The New Model of Prioritizing Orphan Drugs: The Case Study for Hemophilia, SMA, CF and MS

Background: Rare diseases, characterized by low prevalence and high complexity, pose significant challenges to health systems due to the uncertainty surrounding the best diagnostic methods and availability of effective treatments. Objectives: This study aimed to introduce new methods for prioritizing orphan drugs, with a pilot application to hemophilia, spinal muscular atrophy (SMA), cystic fibrosis (CF), and multiple sclerosis (MS). Methods: This quantitative research, conducted at Iran’s National Institute for Health Research from 2021 to 2023, employs multi-criteria decision-making models (MCDA) to evaluate the efficacy of health care technologies for "rare and hard to cure" diseases. A preliminary model was developed based on a pilot selection of seven medications, for which comprehensive data was extracted. The clinical efficacy was assessed using quality adjusted life years (QALY) as a metric. The model design was grounded on three critical factors: The annual cost of intervention per individual, the annual number of eligible patients for intervention, and the Proportion of expenses covered by the governmental budget. Additionally, the model incorporated various constraints and a regulatory coefficient, denoted as “w,” to enhance its robustness. Results: By running the model, the coverage of selected medications through model optimization, revealing the following percentages: Alemtuzumab (30%), ocrelizumab (30%), emicizumab (6%), dornase alfa (29%), tobramycin (2%), and spinraza (0.02%). Additionally, the corresponding monetary coverage in Iranian Rials is reported as follows: Alemtuzumab (27,144,000,000 IRR), ocrelizumab (109,645,200,000 IRR), emicizumab (17,360,490,000 IRR), dornase alfa (43,350,930,000 IRR), tobramycin (1,268,505,000 IRR), and spinraza (350,000,000 IRR). Conclusions: This model has tried to solve the shortcomings of the existing models regarding the prioritization of orphan drugs by combining different factors to improve access to essential treatments for rare diseases, although it can be generally asserted that no unique model can answer all policymakers’ questions regarding budget allocation of rare diseases and orphan products.

Authors

Institutions

Publication Details

Journal
Shiraz E-Medical Journal
Published
2024-12-04
DOI
https://doi.org/10.5812/semj-150989
Primary Topic
Neurogenetic and Muscular Disorders Research
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The New Model of Prioritizing Orphan Drugs: The Case Study for Hemophilia, SMA, CF and MS

Marita Mohammadshahi, Parisa Abooee, Hamid Sharafi, Farhad Hosseinzadeh Lotfı et al.
Shiraz E-Medical Journal
Neurogenetic and Muscular Disorders Research
article

The New Model of Prioritizing Orphan Drugs: The Case Study for Hemophilia, SMA, CF and MS

Marita Mohammadshahi, Parisa Abooee, Hamid Sharafi, Farhad Hosseinzadeh Lotfı, Zeinab Fakoorfard, Alireza Olyaeemanesh, Mohammadreza Mobinizadeh, Ali Akbari Sari, Zahra Gharib-Naseri
article en

Abstract

Background: Rare diseases, characterized by low prevalence and high complexity, pose significant challenges to health systems due to the uncertainty surrounding the best diagnostic methods and availability of effective treatments. Objectives: This study aimed to introduce new methods for prioritizing orphan drugs, with a pilot application to hemophilia, spinal muscular atrophy (SMA), cystic fibrosis (CF), and multiple sclerosis (MS). Methods: This quantitative research, conducted at Iran’s National Institute for Health Research from 2021 to 2023, employs multi-criteria decision-making models (MCDA) to evaluate the efficacy of health care technologies for "rare and hard to cure" diseases. A preliminary model was developed based on a pilot selection of seven medications, for which comprehensive data was extracted. The clinical efficacy was assessed using quality adjusted life years (QALY) as a metric. The model design was grounded on three critical factors: The annual cost of intervention per individual, the annual number of eligible patients for intervention, and the Proportion of expenses covered by the governmental budget. Additionally, the model incorporated various constraints and a regulatory coefficient, denoted as “w,” to enhance its robustness. Results: By running the model, the coverage of selected medications through model optimization, revealing the following percentages: Alemtuzumab (30%), ocrelizumab (30%), emicizumab (6%), dornase alfa (29%), tobramycin (2%), and spinraza (0.02%). Additionally, the corresponding monetary coverage in Iranian Rials is reported as follows: Alemtuzumab (27,144,000,000 IRR), ocrelizumab (109,645,200,000 IRR), emicizumab (17,360,490,000 IRR), dornase alfa (43,350,930,000 IRR), tobramycin (1,268,505,000 IRR), and spinraza (350,000,000 IRR). Conclusions: This model has tried to solve the shortcomings of the existing models regarding the prioritization of orphan drugs by combining different factors to improve access to essential treatments for rare diseases, although it can be generally asserted that no unique model can answer all policymakers’ questions regarding budget allocation of rare diseases and orphan products.

Shiraz E-Medical JournalVol. 26(1)
Islamic Azad University, Science and Research Branch (IR), Iran National Science Foundation (IR), Iranian Institute for Health Sciences Research (IR), Shahid Beheshti University of Medical Sciences (IR), Tehran University of Medical Sciences (IR)
Ministry of Health and Medical Education, Tehran University of Medical Sciences and Health Services
Openalex Percentile: Top 61%
Neurogenetic and Muscular Disorders Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.