ReviewInternational journal of pharmaceutics: X2025
Transformative roles of digital twins from drug discovery to continuous manufacturing: pharmaceutical and biopharmaceutical perspectives.
Review in International journal of pharmaceutics: X, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Protein Therapeutic Quality Control Using Multi-Attribute Method (MAM): Challenges and Current Practice in cGMP Environments.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Machine-Learning-Driven Optimization of Functional Excipients and Their Biointeractions in Drug Formulations.ACS pharmacology & translational science · 2026Review
- From Therapeutic Drug to Xenobiotic in Cancer Repurposing: Clozapine Mechanisms, Metabolic Liabilities, and Human-Relevant Translational Approaches.Journal of xenobiotics · 2026Review
- Machine learning-enhanced nano-QSAR and multiscale modeling for predictive nanomedicine: applications in herbal therapeutics and neglected tropical diseases.Discover nano · 2026Review
- From production to bedside: A tiered QC framework for regulatory alignment and analytical comparability in decentralized CGT.Regenerative therapy · 2026Review
- Stabilization strategies and advancements in lyophilization to preserve integrity and efficacy of next-generation biologicals.International journal of pharmaceutics: X · 2026Review
- Digital Twins in Orthopedics and Trauma: Concepts, Emerging Evidence, and Barriers to Clinical Translation.Journal of clinical medicine · 2026Review
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- Artificial Intelligence and the Transformation of Cell and Gene Therapy Development.Pharmaceutics · 2026Review
- Multitechnological integration advances musculoskeletal regeneration: synergistic progress of organoids, 3D/4D bioprinting, single-cell omics and artificial intelligence.Frontiers in bioengineering and biotechnology · 2026Review
- From fragmented innovation to an integrated plant-to-product framework: integrated digital twins and artificial intelligence approaches in phytomedicine.Frontiers in pharmacology · 2026Article
- Nanomedicine in 2026: Illustrative Quantitative Analyses of EPR Heterogeneity, Clinical Trial Attrition, and Emerging Horizons for Active Nanotherapeutics.International journal of nanomedicine · 2026Review
- AI-Assisted Impedance Biosensing of Yeast Cell Concentration.Biosensors · 2025Article
- Intelligent information management enables quality-by-design in pharmaceutical production.Scientific reports · 2025Article
- Mining the gaps: Deciphering Alzheimer's biology through AI-driven reconciliation.The journal of prevention of Alzheimer's disease · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital Twins (DTs) represent a groundbreaking development tool in the pharmaceutical and biopharmaceutical industries, providing virtual representations of physical entities, processes, or systems. This review investigates the transformative roles of DTs by examining their applications throughout the entire drug development lifecycle, from discovery to continuous manufacturing. By facilitating real-time monitoring and predictive analytics, DTs enhance operational efficiency, reduce costs, and improve product quality. Integration with advanced technologies, such as artificial intelligence and machine learning, further amplifies their capabilities, enabling sophisticated data analysis for preventive maintenance and manufacturing optimization. Despite these advantages, the implementation of DTs faces significant challenges, including data integration, model accuracy, and regulatory complexity. This review discusses these barriers while highlighting opportunities for innovation and automation through emerging technologies, including blockchain, nanotechnology, and dark factory. It also explores the potential of DTs to support personalized medicine through individualized treatments based on patient-specific data. Overall, this review highlights the current state, key challenges, and future perspectives of DT applications in pharmaceutical systems, emphasizing their potential to improve efficiency, quality, and patient outcomes.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.