ArticleNPJ digital medicine2025
Digital twin models for predicting venetoclax and azacitidine-induced neutropenia in patients with acute myeloid leukemia.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The trial behind it
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Who cites it
6 citing papers in PubMed.
- Heterogeneity in cancer: molecular mechanisms and therapeutic strategies.Signal transduction and targeted therapy · 2026Review
- Artificial Intelligence for Personalized Management of Acute Myeloid Leukemia.Journal of personalized medicine · 2026Review
- A Nomogram Prediction Model and Scoring System for Resistance in Acute Myeloid Leukemia Patients Treated with Venetoclax Combined with Hypomethylating Agents.Current oncology (Toronto, Ont.) · 2026Article
- Mechanistic learning to predict and understand minimal residual disease.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence-driven nano-enhanced stem cell therapy for neurodegenerative diseases: from rational design to clinical translation.Journal of nanobiotechnology · 2026Review
- Mapping the future of medicine through digital twins.Frontiers in molecular medicine · 2026Review
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
Therapeutic toxicity, which can be life-threatening, presents a major challenge in treating patients with acute myeloid leukemia (AML). Medical digital twins, which are virtual representations of patient disease, have the potential to forecast disease progression and simulate potential treatments. Using neutrophil counts and blast percentages, we developed mechanistic models to predict toxicity (neutropenia) in AML patients receiving combination venetoclax and azacitidine treatment. We identified a best-fitting model, though patient-specific accuracy was highly variable. To address this variability, we investigated subsets of patients based on their accordance with model assumptions, and were able to identify features predictive of model fit. In addition, we found that continuous updating over time improves model accuracy. The model evaluated in this study could be further validated in a larger clinical setting and may support a digital twin for decision making in forecasting therapeutic toxicity of venetoclax and azacitidine treatment.
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Registered trials
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