A Data-Driven Workflow for Nanomedicine Optimization Using Active Learning and Automated Experimentation
Bibliographic record
Abstract
Nanomedicines are an advanced class of drug formulations that hold significant promise, particularly in enhancing the solubility of hydrophobic drugs. However, current state-of-the-art methodologies for developing nanomedicines are often inefficient, limiting both the systematic screening of dosage forms and the fine-tuning of individual formulations. To overcome these challenges, this study introduces a data-driven workflow that integrates active learning with experimental automation to rapidly identify optimal nanoformulations, using aceclofenac as a model poorly soluble drug. The initial formulation design space comprised combinations of the drug with 12 different excipients, resulting in approximately 17 billion possible formulations. To optimize across four objectives simultaneously, the active learning–robotic system efficiently narrowed this vast space to a manageable subset. This refined subset was further explored using a design of experiments approach, with selected formulations manually prepared and subjected to standardized evaluation. Within weeks, a panel of high-performing lead nanoformulations was identified. Notably, several of these promising formulations represent hybrid nanomedicines that are not well studied in the literature. These findings highlight the power of combining AI-driven design with automation to accelerate nanomedicine development and lay the groundwork for more efficient formulation development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".