ReviewMatter2023
Self-driving laboratories: A paradigm shift in nanomedicine development.
Review in Matter, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed.
- Cancer Drug Delivery with Nanoparticles and Biomolecules: Stimuli-Responsive, Theranostic, and AI-Guided Approaches.Materials (Basel, Switzerland) · 2026Review
- Review
- Sequence-defined polymers for predictable gene delivery.Nature reviews. Chemistry · 2026Review
- Integrated microfluidic biosensors: shaping the future of quantitative life sciences and on-chip molecular diagnostics.Lab on a chip · 2026Review
- Tumor microenvironment-specific nanomedicine: from biology-driven to multi-omics-guided precision engineering.Journal of hematology & oncology · 2026Review
- Smart Nanodelivery Systems for Immunometabolic Modulation in Osteoarthritis.Exploration (Beijing, China) · 2026Review
- Artificial intelligence-driven design and optimization of mitochondria-targeted nanocarriers for chronic heart failure.Frontiers in bioengineering and biotechnology · 2026Review
- Nanotechnology for atherosclerotic plaque stabilisation: bridging innovation and clinical practice.EBioMedicine · 2026Review
- Nanoparticle-Based Drug Delivery Systems: Current Advances and Future Directions.Current drug targets · 2026Review
- Navigating the complexity of oral peptide delivery: challenges and strategies to enhance oral bioavailability.Frontiers in drug delivery · 2026Review
- Self-driving lab discovers principles for steering spontaneous emission beyond conventional Fourier optics.Nature communications · 2025Article
- Design biology and mind-body health.Journal, genetic engineering & biotechnology · 2025Review
- MakingInternational journal of pharmaceutics: X · 2025Article
- Nanomaterials in Drug Delivery: Leveraging Artificial Intelligence and Big Data for Predictive Design.International journal of molecular sciences · 2025Review
- Combining High-Throughput Screening and Machine Learning to Predict the Formation of Both Binary and Ternary Amorphous Solid Dispersion Formulations for Early Drug Discovery and Development.Pharmaceutical research · 2025Article
- Hallmarks of Polymersome Characterization.ACS materials Au · 2025Review
- A dataset on formulation parameters and characteristics of drug-loaded PLGA microparticles.Scientific data · 2025Article
- The Dawn of a New Pharmaceutical Epoch: Can AI and Robotics Reshape Drug Formulation?Advanced healthcare materials · 2024Article
- A translational framework to DELIVER nanomedicines to the clinic.Nature nanotechnology · 2024Review
- Machine learning in drug delivery.Journal of controlled release : official journal of the Controlled Release Society · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nanomedicines have transformed promising therapeutic agents into clinically approved medicines with optimal safety and efficacy profiles. This is exemplified by the mRNA vaccines against COVID-19, which were made possible by lipid nanoparticle technology. Despite the success of nanomedicines to date, their design remains far from trivial, in part due to the complexity associated with their preclinical development. Herein, we propose a nanomedicine materials acceleration platform (NanoMAP) to streamline the preclinical development of these formulations. NanoMAP combines high-throughput experimentation with state-of-the-art advances in artificial intelligence (including active learning and few-shot learning) as well as a web-based application for data sharing. The deployment of NanoMAP requires interdisciplinary collaboration between leading figures in drug delivery and artificial intelligence to enable this data-driven design approach. The proposed approach will not only expedite the development of next-generation nanomedicines but also encourage participation of the pharmaceutical science community in a large data curation initiative.
Indexed as
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.