Evidence map›Paper›PMID 40242045›Full record

ArticleMolecular therapy. Nucleic acids2025

Mechanistic intracellular PK/PD modeling to inform development strategies for small interfering RNA therapeutics.

Lin Chen, Caroline Bosmajian, Sukyung Woo

Abstract read
In one paragraph

Article in Molecular therapy. Nucleic acids, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Article
  3. DNA Nanostructures for siRNA Delivery.Bioconjugate chemistry · 2026
    Review
  4. Article
  5. Article
  6. Article
  7. Review
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.

Lin ChenDivision of Pharmacokinetics-Pharmacodynamics and Systems Pharmacology, Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14214, USA.
Caroline BosmajianDivision of Pharmacokinetics-Pharmacodynamics and Systems Pharmacology, Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14214, USA.
Sukyung WooDivision of Pharmacokinetics-Pharmacodynamics and Systems Pharmacology, Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14214, USA.

Funding

Targeting ovarian cancer spheroid formation and metabolic adaptation by APJ inhibitionR01CA258546 · NCI · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI Sukyung Woo · 2022 to 2026
$2.0M
NCI NIH HHS R01 CA258546
6 · The paper itself

Abstract

Small interfering RNA (siRNA) therapeutics provide a targeted approach to silence disease-related genes, with notable success in liver-targeting applications. However, the quantitative effects of siRNA properties, such as stability and affinity, as well as biological factors like cell proliferation, mRNA turnover, and abundance, on gene silencing, particularly for extrahepatic targets, remain poorly understood. To identify determinants influencing gene knockdown extent and duration, we developed a mechanistic intracellular pharmacokinetic/pharmacodynamic (PK/PD) model for RNAiMAX-delivered siRNA, based on cytoplasmic siRNA disposition, RISC-loaded siRNA exposure, and mRNA knockdown across different targets in MCF7 and BT474 cells. The model highlighted the critical roles of cell proliferation in silencing duration and mRNA turnover rates on knockdown extent. In rapid-dividing cells, mRNA half-life drives knockdown profiles, whereas chemical siRNA stabilization extends silencing in slow-dividing cells. Targets with extremely low or high mRNA abundance pose silencing challenges. While sufficient RISC occupancy is essential, increasing RISC exposure has minimal impact on silencing extent; enhancing siRNA-mRNA target engagement is more effective. The model also defined a quantitative relationship for maximal mRNA knockdown, governed by cell proliferation, mRNA half-life, and RISC-mediated cleavage rates. This mechanistic PK/PD modeling provides insights into optimizing siRNA design and target selection in therapeutic development.

Indexed as

computational modelingmaximal gene silencingmodel-informed drug developmentMT: Non-coding RNAsRISC-loaded siRNAsiRNA pharmacokinetics and pharmacodynamicssiRNA therapeutics

Identifiers

PMID40242045
PMCPMC12002994

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.