Regulatory Challenges for Look-Alike and Sound-Alike Non-Biological Complex Drugs: Molecular Fingerprint Discrimination of <i>Ichthammol</i> Formulations
Bibliographic record
Abstract
Look-alike and sound-alike (LASA) medications are a critical source of medication errors, posing significant risks to patient safety. This issue is particularly relevant for non-biological complex drugs (NBCDs), whose intricate compositions and manufacturing-dependent properties complicate regulatory assessment and comparability of follow-on products. In this study, we employ high-resolution mass spectrometry (HRMS) techniques, including Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) and comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC×GC-HRToF MS), to differentiate between Ichthammol formulations compliant with different pharmacopeia definitions. Despite similar bulk properties, comprehensive molecular-level characterization reveals substantial compositional differences: European Pharmacopeia (Ph. Eur.) compliant samples, which also largely correspond to the U.S. pharmacopeia (USP), predominantly contain sulfonated thiaarenes derived from shale oil, while samples compliant with the Chinese Pharmacopeia (ChP) consist mainly of sulfurized fatty acids and sulfur-linked fatty acid oligomers derived from vegetable oils. Proposed reaction mechanisms describe a classical aromatic sulfonation by sulfuric acid yielding ammonium sulfonates of thiaarenes and, in smaller quantities, ammonium sulfonate arenes in the Ph. Eur. formulations after neutralization with ammonia. In contrast, ChP formulations likely undergo an initial sulfurization via vulcanization, leading to thiophene-containing fatty acids and sulfur-linked oligomers, followed by sulfonation and neutralization. Our findings provide strong evidence for distinct chemical fingerprints, allowing robust differentiation between these complex LASA drugs. These findings were in accordance with the mechanistic pathways for their respective manufacturing processes proposed in this study. These insights highlight the necessity of molecular-level analysis for regulatory assessment of complex pharmaceuticals and underscore the potential risks of relying solely on bulk parameter equivalence in complex drug approval and substitution.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| 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".