Omics and Drug Response
Abstract
A new generation of technologies commonly named omics permits assessment of the entirety of the components of biological systems and produces an explosion of data and a major shift in our concepts of disease. These technologies will likely shape the future of health care. One aspect of these advances is that the data generated document the uniqueness of each human being in regard to disease risk and treatment response. These developments have reemphasized the concept of personalized medicine. Here we review the impact of omics technologies on one key aspect of personalized medicine: the individual drug response. We describe how knowledge of different omics may affect treatment decisions, namely drug choice and drug dose, and how it can be used to improve clinical outcomes.
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Metformin and cancer: from the old medicine cabinet to pharmacological pitfalls and prospects
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Self-citationImmune checkpoint inhibitor combinations: Current efforts and important aspects for success
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Impact of Membrane Drug Transporters on Resistance to Small-Molecule Tyrosine Kinase Inhibitors
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A Rational Approach to Target the Epidermal Growth Factor Receptor in Glioblastoma
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COVID19 Drug Repository: text-mining the literature in search of putative COVID19 therapeutics
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Self-citationBridging Genomics and Phenomics
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Counteracting antibiotic resistance: breaking barriers among antibacterial strategies
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Histology and metabonomics reveal the toxic effects of kresoxim-methyl on adult zebrafish
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Personalized Biochemistry and Biophysics
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Genetic Biomarkers in Epilepsy
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Epigenomic Mapping and Effect Sizes of Noncoding Variants Associated with Psychotropic Drug Response
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Nursing Personnel in the Era of Personalized Healthcare in Clinical Practice
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Systems Approaches in Risk Assessment
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Establishing Serum Reference Ranges for Antihypertensive Drugs
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Pharmacogenetics: Implications for Modern Type 2 Diabetes Therapy
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Malentacchi, F., Mancini, I., Brandslund, I., Vermeersch, P., Schwab, M. G., Marc, J., van Schaik, R. H. N., Siest, G., Theodorsson, E., Pazzagli, M., et al. (2015) · Drug Metabolism and Personalized Therapy · doi:10.1515/dmdi-2015-0012 · Cited by 20 · see citing works
Self-citationClinical Translation in the Virtual Liver Network
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Personalized medicine into the next generation
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Polymorphisms of Phase I and Phase II Enzymes and Breast Cancer Risk
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Multidrug Resistance Proteins of the ABCC Subfamily
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Achieving the World Health Organization's vision for clinical pharmacology
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Self-citationTremmel, R., Klein, K., Battke, F., Fehr, S., Winter, S., Scheurenbrand, T., Schaeffeler, E., Biskup, S., Schwab, M. G., & Zanger, U. M. (2019) · Human Genetics · doi:10.1007/s00439-019-02093-7 · Cited by 17 · see citing works
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MLG2Net: Molecular Global Graph Network for Drug Response Prediction in Lung Cancer Cell Lines
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‘Omics’: an emerging field in pain research and management
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Pharmacometrics and systems pharmacology for metabolic bone diseases
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The Letrozole Phase 1 Metabolite Carbinol as a Novel Probe Drug for UGT2B7
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Genetic and Epigenetic Regulation of Organic Cation Transporters.
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Self-citationEvolutionary Diagnosis of non-synonymous variants involved in differential drug response
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Applying genomics in heart transplantation
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Cytochrome P450 Enzyme Metabolites in Lead Discovery and Development
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Pharmacogenovigilance: A Pharmacogenomics Pharmacovigilance Program
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Effects of SLCO1B1 Genetic Variant on Metabolite Profile in Participants on Simvastatin Treatment
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Arlanov, R., Lang, T., Jedlitschky, G., Schaeffeler, E., Ishikawa, T., Schwab, M. G., & Nies, A. T. (2015) · The Pharmacogenomics Journal · doi:10.1038/tpj.2015.27 · Cited by 9 · see citing works
Self-citationHow paediatric drug development and use could benefit from OMICs: A c4c expert group white paper
Neumann, E., Schreeck, F., Herberg, J. A., Jacqz-Aigrain, É., der Zee, A. H. M., Pérez‐Martínez, A., Hawcutt, D. B., Schaeffeler, E., Rane, A., de Wildt, S. N., et al. (2022) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.15216 · Cited by 9 · see citing works
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The Significance of Transcriptome Sequencing in Personalized Cancer Medicine
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- Authors
- Meyer, U. A., Zanger, U. M., & Schwab, M.
- Affiliations
- University of Basel (CH)
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology (DE)
- Universitätsklinikum Tübingen (DE)
- Venue
- Annual Review of Pharmacology and Toxicology
- Publisher
- Annual Reviews
- Published
- 2013-01-06
- Volume
- 53
- Issue
- 1
- Pages
- 475-502
- ISSN
- 0362-1642 (ISSN-L)
- 1545-4304
- OpenAlex
- W2098156682
- Cited by
- 157
- References
- 196
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 6 · 2023: 5 · 2022: 10 · 2021: 5 · 2020: 12 · 2019: 17 · 2018: 10 · 2017: 15 · 2016: 12 · 2015: 17 · 2014: 17 · 2013: 14 · 2012: 3
- FWCI
- 1.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Annual Review of Pharmacology and Toxicology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Robert Bosch Stiftung
- Bundesministerium für Bildung und Forschung
- National Institutes of Health
- National Human Genome Research Institute
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- omics technologies, drug response, personalized medicine, pharmacogenomics, drug selection, drug dosing, treatment response variability, clinical outcomes, precision medicine
- MeSH terms
- Animals, Humans, Pharmacogenetics, Genomics, Precision Medicine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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