Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: EC:3.6.3.44 (P-glycoprotein)
 13,344 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Estimation of bioavailability and toxicity at the very beginning of the drug development process is one of the big challenges in drug discovery. Most of the processes involved in ADME are driven by rather unspecific interactions between drugs and biological macromolecules. Within the past decade, drug transport pumps such as P-glycoprotein (Pgp) have gained increasing interest in the early ADME profiling process. Due to the high structural diversity of ligands of Pgp, traditional QSAR methods were only successful within analogous series of compounds. We used an approach based on similarity calculations to predict Pgp-inhibitory activity of a series of propafenone analogues. This SIBAR approach is based on selection of a highly diverse reference compound set and calculation of similarity values to these reference compounds. The similarity values (denoted as SIBAR descriptors) are then used for PLS analysis. Our results show, that for a set of 131 propafenone type compounds, models with good predictivity were obtained both in cross validation procedures and with a 31-compound external test set. Thus, these new descriptors might be a versatile tool for generation of predictive ADME models.
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PMID:Similarity based SAR (SIBAR) as tool for early ADME profiling. 1282 90

The ability to permeate across the blood brain barrier (BBB) is essential for drugs acting on the central nervous system (CNS). Thus, for speeding up the drug discovery process in the CNS-area, it is of great importance to develop systems that allow rapid and inexpensive screening of the BBB-permeability properties of novel lead compounds or at least small subsets of combinatorial CNS-libraries. In this field, in silico prediction methods gain increasing importance. Starting with simple regression models based on calculation of lipophilicity and polar surface area, the field developed via PLS methods to grid based approaches (e.g. VolSurf). Additionally, the use of artificial neural networks gain increasing importance. However, permeation through the BBB is also influenced by active transport systems. For nutrients and endogenous compounds, such as amino acids, monocarboxylic acids, amines, hexoses, thyroid hormones, purine bases and nucleosides, several transport systems regulating the entry of the respective compound classes into the brain have been identified. The other way round there is striking evidence that expression of active efflux pumps like the multidrug transporter P-glycoprotein (P-gp) on the luminal membrane of the brain capillary endothelial cells accounts for poor BBB permeability of certain drugs. Undoubtedly, P-gp is an important impediment for the entry of hydrophobic drugs into the brain. Thus, proper prediction models should also take into account the active transport phenomena.
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PMID:In silico prediction models for blood-brain barrier permeation. 1518 May 67

Trying to understand the complex interactions that substrates and inhibitors have with the efflux transporter P-glycoprotein has been the subject of various publications. In this work, we have confined our study to substrates by picking a diverse set of 129 compounds based on the efflux ratios from Caco-2 permeability measurements. These compounds were then evaluated for P-glycoprotein inhibition using a calcein-AM assay. The subsequent data was used in a 3D-QSAR analysis using GRIND pharmacophore-based and physicochemical descriptors. Pharmacophore-based descriptors produced a much more robust model than the one obtained from physicochemical-based descriptors. This supports the process proposed by Seelig and co-workers previously published whereby the substrate enters the membrane as the first step and is then recognized by P-glycoprotein in a second step. The strong correlation, highlighted by PLS statistical analysis, between pharmacophoric descriptors and inhibition values suggests that substrate interaction, with perhaps the mouth of the protein or another binding site, plays a key role in the efflux process, yielding a model in which diffusion across the membrane is less important than substrate-protein interaction. One pharmacophore emerged from the analysis of the model. We pose that the recognition elements, at least determined by the molecules used in this study, are two hydrophobic groups 16.5 A apart and two hydrogen-bond-acceptor groups 11.5 A apart and that the dimensions of the molecule also plays a role in its recognition as a substrate.
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PMID:A pharmacophore hypothesis for P-glycoprotein substrate recognition using GRIND-based 3D-QSAR. 1582 31

Orally administered drugs must overcome several barriers before reaching their target site. Such barriers depend largely upon specific membrane transport systems and intracellular drug-metabolizing enzymes. For the first time, the P-glycoprotein (P-gp) and cytochrome P450s, the main line of defense by limiting the oral bioavailability (OB) of drugs, were brought into construction of QSAR modeling for human OB based on 805 structurally diverse drug and drug-like molecules. The linear (multiple linear regression: MLR, and partial least squares regression: PLS) and nonlinear (support-vector machine regression: SVR) methods are used to construct the models with their predictivity verified with five-fold cross-validation and independent external tests. The performance of SVR is slightly better than that of MLR and PLS, as indicated by its determination coefficient (R(2)) of 0.80 and standard error of estimate (SEE) of 0.31 for test sets. For the MLR and PLS, they are relatively weak, showing prediction abilities of 0.60 and 0.64 for the training set with SEE of 0.40 and 0.31, respectively. Our study indicates that the MLR, PLS and SVR-based in silico models have good potential in facilitating the prediction of oral bioavailability and can be applied in future drug design.
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PMID:A novel chemometric method for the prediction of human oral bioavailability. 2283 74