Ligand-based design of a potent and selective inhibitor of cytochrome P450 2C19

Deep mutational scanning of CYP2C19 in human cells reveals a substrate specificity-abundance tradeoff

The cytochrome P450s enzyme family metabolizes ∼80% of small molecule drugs. Variants in cytochrome P450s can substantially alter drug metabolism, leading to improper dosing and severe adverse drug reactions. Due to low sequence conservation, predicting variant effects across cytochrome P450s is challenging. Even closely related cytochrome P450s like CYP2C9 and CYP2C19, which share 92% amino acid sequence identity, display distinct phenotypic properties. Using variant abundance by massively parallel sequencing, we measured the steady-state protein abundance of 7,660 single amino acid variants in CYP2C19 expressed in cultured human cells. Our findings confirmed critical positions and structural features essential for cytochrome P450 function, and revealed how variants at conserved positions influence abundance. We jointly analyzed 4,670 variants whose abundance was measured in both CYP2C19 and CYP2C9, finding that the homologs have different variant abundances in substrate recognition sites within the hydrophobic core. We also measured the abundance of all single and some multiple wild type amino acid exchanges between CYP2C19 and CYP2C9. While most exchanges had no effect, substitutions in substrate recognition site 4 reduced abundance in CYP2C19. Double and triple mutants showed distinct interactions, highlighting a region that points to differing thermodynamic properties between the 2 homologs. These positions are known contributors to substrate specificity, suggesting an evolutionary tradeoff between stability and enzymatic function. Finally, we analyzed 368 previously unannotated human variants, finding that 43% had decreased abundance. By comparing variant effects between these homologs, we uncovered regions underlying their functional differences, advancing our understanding of this versatile family of enzymes.

Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs

Roughly 2.8% of annual hospitalizations are a result of adverse drug interactions in the United States, representing more than 245,000 hospitalizations. Drug-drug interactions commonly arise from major cytochrome P450 (CYP) inhibition. Various approaches are routinely employed in order to reduce the incidence of adverse interactions, such as altering drug dosing schemes and/or minimizing the number of drugs prescribed; however, often, a reduction in the number of medications cannot be achieved without impacting therapeutic outcomes. Nearly 80% of drugs fail in development due to pharmacokinetic issues, outlining the importance of examining cytochrome interactions during preclinical drug design. In this review, we examined the physiochemical and structural properties of small molecule inhibitors of CYPs 3A4, 2D6, 2C19, 2C9, and 1A2. Although CYP inhibitors tend to have distinct physiochemical properties and structural features, these descriptors alone are insufficient to predict major cytochrome inhibition probability and affinity. Machine learning based in silico approaches may be employed as a more robust and accurate way of predicting CYP inhibition. These various approaches are highlighted in the review.

Orphan Receptor GPR88 as an Emerging Neurotherapeutic Target

Although G protein-coupled receptors (GPCRs) are recognized as pivotal drug targets involved in multiple physiological and pathological processes, the majority of GPCRs including orphan GPCRs (oGPCRs) are unexploited. GPR88, a brain-specific oGPCR with particularly robust expression in the striatum, regulates diverse brain and behavioral functions, including cognition, mood, movement control, and reward-based learning, and is thus emerging as a novel drug target for central nervous system disorders including schizophrenia, Parkinson's disease, anxiety, and addiction. Nevertheless, no effective GPR88 synthetic ligands have yet entered into clinical trials, and GPR88 endogenous ligands remain unknown. Despite the recent discovery and early stage study of several GPR88 agonists, such as 2-PCCA, RTI-13951-33, and phenylglycinol derivatives, further research into GPR88 pharmacology, medicinal chemistry, and chemical biology is urgently needed to yield structurally diversified GPR88-specific ligands. Drug-like pharmacological tool function and relevant signaling elucidation will also accelerate the evaluation of this receptor as a viable neurotherapeutic target.

Enzyme Kinetics and Molecular Docking Studies on Cytochrome 2B6, 2C19, 2E1, and 3A4 Activities by Sauchinone

Sauchinone, an active lignan isolated from the aerial parts of Saururus chinensis (Saururaceae), exhibits anti-inflammatory, anti-obesity, anti-hyperglycemic, and anti-hepatic steatosis effects. As herb–drug interaction (HDI) through cytochrome P450s (CYPs)-mediated metabolism limits clinical application of herbs and drugs in combination, this study sought to explore the enzyme kinetics of sauchinone towards CYP inhibition in in vitro human liver microsomes (HLMs) and in vivo mice studies and computational molecular docking analysis. In in vitro HLMs, sauchinone reversibly inhibited CYP2B6, 2C19, 2E1, and 3A4 activities in non-competitive modes, showing inhibition constant (K_i) values of 14.3, 16.8, 41.7, and 6.84 μM, respectively. Also, sauchinone time-dependently inhibited CYP2B6, 2E1 and 3A4 activities in vitro HLMs. Molecular docking study showed that sauchinone could be bound to a few key amino acid residues in the active site of CYP2B6, 2C19, 2E1, and 3A4. When sibutramine, clopidogrel, or chlorzoxazone was co-administered with sauchinone to mice, the systemic exposure of each drug was increased compared to that without sauchinone, because sauchinone reduced the metabolic clearance of each drug. In conclusion, when sauchinone was co-treated with drugs metabolized via CYP2B6, 2C19, 2E1, or 3A4, sauchinone–drug interactions occurred because sauchinone inhibited the CYP-mediated metabolic activities.

Interactions of omeprazole-based analogues with cytochrome P450 2C19: a computational study

Cytochrome P450 2C19 (CYP2C19) is one of 57 drug metabolizing enzymes in humans and is responsible for the metabolism of ∼7-10% of drugs in clinical use. Recently omeprazole-based analogues were reported to be the potent inhibitors of CYP2C19 and have the potential to be used as the tool compounds for studying the substrate selectivity of CYP2C19. However, the binding modes of these compounds with CYP2C19 remain to be elucidated. In this study, a combination of molecular docking, molecular dynamics (MD), and MM/GBSA calculations was employed to systematically investigate the interactions between these compounds and CYP2C19. The binding modes of these analogues were analyzed in detail. The results indicated that the inclusion of explicit active site water molecules could improve binding energy prediction when the water molecules formed a hydrogen bonding network between the ligand and protein. We also found that the effect of active site water molecules on binding free energy prediction was dependent on the ligand binding modes. Our results unravel the interactions of these omeprazole-based analogues with CYP2C19 and might be helpful for the future design of potent CYP2C19 inhibitors with improved metabolic properties.

Time-Dependent Inhibition of CYP2C19 by Isoquinoline Alkaloids: In Vitro and In Silico Analysis

The cytochrome P450 2C19 (CYP2C19) enzyme plays an important role in the metabolism of many commonly used drugs. Relatively little is known about CYP2C19 inhibitors, including compounds of natural origin, which could inhibit CYP2C19, potentially causing clinically relevant metabolism-based drug interactions. We evaluated a series (N = 49) of structurally related plant isoquinoline alkaloids for their abilities to interact with CYP2C19 enzyme using in vitro and in silico methods. We examined several common active alkaloids found in herbal products such as apomorphine, berberine, noscapine, and papaverine, as well as the previously identified mechanism-based inactivators bulbocapnine, canadine, and protopine. The IC50 values of the alkaloids ranged from 0.11 to 210 µM, and 42 of the alkaloids were confirmed to be time-dependent inhibitors of CYP2C19. Molecular docking and three-dimensional quantitative structure-activity relationship analysis revealed key interactions of the potent inhibitors with the enzyme active site. We constructed a comparative molecular field analysis model that was able to predict the inhibitory potency of a series of independent test molecules. This study revealed that many of these isoquinoline alkaloids do have the potential to cause clinically relevant drug interactions. These results highlight the need for studying more profoundly the potential interactions between drugs and herbal products. When further refined, in silico methods can be useful in the high-throughput prediction of P450 inhibitory potential of pharmaceutical compounds.

Microwave Controlled Reductive Cyclization: A Selective Synthesis of Novel Benzimidazole-alkyloxypyrrolo[1,2-a]quinoxalinones

An efficient cascade synthesis of novel benzimidazole linked alkyloxypyrrolo[1,2-a]quinoxalinones was explored on soluble polymer support under microwave irradiation. Two exclusive protocols have been developed for the partial and full reductive cyclization by controlling the microwave energy. Commencing from the same substrate, ortho nitro pyrrol carboxylates, N-hydroxy pyrroloquinoxalinones were obtained by partial reductive cyclization (60 °C, 7 min), and the synthesis of pyrroloquinoxalinones was accomplished by full reductive cyclization (85 °C, 12 min). This method represents the first synthesis of N-hydroxy pyrroloquinoxalinones using Pd/C and ammonium formate as reducing agents. Further employing a variety of alkyl bromides, the obtained pyrroloquinoxalinones were transformed to their corresponding O- and N-alkylated analogues to deliver the diversified, novel molecular entities.

Predicting CYP2C19 catalytic parameters for enantioselective oxidations using artificial neural networks and a chirality code

Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predict parameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters (k(cat), K(m), and k(cat)/K(m)), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box-Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters (k(cat) and K(m)) were more consistent with experimental values than those for catalytic efficiency (k(cat)/K(m)). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R- and S-propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds.