In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling

Abstract

Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.

Figure 1

The two basic modes of interaction between xenobiotics and biological systems, namely PD (activity and toxicity) and PK events (ADME) (modified from (Testa and Krämer, 2006) and reproduced with the kind permission of the Verlag Helvetica Chimica Acta in Zurich). ADME, absorption, distribution, metabolism and excretion; PD, pharmacodynamic; PK, pharmacokinetic.

Figure 2

Metabolic scheme of galantamine comparing the experimental in vivo results in rats, dogs and humans (Mannens et al., 2002) with the predictions of METEOR (Testa et al., 2005). (Reproduced with the kind permission of the Verlag Helvetica Chimica Acta in Zurich).

Figure 3

(a) An endogenous molecule network generated using Ingenuity Pathways Analysis (Ingenuity Systems Inc., Redwood City, CA, USA). Solid lines represent direct interactions and dashed lines represent indirect interactions. (b) A network showing the connectivity via direct interactions of several key nuclear hormone receptors (rectangles) and their regulation of several transporters (trapezoid) and enzymes (diamonds) involved in drug absorption and metabolism while gene expression data from rats after treatment with 2(S)-((3,5-bis(trifluoromethyl)benzyl)-oxy)-3(S)phenyl-4-((3-oxo-1,2,4-tri azol-5-yl)methyl)morpholine (L-742694) is overlaid (Hartley et al., 2004). The network shows key upregulated transporter and enzyme genes (red symbols). Note that several genes are connected to PXR (NRI13).