly invented pharmacophore ensemble/support vector machine (PhE/SVM) scheme based on the data compiled from the literature. The predictions by the PhE/SVM model were found to be in good agreement with the observed values for those structurally diverse molecules in the training set (n = 31, r(2) = 0.89, q(2) = 0.86, RMSE = 0.40, s = 0.28), the test set (n = 88, r(2) = 0.87, RMSE = 0.39, s = 0.25) and the outlier set (n = 11, r(2) = 0.96, RMSE = 0.10, s = 0.05). The generated PhE/SVM model also showed high accuracy when subjected to those validation criteria generally adopted to gauge the predictivity of a theoretical model. CONCLUSIONS/SIGNIFICANCE: This accurate, fast and robust PhE/SVM model that can take into account the promiscuous nature of P-gp can be applied to predict the P-gp inhibition of structurally diverse compounds that otherwise cannot be done by any other methods in a high-throughput fashion to facilitate drug discovery and development by designing drug candidates with better metabolism profile

The crystal structure of (4SR)-7-(3,4-dichlorobenzyl)-4,8,8-trimethyl-7,8-dihydroimidazo[5,1c][1, 2,4]triazine-3,6(2H,4H)-dione, C15H16Cl2N4O2

Tzvetkov NT, Peeva MI, Tsakovska I, Milella L, Pajeva I, Stammler H-G. The crystal structure of (4SR)-7-(3,4-dichlorobenzyl)-4,8,8-trimethyl-7,8-dihydroimidazo[5,1c][1, 2,4]triazine-3,6(2H,4H)-dione, C15H16Cl2N4O2. Zeitschrift für Kristallographie - New Crystal Structures . 2022.C15H16Cl2N4O2, monoclinic, P2(1)/c (no. 14), a = 26.2014(7) angstrom, b = 7.59320(10) angstrom, c = 17.9766(4) angstrom, beta = 109.217(3)degrees, V = 3377.20(14) angstrom(3), Z = 8, R ( gt )(F) = 0.0503, wR ( ref )(F (2)) = 0.1411, T = 100.0(1) K

New therapeutic strategy for overcoming multidrug resistance in cancer cells with pyrazolo[3,4‐d]pyrimidine tyrosine kinase inhibitors

none9siTyrosine kinase inhibitors (TKIs) often interact with the multidrug resistant (MDR) phenotype of cancer cells. In some cases, TKIs increase the susceptibility of MDR cancer cells to chemotherapy. As the overexpression of membrane transporter P‐glycoprotein (P‐gp) is the most com-mon alteration in MDR cancer cells, we investigated the effects of TKI pyrazolo[3,4‐d]pyrimidines on P‐gp inhibition in two cellular models comprising sensitive and corresponding MDR cancer cells (human non‐small cell lung carcinoma and colorectal adenocarcinoma). Tested TKIs showed collateral sensitivity by inducing stronger inhibition of MDR cancer cell line viability. Moreover, TKIs directly interacted with P‐gp and inhibited its ATPase activity. Their potential P‐gp binding site was proposed by molecular docking simulations. TKIs reversed resistance to doxorubicin and paclitaxel in a concentration‐dependent manner. The expression studies excluded the indirect effect of TKIs on P‐gp through regulation of its expression. A kinetics study showed that TKIs decreased P‐gp activity and this effect was sustained for seven days in both MDR models. Therefore, pyrazolo[3,4‐d]pyrimidines with potential for reversing P‐gp‐mediated MDR even in prolonged treatments can be considered a new therapeutic strategy for overcoming cancer MDR.openPodolski-renic A.; Dinic J.; Stankovic T.; Tsakovska I.; Pajeva I.; Tuccinardi T.; Botta L.; Schenone S.; Pesic M.Podolski-renic, A.; Dinic, J.; Stankovic, T.; Tsakovska, I.; Pajeva, I.; Tuccinardi, T.; Botta, L.; Schenone, S.; Pesic, M