Combination Screening Case Study: CytoSolve® Molecular Digital Twin Enables Ingredient Combination Screening for ALS Biomarkers and Disease Bioprocesses

Gregg Bonheur
Gregg Bonheur is an amyotrophic lateral sclerosis (ALS) patient whose case became the first patient-specific Molecular Digital Twin developed using CytoSolve® technology. His participation marked a foundational advance in applying mechanistic in silico modeling directly to an individual patient context, enabling personalized exploration of disease biology and therapeutic response.

Challenge

ALS is driven by intertwined pathological bioprocesses, including neuroinflammation, oxidative stress, mitochondrial dysfunction, impaired proteostasis, and disrupted neuronal signaling. These processes collectively influence disease biomarkers and clinical progression. While individual bioactive ingredients may show isolated effects on specific pathways, ALS progression is governed by system-wide interactions, making it difficult to predict how ingredient combinations will jointly affect biomarkers and disease-driving bioprocesses.

Traditional experimental approaches are poorly suited to screen ingredient combinations efficiently, especially in a patient-specific context. Clinical trials are slow and expensive, and animal models often fail to reflect human ALS biology. A new approach was required to screen ingredient combinations mechanistically, quantify their effects on ALS biomarkers, and evaluate their efficacy across multiple disease bioprocesses within a single, integrated framework.

How CytoSolve Helped

CytoSolve® created the first patient-specific Molecular Digital Twin™ for ALS, serving as a mechanistic platform for combination screening of bioactive ingredients.

Rather than relying on statistical correlations, the digital twin explicitly simulated molecular kinetics and biochemical interactions underlying ALS pathology. Independent, evidence-based pathway modules representing inflammation, oxidative stress, mitochondrial energy metabolism, neuronal signaling, and protein aggregation were executed in parallel and dynamically linked within CytoSolve®’s distributed systems architecture.

Within this digital twin environment, individual ingredients were introduced as mechanistic perturbations acting on defined molecular targets. Crucially, ingredient combinations were screened as unified interventions, allowing the model to capture non-linear and synergistic effects on ALS-relevant biomarkers and bioprocesses. The system quantified how combinations influenced key disease indicators such as inflammatory mediators, oxidative stress markers, mitochondrial efficiency, and neuronal survival signaling.

This approach enabled virtual combination screening to assess:

  • Whether ingredient combinations produced additive, synergistic, or antagonistic effects
  • How combinations influenced multiple ALS biomarkers simultaneously
  • The degree to which combined interventions altered core disease bioprocesses rather than isolated pathways
All screening was performed in silico, allowing rapid iteration and exploration of combination strategies without physical experimentation.

Key Benefits Realized

  • Patient-specific combination screening using a mechanistic Molecular Digital Twin
  • Quantitative evaluation of ingredient combination effects on ALS biomarkers
  • Systems-level assessment of efficacy across interconnected ALS bioprocesses
  • Identification of synergistic versus non-beneficial ingredient combinations
  • Ability to explore virtual intervention strategies and timing scenarios
  • Reduced reliance on animal models and early-stage human testing

Outcome

The CytoSolve® Molecular Digital Twin of Gregg Bonheur demonstrated that combination screening of bioactive ingredients can be performed mechanistically within a patient-specific, in silico environment. By evaluating ingredient combinations against ALS biomarkers and disease-driving bioprocesses simultaneously, the platform enabled a level of insight not achievable through single-ingredient or single-pathway analysis. This case study establishes a new paradigm for precision combination screening in neurodegenerative disease—using digital twins to explore efficacy, synergy, and biological impact before advancing to real-world testing.