Interest in the role of metabolism in psychiatric disorders and their treatment is rapidly expanding, driven by emerging evidence that neurometabolic dysfunction contributes to the pathophysiology of conditions such as depression, bipolar disorder, and schizophrenia. This chapter provides an overview of strategies for designing mechanistically informed studies and clinical trials that evaluate metabolic interventions in psychiatric populations. Discussion includes the importance of integrating biomarkers, neuroimaging, and computational modeling to identify and stratify patient subgroups most likely to benefit from metabolic therapies. Key considerations for developing proof-of-concept and efficacy studies are presented, including optimal study design, statistical and mechanistic modeling, biomarker selection, and the alignment of mechanistic hypotheses with clinical endpoints. By adopting a translational framework that connects experimental data, clinical observations, and systems-level mathematical modeling, researchers can more effectively evaluate the therapeutic potential of metabolic interventions. This roadmap aims to accelerate the development of personalized and data-driven treatment strategies and establish a rigorous evidence base for incorporating metabolic targets into psychiatric care.