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Applicability and added value of novel methods to improve drug development in rare diseases
- Source :
- Orphanet journal of rare diseases, 13(1). BioMed Central, Orphanet Journal of Rare Diseases, Orphanet Journal of Rare Diseases, Vol 13, Iss 1, Pp 1-16 (2018), Orphanet Journal of Rare Diseases, 13(1), 200. BioMed Central
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
Abstract
- Background The ASTERIX project developed a number of novel methods suited to study small populations. The objective of this exercise was to evaluate the applicability and added value of novel methods to improve drug development in small populations, using real world drug development programmes as reported in European Public Assessment Reports. Methods The applicability and added value of thirteen novel methods developed within ASTERIX were evaluated using data from 26 European Public Assessment Reports (EPARs) for orphan medicinal products, representative of rare medical conditions as predefined through six clusters. The novel methods included were ‘innovative trial designs’ (six methods), ‘level of evidence’ (one method), ‘study endpoints and statistical analysis’ (four methods), and ‘meta-analysis’ (two methods) and they were selected from the methods developed within ASTERIX based on their novelty; methods that discussed already available and applied strategies were not included for the purpose of this validation exercise. Pre-requisites for application in a study were systematized for each method, and for each main study in the selected EPARs it was assessed if all pre-requisites were met. This direct applicability using the actual study design was firstly assessed. Secondary, applicability and added value were explored allowing changes to study objectives and design, but without deviating from the context of the drug development plan. We evaluated whether differences in applicability and added value could be observed between the six predefined condition clusters. Results and discussion Direct applicability of novel methods appeared to be limited to specific selected cases. The applicability and added value of novel methods increased substantially when changes to the study setting within the context of drug development were allowed. In this setting, novel methods for extrapolation, sample size re-assessment, multi-armed trials, optimal sequential design for small sample sizes, Bayesian sample size re-estimation, dynamic borrowing through power priors and fall-back tests for co-primary endpoints showed most promise - applicable in more than 40% of evaluated EPARs in all clusters. Most of the novel methods were applicable to conditions in the cluster of chronic and progressive conditions, involving multiple systems/organs. Relatively fewer methods were applicable to acute conditions with single episodes. For the chronic clusters, Goal Attainment Scaling was found to be particularly applicable as opposed to other (non-chronic) clusters. Conclusion Novel methods as developed in ASTERIX can improve drug development programs. Achieving optimal added value of these novel methods often requires consideration of the entire drug development program, rather than reconsideration of methods for a specific trial. The novel methods tested were mostly applicable in chronic conditions, and acute conditions with recurrent episodes. Electronic supplementary material The online version of this article (10.1186/s13023-018-0925-0) contains supplementary material, which is available to authorized users.
- Subjects :
- Statistical methods
Computer science
lcsh:Medicine
Context (language use)
Machine learning
computer.software_genre
01 natural sciences
Goal Attainment Scaling
Orphan
010104 statistics & probability
03 medical and health sciences
Rare Diseases
Clinical trials
0302 clinical medicine
Drug Development
Prior probability
Added value
Humans
Pharmacology (medical)
0101 mathematics
Genetics (clinical)
business.industry
Research
lcsh:R
Small population
Bayes Theorem
Rare condition
General Medicine
Evidence-based medicine
3. Good health
Drug development
Sequential analysis
Sample size determination
030220 oncology & carcinogenesis
Artificial intelligence
business
computer
Subjects
Details
- ISSN :
- 17501172
- Volume :
- 13
- Database :
- OpenAIRE
- Journal :
- Orphanet Journal of Rare Diseases
- Accession number :
- edsair.doi.dedup.....4263f98e6c0dc4e0ff5a2e0e6885d61f
- Full Text :
- https://doi.org/10.1186/s13023-018-0925-0