Model-Predicted Treatment Candidates

Combining sensitivity analysis (Section Global Sensitivity Analysis and Drug Target Ranking), controllability analysis (Section Network Controllability and Minimum Intervention Sets), and the pharmacodynamic models from earlier chapters, the integrated model generates specific treatment predictions. Table Model-Predicted Treatment Candidates summarizes candidates organized by target parameter and predicted mechanism. For a clinical-translation map that pairs each driver parameter with sustaining-loop description, evidence-tiered intervention list, and recovery-timescale classification, see Section Primary Mechanism Map: Causal Loops, Detection, and Targeted Interventions.

Model-predicted treatment candidates ranked by global sensitivity index. *Model-unique* column indicates whether the prediction requires the mathematical model or could be derived from qualitative reasoning alone.
Target	Agent	Predicted mechanism	$S_T$	Model-unique?
$\alpha_\text{CI}$	CoQ10/ubiquinol	Restore ETC electron transfer	0.22	Dose threshold
$\alpha_\text{CI}$	Methylene blue	Alternative electron carrier bypassing CI	0.22	Bypass kinetics
$\alpha_\text{CI}$	NR/NMN	Increase NAD⁺/NADH ratio	0.22	Carnitine vs NAD⁺ bottleneck ID
$P_0$ (BBB)	Palmitoylethanolamide	Reduce BBB permeability via PPAR-$\alpha$	0.14	Combination-only value
$P_0$ (BBB)	Luteolin	Mast cell stabilization + BBB protection	0.14	Dual-target synergy
$k_\text{exh}$	LDN	Modify immune exhaustion dynamics	0.12	Rebound timing
$k_\text{exh}$	Daratumumab	Deplete CD38⁺ plasma cells (Eq. daratumumab)	0.12	Response delay prediction
$n_F$ (HPA)	Low-dose hydrocortisone	Partial HPA axis restoration	0.10	Taper protocol optimization
$K_\text{MC}$	Cromolyn sodium	Raise mast cell activation threshold	0.08	Monotherapy futility prediction
$K_\text{MC}$	Ketotifen	H1 antagonism + mast cell stabilization	0.08	Histamine loop gain reduction
$\beta_\text{epoxy}$	Nattokinase	Reduce microclot burden	0.07	VO₂ improvement quantification
$\beta_\text{epoxy}$	Triple anticoagulation	Fibrinolysis + antiplatelet + anticoagulant	0.07	Multiplicative O₂ restoration
BH4 synthesis	Sapropterin	Exogenous BH4 supplementation	0.05	Three-way competition resolution
BH4 synthesis	Folinic acid	Support BH4 recycling via DHFR	0.05	Cofactor redistribution
$\mathcal{G}_\text{set}$	Prucalopride	$ 5 _4$ agonist restores MMC cycling	0.04	SIBO recurrence prevention
$\mathcal{G}_\text{set}$	Rifaximin	Reduce $B_\text{SI}$ directly ($\delta_\text{Abx}$ term)	0.04	Relapse without prokinetic

1 Synergy Matrix for Combination Therapy

The model enables systematic evaluation of pairwise treatment synergies by simulating combination effects and comparing to the sum of individual effects. Define the synergy coefficient for treatments $A$ and $B$:

\[ \mathcal{S}_{A B} = \frac{\Delta Y_{A+B}}{\Delta Y_A + \Delta Y_B} - 1 \tag{1}\]

where $\Delta Y$ is the improvement in symptom composite. $\mathcal{S}_{A B} > 0$ indicates synergy (superadditive), $\mathcal{S}_{A B} = 0$ indicates additivity, and $\mathcal{S}_{A B} < 0$ indicates antagonism. The predicted synergy matrix for the top treatment pairs is:

Strongest predicted synergies ($\mathcal{S} > 0.3$):
- CoQ10 + nattokinase ($\mathcal{S} \approx 0.45$): mitochondrial capacity $\\times$ oxygen delivery—multiplicative because O₂ is substrate for the enhanced ETC
- LDN + low-dose hydrocortisone ($\mathcal{S} \approx 0.38$): immune modulation $\\times$ HPA restoration—reduces both inflammatory drive and cortisol-mediated immune dysregulation
- Sapropterin + LDN ($\mathcal{S} \approx 0.35$): BH4 repletion $\\times$ reduced immune BH4 consumption—LDN reduces iNOS-driven BH4 oxidation (Equation bh4 dynamics), making exogenous BH4 more effective
Predicted antagonisms ($\mathcal{S} < -0.1$):
- High-dose antioxidants + exercise therapy ($\mathcal{S} \approx -0.25$): antioxidants blunt the ROS signaling required for exercise-induced mitochondrial biogenesis (Equation biogenesis)
- Immunosuppressants + immune checkpoint activators ($\mathcal{S} \approx -0.40$): opposing mechanisms on the same pathway

Model Insight: Synergy Prediction Requires Nonlinear Models

Pairwise synergy coefficients are fundamentally unpredictable without a mechanistic model. Clinical intuition based on “different targets should combine well” fails to capture the nonlinear interactions: CoQ10 + nattokinase shows high synergy because oxygen delivery and ETC capacity multiply (not add) in the ATP production equation, but CoQ10 + NMN shows lower synergy ($\mathcal{S} \approx 0.12$) because both target the same bottleneck (ETC electron flow) from different angles. The antagonism prediction for high-dose antioxidants + exercise is particularly clinically relevant: it suggests that patients attempting graded exercise therapy while taking high-dose vitamin C or E may be inadvertently blocking the beneficial mitochondrial adaptation pathway. This prediction arises directly from the ROS dual-role captured in the biogenesis equation and could not be anticipated without quantitative modeling.