CytoSolve® In Silico Systems Architecture Enables Computational Modeling of Relaxation Pathways for Innovation in Harm Reduction Research

Partner Description

British American Tobacco
British American Tobacco (BAT) is a global organization with advanced research and development capabilities focused on understanding biological mechanisms relevant to consumer products and harm-reduction science. BAT’s R&D group applies rigorous analytical, biological, and computational methods to study complex physiological systems, supporting evidence-based innovation and scientific transparency.

Challenge

BAT sought a deeper mechanistic understanding of how multiple bioactive ingredients influence biological systems associated with relaxation and stress modulation. These systems span highly interconnected molecular networks, including neurotransmitter signaling, hormonal regulation, immune responses, and neuromodulatory feedback loops. Traditional experimental approaches are limited in their ability to efficiently evaluate non-linear interactions, combinatorial effects, and system-wide responses across multiple pathways.

BAT required a scalable, quantitative, and fully in silico modeling framework capable of evaluating both individual ingredients and ingredient combinations, while reducing reliance on extensive in vitro and in vivo experimentation and enabling rapid hypothesis testing.

How CytoSolve® Helped

CytoSolve® applied its distributed computational systems biology platform to perform in silico combination screening grounded in peer-reviewed scientific literature.

Six core molecular systems governing relaxation biology were identified and modeled:

  • Gut microbiome–neuroinflammation signaling
  • Hypothalamic–pituitary–adrenal (HPA) axis signaling
  • Brain-derived neurotrophic factor (BDNF) signaling
  • Neurotransmitter pathways involving GABA and serotonin
  • Catecholamine signaling via dopamine
  • Endocannabinoid signaling centered on anandamide (AEA)
Each pathway was independently translated into a validated mathematical model using ordinary differential equations and encoded in SBML format. CytoSolve® then dynamically integrated these individual models into a unified in silico systems architecture, preserving pathway-specific behavior while enabling real-time interaction across the full relaxation network.

Six shortlisted bioactive ingredients were computationally simulated across physiologically relevant dose ranges. The in silico framework quantified ingredient-specific and combination effects on key biomarkers associated with inflammation, cortisol regulation, neurotransmission, and neurotrophic signaling. The architecture was explicitly designed to support future optimization of multi-ingredient formulations based on defined biological objectives.

Key Benefits Realized

  • Systems-level, in silico quantification of ingredient effects across interconnected relaxation pathways
  • Mechanistic clarity into modulation of inflammation, cortisol dynamics, neurotransmission, and neurotrophic signaling
  • Rapid, scalable computational screening of multiple ingredients across six molecular systems
  • Combination-ready modeling framework supporting formulation optimization strategies
  • Reduced dependence on early-stage animal and cell-based experimental studies

Outcome

The CytoSolve® Systems Architecture delivered a comprehensive, mechanistic in silico modeling assessment of bioactive ingredients acting on molecular systems underlying relaxation for British American Tobacco. By integrating multiple biological pathways into a single computational framework, the collaboration enabled data-driven ingredient prioritization and established a scalable foundation for future combination optimization. This case study demonstrates how systems architecture–based in silico modeling provides a rigorous, ethical, and efficient approach for investigating complex biological phenomena within modern, science-driven research and harm-reduction programs.