Predicting drug interactions

Recently, Bottino et al. [151] developed mathematical models that use hERG IC50 data and APD results measured from dog Purkinje fibers to predict drug interaction with other cardiac ion currents and dispersion of repolarization in transmural ECG. As regards cardiac Purkinje cells, a recent study suggested that rabbit cells might be more suitable for screening purposes because they possess a lower repolarization reserve than canine cells [152]. 

In previous sections, we discussed pharmacokinetics and pharmacodynamics. Remember, these are simply fancy medical terms for what the body does to a medication and what a medication does to the body. These twin pillars of pharmacology form the basis for understanding and being able to predict drug interactions. 

Despite the limited value of measuring plasma psychotropic drug concentrations to assess clinical response, a knowledge of the pharmacokinetics of such a drug can be of value in predicting drug interactions. 

Wrighton SA, Ring BJ. Predicting drug interactions and pharmacokinetic variability with in vitro methods the olanzapine experience. Drug Metab Rev 1999 31 15-28. 

Most P450 oxidations and drug interactions can be predicted from inhibition studies, since most P450 inhibitors show competitive Michaelis-Menten kinetics. However, there are examples of unusual kinetics, and most of these are associated with CYP3A oxidations. In this chapter, both Michaelis-Menten kinetics and more complex kinetics will be discussed. General experimental protocols that can be used to obtain and analyze kinetic data will be presented, and the implications of the results when predicting drug interactions will be discussed. 

Von Moltke LL, Greenblatt DJ, Cotreaubibbo MM, et al. Inhibition of desipramine hydroxylation in vitro by serotonin-reuptake inhibitor antidepressants, and by qui-nidine and ketoconazole—a model system to predict drug-interactions in vivo. J Pharmacol Exp Ther 1994 268 1278-1283. 

Jamis-Dow CA, Pearl ML, Watkins PB, et al. Predicting drug interactions in vivo from experiments in vitro human studies with paclitaxel and ketoconazole. Am J Clin Oncol 1997 20 592-599. 

Moore JT, Kliewer SA. Use of the nuclear receptor PXR to predict drug interactions. Toxicology 2000 153 1-10. 

Not all predicted drug interactions are expected to be clinically significant. For example, nifedipine and cyclosporine are both CYP3A4 substrates, but there is no clinically important drug interaction noticed.To predict the drug interaction, several parameters are expected to be important. These include notably ... 

In vitro inhibition of CYP2D6 activity by methadone (K = 3 M) predicted drug interactions in vivo. This finding was confirmed in 42 abusers of oral opiates undergoing treatment with methadone (Wu et al. 1993a). 

A further consideration in aiming to predict drug interaction in humans is to ask which human The variation in the human genome may help to explain the many, well-catalogued examples of idiosyncratic toxicity. Genetic, physiological and... 

Clearly, no one animal model or combination of animal models reflects the metabolic capabilities of humans. By having a complete understanding of the factors (e.g., inducers, inhibitors, and effect of disease state) that alter the expression and activity of the enzyme responsible for the metabolism of a particular compound, and by a determination of responsible isoforms and patient phenotyping, it may be possible to predict drug interactions and metabolic clearance. 

Von Moltke LL, Greenblatt DJ, Schmider J, Wright CE, Harmatz JS, Shader RI. In vitro approaches to predicting drug interactions in vivo. Biochem Pharmacol 1998 55 113-122. 

Darvas, F., Futo, I. and Szeredi, P. 1978. Logic-based program system for predicting drug interaction. Int. J. Bio-Med. Comp. 9 259-271. 

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