Editorial: Model organisms in neuropharmacology 2024

the same systems that have driven foundational advances also define many of the field's current limitations. Despite transformative progress in genetics, imaging, and systems-level neuroscience, translating preclinical findings into effective and safe therapies for brain disorders remains limited [3].While neurodegenerative and neuropsychiatric diseases continue to impose a substantial global burden [4], prolonged timelines and escalating costs of preclinical studies, as well as high rates of clinical failure, delay central nervous system drug development. These challenges reflect not only the intrinsic complexity of the brain but also the selection, validation, and integration of experimental models across the preclinical pipeline.As neuropsychiatric disorders emerge from dynamic interactions among genetic susceptibility, biological sex, early-life development, environmental factors, stress, and aging, no single experimental model can capture this multidimensional complexity in full. At the same time, increasing pressure to reduce animal use and concern over the environmental persistence of neuroactive compounds demand approaches that are both scientifically rigorous and ethically responsible. Consequently, a shift away from model-centric thinking toward strategies that explicitly align specific organisms with clearly defined experimental goals is both necessary and timely.Model Organisms in Neuropharmacology was designed to illustrate how mammalian and nonmammalian systems can be used in a complementary way to enhance understanding, ensure translational relevance, and promote responsible research practices. Across the collection, model organisms are presented as interconnected systems that enable the investigation of different neuropharmacological processes at molecular, cellular, behavioral, and organismal levels. Central to this approach is the consideration of experimental validity.Face validity reflects phenotypic similarity to human disease, construct validity denotes correspondence with underlying pathophysiology, and predictive validity captures the ability of a model to forecast therapeutic efficacy. Although these dimensions of validity are analytically distinct, they often overlap in practice and are most informative when considered together. However, because no single model can fully recapitulate all three dimensions, cross-species integration is required (Figure 1). In this Topic, model organisms are strategically adopted to address distinct dimensions of experimental validity and to refine hypotheses across molecular, circuit, and behavioral levels. This framework provides the context for the individual contributions that follow, each of which addresses a specific translational bottleneck in neuropharmacology.One such bottleneck is the limited translation of molecular and circuit-level findings into meaningful functional and cognitive outcomes. This challenge is addressed in the systematic review by Foo et al., which synthesizes evidence from rodent models of genetic epilepsy and demonstrates that neurocognitive and affective impairments are common, yet highly dependent on genotype and developmental stage. By extending analysis beyond seizure-centric phenotypes, this work strengthens face validity and underscores the importance of incorporating behavioral and cognitive endpoints when evaluating disease mechanisms and therapeutic efficacy. Notably, the genotype-and developmentdependent variability highlighted in this review also emphasizes the value of scalable screening platforms, such as larval zebrafish seizure paradigms, for early-stage compound prioritization across developmental windows prior to targeted validation in mammalian systems. Thus, by aligning experimental models with explicit validity goals, this Topic demonstrates that organisms are most informative when strategically integrated within a framework that balances face, construct, and predictive validity, thereby defining a rigorous and scalable path toward neuropharmacological research that is more efficient, mechanistically precise, and genuinely translational.