Bioenergetic Profiling in Glioblastoma Multiforme Patients with Different Clinical Outcomes.

The accumulation of cell biomass is associated with dramatically increased bioenergetic and biosynthetic demand. Metabolic reprogramming, once thought as an epiphenomenon, currently relates to disease progression, also in response to extracellular fate-decisive signals. Glioblastoma multiforme patients often suffer misdiagnosis, short survival time, low quality of life, and poor disease management options. Today, tumor genetic testing and histological analysis guide diagnosis and treatment. We and others appreciate that metabolites complement translational biomarkers and molecular signatures in disease profiling and phenotyping. Herein, we coupled a mixed-methods content analysis to a mass spectrometry-based untargeted metabolomic analysis on plasma samples from glioblastoma multiforme patients to delineate the role of metabolic remodeling in biological plasticity and, hence, disease severity. Following data processing and analysis, we established a bioenergetic profile coordinated by the mitochondrial function and redox state, lipids, and energy substrates. Our findings show that epigenetic modulators are key players in glioblastoma multiforme cell metabolism, in particular when microRNAs are considered. We propose that biological plasticity in glioblastoma multiforme is a mechanism of adaptation and resistance to treatment which is eloquently revealed by bioenergetics.

Pharmacometabolomics-Based Translational Biomarkers: How to Navigate the Data Ocean.

Metabolome is the end point of the genome-environment interplay, and enables an important holistic overview of individual adaptability and host responses to environmental, ecological, as well as endogenous changes such as disease. Pharmacometabolomics is the application of metabolome knowledge to decipher the mechanisms of interindividual and intraindividual variations in drug efficacy and safety. Pharmacometabolomics also contributes to prediction of drug treatment outcomes on the basis of baseline (predose) and postdose metabotypes through mathematical modeling. Thus, pharmacometabolomics is a strong asset for a diverse community of stakeholders interested in theory and practice of evidence-based and precision/personalized medicine: academic researchers, public health scholars, health professionals, pharmaceutical, diagnostics, and biotechnology industries, among others. In this expert review, we discuss pharmacometabolomics in four contexts: (1) an interdisciplinary omics tool and field to map the mechanisms and scale of interindividual variability in drug effects, (2) discovery and development of translational biomarkers, (3) advance digital biomarkers, and (4) empower drug repurposing, a field that is increasingly proving useful in the current era of Covid-19. As the applications of pharmacometabolomics are growing rapidly in the current postgenome era, next-generation proteomics and metabolomics follow the example of next-generation sequencing analyses. Pharmacometabolomics can also empower data reliability and reproducibility through multiomics integration strategies, which use each data layer to correct, connect with, and inform each other. Finally, we underscore here that contextual data remain crucial for precision medicine and drug development that stand the test of time and clinical relevance.

Pharmacometabolomics-aided Pharmacogenomics in Autoimmune Disease.

Inter-individual variability has been a major hurdle to optimize disease management. Precision medicine holds promise for improving health and healthcare via tailor-made therapeutic strategies. Herein, we outline the paradigm of "pharmacometabolomics-aided pharmacogenomics" in autoimmune diseases. We envisage merging pharmacometabolomic and pharmacogenomic data (to address the interplay of genomic and environmental influences) with information technologies to facilitate data analysis as well as sense- and decision-making on the basis of synergy between artificial and human intelligence. Humans can detect patterns, which computer algorithms may fail to do so, whereas data-intensive and cognitively complex settings and processes limit human ability. We propose that better-informed, rapid and cost-effective omics studies need the implementation of holistic and multidisciplinary approaches.

Evaluation of a stable gonadotropin-releasing hormone analog in mice for the treatment of endocrine disorders and prostate cancer.

Gonadotropin-releasing hormone (GnRH) receptor agonists have wide clinical applications including the treatment of prostate cancer and endocrine disorders. However, such agonists are characterized by poor pharmacokinetic properties, often requiring repeated administration or special formulations. Therefore, the development of novel peptide analogs with enhanced in vivo stability could potentially provide therapeutic alternatives. The pharmacological evaluation of a bioactive peptide [Des-Gly¹°,Tyr5(OMe),D-Leu6,Aze-NHEt9]GnRH, analog 1, is presented herein and compared with leuprolide. Peptide stability was evaluated using mouse kidney membrane preparations, followed by a liquid chromatography-tandem mass spectrometry-based approach that afforded identification and quantification of its major metabolites. The analog was significantly more stable in vitro in comparison with leuprolide. In vitro and in vivo stability results correlated well, encouraging us to develop a clinically relevant pharmacokinetic mouse model, which facilitated efficacy measurements using testosterone as a biomarker. Analog 1, an agonist of the GnRH receptor with a binding affinity in the nanomolar range, caused testosterone release in mice that was acutely dose-dependent, an effect blocked by the GnRH receptor antagonist cetrorelix. Repeated dosing studies in mice demonstrated that analog 1 was well tolerated and had potency similar to that of leuprolide, based on plasma and testis testosterone reduction and histopathological findings. Analog 1 also shared with leuprolide similar significant antiproliferative activity on androgen-dependent prostate cancer (LNCaP) cells. On the basis of pharmacokinetic advantages, we expect that analog 1 or analogs based on this new design will be therapeutically advantageous for the treatment of cancer and endocrine disorders.

Simultaneous absolute quantification of the glucose-dependent insulinotropic polypeptides GIP1-42 and GIP3-42 in mouse plasma by LC/ESI-MS/MS: preclinical evaluation of DP-IV inhibitors.

The incretin hormone Glucose-dependent Insulinotropic Polypeptide GIP1-42 (approximately 5 kDa), is released postprandially, and rapidly degraded by Dipeptidyl Peptidase IV (DP-IV) to yield the inactive GIP3-42. Methods for the quantification of the pair of GIP peptides include combinations of immunoassays; however, mass spectrometry based approaches can offer the improved selectivity required for the distinction between the active and inactive forms. In this study, we report an LC/ESI-MS/MS approach for the simultaneous absolute quantification of GIP1-42 and GIP3-42 via the corresponding surrogate proteolytic peptide fragments, GIP1-16 and GIP3-16. These surrogate peptides afford approximately 250-fold improvement in lower limits of quantification (LLOQ) compared to the precursor proteins. The LLOQ of the reported method was 5 ng/mL (5-1000 ng/mL) for GIP1-42 and 10 ng/mL (10-1000 ng/mL) for GIP3-42, using 100 microL of mouse plasma. This is the first reported study in which the GIP1-42 and GIP3-42 polypeptides are quantified simultaneously with LC/ESI-MS/MS via their tryptic surrogate peptides. The approach is suitable for both preclinical and clinical pharmacokinetic studies due to the low volume required for the analysis. The described methodology was applied to a pharmacokinetic study, in which enhanced stability of exogenously administered GIP1-42 was demonstrated in mice treated with a DP-IV inhibitor.