Safety Evaluation of Serendipity Berry Sweet Protein From <i>Komagataella phaffii</i>
Bibliographic record
Abstract
Serendipity Berry Sweet Protein (sweelin) is a novel hyper-sweet thermophilic protein designed using Artificial Intelligence Computational Protein Design (AI-CPD) to improve the stability and sensory profile of the protein found in serendipity berry (Dioscoreophyllum cumminsii). sweelin is produced through precision fermentation by expression in Komagataella phaffii. The safety of sweelin was investigated through an evaluation of its genotoxicity, mutagenicity, systemic toxicity and digestibility potential in in vitro and in vivo models. sweelin was not genotoxic in in vitro reverse mutation and mammalian micronucleus assays and was not associated with systemic toxicity in a 90-day dietary toxicity study in rats. The no-observed-adverse-effect level for sweelin in Sprague Dawley rats was established as 14,300 ppm, the highest dose tested. This dose level corresponds to dietary intakes of 838.3 and 946.0 mg/kg body weight/day in male and female rats, respectively. sweelin was demonstrated to be readily digestible in an in vitro semi-dynamic model of the gastrointestinal tract. The results support the safety of sweelin as a food ingredient for sweetening purposes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".