Cited by 732Animal testing and alternativesImmunotoxicology and immune responsesVeterinary Pharmacology and Anesthesia

The DOI matches its Crossref record; 110 of the 118 references it cites were found in a registry or an index; 23 of the 67 sources its claims cite were checked for support.

Limitations of Animal Studies for Predicting Toxicity in Clinical Trials

Open access
Van Norman, Gail A.·2019·JACC: Basic to Translational Science

Abstract

Animal testing is used in pharmaceutical and industrial research to predict human toxicity, and yet analysis suggests that animal models are poor predictors of drug safety in humans. The cost of animal research is high-in dollars, delays in drug approval, and in the loss of potentially beneficial drugs for human use. Human subjects have been harmed in the clinical testing of drugs that were deemed safe by animal studies. Increasingly, investigators are questioning the scientific merit of animal research. This review discusses issues in using animals to predict human toxicity in pharmaceutical development. Part 1 focuses on scientific concerns over the validity of animal research. Part 2 will discuss alternatives to animal research and their validation and use in production of human pharmaceuticals.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This review argues that animal toxicity testing is a poor predictor of human drug safety, citing low animal-human concordance, high clinical trial failure rates, and cases of severe human harm after animal tests deemed drugs safe. It concludes that animal testing should be abandoned or significantly curtailed in favor of more reliable alternatives, with alternatives to be discussed in Part 2.

Key findings

  • Concordance between animal and human results is low: a 2006 review of 76 animal studies found ~20% were contradicted in humans and only 37% were ever replicated in humans; a review of 221 animal experiments found agreement with human studies just 50% of the time. In 37 chemicals from the U.S. National Toxicology Program, average positive predictive value from mouse to rat was 55.3% for long-term and 44.8% for short-term studies, hovering around 50%—no greater than random chance.
  • Drug development failure rates raise doubts about animal predictivity: about 12% of pharmaceuticals pass preclinical testing to enter clinical trials, only 60% of those complete phase I, and approximately 89% of novel drugs fail human clinical trials, with about one-half of those failures due to unanticipated human toxicity. An analysis of 2,366 drugs concluded animal tests are highly inconsistent predictors of human toxic responses and “little better than what would result merely by chance—or tossing a coin.”
  • Post-approval toxicity is common: of 578 discontinued and withdrawn drugs in Europe and the United States, almost one-half were withdrawn or discontinued in post-approval actions due to toxicity; of 93 post-marketing serious adverse outcomes, only 19% were identified in preclinical animal studies. The most common toxicity types associated with U.S. and European drug withdrawals are hepatic (21%), cardiovascular (16%), hematological (11%), neurological (9%), and carcinogenicity (8%).
  • Costs and delays are substantial: rodent testing in cancer therapeutics adds an estimated 4 to 5 years and $2 to $4 million; registering a single pesticide takes about 10 years and $3 million in required animal studies; animal tests range from 1.5× to >30× as expensive as in vitro testing. A single 2-generation reproductive animal toxicity study averages €318,295 worldwide and €285,842 in Europe (roughly $349,890 and $314,215).

Methods

  • Design: narrative review focused on scientific concerns over the validity of animal research for predicting human toxicity in pharmaceutical development; Part 2 is stated to discuss alternatives.
  • Data: published analyses and reviews of animal-to-human toxicity concordance, drug failure and withdrawal statistics, regulatory and historical examples, and cost estimates; no new primary data are collected.
  • Analysis: narrative synthesis, including use of positive predictive value, negative predictive value, and likelihood ratios to assess the evidentiary value of animal toxicity tests for human outcomes.

Limitations

  • Stated in the paper: no comprehensive reviews of the total overall cost of animal testing in pharmaceutical development appear to exist; even the total number of animals or studies is unknown. The paper also states that a comprehensive study of the accuracy, likelihood ratios, and costs of alternative testing methods compared with animal toxicity testing has not been published, and that the prevalence of human toxicity is unknown once a drug tests positive in animals.
  • Stated in the paper: in some analyses, likelihood ratios could not be determined due to a lack of specificity reporting on the tests.
  • Evident from the text: this is a narrative review rather than a systematic review, it presents no new primary data, and it includes selected historical and contemporary examples; the second part on alternatives is not included in this paper.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1016/j.jacbts.2019.10.008

Cites

110 of 110 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

23 of 67 cited sources checked · 3 supported · 1 flagged · accuracy not yet measured

Show all 36 claims

  • It may state two separate claims as one. 25
  • Probably not a substantial claim. 13
33 of 33
  • The paper relies on this without support.

    Animal testing is used in pharmaceutical and industrial research to predict human toxicity.

    “Animal testing is used in pharmaceutical and industrial research to predict human toxicity”
  • The paper relies on this without support.

    Animal toxicity testing has been the stalwart basis of “ensuring” safety of in-human clinical testing and use.

    “Although animal toxicity testing has been the stalwart basis of “ensuring” safety of in-human clinical testing and use”
  • The paper relies on this without support.

    The published data on animal toxicity testing are sufficient to assess its reliability.

    “examination of the published data raises significant questions about whether it is reliable”
  • The paper relies on this without support.

    Animal models are used to predict human toxicity.

    “reliability of animal models in predicting human toxicity”
4 of 4

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Removing toxicity failures does not produce a 56% clinical-trial success rate

    Major

    The figure says the toxicity-related failures are eliminated before clinical trials, not converted into successful trials. With an 11.7% success rate and roughly half of the 89% failures removed, the success rate among the remaining trials would be about 21%, not 56%.

  • The stated effect of an NLR below one reverses its earlier definition

    Minor

    The paper defines the NLR as indicating a decrease in the probability that a condition exists after a negative result. It later says an NLR below one decreases the probability that the condition is absent-the opposite direction.

  • Likelihood ratios are said to multiply both probabilities and odds

    Minor

    The paper first says to calculate the change by multiplying the pre-test probability by the likelihood ratio, but later says the calculation requires converting probabilities to odds and back. Those are different calculations: likelihood ratios multiply odds, not probabilities.

3 of 3

Cited works that were retracted

Works this paper cites that were later retracted, from the retraction notices publishers deposit. Major when the notice came out before this paper was published.

  • Cites a retracted work: 10.1093/nar/gkv1192

    Info

    The cited work was retracted; the date of the notice is not on record.

    Cited work 10.1093/nar/gkv1192 · retraction date not known · this paper published 2019-11

1 of 1

Citations

Cites

1 retracted

Flagged first, then by citations

Show all 118 references

8 of the 118 references couldn’t be matched to a known paper.

Research works printed but not linked: 2

  1. Schardein, 1976, 5, 49
  2. Sankar, 2005, The delicate toxicity balance in drug discovery, Scientist, 19, 32

Cited by

Sorted by citations

Items 601–688 of 688

Metadata

Authors
Van Norman, G. A.
Venue
JACC: Basic to Translational Science
Publisher
Elsevier BV
Published
2019-11
Volume
4
Issue
7
Pages
845-854
ISSN
2452-302X (ISSN-L)
OpenAlex
W2990376563
Cited by
732
References
118
Citations by year
2026: 128 · 2025: 149 · 2024: 159 · 2023: 110 · 2022: 104 · 2021: 59 · 2020: 21
FWCI
68.93
Type
journal article
Language
en
Open access
Locations
  • JACC Basic to Translational Science (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
  • Life Sciences › Immunology and Microbiology › Immunology › Immunotoxicology and immune responses
  • Health Sciences › Veterinary › Small Animals › Veterinary Pharmacology and Anesthesia
Keywords
animal experimentation, animal models, drug safety, translational research, alternatives to animal testing, clinical trials, drug development, drug approval delays

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.

A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Cited sources are checked with a language model’s help (gpt-6-sol).

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 ›

Are you an author, or do you see a problem with this page? Request a review