Evidence map›Paper›PMID 41698965›Full record

ArticleScientific reports2026

Comparative pharmacoinformatic and quantum descriptor insights from BFM/GBTLI guidelines to phase I/II compounds for acute lymphoblastic leukemia (ALL).

Ian A F Bahia, Maria K da Silva, Emad Rashad Sindi, João F Rodrigues-Neto, Edilson D da Silva, Taha Alqahtani, Yewulsew Kebede Tiruneh, Magdi E A Zaki, Umberto L Fulco, Jonas I N Oliveira

Abstract readComparative Study
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

10 authors.

Ian A F BahiaHealth Sciences Center (CCS), Federal University of Rio Grande Do Norte, Natal, RN, Brazil.
Maria K da SilvaDepartment of Biophysics and Pharmacology, Bioscience Center, Federal University of Rio Grande Do Norte, Natal, RN, Brazil.
Emad Rashad SindiDivision of Clinical Biochemistry, Department of Basic Medical Sciences, College of Medicine, University of Jeddah, 23890, Jeddah, Saudi Arabia.
João F Rodrigues-NetoMulticampi School of Medical Sciences, Federal University of Rio Grande Do Norte, Caicó, RN, Brazil.
Edilson D da SilvaDepartment of Biophysics and Pharmacology, Bioscience Center, Federal University of Rio Grande Do Norte, Natal, RN, Brazil.
Taha AlqahtaniDepartment of Pharmacology, College of Pharmacy, King Khalid University, 62529, Abha, Saudi Arabia.
Yewulsew Kebede TirunehDepartment: Biology, Biomedical Sciences Stream Bahir Dar University, P.O.Box=79, Bahir Dar, Ethiopia. Kebede@bdu.edu.et.
Magdi E A ZakiDepartment of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), P.O. Box 5701, 13317, Riyadh, Kingdom of Saudi Arabia. mezaki@imamu.edu.sa.
Umberto L FulcoDepartment of Biophysics and Pharmacology, Bioscience Center, Federal University of Rio Grande Do Norte, Natal, RN, Brazil.
Jonas I N OliveiraDepartment of Biophysics and Pharmacology, Bioscience Center, Federal University of Rio Grande Do Norte, Natal, RN, Brazil. jonas.nobre@ufrn.br.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acute lymphoblastic leukemia (ALL) remains the most common pediatric malignancy worldwide. Standard protocols such as BFM and GBTLI rely on long-established cytotoxic agents, yet novel targeted compounds have recently entered phase I/II trials. Despite these advances, no prior study has systematically compared the pharmacokinetic, ADMET, and quantum descriptor profiles of protocol-based drugs versus emerging clinical-phase agents. This study addresses that gap by integrating pharmacoinformatic and quantum-chemical approaches to highlight differences with potential clinical implications. We retrieved all small-molecule drugs from the BFM/GBTLI 2009 protocols and a representative set of phase I/II investigational compounds for pediatric ALL. In silico tools were used to assess physicochemical properties, ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles, and quantum chemical descriptors. We evaluated physicochemical and pharmacokinetic properties, including solubility, permeability, metabolic liabilities, and toxicity risks. Quantum chemical descriptors were calculated with density functional theory (DFT) to assess molecular reactivity (HOMO, LUMO, gap, dipole moment, electrophilicity). Multivariate analyses were applied to compare and cluster drug profiles. The comparative analysis revealed significant variability between guideline and clinical-phase compounds. Clinical-phase compounds generally exhibited higher molecular weight and lipophilicity, together with greater variability in permeability and solubility-related descriptors, indicating potential formulation and bioavailability challenges. Several investigational agents were identified as P-gp substrates and hERG inhibitors, suggesting increased risk of efflux-mediated resistance and cardiotoxicity. Quantum chemical analysis revealed that phase I/II compounds (e.g., Pelabresib, Molibresib) displayed smaller HOMO-LUMO gaps and higher electrophilicity, consistent with higher theoretical reactivity, whereas guideline drugs (e.g., Vincristine, Methotrexate) showed more stable electronic profiles. Cluster analysis confirmed distinct grouping between guideline and clinical-phase compounds. This in silico comparison integrates pharmacoinformatic and quantum descriptor analyses of established and emerging ALL therapeutics. By revealing key differences in drug-likeness, ADMET, and electronic reactivity, the study provides a comparative framework that may support the prioritization, optimization, and clinical translation of next-generation therapies for pediatric ALL.

Indexed as

Antineoplastic AgentsPrecursor Cell Lymphoblastic Leukemia-LymphomaClinical Trials, Phase II as TopicComputer SimulationHumansQuantum TheoryAntineoplastic AgentsADMETChemotherapeuticDFTDrug-likenessPediatric cancer

Identifiers

PMID41698965
PMCPMC12953776

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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.