The mechanics by which a drug is absorbed and distributed through the body in terms of pharmacokinetics has great influence on the effects of the drug. Pharmacokinetics can be summarized through the acronym of ADME, which accounts for absorption, distribution, metabolism, and excretion. At every step of this process are factors that can cause difference in pharmacokinetics between formulations. Furthermore, the efficacy of each step varies greatly by mechanism. Dr. Halpert’s research on the structural interactions of different hepatic enzymes is only one arm in the research of drug metabolism and disposition, and even within the study of human drug metabolism.
The study of enzyme structures is highly analytical and uses large amounts of x-ray crystallography and computer modelling. As technologies progress, the time needed to obtain x-ray crystallography decreases, allowing for increasing volume and speed of protein structure analysis. The cytochrome p450 class that is so critical in hepatic drug metabolism has been largely studied, in order from the most commonly implicated. In addition, available information on the interactions made by the cytochrome p450 enzymes can be used in the the analysis of different drug compounds. This includes inter-ligand or multiple-ligand interactions, such as in that of Cl and NH forming ligand complexes within the active site of cytochrome p450 2B4 complexes. In this sense, the way that ligands interact with the active sites can be very unpredictable, and with a large roster of metabolic enzymes in different conformations, their analysis can be difficult. Many molecules may cause shifts in enzyme conformations for what would otherwise be nonobvious interactions. However, with a sufficient number of templates, most ligands can be studied. Such calculations and virtualizations can serve as key part of the preliminary screening for drug molecules, for further analysis and screening in physical in vitro or in vivo experiments.
Outside of potential drug candidate screening, the analysis of metabolism can also involve polymorphisms in the hepatic enzymes involved in the pharmacokinetics of a drug. For example, one CYP1A1 polymorphism found in Japanese populations was shown to have corresponded with higher rates of gastric cancer, in a cohort study held among over thirty six thousand patients, published as “CYP1A1, GSTM1 and GSTT1 genetic polymorphisms and gastric cancer risk among Japanese: A nested case–control study within a large-scale population-based prospective study” in the International Journal of Cancer. Two other genotypes of glutathione transferases studied were shown to have had no statistical significance in correlation with gastric cancers. Their conclusion was that the specific CYP1A1 polymorphism may increase the risks of gastric carcinogenesis, and further studies would need to be done on the mechanisms of how the polymorphism could lead to this outcome.
The study itself as a case control seems to have substantial power, using public health records available through the Japanese government from 1990 until follow ups in 2004, with the only restriction being two prefectures for which health data was unavailable. And thus, it may have served as one of the many jumping points for research delving into enzyme polymorphisms.
Another study conducted analyzed a few common polymorphisms within the cytochrome p450 family, such as CYP2C9, CYP2C19, CYP2D6 and CYP3A5. These four “account for 40% of the metabolism of clinically used drugs” (Dong, 2015), and thus analysis and improved understanding of their genetic and functional differences can make a significant impact in the administration of medicines. The analysis method in this research is only limited by the fact that the genes used were sampled purely from Chinese populations and therefore more studies would have to be conducted in other countries to establish sufficient research across the human gene expression. In particular, the study was able to describe statistically significant differences in polymorphic distributions in each of these enzymes. For example, CYP3A5*3 had an allelic frequency of over 70.69%. This kind of information can be critical if there is a clinical difference in pharmacokinetic functions of this particular polymorphism as compared to the wild type CYP3A5. Another example is the distribution of the CYP2C9*3 polymorphism with an allelic frequency of 10.7% among 19 identified polymorphisms (and the enzyme with the largest number of studied polymorphisms in the entire experiment). This showed a statistically significant different from an even distribution of 5% between the polymorphisms and the wild type CYP2C9. This enzyme in particular is of interest as the type 3 polymorphism has significantly reduced metabolic capabilities, and is involved in the metabolism of common angiotensin II receptor blockers, NSAIDs, sulfonylureas, and more (Van Booven, 2010).
Studies such as the Van Booven 2010 article summarizing CYP2C9 polymorphisms explored the differences through a laboratory setting. This means that it does not contain the sampling seen in the previous study and may not give significant implications for patterns in dosing, but it provides a wide variety of mechanisms and the clinical impact of different polymorphic types of the enzyme. The dose adjustment is particularly prominent in older generation drugs such as warfarin. Evidence regarding warfarin can be found in the in vivo studies published called “The warfarin-sulfinpyrazone interaction: stereochemical considerations” from 1986. This article is one of the many primary sources that can be traced back through Van Booven’s review, Poor metabolizers for warfarin will end up with higher plasma concentrations and may experience adverse reactions at what were prescribed to be a therapeutic dose. There are many other major drugs and conditions listed and cited in this review article. In the case of diabetes treatments like glipizide, there is the danger of incurring hypoglycemia. Having the polymorphism for poor phenytoin is a particularly dangerous one, as phenytoin is highly protein bound and its therapeutic window operates within a very small scale. Overdoses may be more common in patients who have the phenotype for low phenytoin metabolism, as found in this CYP2C9 for 90% of all phenytoin metabolism in the body.
The implications of these studies are vast. It allows the allocation of specific factors via the analysis of polymorphic enzymes expressed in different proteins. Genetic analysis is in clinical use today in pharmacy, mostly to screen for dangerous adverse reactions such as Stephens Johnsons syndrome, found in a certain mutation of the HLA protein. However, there is a more diverse field for adverse reactions lying within the study of genetic variation in metabolism.
The 2010 Van Booven article exceeds in collecting papers that provide evidence of metabolism rates between different enzyme mutations and commonly used drugs through controlled experiments. This has slightly different implications as compared to studies that use x-ray crystallography of the enzymes to model and anticipate interactions or overviewing population genetic variations for disease correlations. The design of experiments is another crucial aspect to the progress of knowledge in this field. Case-control studies can narrow down leads for future experiments, and the object of interest can be observed physically or by their experimental properties. The next step may include conducting experiments clinically with different hypothesis on how these discoveries may have greater implications on healthcare, analyzing their efficacy compared to the null of prescribing and making healthcare recommendations regardless of genetic variants of metabolic enzymes