Endogenous metabolites that are substrates of organic anion transporter’s (OATs) predict methotrexate clearance

Muhrez, Kienana; Bretagne, Isabelle Benz-de; Nadal-Desbarats, Lydie; Blasco, Hélène; Gyan, Emmanuel; Choquet, Sylvain; Montigny, Frédéric; Emond, Patrick; Guellec, Chantal Barin‐Le

doi:10.1016/j.phrs.2016.05.021

Cited by 27 publications

(18 citation statements)

References 39 publications

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“…This is a problem for transporters, which can exhibit marked overlap in substrate specificity (101). Metabolomics approaches could be useful for identifying highly specific endogenous substrates of transporters that would be suitable for characterisation of activity in vivo (102).…”

Section: Discussionmentioning

confidence: 99%

Key to Opening Kidney for In Vitro-In Vivo Extrapolation Entrance in Health and Disease: Part II: Mechanistic Models and In Vitro-In Vivo Extrapolation

Scotcher

Jones²,

Posada

et al. 2016

AAPS J

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Abstract.It is envisaged that application of mechanistic models will improve prediction of changes in renal disposition due to drug-drug interactions, genetic polymorphism in enzymes and transporters and/or renal impairment. However, developing and validating mechanistic kidney models is challenging due to the number of processes that may occur (filtration, secretion, reabsorption and metabolism) in this complex organ. Prediction of human renal drug disposition from preclinical species may be hampered by species differences in the expression and activity of drug metabolising enzymes and transporters. A proposed solution is bottom-up prediction of pharmacokinetic parameters based on in vitro-in vivo extrapolation (IVIVE), mediated by recent advances in in vitro experimental techniques and application of relevant scaling factors. This review is a follow-up to the Part I of the report from the 2015 AAPS Annual Meeting and Exhibition (Orlando, FL; 25th-29th October 2015) which focuses on IVIVE and mechanistic prediction of renal drug disposition. It describes the various mechanistic kidney models that may be used to investigate renal drug disposition. Particular attention is given to efforts that have attempted to incorporate elements of IVIVE. In addition, the use of mechanistic models in prediction of renal drugdrug interactions and potential for application in determining suitable adjustment of dose in kidney disease are discussed. The need for suitable clinical pharmacokinetics data for the purposes of delineating mechanistic aspects of kidney models in various scenarios is highlighted.KEY WORDS: active renal excretion; human kidney transporters; human renal drug clearance; kidney disease; non-hepatic drug metabolism.

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Section: Discussionmentioning

confidence: 99%

Key to Opening Kidney for In Vitro-In Vivo Extrapolation Entrance in Health and Disease: Part II: Mechanistic Models and In Vitro-In Vivo Extrapolation

Scotcher

Jones²,

Posada

et al. 2016

AAPS J

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“…Kienana et al used similar methodology, including external validation, to predict methotrexate clearance in a group of 62 patients with lymphoid malignancies, treated with a high dose methotrexate (>1 g.m −2 ; Muhrez et al, 2016). Methotrexate is an interesting example as genetic polymorphisms in enzymes or transporter proteins involved in methotrexate clearance are known to be associated with PK variation.…”

Section: Progress In Pharmacometabonomics In Animals and Humansmentioning

confidence: 99%

From Metabonomics to Pharmacometabonomics: The Role of Metabolic Profiling in Personalized Medicine

Everett

2016

Front. Pharmacol.

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Variable patient responses to drugs are a key issue for medicine and for drug discovery and development. Personalized medicine, that is the selection of medicines for subgroups of patients so as to maximize drug efficacy and minimize toxicity, is a key goal of twenty-first century healthcare. Currently, most personalized medicine paradigms rely on clinical judgment based on the patient's history, and on the analysis of the patients' genome to predict drug effects i.e., pharmacogenomics. However, variability in patient responses to drugs is dependent upon many environmental factors to which human genomics is essentially blind. A new paradigm for predicting drug responses based on individual pre-dose metabolite profiles has emerged in the past decade: pharmacometabonomics, which is defined as “the prediction of the outcome (for example, efficacy or toxicity) of a drug or xenobiotic intervention in an individual based on a mathematical model of pre-intervention metabolite signatures.” The new pharmacometabonomics paradigm is complementary to pharmacogenomics but has the advantage of being sensitive to environmental as well as genomic factors. This review will chart the discovery and development of pharmacometabonomics, and provide examples of its current utility and possible future developments.

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“…Therefore, it is important to predict the PK of tacrolimus in order to minimise adverse drug adult patients being treated for lymphoid malignancies. [16] Variable PK of MTX is known to be responsible for serious patient toxicities, even death, and over-exposure can occur even in the same patient between MTX treatment courses, thus indicating that genetic factors per se are not responsible for all of the variability observed. [16] In a well-designed study utilising internal and external validation of the models, the pre-dose urine levels of 28 metabolites were shown to be predictive of MTX clearance with mean prediction error and precision of 0.4% and 21% respectively.…”

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confidence: 99%

“…[16] Variable PK of MTX is known to be responsible for serious patient toxicities, even death, and over-exposure can occur even in the same patient between MTX treatment courses, thus indicating that genetic factors per se are not responsible for all of the variability observed. [16] In a well-designed study utilising internal and external validation of the models, the pre-dose urine levels of 28 metabolites were shown to be predictive of MTX clearance with mean prediction error and precision of 0.4% and 21% respectively. An orthogonal PLS discriminant analysis model showed a partial separation between patients with normal or delayed MTX elimination and whilst the specificity was excellent (93%), sensitivity was poor (42%) and model improvements would be required for clinical utility of this element.…”

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confidence: 99%

“…The model for the prediction of MTX clearance is however expected to have clinical utility and also gave insights into the underlying mechanisms of MTX excretion, including the role of organic anion transporters. [16] Pharmacometabonomics, though still an emerging technology, has shown significant promise in the prediction of drug efficacy, safety and metabolism, in addition to the prediction of PK.…”

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confidence: 99%

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The Role of Pharmacometabonomics in Predicting Drug Pharmacokinetics

Varshavi

Everett

2016

Int. J. Pharmacokinet.

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Individual variability in drug response is a key challenge in current clinical practice and in drug discovery and development. Pharmacological response is closely associated with drug concentration at the site of action and therefore knowledge of drug pharmacokinetics is vital to delivering effective therapy. In addition to genetic polymorphisms, environmental factors also play an important role in determining drug efficacy, safety, metabolism and pharmacokinetics. The newly emerging field of pharmacometabonomics uses information from pre-dose metabolite profiles to predict individual drug responses, can be sensitive to both genetic and environmental factors and thus has great promise to help the future delivery of personalized medicine. This article introduces pharmacometabonomics and covers its application to the prediction of pharmacokinetics. EditorialPersonalised or stratified therapy is a key goal for 21 st century medicine in order to maximize therapeutic efficacy and minimize the likelihood of adverse drug reactions for groups of patients. In addition, personalised medicine has the potential to substantially enhance the process of drug discovery and development.[1] Thus far, personalized medicine has been mainly based on pharmacogenomics (PG), where an individual's genetic profile is used to

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Endogenous metabolites that are substrates of organic anion transporter’s (OATs) predict methotrexate clearance

Cited by 27 publications

References 39 publications

Key to Opening Kidney for In Vitro-In Vivo Extrapolation Entrance in Health and Disease: Part II: Mechanistic Models and In Vitro-In Vivo Extrapolation

Key to Opening Kidney for In Vitro-In Vivo Extrapolation Entrance in Health and Disease: Part II: Mechanistic Models and In Vitro-In Vivo Extrapolation

From Metabonomics to Pharmacometabonomics: The Role of Metabolic Profiling in Personalized Medicine

The Role of Pharmacometabonomics in Predicting Drug Pharmacokinetics

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