Pharmaceutical research and metabolomics in the newborn

Metabolomics in pharmacology - a delve into the novel field of pharmacometabolomics

Introduction: Pharmacometabolomics is an emerging science pursuing the application of precision medicine. Combining both genetic and environmental factors, the so-called pharmacometabolomic approach guides patient selection and stratification in clinical trials and optimizes personalized drug dosage, improving efficacy and safety.Areas covered: This review illustrates the progressive introduction of pharmacometabolomics as an innovative solution for enhancing the discovery of novel drugs and improving research and development (R&D) productivity of the pharmaceutical industry. An extended analysis on published pharmacometabolomics studies both in animal models and humans includes results obtained in several areas such as hepatology, gastroenterology, nephrology, neuropsychiatry, oncology, drug addiction, embryonic cells, neonatology, and microbiomics.Expert opinion: a tailored, individualized therapy based on the optimization of pharmacokinetics and pharmacodynamics, the improvement of drug efficacy, and the abolition of drug toxicity and adverse drug reactions is a key issue in precision medicine. Genetics alone has become insufficient for deciphring intra- and inter-individual variations in drug-response, since they originate both from genetic and environmental factors, including human microbiota composition. The association between pharmacogenomics and pharmacometabolomics may be considered the new strategy for an in-deep knowledge on changes and alterations in human and microbial metabolic pathways due to the action of a drug.

Precision Medicine for Neonatal Sepsis

Neonatal sepsis remains a significant cause of morbidity and mortality especially in the preterm infant population. The ability to promptly and accurately diagnose neonatal sepsis based on clinical evaluation and laboratory blood tests remains challenging. Advances in high-throughput molecular technologies have increased investigations into the utility of transcriptomic, proteomic and metabolomic approaches as diagnostic tools for neonatal sepsis. A systems-level understanding of neonatal sepsis, obtained by using omics-based technologies (at the transcriptome, proteome or metabolome level), may lead to new diagnostic tools for neonatal sepsis. In particular, recent omic-based studies have identified distinct transcriptional signatures and metabolic or proteomic biomarkers associated with sepsis. Despite the emerging need for a systems biology approach, future studies have to address the challenges of integrating multi-omic data with laboratory and clinical meta-data in order to translate outcomes into precision medicine for neonatal sepsis. Omics-based analytical approaches may advance diagnostic tools for neonatal sepsis. More research is needed to validate the recent systems biology findings in order to integrate multi-dimensional data (clinical, laboratory and multi-omic) for future translation into precision medicine for neonatal sepsis. This review will discuss the possible applications of omics-based analyses for identification of new biomarkers and diagnostic signatures for neonatal sepsis, focusing on the immune-compromised preterm infant and considerations for clinical translation.

Prediction of serum theophylline concentrations and cytochrome P450 1A2 activity by analyzing urinary metabolites in preterm infants

AIMS

The purpose of this study was to explore clinical markers reflecting developmental changes in drug clearance by preterm infants.

METHODS

Preterm infants administered aminophylline or theophylline to treat apnoea of prematurity were enrolled in this study. Trough and one of 2 h, 4 h or 6 h post-dose blood samples and urine samples were collected during steady state, to determine the concentrations of theophylline and its targeted metabolites. CYP1A2*1C and CYP1A2*1F genotypes were analyzed. Total, renal and nonrenal clearances of theophylline were calculated, and cytochrome P450 1A2 (CYP1A2) activity was obtained from the ratio of 1-methyluric acid and 3-methylxanthine to theophylline in urine. Multiple linear regression analysis was performed to evaluate the relationships between theophylline clearance and the clinical characteristics of preterm infants.

RESULTS

A total of 152 samples from 104 preterm infants were analyzed. A strong association between the serum trough and urine theophylline concentrations was found (P < 0.001). Total, renal and nonrenal clearances of theophylline were 0.50 ± 0.29 ml kg^-1  min^-1 , 0.16 ± 0.06 ml kg^-1  min^-1 and 0.34 ± 0.28 ml kg^-1  min^-1 , respectively. CYP1A2 activity correlated positively with the postnatal age and postmenstrual age. However, CYP1A2 genotype was not associated with CYP1A2 activity, which was significantly associated with nonrenal clearance. CYP1A2 activity, postnatal age , weight and 24-h urine output were significantly associated with total theophylline clearance.

CONCLUSIONS

CYP1A2 activity can be monitored using noninvasive random urine samples, and it can be used to assess developmental changes in theophylline clearance by preterm infants.

Applying metabolomics to cardiometabolic intervention studies and trials: past experiences and a roadmap for the future

Metabolomics and lipidomics are emerging methods for detailed phenotyping of small molecules in samples. It is hoped that such data will: (i) enhance baseline prediction of patient response to pharmacotherapies (beneficial or adverse); (ii) reveal changes in metabolites shortly after initiation of therapy that may predict patient response, including adverse effects, before routine biomarkers are altered; and( iii) give new insights into mechanisms of drug action, particularly where the results of a trial of a new agent were unexpected, and thus help future drug development. In these ways, metabolomics could enhance research findings from intervention studies. This narrative review provides an overview of metabolomics and lipidomics in early clinical intervention studies for investigation of mechanisms of drug action and prediction of drug response (both desired and undesired). We highlight early examples from drug intervention studies associated with cardiometabolic disease. Despite the strengths of such studies, particularly the use of state-of-the-art technologies and advanced statistical methods, currently published studies in the metabolomics arena are largely underpowered and should be considered as hypothesis-generating. In order for metabolomics to meaningfully improve stratified medicine approaches to patient treatment, there is a need for higher quality studies, with better exploitation of biobanks from randomized clinical trials i.e. with large sample size, adjudicated outcomes, standardized procedures, validation cohorts, comparison witth routine biochemistry and both active and control/placebo arms. On the basis of this review, and based on our research experience using clinically established biomarkers, we propose steps to more speedily advance this area of research towards potential clinical impact.

The metabolomic signature of hematologic malignancies

The ongoing accumulation of knowledge raises hopes that understanding tumor metabolism will provide new ways for predicting, diagnosing, and even treating cancers. Some metabolic biomarkers are at present routinely utilized to diagnose cancer and metabolic alterations of tumors are being confirmed as therapeutic targets. The growing utilization of metabolomics in clinical research may rapidly turn it into one of the most potent instruments used to detect and fight tumor. In fact, while the current state and trends of high throughput metabolomics profiling focus on the purpose of discovering biomarkers and hunting for metabolic mechanism, a prospective direction, namely reprogramming metabolomics, highlights the way to use metabolomics approach for the aim of treatment of disease by way of reconstruction of disturbed metabolic pathways. In this review, we present an ample summary of the current clinical appliances of metabolomics in hematological malignancies.

Predictive scores for mortality in full-term infants with necrotizing enterocolitis: experience of a tertiary hospital in Southwest China

BACKGROUND

Although many risk factors for mortality of necrotizing enterocolitis (NEC) were investigated, most of them were obtained from preterm infants, and few works focused on the prognostic risk factors in fullterm infants. This study aimed to identify risk factors and develop a prediction score model for mortality in fullterm neonates with NEC.

METHODS

The risk factors were analyzed retrospectively by bivariate and multivariate logistic regression analysis in 153 full-term neonates with NEC, who were hospitalized in Children's Hospital of Chongqing Medical University from 2000 to 2013. A prediction score model was developed according to the regression coefficients of risk factors.

RESULTS

The mortality of the infants was 19.6% (30/153). The non-survivors had a younger age of diagnosis and advanced stage of NEC (P<0.05). They had a higher prevalence of respiratory failure, intestinal perforation, peritonitis and other complications, compared with the survivors (P<0.05). On the day of diagnosis, the nonsurvivors were more likely to have abnormal laboratory indicators than survivors (P<0.05). Age at diagnosis [odds ratio (OR)=0.91, 95% confidence interval (CI)=0.836-0.99], respiratory failure (OR=2.76, 95% CI=1.10-6.92), and peritonitis (OR=26.36, 95% CI=7.52-173.92) had significant independent contributions to death. A score model predicting death was developed, and the area under the receiver operating characteristic curve was 0.869 (95% CI=0.803-0.935). All infants with scores ≥8 died.

CONCLUSION

Younger age at diagnosis, peritonitis, and respiratory failure might be risk factors for the mortality of full-term infants with NEC. Infants with a predictive score of 8 were at high risk for death.

Assessment of glomerular filtration rate in the neonate: is creatinine the best tool?

PURPOSE OF REVIEW

This article answers the question of whether creatinine is the best biomarker for monitoring neonatal glomerular filtration rate (GFR) in view of recent advances in measuring neonatal renal function.

RECENT FINDINGS

We rely largely on serum creatinine for the estimation of GFR in the newborn, even though creatinine is freely exchanged through the placenta. During the first few days of life, the serum creatinine reflects maternal renal function or the maternal creatinine. Back filtration of creatinine in preterm newborns is also a serious limitation. This review summarizes current knowledge on the prenatal and postnatal handling of creatinine as well as that of other, more novel biomarkers of GFR, such as cystatin C (CysC) and β-trace protein (BTP). Only small amounts of CysC cross the placenta, whereas BTP does not cross the placenta at all. However, BTP measurements are not widely available. Recent studies on renal volumetry are also discussed.

SUMMARY

Currently, CysC may be the most suitable marker of neonatal renal function, but its availability is still limited, it is more costly, and the best method of reporting acute kidney injury and neonatal estimated GFR remains to be established.

Computational Modeling of Human Metabolism and Its Application to Systems Biomedicine

Modern high-throughput techniques offer immense opportunities to investigate whole-systems behavior, such as those underlying human diseases. However, the complexity of the data presents challenges in interpretation, and new avenues are needed to address the complexity of both diseases and data. Constraint-based modeling is one formalism applied in systems biology. It relies on a genome-scale reconstruction that captures extensive biochemical knowledge regarding an organism. The human genome-scale metabolic reconstruction is increasingly used to understand normal cellular and disease states because metabolism is an important factor in many human diseases. The application of human genome-scale reconstruction ranges from mere querying of the model as a knowledge base to studies that take advantage of the model's topology and, most notably, to functional predictions based on cell- and condition-specific metabolic models built based on omics data.An increasing number and diversity of biomedical questions are being addressed using constraint-based modeling and metabolic models. One of the most successful biomedical applications to date is cancer metabolism, but constraint-based modeling also holds great potential for inborn errors of metabolism or obesity. In addition, it offers great prospects for individualized approaches to diagnostics and the design of disease prevention and intervention strategies. Metabolic models support this endeavor by providing easy access to complex high-throughput datasets. Personalized metabolic models have been introduced. Finally, constraint-based modeling can be used to model whole-body metabolism, which will enable the elucidation of metabolic interactions between organs and disturbances of these interactions as either causes or consequence of metabolic diseases. This chapter introduces constraint-based modeling and describes some of its contributions to systems biomedicine.

Pharmacometabonomics in humans: a new tool for personalized medicine

Pharmacogenomics is now over 50 years old and has had some impact in clinical practice, through its use to select patient subgroups who will enjoy efficacy without side effects when treated with certain drugs. However, pharmacogenomics, has had less impact than initially predicted. One reason for this is that many diseases, and the way in which the patients respond to drug treatments, have both genetic and environmental elements. Pure genomics is almost blind to the environmental elements. A new methodology has emerged, termed pharmacometabonomics that is concerned with the prediction of drug effects through the analysis of predose, biofluid metabolite profiles, which reflect both genetic and environmental influences on human physiology. In this review we will cover what pharmacometabonomics is, how it works, what applications exist and what the future might hold in this exciting new area.

Necrotizing enterocolitis: a clinical review on diagnostic biomarkers and the role of the intestinal microbiota

Necrotizing enterocolitis (NEC) remains one of the most frequent gastrointestinal diseases in the neonatal intensive care unit, with a continuing unacceptable high mortality and morbidity rates. Up to 20% to 40% of infants with NEC will need surgical intervention at some point. Although the exact pathophysiology is not yet elucidated, prematurity, use of formula feeding, and an altered intestinal microbiota are supposed to induce an inflammatory response of the immature intestine. The clinical picture of NEC has been well described. However, an early diagnosis and differentiation against sepsis is challenging. Besides, it is difficult to timely identify NEC cases that will deteriorate and need surgical intervention. This may interfere with the most optimal treatment of infants with NEC. In this review, we discuss the pathogenesis, diagnosis, and treatment of NEC with a focus on the role of microbiota in the development of NEC. An overview of different clinical prediction models and biomarkers is given. Some of these are promising tools for accurate diagnosis of NEC and selection of appropriate therapy.

The Importance of Accurately Assessing Renal Function in the Neonate and Infant

Measuring renal function in neonates and small infants is important to ensure that drugs are safely dosed and to detect acute kidney injuries early on. Serum creatinine (Cr) remains the most widely used marker, but its shortcomings are particularly important in neonates. For example, neonatal Cr largely depends on maternal renal function for at least the first 72 h of life. Novel approaches for assessing neonatal renal function include cystatin C and beta-trace protein. Another way to assess renal function is to measure renal volume by ultrasound. Although this approach may assess neonatal nephron endowment, it is insensitive to the postnatal adaptation of renal function in term and preterm neonates. The purpose of this review is to summarize what is known about measuring renal function in term and preterm newborns, and to summarize existing knowledge gaps, including a description of steps to take to close these gaps.

Prediction of intracellular metabolic states from extracellular metabolomic data

Metabolic models can provide a mechanistic framework to analyze information-rich omics data sets, and are increasingly being used to investigate metabolic alternations in human diseases. An expression of the altered metabolic pathway utilization is the selection of metabolites consumed and released by cells. However, methods for the inference of intracellular metabolic states from extracellular measurements in the context of metabolic models remain underdeveloped compared to methods for other omics data. Herein, we describe a workflow for such an integrative analysis emphasizing on extracellular metabolomics data. We demonstrate, using the lymphoblastic leukemia cell lines Molt-4 and CCRF-CEM, how our methods can reveal differences in cell metabolism. Our models explain metabolite uptake and secretion by predicting a more glycolytic phenotype for the CCRF-CEM model and a more oxidative phenotype for the Molt-4 model, which was supported by our experimental data. Gene expression analysis revealed altered expression of gene products at key regulatory steps in those central metabolic pathways, and literature query emphasized the role of these genes in cancer metabolism. Moreover, in silico gene knock-outs identified unique control points for each cell line model, e.g., phosphoglycerate dehydrogenase for the Molt-4 model. Thus, our workflow is well-suited to the characterization of cellular metabolic traits based on extracellular metabolomic data, and it allows the integration of multiple omics data sets into a cohesive picture based on a defined model context.

The interplay between drugs and the kidney in premature neonates

The kidney plays a central role in the clearance of drugs. However, renal drug handling entails more than glomerular filtration and includes tubular excretion and reabsorption, and intracellular metabolization by cellular enzyme systems, such as the Cytochrome P450 isoenzymes. All these processes show maturation from birth onwards, which is one of the reasons why drug dosing in children is not simply similar to dosing in small adults. As kidney development normally finishes around the 36th week of gestation, being born prematurely will result in even more immature renal drug handling. Environmental effects, such as extra-uterine growth restriction, sepsis, asphyxia, or drug treatments like caffeine, aminoglycosides, or non-steroidal anti-inflammatory drugs, may further hamper drug handling in the kidney. Dosing in preterm neonates is therefore dependent on many factors that need to be taken into account. Drug treatment may significantly hamper postnatal kidney development in preterm neonates, just like renal immaturity has an impact on drug handling. The restricted kidney development results in a lower number of nephrons that may have several long-term sequelae, such as hypertension, albuminuria, and renal failure. This review focuses on the interplay between drugs and the kidney in premature neonates.

Metabolomics applied in neonatology

Over the past two decades, host-response biomarkers have been extensively used by clinicians for a better understanding of normal biological processes, the complexity and severity of illnesses, or pharmacological responses to therapeutic intervention. A myriad of information can be drawn from the gender, age, dietary intake and the disease history of an individual. These biomarkers may be promising for the complete phenotyping of a cell, tissue or an organism. In neonatology, these molecular markers may help in prediction of disease severity and its outcome, thus allowing personalized interventions. In this Review, existing data in the literature on metabolites in plasma, urine and maternal milk that may offer a unique insight into the host's dynamic behavior in different neonatal conditions will be examined.

Potential of metabolomics in preclinical and clinical drug development

Metabolomics is an upcoming technology system which involves detailed experimental analysis of metabolic profiles. Due to its diverse applications in preclinical and clinical research, it became an useful tool for the drug discovery and drug development process. This review covers the brief outline about the instrumentation and interpretation of metabolic profiles. The applications of metabolomics have a considerable scope in the pharmaceutical industry, almost at each step from drug discovery to clinical development. These include finding drug target, potential safety and efficacy biomarkers and mechanisms of drug action, the validation of preclinical experimental models against human disease profiles, and the discovery of clinical safety and efficacy biomarkers. As we all know, nowadays the drug discovery and development process is a very expensive, and risky business. Failures at any stage of drug discovery and development process cost millions of dollars to the companies. Some of these failures or the associated risks could be prevented or minimized if there were better ways of drug screening, drug toxicity profiling and monitoring adverse drug reactions. Metabolomics potentially offers an effective route to address all the issues associated with the drug discovery and development.

Metabolomics in neonatology: fact or fiction?

The newest 'omics' science is metabolomics, the latest offspring of genomics, considered the most innovative of the 'omics' sciences. Metabolomics, also called the 'new clinical biochemistry', is an approach based on the systematic study of the complete set of metabolites in a biological sample. The metabolome is considered the most predictive phenotype and is capable of considering epigenetic differences. It is so close to the phenotype that it can be considered the phenotype itself. In the last three years about 5000 papers have been listed in PubMed on this topic, but few data are available in the newborn. The aim of this review, after a description of background and technical procedures, is to analyse the clinical applications of metabolomics in neonatology, covering the following points: gestational age, postnatal age, type of delivery, zygosity, perinatal asphyxia, intrauterine growth restriction, prenatal inflammation and brain injury, respiratory, cardiovascular renal, metabolic diseases; sepsis, necrotizing enterocolitis and antibiotic treatment; nutritional studies on maternal milk and formula, pharma-metabolomics, long-term diseases. Pros and cons of metabolomics are also discussed. All this comes about with the non-invasive collection of a few drops of urine (exceptionally important for the neonate, especially those of low birth weight). Only time and large-scale studies to validate initial results will place metabolomics within neonatology. In any case, it is important for perinatologists to learn and understand this new technology to offer their patients the utmost in diagnostic and therapeutic opportunities.