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Methods and Principles in Medicinal Chemistry - Mannhold R.

Mannhold R., Kubinyi H., Timmerman H. Methods and Principles in Medicinal Chemistry - Wiley-VCH, 2001. - 155 p.
Download (direct link): pharmacokinetiksmedicanalchemistri2001.pdf
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10.7.2
PK Predictions Using QSAR and Neural Networks
Neural networks are a relatively new tool in data modelling in the field of pharmacokinetics [54-56]. Using this approach, non-linear relationships to predicted properties are better taken into account than by multiple linear regression [45]. Human hepatic drug clearance was best predicted from human hepatocyte data, followed by rat hepatocyte data, while in the studied data set animal in vivo data did not significantly contribute to the predictions [56].
10.8 Outlook
10.7.3
Physiologically-Based Pharmacokinetic (PBPK) Modelling
There are several approaches to pharmacokinetic modelling. These include empirical, compartmental, clearance-based and physiological models. In the latter full physiological models of blood flow to and from all major organs and tissues in the body are considered. Such models can be used to study concentration-time profiles in the individual organs and e. g. in the plasma [57-60]. Further progress in this area may result in better PK predictions in humans [61]
10.8
Outlook
Ultimately rapid methods are needed to obtain data adequate at each stage of drug discovery. At candidate selection level the extrapolation from animals to man, through the integration of pharmacokinetics and pharmacodynamics (PK/PD), becomes crucial for success [62, 63]. Such HT methods are currently being implemented and further developed in the pharmaceutical and biotechnology industry.
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