Evidence map›Paper›PMID 38542901›Full record

ReviewMolecules (Basel, Switzerland)2024

Experimental and Computational Methods to Assess Central Nervous System Penetration of Small Molecules.

Mayuri Gupta, Jun Feng, Govinda Bhisetti

Open access · goldAbstract readReview
In one paragraph

Review in Molecules (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
6.8field-weighted citation impact, top 2% 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

15 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. 4-methylbenzyl 5-arylthiophene-2-carboxylates as Multitarget Directed Ligands Scaffolds (MTDLs): Synthesis, in-silico docking studies, and evaluation of dual selective enzymatic inhibition (AChE & MAO-B).Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026
    Article
  5. Review
  6. Review
  7. Review
  8. Article
  9. Article
  10. Prodrug Approach as a Strategy to Enhance Drug Permeability.Pharmaceuticals (Basel, Switzerland) · 2025
    Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. 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 at 2 institutions in 1 country.

Mayuri GuptaDepartment of Modeling and Informatics, Merck & Co., Inc., Rahway, NJ 07065, USA.
Jun FengDepartment of Computational Chemistry, Cellarity, 101 South Street L6, Somerville, MA 02143, USA.
Govinda BhisettiDepartment of Computational Chemistry, Cellarity, 101 South Street L6, Somerville, MA 02143, USA.ORCID 0000-0002-6611-9632
Computational Physics (United States) · USMerck & Co., Inc., Rahway, NJ, USA (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In CNS drug discovery, the estimation of brain exposure to lead compounds is critical for their optimization. Compounds need to cross the blood-brain barrier (BBB) to reach the pharmacological targets in the CNS. The BBB is a complex system involving passive and active mechanisms of transport and efflux transporters such as P-glycoproteins (P-gp) and breast cancer resistance protein (BCRP), which play an essential role in CNS penetration of small molecules. Several in vivo, in vitro, and in silico methods are available to estimate human brain penetration. Preclinical species are used as in vivo models to understand unbound brain exposure by deriving the Kp,uu parameter and the brain/plasma ratio of exposure corrected with the plasma and brain free fraction. The MDCK-mdr1 (Madin Darby canine kidney cells transfected with the MDR1 gene encoding for the human P-gp) assay is the commonly used in vitro assay to estimate compound permeability and human efflux. The in silico methods to predict brain exposure, such as CNS MPO, CNS BBB scores, and various machine learning models, help save costs and speed up compound discovery and optimization at all stages. These methods enable the screening of virtual compounds, building of a CNS penetrable compounds library, and optimization of lead molecules for CNS penetration. Therefore, it is crucial to understand the reliability and ability of these methods to predict CNS penetration. We review the in silico, in vitro, and in vivo data and their correlation with each other, as well as assess published experimental and computational approaches to predict the BBB penetrability of compounds.

Indexed as

BrainNeoplasm ProteinsATP Binding Cassette Transporter, Subfamily G, Member 2Blood-Brain BarrierCentral Nervous SystemHumansReproducibility of ResultsATP Binding Cassette Transporter, Subfamily G, Member 2Neoplasm Proteinsactive transportblood–brain barrier (BBB)breast cancer resistance protein (BCRP)CNS drug discoveryefflux transportersinflux transportersin silico modelspassive diffusionP-glycoproteins (P-gp)

Identifiers

PMID38542901
PMCPMC10975190
OpenAlexW4392752859

What OpenQuestion holds

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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.