Evidence map›Paper›PMID 40871023›Full record

ReviewPharmaceutics2025

In Silico ADME Methods Used in the Evaluation of Natural Products.

Robert Ancuceanu, Beatrice Elena Lascu, Doina Drăgănescu, Mihaela Dinu

Abstract readReview
In one paragraph

Review in Pharmaceutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. In vitro study and in silico reveal Teucrium Polium L. methanolic extract as an apoptosis inducer and a potential Bcl-2 inhibitor for breast cancer.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2026
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  4. PreliminaryLife (Basel, Switzerland) · 2026
    Article
  5. Chemical Characterization and Bioactive Potential ofMolecules (Basel, Switzerland) · 2026
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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

4 authors.

Robert AncuceanuFaculty of Pharmacy, Department of Pharmaceutical Botany and Cell Biology, Carol Davila University of Medicine and Pharmacy, 050474 București, Romania.ORCID 0000-0002-9369-3314
Beatrice Elena LascuFaculty of Pharmacy, Department of Pharmaceutical Botany and Cell Biology, Carol Davila University of Medicine and Pharmacy, 050474 București, Romania.
Doina DrăgănescuFaculty of Pharmacy, Department of Pharmaceutical Physics and Informatics, Carol Davila University of Medicine and Pharmacy, 050474 București, Romania.
Mihaela DinuFaculty of Pharmacy, Department of Pharmaceutical Botany and Cell Biology, Carol Davila University of Medicine and Pharmacy, 050474 București, Romania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The pharmaceutical industry faces significant challenges when promising drug candidates fail during development due to suboptimal ADME (absorption, distribution, metabolism, excretion) properties or toxicity concerns. Natural compounds are subject to the same pharmacokinetic considerations. In silico approaches offer a compelling advantage-they eliminate the need for physical samples and laboratory facilities, while providing rapid and cost-effective alternatives to expensive and time-consuming experimental testing. Computational methods can often effectively address common challenges associated with natural compounds, such as chemical instability and poor solubility. Through a review of the relevant scientific literature, we present a comprehensive analysis of in silico methods and tools used for ADME prediction, specifically examining their application to natural compounds. Whereas we focus on identifying the predominant computational approaches applicable to natural compounds, these tools were developed for conventional drug discovery and are of general use. We examine an array of computational approaches for evaluating natural compounds, including fundamental methods like quantum mechanics calculations, molecular docking, and pharmacophore modeling, as well as more complex techniques such as QSAR analysis, molecular dynamics simulations, and PBPK modeling.

Indexed as

ADMEin silico methodsmolecular dockingmolecular dynamicsnatural compoundsPBPKpharmacophore modelingQSARquantum mechanics

Identifiers

PMID40871023
PMCPMC12389637

What OpenQuestion holds

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LicenceCC BY
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.