Comparative effectiveness of common therapies for Wilson disease: A systematic review and meta‐analysis of controlled studies

Abstract

Background & aims Wilson disease (WD) is a rare disorder of copper metabolism. The objective of this systematic review was to determine the comparative effectiveness and safety of common treatments of WD. Methods We included WD patients of any age or stage and the study drugs D‐penicillamine, zinc salts, trientine and tetrathiomolybdate. The control could be placebo, no treatment or any other treatment. We included prospective, retrospective, randomized and non‐randomized studies. We searched Medline and Embase via Ovid, the Cochrane Central Register of Controlled Trials, and screened reference lists of included articles. Where possible, we applied random‐effects meta‐analyses. Results The 23 included studies reported on 2055 patients and mostly compared D‐penicillamine to no treatment, zinc, trientine or succimer. One study compared tetrathiomolybdate and trientine. Post‐decoppering maintenance therapy was addressed in one study only. Eleven of 23 studies were of low quality. When compared to no treatment, D‐penicillamine was associated with a lower mortality (odds ratio 0.013; 95% CI 0.0010 to 0.17). When compared to zinc, there was no association with mortality (odds ratio 0.73; 95% CI 0.16 to 3.40) and prevention or amelioration of clinical symptoms (odds ratio 0.84; 95% CI 0.48 to 1.48). Conversely, D‐penicillamine may have a greater impact on side effects and treatment discontinuations than zinc. Conclusions There are some indications that zinc is safer than D‐penicillamine therapy while being similarly effective in preventing or reducing hepatic or neurological WD symptoms. Study quality was low warranting cautious interpretation of our findings.

Switching Pharmacological Treatment in Wilson Disease: Case Report and Recommendations

“…While several studies have been done regarding the efficacy of chelating agents 13 and zinc, as well as comparing the various agents available for treatment of WD, 14,15,34,37 there is little to no commentary on dose adjustments or the most effective way to transition between different agents if necessary. As demonstrated by our patient above, patients may be transitioned between therapies for various reasons, whether it be inability to tolerate therapy due to adverse effects, ineffective treatment, comorbidities, or financial concerns.…”

“…9,31 In our patient, despite compliance with zinc therapy, he had rising liver function tests, suggesting ineffective maintenance therapy with zinc. It has also been suggested that zinc may be as effective as chelators as initial therapy, 15,[32][33][34] especially in patients who are unable to tolerate chelating agents, who have significant neuropsychiatric disease, or in patients who are presymptomatic.…”

Application of 9.4T MRI in Wilson Disease Model TX Mice With Quantitative Susceptibility Mapping to Assess Copper Distribution

“…Meanwhile, copper can bind to drugs in the bloodstream, mediate their passage through the damaged blood-brain barrier, and ultimately aggravate braintissue damage (Stuerenburg, 2000). Another hypothesis states that since the deposition of copper in nerve tissue may not be reversed in some patients with WD (even with the use of copperloading drugs, such as PCA) the neurological symptoms of these patients are irreversible, and PCA may even accelerate them (Appenzeller-Herzog et al, 2019).…”

Active learning for screening prioritization in systematic reviews - A simulation study

“…The Wilson dataset [50] -from the field of medicine -is from a review on the effectiveness and safety of treatments of Wilson Disease, a rare genetic disorder of copper metabolism [33]. From the same field, the ACE dataset contains publications on the efficacy of Angiotensin-converting enzyme (ACE) inhibitors, a treatment drug for heart disease [32].…”

“…Models were assessed by conducting a simulation on six systematic review datasets. To assess generalizability of the models across research contexts, datasets containing previous systeamtic reviews were collected from the fields of medical science [32,33,34], computer science [11], and social science [35,36]. The models, datasets and simulations are implemented in a pipeline of active learning for screening prioritization, called…”