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Related Concept Videos

Pharmacokinetics in Pediatric Patients: Drug Metabolism01:24

Pharmacokinetics in Pediatric Patients: Drug Metabolism

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In pediatric care, understanding the nuances of hepatic drug metabolism is crucial, as it significantly differs from that of adults. This divergence is primarily due to the developmental stage of drug-metabolizing enzymes, which affects how medications are processed in the body. In neonates, for instance, the activity of Phase I enzymes—critical for the initial breakdown of drugs—is markedly reduced, functioning at just 20–40% of the levels seen in adults. This reduction poses...
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Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

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Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
A recent model describes pravastatin's hepatobiliary excretion,...
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Pharmacokinetics in Geriatric Patients: Effect of Age on Drug Metabolism01:18

Pharmacokinetics in Geriatric Patients: Effect of Age on Drug Metabolism

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Geriatric patients show significant variation in how their bodies process medications, which can change how effective and safe treatments are. The liver is the primary organ where drug metabolism occurs, involving two main types of chemical reactions: phase I and II. Phase I metabolism is driven by the cytochrome P450 enzyme system, which includes key types such as CYP3A, CYP2D6, and CYP2C9. Research indicates that while aging doesn't notably alter the levels or activity of these enzymes, it...
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Pharmacokinetics in Obese Patients: Drug Metabolism and Excretion01:20

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Drug metabolism, a critical process in the liver, involves two primary phases: Phase I reactions and Phase II conjugation. Obesity introduces significant alterations in this metabolic process, primarily due to fatty infiltration of the liver, leading to conditions such as nonalcoholic fatty liver disease (NAFLD). This condition can modify the activities of both Phase I and II enzymes, impacting how drugs are metabolized in obese patients.Phase I metabolism sees variable effects across...
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Factors Affecting Drug Biotransformation: Physicochemical and Chemical Properties of Drugs01:21

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A drug's physicochemical properties fundamentally influence its metabolism. For instance, a drug's molecular size and shape critically determine its interaction with enzymes and transporters — larger drugs may face difficulty reaching enzyme active sites, altering their metabolic pathways. The pKa of a drug, which establishes its ionization state, can impact its solubility and absorption, thereby influencing metabolism.
The drug's acidity or basicity is essential in...
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Effect of Hepatic Disease on Pharmacokinetics: Drug Dosing and Hepatic Blood Flow01:26

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Chronic liver disease significantly impacts drug metabolism due to alterations in hepatic blood flow and enzyme accessibility. This disruption affects the body's pharmacokinetics—the movement and processing of drugs within the system. Key enzymes crucial for metabolizing medications become less accessible, changing how drugs are processed and utilized. Furthermore, liver disease influences the synthesis of plasma proteins, such as albumin and globulins, which play critical roles in drug...
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Related Experiment Video

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Recent developments in predicting CYP-independent metabolism.

Nikhilesh V Dhuria1, Bianka Haro2, Amit Kapadia3

  • 1Department of Pharmaceutical Sciences, University of Nebraska Medical Center, Omaha, NE, USA.

Drug Metabolism Reviews
|May 4, 2021
PubMed
Summary

Predicting drug metabolism by non-cytochrome P450 (CYP) enzymes is challenging, especially accounting for extrahepatic clearance. This review summarizes advancements in predicting non-CYP drug metabolism and in vitro to in vitro extrapolation for investigational compounds.

Keywords:
Non-CYP enzymes; alcohol and aldehyde dehydrogenase; aldehyde oxidase; carboxylesterase; flavin-containing monooxygenase; glutathione-S-transferase; monoamine oxidase; sulfotransferase; UDP glucuronosyltransferase; xanthine oxidase

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Area of Science:

  • Pharmacology
  • Drug Metabolism and Pharmacokinetics
  • Medicinal Chemistry

Background:

  • Lead optimization has reduced cytochrome P450 (CYP) metabolism issues, increasing focus on non-CYP enzymes.
  • Non-CYP enzymes are better characterized (localization, mechanisms, substrates), but predicting their clearance remains difficult.
  • Accurate prediction of non-CYP-mediated clearance, including extrahepatic contributions, is a significant challenge in drug development.

Purpose of the Study:

  • To comprehensively review recent advancements in predicting drug metabolism by non-CYP enzymes.
  • To summarize progress in in vitro to in vitro extrapolation of clearance for non-CYP substrates.
  • To highlight challenges and solutions in accounting for extrahepatic contributions to non-CYP clearance.

Main Methods:

  • Literature review of recent studies on non-CYP enzyme characterization and substrate identification.
  • Analysis of methodologies for predicting non-CYP-mediated drug clearance.
  • Evaluation of in vitro to in vitro extrapolation techniques for non-CYP clearance prediction.

Main Results:

  • Significant progress has been made in understanding non-CYP enzyme roles in drug metabolism.
  • Newer predictive models are emerging for non-CYP clearance, though challenges persist.
  • In vitro to in vitro extrapolation methods show promise but require further refinement for extrahepatic considerations.

Conclusions:

  • Accurate prediction of non-CYP drug metabolism and clearance is crucial for successful drug development.
  • Addressing extrahepatic contributions is key to improving the reliability of non-CYP clearance predictions.
  • Continued research into non-CYP enzymes and predictive modeling will enhance drug safety and efficacy assessments.