About the Intrinsic Clearance Calculator

This Intrinsic Clearance Calculator is a pharmacokinetic tool designed for researchers in drug discovery and development. It streamlines the process of in vitro-in vivo extrapolation (IVIVE) by converting metabolic stability data from laboratory assays into predictions of a drug's hepatic clearance in a living system.

What This Calculator Does

The primary function is to estimate how efficiently the liver can remove a drug from the bloodstream. It achieves this by:

• Calculating the in vitro intrinsic clearance (CLint) from either a metabolic half-life (t½) or raw time-course data.
• Scaling this in vitro value using species-specific physiological parameters (e.g., liver weight, protein/cell content).
• Predicting the in vivo hepatic clearance (CLh) using the well-stirred liver model, which accounts for liver blood flow and the fraction of drug unbound in plasma.
• Determining the Hepatic Extraction Ratio (ER), which classifies the compound as having low, intermediate, or high clearance.

When to Use It

This calculator is most valuable during the early stages of drug discovery and preclinical development. Key applications include:

• Compound Screening: Quickly rank a series of drug candidates based on their metabolic stability. Compounds with very high clearance may be deprioritized.
• Dose Prediction: Hepatic clearance is a critical parameter for predicting the human oral bioavailability and required dose.
• Cross-Species Comparison: Evaluate how metabolic clearance differs across species (e.g., rat, dog, human) to aid in the selection of appropriate animal models.
• Mechanistic Understanding: Assess whether a compound is likely to be a low or high extraction ratio drug, which has implications for its susceptibility to drug-drug interactions and first-pass metabolism.

Inputs Explained

Calculation Mode

You can provide your experimental data in two ways:

• Enter Half-Life (t½): The simplest method. Input the time (in minutes) it takes for 50% of the drug to be metabolized in your in vitro system.
• Enter Raw Data: Paste time-course data (Time vs. % Remaining). The calculator will perform a linear regression on the natural log of the percent remaining to calculate the degradation rate constant (k) and the half-life. This method also provides an R² value to assess the quality of the data fit.

In Vitro System

• Liver Microsomes: Subcellular fractions containing primary drug-metabolizing enzymes (cytochrome P450s). Best for studying Phase I metabolism. The corresponding scaling factor is Microsomal Protein per Gram of Liver (MPPGL).
• Hepatocytes: Intact, whole liver cells. They contain both Phase I and Phase II enzymes, as well as transporters, offering a more complete picture of liver metabolism. The corresponding scaling factor is Hepatocellularity (millions of cells per gram of liver).

Physiological Parameters

• Incubation Concentration: The concentration of microsomal protein (mg/mL) or hepatocytes (million cells/mL) used in the experiment. This is crucial for normalizing the rate of metabolism.
• Fraction Unbound in Plasma (fu): The proportion of the drug that is not bound to plasma proteins. Only the unbound drug is available to be cleared by the liver. This is a unitless value between 0 and 1 (e.g., 0.05 for 5% unbound).
• Species: Select the species from which the in vitro system was derived. This pre-fills standard physiological values for Liver Blood Flow (Qh), Liver Weight, MPPGL, and Hepatocellularity. You can select "Custom" to enter your own values.

Results Explained

• In Vitro CLint: Intrinsic clearance measured in the experimental system, expressed in units like µL/min/mg protein or µL/min/million cells. It reflects the inherent metabolic activity of the enzymes or cells towards the drug.
• Scaled CLint: The in vitro CLint scaled up to represent the metabolic capacity of the entire liver, expressed in mL/min/kg of body weight. It represents the maximum possible clearance if liver blood flow were not a limiting factor.
• Predicted Hepatic Clearance (CLh): The final, physiologically relevant prediction of clearance by the liver, also in mL/min/kg. It incorporates the limitations of both liver blood flow and plasma protein binding.
• Hepatic Extraction Ratio (ER): Calculated as CLh / Qh, this unitless value (0 to 1) represents the fraction of drug removed from the blood during a single pass through the liver. The calculator categorizes it as:
  • Low (ER < 0.3): Clearance is primarily limited by enzymatic activity (CLint) and is sensitive to changes in protein binding and enzyme inhibition/induction.
  • Intermediate (0.3 ≤ ER ≤ 0.7): Clearance is sensitive to both blood flow and enzymatic activity.
  • High (ER > 0.7): Clearance is primarily limited by the rate of drug delivery to the liver (blood flow). These drugs often exhibit significant first-pass metabolism.

Formula / Method

Step-by-Step Example

Let's predict human hepatic clearance for a new compound.

1. Experiment: The compound is incubated with human liver microsomes.
2. Inputs:
   • Calculation Mode: Enter Half-Life (t½)
   • Half-Life: 25 min
   • In Vitro System: Liver Microsomes
   • Incubation Protein Conc.: 0.5 mg/mL
   • Species: Human (Qh=20.7, Liver Weight=25.7, MPPGL=57.5)
   • Fraction Unbound (fu): 0.1
3. Calculation Process:
   • Rate constant (k): ln(2) / 25 = 0.0277 min⁻¹
   • In Vitro CLint: (0.0277 / 0.5) * 1000 = 55.4 µL/min/mg protein
   • Scaled CLint: (55.4 * 57.5 mg/g * 25.7 g/kg) / 1000 = 81.9 mL/min/kg
   • Predicted CLh: (20.7 * 0.1 * 81.9) / (20.7 + (0.1 * 81.9)) = 169.5 / 28.89 = 5.87 mL/min/kg
   • Extraction Ratio (ER): 5.87 / 20.7 = 0.28
4. Result: The predicted hepatic clearance is 5.87 mL/min/kg, and the compound is classified as having Low Clearance (ER < 0.3).

Tips + Common Errors

• Check Units: Ensure all inputs use the correct units (minutes, mg/mL, million cells/mL, g/kg). The most common error is inconsistent units.
• fu is a Fraction: The fraction unbound (fu) must be a decimal between 0 and 1 (e.g., 0.1 for 10% unbound), not a percentage (10).
• Use Raw Data for Quality Control: When possible, use the raw data input mode. A low R² value (e.g., < 0.9) can indicate poor data quality, non-first-order kinetics, or experimental issues, suggesting the results should be treated with caution.
• Microsomes vs. Hepatocytes: Choose the system that best reflects the expected metabolic pathways. If Phase II metabolism or active transport is significant, hepatocytes are a better choice.
• Assay Linearity: Ensure your experimental t½ was determined within the linear range of the assay. Very short or very long half-lives may be inaccurate.

Frequently Asked Questions (FAQs)

What is the difference between microsomes and hepatocytes?

Microsomes are fragments of the endoplasmic reticulum that contain most Phase I enzymes (like CYPs). Hepatocytes are whole liver cells that contain a full complement of Phase I and Phase II enzymes, as well as uptake and efflux transporters. Hepatocytes provide a more comprehensive model but can be more complex to work with.

Why is fraction unbound (fu) so important for predicting hepatic clearance?

According to the "free drug hypothesis," only the drug that is unbound to plasma proteins can cross cell membranes to be metabolized by the liver. A highly protein-bound drug (low fu) has less free concentration available for clearance, which can significantly reduce its CLh, especially for low ER compounds.

What does a low R² value mean when I use the raw data input?

R² (the coefficient of determination) indicates how well the data fits the first-order decay model. A value close to 1.0 (e.g., >0.95) means a good fit. A low R² value suggests the data points don't form a straight line on a log-linear plot, which could be due to experimental variability, complex kinetics, or inaccurate measurements.

What are the assumptions of the "well-stirred" liver model?

This model assumes the drug concentration within the liver is uniform and equal to the concentration in the exiting blood. It is a simple and widely used model but may not be accurate for all drugs, especially those with very high extraction ratios or that are subject to significant transporter effects.

Can I use this calculator for clearance in other organs like the kidney or intestine?

No. This calculator is specifically parameterized for hepatic (liver) clearance. The scaling factors (MPPGL, Hepatocellularity) and physiological parameters (Qh, Liver Weight) are specific to the liver.

Why is Predicted CLh different from Scaled CLint?

Scaled CLint represents the liver's maximum intrinsic ability to metabolize a drug, assuming no barriers. Predicted CLh is a more realistic estimate that accounts for two key physiological barriers: the rate at which the blood can deliver the drug to the liver (liver blood flow, Qh) and the amount of drug available for metabolism (fraction unbound, fu).

What are the main limitations of this IVIVE approach?

IVIVE is a powerful estimation tool but has limitations. It often assumes metabolism is the only clearance pathway, ignoring renal or biliary excretion. It may not capture complex biology like active transport into the liver, and the accuracy depends heavily on the quality of the in vitro data and the physiological scaling factors used.

What does an Extraction Ratio (ER) of 0.9 mean?

An ER of 0.9 means that 90% of the drug delivered to the liver is removed in a single pass. This is a high-clearance compound. Its overall clearance is highly dependent on liver blood flow and it will likely experience significant first-pass metabolism after oral administration, potentially leading to low bioavailability.

References

The methodologies used in this calculator are based on established principles in pharmacokinetics and drug metabolism. For further reading, please consult authoritative sources:

1. Obach, R. S. (1999). Prediction of human clearance from in vitro drug metabolism data. Drug Metabolism and Disposition, 27(11), 1350-1359. View Article
2. Houston, J. B. (1994). Utility of in vitro drug metabolism data in predicting in vivo metabolic clearance. Biochemical Pharmacology, 47(9), 1469-1479. View Article
3. Baranczewski, P., Stańczak, A., Sundberg, K., et al. (2006). Introduction to in vitro estimation of metabolic stability and drug interactions of new chemical entities in drug discovery and development. Pharmacological Reports, 58(4), 453-472. View Article
4. Narayan, M., & Zanza, A. (2021). In Vitro-In Vivo Extrapolation. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing. View Article