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Clearance Models: Noncompartmental Models

Clearance Models: Noncompartmental Models

Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...

Clearance Models: Physiological Models

Clearance Models: Physiological Models

Drug clearance is a critical pharmacokinetic process involving the irreversible removal of drugs from the body through various organs over a specified time period. Physiological models are indispensable in determining organ-specific clearance, defined by the proportion of the drug eliminated per unit of time from the organ's blood volume.
The organ's clearance rate depends on the blood flow to the organ and the extraction ratio (E). The extraction ratio describes the organ's proficiency in drug...

One-Compartment Open Model for IV Bolus Administration: Estimation of Clearance

One-Compartment Open Model for IV Bolus Administration: Estimation of Clearance

Clearance is a key pharmacokinetic parameter that quantifies the volume of body fluid from which a drug is entirely removed within a specific time frame. It is crucial in assessing how a drug is eliminated from the body and has critical clinical applications.
In the one-compartment open model for intravenous (IV) bolus administration, clearance is estimated by dividing the elimination rate by the plasma drug concentration. This equation leverages the elimination rate constant and the apparent...

Clearance Models: Compartment Models

Clearance Models: Compartment Models

Clearance measures drug elimination from the central compartment, including plasma and highly perfused organs like kidneys and liver. Its calculation varies depending on pharmacokinetic models and administration routes. The one-compartment model, for instance, portrays the pharmacokinetics of polar drugs such as aminoglycoside antibiotics administered intravenously and readily excreted in urine. In this case, clearance is influenced by the terminal rate constant (λz) and the total volume of...

Hepatic Drug Clearance: Restrictive and Nonrestrictive Clearance

Hepatic Drug Clearance: Restrictive and Nonrestrictive Clearance

Hepatic clearance refers to the volume of blood cleared of a drug by the liver per unit of time. It plays a crucial role in drug metabolism and elimination. While hepatic clearance is commonly estimated by subtracting renal clearance from total body clearance, other pathways, such as pulmonary or biliary clearance, may also contribute. However, these pathways are generally less significant than hepatic and renal clearance.
Most drugs undergo restrictive clearance, which is proportional to the...

Drug Elimination: The Concept of Clearance

Drug Elimination: The Concept of Clearance

Drug elimination refers to removing drugs from the body, either through urine by the kidneys or through bile by the liver. Drug clearance is a pharmacokinetic parameter that measures the efficiency of drug removal from the bloodstream within a specific time frame. It is calculated as the rate at which a drug is eliminated from plasma divided by the plasma concentration of the drug.
Drug clearance is not limited to renal excretion but encompasses all organs involved in drug elimination,...

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Related Experiment Video

Updated: Jun 11, 2026

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

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Predicting total clearance in humans from chemical structure.

¹Eisai Incorporated, 4 Corporate Drive, Andover, Massachusetts 01810, USA.

Journal of Chemical Information and Modeling

|July 13, 2010

Summary

A new in silico model predicts human total clearance using k-nearest neighbors (kNN) and molecular descriptors. This computational approach aids early drug discovery by estimating clearance without experimental data.

Area of Science:

Pharmacokinetics
Computational Chemistry
Drug Discovery

Background:

Predicting human total clearance is crucial for early drug discovery.
Existing methods often rely on experimental data or complex allometric scaling.
A need exists for a fully in silico model for rapid clearance prediction.

Purpose of the Study:

To develop and validate a simple, fully in silico model for predicting human total clearance.
To utilize a k-nearest neighbors (kNN) technique based on molecular similarity.
To provide a tool for virtual screening and analogue prioritization in early drug development.

Main Methods:

Employed a k-nearest neighbors (kNN) algorithm for prediction.
Defined molecular similarity using one- and two-dimensional molecular descriptors.

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Combined and curated human pharmacokinetic data from Obach and Berellini sets.

Validated the model using an external test set, cross-validation, and y-randomization techniques.

Main Results:

Achieved average prediction accuracy within two-fold of observed values for various compound types in the external test set.
The in silico kNN model demonstrated competitive performance against allometric scaling methods for predicting clearance from preclinical data.
Outperformed simple allometry and approached combination multiexponential allometry in accuracy for multi-species data.
Showed superior performance over both simple and combination allometry for two-species (rat-dog) data.

Conclusions:

The developed in silico kNN model offers a viable, data-driven approach for predicting human total clearance.
This model can significantly aid early-stage drug discovery by enabling virtual screening and prioritizing compounds before synthesis.
The fully computational nature of the model allows for rapid clearance predictions, accelerating research efforts.