Predicting In Vivo Drug Interactions from In Vitro data by use of Fluorometric Assays.

 

Introduction

It is very common for people using illicit or prescription drugs to take them in combination with one another. When doing so a drug-drug interaction may occur, in which the metabolism of the drugs may be affected. The drug-drug interaction may cause either an induction or an inhibition of enzymes in the body. During an enzyme induction one drug will induce the body to produce more of an enzyme, which can then metabolize the second drug and therefore reduce the effects of the second drug. During an enzyme inhibition one drug will inhibit the metabolism of the second drug therefore allowing an accumulation of the second drug in the body1. The goal of this study is to develop an efficient method to detect the inhibition of various drug combinations simultaneously.

Cytochrome P450 enzymes, including CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4 are responsible for the metabolism of many different drugs in the human body. However, they are also highly susceptible to enzyme inhibition due to their lack of substrate specificity 2. The isoenzyme CYP2D6 will be used in this experiment due to the variability in its expression in the liver. CYP2D6 shows the largest phenotypical variability amongst the CYPs, largely due to genetic polymorphism. The different phenotypes account for normal, reduced and non-existent CYP2D6 function in the human body3.

A well known example of a drug that is metabolized by the CYP2D6 enzyme is MDMA (methylenedioxymethamphetam-ine). During this study, for instance, we can highlight the effects of persons choosing to consume MDMA, better known as ecstasy, in combination with cocaine, which is often used to enhance the effects. Also many anti-depressant drugs such as fluoxetine and paroxetine are taken in combination with ecstasy to regulate its serotonin effects4. Increasing in popularity is the combination of sildenafil, better known as Viagra®, with ecstasy to form a combination known as “sextasy” in which each drug amplifies the weakness of the other4. These are only a few of the many drug-drug combinations using MDMA that are of interest in this study. Consequently, the person consuming these multiple-drug combinations will be at risk of suffering from a drug-drug interaction and therefore may suffer from unwanted reactions in the body, or even toxic side effects.

Additional drugs that are metabolized by the P450 CYP enzymes include caffeine and St. John’s Wort (hypericum perforatum). Although these drugs are not metabolized directly by the CYP2D6 enzyme, it has been found that many drugs that are metabolized by a CYP superfamily enzymes serve to inhibit the metabolism of other CYP enzymes3. Constructing multiple drug-drug combinations in this study will allow for a better understanding of the inhibitors and inducers of metabolism by the CYP2D6 enzyme. Ultimately, testing the interactions of the drug in vitro, will allow us to better predict the interactions of these drugs in vivo.

 

Methods

We propose the development of a rapid high throughput system for drug-drug interaction using a fluorometric assay. The drugs chosen for analysis in this study will be those that are specifically metabolized by the CYP2D6 enzyme, or those that are metabolized by another P450 cytochrome CYP enzyme, with potential to inhibit metabolism by the CYP2D6 enzyme.

The assessment of specific drug metabolism will be conducted using a spectrofluorometer and the contents of the CYP2D6 /AMMC High Throughput Inhibitor screening kit, sold by BD Biosciences, which is designed to rapidly screen for potential inhibitors of CYP2D6 catalytic activity. This kit includes the enzyme of interest, CYP2D6, fluorogenic substrate, AMMC (3-[2-(N,N-diethyl-N-methylammonium)-ethyl]-7-methoxy-4-methylcoumarin)5, a positive control inhibitor, and insect control. It will be modified to allow the simultaneous testing of various drug-drug interactions in a 96-well plate format.  The assays will be analyzed by the spectrofluorometer, which will quantify the conversion of the substrates to highly fluorescent products1. This method will be used to detect the effects of various drug-drug interactions on the metabolic reactions of the isoenzyme CYP2D6.

The experimental results can be analyzed using enzyme-kinetic principles. Since competitive inhibition is being measured in a drug-drug interaction experiment, the metabolic rate can be determined using first-order reaction kinetics, by applying the Michaelis-Menten equation to the specific experimental set-up1. The computer software Matlab® will also be utilized in the analysis of experimental results. 

 

Possible Results and Implications

The results found from these various drug-drug interactions could have many positive effects. The determination of possible toxic drug combinations may be determined, as well as the determination of safe drug combinations. This experimentation will also serve to create a more efficient means to detect the inhibition of various drug combinations simultaneously. The current methods of studying drug-drug inhibition will be improved based upon the efficiency and the results of this experiment.

A potential negative implication in this experimentation is the worth of the predictions made for in vivo drug interactions based upon in vitro studies. Once the drugs have entered the body, other chemical, physiological, or biochemical factors will determine the ultimate fate of the drug-drug interaction in the human body. If the in vitro study inaccurately predicts the in vivo implications of certain drug-drug interactions, a perfectly safe drug-drug combination may be withdrawn in error, or an unsafe drug-drug combination may possibly be created unintentionally.

 

Reference List

 

   1.   Wienkers, L. C.; Heath, T. G. Nature Reviews Drug Discovery 2005, 4, 825-33.

   2.   Obach, R. S.; Walsky, R. L.; Venkatakrishnan, K.; Houston, J. B.; Tremaine, L. M. Clinical Pharmacology & Therapeutics 2005, 78, 582-92.

   3.   Howard, L. A.; Sellers, E. M.; Tyndale, R. F. Pharmacogenomics 2002, 3, 185-99.

   4.   Oesterheld, J. R.; Armstrong, S. C.; Cozza, K. L. Psychosomatics 2004, 45, 84-87.

   5.   Chauret, N.; Dobbs, B.; Lackman, R. L.; Bateman, K.; Nicoll-Griffith, D. A.; Stresser, D. M.; Ackermann, J. M.; Turner, S. D.; Miller, V. P.; Crespi, C. L. Drug Metabolism and Disposition 2001, 29, 1196-200.