Insights into molecular mechanisms of drug metabolism dysfunction of human CYP2C9*30

New tetrahydroisoquinoline-4-carbonitrile derivatives as potent agents against cyclin-dependent kinases, crystal structures, and computational studies

The synthesis of two new hexahydroisoquinoline-4-carbonitrile derivatives (3a and 3b) is reported along with spectroscopic data and their crystal structures. In compound 3a, the intramolecular O-H···O hydrogen bond constraints the acetyl and hydroxyl groups to be syn. In the crystal, inversion dimers are generated by C-H···O hydrogen bonds and are connected into layers parallel to (10-1) by additional C-H···O hydrogen bonds. The layers are stacked with Cl···S contacts 0.17 Å less than the sum of the respective van der Waals radii. The conformation of the compound 3b is partially determined by the intramolecular O-H···O hydrogen bond. A puckering analysis of the tetrahydroisoquinoline unit was performed. In the crystal, O-H···O and C-H···O hydrogen bonds together with C-H···π(ring) interactions form layers parallel to (01-1) which pack with normal van der Waals interactions. To understand the binding efficiency and stability of the title molecules, molecular docking, and 100 ns dynamic simulation analyses were performed with CDK5A1. To rationalize their structure-activity relationship(s), a DFT study at the B3LYP/6-311++G** theoretical level was also done. The 3D Hirshfled surfaces were also taken to investigate the crystal packings of both compounds. In addition, their ADMET properties were explored.Communicated by Ramaswamy H. Sarma.

Assessing the Performance of In silico Tools and Molecular Dynamics Simulations for Predicting Pharmacogenetic Variant Impact

The ability of freely available in silico tools to predict the effect of non-synonymous single nucleotide polymorphisms (nsSNPs) in pharmacogenes on protein function is not well defined. We assessed the performance of seven sequence-based (SIFT, PolyPhen2, mutation accessor, FATHMM, PhD-SNP, MutPred2, and SNPs & Go) and five structure-based (mCSM, SDM, DDGun, CupSat, and MAESTROweb) tools in predicting the impact of 118 nsSNPs in the CYP2C19, CYP2C9, CYP2B6, CYP2D6, and DPYD genes with known function (24 normal, one increased, 42 decreased, and 51 no-function). Sequence-based tools had a higher median (IQR) positive predictive value (89% [89-94%] vs. 12% [10-15%], P < 0.001) and lower negative predictive value (30% [24-34%] vs. 90% [80-93%], P < 0.001) than structure-based tools. Accuracy did not significantly differ between sequence-based (59% [37-67%]) and structure-based (34% [23-44%]) tools (P = 0.070). Notably, the no-function CYP2C9*3 allele and decreased function CYP2C9*8 allele were predicted incorrectly as tolerated by 100% of sequenced-based tools and as stabilizing by 60% and 20% of structure-based tools, respectively. As a case study, we performed mutational analysis for the CYP2C9*1, *3 (I359L), and *8 (R150H) proteins through molecular dynamic (MD) simulations using S-warfarin as the substrate. The I359L variant increased the distance of the major metabolic site of S-warfarin to the oxy-ferryl center of CYP2C9, and I359L and R150H caused shifts in the conformation of S-warfarin to a position less favorable for metabolism. These data suggest that MD simulations may better capture the impact of nsSNPs in pharmacogenes than other tools.

[The interactions of abiraterone and its pharmacologically active metabolite D4A with cytochrome P450 2C9 (CYP2C9)]

Interactions of cytochrome P450 2C9 (CYP2C9) were studied with the antitumor drug abiraterone and its pharmacologically active metabolite D4A, promising as an agent for prostate cancer treatment. It was shown by absorption spectroscopy, that both investigated compounds induced spectral changes of CYP2C9, indicating interactions of the pyridine nitrogen atom with the heme iron ion of the active site of the enzyme, but interactions of the ligands with the enzyme could be mediated by a water molecule bound to the heme iron ion. Based on the spectral changes, the values of dissociation constants (KS) for complexes of abiraterone and D4A with CYP2C9 were calculated as 1.73±0.14 μM and 3.95±0.16 μM. Both compounds inhibited O-demethylase activity of CYP2C9 towards its substrate. At 100 μM concentration of naproxen the concentrations of abiraterone, D4A and sulfaphenazole inhibiting CYP2C9 activity by 50% (IC50) were determined as 13.9 μM, 40 μM and 41 μM, respectively. The obtained results can be used for prognosis of drug-drug interactions at CYP2C9 level during administration of abiraterone or D4A as an antitumor agent for prostate cancer treatment in complex pharmacotherapy.

Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9

Cytochrome P450 2C9 (CYP2C9) is a major drug-metabolizing enzyme that represents 20% of the hepatic CYPs and is responsible for the metabolism of 15% of drugs. A general concern in drug discovery is to avoid the inhibition of CYP leading to toxic drug accumulation and adverse drug-drug interactions. However, the prediction of CYP inhibition remains challenging due to its complexity. We developed an original machine learning approach for the prediction of drug-like molecules inhibiting CYP2C9. We created new predictive models by integrating CYP2C9 protein structure and dynamics knowledge, an original selection of physicochemical properties of CYP2C9 inhibitors, and machine learning modeling. We tested the machine learning models on publicly available data and demonstrated that our models successfully predicted CYP2C9 inhibitors with an accuracy, sensitivity and specificity of approximately 80%. We experimentally validated the developed approach and provided the first identification of the drugs vatalanib, piriqualone, ticagrelor and cloperidone as strong inhibitors of CYP2C9 with IC values <18 μM and sertindole, asapiprant, duvelisib and dasatinib as moderate inhibitors with IC50 values between 40 and 85 μM. Vatalanib was identified as the strongest inhibitor with an IC50 value of 0.067 μM. Metabolism assays allowed the characterization of specific metabolites of abemaciclib, cloperidone, vatalanib and tarafenacin produced by CYP2C9. The obtained results demonstrate that such a strategy could improve the prediction of drug-drug interactions in clinical practice and could be utilized to prioritize drug candidates in drug discovery pipelines.

Computational approach towards the design of artemisinin-thymoquinone hybrids against main protease of SARS-COV-2

Background

The sanitary emergency installed in the world, generated by the pandemic of COVID-19, instigates the search for scientific strategies to mitigate the damage caused by the disease to different sectors of society. The disease caused by the coronavirus, SARS-CoV-2, reached 216 countries/territories, where about 199 million people were reported with the infection. Of these, more than 4 million died. In this sense, strategies involving the development of new antiviral molecules are extremely important. The main protease (Mpro) from SARS-CoV-2 is an important target, which has been widely studied for antiviral treatment. This work aims to perform a screening of pharmacodynamics and pharmacokinetics of synthetic hybrids from thymoquinone and artemisin (THY-ART) against COVID-19.

Results

Molecular docking studies indicated that hybrids of artemisinin and thymoquinone showed a relevant interaction with the active fraction of the enzyme Mpro, when compared to the reference drugs. Furthermore, hybrids show an improvement in the interaction of substances with the enzyme, mainly due to the higher frequency of interactions with the Thr199 residue. ADMET studies indicated that hybrids tend to permeate biological membranes, allowing good human intestinal absorption, with low partition to the central nervous system, potentiation for CYP-450 enzyme inhibitors, low risk of toxicity compared to commercially available drugs, considering mainly mutagenicity and cardiotoxicity, low capacity of hybrids to permeate the blood–brain barrier, high absorption and moderate permeability in Caco-2 cells. In addition, T1–T7 tend to have a better distribution of their available fractions to carry out diffusion and transport across cell membranes, as well as increase the energy of interaction with the SARS-CoV-2 target.

Conclusions

Hybrid products of artemisinin and thymoquinone have the potential to inhibit Mpro, with desirable pharmacokinetic and toxicity characteristics compared to commercially available drugs, being indicated for preclinical and subsequent clinical studies against SARS-CoV-2. Emphasizing the possibility of synergistic use with currently used drugs in order to increase half-life and generate a possible synergistic effect. This work represents an important step for the development of specific drugs against COVID-19.

Insights into the substrate binding mechanism of SULT1A1 through molecular dynamics with excited normal modes simulations

Sulfotransferases (SULTs) are phase II drug-metabolizing enzymes catalyzing the sulfoconjugation from the co-factor 3′-phosphoadenosine 5′-phosphosulfate (PAPS) to a substrate. It has been previously suggested that a considerable shift of SULT structure caused by PAPS binding could control the capability of SULT to bind large substrates. We employed molecular dynamics (MD) simulations and the recently developed approach of MD with excited normal modes (MDeNM) to elucidate molecular mechanisms guiding the recognition of diverse substrates and inhibitors by SULT1A1. MDeNM allowed exploring an extended conformational space of PAPS-bound SULT1A1, which has not been achieved up to now by using classical MD. The generated ensembles combined with docking of 132 SULT1A1 ligands shed new light on substrate and inhibitor binding mechanisms. Unexpectedly, our simulations and analyses on binding of the substrates estradiol and fulvestrant demonstrated that large conformational changes of the PAPS-bound SULT1A1 could occur independently of the co-factor movements that could be sufficient to accommodate large substrates as fulvestrant. Such structural displacements detected by the MDeNM simulations in the presence of the co-factor suggest that a wider range of drugs could be recognized by PAPS-bound SULT1A1 and highlight the utility of including MDeNM in protein–ligand interactions studies where major rearrangements are expected.

Enzyme Models-From Catalysis to Prodrugs

Enzymes are highly specific biological catalysts that accelerate the rate of chemical reactions within the cell. Our knowledge of how enzymes work remains incomplete. Computational methodologies such as molecular mechanics (MM) and quantum mechanical (QM) methods play an important role in elucidating the detailed mechanisms of enzymatic reactions where experimental research measurements are not possible. Theories invoked by a variety of scientists indicate that enzymes work as structural scaffolds that serve to bring together and orient the reactants so that the reaction can proceed with minimum energy. Enzyme models can be utilized for mimicking enzyme catalysis and the development of novel prodrugs. Prodrugs are used to enhance the pharmacokinetics of drugs; classical prodrug approaches focus on alternating the physicochemical properties, while chemical modern approaches are based on the knowledge gained from the chemistry of enzyme models and correlations between experimental and calculated rate values of intramolecular processes (enzyme models). A large number of prodrugs have been designed and developed to improve the effectiveness and pharmacokinetics of commonly used drugs, such as anti-Parkinson (dopamine), antiviral (acyclovir), antimalarial (atovaquone), anticancer (azanucleosides), antifibrinolytic (tranexamic acid), antihyperlipidemia (statins), vasoconstrictors (phenylephrine), antihypertension (atenolol), antibacterial agents (amoxicillin, cephalexin, and cefuroxime axetil), paracetamol, and guaifenesin. This article describes the works done on enzyme models and the computational methods used to understand enzyme catalysis and to help in the development of efficient prodrugs.

Identification of hub genes associated with obesity-induced hepatocellular carcinoma risk based on integrated bioinformatics analysis

Obesity, which has become one of the biggest public health problems of the twenty-first century, accompanies many chronic conditions, including cancer. On the other hand, liver cancer, which is known to be associated with obesity, is considered another serious threat to public health. However, the underlying drivers of the development of obesity-associated hepatocellular carcinoma (HCC) remain blurry. The current study attempted to identify the key genes and pathways in the obesity-induced development of HCC using integrated bioinformatics analyses. Obesity and HCC-associated gene expression datasets were downloaded from Gene Expression Omnibus (GEO) and analyzed to identify overlapping differentially expressed genes (DEGs) and hub genes. The prognostic potentials, survival analysis, and expression levels of hub genes were further assessed. Moreover, the correlation between hub genes and the immune cells infiltration was analyzed. The findings of this research revealed that both mRNA and protein expression levels of the four hub genes (IGF1, ACADL, CYP2C9, and G6PD) involved in many important metabolic pathways are remarkably altered in both obese individuals and patients with HCC. The results demonstrated that these dysregulated genes in both obesity and HCC may serve as considerable targets for the prevention and treatment of HCC development in obese individuals.

A novel four-gene of iron metabolism-related and methylated for prognosis prediction of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a liver disease with a complex underlying mechanism, and patients with HCC have low survival rates. Iron metabolism plays a crucial role in the pathogenesis of HCC; however, the prognostic value of iron metabolism-related and methylated genes for HCC needs to be further explored. In the present study, we identified differentially expressed genes (DEGs) that play a role in iron metabolism and DNA methylation in HCC from The Cancer Genome Atlas. Four of these DEGs, whose expression levels are correlated with HCC prognosis, namely, RRM2, FTCD, CYP2C9, and ATP6V1C1, were further used to construct a prognostic model for HCC, wherein the risk score was calculated using the gene expression of the four DEGs. This could be used to predict the overall survival of HCC patients for 1, 3, and 5 years. Results of a multivariate Cox regression analysis further indicated that the risk score was an independent variable correlated with the prognosis of HCC patients. The identified gene signature was further validated using an independent cohort of HCC patients from the International Cancer Genome Consortium. Weighted gene co-expression network analysis and gene set enrichment analysis were performed to identify potential regulatory mechanisms of the gene signature in HCC. Taken together, we identified key prognostic factors of iron metabolism-related and methylated genes for HCC, providing a potential treatment strategy for HCC.

Deciphering Reaction Determinants of Altered-Activity CYP2D6 Variants by Well-Tempered Metadynamics Simulation and QM/MM Calculations

The xenobiotic metabolizing enzyme CYP2D6 is the P450 cytochrome family member with the highest rate of polymorphism. This causes changes in the enzyme activity and specificity, which can ultimately lead to adverse reactions during drug treatment. To avoid or lower CYP-related toxicity risks, prediction of the most likely positions within a molecule where a metabolic reaction might occur is paramount. In order to obtain accurate predictions, it is crucial to understand all phenomena within the active site of the enzyme that contribute to an efficient substrate recognition and the subsequent catalytic reaction together with their relative weight within the overall thermodynamic context. This study aims to define the weight of the driving forces upon the C-H bond activation within CYP2D6 wild-type and a clinically relevant allelic variant with increased activity (CYP2D6*53) featuring two amino acid mutations in close vicinity of the heme. First, we investigated the steric and electrostatic complementarity of the substrate bufuralol using well-tempered metadynamics simulations with the aim to obtain the free energy profiles for each site of metabolism (SoM) within the different active sites. Second, the stereoelectronic complementarity was determined for each SoM within the two different active-site environments. Relying on the well-tempered metadynamics simulation energy profiles of each SoM, we identified the binding mode that was closest to the preferred transition-state geometry for efficient C-H bond activation. The binding modes were then used as starting structures for the quantum mechanics/molecular mechanics calculations performed to quantify the corresponding activation barriers. Our results show the relevance of the steric component in orienting the SoM in an energetically accessible position toward the heme. However, the corresponding intrinsic reactivity and electronic complementarity within the active site must be accurately evaluated in order to obtain a meaningful reaction prediction, from which the predominant SoM can be determined. The F120I mutation lowered the activation barrier for the major site and one of the minor SoMs. However, it had an impact neither on the CYP2D6 enantioselectivity preference of the oxidation reaction nor on the stereoselectivity from the substrate point of view.

Interaction of indole-3-acetic acid with horseradish peroxidase as a potential anticancer agent: from docking to molecular dynamics simulation

The oxidation process, catalyzed by the peroxidase enzymes, occurs in all domains of life to detoxify the hydrogen peroxide toxicity. The most well-known, applicable and vastly studied member of the peroxidases family is horseradish peroxidase (HRP), especially the isoenzyme C (HRP C). HRP (primarily HRP C) is commercially available and applicable in biotechnology and diagnosis. Recently, a novel application of HRP has been introduced in cancer therapy as the combination of HRP with indole-3-acetic acid (IAA). The anticancer activity of HRP/IAA complex is through oxidation of IAA by HRP in hypoxic tumor condition, which leads to apoptosis and cancerous cell death. However, the molecular interaction of HRP/IAA has not been elucidated. Identifying the interaction of IAA with HRP would provide a better insight into its function and applications. In this study, molecular docking and molecular dynamics (MD) simulation were applied to determine the molecular interaction of the IAA/HRP complex. The docking study represented that IAA bound at the 'exposed' heme edge of the HRP enzyme, and the IAA entrance to the enzyme was situated at the carboxymethyl side-chain of the selected structure. Our computational results showed the HRP/IAA complex structure stability. While hydrogen bond formation with ARG38 and HIS42 stabilized the substrate, hydrophobic interactions with Phe68, Gly69, Leu138, Pro139, Pro141 and Phe179 contributed to IAA/HRP complex stability. The results can help to better understand peroxidase enzyme activity and would pave the way for future development of new therapeutics with improved anticancer efficacy.Communicated by Ramaswamy H. Sarma.

Effects of mongolian medicine Terminalia chebula Retz. on 6 CYP450 enzymes in rats

Terminalia chebula Retz. (TCR) is a medicinal material commonly used in Mongolian medicine. After consulting the literature at home and abroad, current research on TCR focuses on chemical composition, pharmacodynamics, and fingerprints. The pharmacokinetics of TCR has not been reported. Cytochrome P450 (CYP450) is the main drug-metabolizing enzyme, and its activity may be induced or inhibited by certain drugs, resulting in drug interactions in clinical applications. The objective of this study was to establish a high performance liquid chromatography (HPLC) method that can simultaneously detect multiple probe drugs to determine the effect of TCR on the activities of CYP450 enzymes CYP2C19, CYP2E1, CYP2D6, CYP2C9, CYP3A4, and CYP1A2. Wistar rats (male) were divided into 5 groups according to the randomization principle, namely the control group, the positive group, and the high, medium and low dose group. After 15 days of continuous administration, the mixed probe drug was injected into the vein, and then a small amount of blood was collected from the orbital vein at different time points. After the samples were processed, the blood concentration of each probe drug was measured by the established HPLC method. The pharmacokinetic parameters of each probe drug were calculated using DAS software. Compared with the control group, the plasma clearance (CL) of chlorzoxazone and omeprazole decreased, and the maximum plasma concentration (C_max) and area under the curve (AUC) increased in the TCR group. The pharmacokinetic parameters of theophylline, midazolam, metoprolol, and tolbutamide did not differ significantly. The results indicated that TCR mainly inhibited the activities of CYP2E1 and CYP2C19, but had no effect on the activities of CYP1A2, CYP2C9, CYP3A4 and CYP2D6. Extra care should be taken when drugs metabolized by CYP2C19 and CYP2E1 enzymes are used in combination with TCR, as drug-herb interactions may occur. These results can guide the clinical application of related drugs and provide valuable information for drug interactions. The main component that affects enzyme activity may be tannins in the water extract.

Computational Simulations to Guide Enzyme-Mediated Prodrug Activation

Prodrugs are designed to improve pharmaceutical/biopharmaceutical characteristics, pharmacokinetic/pharmacodynamic properties, site-specificity, and more. A crucial step in successful prodrug is its activation, which releases the active parent drug, exerting a therapeutic effect. Prodrug activation can be based on oxidation/reduction processes, or through enzyme-mediated hydrolysis, from oxidoreductases (i.e., Cytochrome P450) to hydrolytic enzymes (i.e., carboxylesterase). This study provides an overview of the novel in silico methods for the optimization of enzyme-mediated prodrug activation. Computational methods simulating enzyme-substrate binding can be simpler like molecular docking, or more complex, such as quantum mechanics (QM), molecular mechanics (MM), and free energy perturbation (FEP) methods such as molecular dynamics (MD). Examples for MD simulations used for elucidating the mechanism of prodrug (losartan, paclitaxel derivatives) metabolism via CYP450 enzyme are presented, as well as an MD simulation for optimizing linker length in phospholipid-based prodrugs. Molecular docking investigating quinazolinone prodrugs as substrates for alkaline phosphatase is also presented, as well as QM and MD simulations used for optimal fit of different prodrugs within the human carboxylesterase 1 catalytical site. Overall, high quality computational simulations may show good agreement with experimental results, and should be used early in the prodrug development process.

Analysis of protein missense alterations by combining sequence- and structure-based methods

BACKGROUND

Different types of in silico approaches can be used to predict the phenotypic consequence of missense variants. Such algorithms are often categorized as sequence based or structure based, when they necessitate 3D structural information. In addition, many other in silico tools, not dedicated to the analysis of variants, can be used to gain additional insights about the possible mechanisms at play.

METHODS

Here we applied different computational approaches to a set of 20 known missense variants present on different proteins (CYP, complement factor B, antithrombin and blood coagulation factor VIII). The tools that were used include fast computational approaches and web servers such as PolyPhen-2, PopMusic, DUET, MaestroWeb, SAAFEC, Missense3D, VarSite, FlexPred, PredyFlexy, Clustal Omega, meta-PPISP, FTMap, ClusPro, pyDock, PPM, RING, Cytoscape, and ChannelsDB.

RESULTS

We observe some conflicting results among the methods but, most of the time, the combination of several engines helped to clarify the potential impacts of the amino acid substitutions.

CONCLUSION

Combining different computational approaches including some that were not developed to investigate missense variants help to predict the possible impact of the amino acid substitutions. Yet, when the modified residues are involved in a salt-bridge, the tools tend to fail, even when the analysis is performed in 3D. Thus, interactive structural analysis with molecular graphics packages such as Chimera or PyMol or others are still needed to clarify automatic prediction.

Bioinformatics-based screening of key genes for transformation of liver cirrhosis to hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is the most common type of liver tumour, and is closely related to liver cirrhosis. Previous studies have focussed on the pathogenesis of liver cirrhosis developing into HCC, but the molecular mechanism remains unclear. The aims of the present study were to identify key genes related to the transformation of cirrhosis into HCC, and explore the associated molecular mechanisms.

Methods

GSE89377, GSE17548, GSE63898 and GSE54236 mRNA microarray datasets from Gene Expression Omnibus (GEO) were analysed to obtain differentially expressed genes (DEGs) between HCC and liver cirrhosis tissues, and network analysis of protein–protein interactions (PPIs) was carried out. String and Cytoscape were used to analyse modules and identify hub genes, Kaplan–Meier Plotter and Oncomine databases were used to explore relationships between hub genes and disease occurrence, development and prognosis of HCC, and the molecular mechanism of the main hub gene was probed using Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis.

Results

In total, 58 DEGs were obtained, of which 12 and 46 were up- and down-regulated, respectively. Three hub genes (CDKN3, CYP2C9 and LCAT) were identified and associated prognostic information was obtained. CDKN3 may be correlated with the occurrence, invasion, and recurrence of HCC. Genes closely related to changes in the CDKN3 hub gene were screened, and Kyoto Encyclopedia of Genes and Genomes (KEGGs) pathway analysis identified numerous cell cycle-related genes.

Conclusion

CDKN3 may affect the transformation of liver cirrhosis into HCC, and represents a new candidate molecular marker of the occurrence and progression of HCC.

Quality Threshold Clustering of Molecular Dynamics: A Word of Caution

Clustering Molecular Dynamics trajectories is a common analysis that allows grouping together similar conformations. Several algorithms have been designed and optimized to perform this routine task, and among them, Quality Threshold stands as a very attractive option. This algorithm guarantees that in retrieved clusters no pair of frames will have a similarity value greater than a specified threshold, and hence, a set of strongly correlated frames are obtained for each cluster. In this work, it is shown that various commonly used software implementations are flawed by confusing Quality Threshold with another simplistic well-known clustering algorithm published by Daura et al. (Daura, X.; van Gunsteren, W. F.; Jaun, B.; Mark, A. E.; Gademann, K.; Seebach, D. Peptide Folding: When Simulation Meets Experiment. Angew. Chemie Int. Ed. 1999, 38 (1/2), 236-240). Daura's algorithm does not impose any quality threshold for the frames contained in retrieved clusters, bringing unrelated structural configurations altogether. The advantages of using Quality Threshold whenever possible to explore Molecular Dynamic trajectories is exemplified. An in-house implementation of the original Quality Threshold algorithm has been developed in order to illustrate our comments, and its code is freely available for further use by the scientific community.