Evidence map›Paper›PMID 40877500›Full record

ReviewMethods in molecular biology (Clifton, N.J.)2025

Chemical Modifications in Nucleic Acid Therapeutics.

Kim A Lennox, Rebecca C Young, Mark A Behlke

Abstract readReview
PubMed Publisher
In one paragraph

Review in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. LncRNA Knockdown Using Gapmer Antisense Oligonucleotides.Methods in molecular biology (Clifton, N.J.) · 2026
    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

3 authors.

Kim A LennoxIntegrated DNA Technologies, Inc., Coralville, IA, USA. klennox@idtdna.com.
Rebecca C YoungAldeveron, Fargo, ND, USA.
Mark A BehlkeIntegrated DNA Technologies, Inc., Coralville, IA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nucleic acid-based therapies (NATs) have become an increasingly prominent class of drugs due to the recent clinical successes made possible by nucleic acid chemical modifications. This class of therapies includes reagents that inhibit gene expression (antisense oligonucleotides (ASOs) or RNA interference (RNAi)), modulate gene structure (splice-shifting ASOs), increase protein expression (messenger RNA (mRNA)) or direct specific editing of the mammalian genome (CRISPR/Cas gene editing). Each of these technologies relies on specific combinations of chemically modified nucleic acids to increase drug efficacy, safety, and uptake efficiency in desired cell types. The knowledge gained from years of characterizing the biochemical properties of chemically modified oligonucleotides (ONs) combined with recent regulatory approvals will hopefully accelerate more NATs into the clinic to treat currently undruggable or ultrarare diseases. This review discusses the most employed chemical modifications in each of the aforementioned nucleic acid-based technologies and provides an overview of select publications that have demonstrated milestones and successes in improving ON efficacy and/or mitigating undesired off-target effects. Key innovations in chemical modifications that are expanding clinical capabilities are highlighted, casting a positive light on the future of nucleic acid medicine.

Indexed as

Genetic TherapyNucleic AcidsOligonucleotides, AntisenseAnimalsCRISPR-Cas SystemsGene EditingHumansRNA InterferenceNucleic AcidsOligonucleotides, AntisenseAntisenseASOCas12aCas9Chemical modificationCRISPRcrRNAGuide RNAmRNAOligonucleotidesRNAisgRNAsiRNA

Identifiers

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.