By Devin G. Bost
Feb. 17, 1014
Problem One: Identifying the metabolites
Many people overlook the fact that after a drug is consumed, it is broken down by the body. A finite series of mechanisms exist to process the drug. The P450 set of enzymes are key in metabolizing most known drugs, and through the liver, they break the drug down into various components. Many of those components, called metabolites, are still biologically active. In some cases, the metabolites of a drug are much more reactive than the initial drug. Thalidomide is a famous example of this. Other good examples include intermediates, coordination complexes, and adducts formed by metabolites of drugs of abuse. Ethanol, for example, is much less dangerous than its metabolite, acetaldehyde, which reacts with DNA, initiating carcinogenesis, and can spontaneously detonate at room temperature.
Solution: Representing the metabolites as an XML data tree
Large chemical databases of allow developers to computationally predict the potential metabolites formed by a given drug. XML is known as a hierarchal data structure. Recently, advances in SQL database technology has improved the functionality and usability of this data type. Using known chemical degradation processes, stored procedures (in the database) can be used to compute each potential metabolite of a given source drug. Mathematically, limitations to SQL stored procedures can be overcome by linking to external software libraries. For example, using Microsoft SQL Server 2012, a database administrator can easily import a dynamic linked library (DLL) to extend database functionality to include a program written in an object-oriented language (e.g. C++, C#, or VB.NET) over the Common Language Runtime (CLR). This technique enables database developers to extend the typical functionality of the database and perform advanced computations with external software libraries designed for mathematical modeling and simulation. Using an XML data type, databases can store the entire computed tree of metabolites for each initial drug. After the computations are performed, the data is easily stored in the database for later use.
Problem Two: How do we identify interactions between drugs and/or metabolites?
According to (Wishart & Materi, 2007), “In the areas of drug discovery and development, [pharmacokinetic (PK)] modeling might be regarded as one of the ﬁrst and most successful examples of computational system biology,” considering also the discoveries of (Huisinga, Telgmann, & Wulkow, 2006) and (Mager, 2006), “A key limitation of ODEs or systems of ODEs is the need for complete and quantitative data on concentrations, reaction rates, diffusion rates, degradation rates and many other parameters.” With special thanks to Henry Eyring
Large metabolomics databases, such as the ones listed by (Wishart & Materi, 2007), reduce the quantity of computational predictions that are required by the developer. Furthermore, these databases provide a means for testing the accuracy of predictions against known empirical data and experimental findings. Developing and integrating the models for computing and joining the required parameters then remains the ultimate task for the developer.
Huisinga, W., Telgmann, R., & Wulkow, M. (2006). The virtual laboratory approach to pharmacokinetics: design principles and concepts. Drug discovery today, 11(17), 800-805.
Mager, D. E. (2006). Quantitative structure–pharmacokinetic/pharmacodynamic relationships. Advanced drug delivery reviews, 58(12), 1326-1356.
Wishart, D. S., & Materi, W. (2007). Current Progress in computational metabolomics. Briefings in Bioinformatics, 8(5), 279-293.
 Henry Eyring is best known for his contributions on transition state theory. He has been recognized by numerous awards in chemistry, including a Nobel Prize. Interestingly, Henry Eyring is the father of a religious leader, Henry B. Eyring. As of Feb. 17, 2014, Henry B. Eyring is serving as a member of the First Presidency of The Church of Jesus Christ of Latter-Day Saints.