Evidence map›Paper›PMID 34549885›Full record

ArticleChemical biology & drug design2022

Lack of efficacy of mono-mode of action therapeutics in COVID-19 therapy - How the lack of predictive power of preclinical cell and animal studies leads developments astray.

Annekathrin Haberland, Johannes Müller

Open access · hybridAbstract read
In one paragraph

Article in Chemical biology & drug design, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 57% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

2 authors at 1 institution in 1 country.

Annekathrin HaberlandBerlin Cures GmbH, Berlin, Germany.
Johannes MüllerBerlin Cures GmbH, Berlin, Germany.
Berlin Heart (Germany) · DE

Funding

Berlin Cures
6 · The paper itself

Abstract

The diverse experiences regarding the failure of tested drugs in the fight against COVID-19 made it clear that one should at least question the requirement to apply classical preclinical development strategies that demand cell and animal efficacy models to be tested before going into clinical trials. Most animals are not susceptible to infection with SARS-CoV-2, and so this led to one-sided virus replication experiments in cells and the use of animal models that have little in common with the complex pathogenesis of COVID-19 in humans. Therefore, non-clinical development strategies were designed to meet regulatory requirements, but they did not truly reflect the situation in the clinic. This has led the search for effective agents astray in many cases. As proof of this statement, we now bring together the results of such required preclinical experiments and compare with the results in clinical trials. Two clear conclusions that can be drawn from the experience to date: The required preclinical models are unsuitable for the development of innovative treatments medical devices in the case of COVID-19 and mono-action strategies (e.g. direct antivirals) are of very little or no benefit to patients under randomized,blinded conditions. Our hypothesis is that the complex situation of COVID-19 may benefit from multi-mode drugs. Here, the molecular class of aptamers could be a solution.

Indexed as

COVID-19 Drug TreatmentDrug DiscoveryDrug Evaluation, PreclinicalAnimalsAntiviral AgentsDisease Models, AnimalHumansSARS-CoV-2Antiviral AgentsaptamerCOVID-19COVID-19 animal experimentsinnovative thinkingmode of actionpredictive valueSARS-CoV-2 cell assays

Identifiers

PMID34549885
PMCPMC8653042
OpenAlexW3199557988

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.