desonide and Drug-Related-Side-Effects-and-Adverse-Reactions

Low to mid potency corticosteroids remain a cornerstone of therapy for atopic dermatitis (AD). Since AD is most prevalent in the younger pediatric population and is chronic in nature, safety is of particular concern especially for children under 2 years of age. A novel desonide (0.05%) formulation was developed in a nonirritating and moisturizing aqueous gel (hydrogel) that is free of alcohol and surfactants. The safety and efficacy of this new class VI low potency topical steroid was substantiated in 2 phase III clinical trials in mild to moderate AD subjects aged 3 months to 18 years (mean age 6.7 years and 30% under 3 years). A total of 425 subjects were treated with desonide hydrogel and 157 subjects with the hydrogel vehicle. Desonide hydrogel 0.05% was extremely well-tolerated and provided statistically significant improvements in all primary (P < .001) and secondary (P < .006) efficacy endpoints in both studies. This novel desonide formulation represents an advancement in the treatment of AD.

Topics: Adolescent; Anti-Inflammatory Agents; Child; Child, Preschool; Dermatitis, Atopic; Desonide; Double-Blind Method; Drug-Related Side Effects and Adverse Reactions; Female; Humans; Hydrogels; Infant; Male; Steroids; Treatment Outcome

Drug-induced liver injury (DILI) is a significant concern in drug development due to the poor concordance between preclinical and clinical findings of liver toxicity. We hypothesized that the DILI types (hepatotoxic side effects) seen in the clinic can be translated into the development of predictive in silico models for use in the drug discovery phase. We identified 13 hepatotoxic side effects with high accuracy for classifying marketed drugs for their DILI potential. We then developed in silico predictive models for each of these 13 side effects, which were further combined to construct a DILI prediction system (DILIps). The DILIps yielded 60-70% prediction accuracy for three independent validation sets. To enhance the confidence for identification of drugs that cause severe DILI in humans, the "Rule of Three" was developed in DILIps by using a consensus strategy based on 13 models. This gave high positive predictive value (91%) when applied to an external dataset containing 206 drugs from three independent literature datasets. Using the DILIps, we screened all the drugs in DrugBank and investigated their DILI potential in terms of protein targets and therapeutic categories through network modeling. We demonstrated that two therapeutic categories, anti-infectives for systemic use and musculoskeletal system drugs, were enriched for DILI, which is consistent with current knowledge. We also identified protein targets and pathways that are related to drugs that cause DILI by using pathway analysis and co-occurrence text mining. While marketed drugs were the focus of this study, the DILIps has a potential as an evaluation tool to screen and prioritize new drug candidates or chemicals, such as environmental chemicals, to avoid those that might cause liver toxicity. We expect that the methodology can be also applied to other drug safety endpoints, such as renal or cardiovascular toxicity.

Topics: Animals; Anti-Infective Agents; Anti-Inflammatory Agents; Chemical and Drug Induced Liver Injury; Databases, Factual; Drug-Related Side Effects and Adverse Reactions; Humans; Liver; Models, Biological; Predictive Value of Tests