Werner Kalow, Urs A. Meyer and Rachel F. Tyndale (eds): Pharmacogenomics
Bibliographic record
Abstract
Pharmacogenomics Kalow, Werner, Meyer, Urs A. and Tyndale, Rachel F. (eds.) Drugs and the Pharmaceuticals , Volume 113 . Marcel Dekker , 2001 , $165 , 403 pp. ISBN 0-8247-0544-0 Werner Kalow, Professor Emeritus at the University of Toronto, has been in the business of pharmacogenetics for nearly 60 years. In fact, it would be fair to say that Professor Kalow helped to set the course for investigation into the genetic bases for individual differences in drug response in the 1950s. Urs A. Meyer is Professor of Pharmacology and Neurobiology at the University of Basel and has studied extensively genetic polymorphisms of drug-metabolizing enzymes and the effect of drugs on the transcription of numerous genes. Rachel F. Tyndale is Associate Professor of Pharmacology at the University of Toronto. Her research areas include genetic influences on drug receptors and drug-metabolizing enzymes. This team has done well to present a newly emerging discipline. Pharmacogenetics has followed two courses of inquiry over the past five decades. The older tradition, and the one associated with Kalow's work, concerns individual differences in drug absorption, distribution, biotransformation and clearance (mostly biotransformation), or pharmacokinetics. The other, more recent, tradition has involved the study of genetic-based differential target-tissue sensitivity, an example of which is the work by McClearn and Kakihana (1981) who showed that high and low alcohol sensitivity respond to selective breeding in mice. The latter, pharmacodynamic approach, thus involves the study of receptors and other means of drug–tissue interaction and more frequently than the former approach, relies on animal models. As Kalow and colleagues cogently explain, pharmacogenetics is not fundamentally different from other areas that examine genetic-evolutionary processes. Thus the same principles that render some insects less vulnerable to pesticides, bacteria less susceptible to antibiotics and underlie phenylketonuria are fundamental to the understanding of, for example, slow and fast drug metabolizers in humans. Pharmacogenomics, in contrast to pharmacogenetics, expands the horizon of inquiry from individual genes to the participation of the entire genome. It shifts the balance of investigation from phenotype to genotype and considers not only individual differences in susceptibility to adverse drug effects, but genetic differences in drug effectiveness. The overall goal is therefore to make the best possible match between individuals and their drug treatment regimen, using genomic information. Pharmacogenomics also broadens the base of inquiry to include both pharmacokinetics and pharmacodynamics. The book is an edited volume representing the intellectual contributions from 30 researchers in addition to Kalow. The intended audience is broad and includes clinicians and scientists working at molecular to behavioral levels of investigation. The work is two books in one. The first eight chapters define the domain. As it stands, most of the best-known texts in pharmacology give pharmacogenetics only a cursory treatment. As such, the first part could stand alone as text material for medical students and graduate students in pharmacology. The second part of the book contains chapters on specific technical aspects, including gene identification, sequencing, expression, proteomics, mapping and bioinformatics. What this part provides the reader is not only technical aspects in their current development, but also how to negotiate a veritable methodological labyrinth. Pharmacogenomics gives the practitioner and scientist some new ways to consider genes, environment, populations, and disease. For example, pharmacogenomics includes the study of polygenic disorders to help tailor effective drug treatment, based on allelic profiles. The study of pharmacogenomics includes genetic-based differences among ethnic groups that differ from others in drug metabolism and response, and in disease susceptibility. Particularly compelling is the finding that primaquine, an antimalarial agent, causes hemolytic anemia among a high proportion of individuals of recent African descent and among these people nearly exclusively. This susceptibility was shown to be caused by a variant in the gene for glucose-6-phosphate dehydrogenase. Importantly, the variant allele afforded protection from malaria, the very disease for which primaquine was developed! Now that the case for pharmacogenomics is made, what about therapeutic implementation and ethical considerations? In the case of the former, it seems that individualizing medications could be a nightmare for the pharmaceutical industry. How could drugs for individuals or even subsets of populations be developed and manufactured in a cost-effective manner? Who should be genotyped and how should the information be disseminated? For the former challenge, the answer is likely to be found in technology, for example the development of rapid genotyping methods. Analysis of single repeat nucleotide polymorphisms and development of proteomics holds promise for high throughput for efficient identification. Another promising development which is still in its infancy is rational drug design. Targeting specific drugs to protein variants may seem far-fetched now, but could very well be the standard in the not too far future. What about ethical concerns? Who should be genotyped and what should happen to the information? One project that is exciting, promising, and troublesome all at once is the Iceland Genome Project. The Swiss pharmaceutical house, Roche Holding of Basel paid 200 million dollars to gain access to the genomic data of 270 000 Icelanders. Because of the small population, genetic homogeneity, and genealogical data spanning many centuries, Iceland may be an ideal laboratory for pharmacogenomics. On the promising side is the knowledge of an individual's genotype to know, for example, if he or she is drug-sensitive or insensitive, prone to adverse reactions, etc. This can be seen as advantageous both to the individual and to the drug industry. Screening would, ostensibly, help to fine-tune clinical trials, saving money. On the troubling side is the protection of patient confidentiality. We know that confidentiality does not necessarily protect anonymity. There are also proprietary concerns such as how to balance general public health benefits against the need to recoup costs incurred for drug development and to make profit. In his book, Forbidden Knowledge, Shattuck (1996) presents a sharp critique of the Human Genome Project. The charges are that the project is (1) overblown in importance, (2) open to misuse by unscrupulous entrepreneurs and others with a conflict of interest, (3) takes more than its fair share of federal funds, thus depriving other, more worthy projects, (4) asserts the overly simple notion that all we have to do is find the gene for (name your disease) and (5) raises myriad ethical, legal and social implications. The scientific community would do well to listen to such criticism. How the development and effective application of pharmacogenomics will proceed depends not only on technical developments, but on how it passes judgement by those whom it would benefit the most. The authors of Pharmacogenomics present their case and methods convincingly and elegantly. In fact, it is a challenge to the scientific and clinical communities to rethink the science and art of therapeutics. Byron C. Jones Professor of Biobehavioral Health and Pharmacology The Pennsylvania State University 315 E. HHD Bldg University Park PA 16802-6508 USA E-mail: bcj1@psu.edu
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".