Kinetics and mechanisms in lipopolysaccharide-induced neuroinflammation: a framework for phytochemicals screening.

Neuroinflammation is an innate immune response of the central nervous system in which microglia, astrocytes and neuronal stress pathways regulate the production of cytokines, inflammatory enzymes and redox mediators. Among experimental paradigms, lipopolysaccharide (LPS) provides a stimulus for probing inflammatory signalling and for evaluation of phytochemicals. However, heterogeneity in LPS provenance, chemotype, purification grade, dose, route, exposure duration and endpoint timing compromises cross-study comparability, mechanistic attribution and translational interpretation. This review synthesises evidence from in vitro and in vivo LPS-induced neuroinflammation models used for phytochemical screening and proposes a kinetics- and mechanism-informed framework for experimental design. In vitro, murine microglial systems predominate, particularly BV-2, N9 and primary microglia, with complementary use of astroglia, neuronal and human myeloid or microglia-like platforms. LPS concentrations range from 100 ng/mL to 1 µg/mL, with signalling activation occurring within minutes to hours and inflammatory phenotypes typically assessed at 24-48 h. Frequently used pharmacodynamic readouts include tumour necrosis factor-α, interleukin-1β, interleukin-6, inducible nitric oxide synthase/nitric oxide, cyclooxygenase-2/prostaglandin E2, reactive oxygen species, glial activation markers and viability indices, combined with mechanistic interrogation of NF-κB, MAPK, PI3K-Akt, Nrf2-HO-1 and NLRP3-associated pathways. In vivo, repeated intraperitoneal LPS administration predominates in rodent models, whereas intracerebroventricular and zebrafish models provide complementary spatial and kinetic insights. Overall, LPS challenge should be interpreted as a controlled inflammatory trigger rather than a disease-equivalent surrogate. Rigorous LPS reporting, closer alignment of endpoints with inflammatory kinetics, and multi-endpoint validation across models should strengthen mechanistic inference and support translational evaluation of phytochemical leads.