Evidence map›Paper›PMID 33423212›Full record

ReviewMolecular biotechnology2021

Oncological Ligand-Target Binding Systems and Developmental Approaches for Cancer Theranostics.

Jaison Jeevanandam, Godfred Sabbih, Kei X Tan, Michael K Danquah

Abstract readReview
PubMed Publisher
In one paragraph

Review in Molecular biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

4 authors.

Jaison JeevanandamCQM-Centro de Química da Madeira, MMRG, Universidade da Madeira, Campus da Penteada, 9020-105, Funchal, Portugal.
Godfred SabbihChemical Engineering Department, University of Tennessee, Chattanooga, TN, 37403, USA.
Kei X TanSchool of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore.
Michael K DanquahChemical Engineering Department, University of Tennessee, Chattanooga, TN, 37403, USA. michael-danquah@utc.edu.ORCID http://orcid.org/0000-0002-4232-7138

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Targeted treatment of cancer hinges on the identification of specific intracellular molecular receptors on cancer cells to stimulate apoptosis for eventually inhibiting growth; the development of novel ligands to target biomarkers expressed by the cancer cells; and the creation of novel multifunctional carrier systems for targeted delivery of anticancer drugs to specific malignant sites. There are numerous receptors, antigens, and biomarkers that have been discovered as oncological targets (oncotargets) for cancer diagnosis and treatment applications. Oncotargets are critically important to navigate active anticancer drug ingredients to specific disease sites with no/minimal effect on surrounding normal cells. In silico techniques relating to genomics, proteomics, and bioinformatics have catalyzed the discovery of oncotargets for various cancer types. Effective oncotargeting requires high-affinity probes engineered for specific binding of receptors associated with the malignancy. Computational methods such as structural modeling and molecular dynamic (MD) simulations offer opportunities to structurally design novel ligands and optimize binding affinity for specific oncotargets. This article proposes a streamlined approach for the development of ligand-oncotarget bioaffinity systems via integrated structural modeling and MD simulations, making use of proteomics, genomic, and X-ray crystallographic resources, to support targeted diagnosis and treatment of cancers and tumors.

Indexed as

Antineoplastic AgentsBiomarkers, TumorComputer SimulationGene Expression Regulation, NeoplasticHumansLigandsModels, MolecularMolecular Targeted TherapyNeoplasmsPrecision MedicineAntineoplastic AgentsBiomarkers, TumorLigandsBioaffinity ligandCancerGenomicsMD simulationOncotargetsProteomics

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.