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Metabolomic screening and identification of the bioactivation pathways of ritonavir.

ABSTRACT: Ritonavir-boosted protease inhibitor regimens are widely used for HIV chemotherapy. However, ritonavir causes multiple side effects, and the mechanisms are not fully understood. The current study was designed to explore the metabolic pathways of ritonavir that may be related to its toxicity. Metabolomic analysis screened out 26 ritonavir metabolites in mice, and half of them are novel. These novel ritonavir metabolites include two glycine conjugated, two N-acetylcysteine conjugated, and three ring-open products. Accompanied with the generation of ritonavir ring-open metabolites, the formation of methanethioamide and 2-methylpropanethioamide were expected. Upon the basis of the structures of these novel metabolites, five bioactivation pathways are proposed, which may be associated with sulfation and epoxidation. By using Cyp3a-null mice, we confirmed that CYP3A is involved in four pathways of RTV bioactivation. In addition, all these five bioactivation pathways were recapitulated in the incubation of ritonavir in human liver microsomes. Further studies are suggested to determine the role of CYP3A and these bioactivation pathways in ritonavir toxicity.

SUBMITTER: Li F

PROVIDER: S-EPMC3243766 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature

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Metabolomic screening and identification of the bioactivation pathways of ritonavir.

Li Feng F Lu Jie J Ma Xiaochao X

Chemical research in toxicology 20111117 12

Ritonavir-boosted protease inhibitor regimens are widely used for HIV chemotherapy. However, ritonavir causes multiple side effects, and the mechanisms are not fully understood. The current study was designed to explore the metabolic pathways of ritonavir that may be related to its toxicity. Metabolomic analysis screened out 26 ritonavir metabolites in mice, and half of them are novel. These novel ritonavir metabolites include two glycine conjugated, two N-acetylcysteine conjugated, and three ri ...[more]

PMID: 22040299

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Project description:BackgroundA better understanding of the pathophysiology involving coronary artery calcification (CAC) in patients on hemodialysis (HD) will help to develop new therapies. We sought to identify the differences in metabolomics profiles between patients on HD with and without CAC.MethodsIn this case-control study, nested within a cohort of 568 incident patients on HD, the cases were patients without diabetes with a CAC score >100 (n=51), and controls were patients without diabetes with a CAC score of zero (n=48). We measured 452 serum metabolites in each participant. Metabolites and pathway scores were compared using Mann-Whitney U tests, partial least squares-discriminant analyses, and pathway enrichment analyses.ResultsCompared with controls, cases were older (64±13 versus 42±12 years) and were less likely to be Black (51% versus 94%). We identified three metabolites in bile-acid synthesis (chenodeoxycholic, deoxycholic, and glycolithocholic acids) and one pathway (arginine/proline metabolism). After adjusting for demographics, higher levels of chenodeoxycholic, deoxycholic, and glycolithocholic acids were associated with higher odds of having CAC; comparing the third with the first tertile of each bile acid, the OR was 6.34 (95% CI, 1.12 to 36.06), 6.73 (95% CI, 1.20 to 37.82), and 8.53 (95% CI, 1.50 to 48.49), respectively. These associations were no longer significant after further adjustment for coronary artery disease and medication use. Per 1 unit higher in the first principal component score, arginine/proline metabolism was associated with CAC after adjusting for demographics (OR, 1.83; 95% CI, 1.06 to 3.15), and the association remained significant with additional adjustments for statin use (OR, 1.84; 95% CI, 1.04 to 3.27).ConclusionsAmong patients on HD without diabetes mellitus, chenodeoxycholic, deoxycholic, and glycolithocholic acids may be potential biomarkers for CAC, and arginine/proline metabolism is a plausible mechanism to study for CAC. These findings need to be confirmed in future studies.

Project description:Rheumatoid arthritis (RA), which normally manifests as a multi‑joint inflammatory reaction, is a common immunological disease in clinical practice. However, the pathogenesis of RA has not yet been fully elucidated. Rituximab (RTX) is an effective drug in the treatment of RA, however its therapeutic efficacy and mechanism of action require further investigation. Thus, the present study aimed to screen the candidate key regulatory genes and explain the potential mechanisms of RA. Gene chips of RA and normal joint tissues were analyzed and, gene chips of RTX before and after treatment were investigated. In the present study, strong evidence supporting the pathogenesis of RA and mechanism of action of RTX were also revealed. Differentially expressed genes (DEGs) were analyzed using the limma package of RStudio software. A total of 1,150 DEGs were detected in RA compared with normal joint tissues. The upregulated genes were enriched in 'interleukin‑12 production', 'I‑κB kinase/NF‑κB signaling', 'regulation of cytokine production involved in immune response' and 'cytokine metabolic process'. Functional enrichment analysis showed that RTX was primarily involved in the inhibition of 'adaptive immune response', 'B cell activation involved in immune response' and 'immune effector process'. Subsequently, leukocyte immunoglobulin‑like receptor subfamily B member 1 (LILRB1), a hub gene with high connectivity degree, was selected, and traditional Chinese medicine libraries were molecularly screened according to the structure of the LILRB1 protein. The results indicated that kaempferol 3‑O‑β‑D‑glucosyl‑(1→2)‑β‑D‑glucoside exhibited the highest docking score. In the present study, the DEGs and their biological functions in RA and the pharmacological mechanism of RTX action were determined. Taken together, the results suggested that LILRB1 may be used as a molecular target for RA treatment, and kaempferol 3‑O‑β‑D‑glucosyl‑(1→2)‑β‑D‑glucoside may inhibit the pathological process of RA.

Project description:Understanding non-covalent biomolecular recognition, which includes drug-protein bound states and their binding/unbinding processes, is of fundamental importance in chemistry, biology, and medicine. Fully revealing the factors that govern the binding/unbinding processes can further assist in designing drugs with desired binding kinetics. HIV protease (HIVp) plays an integral role in the HIV life cycle, so it is a prime target for drug therapy. HIVp has flexible flaps, and the binding pocket can be accessible by a ligand via various pathways. Comparing ligand association and dissociation pathways can help elucidate the ligand-protein interactions such as key residues directly involved in the interaction or specific protein conformations that determine the binding of a ligand under certain pathway(s). Here, we investigated the ligand unbinding process for a slow binder, ritonavir, and a fast binder, xk263, by using unbiased all-atom accelerated molecular dynamics (aMD) simulation with a re-seeding approach and an explicit solvent model. Using ritonavir-HIVp and xk263-HIVp ligand-protein systems as cases, we sampled multiple unbinding pathways for each ligand and observed that the two ligands preferred the same unbinding route. However, ritonavir required a greater HIVp motion to dissociate as compared with xk263, which can leave the binding pocket with little conformational change of HIVp. We also observed that ritonavir unbinding pathways involved residues which are associated with drug resistance and are distal from catalytic site. Analyzing HIVp conformations sampled during both ligand-protein binding and unbinding processes revealed significantly more overlapping HIVp conformations for ritonavir-HIVp rather than xk263-HIVp. However, many HIVp conformations are unique in xk263-HIVp unbinding processes. The findings are consistent with previous findings that xk263 prefers an induced-fit model for binding and unbinding, whereas ritonavir favors a conformation selection model. This study deepens our understanding of the dynamic process of ligand unbinding and provides insights into ligand-protein recognition mechanisms and drug discovery.

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Metabolomic screening and identification of the bioactivation pathways of ritonavir.

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Metabolomic screening and identification of the bioactivation pathways of ritonavir.

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