Deciphering Cannabidiol Neuroregulatory Role in Addiction Pathways , A Systems Level Comparison with THC via Intrinsic Network Pharmacology

Cannabidiol (CBD), a nonpsychoactive phytocannabinoid from Cannabis sativa, has demonstrated potent neuroprotection in a number of neuropsychiatric disorders. Unlike {Delta}9-tetrahydrocannabinol (THC), which acts to stimulate the brains reward circuitry through CB1 receptor activation, CBD seems to stabilize several neuroregulatory circuits without causing addictive reinforcement. Yet a mechanistic systems-level distinction between these two cannabinoids is relatively unexplored. This research uses an eight-layer Intrinsic Network Pharmacology (INP) system to model and compare the molecular effects of CBD and THC on addiction-related networks, combining computational modeling and network pharmacology. The INP system includes molecular trigger mapping, feedback loop dynamics, redox balance, immune signaling, autophagy repair, therapeutic fit, dynamic simulations, and multi-target synergy overlays. In this model, we replicated the action of CBD on major molecules such as Nrf2, ROS, IL-6, dopamine (DA), D2 receptor, and BDNF. Ordinary differential equation (ODE) models were employed to model time-dependent transitions, whereas Boolean logic models simulated binary molecular switches. Comparative reward pathway diagrams were drawn to represent divergent neuropharmacological cascades triggered by CBD and THC. Our simulations show that CBD triggers redox restoration, inhibits inflammatory cytokines, and induces dopaminergic and synaptic stability. Withdrawal simulation also showed that relapse of the partial system could be triggered if CBD is withdrawn suddenly, substantiating its status as a homeostatic stabilizer and not a suppressive one. Network pharmacology fit scoring exhibited CBDs high concordance with antioxidant and neuroplasticity targets, whereas THC mostly mapped onto reward-amplifying nodes. Though these results are hypothesis-driven and computational, they also offer a useful framework for the experimental validation. This work illustrates the utility of INP-based simulations in deciphering polypharmacological drugs such as CBD and presents a scalable paradigm for assaying future candidates for neurotherapeutic use in the study of addiction.