Oral Bioavailability

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
  • 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

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.
   

   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.
   
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 all other predictions (i.e., do not pay attention to predicted values in this particular case).

Passive absorption – ability to cross human intestinal membrane by passive diffusion:

• Red – intestinal passive absorption <30%. Poor bioavailability, as %F (oral) cannot exceed the extent of passive absorption.
• Green – good (>70%) passive absorption across intestinal barrier. Passive absorption does not affect %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, including both detailed pharmacokinetic characterization studies (i.e., usually only several compounds per article), and larger data compilations

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 (f_a) 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: f_a ≤ 33%
  • Moderate: 33% < f_a < 66%
  • High: f_a ≥ 66%
• Quantitatively, using the Residual Mean Square Error (RMSE) statistic and visual inspection of the correlation between observed and predicted f_a values of the considered drugs.

Validation results

The model performance for validation set compounds is demonstrated in the Table below. Notably, the software did not produce any two-class misclassification errors (when a molecule having low f_a is predicted to be well-absorbed or vice versa) except pirenzepine (exp. f_a = 27% at 50 mg) which is known as P-glycoprotein substrate [3] and its bioavailability is significantly limited by P-gp efflux.

Table. Qualitative and 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
Statistics	No. of compounds: 28	RMSE: 22%*	RMSE: 17%**	Correct: 20/28 (71%)*	Correct: 20/25 (80%)**

* - P-gp substrates included
** - P-gp substrates excluded

Qualitative evaluation does not reveal the full picture, since compounds with f_a values near the class boundaries would often be misclassified, even though the actual prediction would not be far off the experimental value. Therefore, quantitative predictions were also visualized in the scatter plot below:

Figure. Correlation between observed and predicted f_a for the validation set of 28 drugs.

Since carrier-mediated transport effects can be reliably accounted in the simulation only if experimentally measured permeabilities are used, Pinrenzepine and two other compounds clearly affected by P-gp efflux (Saquinavir and Etoposide) were excluded from quantitative analysis - these are marked by red crosses in the scatter plot and the respective experimental values are highlighted in red in the data table. For the majority of remaining drugs, calculated values are in good agreement with clinical data, except several basic molecules (such as terbutaline, amiloride) underpredicted by more than 30%. However, one cannot disregard the possible contribution of carrier-mediated influx for these small cationic compounds. Once again, the overall RMSE of 17% was obtained with the software operating in 'pure' in silico mode, i.e., the predictions were performed using only the compounds' structure as input. On the other hand, ACD/PK Explorer module provides the potential for further improvement of prediction accuracy by offering full in combo simulation possibilities with a wide range of accepted input parameters including basic physicochemical properties (LogP and pKa), solubility, permeability, elimination rate constant, and volume of distribution.