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It's a Mindset Thing

One theme that I try to present is that very small differences between humans can result in very important differences in terms of drugs and disease. A good example of this notion is the fact that monozygotic (formerly called identical) twins are not identical genetically and hence have different susceptibilities to diseases like schizophrenia. A press release from the University of Western Ontario states:

Just like snowflakes, no two people are alike, even if they're identical twins according to new genetic research from The University of Western Ontario. Molecular geneticist Shiva Singh has been working with psychiatrist Dr. Richard O'Reilly to determine the genetic sequencing of schizophrenia using identical or monozygotic twins. The study is published in this month's PLoS ONE.

Singh looked at about one million markers of identical twins (and their two parents) where only one twin had schizophrenia. "The most informative feature of schizophrenia is that it sometimes runs in the family. So, for example, the risk of developing schizophrenia is much higher if your brother, sister, mother or father have the disease," says Singh, noting in the general population about one percent have schizophrenia. "We started with the belief that monozygotic twins are genetically identical, so if one member of identical twins has schizophrenia, then the risk for the other twin should be 100 percent, if it's all due to genes. However, studies over the years have shown that the risk of the disease in both twins is only 50 percent." That means either the twins are genetically not identical or the familial disease involves non-genetic (random) effects.

Singh and his team have now demonstrated that the monozygotic twins are not genetically identical. "So if schizophrenia is in the genes, then the difference in the genetic makeup of monozygotic twins, with only one disease twin, must have something to do with the disease." Singh found about 12 per cent of DNA can vary across individuals, "Cells are dividing as we develop and differentiate. More importantly, these cells may lose or acquire additional DNA. The genome is not static."

The abstract of the above-mentioned article states:

Genetic individuality is the foundation of personalized medicine, yet its determinants are currently poorly understood. One issue is the difference between monozygotic twins that are assumed identical and have been extensively used in genetic studies for decades [1]. Here, we report genome-wide alterations in two nuclear families each with a pair of monozygotic twins discordant for schizophrenia evaluated by the Affymetrix 6.0 human SNP array. The data analysis includes characterization of copy number variations (CNVs) and single nucleotide polymorphism (SNPs). The results have identified genomic differences between twin pairs and a set of new provisional schizophrenia genes. Samples were found to have between 35 and 65 CNVs per individual. The majority of CNVs (~80%) represented gains. In addition, ~10% of the CNVs were de novo (not present in parents), of these, 30% arose during parental meiosis and 70% arose during developmental mitosis. We also observed SNPs in the twins that were absent from both parents. These constituted 0.12% of all SNPs seen in the twins. In 65% of cases these SNPs arose during meiosis compared to 35% during mitosis. The developmental mitotic origin of most CNVs that may lead to MZ twin discordance may also cause tissue differences within individuals during a single pregnancy and generate a high frequency of mosaics in the population. The results argue for enduring genome-wide changes during cellular transmission, often ignored in most genetic analyses. (Maiti et al. 2011)

Granted these twins have hearts, livers, and lungs that perform exactly the same function in each, but in terms of responding to certain drugs and susceptibility to certain diseases, the differences are more important than gross similarities.

Genetic differences can take the form of changes in the genes themselves, like those above, or in how the same genes are regulated. Many of the differences between species can be explained by the regulation of genes. Pai et al.:

It has long been hypothesized that changes in gene regulation have played an important role in primate evolution. However, despite the wealth of comparative gene expression data, there are still only few studies that focus on the mechanisms underlying inter-primate differences in gene regulation. In particular, we know relatively little about the degree to which changes in epigenetic profiles might explain differences in gene expression levels between primates. To this end, we studied DNA methylation and gene expression levels in livers, hearts, and kidneys from multiple humans and chimpanzees. Using these comparative data, we were able to study the evolution of gene regulation in the context of conservation of or changes in DNA methylation profiles across tissues and species. We found that inter-tissue methylation patterns are often conserved between humans and chimpanzees. In addition, we also found a large number of gene expression differences between species, which might be explained, at least in part, by corresponding differences in methylation levels. We estimate that, in the tissues we studied, interspecies differences in methylation levels might underlie as much as 12%–18% of differences in gene expression levels between humans and chimpanzees. (Pai et al. 2011)

The above illustrates why predicting the response of an individual to drugs or disease is problematic. It is problematic within a given species and more or less impossible between species. The answer to the prediction problem will not come from more genetically modified mice. Regardless of how many genes you put in the mouse, you still have a mouse. Technology, on the other hand, presents some opportunities.

Yen et al (Yen, Mital, and Srinivasan 2011) have used artificial neural networks (ANNs) to predict adverse drug reactions. They programmed the ANNs with data from old drugs and claim the ANNs technique is “99.87 percent accurate in predicting adverse drug reactions among 10,000 observations and 100 percent for non-serious ADRs.” If this accuracy holds up to further testing, this technique will be a huge advance in drug testing. (We discussed ANNs in our book What Will We Do If We Don't Experiment On Animals? Medical Research for the Twenty-first Century.)

From Elferink et al. Gene expression analysis of precision-cut human liver slices indicate stable expression of ADME-Tox related genes:

In the process of drug development it is of high importance to test the safety of new drugs with predictive value for human toxicity. A promising approach of toxicity testing is based on shifts in gene expression profiling of the liver. Toxicity screening based on animal liver cells cannot be directly extrapolated to humans due to species differences. The aim of this study was to evaluate precision-cut human liver slices as in vitro method for the prediction of human specific toxicity by toxicogenomics. The liver slices contain all cell types of the liver in their natural architecture. This is important since drug-induced toxicity often is a multi-cellular process. Previously we showed that toxicogenomic analysis of rat liver slices is highly predictive for rat in vivo toxicity. In this study we investigated the levels of gene expression during incubation up to 24 hours with Affymetrix microarray technology. The analysis was focused on a broad spectrum of genes related to stress and toxicity, and on genes encoding for phase-I,-II and -III metabolizing enzymes and transporters. Observed changes in gene expression were associated with cytoskeleton remodeling, extracellular matrix and cell adhesion, but for the ADME-Tox related genes only minor changes were observed. PCA analysis showed that changes in gene expression were not associated with age, sex or source of the human livers. Slices treated with acetaminophen showed patterns of gene expression related to its toxicity. These results indicate that precision-cut human liver slices are relatively stable during 24h of incubation and represent a valuable model for human in vitro hepatotoxicity testing despite the human inter-individual variability. (Elferink et al. 2011)

And this from the NIH press release New robot system to test 10,000 chemicals for toxicity:

Several federal agencies, including the National Institutes of Health, today unveiled a new high-speed robot screening system that will test 10,000 different chemicals for potential toxicity. The system marks the beginning of a new phase of an ongoing collaboration, referred to as Tox21, that is working to protect human health by improving how chemicals are tested in the United States.

The robot system, which is located at the NIH Chemical Genomics Center (NCGC) in Rockville, Md., was purchased as part of the Tox21 collaboration. Tox21 was established in 2008 between the National Institute of Environmental Health Sciences National Toxicology Program (NTP), the National Human Genome Research Institute (NHGRI), and the U.S. Environmental Protection Agency (EPA), with the addition of the U.S. Food and Drug Administration (FDA) in 2010. Tox21 merges existing agency resources (research, funding, and testing tools) to develop ways to more effectively predict how chemicals will affect human health and the environment.

Animals and humans have traits in common but those commonalities are insufficient to allow prediction of drug and disease response. Technology is allowing scientists to test each individual then prescribe treatment based on that individual’s genetic makeup. Personalized medicine is based a very different mindset and set of facts than animal-based research.

Pointing out instances of correlation between species, as vivisection activists love to do, is disingenuous. The question is not: “Do species respond similarly some of the time?” but rather: “Is the frequency of similar responses high enough for the animal model to be classified as scientifically predictive?” An understanding of the genetics as illustrated in the above studies explains why the answer is no.


Elferink, M. G. L., P. Olinga, E. M. van Leeuwen, S. Bauerschmidt, J. Polman, W. G. Schoonen, S. H. Heisterkamp, and G. M. M. Groothuis. 2011. Gene expression analysis of precision-cut human liver slices indicate stable expression of ADME-Tox related genes. Toxicology and Applied Pharmacology In Press, Accepted Manuscript.

Maiti, Sujit, Kiran Halagur Bhoge Gowda Kumar, Christina A. Castellani, Richard O'Reilly, and Shiva M. Singh. 2011. Ontogenetic <italic>De Novo</italic> Copy Number Variations (CNVs) as a Source of Genetic Individuality: Studies on Two Families with MZD Twins for Schizophrenia. PLoS ONE 6 (3):e17125.

Pai, Athma A., Jordana T. Bell, John C. Marioni, Jonathan K. Pritchard, and Yoav Gilad. 2011. A Genome-Wide Study of DNA Methylation Patterns and Gene Expression Levels in Multiple Human and Chimpanzee Tissues. PLoS Genet 7 (2):e1001316.

Yen, Peng-fang, Dinesh P. Mital, and Shankar Srinivasan. 2011. Prediction of the serious adverse drug reactions using an artificial neural network model. International Journal of Medical Engineering and Informatics 3 (1):53 - 59.


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