Background: Curcumin has a wide spectrum of biological and pharmacological activities, but it has not yet been approved as a therapeutic agent because of its low solubility and stability in aqueous solution, and the relatively low bioavailability in vivo. To overcome these limitations, self-microemulsifying drug delivery system (SMEDDS) of curcumin was developed. Method: Various oils, surfactants, and cosurfactants were selected to optimize the formulation. Pseudoternary phase diagrams were constructed and orthogonal design was used to compare the oil-in-water (o/w) microemulsion-forming capacity of different oils/surfactants/cosurfactants. The solubility of curcumin in various oils and cosurfactants was determined to find suitable ingredients with a good solubilizing capacity. Droplet size was measured to obtain the concentration of oil, surfactant, and cosurfactant for forming stable microemulsion. Furthermore, its quality and bioavailability in mice were assessed. Results: Pseudoternary phase diagrams and solubility test showed that the formulation of SMEDDS composed of 20% ethanol, 60% Cremophor RH40®, and 20% isopropyl myristate, in which the concentration of curcumin reached 50 mg/mL. Curcumin was released completely from SMEDDS at 10 minutes. The developed SMEDDS formulation improved the oral bioavailability of curcumin significantly, and the relative oral bioavailability of SMEDDS compared with curcumin suspension was 1213%. Conclusion: The SMEDDS can significantly increase curcumin dissolution in vitro and bioavailability in vivo.

Background: Establishing whether there is a potential relationship between glucagon-like peptide 1 receptor agonists (GLP-1RAs) and suicidal or self-injurious behaviors (SSIBs) is crucial for public safety. This study investigated the potential association between GLP-1RAs and SSIBs by exploring the FDA Adverse Event Reporting System (FAERS) database. Methods: A disproportionality analysis was conducted using post-marketing data from the FAERS repository (2018 Q1 to 2022 Q4). SSIB cases associated with GLP-1RAs were identified and analyzed through disproportionality analysis using the information component. The parametric distribution with a goodness-of-fit test was employed to analyze the time-to-onset, and the Ω shrinkage was used to evaluate the potential effect of co-medication on the occurrence of SSIBs. Results: In total, 204 cases of SSIBs associated with GLP-1RAs, including semaglutide, liraglutide, dulaglutide, exenatide, and albiglutide, were identified in the FAERS database. Time-of-onset analysis revealed no consistent mechanism for the latency of SSIBs in patients receiving GLP-1RAs. The disproportionality analysis did not indicate an association between GLP-1RAs and SSIBs. Co-medication analysis revealed 81 cases with antidepressants, antipsychotics, and benzodiazepines, which may be proxies of mental health comorbidities. Conclusions: We found no signal of disproportionate reporting of an association between GLP-1RA use and SSIBs. Clinicians need to maintain heightened vigilance on patients premedicated with neuropsychotropic drugs. This contributes to the greater acceptance of GLP-1RAs in patients with type 2 diabetes mellitus or obesity. Graphical

Isavuconazole may be useful in treating and preventing fungal infections in solid-organ transplant (SOT) recipients due to its safety profile and activity against Aspergillus and some Mucorales . Isavuconazole has favorable pharmacokinetics based on clinical trials in various patient populations, but data are limited in SOT recipients.

Triazole antifungal drugs (TAD) are widely used to treat invasive fungal infections due to their broad antifungal spectrum and low toxicity. Despite their preference in the clinic, multiple Adverse Events (AE) are still reported each year. Objective: We aimed to characterize the distribution of Adverse Events associated with Triazole antifungal drugs in different systems and to identify Important Medical Events (IME) signals for Triazole antifungal drugs. Methods: The U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) was queried for Adverse Events related to Triazole antifungal drugs from 2012 to 2022. The Adverse Events caused by all other drugs and non-TAD antifungal drugs were analyzed as references. Reporting odds ratio and Bayesian confidence propagation neural network of information components were used to evaluate the association between Triazole antifungal drugs and Important Medical Events. Visual signal spectrum is mapped to identify potential adverse reaction signals. Results: Overall, 10,262 Adverse Events were reported to be associated with Triazole antifungal drugs, of which 5,563 cases were defined as Important Medical Events. Common adverse drug reactions (ADR) mentioned in the instructions such as delirium and hypokalemia were detected, as well as unlabeled ADRs such as rhabdomyolysis and hepatitis fulminant. Cholestasis, drug-induced liver injury, QT interval prolongation and renal impairment have notable signals in all Triazole antifungal drugs, with 50 percent of patients developing a severe clinical outcome. Isavuconazole had the lowest signal intensity and demonstrated a superior safety profile. Conclusion: Most results are generally consistent with previous studies and are documented in the prescribing instructions, but some IMEs are not included, such as hepatitis fulminant. Additional pharmaco-epidemiological or experimental studies are required to validate the small number of unlabeled ADRs. TAD-related Important Medical Eventshave a considerable potential to cause clinically serious outcomes. Clinical use of Triazole antifungal drugs requires more attention.

Isavuconazole (ISA) is a triazole antifungal with activity against yeasts and molds. We established a population pharmacokinetic (pop PK) model of intravenous (i.v.) ISA to identify covariates that affect pharmacokinetics, using plasma samples from solid-organ transplant (SOT) recipients receiving peritransplant prophylaxis. Samples ( n = 471) from 79 SOT recipients were utilized for pop PK analysis using nonlinear mixed-effect modeling NONMEM software.