Evidence map›Paper›PMID 23979119›Full record

ReviewNeuropsychopharmacology : official publication of the American College of Neuropsychopharmacology2014

Biomarkers for the development of new medications for cocaine dependence.

Kristopher J Bough, Shashi Amur, Guifang Lao, Scott E Hemby, Nilesh S Tannu, Kyle M Kampman, Joy M Schmitz, Diana Martinez, Kalpana M Merchant, Charles Green and 3 more

Open access · bronzeAbstract readReview
In one paragraph

Review in Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
1.8field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 36 citations in OpenAlex.

  1. Trial
  2. D2 autoreceptors gate vulnerability to cocaine use disorder.bioRxiv : the preprint server for biology · 2026
    Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Neuroimaging Biomarkers in Schizophrenia.The American journal of psychiatry · 2021
    Review
  8. Methylation Patterns of theFrontiers in psychiatry · 2020
    Article
  9. Review
  10. A Clinician's Perspective on Biomarkers.Focus (American Psychiatric Publishing) · 2018
    Review
  11. Review
  12. Review
  13. Review
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 8 institutions in 1 country.

Kristopher J BoughDivision of Basic Neuroscience and Behavioral Research, National Institute on Drug Abuse, Bethesda, MD, USA.
Shashi Amur
Guifang Lao
Scott E Hemby
Nilesh S Tannu
Kyle M Kampman
Joy M Schmitz
Diana Martinez
Kalpana M Merchant
Charles Green
Jyoti Sharma
Anne H Dougherty
F Gerard Moeller
The University of Texas Health Science Center at Houston · USNational Institute on Drug Abuse · USCenter for Drug Evaluation and Research · USEli Lilly (United States) · USState University of New York · USUniversity of Pennsylvania · USVirginia Commonwealth University · USWake Forest University · US

Funding

TROPANE EVALUATION AND GENERAL SCIENTIFIC SERVICES COREP50DA006634 · NIDA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI PORRINO, LINDA J. · 1991 to 2019
$30.8M
Center for the Development of Novel Medications for Cocaine DependenceU54DA039002 · NIDA · UNIVERSITY OF PENNSYLVANIA · PI KAMPMAN, KYLE MATTHEW · 2014 to 2018
$5.1M
NEUROBIOLOGY OF SPEEDBALL SELF-ADMINISTRATIONR01DA012498 · NIDA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI HEMBY, SCOTT EDWARDS · 2000 to 2011
$2.4M
NIDA NIH HHS P50 DA006634NIDA NIH HHS R01 DA012498NIDA NIH HHS U54 DA039002
6 · The paper itself

Abstract

There has been significant progress in personalized drug development. In large part, this has taken place in the oncology field and been due to the ability of researchers/clinicians to discover and develop novel drug development tools (DDTs), such as biomarkers. In cancer treatment research, biomarkers have permitted a more accurate pathophysiological characterization of an individual patient, and have enabled practitioners to target mechanistically the right drug, to the right patient, at the right time. Similar to cancer, patients with substance use disorders (SUDs) present clinically with heterogeneous symptomatology and respond variably to therapeutic interventions. If comparable biomarkers could be identified and developed for SUDs, significant diagnostic and therapeutic advances could be made. In this review, we highlight current opportunities and difficulties pertaining to the identification and development of biomarkers for SUDs. We focus on cocaine dependence as an example. Putative diagnostic, pharmacodynamic (PD), and predictive biomarkers for cocaine dependence are discussed across a range of methodological approaches. A possible cocaine-dependent clinical outcome assessment (COA)--another type of defined DDT--is also discussed. At present, biomarkers for cocaine dependence are in their infancy. Much additional research will be needed to identify, validate, and qualify these putative tools prior to their potential use for medications development and/or application to clinical practice. However, with a large unmet medical need and an estimated market size of several hundred million dollars per year, if developed, biomarkers for cocaine dependence will hold tremendous value to both industry and public health.

Indexed as

AnimalsBiomarkers, PharmacologicalCardiovascular SystemCocaine-Related DisordersDrug DiscoveryHumansNeuroimagingSubstance-Related DisordersTreatment OutcomeBiomarkers, Pharmacological

Identifiers

PMID23979119
PMCPMC3857653
OpenAlexW2073929040

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.