Academic Journal
Integrating single-cell atlases and machine learning to construct 'in silico patients' for predicting individualized drug responses.
| Τίτλος: | Integrating single-cell atlases and machine learning to construct 'in silico patients' for predicting individualized drug responses. |
|---|---|
| Συγγραφείς: | Zuo Z; School of Life Science and Technology, Key Laboratory for Space Biosciences & Biotechnology, Institute of Special Environmental Biophysics, Research Center of Special Environmental Biomechanics and Medical Engineering, Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology and Equipment, Northwestern Polytechnical University, Xi'an, Shaanxi Province 710072, China., Sun Y; School of Life Science and Technology, Key Laboratory for Space Biosciences & Biotechnology, Institute of Special Environmental Biophysics, Research Center of Special Environmental Biomechanics and Medical Engineering, Engineering Research Center of Chinese Ministry of Education for Biological Diagnosis, Treatment and Protection Technology and Equipment, Northwestern Polytechnical University, Xi'an, Shaanxi Province 710072, China. Electronic address: yulongsun@nwpu.edu.cn. |
| Πηγή: | Biochemical pharmacology [Biochem Pharmacol] 2026 Jun; Vol. 248, pp. 117873. Date of Electronic Publication: 2026 Mar 06. |
| Τύπος έκδοσης: | Journal Article; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Science Country of Publication: England NLM ID: 0101032 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-2968 (Electronic) Linking ISSN: 00062952 NLM ISO Abbreviation: Biochem Pharmacol Subsets: MEDLINE |
| Imprint Name(s): | Publication: Oxford : Elsevier Science Original Publication: Oxford, New York [etc.] Paragamon Press. |
| Ιατρικοί όροι (MeSH): | Single-Cell Analysis*/methods , Neoplasms*/drug therapy , Neoplasms*/genetics , Neoplasms*/metabolism , Machine Learning*/trends , Precision Medicine*/methods , Precision Medicine*/trends , Antineoplastic Agents*/therapeutic use , Antineoplastic Agents*/pharmacology , Computer Simulation*, Tumor Microenvironment/drug effects ; Tumor Microenvironment/physiology ; Humans ; Animals ; Single-Cell Gene Expression Analysis |
| Περίληψη: | Intratumoral cellular heterogeneity is a core challenge that drives drug resistance and hinders the advancement of precision oncology. Single-cell RNA sequencing (scRNA-seq) has revealed the complexity of the tumor ecosystem at unprecedented resolution, offering new opportunities for predicting therapeutic responses. This review synthesizes the emerging concept of the "in silico patient", a predictive framework that integrates multi-source data. This framework leverages large-scale single-cell atlases as references for cellular identity, combines massive pharmacogenomic databases to train models, and incorporates patient-specific scRNA-seq data to achieve individualized predictions. Artificial intelligence (AI), particularly deep learning and transfer learning algorithms, acts as the core driver of this framework, effectively applying knowledge gained from cell line data to clinically relevant patient single-cell data. By integrating the impact of the tumor microenvironment (TME) and using advanced preclinical models that preserve tissue architecture (such as acute tissue slice cultures) for rapid experimental validation, a critical "prediction-validation-optimization" closed loop is being formed. This review systematically outlines the data foundations, core computational strategies, current challenges, and future directions, including multi-omics and spatial information integration, necessary to construct "in silico patients", aiming to provide a comprehensive conceptual blueprint for developing the next generation of individualized drug response prediction tools. (Copyright © 2026 Elsevier Inc. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Artificial intelligence; Drug response prediction; In silico patient; Single-cell RNA sequencing; Tumor heterogeneity |
| Substance Nomenclature: | 0 (Antineoplastic Agents) |
| Entry Date(s): | Date Created: 20260309 Date Completed: 20260710 Latest Revision: 20260710 |
| Update Code: | 20260711 |
| DOI: | 10.1016/j.bcp.2026.117873 |
| PMID: | 41796725 |
| Βάση Δεδομένων: | MEDLINE |
καταχωρήστε σχόλιο πρώτοι!