Evidence map›Paper›PMID 19758437›Full record

ArticleBMC pharmacology2009

Evaluation of molecular descriptors for antitumor drugs with respect to noncovalent binding to DNA and antiproliferative activity.

José Portugal

Open access · hybridAbstract read
In one paragraph

Article in BMC pharmacology, 2009. 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
1.2field-weighted citation impact, top 22% 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

3 citing papers in PubMed, 22 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 at 1 institution in 1 country.

José PortugalInstituto de Biología Molecular de Barcelona, CSIC, Parc Cientific de Barcelona, Baldiri Reixac, 10, E-08028 Barcelona, Spain. jpmbmc@ibmb.csic.es
Institut de Biologia Molecular de Barcelona · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSmall molecules that bind reversibly to DNA are among the antitumor drugs currently used in chemotherapy. In the pursuit of a more rational approach to cancer chemotherapy based upon these molecules, it is necessary to exploit the interdependency between DNA-binding affinity, sequence selectivity and cytotoxicity. For drugs binding noncovalently to DNA, it is worth exploring whether molecular descriptors, such as their molecular weight or the number of potential hydrogen acceptors/donors, can account for their DNA-binding affinity and cytotoxicity.

resultsFifteen antitumor agents, which are in clinical use or being evaluated as part of the National Cancer Institute's drug screening effort, were analyzed in silico to assess the contribution of various molecular descriptors to their DNA-binding affinity, and the capacity of the descriptors and DNA-binding constants for predicting cell cytotoxicity. Equations to predict drug-DNA binding constants and growth-inhibitory concentrations were obtained by multiple regression following rigorous statistical procedures.

conclusionFor drugs binding reversibly to DNA, both their strength of binding and their cytoxicity are fairly predicted from molecular descriptors by using multiple regression methods. The equations derived may be useful for rational drug design. The results obtained agree with that compounds more active across the National Cancer Institute's 60-cell line data set tend to have common structural features.

Indexed as

Models, StatisticalAnimalsAntineoplastic AgentsCell ProliferationCluster AnalysisDatabases, FactualDNADrug Screening Assays, AntitumorIntercalating AgentsNational Cancer Institute (U.S.)Numerical Analysis, Computer-AssistedPrincipal Component AnalysisRegression AnalysisUnited StatesAntineoplastic AgentsDNAIntercalating Agents

Identifiers

PMID19758437
PMCPMC2758867
OpenAlexW2086439329

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Registered trials

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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.