1 2-Docosahexanoyl-Sn-Glycero-3-Phosphoserine Calc

證據等級: L5

預測適應症: 0 個

Multi-Nutrient Complex (DHA-PS / EPA-PS / B-Vitamins / Minerals): From Unknown Indication to No Prediction Available

One-Sentence Summary

This candidate is a 16-ingredient multi-nutrient combination comprising phospholipids (DHA-PS, EPA-PS, phosphatidylserine), B-vitamin cofactors (cobamamide, cocarboxylase, FAD, folic acid, leucovorin, levomefolate, pyridoxal phosphate, NADH), and mineral complexes (magnesium ascorbate, magnesium L-threonate, ferrous cysteine glycinate, zinc ascorbate, betaine). The TxGNN pipeline returned no predicted indications for this candidate, most likely because the multi-ingredient query string could not be resolved to a single DrugBank node in the knowledge graph. As a result, no clinical evidence table can be generated, and the evaluation must be classified as Hold pending data remediation.

Quick Overview

Item	Content
Original Indication	Not established (no regulatory authorization found)
Predicted New Indication	None (TxGNN returned no results)
TxGNN Prediction Score	N/A
Evidence Level	L5 — Model prediction only; no actual predictions returned
US Market Status	Not marketed
Number of NDAs	0
Recommended Decision	Hold

Why is This Prediction Reasonable?

No TxGNN prediction is available for this candidate, so a mechanistic plausibility analysis cannot be performed at this time.

The combination consists entirely of endogenous cofactors and micronutrients. Several ingredients — phosphatidylserine, DHA-PS, EPA-PS, magnesium L-threonate, and NADH — have individual literature suggesting roles in neuronal membrane integrity, mitochondrial energy metabolism, and one-carbon cycle regulation. Betaine, folic acid, leucovorin, levomefolate, and pyridoxal phosphate collectively support methylation pathways. This profile is consistent with formulas targeting cognitive decline or neurodegenerative conditions, but this is a literature-level inference only, not a TxGNN-derived prediction.

The fundamental obstacle is identity resolution: the Evidence Pack contains 16 named ingredients in a single query string with no DrugBank ID assigned. The TxGNN knowledge graph operates on individual DrugBank nodes; a compound multi-ingredient string cannot be matched. Each ingredient would need to be evaluated independently or the combination would need a registered product identity before a meaningful repurposing prediction can be generated.

Conclusion and Next Steps

Decision: Hold

Rationale: The TxGNN pipeline produced no predicted indications because the system could not resolve a 16-ingredient query string to any node in the drug–disease knowledge graph. Without a prediction score, a repurposing hypothesis, or regulatory authorization, there is no evaluable candidate at this stage.

To proceed, the following is needed:

Resolve drug identity: Assign a DrugBank ID to the combination product, or decompose the query into individual ingredient-level TxGNN runs (one prediction per active ingredient), then aggregate results.
Establish original indication: Determine the registered therapeutic indication or intended use category for this formulation (e.g., dietary supplement, pharmaceutical, OTC) in at least one jurisdiction.
Obtain safety data: Download the package insert or regulatory dossier to fill DG001 (warnings/contraindications) and DG002 (MOA), both of which are currently blocking.
Re-run Evidence Pack pipeline: Once a valid DrugBank node is identified, re-query TxGNN, ClinicalTrials.gov, and PubMed to populate the predicted_indications array.
Consider ingredient-level analysis: If the combination has no single regulatory identity, a parallel multi-drug report (one per ingredient) may be the only viable path to generating actionable repurposing hypotheses.
Disclaimer

This content is for research purposes only and does not constitute medical advice. Clinical validation is required before any clinical application.