Advancing Disease Modeling in Animal-Based Research in Support of Precision Medicine: Proceedings of a Workshop by unknow

Author:unknow
Language: eng
Format: epub
Tags: Biology and Life Sciences: Laboratory Animal Research, Biology and Life Sciences: Policy, Reviews and Evaluations
Publisher: The National Academies Press
Published: 2018-06-04T00:00:00+00:00

THE PARADOXES OF PRECISION MEDICINE

Jonathan Kimmelman, associate professor in the Biomedical Studies Unit/Social Studies of Medicine at McGill University, Canada, introduced workshop participants to the five paradoxes of precision medicine. These paradoxes, he said, are creating a very heavy demand for and an expectation about the quality of preclinical evidence, while concomitantly reducing the ability to produce high-quality, reproducible evidence.

The first paradox, said Kimmelman, involves the issue of smaller samples. Precision medicine is aimed at taking heterogeneous populations and dividing them into smaller groups that are more homogeneous (e.g., in terms of response to medication). However, by breaking up the large groups into smaller ones, research on the small groups has lower statistical power with a higher degree of variance. Relying on traditional randomized controlled trials may not be possible in these situations, said Kimmelman, so researchers may need to combine different forms of evidence.

The second paradox is the issue of boundaries. Kimmelman explained that one goal of precision medicine is to develop diagnostic techniques that allow the stratification of patients. Diagnostic techniques typically rely on establishing a boundary—a cut-off point that assigns individuals into different categories. However, there may be a variation of alleles whose location in regard to the boundaries is unclear. While it may be possible to determine with a high degree of certainty how a specific individual with a specific allele reacts to a drug, there may be uncertainty about how other alleles will affect this response. Using multiple diagnostic techniques further muddies the waters, said Kimmelman, because each technique has its own boundaries and commensurate uncertainty about whether these boundaries are drawn correctly. This creates a “proliferation of imprecision.”

The third paradox is the issue of algorithms and interpretation of data. The data and algorithms used to determine how to match patients to treatments are, obviously, critical to an accurate matching. The use of preclinical data in these determinations can be controversial, said Kimmelman. Some clinicians have chosen not to include any preclinical evidence due to concerns about reproducibility, while others have determined that preclinical evidence is clinically actionable in the absence of other higher forms of evidence. The need for high-quality evidence to inform patient classification algorithms compounds the demands on the quality of preclinical evidence.

The fourth paradox concerns the rapid evolution of knowledge and diagnostic techniques in the area of precision medicine. As precision medicine advances, information is constantly accruing about treatment approaches and patient populations, and techniques that evolve based on these new data. By the time a study is published, said Kimmelman, the techniques or algorithms used may already be outdated.

Finally, the fifth paradox is about integrating diverse datasets. Kimmelman said that in non-precision medicine, large clinical trials with a low degree of variance can be synthesized into a meta-analysis regarding the clinical utility of a treatment. However, in precision medicine, the trials are much smaller and may be testing different drugs or different diagnostic techniques to classify patients. Aggregating such disparate information—together with preclinical research—is a considerable challenge.

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