Evidence map›Paper›PMID 39138274›Full record

ArticleScientific reports2024

Correlating physico-chemical properties of analytes with Hansen solubility parameters of solvents using machine learning algorithm for predicting suitable extraction solvent.

Eman A Mostafa, Mohammad Abdul Azim, Asmaa A ElZaher, Ehab F ElKady, Marwa A Fouad, Fatma H Ghazy, Esraa A Radi, Mahmoud Abo El Makarim Saleh, Ahmed M El Kerdawy

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing 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

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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

3 citing papers in PubMed.

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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

9 authors.

Eman A MostafaPharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St., P.O. Box 11562, Cairo, Egypt. eman.saleh@pharma.cu.edu.eg.ORCID http://orcid.org/0000-0003-0570-5731
Mohammad Abdul AzimPharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St., P.O. Box 11562, Cairo, Egypt.
Asmaa A ElZaherPharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St., P.O. Box 11562, Cairo, Egypt.
Ehab F ElKadyPharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St., P.O. Box 11562, Cairo, Egypt.
Marwa A FouadPharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St., P.O. Box 11562, Cairo, Egypt.
Fatma H GhazyPharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St., P.O. Box 11562, Cairo, Egypt.
Esraa A RadiHealth Minister's Technical Office, Ministry of Health, Cairo, Egypt.
Mahmoud Abo El Makarim SalehEgyptian Drug Authority, Giza, Egypt.
Ahmed M El KerdawyPharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr El-Aini St., P.O. Box 11562, Cairo, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial neural networks (ANNs) are biologically inspired algorithms designed to simulate the way in which the human brain processes information. In sample preparation for bioanalysis, liquid-liquid extraction (LLE) represents an important step with the extraction solvent selection is the key laborious step. In the current work, a robust and reliable ANNs model for LLE solvent prediction was generated which could predict the suitable solvent for analyte extraction. The developed ANNs model takes a set of chosen descriptors for the cited analyte as an input and predicts the corresponding Hansen solubility parameters of the suitable extraction solvent as a model output. Then, from the solvent combination's appendix, the analyst can identify the proposed extraction solvents' combination for the cited analyte easily and efficiently. For the experimental validation of the model prediction capabilities, twenty structurally diverse drugs belonging to different pharmacological classes were extracted from human plasma. The extraction process was performed using the predicted extraction solvent combination for each drug and quantitively estimated by HPLC/UV methods to assess their extraction recovery. The developed LLE solvent prediction model is in- line with the global trend towards green chemistry since it limits the consumption of organic solvents.

Indexed as

Artificial Neural Network modelExtraction solvent predictionHansen solubility parametersLiquid–liquid extractionModel validation

Identifiers

PMID39138274
PMCPMC11322549

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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.