ADME T Predictions in Drug Discovery

This observation catalyzed a change in how drug metabolism and pharmacokinetics was applied in the mid 1990s. Scientists started to examine how the descriptive nature of development drug metabolism and pharmacokinetics could be applied in a predictive way into drugs at the design stage. 

The discipline of DMPK has always been a drug development activity. In this realm, it still plays a pivotal role in describing the kinetic profile of the drug in both preclinical species and humans, where this information guides the selection of safe therapeutic doses for man for the first clinical studies, as well guiding the choice of dose frequency in patients. It is also important to describe accumulation, distribution and elimination profiles of the drug, its metabolic fate and the profiles of those key metabolites. The use of predictive tools to indicate metabolic fate is described in Chapter 11. The study of competition 

By its nature, DMPK is a mathematical discipline that describes the concentration-time profile of a drug and links this to its efficacy response and safety profile. From its mathematical basis, DMPK has developed as a truly predictive quantitative discipline. A number of fundamental DMPK principles and mathematical equations have underpinned this transformation. 

These principles include the role of physico-chemical properties the free drug hypothesis and the concept of clearance and scaling. Pharmacokinetic-pharmacodynamic simply stated these principles are  

Physico-chemical properties underpin many of the structure-activity relationships of absorption, distribution, metabolism and elimination. The control of physico-chemical properties is a first and major step in solving many DMPK problems. 

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Hou, T., Wang, J., Zhang, W. and Xu, X. (2007) ADM E evaluation in drug discovery. 6. Can oral bioavailability in humans be effectively predicted by simple molecular property-based rules Journal of Chemical Information and Modeling, 47, 460-463. 

Each year, a growing number of publications report on computational methods for the development of predictive ADME/T models. However, currently available methods are not reliable enough and are limited in their application, despite the recognition of their importance in the drug discovery process. Are we able to generate such reliable models, considering the severe limitations related to the intrinsic chemical diversity, the quantity and quality of the data In this chapter, we critically review data and approaches used to develop physicochemical and biological ADME/T models, in an attempt to address this question. 

Hou, T.-J. and Xu, X. (2002) ADME evaluation in drug discovery. 1. Applications of genetic algorithms on the prediction of blood-brain partitioning of a large set drugs./. Mol, Model, 8, 337-349. 

T. Hou, J. Wang, W. Zhang and X. Xu, ADME evaluation in drug discovery. 7. Prediction of oral absorption by correlation and classification, /. Chem. Inf. Model, 2007, 47, 208-218. 

In silico techniques have gained wide acceptance as a tool to support the drug discovery and optimization process. Binding mode predictions via docking, affinity predictions via QSAR and CoMFA, or the prediction of ADME(T) properties are routinely applied [1-3]. 

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