The present study aimed to establish an LC-MS/MS method for the quantification of tiamulin in minipig plasma and to further conduct a pharmacokinetic comparison of different formulations. The plasma samples were extracted with acetonitrile (meloxicam as internal standard), separated on a C18 column, and quantified by multiple reaction monitoring mode (ESI+). Sanyuan minipigs were used as experimental animals. Plasma samples were collected after intravenous injection (10 mg/kg) and intragastric administration (20 mg/kg). The method showed good linearity, with intra- and inter-batch RSD of 1.00%–8.13% and RE within ±15%. The extraction recovery, matrix effect and stability of the analytical methods met the relevant requirements. Tiamulin fumarate active pharmaceutical ingredient was intravenously administered, with c0 of about (4383.73±2676.78) ng/mL, AUC0-t of about (4803.50±965.68) h·ng/mL, t1/2 of about (4.66±1.68) h, and CL of about (2.14±0.46) L/(kg·h). Three tiamulin formulations were intragastrically administered, with cmax of (552.00±328.55), (545.00±136.97) and (590.60±237.02) ng/mL, tmax of (1.47±0.68), (0.69±0.75) and (0.72±0.72) h, and F of 24.85%, 15.28% and 16.97%, respectively. The validated method meets the requirements for biological sample analysis and is applicable for the pharmacokinetic evaluation of tiamulin formulations in minipigs.

BACKGROUND: Retinal ganglion cell (RGC) death caused by acute ocular hypertension is an important characteristic of acute glaucoma. Receptor-interacting protein kinase 3 (RIPK3) that mediates necroptosis is a potential therapeutic target for RGC death. However, the current understanding of the targeting agents and mechanisms of RIPK3 in the treatment of glaucoma remains limited. Notably, artificial intelligence (AI) technologies have significantly advanced drug discovery. This study aimed to discover RIPK3 inhibitor with AI assistance. METHODS: An acute ocular hypertension model was used to simulate pathological ocular hypertension in vivo . We employed a series of AI methods, including large language and graph neural network models, to identify the target compounds of RIPK3. Subsequently, these target candidates were validated using molecular simulations (molecular docking, absorption, distribution, metabolism, excretion, and toxicity [ADMET] prediction, and molecular dynamics simulations) and biological experiments (Western blotting and fluorescence staining) in vitro and in vivo . RESULTS: AI-driven drug screening techniques have the potential to greatly accelerate drug development. A compound called HG9-91-01, identified using AI methods, exerted neuroprotective effects in acute glaucoma. Our research indicates that all five candidates recommended by AI were able to protect the morphological integrity of RGC cells when exposed to hypoxia and glucose deficiency, and HG9-91-01 showed a higher cell survival rate compared to the other candidates. Furthermore, HG9-91-01 was found to protect the retinal structure and reduce the loss of retinal layers in an acute glaucoma model. It was also observed that the neuroprotective effects of HG9-91-01 were highly correlated with the inhibition of PANoptosis (apoptosis, pyroptosis, and necroptosis). Finally, we found that HG9-91-01 can regulate key proteins related to PANoptosis, indicating that this compound exerts neuroprotective effects in the retina by inhibiting the expression of proteins related to apoptosis, pyroptosis, and necroptosis. CONCLUSION AI-enabled drug discovery revealed that HG9-91-01 could serve as a potential treatment for acute glaucoma.
Animals ; Glaucoma/metabolism* ; Neuroprotective Agents/pharmacology* ; Mice ; Receptor-Interacting Protein Serine-Threonine Kinases/metabolism* ; Artificial Intelligence ; Retinal Ganglion Cells/metabolism* ; Disease Models, Animal ; Molecular Docking Simulation ; Mice, Inbred C57BL ; Male

Chronic spontaneous urticaria (CSU) is characterized by recurrent wheals with severe itching, and greatly affects the life quality of patients. The European guideline on chronic urticaria recommends the anti-IgE monoclonal antibody omalizumab as the only third-line therapy for patients with CSU whose condition can not be controlled by high doses of antihistamines. Although a lot of researches have shown that omalizumab is effective and safe for the treatment of CSU, its therapeutic mechanisms have not yet been fully elucidated. This review summarizes therapeutic mechanisms of omalizumab in the treatment of CSU, and indices for predicting and monitoring its clinical efficacy.