Exploration of the binding of proton pump inhibitors to human P450 2C9 based on docking and molecular dynamics simulation

Artificial neural network cascade identifies multi-P450 inhibitors in natural compounds

Substantial evidence has shown that most exogenous substances are metabolized by multiple cytochrome P450 (P450) enzymes instead of by merely one P450 isoform. Thus, multi-P450 inhibition leads to greater drug-drug interaction risk than specific P450 inhibition. Herein, we innovatively established an artificial neural network cascade (NNC) model composed of 23 cascaded networks in a ladder-like framework to identify potential multi-P450 inhibitors among natural compounds by integrating 12 molecular descriptors into a P450 inhibition score (PIS). Experimental data reporting in vitro inhibition of five P450 isoforms (CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4) were obtained for 8,148 compounds from the Cytochrome P450 Inhibitors Database (CPID). The results indicate significant positive correlation between the PIS values and the number of inhibited P450 isoforms (Spearman’s ρ = 0.684, p < 0.0001). Thus, a higher PIS indicates a greater possibility for a chemical to inhibit the enzyme activity of at least three P450 isoforms. Ten-fold cross-validation of the NNC model suggested an accuracy of 78.7% for identifying whether a compound is a multi-P450 inhibitor or not. Using our NNC model, 22.2% of the approximately 160,000 natural compounds in TCM Database@Taiwan were identified as potential multi-P450 inhibitors. Furthermore, chemical similarity calculations suggested that the prevailing parent structures of natural multi-P450 inhibitors were alkaloids. Our findings show that dissection of chemical structure contributes to confident identification of natural multi-P450 inhibitors and provides a feasible method for virtually evaluating multi-P450 inhibition risk for a known structure.

Influence of proton pump inhibitors and VKORC1 mutations on CYP2C9-mediated dose requirements of vitamin K antagonist therapy: a pilot study

Interindividual variations in dose requirements of oral vitamin K antagonists (VKA) are attributed to several factors, including genetic variant alleles of vitamin K epoxide reductase complex subunit 1 (VKORC1) and cytochrome P450 2C9 (CYP2C9), but also interaction with co-medications. In this context, proton pump inhibitor (PPI)-related alterations of VKA maintenance dose requirements have been published. The present investigation aimed to test for an interaction profile of oral VKA-therapy and PPIs in relation to the CYP2C9 genotype. Median weekly stable VKA dose requirements over 1 year were recorded in 69 patients. Patients were genotyped for CYP2C9*2, CYP2C9*3, VKORC1c.-1639G>A and VKORC1c.174-136C>T and assessed for an association with PPI use and total VKA maintenance dose requirements. PPI users with CYP2C9 genetic variations required significantly lower weekly VKA maintenance doses than those with the wild-type genotype (t-test: P = 0·02). In contrast, in subjects without PPI use, the CYP2C9 genotype had no significant influence on oral VKA dose requirements. Further, the combined CYP2C9/VKORC1 genotype was a significant predictor for VKA dose requirements [linear regression: estimate: -1·47, standard error: 0·58 (P = 0·01)]. In conclusion, in carriers of CYP2C9 gene variations, the interference with the VKA metabolism is modified by PPI co-medication and the VCKORC1 genotype. Preceding knowledge of the genetic profile and the awareness for potentially occurring severe over-anticoagulation problems under PPI co-medication could contribute to a safer and personalized VKA pharmacotherapy.

Human cytochrome P450 17A1 conformational selection: modulation by ligand and cytochrome b5

Crystallographic studies of different membrane cytochrome P450 enzymes have provided examples of distinct structural conformations, suggesting protein flexibility. It has been speculated that conformational selection is an integral component of substrate recognition and access, but direct evidence of such substate interconversion has thus far remained elusive. In the current study, solution NMR revealed multiple and exchanging backbone conformations for certain structural features of the human steroidogenic cytochrome P450 17A1 (CYP17A1). This bifunctional enzyme is responsible for pregnenolone C17 hydroxylation, followed by a 17,20-lyase reaction to produce dehydroepiandrosterone, the key intermediate in human synthesis of androgen and estrogen sex steroids. The distribution of CYP17A1 conformational states was influenced by temperature, binding of these two substrates, and binding of the soluble domain of cytochrome b5 (b5). Notably, titration of b5 to CYP17A1·pregnenolone induced a set of conformational states closely resembling those of CYP17A1·17α-hydroxypregnenolone without b5, providing structural evidence consistent with the reported ability of b5 to selectively enhance 17,20-lyase activity. Solution NMR thus revealed a set of conformations likely to modulate human steroidogenesis by CYP17A1, demonstrating that this approach has the potential to make similar contributions to understanding the functions of other membrane P450 enzymes involved in drug metabolism and disease states.

Structure-based ligand design to overcome CYP inhibition in drug discovery projects

Cytochrome P450 (CYP) enzymes are key players in xenobiotic metabolism, and inhibition of CYPs can therefore result in unwanted drug-drug interactions. Within drug discovery, CYP inhibition can cause delays in the progression of candidate drugs, or even premature closure of projects. During the past decade, a massive effort in the pharmaceutical industry and academic research has produced a wealth of structural information in the CYP field. In this short review, we will describe how structure-based approaches can be used in the pharmaceutical industry to work away from CYP inhibition, with a focus on the opportunities and challenges. We will show two examples from our own work where structural information on CYP2C9 and CYP3A4 inhibitor complexes have been successfully exploited in ongoing drug discovery projects.

Toward in silico structure-based ADMET prediction in drug discovery

Quantitative structure-activity relationship (QSAR) methods and related approaches have been used to investigate the molecular features that influence the absorption, distribution, metabolism, excretion and toxicity (ADMET) of drugs. As the three-dimensional structures of several major ADMET proteins become available, structure-based (docking-scoring) computations can be carried out to complement or to go beyond QSAR studies. Applying docking-scoring methods to ADMET proteins is a challenging process because they usually have a large and flexible binding cavity; however, promising results relating to metabolizing enzymes have been reported. After reviewing current trends in the field we applied structure-based methods in the context of receptor flexibility in a case study involving the phase II metabolizing sulfotransferases. Overall, the explored concepts and results suggested that structure-based ADMET profiling will probably join the mainstream during the coming years.