TY  - JOUR
T1  - When Does a Pharmaceutical Model Become a Digital Twin? An Umbrella Review across Pharmacology, Pharmaceutics, and Drug Delivery
A1  - Thabo Nkosi
A1  - Lerato Molefe
A1  - Sipho Dlamini
A1  - Ayanda Mokoena
A1  - Kabelo Ndlovu
JF  - Pharmacophore
JO  - Pharmacophore
SN  - 2229-5402
Y1  - 2026
VL  - 17
IS  - 4
DO  - 10.51847/HXSioqastj
SP  - 135
EP  - 145
N2  - Digital-twin terminology is increasingly applied to computational models used in drug discovery, pharmacology, formulation development, drug delivery, precision dosing, clinical trials, and pharmaceutical manufacturing. However, patient-specific simulation, mechanistic modeling, adaptive prediction, virtual populations, and process monitoring are frequently described as digital twins without consistent requirements for real-entity linkage, updating, uncertainty assessment, or decision use. This umbrella review critically evaluates review-level evidence across pharmaceutical domains to determine when a model warrants the digital-twin designation. The synthesis distinguishes digital models, adaptive models, virtual patients, digital shadows, and decision-coupled twins according to counterpart identity, data connection, update logic, predictive purpose, bidirectional information flow, validation, and governance. Review selection, methodological quality, primary-study overlap, consistency, and context of use are treated as determinants of evidentiary strength rather than assuming that multiple reviews constitute independent confirmation. The evidence indicates that pharmacometric, systems-pharmacology, formulation, delivery, and manufacturing models provide important twin-enabling capabilities, but most reported applications demonstrate only subsets of the functions implied by a mature digital twin. Patient specificity, mechanistic sophistication, or benchmark performance alone does not establish twin status. The central contribution is a proposed operational boundary in which a pharmaceutical digital twin must represent a specified counterpart, receive state-relevant updates, generate uncertainty-aware prospective predictions, and connect those predictions to an auditable and bounded decision pathway. This framework is conceptual and requires domain-specific validation. Its purpose is not to promote universal adoption of digital twins, but to improve terminology, evidence appraisal, comparative evaluation, and translational judgment across computational pharmaceutical science.
UR  - https://pharmacophorejournal.com/article/when-does-a-pharmaceutical-model-become-a-digital-twin-an-umbrella-review-across-pharmacology-phar-stymm4yaeycckb8
ER  -