Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Kell, Douglas B.; Goodacre, Royston

doi:10.1016/j.drudis.2013.07.014

Cited by 147 publications

(132 citation statements)

References 436 publications

(265 reference statements)

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“…Metabolomics is an important tool to elucidate phenotypes of biological systems and has wide application in biomarker discovery, drug development, and drug metabolism analysis (16)(17)(18). Untargeted metabolomics of biofluids collected from TB patients undergoing therapy may provide information on multiple drugs and a majority of their metabolites.…”

mentioning

confidence: 99%

Global Urine Metabolomics in Patients Treated with First-Line Tuberculosis Drugs and Identification of a Novel Metabolite of Ethambutol

Das

Arya

Debnath

et al. 2016

Antimicrob Agents Chemother

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Population level variation of drug metabolism phenotype (DMP) has great implications in treatment outcome, drug-related side effects, and resistance development. In this study, we used a gas chromatography-time of flight-mass spectrometry (GC-TOF-MS)-based untargeted urine metabolomics approach to understand the DMP of a tuberculosis (TB) patient cohort (n ‫؍‬ 20) from Tripura, a state in the northeastern part of India. Urine samples collected at different postdose time points (2 h, 6 h, 12 h, 24 h, 36 h, and 48 h) from these newly diagnosed TB patients receiving first-line anti-TB drugs were analyzed, and we have successfully detected three of the four first-line drugs, viz., isoniazid (INH), ethambutol (ETB), and pyrazinamide (PZA). The majority of their known metabolites, acetyl-isoniazid (AcINH), isonicotinic acid (INA), isonicotinuric acid (INTA), 2,2=-(ethylenediimino)-dibutyric acid (EDBA), 5-hydroxypyrazinamide (5OH-PZA), pyrazinoic acid (POA), and 5-hydroxypyrazinoic acid (5OH-POA), were also detected. Analyzing the variation in abundances of drugs and their known metabolites and calculating the metabolic ratios in these samples, we offer comprehensive DMP information on this small patient cohort that represents Tripura, India. The majority (75%) of these patients are found to be slow acetylators of INH. The average metabolic ratios of POA/PZA and 5OH-POA/POA are 3.16 ؎ 3.03 and 6.09 ؎ 6.15, respectively. Employing correlation analysis of the metabolomics metadata and a manual prediction of drug catabolism, we have proposed 2-aminobutyric acid (AABA) as a novel metabolite of ETB. These observations indicate the usefulness of GC-MS-based metabolomics to characterize the DMP at a population level and also to identify novel drug metabolites.

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mentioning

confidence: 99%

Global Urine Metabolomics in Patients Treated with First-Line Tuberculosis Drugs and Identification of a Novel Metabolite of Ethambutol

Das

Arya

Debnath

et al. 2016

Antimicrob Agents Chemother

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“…Kell and Goodacre recently stated that metabolomics network (more than transcriptomics and proteomics networks) could be more relevant for fi nding disease biomarkers because the change in metabolite concentration is likely to be more robust than the change of the enzyme that controls the metabolic fl uxes [ 57 ]. Although we do not discuss the evaluation and interpretation of metabolomics data in this chapter, the relevant tools are increasingly available.…”

Section: Notesmentioning

confidence: 97%

Xenobiotic Metabolism Activation as a Biomarker of Cigarette Smoke Exposure Response

Iskandar

2015

Methods in Pharmacology and Toxicology

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The recent advances in "omics" technologies have generated various in silico approaches for toxicity assessment. In silico-based toxicity predictions can overcome certain major drawbacks of laboratory experiments, including the limitation of conducting experiments in a chemical-by-chemical basis that can be expensive. This chapter discusses some recent applications of in silico approaches utilizing xenobiotic metabolism that can be used to assess the impact of cigarette smoke (CS). We fi rst outline recent studies using quantum mechanics/molecular modeling and quantitative structure-activity relationships that focus on smokingrelevant cytochrome P450 (CYP) enzymes. Subsequently, we describe several network-based approaches for toxicity assessment and relevant use cases leveraging a xenobiotic metabolism network model for a quantitative assessment of CS impact.

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“…Based on systems biology, systems pharmacology focuses on complex interactions within biological systems to quantitatively simulate the interaction between a drug and various systems of the body (Vicini & van der Graaf, 2013). As noted above, the ADME/T of drugs includes complex processes and has a significant impact on the effects of the drug, including transcriptome, proteome, and enzyme activities, as also represented as the 'sharp end' of systems biology (Kell & Goodacre, 2014). Systems biology and systems pharmacology, which leverage knowledge based on the systematic understanding of the interaction between drugs and the human body, may provide a critically needed blueprint for the ADME/T processes of drugs and help to develop a robust systematic model for drug ADME/T prediction.…”

Section: Discussionmentioning

confidence: 99%

“…As a major safety concern to pharmaceutical research, metabolic liability can lead to a number of issues, such as poor bioavailability due to enhanced clearance; toxic effects caused by drug accumulation; and DDIs, including enzyme inhibition, induction, and mechanism-based inactivation (Kell & Goodacre, 2014;Kirchmair et al 2012). In addition, metabolic information can offer prospective advice for drug development, for example, to guide the design of a pro-drug for some metabolically unstable drug to enhance bioavailability (Stella et al 2007).…”

Section: Metabolismmentioning

confidence: 99%

In silicoADME/T modelling for rational drug design

Wang

Xing

Yue

et al. 2015

Quart. Rev. Biophys.

280

155

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Abstract. In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.

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Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Cited by 147 publications

References 436 publications

Global Urine Metabolomics in Patients Treated with First-Line Tuberculosis Drugs and Identification of a Novel Metabolite of Ethambutol

Global Urine Metabolomics in Patients Treated with First-Line Tuberculosis Drugs and Identification of a Novel Metabolite of Ethambutol

Xenobiotic Metabolism Activation as a Biomarker of Cigarette Smoke Exposure Response

In silicoADME/T modelling for rational drug design

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