Evidence map›Paper›PMID 37189659›Full record

ArticleBiomedicines2023

Artificial Intelligence Assisted Pharmacophore Design for Philadelphia Chromosome-Positive Leukemia with Gamma-Tocotrienol: A Toxicity Comparison Approach with Asciminib.

Muhammad Naveed, Noor Ul Ain, Tariq Aziz, Khushbakht Javed, Muhammad Aqib Shabbir, Metab Alharbi, Abdulrahman Alsahammari, Abdullah F Alasmari

Open access · goldAbstract read
In one paragraph

Article in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
5.3field-weighted citation impact, top 4% 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

14 citing papers in PubMed, 27 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

8 authors at 3 institutions in 3 countries.

Muhammad NaveedDepartment of Biotechnology, Faculty of Science & Technology, University of Central Punjab, Lahore 54590, Pakistan.
Noor Ul AinDepartment of Biotechnology, Faculty of Science & Technology, University of Central Punjab, Lahore 54590, Pakistan.ORCID 0000-0003-3692-980X
Tariq AzizDepartment of Agriculture, University of Ioannina, 47100 Arta, Greece.ORCID 0000-0003-0905-8076
Khushbakht JavedDepartment of Biotechnology, Faculty of Science & Technology, University of Central Punjab, Lahore 54590, Pakistan.
Muhammad Aqib ShabbirDepartment of Biotechnology, Faculty of Science & Technology, University of Central Punjab, Lahore 54590, Pakistan.ORCID 0000-0001-7912-3983
Metab AlharbiDepartment of Pharmacology and Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia.
Abdulrahman AlsahammariDepartment of Pharmacology and Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia.ORCID 0000-0002-9888-9826
Abdullah F AlasmariDepartment of Pharmacology and Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia.ORCID 0000-0001-7569-4287
University of Central Punjab · PKKing Saud University · SAUniversity of Ioannina · GR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BCR-ABL1 is a fusion protein as a result of a unique chromosomal translocation (producing the so-called Philadelphia chromosome) that serves as a clinical biomarker primarily for chronic myeloid leukemia (CML); the Philadelphia chromosome also occurs, albeit rather rarely, in other types of leukemia. This fusion protein has proven itself to be a promising therapeutic target. Exploiting the natural vitamin E molecule gamma-tocotrienol as a BCR-ABL1 inhibitor with deep learning artificial intelligence (AI) drug design, this study aims to overcome the present toxicity that embodies the currently provided medications for (Ph+) leukemia, especially asciminib. Gamma-tocotrienol was employed in an AI server for drug design to construct three effective de novo drug compounds for the BCR-ABL1 fusion protein. The AIGT's (Artificial Intelligence Gamma-Tocotrienol) drug-likeliness analysis among the three led to its nomination as a target possibility. The toxicity assessment research comparing AIGT and asciminib demonstrates that AIGT, in addition to being more effective nonetheless, is also hepatoprotective. While almost all CML patients can achieve remission with tyrosine kinase inhibitors (such as asciminib), they are not cured in the strict sense. Hence it is important to develop new avenues to treat CML. We present in this study new formulations of AIGT. The docking of the AIGT with BCR-ABL1 exhibited a binding affinity of -7.486 kcal/mol, highlighting the AIGT's feasibility as a pharmaceutical option. Since current medical care only exclusively cures a small number of patients of CML with utter toxicity as a pressing consequence, a new possibility to tackle adverse instances is therefore presented in this study by new formulations of natural compounds of vitamin E, gamma-tocotrienol, thoroughly designed by AI. Even though AI-designed AIGT is effective and adequately safe as computed, in vivo testing is mandatory for the verification of the in vitro results.

Indexed as

artificial intelligenceasciminibchronic myeloid leukemiagamma-tocotrienolPhiladelphia chromosome-positive leukemiatoxicity

Identifiers

PMID37189659
PMCPMC10135824
OpenAlexW4361210123

What OpenQuestion holds

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LicenceCC BY
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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.