P-glycoprotein substrate models using support vector machines based on a comprehensive data set

Modeling Substrate Entry into the P-Glycoprotein Efflux Pump at the Blood-Brain Barrier

We report molecular dynamics simulations of rhodamine entry into the central binding cavity of P-gp in the inward open conformation. Rhodamine can enter the inner volume via passive transport across the luminal membrane or lateral diffusion in the lipid bilayer. Entry into the inner volume is determined by the aperture angle at the apex of the protein, with a critical angle of 27° for rhodamine. The central binding cavity has an aqueous phase with a few lipids, which significantly reduces substrate diffusion. Within the central binding cavity, we identified regions with relatively weak binding, suggesting that the combination of reduced mobility and weak substrate binding confines rhodamine to enable the completion of the efflux cycle. Tariquidar, a P-gp inhibitor, aggregates at the lower arms of the P-gp, suggesting that inhibition involves steric hindrance of entry into the inner volume and/or steric hindrance of access of ATP to the nucleotide-binding domains.

Structural Dynamics of P-Rex1 Complexed with Natural Leads Establishes the Protein as an Attractive Target for Therapeutics to Suppress Cancer Metastasis

Phosphatidylinositol 3,4,5-trisphosphate- (PIP3-) dependent Rac exchanger 1 (P-Rex1) functions as Rho guanine nucleotide exchange factor and is activated by synergistic activity of Gβγ and PIP3 of the heterotrimeric G protein. P-Rex1 activates Rac GTPases for regulating cell invasion and migration and promotes metastasis in several human cancers including breast, prostate, and skin cancer. The protein is a promising therapeutic target because of its multifunction roles in human cancers. Herein, the present study attempts to identify selective P-Rex1 natural inhibitors by targeting PIP3-binding pocket using large-size multiple natural molecule libraries. Each library was filtered subsequently in FAF-Drugs4 based on Lipinski's rule of five (RO5), toxicity, and filter pan assay interference compounds (PAINS). The output hits were virtually screened at the PIP3-binding pocket through PyRx AutoDock Vina and cross-checked by GOLD. The best binders at the PIP3-binding pocket were prioritized using a comparative analysis of the docking scores. Top-ranked two compounds with high GOLD fitness score (>80) and lowest AutoDock binding energy (< -12.7 kcal/mol) were complexed and deciphered for molecular dynamics along with control-P-Rex1 complex to validate compound binding conformation and disclosed binding interaction pattern. Both the systems were seen in good equilibrium, and along the simulation time, the compounds are in strong contact with the P-Rex1 PIP3-binding site. Hydrogen bonding analysis towards simulation end identified the formation of 16 and 22 short- and long-distance hydrogen bonds with different percent of occupancy to the PIP3 residues for compound I and compound 2, respectively. Radial distribution function (RDF) analysis of the key hydrogen bonds between the compound and the PIP3 residues demonstrated a strong affinity of the compounds to the mentioned PIP3 pocket. Additionally, MMGB/PBSA energies were performed that confirmed the dominance of Van der Waals energy in complex formation along with favorable contribution from hydrogen bonding. These findings were also cross-validated by a more robust WaterSwap binding energy predictor, and the results are in good agreement with a strong binding affinity of the compounds for the protein. Lastly, the key contribution of residues in interaction with the compounds was understood by binding free energy decomposition and alanine scanning methods. In short, the results of this study suggest that P-Rex1 is a good druggable target to suppress cancer metastasis; therefore, the screened druglike molecules of this study need in vitro and in vivo anti-P-Rex1 validation and may serve as potent leads to fight cancer.

Ensemble Machine Learning Approaches Based on Molecular Descriptors and Graph Convolutional Networks for Predicting the Efflux Activities of MDR1 and BCRP Transporters

Multidrug resistance (MDR1) and breast cancer resistance protein (BCRP) play important roles in drug absorption and distribution. Computational prediction of substrates for both transporters can help reduce time in drug discovery. This study aimed to predict the efflux activity of MDR1 and BCRP using multiple machine learning approaches with molecular descriptors and graph convolutional networks (GCNs). In vitro efflux activity was determined using MDR1- and BCRP-expressing cells. Predictive performance was assessed using an in-house dataset with a chronological split and an external dataset. CatBoost and support vector regression showed the best predictive performance for MDR1 and BCRP efflux activities, respectively, of the 25 descriptor-based machine learning methods based on the coefficient of determination (R2). The single-task GCN showed a slightly lower performance than descriptor-based prediction in the in-house dataset. In both approaches, the percentage of compounds predicted within twofold of the observed values in the external dataset was lower than that in the in-house dataset. Multi-task GCN did not show any improvements, whereas multimodal GCN increased the predictive performance of BCRP efflux activity compared with single-task GCN. Furthermore, the ensemble approach of descriptor-based machine learning and GCN achieved the highest predictive performance with R2 values of 0.706 and 0.587 in MDR1 and BCRP, respectively, in time-split test sets. This result suggests that two different approaches to represent molecular structures complement each other in terms of molecular characteristics. Our study demonstrated that predictive models using advanced machine learning approaches are beneficial for identifying potential substrate liability of both MDR1 and BCRP.

Identification of in vitro metabolites of an Allium organosulfur compound and environmental toxicity prediction as part of its risk assessment

Propyl-propane-thiosulfonate (PTSO) is an organosulfur compound found inAllium spp. Due to its antioxidant and antimicrobial activities, PTSO has been proposed for applications in the agri-food sector, such as feed additive. However, its use with commercial purposes depends on its toxicity evaluation. The present work aimed to perform a pilot-study of toxicokinetic profile of PTSO combining in silico and in vitro techniques, important steps in the risk assessment process. In silico ecotoxicity studies were also performed considering the importance of the environmental impact of the compound before its commercial use. First, an analytical method has been developed and validated to determine the original compound and its metabolites by ultra-performance liquid chromatography-tandem mass spectrometry. The phase I and II metabolism of PTSO was predicted using Meta-Pred Web Server. For the phase I metabolism, rat (male and female) and human liver microsomes were incubated with PTSO and NADPH regeneration system. Furthermore, in the phase II, microsomes were incubated with PTSO and glutathione or uridine 5'- diphosphoglucuronic acid. The analysis revealed the presence of propylpropane thiosulfinate (PTS) originated by redox reaction in phase I, and two conjugates from the phase II: S-propylmercaptoglutathione (GSSP) and S-propylmercaptocysteine (CSSP). Additionally, considering the environmental fate of PTSO and its metabolites, the ADME parameters and the potential ecotoxicity were also predicted using in silico softwares. The results of the ecotoxicity in silico study evidenced that the metabolism induced the formation of detoxified metabolites from the parent compound, except for dimercaprol and 3-mercaptopropane1,2-diol. Further in vivo assays are needed to confirm this prediction.

Exhaustive in silico design and screening of novel antipsychotic compounds with improved pharmacodynamics and blood-brain barrier permeation properties

Antipsychotic drugs or neuroleptics are widely used in the treatment of psychosis as a manifestation of schizophrenia and bipolar disorder. However, their effectiveness largely depends on the blood-brain barrier (BBB) permeation (pharmacokinetics) and drug-receptor pharmacodynamics. Therefore, in this study, we developed and implemented the in silico pipeline to design novel compounds (n = 260) as leads using the standard drug scaffolds with improved PK/PD properties from the standard scaffolds. As a result, the best candidates (n = 3) were evaluated in molecular docking to interact with serotonin and dopamine receptors. Finally, haloperidol (HAL) derivative (1-(4-fluorophenyl)-4-(4-hydroxy-4-{4-[(2-phenyl-1,3-thiazol-4-yl)methyl]phenyl}piperidin-1-yl)butan-1-one) was identified as a "magic shotgun" lead compound with better affinity to the 5-HT2A, 5-HT1D, D2, D3, and 5-HT1B receptors than the control molecule. Additionally, this hit substance was predicted to possess similar BBB permeation properties and much lower toxicological profiles in comparison to HAL. Overall, the proposed rational drug design platform for novel antipsychotic drugs based on the BBB permeation and receptor binding might be an invaluable asset for a medicinal chemist or translational pharmacologist.Communicated by Ramaswamy H. Sarma.

The Molecular Mechanisms of Oleanane Aldehyde-β-enone Cytotoxicity against Doxorubicin-Resistant Cancer Cells

Oleanane aldehyde-β-enone (OA), being the semi-synthetic derivative of the triterpenoid betulin, effectively inhibits the proliferation of HBL-100 and K562 cancer cells (IC50 0.47–0.53 µM), as well as the proliferation of their resistant subclones with high P-gp expression HBL-100/Dox, K562/i-S9 and K562/i-S9_Dox (IC50 0.45−1.24 µM). A molecular docking study, rhodamine efflux test, synergistic test with Dox, and ABC transporter gene expression were used to investigate the ability of OA to act as a P-gp substrate or inhibitor against Dox-resistant cells. We noted a trend toward a decrease in ABCB1, ABCC1 and ABCG2 expression in HBL-100 cells treated with OA. The in silico and in vitro methods suggested that OA is neither a direct inhibitor nor a competitive substrate of P-gp in overexpressing P-gp cancer cells. Thus, OA is able to overcome cellular resistance and can accumulate in Dox-resistant cells to realize toxic effects. The set of experiments suggested that OA toxic action can be attributed to activating intrinsic/extrinsic or only intrinsic apoptosis pathways in Dox-sensitive and Dox-resistant cancer cells, respectively. The cytotoxicity of OA in resistant cells is likely mediated by a mitochondrial cell death pathway, as demonstrated by positive staining with Annexin V–FITC, an increasing number of cells in the subG0/G1 phase, reactive oxygen species generation, mitochondrial dysfunction, cytochrome c migration and caspases-9,-6 activation.

DenovoProfiling: A webserver for de novo generated molecule library profiling

Various deep learning-based architectures for molecular generation have been proposed for de novo drug design. The flourish of the de novo molecular generation methods and applications has created a great demand for the visualization and functional profiling for the de novo generated molecules. An increasing number of publicly available chemogenomic databases sets good foundations and creates good opportunities for comprehensive profiling of the de novo library. In this paper, we present DenovoProfiling, a webserver dedicated to de novo library visualization and functional profiling. Currently, DenovoProfiling contains six modules: (1) identification & visualization module for chemical structure visualization and identify the reported structures, (2) chemical space module for chemical space exploration using similarity maps, principal components analysis (PCA), drug-like properties distribution, and scaffold-based clustering, (3) ADMET prediction module for predicting the ADMET properties of the de novo molecules, (4) molecular alignment module for three dimensional molecular shape analysis, (5) drugs mapping module for identifying structural similar drugs, and (6) target & pathway module for identifying the reported targets and corresponding functional pathways. DenovoProfiling could provide structural identification, chemical space exploration, drug mapping, and target & pathway information. The comprehensive annotated information could give users a clear picture of their de novo library and could guide the further selection of candidates for chemical synthesis and biological confirmation. DenovoProfiling is freely available at http://denovoprofiling.xielab.net.

Inhibition of Mycobacterium tuberculosis InhA (Enoyl-acyl carrier protein reductase) by synthetic Chalcones: a molecular modelling analysis and in-vitro evidence

Tuberculosis (TB) is a serious infectious disease caused by the bacillus Mycobacterium tuberculosis (Mtb). The World Health Organization (WHO) estimates that 1.8 million people die each year from TB, with 10 million new cases being registered each year. In this study, 50 Chalcones were developed, five of which were synthesized, and their inhibitory effects against Mtb were studied. The discovery of new powerful inhibitors with IC₅₀ values in the sub-micro molar range resulted from the development of structure-activity relationships (SAR). The goal of the molecular modelling studies was to uncover the most important structural criteria underpinning the binding affinity and selectivity of this class of inhibitors as possible anti-TB drugs. Because of their great efficacy and selectivity, our developed nitro and benzyloxy substituted Chalcones compounds appear to be promising anti-TB therapies.Communicated by Ramaswamy H. Sarma.

HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer

MOTIVATION

Accurate ADMET (an abbreviation for "absorption, distribution, metabolism, excretion, and toxicity") predictions can efficiently screen out undesirable drug candidates in the early stage of drug discovery. In recent years, multiple comprehensive ADMET systems that adopt advanced machine learning models have been developed, providing services to estimate multiple endpoints. However, those ADMET systems usually suffer from weak extrapolation ability. First, due to the lack of labelled data for each endpoint, typical machine learning models perform frail for the molecules with unobserved scaffolds. Second, most systems only provide fixed built-in endpoints and cannot be customised to satisfy various research requirements. To this end, we develop a robust and endpoint extensible ADMET system, HelixADMET (H-ADMET). H-ADMET incorporates the concept of self-supervised learning to produce a robust pre-trained model. The model is then fine-tuned with a multi-task and multi-stage framework to transfer knowledge between ADMET endpoints, auxiliary tasks, and self-supervised tasks.

RESULTS

Our results demonstrate that H-ADMET achieves an overall improvement of 4%, compared with existing ADMET systems on comparable endpoints. Additionally, the pre-trained model provided by H-ADMET can be fine-tuned to generate new and customised ADMET endpoints, meeting various demands of drug research and development requirements.

AVAILABILITY

H-ADMET is freely accessible at https://paddlehelix.baidu.com/app/drug/admet/train.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Molecular docking and pharmacokinetic studies of phytocompounds from Nigerian Medicinal Plants as promising inhibitory agents against SARS-CoV-2 methyltransferase (nsp16)

Background

Since the index case was reported in China, COVID-19 has led to the death of at least 4 million people globally. Although there are some vaccine cocktails in circulation, the emergence of more virulent variants of SARS-CoV-2 may make the eradication of COVID-19 more difficult. Nsp16 is an S-adenosyl-L-Methionine-dependent methyltransferase that plays an important role in SARS-CoV-2 viral RNA cap formation—a crucial process that confers viral stability and prevents virus detection by cell innate immunity mechanisms. This unique property makes nsp16 a promising molecular target for COVID-19 drug design. Thus, this study aimed to identify potent phytocompounds that can effectively inhibit SARS-CoV-2 nsp16. We performed in silico pharmacokinetic screening and molecular docking studies using 100 phytocompounds—isolated from fourteen Nigerian plants—as ligands and nsp16 (PDB: 6YZ1) as the target.

Results

We found that only 59 phytocompounds passed the drug-likeness analysis test. However, after the docking analysis, only six phytocompounds (oxopowelline, andrographolide, deacetylbowdensine, 11, 12-dimethyl sageone, sageone, and quercetin) isolated from four Nigerian plants (Crinum jagus, Andrographis paniculata, Sage plants (Salvia officinalis L.), and Anacardium occidentale) showed good binding affinity with nsp16 at its active site with docking score ranging from − 7.9 to − 8.4 kcal/mol.

Conclusions

Our findings suggest that the six phytocompounds could serve as therapeutic agents to prevent viral survival and replication in cells. However, further studies on the in vitro and in vivo inhibitory activities of these 6 hit phytocompounds against SARS-CoV-2 nsp16 are needed to confirm their efficacy and dose.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43141-021-00273-5.

ATP-binding cassette transporters mediate differential biosynthesis of glycosphingolipid species

The cytosolic-oriented glucosylceramide (GlcCer) synthase is enigmatic, requiring nascent GlcCer translocation to the luminal Golgi membrane to access glycosphingolipid (GSL) anabolic glycosyltransferases. The mechanism by which GlcCer is flipped remains unclear. To investigate the role of GlcCer-binding partners in this process, we previously made cleavable, biotinylated, photoreactive GlcCer analogs in which the reactive nitrene was closely apposed to the GlcCer head group, while maintaining a C16-acyl chain. GlcCer-binding protein specificity was validated for both photoprobes. Using one probe, XLB, here we identified ATP-binding cassette (ABC) transporters ABCA3, ABCB4, and ABCB10 as unfractionated microsomal GlcCer-binding proteins in DU-145 prostate tumor cells. siRNA knockdown (KD) of these transporters differentially blocked GSL synthesis assessed in toto and via metabolic labeling. KD of ABCA3 reduced acid/neutral GSL levels, but increased those of LacCer, while KD of ABCB4 preferentially reduced neutral GSL levels, and KD of ABCB10 reduced levels of both neutral and acidic GSLs. Depletion of ABCA12, implicated in GlcCer transport, preferentially decreased neutral GSL levels, while ABCB1 KD preferentially reduced gangliosides, but increased neutral GSL Gb3. These results imply that multiple ABC transporters may provide distinct but overlapping GlcCer and LacCer pools within the Golgi lumen for anabolism of different GSL series by metabolic channeling. Differential ABC family member usage may fine-tune GSL biosynthesis depending on cell/tissue type. We conclude that ABC transporters provide a new tool for the regulation of GSL biosynthesis and serve as potential targets to reduce selected GSL species/subsets in diseases in which GSLs are dysregulated.

Rational design of potent anti-COVID-19 main protease drugs: An extensive multi-spectrum in silico approach

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a novel coronavirus and the etiological agent of global pandemic coronavirus disease (COVID-19) requires quick development of potential therapeutic strategies. Computer aided drug design approaches are highly efficient in identifying promising drug candidates among an available pool of biological active antivirals with safe pharmacokinetics. The main protease (M^Pro) enzyme of SARS-CoV-2 is considered key in virus production and its crystal structures are available at excellent resolution. This marks the enzyme as a good starting receptor to conduct an extensive structure-based virtual screening (SBVS) of ASINEX antiviral library for the purpose of uncovering valuable hits against SARS-CoV-2 M^Pro. A compound hit (BBB_26580140) was stand out in the screening process, as opposed to the control, as a potential inhibitor of SARS-CoV-2 M^Pro based on a combined approach of SBVS, drug likeness and lead likeness annotations, pharmacokinetics, molecular dynamics (MD) simulations, and end point MM-PBSA binding free energy methods. The lead was further used in ligand-based similarity search (LBSS) that found 33 similar compounds from the ChEMBL database. A set of three compounds (SCHEMBL12616233, SCHEMBL18616095, and SCHEMBL20148701), based on their binding affinity for M^Pro, was selected and analyzed using extensive MD simulation, hydrogen bond profiling, MM-PBSA, and WaterSwap binding free energy techniques. The compounds conformation with M^Pro show good stability after initial within active cavity moves, a rich intermolecular network of chemical interactions, and reliable relative and absolute binding free energies. Findings of the study suggested the use of BBB_26580140 lead and its similar analogs to be explored in vivo which might pave the path for rational drug discovery against SARS-CoV-2 M^Pro.

Interactions between T-2 toxin and its metabolites in HepG2 cells and in silico approach

The T-2 toxin (T-2) is commonly metabolized to HT-2 toxin (HT-2), Neosolaniol (NEO), T2-triol and T2-tetraol and they can modify the toxicity of T-2. In this study, T-2 and its modified forms were evaluated by in vitro and in silico methods. The in vitro cytotoxicity individually was evaluated by MTT and Total Protein Content (PC) assays in human hepatocarcinoma (HepG2) cells. The order of IC₅₀ was T-2 tetraol > T-2 triol > NEO > T-2 = HT-2. The T-2 and HT-2 evidenced the highest cytotoxic effect in HepG2 cells individually. No differences were observed in binary combinations tested and the two mycotoxins in the mixture tested individually. The T-2+HT-2 combination showed the highest toxic potential with the lowest IC₅₀ value of 34.42 ± 0.58 nM at 24 h. All binary combinations exhibited antagonistic interactions. The ADME and toxicity profile of mycotoxins were obtained by the in silico admetSAR predictive model which determines the metabolic and toxicological approaches in order to know if these mycotoxins might be taken into consideration to support a more realistic and adequate risk assessment.

Cellular cholesterol prediction of mammalian ATP-binding cassette (ABC) proteins based on fuzzy c-means with support vector machine algorithms

Over the years protein interaction and prediction of membrane protein have been a pivotal research area for all researchers. For both prokaryotes and eukaryotes Adenosine Triphosphate-(ATP) binding cassette (ABC) genes plays a significant role. In our analysis, we concentrate on human part of ABC genes. In case of living organisms transport of precise molecules across lipid membranes has been treated as vital part and for that reason a bigger transporter is required to carry out the molecules. Here ABC transporter families are evolved to transport the specific molecules such as sugars, amino acid, peptides, proteins, ions etc. within the plasma membrane. As we know another important component of human being is cholesterol, which is a major component in cell membrane and its main functions are to maintain integrity and mechanical stability. Each and every time, membrane cholesterolsareinteracted with membrane protein in both N-C terminuses and target valid sequence(s) which has relevance in human diseases. In this manuscript we have applied Fuzzy C-Means (FCM) with Support Vector Machine (SVM) algorithm for prediction of cellular cholesterol with ABC genes. Our experiments have been performed well using ABCdata set.

STS-NLSP: A Network-Based Label Space Partition Method for Predicting the Specificity of Membrane Transporter Substrates Using a Hybrid Feature of Structural and Semantic Similarity

Membrane transport proteins play crucial roles in the pharmacokinetics of substrate drugs, the drug resistance in cancer and are vital to the process of drug discovery, development and anti-cancer therapeutics. However, experimental methods to profile a substrate drug against a panel of transporters to determine its specificity are labor intensive and time consuming. In this article, we aim to develop an in silico multi-label classification approach to predict whether a substrate can specifically recognize one of the 13 categories of drug transporters ranging from ATP-binding cassette to solute carrier families using both structural fingerprints and chemical ontologies information of substrates. The data-driven network-based label space partition (NLSP) method was utilized to construct the model based on a hybrid of similarity-based feature by the integration of 2D fingerprint and semantic similarity. This method builds predictors for each label cluster (possibly intersecting) detected by community detection algorithms and takes union of label sets for a compound as final prediction. NLSP lies into the ensembles of multi-label classifier category in multi-label learning field. We utilized Cramér's V statistics to quantify the label correlations and depicted them via a heatmap. The jackknife tests and iterative stratification based cross-validation method were adopted on a benchmark dataset to evaluate the prediction performance of the proposed models both in multi-label and label-wise manner. Compared with other powerful multi-label methods, ML-kNN, MTSVM, and RAkELd, our multi-label classification model of NLPS-RF (random forest-based NLSP) has proven to be a feasible and effective model, and performed satisfactorily in the predictive task of transporter-substrate specificity. The idea behind NLSP method is intriguing and the power of NLSP remains to be explored for the multi-label learning problems in bioinformatics. The benchmark dataset, intermediate results and python code which can fully reproduce our experiments and results are available at https://github.com/dqwei-lab/STS.

Micelle-Forming Block Copolymers Tailored for Inhibition of P-gp-Mediated Multidrug Resistance: Structure to Activity Relationship

Multidrug resistance (MDR) is often caused by the overexpression of efflux pumps, such as ABC transporters, in particular, P-glycoprotein (P-gp). Here, we investigate the di- and tri- block amphiphilic polymer systems based on polypropylene glycol (PPO) and copolymers of (N-(2-hydroxypropyl)methacrylamide) (PHPMA) as potential macromolecular inhibitors of P-gp, and concurrently, carriers of drugs, passively targeting solid tumors by the enhanced permeability and retention (EPR) effect. Interestingly, there were significant differences between the effects of di- and tri- block polymer-based micelles, with the former being significantly more thermodynamically stable and showing much higher P-gp inhibition ability. The presence of Boc-protected hydrazide groups or the Boc-deprotection method did not affect the physico-chemical or biological properties of the block copolymers. Moreover, diblock polymer micelles could be loaded with free PPO containing 5–40 wt % of free PPO, which showed increased P-gp inhibition in comparison to the unloaded micelles. Loaded polymer micelles containing more than 20 wt % free PPO showed a significant increase in toxicity; thus, loaded diblock polymer micelles containing 5–15 wt % free PPO are potential candidates for in vitro and in vivo application as potent MDR inhibitors and drug carriers.

Design, Synthesis, and Pharmacological Evaluation of Potent Positive Allosteric Modulators of the Glucagon-like Peptide-1 Receptor (GLP-1R)

The therapeutic success of peptidic GLP-1 receptor agonists for treatment of type 2 diabetes mellitus (T2DM) motivated our search for orally bioavailable small molecules that can activate the GLP-1 receptor (GLP-1R) as a well-validated target for T2DM. Here, the discovery and characterization of a potent and selective positive allosteric modulator (PAM) for GLP-1R based on a 3,4,5,6-tetrahydro-1H-1,5-epiminoazocino[4,5-b]indole scaffold is reported. Optimization of this series from HTS was supported by a GLP-1R ligand binding model. Biological in vitro testing revealed favorable ADME and pharmacological profiles for the best compound 19. Characterization by in vivo pharmacokinetic and pharmacological studies demonstrated that 19 activates GLP-1R as positive allosteric modulator (PAM) in the presence of the much less active endogenous degradation product GLP1(9-36)NH₂ of the potent endogenous ligand GLP-1(7-36)NH₂. While these data suggest the potential of small molecule GLP-1R PAMs for T2DM treatment, further optimization is still required towards a clinical candidate.

Novel Heat Shock Protein 90 Inhibitors Suppress P-Glycoprotein Activity and Overcome Multidrug Resistance in Cancer Cells

Heat Shock Protein 90 (Hsp90) chaperone interacts with a broad range of client proteins involved in cancerogenesis and cancer progression. However, Hsp90 inhibitors were unsuccessful as anticancer agents due to their high toxicity, lack of selectivity against cancer cells and extrusion by membrane transporters responsible for multidrug resistance (MDR) such as P-glycoprotein (P-gp). Recognizing the potential of new compounds to inhibit P-gp function and/or expression is essential in the search for effective anticancer drugs. Eleven Hsp90 inhibitors containing an isoxazolonaphtoquinone core were synthesized and evaluated in two MDR models comprised of sensitive and corresponding resistant cancer cells with P-gp overexpression (human non-small cell lung carcinoma and colorectal adenocarcinoma). We investigated the effect of Hsp90 inhibitors on cell growth inhibition, P-gp activity and P-gp expression. Structure-activity relationship analysis was performed in respect to cell growth and P-gp inhibition. Compounds 5, 7, and 9 directly interacted with P-gp and inhibited its ATPase activity. Their potential P-gp binding site was identified by molecular docking studies. In addition, these compounds downregulated P-gp expression in MDR colorectal carcinoma cells, showed good relative selectivity towards cancer cells, while compound 5 reversed resistance to doxorubicin and paclitaxel in concentration-dependent manner. Therefore, compounds 5, 7 and 9 could be promising candidates for treating cancers with P-gp overexpression.

Sorcin: a novel potential target in therapies of cancers

Soluble resistance-related calcium-binding protein (sorcin) is a member of the penta-EF-hand protein family. Sorcin is widely distributed in normal human tissues, such as the brain, heart, lymphocytes, kidneys, breast and skin. Findings suggest that sorcin is associated with the regulation of calcium homeostasis, cell cycle and vesicle trafficking. It has been reported that many types of non-neoplastic diseases such as diabetes, viral infection, infertility, and nervous system diseases were affected by the expression of sorcin. One of the main issues is the role of sorcin in neoplastic diseases. Research proved that sorcin can be found to overexpress in cells of several cancers, particularly in the case of multidrug-resistant cancers. Additionally, the researchers proposed that the expression of sorcin was significantly associated with the foundation of multidrug resistance (MDR). All the findings mentioned above emphasized the importance of studying sorcin. This review mainly includes the following aspects: functions of sorcin, role in non-neoplastic and neoplastic diseases, and research related to drugs. To sum up, sorcin is a potential novel target to be studied to deal with MDR.

Multiclass Classifier for P-Glycoprotein Substrates, Inhibitors, and Non-Active Compounds

P-glycoprotein (P-gp) is a transmembrane protein that actively transports a wide variety of chemically diverse compounds out of the cell. It is highly associated with the ADMET (absorption, distribution, metabolism, excretion and toxicity) properties of drugs/drug candidates and contributes to decreasing toxicity by eliminating compounds from cells, thereby preventing intracellular accumulation. Therefore, in the drug discovery and toxicological assessment process it is advisable to pay attention to whether a compound under development could be transported by P-gp or not. In this study, an in silico multiclass classification model capable of predicting the probability of a compound to interact with P-gp was developed using a counter-propagation artificial neural network (CP ANN) based on a set of 2D molecular descriptors, as well as an extensive dataset of 2512 compounds (1178 P-gp inhibitors, 477 P-gp substrates and 857 P-gp non-active compounds). The model provided a good classification performance, producing non error rate (NER) values of 0.93 for the training set and 0.85 for the test set, while the average precision (AvPr) was 0.93 for the training set and 0.87 for the test set. An external validation set of 385 compounds was used to challenge the model’s performance. On the external validation set the NER and AvPr values were 0.70 for both indices. We believe that this in silico classifier could be effectively used as a reliable virtual screening tool for identifying potential P-gp ligands.

Characterizing ABC-Transporter Substrate-Likeness Using a Clean-Slate Genetic Background

Mutations in ATP Binding Cassette (ABC)-transporter genes can have major effects on the bioavailability and toxicity of the drugs that are ABC-transporter substrates. Consequently, methods to predict if a drug is an ABC-transporter substrate are useful for drug development. Such methods traditionally relied on literature curated collections of ABC-transporter dependent membrane transfer assays. Here, we used a single large-scale dataset of 376 drugs with relative efficacy on an engineered yeast strain with all ABC-transporter genes deleted (ABC-16), to explore the relationship between a drug’s chemical structure and ABC-transporter substrate-likeness. We represented a drug’s chemical structure by an array of substructure keys and explored several machine learning methods to predict the drug’s efficacy in an ABC-16 yeast strain. Gradient-Boosted Random Forest models outperformed all other methods with an AUC of 0.723. We prospectively validated the model using new experimental data and found significant agreement with predictions. Our analysis expands the previously reported chemical substructures associated with ABC-transporter substrates and provides an alternative means to investigate ABC-transporter substrate-likeness.

Development of Simplified in Vitro P-Glycoprotein Substrate Assay and in Silico Prediction Models To Evaluate Transport Potential of P-Glycoprotein

For efficient drug discovery and screening, it is necessary to simplify P-glycoprotein (P-gp) substrate assays and to provide in silico models that predict the transport potential of P-gp. In this study, we developed a simplified in vitro screening method to evaluate P-gp substrates by unidirectional membrane transport in P-gp-overexpressing cells. The unidirectional flux ratio positively correlated with parameters of the conventional bidirectional P-gp substrate assay ( R² = 0.941) and in vivo K_p,brain ratio (mdr1a/1b KO/WT) in mice ( R² = 0.800). Our in vitro P-gp substrate assay had high reproducibility and required approximately half the labor of the conventional method. We also constructed regression models to predict the value of P-gp-mediated flux and three-class classification models to predict P-gp substrate potential (low-, medium-, and high-potential) using 2397 data entries with the largest data set collected under the same experimental conditions. Most compounds in the test set fell within two- and three-fold errors in the random forest regression model (71.3 and 88.5%, respectively). Furthermore, the random forest three-class classification model showed a high balanced accuracy of 0.821 and precision of 0.761 for the low-potential classes in the test set. We concluded that the simplified in vitro P-gp substrate assay was suitable for compound screening in the early stages of drug discovery and that the in silico regression model and three-class classification model using only chemical structure information could identify the transport potential of compounds including P-gp-mediated flux ratios. Our proposed method is expected to be a practical tool to optimize effective central nervous system (CNS) drugs, to avoid CNS side effects, and to improve intestinal absorption.

A Structural View on Medicinal Chemistry Strategies against Drug Resistance

The natural phenomenon of drug resistance is a widespread issue that hampers the performance of drugs in many major clinical indications. Antibacterial and antifungal drugs are affected, as well as compounds for the treatment of cancer, viral infections, or parasitic diseases. Despite the very diverse set of biological targets and organisms involved in the development of drug resistance, the underlying molecular mechanisms have been identified to understand the emergence of resistance and to overcome this detrimental process. Detailed structural information on the root causes for drug resistance is nowadays frequently available, so next-generation drugs can be designed that are anticipated to suffer less from resistance. This knowledge-based approach is essential for fighting the inevitable occurrence of drug resistance.

ADMET-score - a comprehensive scoring function for evaluation of chemical drug-likeness

Chemical absorption, distribution, metabolism, excretion, and toxicity (ADMET), play key roles in drug discovery and development. A high-quality drug candidate should not only have sufficient efficacy against the therapeutic target, but also show appropriate ADMET properties at a therapeutic dose. A lot of in silico models are hence developed for prediction of chemical ADMET properties. However, it is still not easy to evaluate the drug-likeness of compounds in terms of so many ADMET properties. In this study, we proposed a scoring function named the ADMET-score to evaluate drug-likeness of a compound. The scoring function was defined on the basis of 18 ADMET properties predicted via our web server admetSAR. The weight of each property in the ADMET-score was determined by three parameters: the accuracy rate of the model, the importance of the endpoint in the process of pharmacokinetics, and the usefulness index. The FDA-approved drugs from DrugBank, the small molecules from ChEMBL and the old drugs withdrawn from the market due to safety concerns were used to evaluate the performance of the ADMET-score. The indices of the arithmetic mean and p-value showed that the ADMET-score among the three data sets differed significantly. Furthermore, we learned that there was no obvious linear correlation between the ADMET-score and QED (quantitative estimate of drug-likeness). These results suggested that the ADMET-score would be a comprehensive index to evaluate chemical drug-likeness, and might be helpful for users to select appropriate drug candidates for further development.

Ovarian hormones modulate multidrug resistance transporters in the ovary

BACKGROUND

Multidrug resistance transporters (MDRs) are transmembrane proteins that efflux metabolites and xenobiotics. They are highly conserved in sequence and function in bacteria and eukaryotes and play important roles in cellular homeostasis, as well as in avoidance of antibiotics and cancer therapies. Recent evidence also documents a critical role in reproductive health and in protecting the ovary from environmental toxicant effects. The most well understood MDRs are MDR-1 (P-glycoprotein (P-gp) also known as ABCB1) and BCRP (breast cancer resistance protein) and are both expressed in the ovary. We have previously shown that MDR-1 mRNA steady state expression changes throughout the murine estrous cycle, but expression appears to increase in association with the surge in estradiol during proestrus.

METHODS

Here we test the model that MDR-1 and BCRP are regulated by estrogen, the major hormonal product of the ovary. This was performed by administering 6-week-old female mice either sesame oil (vehicle control) or oral ethinyl estradiol at 1 μg, 10 μg, and 100 μg or PROGESTERONE at 0.25mg, 0.5 mg or 1 mg or a combination of both for 5 days. The mice were then sacrificed, and the ovaries were removed and cleaned. Ovaries were used for qPCR, immunoblotting, and immnunolabeling.

RESULTS

We found that oral ethinyl estradiol did not influence the steady state mRNA of MDR-1 or BCRP. Remarkably, the effect on mRNA levels neither increased or decreased in abundance upon estrogen exposures. Conversely, we observed less MDR-1 protein expression in the groups treated with 1 μg and 10 μg, but not 100 μg of ethinyl estradiol compared to controls. MDR-1 and BCRP are both expressed in pre-ovulatory follicles. When we tested progesterone, we found that MDR-1 mRNA increased at the dosages of 0.25 mg and 0.5 mg, but protein expression levels were not statistically significant. Combined oral ethinyl estradiol and progesterone significantly lowered both MDR-1 mRNA and protein.

CONCLUSIONS

Progesterone appears to influence MDR-1 transcript levels, or steady state levels. This could have implications for better understanding how MDR-1 can be modulated during times of toxic exposure. Understanding the normal physiology of MDR-1 in the ovary will expand the current knowledge in cancer biology and reproduction.

Boosted feature selectors: a case study on prediction P-gp inhibitors and substrates

Feature selection is commonly used as a preprocessing step to machine learning for improving learning performance, lowering computational complexity and facilitating model interpretation. This paper proposes the application of boosting feature selection to improve the classification performance of standard feature selection algorithms evaluated for the prediction of P-gp inhibitors and substrates. Two well-known classification algorithms, decision trees and support vector machines, were used to classify the chemical compounds. The experimental results showed better performance for boosting feature selection with respect to the standard feature selection algorithms while maintaining the capability for feature reduction.

Theoretical Prediction of the Complex P-Glycoprotein Substrate Efflux Based on the Novel Hierarchical Support Vector Regression Scheme

P-glycoprotein (P-gp), a membrane-bound transporter, can eliminate xenobiotics by transporting them out of the cells or blood⁻brain barrier (BBB) at the expense of ATP hydrolysis. Thus, P-gp mediated efflux plays a pivotal role in altering the absorption and disposition of a wide range of substrates. Nevertheless, the mechanism of P-gp substrate efflux is rather complex since it can take place through active transport and passive permeability in addition to multiple P-gp substrate binding sites. A nonlinear quantitative structure⁻activity relationship (QSAR) model was developed in this study using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to explore the perplexing relationships between descriptors and efflux ratio. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 50, r² = 0.96, qCV2 = 0.94, RMSE = 0.10, s = 0.10) and test set (n = 13, q² = 0.80⁻0.87, RMSE = 0.21, s = 0.22). When subjected to a variety of statistical validations, the developed HSVR model consistently met the most stringent criteria. A mock test also asserted the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

IMPPAT: A curated database of Indian Medicinal Plants, Phytochemistry And Therapeutics

Phytochemicals of medicinal plants encompass a diverse chemical space for drug discovery. India is rich with a flora of indigenous medicinal plants that have been used for centuries in traditional Indian medicine to treat human maladies. A comprehensive online database on the phytochemistry of Indian medicinal plants will enable computational approaches towards natural product based drug discovery. In this direction, we present, IMPPAT, a manually curated database of 1742 Indian Medicinal Plants, 9596 Phytochemicals, And 1124 Therapeutic uses spanning 27074 plant-phytochemical associations and 11514 plant-therapeutic associations. Notably, the curation effort led to a non-redundant in silico library of 9596 phytochemicals with standard chemical identifiers and structure information. Using cheminformatic approaches, we have computed the physicochemical, ADMET (absorption, distribution, metabolism, excretion, toxicity) and drug-likeliness properties of the IMPPAT phytochemicals. We show that the stereochemical complexity and shape complexity of IMPPAT phytochemicals differ from libraries of commercial compounds or diversity-oriented synthesis compounds while being similar to other libraries of natural products. Within IMPPAT, we have filtered a subset of 960 potential druggable phytochemicals, of which majority have no significant similarity to existing FDA approved drugs, and thus, rendering them as good candidates for prospective drugs. IMPPAT database is openly accessible at: https://cb.imsc.res.in/imppat.

Molecular structure of human P-glycoprotein in the ATP-bound, outward-facing conformation

The multidrug transporter permeability (P)-glycoprotein is an adenosine triphosphate (ATP)-binding cassette exporter responsible for clinical resistance to chemotherapy. P-glycoprotein extrudes toxic molecules and drugs from cells through ATP-powered conformational changes. Despite decades of effort, only the structures of the inward-facing conformation of P-glycoprotein are available. Here we present the structure of human P-glycoprotein in the outward-facing conformation, determined by cryo-electron microscopy at 3.4-angstrom resolution. The two nucleotide-binding domains form a closed dimer occluding two ATP molecules. The drug-binding cavity observed in the inward-facing structures is reorientated toward the extracellular space and compressed to preclude substrate binding. This observation indicates that ATP binding, not hydrolysis, promotes substrate release. The structure evokes a model in which the dynamic nature of P-glycoprotein enables translocation of a large variety of substrates.

A novel adaptive ensemble classification framework for ADME prediction

AECF is a GA based ensemble method. It includes four components which are (1) data balancing, (2) generating individual models, (3) combining individual models, and (4) optimizing the ensemble.

In silico Study of the Pharmacologic Properties and Cytotoxicity Pathways in Cancer Cells of Various Indolylquinone Analogues of Perezone

Several indolylquinone analogues of perezone, a natural sesquiterpene quinone, were characterized in this work by theoretical methods. In addition, some physicochemical, toxicological and metabolic properties were predicted using bioinformatics software. The predicted physicochemical properties are in agreement with the solubility and cLogP values, the penetration across the cell membrane, and absorption values, as well as with a possible apoptosis-activated mechanism of cytotoxic action. The toxicological predictions suggest no mutagenic, tumorigenic or reproductive effects of the four target molecules. Complementarily, the results of a performed docking study show high scoring values and hydrogen bonding values in agreement with the cytotoxicity IC50 value ranking, i.e: indolylmenadione > indolylperezone > indolylplumbagine > indolylisoperezone. Consequently, it is possible to suggest an appropriate apoptotic pathway for each compound. Finally, potential metabolic pathways of the molecules were proposed.

Implicating ABC Transporters in Insecticide Resistance: Research Strategies and a Decision Framework

Pest insects damage crops, transmit diseases, and are household nuisances. Historically, they have been controlled with insecticides, but overuse often leads to resistance to one or more of these chemicals. Insects gain resistance to insecticides through behavioral, metabolic, genetic, and physical mechanisms. One frequently overlooked strategy is through the use of ATP-binding cassette (ABC) transporters. ABC transporters, present in all domains of life, perform natural excretory functions, thus the exploitation of these transporters to excrete insecticides and contribute to resistance is highly plausible. Previous work has implicated ABC transporters in some cases of insecticide resistance. Proposed herein is a framework meant as a formal guide for more easily incorporating the analysis of ABC transporters into existing resistance monitoring using suggested simple research methods. This framework functions as a simple decision tree and its utility is demonstrated using case examples. Determining a role for ABC transporters in insecticide resistance would help to shape future resistance management plans and guide the design of new insecticides.

SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules

To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours.

Inhibition of CDK4 sensitizes multidrug resistant ovarian cancer cells to paclitaxel by increasing apoptosiss

PURPOSE

Overexpression of cyclin-dependent kinase (CDK) 4 has been observed in a variety of cancers and has been found to contribute to tumor cell growth and proliferation. However, the effect of inhibition of CDK4 in ovarian cancer is unknown. We investigated the therapeutic effect of the CDK4 inhibitor palbociclib in combination with paclitaxel in ovarian cancer cells.

METHODS

Cell viabilities were determined by MTT assay after exposure to different dosages of palbociclib and/or paclitaxel. Western blot, immunofluorescence, and Calcein AM assays were conducted to determine the mechanisms underlying the cytotoxic effects of palbociclib in combination with paclitaxel. CDK4 siRNA was used to validate the outcome of targeting CDK4 by palbociclib in ovarian cancer cells.

RESULTS

We found that combinations of palbociclib and paclitaxel significantly enhanced drug sensitivity in both Rb-positive (SKOV3TR) and Rb-negative (OVCAR8TR) ovarian cancer-derived cells. When combined with paclitaxel, palbociclib induced apoptosis in both SKOV3TR and OVCAR8TR cells. We also found that palbociclib inhibited the activity of P-glycoprotein (Pgp), and that siRNA-mediated CDK4 knockdown sensitized multidrug resistant (MDR) SKOV3TR and OVCAR8TR cells to paclitaxel.

CONCLUSIONS

Inhibition of CDK4 by palbociclib can enhance paclitaxel sensitivity in both Rb-positive and Rb-negative MDR ovarian cancer cells by increasing apoptosis. CDK4 may serve as a promising target in the treatment of ovarian cancer.

Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors

P-glycoprotein (Pgp) inhibition has been considered as an effective strategy towards combating multidrug-resistant cancers. Owing to the substrate promiscuity of Pgp, the classification of its interacting ligands is not an easy task and is an ongoing issue of debate. Chemical structures can be represented by the simplified molecular input line entry system (SMILES) in the form of linear string of symbols. In this study, the SMILES notations of 2254 Pgp inhibitors including 1341 active, and 913 inactive compounds were used for the construction of a SMILE-based classification model using CORrelation And Logic (CORAL) software. The model provided an acceptable predictive performance as observed from statistical parameters consisting of accuracy, sensitivity and specificity that afforded values greater than 70% and MCC value greater than 0.6 for training, calibration and validation sets. In addition, the CORAL method highlighted chemical features that may contribute to increased and decreased Pgp inhibitory activities. This study highlights the potential of CORAL software for rapid screening of prospective compounds from a large chemical space and provides information that could aid in the design and development of potential Pgp inhibitors.

Molecular Properties of Drugs Interacting with SLC22 Transporters OAT1, OAT3, OCT1, and OCT2: A Machine-Learning Approach

Statistical analysis was performed on physicochemical descriptors of ∼250 drugs known to interact with one or more SLC22 "drug" transporters (i.e., SLC22A6 or OAT1, SLC22A8 or OAT3, SLC22A1 or OCT1, and SLC22A2 or OCT2), followed by application of machine-learning methods and wet laboratory testing of novel predictions. In addition to molecular charge, organic anion transporters (OATs) were found to prefer interacting with planar structures, whereas organic cation transporters (OCTs) interact with more three-dimensional structures (i.e., greater SP3 character). Moreover, compared with OAT1 ligands, OAT3 ligands possess more acyclic tetravalent bonds and have a more zwitterionic/cationic character. In contrast, OCT1 and OCT2 ligands were not clearly distinquishable form one another by the methods employed. Multiple pharmacophore models were generated on the basis of the drugs and, consistent with the machine-learning analyses, one unique pharmacophore created from ligands of OAT3 possessed cationic properties similar to OCT ligands; this was confirmed by quantitative atomic property field analysis. Virtual screening with this pharmacophore, followed by transport assays, identified several cationic drugs that selectively interact with OAT3 but not OAT1. Although the present analysis may be somewhat limited by the need to rely largely on inhibition data for modeling, wet laboratory/in vitro transport studies, as well as analysis of drug/metabolite handling in Oat and Oct knockout animals, support the general validity of the approach-which can also be applied to other SLC and ATP binding cassette drug transporters. This may make it possible to predict the molecular properties of a drug or metabolite necessary for interaction with the transporter(s), thereby enabling better prediction of drug-drug interactions and drug-metabolite interactions. Furthermore, understanding the overlapping specificities of OATs and OCTs in the context of dynamic transporter tissue expression patterns should help predict net flux in a particular tissue of anionic, cationic, and zwitterionic molecules in normal and pathophysiological states.

Development of a Support Vector Machine-Based System to Predict Whether a Compound Is a Substrate of a Given Drug Transporter Using Its Chemical Structure

The aim of this study was to develop an in silico prediction system to assess which of 7 categories of drug transporters (organic anion transporting polypeptide [OATP] 1B1/1B3, multidrug resistance-associated protein [MRP] 2/3/4, organic anion transporter [OAT] 1, OAT3, organic cation transporter [OCT] 1/2/multidrug and toxin extrusion [MATE] 1/2-K, multidrug resistance protein 1 [MDR1], and breast cancer resistance protein [BCRP]) can recognize compounds as substrates using its chemical structure alone. We compiled an internal data set consisting of 260 compounds that are substrates for at least 1 of the 7 categories of drug transporters. Four physicochemical parameters (charge, molecular weight, lipophilicity, and plasma unbound fraction) of each compound were used as the basic descriptors. Furthermore, a greedy algorithm was used to select 3 additional physicochemical descriptors from 731 available descriptors. In addition, transporter nonsubstrates tend not to be in the public domain; we, thus, tried to compile an expert-curated data set of putative nonsubstrates for each transporter using personal opinions of 11 researchers in the field of drug transporters. The best prediction was finally achieved by a support vector machine based on 4 basic and 3 additional descriptors. The model correctly judged that 364 of 412 compounds (internal data set) and 111 of 136 compounds (external data set) were substrates, indicating that this model performs well enough to predict the specificity of transporter substrates.

Cell permeability beyond the rule of 5

Drug discovery for difficult targets that have large and flat binding sites is often better suited to compounds beyond the "rule of 5" (bRo5). However, such compounds carry higher pharmacokinetic risks, such as low solubility and permeability, and increased efflux and metabolism. Interestingly, recent drug approvals and studies suggest that cell permeable and orally bioavailable drugs can be discovered far into bRo5 space. Tactics such as reduction or shielding of polarity by N-methylation, bulky side chains and intramolecular hydrogen bonds may be used to increase cell permeability in this space, but often results in decreased solubility. Conformationally flexible compounds can, however, combine high permeability and solubility, properties that are keys for cell permeability and intestinal absorption. Recent developments in computational conformational analysis will aid design of such compounds and hence prediction of cell permeability. Transporter mediated efflux occurs for most investigated drugs in bRo5 space, however it is commonly overcome by high local intestinal concentrations on oral administration. In contrast, there is little data to support significant impact of transporter-mediated intestinal absorption in bRo5 space. Current knowledge of compound properties that govern transporter effects of bRo5 drugs is limited and requires further fundamental and comprehensive studies.

Pharmacokinetics and tolerability of NSC23925b, a novel P-glycoprotein inhibitor: preclinical study in mice and rats

Overexpression of P-glycoprotein (Pgp) increases multidrug resistance (MDR) in cancer, which greatly impedes satisfactory clinical treatment and outcomes of cancer patients. Due to unknown pharmacokinetics, the use of Pgp inhibitors to overcome MDR in the clinical setting remains elusive despite promising in vitro results. The purpose of our current preclinical study is to investigate the pharmacokinetics and tolerability of NSC23925b, a novel and potent P-glycoprotein inhibitor, in rodents. Plasma pharmacokinetic studies of single-dose NSC23925b alone or in combination with paclitaxel or doxorubicin were conducted in male BALB/c mice and Sprague-Dawley rats. Additionally, inhibition of human cytochrome P450 (CYP450) by NSC23925b was examined in vitro. Finally, the maximum tolerated dose (MTD) of NSC23925b was determined. NSC23925b displayed favorable pharmacokinetic profiles after intraperitoneal/intravenous (I.P./I.V.) injection alone or combined with chemotherapeutic drugs. The plasma pharmacokinetic characteristics of the chemotherapy drugs were not affected when co-administered with NSC23925b. All the animals tolerated the I.P./I.V. administration of NSC23925b. Moreover, the enzymatic activity of human CYP450 was not inhibited by NSC23925b. Our results demonstrated that Pgp inhibitor NSC23925b exhibits encouraging preclinical pharmacokinetic characteristics and limited toxicity in vivo. NSC23925b has the potential to treat cancer patients with MDR in the future.

In silico approach for the discovery of new PPARγ modulators among plant-derived polyphenols

Peroxisome proliferator-activated receptor gamma (PPARγ) is a well-characterized member of the PPAR family that is predominantly expressed in adipose tissue and plays a significant role in lipid metabolism, adipogenesis, glucose homeostasis, and insulin sensitization. Full agonists of synthetic thiazolidinediones (TZDs) have been therapeutically used in clinical practice to treat type 2 diabetes for many years. Although it can effectively lower blood glucose levels and improve insulin sensitivity, the administration of TZDs has been associated with severe side effects. Based on recent evidence obtained with plant-derived polyphenols, the present in silico study aimed at finding new selective human PPARγ (hPPARγ) modulators that are able to improve glucose homeostasis with reduced side effects compared with TZDs. Docking experiments have been used to select compounds with strong binding affinity (ΔG values ranging from −10.0±0.9 to −11.4±0.9 kcal/mol) by docking against the binding site of several X-ray structures of hPPARγ. These putative modulators present several molecular interactions with the binding site of the protein. Additionally, most of the selected compounds have favorable druggability and good ADMET properties. These results aim to pave the way for further bench-scale analysis for the discovery of new modulators of hPPARγ that do not induce any side effects.

Cheminformatics Research at the Unilever Centre for Molecular Science Informatics Cambridge

The Centre for Molecular Informatics, formerly Unilever Centre for Molecular Science Informatics (UCMSI), at the University of Cambridge is a world-leading driving force in the field of cheminformatics. Since its opening in 2000 more than 300 scientific articles have fundamentally changed the field of molecular informatics. The Centre has been a key player in promoting open chemical data and semantic access. Though mainly focussing on basic research, close collaborations with industrial partners ensured real world feedback and access to high quality molecular data. A variety of tools and standard protocols have been developed and are ubiquitous in the daily practice of cheminformatics. Here, we present a retrospective of cheminformatics research performed at the UCMSI, thereby highlighting historical and recent trends in the field as well as indicating future directions.

Classification of P-glycoprotein-interacting compounds using machine learning methods

P-glycoprotein (Pgp) is a drug transporter that plays important roles in multidrug resistance and drug pharmacokinetics. The inhibition of Pgp has become a notable strategy for combating multidrug-resistant cancers and improving therapeutic outcomes. However, the polyspecific nature of Pgp, together with inconsistent results in experimental assays, renders the determination of endpoints for Pgp-interacting compounds a great challenge. In this study, the classification of a large set of 2,477 Pgp-interacting compounds (i.e., 1341 inhibitors, 913 non-inhibitors, 197 substrates and 26 non-substrates) was performed using several machine learning methods (i.e., decision tree induction, artificial neural network modelling and support vector machine) as a function of their physicochemical properties. The models provided good predictive performance, producing MCC values in the range of 0.739-1 for internal cross-validation and 0.665-1 for external validation. The study provided simple and interpretable models for important properties that influence the activity of Pgp-interacting compounds, which are potentially beneficial for screening and rational design of Pgp inhibitors that are of clinical importance.

Prediction of drug-ABC-transporter interaction--Recent advances and future challenges

With the discovery of P-glycoprotein (P-gp), it became evident that ABC-transporters play a vital role in bioavailability and toxicity of drugs. They prevent intracellular accumulation of toxic compounds, which renders them a major defense mechanism against xenotoxic compounds. Their expression in cells of all major barriers (intestine, blood–brain barrier, blood–placenta barrier) as well as in metabolic organs (liver, kidney) also explains their influence on the ADMET properties of drugs and drug candidates. Thus, in silico models for the prediction of the probability of a compound to interact with P-gp or analogous transporters are of high value in the early phase of the drug discovery process. Within this review, we highlight recent developments in the area, with a special focus on the molecular basis of drug–transporter interaction. In addition, with the recent availability of X-ray structures of several ABC-transporters, also structure-based design methods have been applied and will be addressed.

Influence of multidrug resistance and drug transport proteins on chemotherapy drug metabolism

INTRODUCTION

Chemotherapy involving the use of anticancer drugs remains an important strategy in the overall management of patients with metastatic cancer. Acquisition of multidrug resistance remains a major impediment to successful chemotherapy. Drug transporters in cell membranes and intracellular drug metabolizing enzymes contribute to the resistance phenotype and determine the pharmacokinetics of anticancer drugs in the body.

AREAS COVERED

ATP-binding cassette (ABC) transporters mediate the transport of endogenous metabolites and xenobiotics including cytotoxic drugs out of cells. Solute carrier (SLC) transporters mediate the influx of cytotoxic drugs into cells. This review focuses on the substrate interaction of these transporters, on their biology and what role they play together with drug metabolizing enzymes in eliminating therapeutic drugs from cells.

EXPERT OPINION

The majority of anticancer drugs are substrates for the ABC transporter and SLC transporter families. Together, these proteins have the ability to control the influx and the efflux of structurally unrelated chemotherapeutic drugs, thereby modulating the intracellular drug concentration. These interactions have important clinical implications for chemotherapy because ultimately they determine therapeutic efficacy, disease progression/relapse and the success or failure of patient treatment.

A highly tumor-targeted nanoparticle of podophyllotoxin penetrated tumor core and regressed multidrug resistant tumors

Podophyllotoxin (PPT) exhibited significant activity against P-glycoprotein mediated multidrug resistant (MDR) tumor cell lines; however, due to its poor solubility and high toxicity, PPT cannot be dosed systemically, preventing its clinical use for MDR cancer. We developed a nanoparticle dosage form of PPT by covalently conjugating PPT and polyethylene glycol (PEG) with acetylated carboxymethyl cellulose (CMC-Ac) using one-pot esterification chemistry. The polymer conjugates self-assembled into nanoparticles (NPs) of variable sizes (20-120 nm) depending on the PPT-to-PEG molar ratio (2-20). The conjugate with a low PPT/PEG molar ratio of 2 yielded NPs with a mean diameter of 20 nm and released PPT at ∼5%/day in serum, while conjugates with increased PPT/PEG ratios (5 and 20) produced bigger particles (30 nm and 120 nm respectively) that displayed slower drug release (∼2.5%/day and ∼1%/day respectively). The 20 nm particles exhibited 2- to 5-fold enhanced cell killing potency and 5- to 20-fold increased tumor delivery compared to the larger NPs. The biodistribution of the 20 nm PPT-NPs was highly selective to the tumor with 8-fold higher accumulation than all other examined tissues, while the larger PPT-NPs (30 and 120 nm) exhibited increased liver uptake. Within the tumor, >90% of the 20 nm PPT-NPs penetrated to the hypovascular core, while the larger particles were largely restricted in the hypervascular periphery. The 20 nm PPT-NPs displayed significantly improved efficacy against MDR tumors in mice compared to the larger PPT-NPs, native PPT and the standard taxane chemotherapies, with minimal toxicity.

Homology modelling and molecular docking of MDR1 with chemotherapeutic agents in non-small cell lung cancer

MDR1, a protein commonly involved in drug transport, has been linked to multi drug resistance and disease progression in cancers such as non-small cell lung cancer. Hence, targeting this protein is essential for improving drug design and preventing adverse drug-drug interactions. The aim of the study was to examine chemotherapeutic drug binding to MDR1 and the interactions therein. We have used Schrödinger suite 2014, to perform homology modelling of human MDR1 based on Mouse MDR1, followed by Induced Fit Docking with Paclitaxel, Docetaxel, Gemcitabine, Carboplatin and Cisplatin drugs. Finally, we evaluated drug binding affinities using Prime/MMGBSA and using these scores we compared the affinities of combination therapies against MDR1. Analysis of the docking results showed Paclitaxel>Docetaxel>Gemcitabine>Carboplatin>Cisplatin as the order of binding affinities, with Paclitaxel having the best docking score. The combination drug binding affinity analysis showed Paclitaxel+Gemcitabine to have the best docking score and hence, efficacy. Through our investigation we have identified the residues Gln 195 and Gln 946 to be more frequently involved in drug binding interactions with MDR1. Our results suggest that, Paclitaxel or combination of Paclitaxel+Gemcitabine could serve as a suitable therapy against MDR1 in NSCLC patients. Thus, our study provides new insight into the possible repurposing of chemotherapeutic drugs in targeting elevated MDR1 levels in NSCLC patients, thereby ensuring better overall outcome. Further our study highlights the use of in silico methodologies in understanding drug binding to protein targets and its relevance to advancing lung cancer therapy.