Integration of biological avatars and digital twins for “ex vivo clinical trials”

Drug development is slow, costly, and prone to late-stage failure, in part because animal models poorly predict human responses. Two human-relevant technologies are maturing in parallel: biological avatars, defined as patient- or stem-cell-derived models such as organoids and organ-on-a-chip systems, and digital twins, defined as computational models that integrate a patient's molecular and clinical data to forecast treatment responses. We propose the ex vivo clinical trial concept, in which an avatar and a digital twin are coupled in an iterative loop so that laboratory measurements refine the computational prediction and the prediction guides the next experiment, allowing candidate therapies to be tested and prioritised before a patient is exposed. We review the platforms, their predictive performance in cancer, cystic fibrosis, and liver toxicity, the conditions under which they fail, and the qualification, turnaround, and standardisation requirements that must be met before such trials can inform drug development or clinical care.

Authors

Institutions

Publication Details

Journal
EBioMedicine
Published
2026-09-01
DOI
https://doi.org/10.1016/j.ebiom.2026.106467
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Integration of biological avatars and digital twins for “ex vivo clinical trials”

Charles L. Howe
EBioMedicine
Neuroscience and Neural Engineering
article

Integration of biological avatars and digital twins for “ex vivo clinical trials”

Charles L. Howe
article en

Abstract

Drug development is slow, costly, and prone to late-stage failure, in part because animal models poorly predict human responses. Two human-relevant technologies are maturing in parallel: biological avatars, defined as patient- or stem-cell-derived models such as organoids and organ-on-a-chip systems, and digital twins, defined as computational models that integrate a patient's molecular and clinical data to forecast treatment responses. We propose the ex vivo clinical trial concept, in which an avatar and a digital twin are coupled in an iterative loop so that laboratory measurements refine the computational prediction and the prediction guides the next experiment, allowing candidate therapies to be tested and prioritised before a patient is exposed. We review the platforms, their predictive performance in cancer, cystic fibrosis, and liver toxicity, the conditions under which they fail, and the qualification, turnaround, and standardisation requirements that must be met before such trials can inform drug development or clinical care.

EBioMedicineVol. 132
Mayo Clinic (US), Mayo Clinic in Arizona (US), Mayo Clinic in Florida (US)
Mayo Foundation for Medical Education and Research
Openalex Percentile: Top 16%
Neuroscience and Neural Engineering
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.