Gout therapeutics and drug delivery

Peng, Xiuju; Li, Xiaotong; Xie, Bing; Lai, Yaoyao; Sosnik, Alejandro; Boucetta, Hamza; Chen, Zhongjian; He, Wei

doi:10.1016/j.jconrel.2023.09.011

Cited by 15 publications

(3 citation statements)

References 393 publications

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“…Typically, gout patients are associated with comorbidities, involving hypertension, diabetes, CKD, cardiovascular disease, and obesity . It is recommended to initiate gout treatment as early as possible before the symptoms further worsen, which may cause physical disability and subsequently affect life quality. , Some reported studies provide insight into pathological characteristics, therapeutic targets, and advanced drug delivery systems for improved gout treatment. − …”

Section: Treatment Strategies For Goutmentioning

confidence: 99%

Gout Management Using Nanocarrier Systems: A Review

Qi,

Mohd Nordin,

Mahmood

et al. 2024

ACS Appl. Nano Mater.

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Gout is an inflammatory arthropathy resulting from the deposition of monosodium urate crystals in and around joints, triggering the release of pro-inflammatory cytokines. Some of the key principles for managing gout are encompassing comorbidity screening, gout flare treatment, urate-lowering therapy, and antiinflammatory prophylaxis. One of the major concerns with gout treatment is the side effects of drug due to uncontrolled biodistribution. Nanocarrier systems are utilized to surmount the limitations of current drugs, including not only biodistribution issues but also the stability and bioavailability of the drugs. Nanocarriers as promising drug delivery systems can effectively deliver drugs by specifically targeting inflamed tissues through enhanced permeability and retention effect mediated delivery. Notable examples include liposomes, niosomes, ethosomes, transfersomes, solid lipid nanoparticles, polymeric nanoparticles, nanoemulsions, and liquid crystalline nanoparticles, namely, cubosomes, hexosomes, and spongosomes. These nanocarriers hold great promise as vehicles for transporting poorly therapeutic agents to precise target sites, associated with controlled release capabilities to bolster bioavailability. Preclinical studies have demonstrated the efficacy of nanocarrier-based therapies in reducing inflammation by specifically targeting inflamed tissues, inhibiting urate crystal formation, and improving joint function in animal models of gout. In this review, we explore the main nanocarrier systems, including their respective advantages and drawbacks, and emphasize their role in nanoformulations for advancing gout treatment. The encapsulation of active agents within nanocarriers for gout therapy represents a significant advancement in the pharmaceutical area and nanomedicine application to succeed in the overall context of gout treatment in the near future.

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Section: Treatment Strategies For Goutmentioning

confidence: 99%

Gout Management Using Nanocarrier Systems: A Review

Qi,

Mohd Nordin,

Mahmood

et al. 2024

ACS Appl. Nano Mater.

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“…Gout is a metabolic disease caused by the accumulation of urate crystals in the joints of the limbs as well as in the tissues and organs ( 1 ). Hyperuricemia is defined when the blood uric acid (UA) concentration is greater than 7.0 mg/dl for men and 6.0 mg/dl for women, the blood is oversaturated with UA, and urate crystals begin to crystallize, making hyperuricemia the most critical risk factor for gout ( 2 ).…”

Section: Introductionmentioning

confidence: 99%

Creating machine learning models that interpretably link systemic inflammatory index, sex steroid hormones, and dietary antioxidants to identify gout using the SHAP (SHapley Additive exPlanations) method

Cao,

2024

Front. Immunol.

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BackgroundThe relationship between systemic inflammatory index (SII), sex steroid hormones, dietary antioxidants (DA), and gout has not been determined. We aim to develop a reliable and interpretable machine learning (ML) model that links SII, sex steroid hormones, and DA to gout identification.MethodsThe dataset we used to study the relationship between SII, sex steroid hormones, DA, and gout was from the National Health and Nutrition Examination Survey (NHANES). Six ML models were developed to identify gout by SII, sex steroid hormones, and DA. The seven performance discriminative features of each model were summarized, and the eXtreme Gradient Boosting (XGBoost) model with the best overall performance was selected to identify gout. We used the SHapley Additive exPlanation (SHAP) method to explain the XGBoost model and its decision-making process.ResultsAn initial survey of 20,146 participants resulted in 8,550 being included in the study. Selecting the best performing XGBoost model associated with SII, sex steroid hormones, and DA to identify gout (male: AUC: 0.795, 95% CI: 0.746- 0.843, accuracy: 98.7%; female: AUC: 0.822, 95% CI: 0.754- 0.883, accuracy: 99.2%). In the male group, The SHAP values showed that the lower feature values of lutein + zeaxanthin (LZ), vitamin C (VitC), lycopene, zinc, total testosterone (TT), vitamin E (VitE), and vitamin A (VitA), the greater the positive effect on the model output. In the female group, SHAP values showed that lower feature values of E2, zinc, lycopene, LZ, TT, and selenium had a greater positive effect on model output.ConclusionThe interpretable XGBoost model demonstrated accuracy, efficiency, and robustness in identifying associations between SII, sex steroid hormones, DA, and gout in participants. Decreased TT in males and decreased E2 in females may be associated with gout, and increased DA intake and decreased SII may reduce the potential risk of gout.

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“…Gout is one of the most common forms of inflammatory arthritis caused by a sustained elevation of serum uric acid (UA) above the dissolved saturation level of 408 µmol/L, leading to an inflammatory response and pain due to the precipitation of sodium urate crystals and their deposition in the joints and soft tissues [ 1 ]. Epidemiologic surveys have shown that the prevalence of gout is 2.7–6.7% in countries with a Western lifestyle and that the heritability of hyperuricemia and gout ranges from 27 to 41% worldwide to about 30% in Europe [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index

Cao,

2024

Nutr Metab (Lond)

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Background Gout prediction is essential for the development of individualized prevention and treatment plans. Our objective was to develop an efficient and interpretable machine learning (ML) model using the SHapley Additive exPlanation (SHAP) to link dietary fiber and triglyceride-glucose (TyG) index to predict gout. Methods Using datasets from the National Health and Nutrition Examination Survey (NHANES) (2005–2018) population to study dietary fiber, the TyG index was used to predict gout. After evaluating the performance of six ML models and selecting the Light Gradient Boosting Machine (LGBM) as the optimal algorithm, we interpret the LGBM model for predicting gout using SHAP and reveal the decision-making process of the model. Results An initial survey of 70,190 participants was conducted, and after a gradual exclusion process, 12,645 cases were finally included in the study. Selection of the best performing LGBM model for prediction of gout associated with dietary fiber and TyG index (Area under the ROC curve (AUC): 0.823, 95% confidence interval (CI): 0.798–0.848, Accuracy: 95.3%, Brier score: 0.077). The feature importance of SHAP values indicated that age was the most important feature affecting the model output, followed by uric acid (UA). The SHAP values showed that lower dietary fiber values had a more pronounced effect on the positive prediction of the model, while higher values of the TyG index had a more pronounced effect on the positive prediction of the model. Conclusion The interpretable LGBM model associated with dietary fiber and TyG index showed high accuracy, efficiency, and robustness in predicting gout. Increasing dietary fiber intake and lowering the TyG index are beneficial in reducing the potential risk of gout.

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Gout therapeutics and drug delivery

Cited by 15 publications

References 393 publications

Gout Management Using Nanocarrier Systems: A Review

Gout Management Using Nanocarrier Systems: A Review

Creating machine learning models that interpretably link systemic inflammatory index, sex steroid hormones, and dietary antioxidants to identify gout using the SHAP (SHapley Additive exPlanations) method

Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index

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