Identification of metabolic vulnerabilities and synthetic lethal targets utilizing artificial intelligence-driven precision medicine
5 affiliations
- 1Prince Fahad Bin Sultan Chair for Biomedical Research, University of Tabuk
- 2Department of Medical Laboratory Technology,Faculty of Applied Medical Sciences, University of Tabuk
- 3Molecular Medicine, Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk
- 4Department of Surgical Oncology, Faculty of Medicine, University of Tabuk
- 5Department of Biology,Faculty of Science, University of Tabuk
Abstract
Cancer metabolism is highly plastic, and most attempts to turn that plasticity into a therapeutic target fail for a specific, recurring reason: a computational prediction of a metabolic vulnerability is not the same evidentiary object as an experimentally confirmed one, and the two are routinely discussed as if they were interchangeable. This review takes that distinction as its starting point rather than its conclusion. Artificial intelligence and genome-scale systems biology have made it easy to generate metabolic hypotheses at a scale no experimental program could match — machine learning models trained on genomic, transcriptomic, proteomic, and metabolomic data now output candidate vulnerabilities, biomarkers, and drug combinations faster than they can be tested — but the resulting literature has not been consistently honest about how far any given prediction has actually traveled toward clinical relevance. We examine six areas where this gap is most visible: multi-omics data integration, genome-scale metabolic network modeling, computational discovery of metabolic vulnerabilities and synthetic lethal interactions, single-cell and spatial resolution of metabolic heterogeneity, AI-guided drug and biomarker discovery, and the small set of studies that have actually reached clinical testing. Rather than summarizing these areas as separate literatures, we grade every substantive finding discussed against an explicit four-tier evidence standard — associative, mechanistic, perturbation-validated, or clinically tested — and we apply that standard critically rather than deferentially: several widely cited computational vulnerabilities remain unconfirmed outside the cell lines in which they were discovered, genome-scale models continue to carry unresolved gaps in reaction coverage that limit their predictive reach, and, of the AI-derived metabolic predictions discussed in this review, only one has so far been tested in a completed prospective clinical trial. We argue that this shortage of prospective validation, not a shortage of computational sophistication, is the field’s central constraint, and we close by identifying where methodological effort would do the most good: standardized validation reporting, tissue-resolved rather than bulk metabolic models, and closer coupling between computational prediction and the specific experimental designs capable of falsifying it.
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BibTeX
@article{mir2026identification,
title = {{Identification of metabolic vulnerabilities and synthetic lethal targets utilizing artificial intelligence-driven precision medicine}},
author = {Rashid Mir and Manal Aljammaz and Amr Ammar Alsufyani and Yahya Ali Khubrani and Aziz Dhaher Albalawi and Shouq S. Alsubaie and Wed Albalawi and Naseh A. Algehainy and Mohammed M. Jalal and Malik A. Altayar and Reema M. Almotairi and Faris J. Tayeb and Ruqaiah I. Bedaiwi and Faisal H. Altemani and Kholoud S. Almasoudi and Ibrahim Altedlawi Albalawi and Aisha Ali Awaji},
journal = {Frontiers in Oncology},
year = {2026},
month = oct,
doi = {10.3389/fonc.2026.1972698},
url = {https://www.frontiersin.org/articles/10.3389/fonc.2026.1972698/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Identification of metabolic vulnerabilities and synthetic lethal targets utilizing artificial intelligence-driven precision medicine
AU - Rashid Mir
AU - Manal Aljammaz
AU - Amr Ammar Alsufyani
AU - Yahya Ali Khubrani
AU - Aziz Dhaher Albalawi
AU - Shouq S. Alsubaie
AU - Wed Albalawi
AU - Naseh A. Algehainy
AU - Mohammed M. Jalal
AU - Malik A. Altayar
AU - Reema M. Almotairi
AU - Faris J. Tayeb
AU - Ruqaiah I. Bedaiwi
AU - Faisal H. Altemani
AU - Kholoud S. Almasoudi
AU - Ibrahim Altedlawi Albalawi
AU - Aisha Ali Awaji
PY - 2026
DA - 2026/10/27
JO - Frontiers in Oncology
DO - 10.3389/fonc.2026.1972698
UR - https://www.frontiersin.org/articles/10.3389/fonc.2026.1972698/full
AB - Cancer metabolism is highly plastic, and most attempts to turn that plasticity into a therapeutic target fail for a specific, recurring reason: a computational prediction of a metabolic vulnerability is not the same evidentiary object as an experimentally confirmed one, and the two are routinely discussed as if they were interchangeable. This review takes that distinction as its starting point rather than its conclusion. Artificial intelligence and genome-scale systems biology have made it easy to generate metabolic hypotheses at a scale no experimental program could match — machine learning models trained on genomic, transcriptomic, proteomic, and metabolomic data now output candidate vulnerabilities, biomarkers, and drug combinations faster than they can be tested — but the resulting literature has not been consistently honest about how far any given prediction has actually traveled toward clinical relevance. We examine six areas where this gap is most visible: multi-omics data integration, genome-scale metabolic network modeling, computational discovery of metabolic vulnerabilities and synthetic lethal interactions, single-cell and spatial resolution of metabolic heterogeneity, AI-guided drug and biomarker discovery, and the small set of studies that have actually reached clinical testing. Rather than summarizing these areas as separate literatures, we grade every substantive finding discussed against an explicit four-tier evidence standard — associative, mechanistic, perturbation-validated, or clinically tested — and we apply that standard critically rather than deferentially: several widely cited computational vulnerabilities remain unconfirmed outside the cell lines in which they were discovered, genome-scale models continue to carry unresolved gaps in reaction coverage that limit their predictive reach, and, of the AI-derived metabolic predictions discussed in this review, only one has so far been tested in a completed prospective clinical trial. We argue that this shortage of prospective validation, not a shortage of computational sophistication, is the field’s central constraint, and we close by identifying where methodological effort would do the most good: standardized validation reporting, tissue-resolved rather than bulk metabolic models, and closer coupling between computational prediction and the specific experimental designs capable of falsifying it.
SN - 2234-943X
ER -