Simultaneous Chromatographic Quantitation of Drug Substance and Excipients in Nanoformulations Using a Combination of Evaporative Light Scattering and Absorbance Detectors
Bibliographic record
Abstract
Nanomedicines including lipid- and polymer-based nanoparticles and polymer-drug conjugates enable targeted drug delivery for the treatment of numerous diseases. Quantitative analysis of components in nanomedicines is routinely performed to characterize the products to ensure quality and property consistency but has been mainly focused on the active pharmaceutical ingredients (APIs) in academic publications. It has been increasingly recognized that excipients in nanomedicines are critical in determining the product quality, stability, consistency, and safety. APIs are often analyzed by high-performance liquid chromatography (HPLC), and it would be convenient if the same method can be applied to excipients to robustly quantify all components in nanomedicines. Here, we report the development of a HPLC method that combined an evaporative light scattering (ELS) detector with an UV-vis detector to simultaneously analyze drugs and excipients in nanomedicines. This method was tested on diverse nanodrug delivery systems, including a niosomal nanoparticle encapsulating a phytotherapeutic, a liposome encapsulating an immune boosting agent, and a PEGylated peptide. This method can be utilized for a variety of applications, such as monitoring drug loading, studying drug release, and storage stability. The information obtained from the analyses is of importance for nanomedicine 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".