In Silico Ingredient Analysis of Redoxx® and Bug Check® Using CytoSolve® Systems Architecture for Synergy, Safety, and Regulatory Validation Framework

Partner Description

The Natural Vet
The Natural Vet® is a science-focused animal health company developing nutraceutical and topical solutions for companion and performance animals. Its portfolio emphasizes natural ingredients, safety, and evidence-based validation. To support regulatory compliance and NASC expectations, The Natural Vet® prioritizes mechanistic substantiation for complex, multi-ingredient formulations beyond traditional single-ingredient testing.

Challenge

The Natural Vet® required rigorous in silico, ingredient-level scientific validation for two complex, multi-ingredient products with distinct biological objectives.

Redoxx® required demonstration that its combined antioxidant ingredients collectively support mitochondrial energy metabolism and redox homeostasis through coordinated, pathway-level effects, rather than isolated ingredient claims.

Bug Check® required validation that repellent efficacy emerges from systems-level interactions among multiple ingredients acting on insect sensory and avoidance mechanisms, while simultaneously maintaining safety relevance for horses, dogs, and cats.

Conventional validation approaches struggle to quantify ingredient synergy, rely heavily on in vivo testing, and offer limited capability to compare pathway-level contributions across ingredients and species in a repeatable, regulatory-aligned manner.

How CytoSolve® Helped

CytoSolve applied its computational systems biology and in silico biosimulation platform to perform ingredient analysis and combination screening for both Redoxx® and Bug Check®.

For Redoxx®, CytoSolve® constructed an ingredient-centered biological network spanning oxidative stress regulation, mitochondrial respiration, ATP synthesis, and antioxidant defense systems. Individual ingredients were computationally mapped to molecular targets and biological pathways, then evaluated collectively using in silico simulations to assess how ingredient interactions influence energy metabolism and redox balance. This systems architecture enabled quantitative assessment of synergistic and complementary effects beyond single-ingredient activity.

For Bug Check®, CytoSolve® developed an in silico, ingredient-driven repellent architecture modeling insect olfactory sensing, neural signaling, and behavioral avoidance pathways. Multi-ingredient interactions were simulated to evaluate collective repellent efficacy without reliance on single-compound assumptions. In parallel, the platform screened ingredient-pathway alignment against mammalian biological systems relevant to horses, dogs, and cats, supporting safety-relevant interpretation within a unified computational framework.

Key Benefits Realized

  • In silico ingredient-level mechanistic validation confirming systems-level synergy
  • Computational substantiation of mitochondrial energy metabolism and redox homeostasis for Redoxx®
  • In silico validation of multi-pathway, ingredient-driven repellent mechanisms for Bug Check®
  • Cross-species safety relevance addressed through unified computational modeling for horses, dogs, and cats
  • NASC-ready, model-driven substantiation supporting regulatory and quality review
  • Reduced reliance on animal testing through advanced in silico ingredient screening

Outcome

Using CytoSolve®’s computational biosimulation and systems architecture platform, The Natural Vet® achieved robust in silico, ingredient-level validation for both Redoxx® and Bug Check®. The modeling substantiated coordinated combination mechanisms—supporting Redoxx®’s role in mitochondrial energy production and redox balance, and validating Bug Check®’s multi-ingredient repellent activity while addressing cross-species safety considerations. This case study demonstrates how in silico, ingredient-focused systems modeling can de-risk product development, reduce animal testing, and deliver regulatory-ready validation aligned with modern NASC and animal health expectations.