Fingerprint-based in silico models for the prediction of P-glycoprotein substrates and inhibitors

AbstractP-Glycoprotein (P-gp, ABCB1) plays a significant role in determining the ADMET properties of drugs and drug candidates. Substrates of P-gp are not only subject to multidrug resistance (MDR) in tumor therapy, they are also associated with poor pharmacokinetic profiles. In contrast, inhibitors of P-gp have been advocated as modulators of MDR. However, due to the polyspecificity of P-gp, knowledge on the molecular basis of ligand–transporter interaction is still poor, which renders the prediction of whether a compound is a P-gp substrate/non-substrate or an inhibitor/non-inhibitor quite challenging. In the present investigation, we used a set of fingerprints representing the presence/absence of various functional groups for machine learning based classification of a set of 484 substrates/non-substrates and a set of 1935 inhibitors/non-inhibitors. Best models were obtained using a combination of a wrapper subset evaluator (WSE) with random forest (RF), kappa nearest neighbor (kNN) and support vector machine (SVM), showing accuracies >70%. Best P-gp substrate models were further validated with three sets of external P-gp substrate sources, which include Drug Bank (n=134), TP Search (n=90) and a set compiled from literature (n=76). Association rule analysis explores the various structural feature requirements for P-gp substrates and inhibitors

Selectivity profiling of BCRP versus P-gp inhibition: from automated collection of polypharmacology data to multi-label learning

Additional file 1. The list of descriptor names, instructions on how to run the python script, the distribution plots for the important descriptors, the heat map of activities for the dense dataset, the structure of the over-represented scaffolds in the sparse dataset, a 2D representation of a PCA run on Morgan fingerprints (ECFP-like) for both dense and sparse datasets, and the structures of the 9 misclassified compounds

K → πππγ in chiral perturbation theory

We present a complete analysis K → 3ππγ decays to Open image in new window(p4) in the low-energy expansion of the Standard Model. We employ the notion of "generalized bremsstrahlung" to take full advantage of experimental information on the corresponding non-radiative K → 3π decay

The high-affinity binding site for tricyclic antidepressants resides in the outer vestibule of the serotonin transporter

The structure of the bacterial leucine transporter from Aquifex aeolicus (LeuT(Aa)) has been used as a model for mammalian Na+/Cl--dependent transporters, in particular the serotonin transporter (SERT). The crystal structure of LeuT(Aa) liganded to tricyclic antidepressants predicts simultaneous binding of inhibitor and substrate. This is incompatible with the mutually competitive inhibition of substrates and inhibitors of SERT. We explored the binding modes of tricyclic antidepressants by homology modeling and docking studies. Two approaches were used subsequently to differentiate between three clusters of potential docking poses: 1) a diagnostic SERTY95F mutation, which greatly reduced the affinity for [H-3] imipramine but did not affect substrate binding; 2) competition binding experiments in the presence and absence of carbamazepine (i.e., a tricyclic imipramine analog with a short side chain that competes with [3H] imipramine binding to SERT). Binding of releasers (para-chloroamphetamine, methylene-dioxy-methamphetamine/ecstasy) and of carbamazepine were mutually exclusive, but Dixon plots generated in the presence of carbamazepine yielded intersecting lines for serotonin, MPP+, paroxetine, and ibogaine. These observations are consistent with a model, in which 1) the tricyclic ring is docked into the outer vestibule and the dimethyl-aminopropyl side chain points to the substrate binding site; 2) binding of amphetamines creates a structural change in the inner and outer vestibule that precludes docking of the tricyclic ring; 3) simultaneous binding of ibogaine (which binds to the inward-facing conformation) and of carbamazepine is indicative of a second binding site in the inner vestibule, consistent with the pseudosymmetric fold of monoamine transporters. This may be the second low-affinity binding site for antidepressants