What influences oral bioavailability?

    PACT-F is the fundamental knowledge base which allows to analyse the factors which influence bioavailability.

    Several factors can influence the bioavailability of a drug. These include drug formulation, coadministration of another drug, feeding condition, the age and gender of the subjects involved, dosing scheme, genetic differences and specific populations of a trial.

    PACT-F helps to determine the reliability of a trial, such as which experimental and analytical procedure was used, which detailed chemical entity has been measured, how many subjects were involved in a trial, were the subjects healthy or did an abnormal state of health influenced the bioavailability result.

    Some other fields in PACT-F describe the comparability of trials. This is important for the proper comparison of trials and for the development of qualitative structure bioavailability relationships (QSBR) models: Was the clinical trial conducted with average humans or with elderly, children or neonates? Was it conducted with healthy subjects or with ill patients? Which diseases do have an impact on bioavailability?

    In order to identify and analyse the factors which influence bioavailability, all bioavailability results and their associated data have been integrated into one file (SD-format). This enables user-friendly database queries to receive information about detailed trials on specific diseases, the effect of coadministrated drugs, different drug formulations, multiple drug administrations, food intake and specific experimental conditions. The effect on bioavailability of age, gender, genetic or ethnic group and other subject specific conditions can be directly analysed.

    PACT-F is a structure-based knowledge base: every record contains the chemical structure of the compound which was used in that trial. This enables scientists to build relationships between structures and properties, such as which molecular patterns increase or decrease the bioavailability of a drug, either generally in all organisms or only in some specific species, see use of PACT-F.

    PACT-F can be used to identify functional groups or molecular patterns which commonly lead to a low bioavailability in some species, like rats, but which don’t affect human bioavailability. This could be due to genetic species differences or different metabolic pathways, making animal trials less reliable to project human bioavailability. In these cases, a bioavailability trial in this species will mislead the candidate selection process in drug development.

    Small changes within the chemical structure can modify the bioavailability of an investigational drug dramatically. PACT-F can be used to identify and quantify the effects of substitution of functional groups. Based on the large amount of available structures and bioavailability values in PACT-F, decision trees and rules can be evolved which guide the future optimisation of novel compounds.

    Last, but not least, the expert system IMPACT-F, which predicts oral bioavailability of future drug candidates in humans, is based on PACT-F.


    Frequently Asked Questions (FAQ):

    Is IMPACT-F restricted to a specific therapeutic area, such as cardiovascular drugs or cancer?

    What is required to predict oral bioavailability with IMPACT-F?

    Can IMPACT-F predict human oral bioavailability of entirely new molecules (NCEs)?


PACT-F:
The largest
and
most
annotated knowledge base
on
bioavailability worldwide.
 

 
Further Links:
 
Drug discovery
 
Drug development
 

Cheminformatics
 
Software for ADME prediction
 
Pharmacokinetics
 
Computational chemistry
 
Molecular
modeling

 
Clinical trials
 

Preclinical
Research

 


Recent News:

PharmaInformatic and
UNIZYME Laboratories
sign
cooperation agreement

(more)
 

© Copyright 2004-2021 PharmaInformatic Boomgaarden. All rights reserved.           Site map           Contact         Terms of Use         Imprint        


PharmaInformatic provides
ADME/TOX
 Knowledge Bases

 and Expert Systems
to improve drug discovery
and development.