Clinical and Economic Evaluation of Repository Corticotropin Injection: A Narrative Literature Review of Treatment Efficacy and Healthcare Resource Utilization for Seven Key Indications
Bibliographic record
Abstract
Repository corticotropin injection (RCI; H.P. Acthar ® Gel; Mallinckrodt Pharmaceuticals Inc., Hampton, NJ) is a highly purified, prolonged-release porcine preparation of adrenocorticotropic hormone (ACTH) analogue that is FDA-approved for treatment of 19 autoimmune and inflammatory disorders. The diverse physiological actions of RCI at the melanocortin receptors (MCRs) affect processes involved in inflammation, pigmentation, steroidogenesis, and immunomodulation. Although RCI has been approved to treat inflammatory and autoimmune diseases for more than 60 years, recent progress in understanding both MCRs and the effects of RCI in modulating immune responses has led to increased interest in RCI as a therapeutic choice. The objective of this narrative literature review is to summarize key clinical and economic data on RCI treatment of seven disorders: infantile spasms (IS), multiple sclerosis (MS) relapses, proteinuria in nephrotic syndrome, rheumatoid arthritis (RA), dermatomyositis/polymyositis (DM/PM), systemic lupus erythematosus (SLE), and symptomatic sarcoidosis based on published literature and product information. An extended report is available as the Academy of Managed Care Pharmacy (AMCP) Formulary dossier for H.P. Acthar ® Gel. Key studies of clinical efficacy and healthcare utilization and cost from 1956 to 2016 are summarized. The evidence supports the efficacy of RCI across the seven indications. RCI is effective as a first-line therapy for IS. For the other six conditions, RCI may improve clinical outcomes during exacerbations or when the condition is resistant to conventional treatments. Use of RCI is associated with reduced use of biologics, corticosteroids, and disease-modifying antirheumatic drugs. Initiation of RCI therapy in patients with IS, MS, RA, SLE, or DM/PM has been associated with lower post-therapy healthcare utilization and medical costs, including decreases in hospitalizations, hospital length of stay, outpatient visits, and emergency department visits. The evidence suggests that RCI may improve inflammatory and autoimmune disease control and patient quality of life, particularly in complex patients, and yield healthcare cost savings that demonstrate the medicine’s value. Mallinckrodt Pharmaceuticals Inc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".