Evidence map›Paper›PMID 32993453›Full record

ArticleHuman vaccines & immunotherapeutics2021

COVID-19 vaccine development during pandemic: gap analysis, opportunities, and impact on future emerging infectious disease development strategies.

Shyam Rele

Open access · bronzeAbstract read
In one paragraph

Article in Human vaccines & immunotherapeutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 13% 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

23 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.

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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

1 author.

Shyam RelePrincipal Consultant, Scientific Strategy, Product Development, Manufacturing, Outsourcing and Emerging Technologies, Gaithersburg, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The world remains cautiously optimistic about a COVID-19 vaccine that is relatively safe and efficacious and that offers sufficient long-lasting protection/immunity by neutralizing the virus infectivity. However, key technical hurdles pertaining to antigen-adjuvant formulation, delivery, and manufacturing challenges of lipid nanoparticles (LNPs) for mRNA vaccines and stability of formulations need to be addressed for successful product development and stockpiling. In addition, the dosage form, the dosage level and regimen for eliciting a protective immune response remain to be established. The high dependence of global supply chains and demand-supply to sourcing quality raw materials, glassware and other supplies, along with the stress on existing production capacities and platform-specific manufacturing challenges could impede vaccine development and access. This article provides critical analysis of vaccine development processes and unit operations that can derail the pandemic response, and also extends to other emerging infectious disease development efforts - issues that take on added significance given the global mandate for an accelerated and at-risk development path to tackle the COVID-19 pandemic.

Indexed as

Drug DevelopmentAdjuvants, ImmunologicCOVID-19COVID-19 VaccinesHumansImmunogenicity, VaccineSARS-CoV-2Vaccine PotencyVaccines, DNAVaccines, InactivatedVaccines, SyntheticVaccines, Virus-Like ParticleAdjuvants, ImmunologicCOVID-19 VaccinesVaccines, DNAVaccines, InactivatedVaccines, SyntheticVaccines, Virus-Like ParticleadjuvantsCOVIDGMP manufacturingimmune responsemRNA-lipid nanoparticlesupply chainsvaccine dosevaccines

Identifiers

PMID32993453
PMCPMC8018474
OpenAlexW3087865356

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.