Evidence map›Paper›PMID 30754680›Full record

ArticleJournal of clinical medicine2019

In Silico Study Probes Potential Inhibitors of Human Dihydrofolate Reductase for Cancer Therapeutics.

Rabia Mukhtar Rana, Shailima Rampogu, Amir Zeb, Minky Son, Chanin Park, Gihwan Lee, Sanghwa Yoon, Ayoung Baek, Sarvanan Parameswaran, Seok Ju Park and 1 more

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.0field-weighted citation impact, top 13% 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

8 citing papers in PubMed, 21 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

11 authors at 2 institutions in 1 country.

Rabia Mukhtar RanaDivision of Life Sciences, Division of Applied Life Science (BK21 Plus), Research Institute of NaturalScience (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Korea. rabia.mukhtar.rana@gmail.com.
Shailima RampoguDivision of Life Sciences, Division of Applied Life Science (BK21 Plus), Research Institute of NaturalScience (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Korea. shailima.rampogu@gmail.com.
Amir ZebDivision of Life Sciences, Division of Applied Life Science (BK21 Plus), Research Institute of NaturalScience (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Korea. zebamir85@gmail.com.
Minky SonDivision of Life Sciences, Division of Applied Life Science (BK21 Plus), Research Institute of NaturalScience (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Korea. minky@gnu.ac.kr.
Chanin ParkDivision of Life Sciences, Division of Applied Life Science (BK21 Plus), Research Institute of NaturalScience (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Korea. chaninpark0806@gmail.com.
Gihwan LeeDivision of Life Sciences, Division of Applied Life Science (BK21 Plus), Research Institute of NaturalScience (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Korea. pika890131@gmail.com.
Sanghwa YoonDivision of Life Sciences, Division of Applied Life Science (BK21 Plus), Research Institute of NaturalScience (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Korea. ayoung@gnu.ac.kr.
Ayoung BaekDivision of Life Sciences, Division of Applied Life Science (BK21 Plus), Research Institute of NaturalScience (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Korea. jsyoon0517@gmail.com.
Sarvanan ParameswaranDivision of Life Sciences, Division of Applied Life Science (BK21 Plus), Research Institute of NaturalScience (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Korea. dr.p.saravanan.bi@gmail.com.ORCID 0000-0001-9279-2620
Seok Ju ParkDepartment of Internal Medicine, College of Medicine, Busan Paik Hospital, Inje University,Busan 47392, Korea. psjgenesis@hanmail.net.
Keun Woo LeeDivision of Life Sciences, Division of Applied Life Science (BK21 Plus), Research Institute of NaturalScience (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Korea. kwlee@gnu.ac.kr.
Gyeongsang National University · KRInje University Busan Paik Hospital · KR

Funding

This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) & funded by the Korean government (MSIT) and also supported by Basic Science Research Program (2017R1D1A3B03035738) through the Natio No. NRF-2018M3A9A7057263
6 · The paper itself

Abstract

Dihydrofolate reductase (DHFR) is an essential cellular enzyme and thereby catalyzes thereduction of dihydrofolate to tetrahydrofolate (THF). In cancer medication, inhibition of humanDHFR (hDHFR) remains a promising strategy, as it depletes THF and slows DNA synthesis and cellproliferation. In the current study, ligand-based pharmacophore modeling identified and evaluatedthe critical chemical features of hDHFR inhibitors. A pharmacophore model (Hypo1) was generatedfrom known inhibitors of DHFR with a correlation coefficient (0.94), root mean square (RMS)deviation (0.99), and total cost value (125.28). Hypo1 was comprised of four chemical features,including two hydrogen bond donors (HDB), one hydrogen bond acceptor (HBA), and onehydrophobic (HYP). Hypo1 was validated using Fischer's randomization, test set, and decoy setvalidations, employed as a 3D query in a virtual screening at Maybridge, Chembridge, Asinex,National Cancer Institute (NCI), and Zinc databases. Hypo1-retrieved compounds were filtered byan absorption, distribution, metabolism, excretion, and toxicity (ADMET) assessment test andLipinski's rule of five, where the drug-like hit compounds were identified. The hit compounds weredocked in the active site of hDHFR and compounds with Goldfitness score was greater than 44.67(docking score for the reference compound), clustering analysis, and hydrogen bond interactionswere identified. Furthermore, molecular dynamics (MD) simulation identified three compounds asthe best inhibitors of hDHFR with the lowest root mean square deviation (1.2 Å to 1.8 Å), hydrogenbond interactions with hDHFR, and low binding free energy (-127 kJ/mol to -178 kJ/mol). Finally,the toxicity prediction by computer (TOPKAT) affirmed the safety of the novel inhibitors of hDHFRin human body. Overall, we recommend novel hit compounds of hDHFR for cancer and rheumatoidarthritis chemotherapeutics.

Indexed as

binding free energydihydrofolate reductase inhibitionmolecular dockingmolecular dynamics simulationpharmacophore modeling

Identifiers

PMID30754680
PMCPMC6406960
OpenAlexW2911265795

What OpenQuestion holds

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LicenceCC BY
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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.