Comparing the Efficacy of Two Generations of EGFR-TKIs: An Integrated Drug-Disease Mechanistic Model Approach in EGFR-Mutated Lung Adenocarcinoma.

Mutationsin epidermal growth factor receptor (EGFR) are found in approximately 48% of Asian and 19% of Western patients with lung adenocarcinoma (LUAD), leading to aggressive tumor growth. While tyrosine kinase inhibitors (TKIs) like gefitinib and osimertinib target this mutation, treatments often face challenges such as metastasis and resistance. To address this, we developed physiologically based pharmacokinetic (PBPK) models for both drugs, simulating their distribution within the primary tumor and metastases following oral administration. These models, combined with a mechanistic knowledge-based disease model of EGFR-mutated LUAD, allow us to predict the tumor's behavior under treatment considering the diversity within the tumor cells due to different mutations. The combined model reproduces the drugs' distribution within the body, as well as the effects of both gefitinib and osimertinib on EGFR-activation-induced signaling pathways. In addition, the disease model encapsulates the heterogeneity within the tumor through the representation of various subclones. Each subclone is characterized by unique mutation profiles, allowing the model to accurately reproduce clinical outcomes, including patients' progression, aligning with RECIST criteria guidelines (version 1.1). Datasets used for calibration came from NEJ002 and FLAURA clinical trials. The quality of the fit was ensured with rigorous visual predictive checks and statistical tests (comparison metrics computed from bootstrapped, weighted log-rank tests: 98.4% (NEJ002) and 99.9% (FLAURA) similarity). In addition, the model was able to predict outcomes from an independent retrospective study comparing gefitinib and osimertinib which had not been used within the model development phase. This output validation underscores mechanistic models' potential in guiding future clinical trials by comparing treatment efficacies and identifying patients who would benefit most from specific TKIs. Our work is a step towards the design of a powerful tool enhancing personalized treatment in LUAD. It could support treatment strategy evaluations and potentially reduce trial sizes, promising more efficient and targeted therapeutic approaches. Following its consecutive prospective validations with the FLAURA2 and MARIPOSA trials (validation metrics computed from bootstrapped, weighted log-rank tests: 94.0% and 98.1%, respectively), the model could be used to generate a synthetic control arm.

Peripheral tryptophan, serotonin, kynurenine, and their metabolites in major depression: A case-control study.

AIM: Tryptophan is the sole precursor of both peripherally and centrally produced serotonin and kynurenine. In depressed patients, tryptophan, serotonin, kynurenine, and their metabolite levels remain unclear. Therefore, peripheral tryptophan and metabolites of serotonin and kynurenine were investigated extensively in 173 patients suffering from a current major depressive episode (MDE) and compared to 214 healthy controls (HC). METHODS: Fasting plasma levels of 11 peripheral metabolites were quantified: tryptophan, serotonin pathway (serotonin, its precursor 5-hydroxytryptophan and its metabolite 5-hydroxyindoleacetic acid), and kynurenine pathway (kynurenine and six of its metabolites: anthranilic acid, kynurenic acid, nicotinamide, picolinic acid, xanthurenic acid, and 3-hydroxyanthranilic acid). RESULTS: Sixty (34.7%) patients were antidepressant-drug free. Tryptophan levels did not differ between MDE patients and HC. Serotonin and its precursor (5-hydroxytryptophan) levels were lower in MDE patients than in HC, whereas, its metabolite (5-hydroxyindoleacetic acid) levels were within the standard range. Kynurenine and four of its metabolites (kynurenic acid, nicotinamide, picolinic acid, and xanthurenic acid) were lower in MDE patients. CONCLUSION: Whilst the results of this study demonstrate an association between the metabolites studied and depression, conclusions about causality cannot be made. This study uses the largest ever sample of MDE patients, with an extensive assessment of peripheral tryptophan metabolism in plasma. These findings provide new insights into the peripheral signature of MDE. The reasons for these changes should be further investigated. These results might suggest new antidepressant therapeutic strategies.

The human plasma-metabolome: Reference values in 800 French healthy volunteers; impact of cholesterol, gender and age.

Metabolomic approaches are increasingly used to identify new disease biomarkers, yet normal values of many plasma metabolites remain poorly defined. The aim of this study was to define the "normal" metabolome in healthy volunteers. We included 800 French volunteers aged between 18 and 86, equally distributed according to sex, free of any medication and considered healthy on the basis of their medical history, clinical examination and standard laboratory tests. We quantified 185 plasma metabolites, including amino acids, biogenic amines, acylcarnitines, phosphatidylcholines, sphingomyelins and hexose, using tandem mass spectrometry with the Biocrates AbsoluteIDQ p180 kit. Principal components analysis was applied to identify the main factors responsible for metabolome variability and orthogonal projection to latent structures analysis was employed to confirm the observed patterns and identify pattern-related metabolites. We established a plasma metabolite reference dataset for 144/185 metabolites. Total blood cholesterol, gender and age were identified as the principal factors explaining metabolome variability. High total blood cholesterol levels were associated with higher plasma sphingomyelins and phosphatidylcholines concentrations. Compared to women, men had higher concentrations of creatinine, branched-chain amino acids and lysophosphatidylcholines, and lower concentrations of sphingomyelins and phosphatidylcholines. Elderly healthy subjects had higher sphingomyelins and phosphatidylcholines plasma levels than young subjects. We established reference human metabolome values in a large and well-defined population of French healthy volunteers. This study provides an essential baseline for defining the "normal" metabolome and its main sources of variation.

Bariatric surgery modulates circulating and cardiac metabolites.

Bariatric procedures such as the Roux-en-Y gastric bypass (RYGB) operation offer profound metabolic enhancement in addition to their well-recognized weight loss effects. They are associated with significant reduction in cardiovascular disease risk and mortality, which suggests a surgical modification on cardiac metabolism. Metabolic phenotyping of the cardiac tissue and plasma postsurgery may give insight into cardioprotective mechanisms. The aim of the study was to compare the metabolic profiles of plasma and heart tissue extracts from RYGB- and sham-operated Wistar rats to identify the systemic and cardiac signature of metabolic surgery. A total of 27 male Wistar rats were housed individually for a week and subsequently underwent RYGB (n = 13) or sham (n = 14) operation. At week 8 postoperation, a total of 27 plasma samples and 16 heart tissue samples (8 RYGB; 8 Sham) were collected from animals and analyzed using (1)H nuclear magnetic resonance (NMR) spectroscopy and ultra performance liquid chromatography (UPLC-MS) to characterize the global metabolite perturbation induced by RYGB operation. Plasma bile acids, phosphocholines, amino acids, energy-related metabolites, nucleosides and amine metabolites, and cardiac glycogen and amino acids were found to be altered in the RYGB operated group. Correlation networks were used to identify metabolite association. The metabolic phenotype of this bariatric surgical model inferred systematic change in both myocardial and systemic activity post surgery. The altered metabolic profile following bariatric surgery reflects an enhancement of cardiac energy metabolism through TCA cycle intermediates, cardiorenal protective activity, and biochemical caloric restriction. These surgically induced metabolic shifts identify some of the potential mechanisms that contribute toward bariatric cardioprotection through gut microbiota ecological fluxes and an enterocardiac axis to shield against metabolic syndrome of cardiac dysfunction.

Global metabolic profiling of animal and human tissues via UPLC-MS.

Obtaining comprehensive, untargeted metabolic profiles for complex solid samples, e.g., animal tissues, requires sample preparation and access to information-rich analytical methodologies such as mass spectrometry (MS). Here we describe a practical two-step process for tissue samples that is based on extraction into 'aqueous' and 'organic' phases for polar and nonpolar metabolites. Separation methods such as ultraperformance liquid chromatography (UPLC) in combination with MS are needed to obtain sufficient resolution to create diagnostic metabolic profiles and identify candidate biomarkers. We provide detailed protocols for sample preparation, chromatographic procedures, multivariate analysis and metabolite identification via tandem MS (MS/MS) techniques and high-resolution MS. By using these optimized approaches, analysis of a set of samples using a 96-well plate format would take ~48 h: 1 h for system setup, 8-10 h for sample preparation, 34 h for UPLC-MS analysis and 2-3 h for preliminary/exploratory data processing, representing a robust method for untargeted metabolic screening of tissue samples.

Optimizing the use of quality control samples for signal drift correction in large-scale urine metabolic profiling studies.

The evident importance of metabolic profiling for biomarker discovery and hypothesis generation has led to interest in incorporating this technique into large-scale studies, e.g., clinical and molecular phenotyping studies. Nevertheless, these lengthy studies mandate the use of analytical methods with proven reproducibility. An integrated experimental plan for LC-MS profiling of urine, involving sample sequence design and postacquisition correction routines, has been developed. This plan is based on the optimization of the frequency of analyzing identical quality control (QC) specimen injections and using the QC intensities of each metabolite feature to construct a correction trace for all the samples. The QC-based methods were tested against other current correction practices, such as total intensity normalization. The evaluation was based on the reproducibility obtained from technical replicates of 46 samples and showed the feature-based signal correction (FBSC) methods to be superior to other methods, resulting in ~1000 and 600 metabolite features with coefficient of variation (CV) < 15% within and between two blocks, respectively. Additionally, the required frequency of QC sample injection was investigated and the best signal correction results were achieved with at least one QC injection every 2 h of urine sample injections (n = 10). Higher rates of QC injections (1 QC/h) resulted in slightly better correction but at the expense of longer total analysis time.

Optimized preprocessing of ultra-performance liquid chromatography/mass spectrometry urinary metabolic profiles for improved information recovery.

Ultra-performance liquid chromatography coupled to mass spectrometry (UPLC/MS) has been used increasingly for measuring changes of low molecular weight metabolites in biofluids/tissues in response to biological challenges such as drug toxicity and disease processes. Typically samples show high variability in concentration, and the derived metabolic profiles have a heteroscedastic noise structure characterized by increasing variance as a function of increased signal intensity. These sources of experimental and instrumental noise substantially complicate information recovery when statistical tools are used. We apply and compare several preprocessing procedures and introduce a statistical error model to account for these bioanalytical complexities. In particular, the use of total intensity, median fold change, locally weighted scatter plot smoothing, and quantile normalizations to reduce extraneous variance induced by sample dilution were compared. We demonstrate that the UPLC/MS peak intensities of urine samples should respond linearly to variable sample dilution across the intensity range. While all four studied normalization methods performed reasonably well in reducing dilution-induced variation of urine samples in the absence of biological variation, the median fold change normalization is least compromised by the biologically relevant changes in mixture components and is thus preferable. Additionally, the application of a subsequent log-based transformation was successful in stabilizing the variance with respect to peak intensity, confirming the predominant influence of multiplicative noise in peak intensities from UPLC/MS-derived metabolic profile data sets. We demonstrate that variance-stabilizing transformation and normalization are critical preprocessing steps that can benefit greatly metabolic information recovery from such data sets when widely applied chemometric methods are used.

Processing and analysis of GC/LC-MS-based metabolomics data.

Data processing forms a crucial step in metabolomics studies, impacting upon data output quality, analysis potential and subsequent biological interpretation. This chapter provides an overview of data processing and analysis of GC-MS- and LC-MS-based metabolomics data. Data preprocessing steps are described, including the different software available for dealing with such complex datasets. Multivariate techniques for the subsequent analysis of metabolomics data, including principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA), are described with illustrations. Steps for the identification of potential biomarkers and the use of metabolite databases are also outlined.

Technical and biological variation in UPLC-MS-based untargeted metabolic profiling of liver extracts: application in an experimental toxicity study on galactosamine.

The relative importance of technical versus bio-logical variation in UPLC-MS liver metabolic profiling studies was assessed on liver samples collected as part of an in vivo hepatotoxicity study. Biological variability within and between two treatment groups (three rats treated with galactosamine and three with galactosamine+uridine) was compared with sampling/extraction variability (three portions extracted from each rat liver section) and UPLC-MS platform variability (triplicate injections of each extract) for aqueous and organic extracts. The impact of scaling on error measurement was investigated on replicate injections of a quality control sample, and consequently started log-transformation was used to stabilize the variance across the ion intensity range. For aqueous extracts, technical variability was two to four times lower than within group interanimal variability. Similar results were obtained for organic extracts for the galactosamine group, sampling/extraction variability being more elevated in the galactosamine+uridine group. For both extract types, differences between treatment groups were the principal source of observed variation, and triplicate injections clustered closely in PCA plots and in HCA dendrograms, indicating small instrument variability compared to observed biological variation. This protocol can be applied to investigate differences in liver metabolic profiles between animal groups in toxicology studies and clinical investigations of liver disease.

HILIC-UPLC-MS for exploratory urinary metabolic profiling in toxicological studies.

Hydrophilic interaction ultra performance liquid chromatography (HILIC-UPLC) permits the analysis of highly polar metabolites, providing complementary information to reversed-phase (RP) chromatography. HILIC-UPLC-TOF-MS was investigated for the global metabolic profiling of rat urine samples generated in an experimental hepatotoxicity study of galactosamine (galN) and the concomitant investigation of the protective effect of glycine. Within-run repeatability and stability over a large sample batch (>200 samples, 60 h run-time) was assessed through the repeat analysis of a quality control sample. Following system equilibration, excellent repeatability was observed in terms of retention time (CV < 1.7%), signal intensity (CV < 14%), and mass variability (<0.005 amu), providing a good measure of reproducibility. Classification of urinary metabolic profiles according to treatment was observed, with significant changes in specific metabolites after galN exposure, including increased urocanic acid, N-acetylglucosamine, and decreased 2-oxoglutarate. A novel finding from this HILIC-UPLC-MS approach was elevated urinary tyramine in galN-treated rats, reflecting disturbed amino acid metabolism. These results show HILIC-UPLC-MS to be a promising method for global metabolic profiling, demonstrating high within-run repeatability, even over an extended run time. Retention of polar endogenous analytes and xenobiotic metabolites was improved compared with RP studies, including galN, N-acetylglucosamine, oxoglutarate, and urocanic acid, enhancing metabolome coverage and potentially improving biomarker discovery.

Optimization and evaluation of metabolite extraction protocols for untargeted metabolic profiling of liver samples by UPLC-MS.

A series of six protocols were evaluated for UPLC-MS based untargeted metabolic profiling of liver extracts in terms of reproducibility and number of metabolite features obtained. These protocols, designed to extract both polar and nonpolar metabolites, were based on (i) a two stage extraction approach or (ii) a simultaneous extraction in a biphasic mixture, employing different volumes and combinations of extraction and resuspension solvents. A multivariate statistical strategy was developed to allow comparison of the multidimensional variation between the methods. The optimal protocol for profiling both polar and nonpolar metabolites was found to be an aqueous extraction with methanol/water followed by an organic extraction with dichloromethane/methanol, with resuspension of the dried extracts in methanol/water before UPLC-MS analysis. This protocol resulted in a median CV of feature intensities among experimental replicates of <20% for aqueous extracts and <30% for organic extracts. These data demonstrate the robustness of the proposed protocol for extracting metabolites from liver samples and make it well suited for untargeted liver profiling in studies exploring xenobiotic hepatotoxicity and clinical investigations of liver disease. The generic nature of this protocol facilitates its application to other tissues, for example, brain or lung, enhancing its utility in clinical and toxicological studies.

Ultra performance liquid chromatography-mass spectrometry profiling of bile acid metabolites in biofluids: application to experimental toxicology studies.

We have developed an ultra performance liquid chromatography-mass spectrometry (UPLC-MS(E)) method to measure bile acids (BAs) reproducibly and reliably in biological fluids and have applied this approach for indications of hepatic damage in experimental toxicity studies. BAs were extracted from serum using methanol, and an Acquity HSS column coupled to a Q-ToF mass spectrometer was used to separate and identify 25 individual BAs within 5 min. Employing a gradient elution of water and acetonitrile over 21 min enabled the detection of a wide range of endogenous metabolites, including the BAs. The utilization of MS(E) allowed for characteristic fragmentation information to be obtained in a single analytical run, easily distinguishing glycine and taurine BA conjugates. The proportions of these conjugates were altered markedly in an experimental toxic state induced by galactosamine exposure in rats. Principally, taurine-conjugated BAs were greatly elevated ( approximately 50-fold from control levels), and were highly correlated to liver damage severity as assessed by histopathological scoring (r = 0.83), indicating their potential as a sensitive measure of hepatic damage. The UPLC-MS approach to BA analysis offers a sensitive and reproducible tool that will be of great value in exploring both markers and mechanisms of hepatotoxicity and can readily be extended to clinical studies of liver damage.