Oral Bioavailability: Difference between revisions

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<!-- TABLE 1 -->
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{| cellpadding="2" cellspacing="0" style="border-top:2px solid black"  
{| cellpadding="2" cellspacing="0" style="border-top:1px solid black"  
|+ <b>Table 1.</b> Classification performance of the lower threshold (%F (oral) > 30%) probabilistic bioavailability model on the training set compounds.
|+ <b>Table.</b> Qualitative nad quantitative fraction absorbed predictions for the validation set of 28 drugs.
|-
|-
! style="border-bottom:1px solid black; background:#EAEAEA" width="250" rowspan="2" | Subset
! style="border-bottom:1px solid black; background:#EAEAEA" width="250" | Compound name
! style="border-bottom:1px solid black; background:#EAEAEA" width="150" rowspan="2" | Observed
! style="border-bottom:1px solid black; background:#EAEAEA" width="150" | Dose, mg
! style="border-bottom:1px solid black; background:#EAEAEA" width="300" colspan="2" | Calculated probability (<i>p</i>)
! style="border-bottom:1px solid black; background:#EAEAEA" width="150" | Experimental ''f<sub>a</sub>'' (%)
! style="border-bottom:1px solid black; background:#EAEAEA" width="150" | Predicted ''f<sub>a</sub>'' (%)
! style="border-bottom:1px solid black; background:#EAEAEA" width="150" | Experimental class
! style="border-bottom:1px solid black; background:#EAEAEA" width="150" | Predicted class
|-
|-
! style="border-bottom:1px solid black; background:#EAEAEA" width="150" | >0.5
| Acyclovir
! style="border-bottom:1px solid black; background:#EAEAEA" width="150" | <0.5
| align="center" | 350
| align="center" | 23
| align="center" | 17.9
| align="center" style="background:#FFCCCC" | Low
| align="center" style="background:#FFCCCC" | Low
|-
|-
| style="border-bottom:2px solid black" align="center" rowspan="2" | Entire training set<br>N = 788*
| Amiloride
| style="border-bottom:1px solid black" | True || align="center" style="border-bottom:1px solid black; background:#E1FFE1" | <b>415</b> <br> <span style="font-size:8pt">(52.7%)</span> || align="center" style="border-bottom:1px solid black; background:#FFE1E1" | 114 <br> <span style="font-size:8pt">(14.5%)</span>
| align="center" | 10
| align="center" | 50
| align="center" | 9.1
| align="center" style="background:#FFCCCC" | Moderate
| align="center" style="background:#FFCCCC" | Low
|-
|-
| style="border-bottom:2px solid black" | False || align="center" style="border-bottom:2px solid black; background:#FFE1E1" | 81 <br> <span style="font-size:8pt">(10.3%)</span> || align="center" style="border-bottom:2px solid black; background:#E1FFE1" | <b>178</b> <br> <span style="font-size:8pt">(22.6%)</span>
| Antipyrine
| align="center" | 600
| align="center" | 97
| align="center" | 99.5
| align="center" style="background:#CCFFCC" | High
| align="center" style="background:#CCFFCC" | High
|-
|-
| style="height:30px" | &nbsp; || Accuracy
| Atenolol
| colspan="2" align="center" |
| align="center" | 50
{| cellpadding="0" cellspacing="0" style="width:80%; height:20px"
| align="center" | 50
| style="color:white; background:#B9CDE5" align="right" width="75.2%" | <b>75.2%&nbsp;</b> || style="background:#EDF2F9" width="24.8%" | &nbsp;
| align="center" | 13.9
|}
| align="center" style="background:#FFCCCC" | Moderate
 
| align="center" style="background:#FFCCCC" | Low
|-
|-
| style="height:30px" | &nbsp; || Sensitivity
| Carbamazepine
| colspan="2" align="center" |
| align="center" | 200
{| cellpadding="0" cellspacing="0" style="width:80%; height:20px"
| align="center" | 70
| style="color:white; background:#B9CDE5" align="right" width="78.4%" | <b>78.4%&nbsp;</b> || style="background:#EDF2F9" width="21.6%" | &nbsp;
| align="center" | 78
|}
| align="center" style="background:#CCFFCC" | High
 
| align="center" style="background:#CCFFCC" | High
|-
|-
| style="height:30px" | &nbsp; || Specificity
| Chloramphenicol
| colspan="2" align="center" |
| align="center" | 250
{| cellpadding="0" cellspacing="0" style="width:80%; height:20px"
| align="center" | 90
| style="color:white; background:#B9CDE5" align="right" width="68.7%" | <b>68.7%&nbsp;</b> || style="background:#EDF2F9" width="31.3%" | &nbsp;
| align="center" | 98.9
|}
| align="center" style="background:#CCFFCC" | High
 
| align="center" style="background:#CCFFCC" | High
|}
<span style="font-size:8pt">* - Since ACD/Bioavailability model does not utilize GALAS modeling methodology, no additional parameters (i.e., <i>RI</i>) are available to enable additional filtering of more reliable predictions.</span>
 
 
<!-- TABLE 2 -->
{| cellpadding="2" cellspacing="0" style="border-top:2px solid black"  
|+ <b>Table 2.</b> Classification performance of the upper threshold (%F (oral) > 70%) probabilistic bioavailability model on the training set compounds.
|-
|-
! style="border-bottom:1px solid black; background:#EAEAEA" width="250" rowspan="2" | Subset
| Desipramine
! style="border-bottom:1px solid black; background:#EAEAEA" width="150" rowspan="2" | Observed
| align="center" | 150
! style="border-bottom:1px solid black; background:#EAEAEA" width="300" colspan="2" | Calculated probability (<i>p</i>)
| align="center" | 100
| align="center" | 99.2
| align="center" style="background:#CCFFCC" | High
| align="center" style="background:#CCFFCC" | High
|-
|-
! style="border-bottom:1px solid black; background:#EAEAEA" width="150" | >0.5
| Diazepam
! style="border-bottom:1px solid black; background:#EAEAEA" width="150" | <0.5
| align="center" | 5
| align="center" | 100
| align="center" | 99
| align="center" style="background:#CCFFCC" | High
| align="center" style="background:#CCFFCC" | High
|-
|-
| style="border-bottom:2px solid black" align="center" rowspan="2" | Entire training set<br>N = 788*
| Diltiazem
| style="border-bottom:1px solid black" | True || align="center" style="border-bottom:1px solid black; background:#E1FFE1" | <b>172</b> <br> <span style="font-size:8pt">(21.8%)</span> || align="center" style="border-bottom:1px solid black; background:#FFE1E1" | 126 <br> <span style="font-size:8pt">(16.0%)</span>
| align="center" | 90
| align="center" | 90
| align="center" | 99.7
| align="center" style="background:#CCFFCC" | High
| align="center" style="background:#CCFFCC" | High
|-
|-
| style="border-bottom:2px solid black" | False || align="center" style="border-bottom:2px solid black; background:#FFE1E1" | 44 <br> <span style="font-size:8pt">(5.6%)</span> || align="center" style="border-bottom:2px solid black; background:#E1FFE1" | <b>446</b> <br> <span style="font-size:8pt">(56.6%)</span>
| Etoposide
| align="center" | 350
| align="center" | 50
| align="center" | 95.8
| align="center" style="background:#FFCCCC" | Moderate
| align="center" style="background:#CCFFCC" | High
|-
|-
| style="height:30px" | &nbsp; || Accuracy
| Furosemide
| colspan="2" align="center" |
| align="center" | 80
{| cellpadding="0" cellspacing="0" style="width:80%; height:20px"
| align="center" | 61
| style="color:white; background:#B9CDE5" align="right" width="78.4%" | <b>78.4%&nbsp;</b> || style="background:#EDF2F9" width="21.6%" | &nbsp;
| align="center" | 30.6
|}
| align="center" style="background:#FFCCCC" | Moderate
 
| align="center" style="background:#FFCCCC" | Low
|-
|-
| style="height:30px" | &nbsp; || Sensitivity
| Ganciclovir
| colspan="2" align="center" |
| align="center" | 75
{| cellpadding="0" cellspacing="0" style="width:80%; height:20px"
| align="center" | 3
| style="color:white; background:#B9CDE5" align="right" width="57.7%" | <b>57.7%&nbsp;</b> || style="background:#EDF2F9" width="42.3%" | &nbsp;
| align="center" | 8.9
|}
| align="center" style="background:#FFCCCC" | Low
 
| align="center" style="background:#FFCCCC" | Low
|-
| Hydrochlorothiazide
| align="center" | 50
| align="center" | 69
| align="center" | 63.1
| align="center" style="background:#CCFFCC" | High
| align="center" style="background:#FFCCCC" | Moderate
|-
| Ketoprofen
| align="center" | 75
| align="center" | 92
| align="center" | 99.4
| align="center" style="background:#CCFFCC" | High
| align="center" style="background:#CCFFCC" | High
|-
| Metoprolol
| align="center" | 100
| align="center" | 95
| align="center" | 96.9
| align="center" style="background:#CCFFCC" | High
| align="center" style="background:#CCFFCC" | High
|-
| Naproxen
| align="center" | 500
| align="center" | 99
| align="center" | 99.5
| align="center" style="background:#CCFFCC" | High
| align="center" style="background:#CCFFCC" | High
|-
| Penicillin V
| align="center" | 200
| align="center" | 38
| align="center" | 65
| align="center" style="background:#FFCCCC" | Moderate
| align="center" style="background:#FFCCCC" | Moderate
|-
| Pirenzepine
| align="center" | 50
| align="center" | 27
| align="center" | 94
| align="center" style="background:#FFCCCC" | Low
| align="center" style="background:#CCFFCC" | High
|-
| Piroxicam
| align="center" | 20
| align="center" | 100
| align="center" | 94.5
| align="center" style="background:#CCFFCC" | High
| align="center" style="background:#CCFFCC" | High
|-
| Progesterone
| align="center" | 2.5
| align="center" | 100
| align="center" | 93.6
| align="center" style="background:#CCFFCC" | High
| align="center" style="background:#CCFFCC" | High
|-
| Propranolol
| align="center" | 240
| align="center" | 99
| align="center" | 99.3
| align="center" style="background:#CCFFCC" | High
| align="center" style="background:#CCFFCC" | High
|-
| Ranitidine
| align="center" | 60
| align="center" | 63
| align="center" | 51.1
| align="center" style="background:#FFCCCC" | Moderate
| align="center" style="background:#FFCCCC" | Moderate
|-
| Saquinavir
| align="center" | 600
| align="center" | 30
| align="center" | 48.4
| align="center" style="background:#FFCCCC" | Low
| align="center" style="background:#FFCCCC" | Moderate
|-
| Sulpiride
| align="center" | 200
| align="center" | 44
| align="center" | 41.9
| align="center" style="background:#FFCCCC" | Moderate
| align="center" style="background:#FFCCCC" | Moderate
|-
| Terbutaline
| align="center" | 10
| align="center" | 62
| align="center" | 19.5
| align="center" style="background:#FFCCCC" | Moderate
| align="center" style="background:#FFCCCC" | Low
|-
| Theophylline
| align="center" | 200
| align="center" | 100
| align="center" | 99.5
| align="center" style="background:#CCFFCC" | High
| align="center" style="background:#CCFFCC" | High
|-
| Verapamil
| align="center" | 120
| align="center" | 100
| align="center" | 99.1
| align="center" style="background:#CCFFCC" | High
| align="center" style="background:#CCFFCC" | High
|-
| Warfarin
| align="center" | 5
| align="center" | 98
| align="center" | 99.1
| align="center" style="background:#CCFFCC" | High
| align="center" style="background:#CCFFCC" | High
|-
|-
| style="height:30px" | &nbsp; || Specificity
| colspan="2" align="center" |
{| cellpadding="0" cellspacing="0" style="width:80%; height:20px"
| style="color:white; background:#B9CDE5" align="right" width="91.0%" | <b>91.0%&nbsp;</b> || style="background:#EDF2F9" width="9.0%" | &nbsp;
|}
|}
|}


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Revision as of 14:17, 17 July 2013

Overview

Oral Bioavailability module predicts the fraction of the specified drug dose that reaches systemic circulation after oral administration (%F). For calculation of quantitative %F values, Oral bioavailability module uses the same kind of absorption simulation that is implemented in ACD/PK Explorer.

Features

  • Predicts %F after oral administration with the possibility to explore dose-dependence of bioavailability
  • Predicts a number of endpoints that affect oral bioavailability:
    • Solubility (dose/solubility ratio)
    • Stability in acidic media
    • Intestinal membrane permeability by passive or active transport
    • Likelihood of P-gp efflux
    • Likelihood of first pass metabolism in the liver
  • Visualizes the contributions of underlying properties with traffic-lights (green = good, red = problematic) for easy interpretation
  • Displays experimental %F values for up to 5 similar structures from Bioavailability DB along with literature references.


Interface

Oral bioavailability.png
  1. 50 mg is the default oral drug dose used in calculations. Enter any desired value to explore the effect of dose on oral bioavailability.
    Oral bioavailability simulation.png
    a. Click the "Undo" button to reset the specified drug dose and to recalculate %F using the default settings.
    b. Click to recalculate %F using the currently specified dose.
  2. Predicted %F (Oral) value.
    Oral Bioavailability Ranges.png
  3. Click to see more details regarding the calculation of the particular property.
  4. Factors affecting oral bioavailability (see below for details).
  5. Hover over a title to view a screentip with a short description.
  6. Up to 5 similar structures in the Bioavailability DB with experimental values and references.


Traffic-lights system explanation

Solubility in gastrointestinal tract:
  • Green – good – dose/solubility ratio < 1.
  • Yellow – moderate – dose/solubility ratio between 1 and 10.
  • Red – poor – dose/solubility ratio > 10.
Stability – susceptibility to acid hydrolysis in stomach:
  • Red – only assigned to highly reactive compounds that decompose in stomach very quickly. Red light means that F (oral) <10%, overriding any probabilistic prediction (i.e., do not pay attention to predicted probabilities in this particular case).
Passive absorption – ability to cross human intestinal membrane by passive diffusion:
  • Red – intestinal passive absorption <30%. %F (oral) is always less than passive absorption.
  • Green – good (>70%) passive absorption across intestinal barrier. Passive absorption does not effect %F.
First-pass metabolism – susceptibility to metabolic transformations catalyzed by enzymes in liver and intestine:
  • Red – high probability that first-pass metabolism is >50%. In this case %F (oral) is likely to be dose dependent and not to exceed 40%.
  • Green – compound probably does not undergo significant first-pass metabolism.
P-gp efflux – susceptibility to backward transport through intestinal membrane:
  • Red – compound is P-glycoprotein substrate. This effect is mostly important when compound is metabolized by CYP3A4
Active transport – susceptibility to active transport through intestinal membrane:
  • Green – compound is actively transported by PepT1, ASBT or other enzymes.
  • Red light never appears, as this factor can only increase %F (oral).


Technical information


Bioavailability DB

Number of compounds: 788
Main sources of experimental data:

  • Reference books:
    • Therapeutic Drugs, Dolery, C., Ed. 2nd Edition, Churchill Livingstone, New York, NY, 1999
    • Clarke's Isolation and Identification of Drugs, Moffat, A.C., Jackson, J.V., Moss, M.S., Widdop, B., Eds. 2nd Edition, The Pharmaceutical Press, London, 1986
  • Various articles from peer-reviewed scientific journals*

* - Both articles reporting oral bioavailability models by other authors (i.e. with larger collections of experimental data per article) and dealing with detailed experimental pharmacokinetic characterization (i.e. usually only several compounds per article) were available for the training set construction.


Simulation model

The mathematical model that is used for simulations performed by PK Explorer and Oral Bioavailability modules is based on differential equations describing solubility in the gastrointestinal tract, passive-absorption in jejunum and elimination (total body clearance). Other pharmacokinetic properties such as first-pass effect in liver and gut and volume of distribution are also considered in simulations. Quantitative %F values are calculated as a ratio of AUCs after oral and intravenous administration. Also, the employed simulation model allows evaluating the dose dependence of bioavailability.


Validation

Validation Set & Assessment Procedure

Since the predictions are based on a mechanistic simulation model rather that formal statistical fitting to a set of data points, the validation procedure was not based on a conventional training/test set approach. Instead, a set of clinical fraction absorbed (fa) data together with dosage information for 28 drugs reported by Parrott & Lavé [1] (originating mainly from a compilation by Zhao et al. [2]) was used for validation purposes.

The model performance was assessed in several ways:

  • Qualitatively, by evaluating the accuracy of three-class classification, where the compounds were categorized by their calculated extent of absorption in the following manner:
    • Low: fa ≤ 33%
    • Moderate: 33% < fa < 66%
    • High: fa ≥ 66%
    • Quantitatively, using the Residual Mean Square Error (RMSE) statistic and visual inspection of the correlation between observed and predicted fa values of the considered drugs.

Validation results


Table. Qualitative nad quantitative fraction absorbed predictions for the validation set of 28 drugs.
Compound name Dose, mg Experimental fa (%) Predicted fa (%) Experimental class Predicted class
Acyclovir 350 23 17.9 Low Low
Amiloride 10 50 9.1 Moderate Low
Antipyrine 600 97 99.5 High High
Atenolol 50 50 13.9 Moderate Low
Carbamazepine 200 70 78 High High
Chloramphenicol 250 90 98.9 High High
Desipramine 150 100 99.2 High High
Diazepam 5 100 99 High High
Diltiazem 90 90 99.7 High High
Etoposide 350 50 95.8 Moderate High
Furosemide 80 61 30.6 Moderate Low
Ganciclovir 75 3 8.9 Low Low
Hydrochlorothiazide 50 69 63.1 High Moderate
Ketoprofen 75 92 99.4 High High
Metoprolol 100 95 96.9 High High
Naproxen 500 99 99.5 High High
Penicillin V 200 38 65 Moderate Moderate
Pirenzepine 50 27 94 Low High
Piroxicam 20 100 94.5 High High
Progesterone 2.5 100 93.6 High High
Propranolol 240 99 99.3 High High
Ranitidine 60 63 51.1 Moderate Moderate
Saquinavir 600 30 48.4 Low Moderate
Sulpiride 200 44 41.9 Moderate Moderate
Terbutaline 10 62 19.5 Moderate Low
Theophylline 200 100 99.5 High High
Verapamil 120 100 99.1 High High
Warfarin 5 98 99.1 High High