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Carboxylesterase 1 and Precision Pharmacotherapy: Pharmacogenetics and Nongenetic Regulators.

ABSTRACT: Carboxylesterase (CES) 1 is the most abundant drug-metabolizing enzyme in human livers, comprising approximately 1% of the entire liver proteome. CES1 is responsible for 80%-95% of total hydrolytic activity in the liver and plays a crucial role in the metabolism of a wide range of drugs (especially ester-prodrugs), pesticides, environmental pollutants, and endogenous compounds. Expression and activity of CES1 vary markedly among individuals, which is a major contributing factor to interindividual variability in the pharmacokinetics (PK) and pharmacodynamics (PD) of drugs metabolized by CES1. Both genetic and nongenetic factors contribute to CES1 variability. Here, we discuss genetic polymorphisms, including single-nucleotide polymorphisms (SNPs), and copy number variants and nongenetic contributors, such as developmental status, genders, and drug-drug interactions, that could influence CES1 functionality and the PK and PD of CES1 substrates. Currently, the loss-of-function SNP G143E (rs71647871) is the only clinically significant CES1 variant identified to date, and alcohol is the only potent CES1 inhibitor that could alter the therapeutic outcomes of CES1 substrate medications. However, G143E and alcohol can only explain a small portion of the interindividual variability in the CES1 function. A better understanding of the regulation of CES1 expression and activity and identification of biomarkers for CES1 function in vivo could lead to the development of a precision pharmacotherapy strategy to improve the efficacy and safety of many CES1 substrate drugs. SIGNIFICANCE STATEMENT: The clinical relevance of CES1 has been well demonstrated in various clinical trials. Genetic and nongenetic regulators can affect CES1 expression and activity, resulting in the alteration of the metabolism and clinical outcome of CES1 substrate drugs, such as methylphenidate and clopidogrel. Predicting the hepatic CES1 function can provide clinical guidance to optimize pharmacotherapy of numerous medications metabolized by CES1.

SUBMITTER: Her L

PROVIDER: S-EPMC7031766 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Carboxylesterase 1 and Precision Pharmacotherapy: Pharmacogenetics and Nongenetic Regulators.

Her Lucy L Zhu Hao-Jie HJ

Drug metabolism and disposition: the biological fate of chemicals 20191223 3

Carboxylesterase (CES) 1 is the most abundant drug-metabolizing enzyme in human livers, comprising approximately 1% of the entire liver proteome. CES1 is responsible for 80%-95% of total hydrolytic activity in the liver and plays a crucial role in the metabolism of a wide range of drugs (especially ester-prodrugs), pesticides, environmental pollutants, and endogenous compounds. Expression and activity of CES1 vary markedly among individuals, which is a major contributing factor to interindividua ...[more]

PMID: 31871135

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Project description:PurposeCoronavirus disease-2019 (COVID-19) continues to be a global threat and remains a significant cause of hospitalizations. Recent clinical guidelines have supported the use of corticosteroids or remdesivir in the treatment of COVID-19. However, uncertainty remains about which patients are most likely to benefit from treatment with either drug; such knowledge is crucial for avoiding preventable adverse effects, minimizing costs, and effectively allocating resources. This study presents a machine-learning system with the capacity to identify patients in whom treatment with a corticosteroid or remdesivir is associated with improved survival time.MethodsGradient-boosted decision-tree models used for predicting treatment benefit were trained and tested on data from electronic health records dated between December 18, 2019, and October 18, 2020, from adult patients (age ≥18 years) with COVID-19 in 10 US hospitals. Models were evaluated for performance in identifying patients with longer survival times when treated with a corticosteroid versus remdesivir. Fine and Gray proportional-hazards models were used for identifying significant findings in treated and nontreated patients, in a subset of patients who received supplemental oxygen, and in patients identified by the algorithm. Inverse probability-of-treatment weights were used to adjust for confounding. Models were trained and tested separately for each treatment.FindingsData from 2364 patients were included, with men comprising slightly more than 50% of the sample; 893 patients were treated with remdesivir, and 1471 were treated with a corticosteroid. After adjustment for confounding, neither corticosteroids nor remdesivir use was associated with increased survival time in the overall population or in the subpopulation that received supplemental oxygen. However, in the populations identified by the algorithms, both corticosteroids and remdesivir were significantly associated with an increase in survival time, with hazard ratios of 0.56 and 0.40, respectively (both, P = 0.04).ImplicationsMachine-learning methods have the capacity to identify hospitalized patients with COVID-19 in whom treatment with a corticosteroid or remdesivir is associated with an increase in survival time. These methods may help to improve patient outcomes and allocate resources during the COVID-19 crisis.

Dataset Information

Carboxylesterase 1 and Precision Pharmacotherapy: Pharmacogenetics and Nongenetic Regulators.

Publications

Carboxylesterase 1 and Precision Pharmacotherapy: Pharmacogenetics and Nongenetic Regulators.

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