CYP7A1 Gene Induction via SHP-Dependent or Independent Mechanisms can Increase the Risk of Drug-Induced Liver Injury Independently or in Synergy with BSEP Inhibition.

Drug-induced liver injury (DILI) is a frequent cause of clinical trial failures during drug development. While inhibiting bile salt export pump (BSEP) is a well-documented DILI mechanism, interference with genes related to bile acid (BA) metabolism and transport can further complicate DILI development. Here, the effects of twenty-eight compounds on genes associated with BA metabolism and transport were evaluated, including those with discontinued development or use, boxed warnings, and clean labels for DILI. The study also included rifampicin and omeprazole, pregnane X receptor and aryl hydrocarbon receptor ligands, and four mitogen-activated protein kinase kinase (MEK1/2) inhibitors. BSEP inhibitors with more severe DILI, notably pazopanib and CP-724714, significantly upregulated the expression of 7 alpha-hydroxylase (CYP7A1), independent of small heterodimer partner (SHP) expression. CYP7A1 expression was marginally induced by omeprazole. In contrast, its expression was suppressed by mometasone (10-fold), vinblastine (18-fold), hexachlorophene (2-fold), bosentan (2.1-fold), and rifampin (2-fold). All four MEK1/2 inhibitors that show clinical DILI were not potent BSEP inhibitors but significantly induced CYP7A1 expression, accompanied by a significant SHP gene suppression. Sulfotransferase 2A1 and BSEP were marginally upregulated, but no other genes were altered by the drugs tested. Protein levels of CYP7A1 were increased with the treatment of CYP7A1 inducers and decreased with obeticholic acid, an farnesoid X receptor ligand. CYP7A1 inducers significantly increased bile acid (BA) production in hepatocytes, indicating the overall regulatory effects of BA metabolism. This study demonstrates that CYP7A1 induction via various mechanisms can pose a risk for DILI, independently or in synergy with BSEP inhibition, and it should be evaluated early in drug discovery. SIGNIFICANCE STATEMENT: Kinase inhibitors, pazopanib and CP-724714, inhibit BSEP and induce CYP7A1 expression independent of small heterodimer partner (SHP) expression, leading to increased bile acid (BA) production and demonstrating clinically elevated drug-induced liver toxicity. MEK1/2 inhibitors that show BSEP-independent drug-induced liver injury (DILI) induced the CYP7A1 gene accompanied by SHP suppression. CYP7A1 induction via SHP-dependent or independent mechanisms can pose a risk for DILI, independently or in synergy with BSEP inhibition. Monitoring BA production in hepatocytes can reliably detect the total effects of BA-related gene regulation for de-risking.

Assessing Pleiotropic Effects of a Mixed-Mode Perpetrator Drug, Rifampicin, by Multiple Endogenous Biomarkers in Dogs.

Rifampicin (RIF) is a mixed-mode perpetrator that produces pleiotropic effects on liver cytochrome P450 enzymes and drug transporters. To assess the complex drug-drug interaction liabilities of RIF in vivo, a known probe substrate, midazolam (MDZ), along with multiple endogenous biomarkers were simultaneously monitored in beagle dogs before and after a 7-day treatment period by RIF at 20 mg/kg per day. Confirmed by the reduced MDZ plasma exposure and elevated 4ß-hydroxycholesterol (4ß-HC, biomarker of CYP3A activities) level, CYP3A was significantly induced after repeated RIF doses, and such induction persisted for 3 days after cessation of the RIF administration. On the other hand, increased plasma levels of coproporphyrin (CP)-I and III [biomarkers of organic anion transporting polypeptides 1b (Oatp1b) activities] were observed after the first dose of RIF. Plasma CPs started to decline as RIF exposure decreased, and they returned to baseline 3 days after cessation of the RIF administration. The data suggested the acute (inhibitory) and chronic (inductive) effects of RIF on Oatp1b and CYP3A enzymes, respectively, and a 3-day washout period is deemed adequate to remove superimposed Oatp1b inhibition from CYP3A induction. In addition, apparent self-induction of RIF was observed as its terminal half-life was significantly altered after multiple doses. Overall, our investigation illustrated the need for appropriate timing of modulator dosing to differentiate between transporter inhibition and enzyme induction. As further indicated by the CP data, induction of Oatp1b activities was not likely after repeated RIF administration. SIGNIFICANCE STATEMENT: This investigation demonstrated the utility of endogenous biomarkers towards complex drug-drug interactions by rifampicin (RIF) and successfully determined the optimal timing to differentiate between transporter inhibition and enzyme induction. Based on experimental evidence, Oatp1b induction following repeated RIF administration was unlikely, and apparent self-induction of RIF elimination was observed.

Quantifying Cell Heterogeneity and Subpopulations Using Single Cell Metabolomics.

Mass spectrometry (MS) has become an indispensable tool for metabolomics studies. However, due to the lack of applicable experimental platforms, suitable algorithm, software, and quantitative analyses of cell heterogeneity and subpopulations, investigating global metabolomics profiling at the single cell level remains challenging. We combined the Single-probe single cell MS (SCMS) experimental technique with a bioinformatics software package, SinCHet-MS (Single Cell Heterogeneity for Mass Spectrometry), to characterize changes of tumor heterogeneity, quantify cell subpopulations, and prioritize the metabolite biomarkers of each subpopulation. As proof of principle studies, two melanoma cancer cell lines, the primary (WM115; with a lower drug resistance) and the metastatic (WM266-4; with a higher drug resistance), were used as models. Our results indicate that after the treatment of the anticancer drug vemurafenib, a new subpopulation emerged in WM115 cells, while the proportion of the existing subpopulations was changed in the WM266-4 cells. In addition, metabolites for each subpopulation can be prioritized. Combining the SCMS experimental technique with a bioinformatics tool, our label-free approach can be applied to quantitatively study cell heterogeneity, prioritize markers for further investigation, and improve the understanding of cell metabolism in human diseases and response to therapy.

Characterization of Elimination Pathways and the Feasibility of Endogenous Metabolites as Biomarkers of Organic Anion Transporter 1/3 Inhibition in Cynomolgus Monkeys.

Organic anion transporters 1 and 3 (OAT1/3) occupy a key role in mediating renal elimination. Kynurenic acid (KYNA) was previously discovered as an effective endogenous biomarker to assess drug-drug interaction (DDI) for OAT inhibitors. Here, further in vitro and in vivo investigation was performed to characterize the elimination routes and feasibility of KYNA, along with other reported endogenous metabolites, as biomarkers of Oat1/3 inhibition in bile duct-cannulated (BDC) cynomolgus monkeys. Our results suggested that KYNA is a substrate of OAT1/3 and OAT2, but not OCT2, MATE1/2K, or NTCP, and that it shares comparable affinities between OAT1 and OAT3. Renal and biliary excretions and plasma concentration-time profiles of KYNA, pyridoxic acid (PDA), homovanillic acid (HVA), and coproporphyrin I (CP-I) were assessed in BDC monkeys dosed with either probenecid (PROB) at 100 mg/kg or the control vehicle. Renal excretion of KYNA, PDA, and HVA was determined to be the major elimination route. The maximum concentration and the area under the plasma concentration-time curve (Cmax and AUC0-24h) of KYNA were about 11.6- and 3.7-fold higher in the PROB group than in the vehicle group. Renal clearance of KYNA decreased by 3.2-fold, but biliary clearance (CLbile) was not altered after PROB administration. A similar trend was observed for PDA and HVA. Interestingly, an elevation of plasma concentration and reduction of CP-I CLbile were observed after PROB treatment, which suggested inhibition of the CP-I Oatp-Mrp2 transport axis by PROB. Overall, our results indicated that KYNA could potentially facilitate early and reliable assessment of DDI liabilities of Oat inhibition in monkeys. SIGNIFICANCE STATEMENT: This work reported renal excretion as the major elimination pathway for kynurenic acid, pyridoxic acid, and homovanillic acid. Administration of probenecid reduced renal clearance and increased plasma exposure of these biomarkers in monkeys, consistent with the observation in humans. These endogenous biomarkers discovered in monkeys could be potentially used to evaluate the clinical drug-drug interactions in the early phase of drug development.

Single-Cell Mass Spectrometry Enables Insight into Heterogeneity in Infectious Disease.

Cellular heterogeneity is generally overlooked in infectious diseases. In this study, we investigated host cell heterogeneity during infection with Trypanosoma cruzi (T. cruzi) parasites, causative agents of Chagas disease (CD). In chronic-stage CD, only a few host cells are infected with a large load of parasites and symptoms may appear at sites distal to parasite colonization. Furthermore, recent work has revealed T. cruzi heterogeneity with regard to replication rates and drug susceptibility. However, the role of cellular-level metabolic heterogeneity in these processes has yet to be assessed. To fill this knowledge gap, we developed a Single-probe SCMS (single-cell mass spectrometry) method compatible with biosafety protocols, to acquire metabolomics data from individual cells during T. cruzi infection. This study revealed heterogeneity in the metabolic response of the host cells to T. cruzi infection in vitro. Our results showed that parasite-infected cells possessed divergent metabolism compared to control cells. Strikingly, some uninfected cells adjacent to infected cells showed metabolic impacts as well. Specific metabolic changes include increases in glycerophospholipids with infection. These results provide novel insight into the pathogenesis of CD. Furthermore, they represent the first application of bioanalytical SCMS to the study of mammalian-infectious agents, with the potential for broad applications to study infectious diseases.

T-Probe: An Integrated Microscale Device for Online In Situ Single Cell Analysis and Metabolic Profiling Using Mass Spectrometry.

The exploration of single cells reveals cell heterogeneity and biological principle of cellular metabolism. Although a number of mass spectrometry (MS) based single cell MS (SCMS) techniques have been dedicatedly developed with high efficiency and sensitivity, limitations still exist. In this work, we introduced a microscale multifunctional device, the T-probe, which integrates cellular contents extraction and immediate ionization, to implement online in situ SCMS analysis at ambient conditions with minimal sample preparation. With high sensitivity and reproducibility, the T-probe was employed for MS analysis of single HeLa cells under control and anticancer drug treatment conditions. Intracellular species and xenobiotic metabolites were detected, and changes of cellular metabolic profiles induced by drug treatment were measured. Combining SCMS experiments with statistical data analyses, including Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) and two-sample t-test, we provided biological insights into cellular metabolic response to drug treatment. Online MS/MS analysis was conducted at single cell level to identify species of interest, including endogenous metabolites and the drug compound. Using the T-probe SCMS technique combined with comprehensive data analyses, we provide an approach to understanding cellular metabolism and evaluate chemotherapies at the single cell level.

The single-probe: a miniaturized multifunctional device for single cell mass spectrometry analysis.

We have developed a new mass spectrometry (MS) technology, the Single-probe MS, capable of real-time, in situ metabolomic analysis of individual living cells. The Single-probe is a miniaturized multifunctional sampling and ionization device that is directly coupled to the mass spectrometer. With a sampling tip smaller than individual eukaryotic cells (<10 µm), the Single-probe can be inserted into single cells to sample the intracellular compounds for real-time MS analysis. We have used the Single-probe to detect several cellular metabolites and the anticancer small molecules paclitaxel, doxorubicin, and OSW-1 in individual cervical cancer cells (HeLa). Single cell mass spectrometry (SCMS) is an emerging scientific technology that could reshape the analytical science of many research disciplines, and the Single-probe MS technology is a novel method for SCMS that, through its accessible fabrication protocols, can be broadly applied to different research areas.

Discovery of GS-5245 (Obeldesivir), an Oral Prodrug of Nucleoside GS-441524 That Exhibits Antiviral Efficacy in SARS-CoV-2-Infected African Green Monkeys.

Remdesivir 1 is an phosphoramidate prodrug that releases the monophosphate of nucleoside GS-441524 (2) into lung cells, thereby forming the bioactive triphosphate 2-NTP. 2-NTP, an analog of ATP, inhibits the SARS-CoV-2 RNA-dependent RNA polymerase replication and transcription of viral RNA. Strong clinical results for 1 have prompted interest in oral approaches to generate 2-NTP. Here, we describe the discovery of a 5'-isobutyryl ester prodrug of 2 (GS-5245, Obeldesivir, 3) that has low cellular cytotoxicity and 3-7-fold improved oral delivery of 2 in monkeys. Prodrug 3 is cleaved presystemically to provide high systemic exposures of 2 that overcome its less efficient metabolism to 2-NTP, leading to strong SARS-CoV-2 antiviral efficacy in an African green monkey infection model. Exposure-based SARS-CoV-2 efficacy relationships resulted in an estimated clinical dose of 350-400 mg twice daily. Importantly, all SARS-CoV-2 variants remain susceptible to 2, which supports development of 3 as a promising COVID-19 treatment.

Development and validation of an LC-MS/MS method to quantify kynurenic acid in human plasma.

Background: Monitoring levels of endogenous biomarkers has become an alternative approach to assess transporter-mediated drug-drug interactions in clinical trials. Among the biomarkers of interest, kynurenic acid is effective for the human organic anion transporters OAT1 and OAT3. Here, a simple and robust bioanalytical method was developed using LC-MS/MS to quantify kynurenic acid in human plasma. Results: This method achieved a LLOQ of 10 nm with acceptable signal-to-noise ratio (S/N >5). In addition, an interfering agent, tryptophan, was identified and separated chromatographically. A full method validation was performed in the spirit of GLP. Conclusion: This method can serve as a tool readily available to assess potential drug-drug interactions mediated by inhibition of OAT1 and OAT3 activities.

Single cell metabolomics using mass spectrometry: Techniques and data analysis.

Mass spectrometry (MS) based techniques are gaining popularity for metabolomics research due to their high sensitivity, wide detection range, and capability of molecular identification. Utilizing such powerful technique to explore the cellular metabolism at the single cell level not only appreciates the subtle cell-to-cell difference (i.e., cell heterogeneity), but also gains biological merits corresponding to individual cells or small cell subpopulations. In this review article, we first briefly summarize recent advances in single cell MS experimental techniques, and then emphasize on the single cell metabolomics data analysis approaches. Through implementation of statistical analysis and more advanced data analysis methods, single cell metabolomics is expected to find more potential applications in the translational and clinical fields in the future.

Towards rapid prediction of drug-resistant cancer cell phenotypes: single cell mass spectrometry combined with machine learning.

Combined single cell mass spectrometry and machine learning methods is demonstrated for the first time to achieve rapid and reliable prediction of the phenotype of unknown single cells based on their metabolomic profiles, with experimental validation. This approach can be potentially applied towards prediction of drug-resistant phenotypes prior to chemotherapy.

Redesigning the T-probe for mass spectrometry analysis of online lysis of non-adherent single cells.

Single cell mass spectrometry (SCMS) allows for molecular analysis of individual cells while avoiding the inevitable drawbacks of using cell lysate prepared from populations of cells. Based on our previous design of the T-probe, a microscale sampling and ionization device for SCMS analysis, we further developed the device to perform online, and real time lysis of non-adherent live single cells for mass spectrometry (MS) analysis at ambient conditions. This redesigned T-probe includes three parts: a sampling probe with a small tip to withdraw a whole cell, a solvent-providing capillary to deliver lysis solution (i.e., acetonitrile), and a nano-ESI emitter in which rapid cell lysis and ionization occur followed by MS analysis. These three components are embedded between two polycarbonate slides and are jointed through a T-junction to form an integrated device. Colon cancer cells (HCT-116) under control and treatment (using anticancer drug irinotecan) conditions were analyzed. We detected a variety of intracellular species, and structural identification of selected ions was conducted using tandem MS (MS2). We further conducted statistical analysis (e.g., PLS-DA and t-test) to gain biological insights of cellular metabolism. Our results indicate that the influence of anticancer drugs on cellular metabolism of live non-adherent cells can be obtained using the SCMS experiments combined with statistical data analysis.

Integrating a generalized data analysis workflow with the Single-probe mass spectrometry experiment for single cell metabolomics.

We conducted single cell metabolomics studies of live cancer cells through online single cell mass spectrometry (SCMS) experiments combined with a generalized comprehensive data analysis workflow. The SCMS experiments were carried out using the Single-probe device coupled with a mass spectrometer to measure molecular profiles of cells in response to two mitotic inhibitors, taxol and vinblastine, under a series of treatment conditions. SCMS metabolomic data were analyzed using a comprehensive approach, including data pre-treatment, visualization, statistical analysis, machine learning, and pathway enrichment analysis. For comparative studies, traditional liquid chromatography-MS (LC-MS) experiments were conducted using lysates prepared from bulk cell samples. Metabolomic profiles of single cells were visualized through Partial Least Square-Discriminant Analysis (PLS-DA), and the phenotypic biomarkers associated with emerging phenotypes induced by drug treatment were discovered and compared through a series of rigorous statistical analysis. Species of interest were further identified at both the single cell and population levels. In addition, four biological pathways potentially involved in the drug treatment were determined through pathway enrichment analysis. Our work demonstrated the capability of comprehensive pipeline studies of single cell metabolomics. This method can be potentially applied to broader types of SCMS datasets for future pharmaceutical and chemotherapeutic research.

Towards early monitoring of chemotherapy-induced drug resistance based on single cell metabolomics: Combining single-probe mass spectrometry with machine learning.

Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absent. Herein, we report an analytical approach that combines single cell mass spectrometry (SCMS) based metabolomics with machine learning (ML) models to address the existing challenges. Metabolomic profiles of live cancer cells (HCT-116) with different levels (i.e., no, low, and high) of chemotherapy-induced drug resistance were measured using the Single-probe SCMS technique. A series of ML models, including random forest (RF), artificial neural network (ANN), and penalized logistic regression (LR), were constructed to predict the degrees of drug resistance of individual cells. A systematic comparison of performance was conducted among multiple models, and the method validation was carried out experimentally. Our results indicate that these ML models, especially the RF model constructed on the obtained SCMS datasets, can rapidly and accurately predict different degrees of drug resistance of live single cells. With such rapid and reliable assessment of drug resistance demonstrated at the single cell level, our method can be potentially employed to evaluate chemotherapeutic efficacy in the clinic.