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Figure 1. 

Integrated biological biotypes of depression and their underlying mechanistic pathways. (a) Immuno-metabolic/inflammatory biotype, where peripheral inflammation drives microglial activation, and shifts tryptophan metabolism toward neurotoxic kynurenine metabolites, reducing serotonin; (b) neural circuit/reward-cognitive biotype, characterized by disrupted prefrontal amygdala connectivity, blunted ventral striatum response, and impaired default mode executive network interactions; and (c) genomic/plasticity biotype, marked by genetic risk variants that impair neurotrophic signaling and synaptic plasticity. Each biotype maps to specific therapeutic strategies, anti-inflammatory/kynurenine modulators, circuit targeted neuromodulation, and plasticity enhancing agents, respectively, illustrating a mechanism-based framework for precision treatment in depression.

Figure 2. 

The paradigm shifts toward precision drug development in depression. This framework illustrates the transition from empirical screening to a mechanism based approach across three stages: (a) human-anchored discovery, where large scale genomics (GWAS) and Mendelian randomization replace traditional animal models to identify causally valid targets; (b) mechanism tailored therapeutics, developing specific agents for distinct biotypes, such as anti-inflammatory biologics, rapid acting glutamatergic modulators, and microbiome interventions; and (c) next-generation evidence generation, utilizing biomarker enriched adaptive platform trials and digital endpoints to efficiently validate efficacy in specific patient subgroups.

Figure 3. 

Mechanism-based clinical decision framework for precision treatment in depression.

Figure 4. 

An integrated roadmap for a precision psychiatry ecosystem. This framework illustrates the translational pathway from; (a) discovery and validation, where multi-omics and digital data inform AI-driven biotype identification; to (b) adaptive platform trials, utilizing master protocols to dynamically assign patients to mechanism-based treatments; and finally to (c) AI enabled closed loop care, where real-world clinical outcomes continuously refine predictive models, creating a learning health system that bridges research and clinical practice.