Plasma Metabolomic Changes following PI3K Inhibition as Pharmacodynamic Biomarkers: Preclinical Discovery to Phase I Trial Evaluation

Targeting PI3K family with small-molecule inhibitors in cancer therapy: current clinical status and future directions

The Phosphatidylinositol-3-kinase (PI3K) family is well-known to comprise three classes of intracellular enzymes. Class I PI3Ks primarily function in signaling by responding to cell surface receptor stimulation, while class II and III are more involved in membrane transport. Under normal physiological conditions, the PI3K signaling network orchestrates cell growth, division, migration and survival. Aberrant activation of the PI3K signaling pathway disrupts cellular activity and metabolism, often marking the onset of cancer. Currently, the Food and Drug Administration (FDA) has approved the clinical use of five class I PI3K inhibitors. These small-molecule inhibitors, which exhibit varying selectivity for different class I PI3K family members, are primarily used in the treatment of breast cancer and hematologic malignancies. Therefore, the development of novel class I PI3K inhibitors has been a prominent research focus in the field of oncology, aiming to enhance potential therapeutic selectivity and effectiveness. In this review, we summarize the specific structures of PI3Ks and their functional roles in cancer progression. Additionally, we critically evaluate small molecule inhibitors that target class I PI3K, with a particular focus on their clinical applications in cancer treatment. Moreover, we aim to analyze therapeutic approaches for different types of cancers marked by aberrant PI3K activation and to identify potential molecular targets amenable to intervention with small-molecule inhibitors. Ultimately, we propose future directions for the development of therapeutic strategies that optimize cancer treatment outcomes by modulating the PI3K family.

Metabolomics and lipidomics strategies in modern drug discovery and development

Metabolomics and lipidomics have an increasingly pivotal role in drug discovery and development. In the context of drug discovery, monitoring changes in the levels or composition of metabolites and lipids relative to genetic variations yields functional insights, bolstering human genetics and (meta)genomic methodologies. This approach also sheds light on potential novel targets for therapeutic intervention. In the context of drug development, metabolite and lipid biomarkers contribute to enhanced success rates, promising a transformative impact on precision medicine. In this review, we deviate from analytical chemist-focused perspectives, offering an overview tailored to drug discovery. We provide introductory insight into state-of-the-art mass spectrometry (MS)-based metabolomics and lipidomics techniques utilized in drug discovery and development, drawing from the collective expertise of our research teams. We comprehensively outline the application of metabolomics and lipidomics in advancing drug discovery and development, spanning fundamental research, target identification, mechanisms of action, and the exploration of biomarkers.

Identification of Plasma Metabolites Associated with Lung Cancer Survival

Limited knowledge has been reported regarding the performance of plasma metabolomics for predicting lung cancer prognosis. In this chapter, we compared the plasma metabolomics of lung cancer patients with differential disease-free survival (DFS, <3 years vs. >4 years) using liquid chromatography-mass spectrometry. We identified 29 survival-related aqueous metabolites but no lipid metabolites. Amino acids and organic acids constitute the majority of these metabolites. The metabolic pathways of these metabolites were cysteine and methionine metabolism and arginine biosynthesis. The Cox proportional hazards regression models confirmed the predictive values of 18 metabolites for DFS, while the phosphocholine and xanthine showed independent predictive values. Regarding cancer phenotypes, thelephoric acid, phosphocholine, inosine, 3-hydroxyanthranilic acid, hypoxanthine, xanthine, and 4-hydroxybenzoic acid showed good correction with lymph node metastasis. Taken together, plasma metabolomics is a powerful tool for identifying prognostic metabolites of lung cancer.

Recent advances in metabolomics analysis for early drug development

The pharmaceutical industry adapted proteomics and other 'omics technologies for drug research early following their initial introduction. Although metabolomics lacked behind in this development, it has now become an accepted and widely applied approach in early drug development. Over the past few decades, metabolomics has evolved from a pure exploratory tool to a more mature and quantitative biochemical technology. Several metabolomics-based platforms are now applied during the early phases of drug discovery. Metabolomics analysis assists in the definition of the physiological response and target engagement (TE) markers as well as elucidation of the mode of action (MoA) of drug candidates under investigation. In this review, we highlight recent examples and novel developments of metabolomics analyses applied during early drug development.

The associations of nitrated polycyclic aromatic hydrocarbon exposures with plasma glucose and amino acids

Nitrated polycyclic aromatic hydrocarbons (nitro-PAHs) have been widely studied for their mutagenic and carcinogenic effects. This study aims to investigate whether exposure to nitro-PAHs is associated with biomarkers of carbohydrate metabolism, an underlying risk factor for metabolic disorder. Early morning urine and blood samples were longitudinally collected two times with a four-week interval from 43 healthy adults. Five urinary amino-PAHs (1-aminonaphthalene, 2-aminonaphthalene, 9-aminophenanthrene, 2-aminofluorene, and 1-aminopyrene) were measured as biomarkers of nitro-PAH exposures. We measured plasma concentrations of glucose and six amino acids that can regulate insulin secretion, including aspartate (Asp), glutamate (Glu), glutamine (Gln), alanine (Ala), Arginine (Arg), and ornithine (Orn). We found that increasing concentrations of 9-aminophenanthrene were significantly associated with increasing glucose levels and with decreasing Asp, Glu, Ala, and Orn levels. We estimated that 26.4 %-43.8 % of the 9-aminophenanthrene-associated increase in glucose level was mediated by Asp, Glu, and Orn. These results suggest that exposure to certain nitro-PAHs affects glucose homeostasis, partly resulting from the depletion of insulin-stimulating amino acids (Asp, Glu, and Orn).

Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics

Metabolomics has been widely used for investigating the biological functions of disease expression and has the potential to discover biomarkers in circulating biofluids or tissue extracts that reflect in phenotypic changes. Metabolic profiling has advantages because of the use of unbiased techniques, including multivariate analysis, and has been applied in pharmacological studies to predict therapeutic and adverse reactions of drugs, which is called pharmacometabolomics (PMx). Nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based metabolomics has contributed to the discovery of recent disease biomarkers; however, the optimal strategy for the study purpose must be selected from many established protocols, methodologies and analytical platforms. Additionally, information on molecular localization in tissue is essential for further functional analyses related to therapeutic and adverse effects of drugs in the process of drug development. MS imaging (MSI) is a promising technology that can visualize molecules on tissue surfaces without labeling and thus provide localized information. This review summarizes recent uses of MS-based global and wide-targeted metabolomics technologies and the advantages of the MSI approach for PMx and highlights the PMx technique for the biomarker discovery of adverse drug effects.

Method comparison of HPLC-ninhydrin-photometry and UHPLC-PITC-tandem mass spectrometry for serum amino acid analyses in patients with complex congenital heart disease and controls

INTRODUCTION

Metabolomics studies are not routine when quantifying amino acids (AA) in congenital heart disease (CHD).

OBJECTIVES

Comparative analysis of 24 AA in serum by traditional high-performance liquid chromatography (HPLC) based on ion exchange and ninhydrin derivatisation followed by photometry (PM) with ultra-high-performance liquid chromatography and phenylisothiocyanate derivatisation followed by tandem mass spectrometry (TMS); interpretation of findings in CHD patients and controls.

METHODS

PM: Sample analysis as above (total run time, ~ 119 min). TMS: Sample analysis by AbsoluteIDQ® p180 kit assay (BIOCRATES Life Sciences AG, Innsbruck, Austria), which employs PITC derivatisation; separation of analytes on a Waters Acquity UHPLC BEH18 C18 reversed-phase column, using water and acetonitrile with 0.1% formic acid as the mobile phases; and quantification on a Triple-Stage Quadrupole tandem mass spectrometer (Thermo Fisher Scientific, Waltham, MA) with electrospray ionisation in the presence of internal standards (total run time, ~ 8 min). Calculation of coefficients of variation (CV) (for precision), intra- and interday accuracies, limits of detection (LOD), limits of quantification (LOQ), and mean concentrations.

RESULTS

Both methods yielded acceptable results with regard to precision (CV < 10% PM, < 20% TMS), accuracies (< 10% PM, < 34% TMS), LOD, and LOQ. For both Fontan patients and controls AA concentrations differed significantly between methods, but patterns yielded overall were parallel.

CONCLUSION

Serum AA concentrations differ with analytical methods but both methods are suitable for AA pattern recognition. TMS is a time-saving alternative to traditional PM under physiological conditions as well as in patients with CHD.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Identifier NCT03886935, date of registration March 27th, 2019 (retrospectively registered).

Cerebrospinal fluid ctDNA and metabolites are informative biomarkers for the evaluation of CNS germ cell tumors

Serum and cerebrospinal fluid (CSF) levels of α-fetoprotein and β-subunit of human chorionic gonadotropin are used as biomarkers for the management of central nervous system (CNS) germ cell tumors (GCTs). However, additional discriminating biomarkers are required. Especially, biomarkers to differentiate non-germinomatous germ cell tumors (NGGCTs) from germinomas are critical, as these have a distinct prognosis. We investigated CSF samples from 12 patients with CNS-GCT patients (8 germinomas and 4 NGGCTs). We analyzed circulating tumor DNA (ctDNA) in CSF to detect mutated genes. We also used liquid chromatography-mass spectrometry to characterize metabolites in CSF. We detected KIT and/or NRAS mutation, known as frequently mutated genes in GCTs, in 3/12 (25%) patients. We also found significant differences in the abundance of 15 metabolites between control and GCT, with unsupervised hierarchical clustering analysis. Metabolites related to the TCA cycle were increased in GCTs. Urea, ornithine, and short-chain acylcarnitines were decreased in GCTs. Moreover, we also detected several metabolites (e.g., betaine, guanidine acetic acid, and 2-aminoheptanoic acid) that displayed significant differences in abundance in patients with germinomas and NGGCTs. Our results suggest that ctDNA and metabolites in CSF can serve as novel biomarkers for CNS-GCTs and can be useful to differentiate germinomas from NGGCTs.

Metabolomic changes of the multi (-AGC-) kinase inhibitor AT13148 in cells, mice and patients are associated with NOS regulation

INTRODUCTION

To generate biomarkers of target engagement or predictive response for multi-target drugs is challenging. One such compound is the multi-AGC kinase inhibitor AT13148. Metabolic signatures of selective signal transduction inhibitors identified in preclinical models have previously been confirmed in early clinical studies. This study explores whether metabolic signatures could be used as biomarkers for the multi-AGC kinase inhibitor AT13148.

OBJECTIVES

To identify metabolomic changes of biomarkers of multi-AGC kinase inhibitor AT13148 in cells, xenograft / mouse models and in patients in a Phase I clinical study.

METHODS

HILIC LC-MS/MS methods and Biocrates AbsoluteIDQ™ p180 kit were used for targeted metabolomics; followed by multivariate data analysis in SIMCA and statistical analysis in Graphpad. Metaboanalyst and String were used for network analysis.

RESULTS

BT474 and PC3 cells treated with AT13148 affected metabolites which are in a gene protein metabolite network associated with Nitric oxide synthases (NOS). In mice bearing the human tumour xenografts BT474 and PC3, AT13148 treatment did not produce a common robust tumour specific metabolite change. However, AT13148 treatment of non-tumour bearing mice revealed 45 metabolites that were different from non-treated mice. These changes were also observed in patients at doses where biomarker modulation was observed. Further network analysis of these metabolites indicated enrichment for genes associated with the NOS pathway. The impact of AT13148 on the metabolite changes and the involvement of NOS-AT13148- Asymmetric dimethylarginine (ADMA) interaction were consistent with hypotension observed in patients in higher dose cohorts (160-300 mg).

CONCLUSION

AT13148 affects metabolites associated with NOS in cells, mice and patients which is consistent with the clinical dose-limiting hypotension.

Exploring Cancer Metabolism: Applications of Metabolomics and Metabolic Phenotyping in Cancer Research and Diagnostics

Altered metabolism is one of the key hallmarks of cancer. The development of sensitive, reproducible and robust bioanalytical tools such as Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry techniques offers numerous opportunities for cancer metabolism research, and provides additional and exciting avenues in cancer diagnosis, prognosis and for the development of more effective and personalized treatments. In this chapter, we introduce the current state of the art of metabolomics and metabolic phenotyping approaches in cancer research and clinical diagnostics.

Recent advances in microfluidic methods in cancer liquid biopsy

Early cancer detection, its monitoring, and therapeutical prediction are highly valuable, though extremely challenging targets in oncology. Significant progress has been made recently, resulting in a group of devices and techniques that are now capable of successfully detecting, interpreting, and monitoring cancer biomarkers in body fluids. Precise information about malignancies can be obtained from liquid biopsies by isolating and analyzing circulating tumor cells (CTCs) or nucleic acids, tumor-derived vesicles or proteins, and metabolites. The current work provides a general overview of the latest on-chip technological developments for cancer liquid biopsy. Current challenges for their translation and their application in various clinical settings are discussed. Microfluidic solutions for each set of biomarkers are compared, and a global overview of the major trends and ongoing research challenges is given. A detailed analysis of the microfluidic isolation of CTCs with recent efforts that aimed at increasing purity and capture efficiency is provided as well. Although CTCs have been the focus of a vast microfluidic research effort as the key element for obtaining relevant information, important clinical insights can also be achieved from alternative biomarkers, such as classical protein biomarkers, exosomes, or circulating-free nucleic acids. Finally, while most work has been devoted to the analysis of blood-based biomarkers, we highlight the less explored potential of urine as an ideal source of molecular cancer biomarkers for point-of-care lab-on-chip devices.

Multifocal demyelinating motor neuropathy and hamartoma syndrome associated with a de novo PTEN mutation

OBJECTIVE

To describe a patient with a multifocal demyelinating motor neuropathy with onset in childhood and a mutation in phosphatase and tensin homolog (PTEN), a tumor suppressor gene associated with inherited tumor susceptibility conditions, macrocephaly, autism, ataxia, tremor, and epilepsy. Functional implications of this protein have been investigated in Parkinson and Alzheimer diseases.

METHODS

We performed whole-exome sequencing in the patient's genomic DNA validated by Sanger sequencing. Immunoblotting, in vitro enzymatic assay, and label-free shotgun proteomic profiling were performed in the patient's fibroblasts.

RESULTS

The predominant clinical presentation of the patient was a childhood onset, asymmetric progressive multifocal motor neuropathy. In addition, he presented with macrocephaly, autism spectrum disorder, and skin hamartomas, considered as clinical criteria for PTEN-related hamartoma tumor syndrome. Extensive tumor screening did not detect any malignancies. We detected a novel de novo heterozygous c.269T>C, p.(Phe90Ser) PTEN variant, which was absent in both parents. The pathogenicity of the variant is supported by altered expression of several PTEN-associated proteins involved in tumorigenesis. Moreover, fibroblasts showed a defect in catalytic activity of PTEN against the secondary substrate, phosphatidylinositol 3,4-trisphosphate. In support of our findings, focal hypermyelination leading to peripheral neuropathy has been reported in PTEN-deficient mice.

CONCLUSION

We describe a novel phenotype, PTEN-associated multifocal demyelinating motor neuropathy with a skin hamartoma syndrome. A similar mechanism may potentially underlie other forms of Charcot-Marie-Tooth disease with involvement of the phosphatidylinositol pathway.

Modulation of Plasma Metabolite Biomarkers of the MAPK Pathway with MEK Inhibitor RO4987655: Pharmacodynamic and Predictive Potential in Metastatic Melanoma

MAPK pathway activation is frequently observed in human malignancies, including melanoma, and is associated with sensitivity to MEK inhibition and changes in cellular metabolism. Using quantitative mass spectrometry-based metabolomics, we identified in preclinical models 21 plasma metabolites including amino acids, propionylcarnitine, phosphatidylcholines, and sphingomyelins that were significantly altered in two B-RAF-mutant melanoma xenografts and that were reversed following a single dose of the potent and selective MEK inhibitor RO4987655. Treatment of non-tumor-bearing animals and mice bearing the PTEN-null U87MG human glioblastoma xenograft elicited plasma changes only in amino acids and propionylcarnitine. In patients with advanced melanoma treated with RO4987655, on-treatment changes of amino acids were observed in patients with disease progression and not in responders. In contrast, changes in phosphatidylcholines and sphingomyelins were observed in responders. Furthermore, pretreatment levels of seven lipids identified in the preclinical screen were statistically significantly able to predict objective responses to RO4987655. The RO4987655 treatment-related changes were greater than baseline physiological variability in nontreated individuals. This study provides evidence of a translational exo-metabolomic plasma readout predictive of clinical efficacy together with pharmacodynamic utility following treatment with a signal transduction inhibitor. Mol Cancer Ther; 16(10); 2315-23. ©2017 AACR.

Interlaboratory Reproducibility of a Targeted Metabolomics Platform for Analysis of Human Serum and Plasma

A critical question facing the field of metabolomics is whether data obtained from different centers can be effectively compared and combined. An important aspect of this is the interlaboratory precision (reproducibility) of the analytical protocols used. We analyzed human samples in six laboratories using different instrumentation but a common protocol (the AbsoluteIDQ p180 kit) for the measurement of 189 metabolites via liquid chromatography (LC) or flow injection analysis (FIA) coupled to tandem mass spectrometry (MS/MS). In spiked quality control (QC) samples 82% of metabolite measurements had an interlaboratory precision of <20%, while 83% of averaged individual laboratory measurements were accurate to within 20%. For 20 typical biological samples (serum and plasma from healthy individuals) the median interlaboratory coefficient of variation (CV) was 7.6%, with 85% of metabolites exhibiting a median interlaboratory CV of <20%. Precision was largely independent of the type of sample (serum or plasma) or the anticoagulant used but was reduced in a sample from a patient with dyslipidaemia. The median interlaboratory accuracy and precision of the assay for standard reference plasma (NIST SRM 1950) were 107% and 6.7%, respectively. Likely sources of irreproducibility were the near limit of detection (LOD) typical abundance of some metabolites and the degree of manual review and optimization of peak integration in the LC-MS/MS data after acquisition. Normalization to a reference material was crucial for the semi-quantitative FIA measurements. This is the first interlaboratory assessment of a widely used, targeted metabolomics assay illustrating the reproducibility of the protocol and how data generated on different instruments could be directly integrated in large-scale epidemiological studies.

Metabolomics combined with pattern recognition and bioinformatics analysis methods for the development of pharmacodynamic biomarkers on liver fibrosis

Metabolomics combined with pattern recognition and network analysis maybe an attractive strategy for the pharmacodynamics biomarkers development on liver fibrosis.

Progress in Metabolomics Standardisation and its Significance in Future Clinical Laboratory Medicine

Today, the technology of 'targeted' based metabolomics is pivotal in the clinical analysis workflow as it provides information of metabolic phenotyping (metabotypes) by enhancing our understanding of metabolism of complex diseases, biomarker discovery for disease development, progression, treatment, and drug function and assessment. This review is focused on surveying and providing a gap analysis on metabolic phenotyping with a focus on targeted based metabolomics from an instrumental, technical point-of-view discussing the state-of-the-art instrumentation, pre- to post- analytical aspects as well as an overall future necessity for biomarker discovery and future (pre-) clinical routine application.