Development and Validation of a Robust and Efficient HPLC Method for the Simultaneous Quantification of Levodopa, Carbidopa, Benserazide and Entacapone in Complex Matrices
Bibliographic record
Abstract
PURPOSE: A variety of fixed-dose combination products is used in the therapy of Parkinson Disease. However, to date a proper analytical method applicable for comparative screening of different antiparkinson products was not available. The objective of the present work was thus to develop and validate an analytical method for the simultaneous quantification of levodopa, carbidopa, benserazide and entacapone. The method should be applicable for quantifying samples from drug release experiments with marketed products and prototype formulations performed under compendial and biorelevant test conditions. METHODS: A fast and robust method applicable for separation and quantification of the four compounds was developed and validated according to International Conference on Harmonization guidelines. Method validation covered applicability to a wide concentration range of all compounds and peak separation in complex sample matrices such as biorelevant dissolution media. RESULTS: The compounds were successfully separated by using a gradient elution method on an endcapped LiChrospher 100 RP-18 (250 x 4.6 mm, 5 µm) column coupled with a LiChrospher 100 RP-18 precolumn (4 x 4 mm, 5 µm) at a column temperature of 35.0 °C and a flow rate of 1.50 mL/min. The injection volume was 30 µL and the detection wavelengths were 280 and 210 nm, respectively. For all drug/media combinations the method was linear (r2 > 0.999) for a concentration range corresponding to 1.25 - 125 % label claim (i.e. 200 mg levodopa/entacapone and 50 mg carbidopa/benserazide) released. All other validation parameters were in the specified limits over the same concentration range. CONCLUSION: The new method allows for robust and fast separation of levodopa, carbidopa, benserazide and entacapone without any interference caused by excipients or ingredients of compendial and biorelevant dissolution media and thus presents a valuable tool in both formulation development and in vitro drug release screening of numerous fixed-dose combinations of antiparkinson drugs. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".