ADMET prediction

The attraction of lipophilicity in medicinal chemistry is mainly due to Corwin Hansch s work and thus it is traditionally related to pharmacodynamic processes. However, following the evolution of the drug discovery process, lipophilicity is today one of the most relevant properties also in absorption, distribuhon, metabolism, excretion and toxicity (ADMET) prediction, and thus in drug profiling (details are given in Chapter 2). 

Lead optimization Application of early ADMET predictive techniques, structure-activity relationships and medicinal chemistry testing of homologs... 

Riley, R. J., Kenna, J. G. Cellular models for ADMET predictions and evaluation of drug-drug interactions. Curr. Opin. Drug Discov. Dev. 2004, 7 86-89. 

O Brien SE, de Groot MJ. Greater than the sum of its parts Combining models for useful ADMET prediction. J Med Chem 2005 48 1287-91. 

Tetko IV, Bruneau P, Mewes HW, Rohrer DC, Poda GI. Can we estimate the accuracy of ADMET predictions 232th ACS National Meeting. San Francisco, CA, 2006. 

New Computational Tools for Data Modeling, ADMET Prediction, and Virtual Screening... 

Prediction of various physicochemical properties such as solubihty, lipophhicity log P, pfQ, number of H-donor and acceptor atoms, number of rotatable bonds, polar surface area), drug-likeness, lead-likeness, and pharmacokinetic properties (ADMET profile). These properties can be applied as a filter in the prescreening step in virtual screening. 

Van de Waterbeemd H, Gifford E. ADMET in silico modelling towards prediction paradise Nat Rev Drug Discov 2003 2 192-204. 

Davis AM, Riley RJ. Predictive ADMET studies. The challenges and the opportunities. Curr Opin Chem Biol 2004 8 378-86. 

As discussed above, all ADMET aspects are dependent on each other and should all be considered when making predictions. Integrated analysis of different aspects of drug pharmacokinetic profiles is yet another future trend. Ultimately, drug ADMET properties should be predicted based on an integration of a compilation of in silico models reflecting different aspects of the process. 

Van de Waterbeemd, H., Gifford, E. ADMET in silica modelling towards prediction paradise Nat. Rev. Drug. Discov. 2003, 2, 192-204. 

The Jamieson paper reports the results of a number of studies, some successful, others not. Failures can be ascribed to the difficulties encountered in log P control. The first evident trouble concerns the choice of the lipophilicity descriptor many prefer log P, but this choice is questionable as has been outlined by Lombardo (see Chapter 16). Secondly, variations in lipophilicity profile influence not only hERG activity, but also target selectivity and also ADMET properties. Lipophilicity is a bulk property and its modification can involve different moieties of the molecules. Once the chemical modulation has been designed, but before moving to the bench, the research group should predict the consequences of this change on each step of the drug s action, but unfortunately this is not always done. 

Hansch and Leo [13] described the impact of Hpophihdty on pharmacodynamic events in detailed chapters on QSAR studies of proteins and enzymes, of antitumor drugs, of central nervous system agents as well as microbial and pesticide QSAR studies. Furthermore, many reviews document the prime importance of log P as descriptors of absorption, distribution, metabolism, excretion and toxicity (ADMET) properties [5-18]. Increased lipophilicity was shown to correlate with poorer aqueous solubility, increased plasma protein binding, increased storage in tissues, and more rapid metabolism and elimination. Lipophilicity is also a highly important descriptor of blood-brain barrier (BBB) permeability [19, 20]. Last, but not least, lipophilicity plays a dominant role in toxicity prediction [21]. 

DeWitte, R., Gorohov, F., Kolovanov, E. Using targeted measurements to improve the accuracy of physical property prediction. Presented at ADMET-1 Conference 2004, San Diego, 2004. 

This is just the first example of how the ADMET reaction can be used to model branching behavior and precisely control the structure in olefin-based polymer backbones. Other polymers under study include polyalcohols, polyvinyl acetates, and ethylene-styrene copolymers. The ultimate goal of this research is to be able to define, or even predict, crystallization limits and behavior for many polymers, some of which have not yet been prepared in a crystallized form. 

In the above-mentioned examples, the prediction of CYP-mediated compound interactions is a starting point in any metabolic pathway prediction or enzyme inactivation. This chapter presents an evolution of a standard method [1], widely used in pharmaceutical research in the early-ADMET (absorption, distribution, metabolism, excretion and toxicity) field, which provides information on the biotransformations produced by CYP-mediated substrate interactions. The methodology can be applied automatically to all the cytochromes whose 3 D structure can be modeled or is known, including plants as well as phase II enzymes. It can be used by chemists to detect molecular positions that should be protected to avoid metabolic degradation, or to check the suitability of a new scaffold or prodrug. The fully automated procedure is also a valuable new tool in early-ADMET where metabolite- or mechanism based inhibition (MBI) must be evaluated as early as possible. 

Among chemical-physics properties, lipophilicity is certainly a key parameter to understand and predict absorption, distribution, metabolism, excretion, and toxicity (ADMET) of NCE furthermore, it contributes to model ligand-target interactions underlying the pharmacodynamic phase [15],... 

Pharmacokinetics and toxicity have been identified as important causes of costly late-stage failures in drug development. Hence, physicochemical as well as ADMET properties need to be fine-tuned even in the lead optimization phase. Recently developed in silica approaches will further increase model predictivity in this area to improve compound design and to focus on the most promising compounds only. A recent overview on ADME in silica models is given in Ref [128]. 

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