Oral Bioavailability

Overview

This module predicts human oral bioavailability for new compounds using a combination of two methods - probabilistic and "mechanistic". First, the "mechanistic" part of the module tries to evaluate the query compound with regard to several crucial bioavailability limiting factors: solubility in stomach or intestine, stability in acidic media, intestinal membrane permeability by passive or active transport, P-gp efflux, and first pass metabolism in liver. All of these properties are predicted using independent algorithms, and experimental data sets. Results of the "mechanistic" evaluation of the human oral bioavailability are presented as easily interpretable "traffic-lights". In addition, this color-coded profile is converted to the probabilities for the compound's oral bioavailability to exceed 30% and 70% respectively with the help of multiple Recursive Partitioning trees. The final compound classification with regards to its oral bioavailability at clinically relevant doses is based on the cumulative result of those two statistical models.

Features

• Provides a unified interface that summarizes all relevant information regarding BBB penetration potential of the compound of interest
• Classifies the compound as CNS permeable on non-permeable and displays the values of quantitative parameters supporting the prediction:
  • Rate of brain penetration (LogPS)
  • Extent of brain penetration (LogBB)
  • Brain/plasma equilibration rate (Log (PS * f_{u, brain}))
• Visualizes the predictions using the 'traffic lights' scheme showing which parameters preclude brain delivery of the analyzed compound
• Allows altering the values of main physicochemical determinants to get an insight on structural changes needed to achieve desired permeation characteristics
• Displays a scatter plot providing a possibility to compare relevant brain penetration characteristics of the analyzed compound to a set of well-known CNS and peripheral drugs.
• Displays carrier-mediated transport alerts for compounds that undergo facilitated diffusion or active efflux across BBB

Interface

File:oral bioavailability.png

1. logP and fraction unbound in plasma (f_{u, plasma}) are calculated automatically. Alter these values to simulate the limiting effect of the compound's lipophilicity and/or plasma protein binding on its brain disposition.
   File:oral bioavailability simulation.png

   a. Click the "Undo" button to restore the automatically calculated property value (f_{u, plasma} in the current example) for a compound and recalculate LogBB using default parameter values

   b. Click to recalculate the unbound fraction in brain and LogBB using the currently specified parameter values

   1. Classification of compounds based on predicted oral bioavailability (poor <30%, moderate 30-70%, good >70%). Probabilities that %F is greater than standard cut-off values are also presented.
   2. Click to see more details regarding the calculation of the particular property
   3. Factors affecting oral bioavailability (see below for details)
   4. Hover over a title to view a screentip with a short description
   5. Up to 5 similar structures in the %F (oral) DB with experimental values and references

   Traffic-lights system explanation

   1. Solubility in unbuffered water:
      • Green – good solubility in unbuffered water, Log S_w > -4 for electrolytes and Log S_w > -3 for non-electrolytes.
      • Red – very poor solubility in unbuffered water (25°C), Log S_w < -6 for electrolytes and Log S_w < -4.5 for non-electrolytes.
      • Yellow – moderate solubility.
   2. 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).
   3. 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.
   4. 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.
   5. 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
   6. 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

   
   

   Training set size: 788
   Internal validation set size: N/A*
   * - Since the formal basis for the probabilistic prediction still are "mechanistic" considerations, there was no particular emphasis on external model validation - similarly to the knowledge based "expert-like". As result, the statistical results obtained for the training set compounds are the only available quantitative characteristics for the ACD/Oral Bioavailability model.

   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.

   
   

   
   

   Training set statistics

   
   

   Table 1. Classification performance of the lower threshold (%F (oral) > 30%) probabilistic bioavailability model on the training set compounds.

   Subset	Observed	Calculated probability (p)
   		>0.5	<0.5
   Entire training set N = 788*	True	415 (52.7%)	114 (14.5%)
   	False	81 (10.3%)	178 (22.6%)
   	Accuracy	75.2%
   	Sensitivity	78.4%
   	Specificity	68.7%

   * - Since ACD/Bioavailability model does not utilize GALAS modeling methodology, no additional parameters (i.e., RI) are available to enable additional filtering of more reliable predictions.

   
   

   Table 2. Classification performance of the upper threshold (%F (oral) > 70%) probabilistic bioavailability model on the training set compounds.

   Subset	Observed	Calculated probability (p)
   		>0.5	<0.5
   Entire training set N = 788*	True	172 (21.8%)	126 (16.0%)
   	False	44 (5.6%)	446 (56.6%)
   	Accuracy	78.4%
   	Sensitivity	57.7%
   	Specificity	91.0%

   * - Since ACD/Bioavailability model does not utilize GALAS modeling methodology, no additional parameters (i.e., RI) are available to enable additional filtering of more reliable predictions.