Expectations, validity, and reality in omics

Ioannidis, John P. A.

doi:10.1016/j.jclinepi.2010.04.002

Cited by 53 publications

(40 citation statements)

References 47 publications

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“…Current medical research is focused on a molecular understanding of physiology and disease, centring around genomics, proteomics, and other 'omics'. 18 There is undoubtedly much to be gained from an understanding of humans and our diseases at a molecular level but we need to balance this with an integrational perspective, recognising that combinations of conditions have complex effects in individuals.…”

Section: Lessons From Mackenzie That Still Resonatementioning

confidence: 99%

Lessons from Mackenzie that still resonate

Hecke

Smith²,

Sullivan³

2013

Br J Gen Pract

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Section: Lessons From Mackenzie That Still Resonatementioning

confidence: 99%

Lessons from Mackenzie that still resonate

Hecke

Smith²,

Sullivan³

2013

Br J Gen Pract

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“…However, the methodology for performing such examination is still underdeveloped and, despite the fact that corrections for multiple comparisons are routinely used in these studies, many of the genes that are identified as significant in these studies are false positives [33].…”

Section: Random-effects Linear Models and Pharmacogenomicsmentioning

confidence: 99%

Role of Statistical Random-Effects Linear Models in Personalized Medicine

Díaz¹

2012

cppm

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Some empirical studies and recent developments in pharmacokinetic theory suggest that statistical randomeffects linear models are valuable tools that allow describing simultaneously patient populations as a whole and patients as individuals. This remarkable characteristic indicates that these models may be useful in the development of personalized medicine, which aims at finding treatment regimes that are appropriate for particular patients, not just appropriate for the average patient. In fact, published developments show that random-effects linear models may provide a solid theoretical framework for drug dosage individualization in chronic diseases. In particular, individualized dosages computed with these models by means of an empirical Bayesian approach may produce better results than dosages computed with some methods routinely used in therapeutic drug monitoring. This is further supported by published empirical and theoretical findings that show that random effects linear models may provide accurate representations of phase III and IV steady-state pharmacokinetic data, and may be useful for dosage computations. These models have applications in the design of clinical algorithms for drug dosage individualization in chronic diseases; in the computation of dose correction factors; computation of the minimum number of blood samples from a patient that are necessary for calculating an optimal individualized drug dosage in therapeutic drug monitoring; measure of the clinical importance of clinical, demographic, environmental or genetic covariates; study of drug-drug interactions in clinical settings; the implementation of computational tools for web-site-based evidence farming; design of pharmacogenomic studies; and in the development of a pharmacological theory of dosage individualization.

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“…There is a hope that proteomics will be used in a similar fashion to inform clinical care; however, there is a clear need to validate findings across these new fields as well as to creatively translate true positives into clinical applications. 7 …”

Section: Genetic and Molecular Medicine: Personalized Information Or mentioning

confidence: 99%

“…The huge number of genes available for demonstrated associations and the wealth of information being churned out at an increasing pace leave some with the feeling that we are producing more data than we can analyze or understand. 7 The personalized medicine vision of the future will most likely not simplify clinical decision making but rather make it more complex and will be unable to fulfill the promise of informing patients with certainties. Just as clinical algorithms either increased or decreased patients' probabilities of developing disease or responding to therapy, the personalized medicine platform will mostly likely do the same.…”

Section: Personalized Medicine and Health Care Systemsmentioning

confidence: 99%

Personalized Medicine: Understanding Probabilities and Managing Expectations

Laksman

Detsky

2010

J GEN INTERN MED

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Personalized medicine promises to represent a transformation in clinical care that will be ushered in by the unprecedented growth and development in the field of human genetics. Further examination of the scientific foundations of this new hope reveals a great number of challenges that lie ahead. While basic science research feverishly races to produce solutions, we continue to wait for the translation of deliverables. Products that have and will come to market may leave our clinical communities and systems exposed and unprepared. At each step, from basic science research to infrastructure development, a great deal of creativity and investment are required before the arsenal of more personalized tools can be assimilated into our current models of health care. This commentary seeks to share perspectives on the current status of personalized medicine from the vantage point of several potential investors, and integrate them into a common set of goals and understanding. We conclude that the stylized model of personalized medicine is more akin to a marketing tool than a literal prediction of the future.

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Expectations, validity, and reality in omics

Cited by 53 publications

References 47 publications

Lessons from Mackenzie that still resonate

Lessons from Mackenzie that still resonate

Role of Statistical Random-Effects Linear Models in Personalized Medicine

Personalized Medicine: Understanding Probabilities and Managing Expectations

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