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Removing Obstacles in Neuroscience Drug Discovery: The Future Path for Animal Models
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Despite great advances in basic neuroscience knowledge, the improved understanding of brain functioning has not yet led to the introduction of truly novel pharmacological approaches to the treatment of central nervous system (CNS) disorders. This situation has been partly attributed to the difficulty of predicting efficacy in patients based on results from preclinical studies. To address these issues, this review critically discusses the traditional role of animal models in drug discovery, the difficulties encountered, and the reasons why this approach has led to suboptimal utilization of the information animal models provide. The discussion focuses on how animal models can contribute most effectively to translational medicine and drug discovery and the changes needed to increase the probability of achieving clinical benefit. Emphasis is placed on the need to improve the flow of information from the clinical/human domain to the preclinical domain and the benefits of using truly translational measures in both preclinical and clinical testing. Few would dispute the need to move away from the concept of modeling CNS diseases in their entirety using animals. However, the current emphasis on specific dimensions of psychopathology that can be objectively assessed in both clinical populations and animal models has not yet provided concrete examples of successful preclinical-clinical translation in CNS drug discovery. The purpose of this review is to strongly encourage ever more intensive clinical and preclinical interactions to ensure that basic science knowledge gained from improved animal models with good predictive and construct validity readily becomes available to the pharmaceutical industry and clinical researchers to benefit patients as quickly as possible.
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“conservation and divergence in brain function between humans and other species provide essential information for the understanding of drug action, which is often poorly conserved across species6.”
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Metadata
- Authors
- Markou, A., Chiamulera, C., Geyer, M. A., Tricklebank, M., & Steckler, T.
- Affiliations
- University of California San Diego (US)
- Eli Lilly (United Kingdom) (GB)
- Corresponding author
- Markou, Athina
- Venue
- Neuropsychopharmacology
- Published
- 2008-10-01
- Volume
- 34
- Issue
- 1
- Pages
- 74-89
- ISSN
- 0893-133X (ISSN-L)
- 1740-634X
- PMC
- PMC2651739
- OpenAlex
- W2142885788
- Cited by
- 328
- References
- 167
- Citations by year
- 2026: 6 · 2025: 14 · 2024: 10 · 2023: 8 · 2022: 8 · 2021: 14 · 2020: 12 · 2019: 14 · 2018: 18 · 2017: 16 · 2016: 15 · 2015: 30 · 2014: 28 · 2013: 27 · 2012: 35
- FWCI
- 6.32
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Neuropsychopharmacology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Veterans Affairs
- Tobacco-Related Disease Research Program
- National Institutes of Health
- National Institute of Mental Health (U01 MH 69062, 5R01MH52885, R01MH071916, U01MH069062, R01MH052885, R01MH062527)
- National Institute on Drug Abuse (R01DA023926, R56DA011946, R01DA002925, 2R01DA11946, R01DA023209, 5R01 DA02925, R01DA011946)
- NIH Clinical Center
- Topics
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- animal models, CNS drug discovery, translational medicine, predictive validity, construct validity, central nervous system disorders, psychopathology, clinical trials, preclinical drug testing
- MeSH terms
- Animals, Central Nervous System Diseases, Clinical Trials as Topic, Disease Models, Animal, Drug Evaluation, Preclinical, Forecasting, Humans, Neurosciences, Reproducibility of Results, Models, Animal, Biomarkers, Pharmacological, Drug Discovery
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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