Concept: Orphan drug
- Proceedings of the National Academy of Sciences of the United States of America
- Published over 5 years ago
A cornerstone of modern biomedical research is the use of mouse models to explore basic pathophysiological mechanisms, evaluate new therapeutic approaches, and make go or no-go decisions to carry new drug candidates forward into clinical trials. Systematic studies evaluating how well murine models mimic human inflammatory diseases are nonexistent. Here, we show that, although acute inflammatory stresses from different etiologies result in highly similar genomic responses in humans, the responses in corresponding mouse models correlate poorly with the human conditions and also, one another. Among genes changed significantly in humans, the murine orthologs are close to random in matching their human counterparts (e.g., R(2) between 0.0 and 0.1). In addition to improvements in the current animal model systems, our study supports higher priority for translational medical research to focus on the more complex human conditions rather than relying on mouse models to study human inflammatory diseases.
Since its enactment in 2000, the European Orphan Medicinal Products Regulation has allowed the review and approval of approaching 70 treatments for some 55 different conditions in Europe. Success does not come without a price, however. Many of these so-called “orphan drugs” have higher price points than treatments for more common diseases. This has been raising debate as to whether the treatments are worth it, which, in turn risks blocking patient access to treatment. To date, orphan drugs have only accounted for a small percentage of the overall drug budget. It would appear that, with increasing numbers of orphan drugs, governments are concerned about the future budget impact and their cost-effectiveness in comparison with other healthcare interventions. Orphan drugs are under the spotlight, something that is likely to continue as the economic crisis in Europe takes hold and governments respond with austerity measures that include cuts to healthcare expenditures. Formally and informally, governments are looking at how they are going to handle orphan drugs in the future. Collaborative proposals between EU governments to better understand the value of orphan drugs are under consideration. In recent years there has been increasing criticism of behaviours in the orphan drug field, mainly centring on two key perceptions of the system: the high prices of orphan drugs and their inability to meet standard cost-effectiveness thresholds; and the construct of the system itself, which allows companies to gain the benefits that accrue from being badged as an orphan drug. The authors hypothesise that, by examining these criticisms individually, one might be able to turn these different “behaviours” into criteria for the creation of a system to evaluate new orphan drugs coming onto the market. It has been acknowledged that standard methodologies for Health Technology Assessments (HTA) will need to be tailored to take into account the specificities of orphan drugs given that the higher price-points claimed by orphan drugs are unlikely to meet current cost-effectiveness thresholds. The authors propose the development of a new assessment system based on several evaluation criteria, which would serve as a tool for Member State governments to evaluate each new orphan drug at the time of pricing and reimbursement. These should include rarity, disease severity, the availability of other alternatives (level of unmet medical need), the level of impact on the condition that the new treatment offers, whether the product can be used in one or more indications, the level of research undertaken by the developer, together with other factors, such as manufacturing complexity and follow-up measures required by regulatory or other authorities. This will allow governments to value an orphan drug that fulfilled all the criteria very differently from one that only met some of them. An individual country could determine the (monetary) value that it places on each of the different criteria, according to societal preferences, the national healthcare system and the resources at its disposal – each individual government deciding on the weighting attributed to each of the criteria in question, based on what each individual society values most. Such a systematic and transparent system will help frame a more structured dialogue between manufacturers and payers, with the involvement of the treating physicians and the patients; and foster a more certain environment to stimulate continued investment in the field. A new approach could also offer pricing and reimbursement decision-makers a tool to handle the different characteristics amongst new orphan drugs, and to redistribute the national budgets in accordance with the outcome of a differentiated assessment. The authors believe that this could, therefore, facilitate the approach for all stakeholders.
Adulthood acute lymphoblastic leukemia (ALL) is a rare disease. In contrast to childhood ALL, survival for adults with ALL is poor. Recently, new protocols, including use of pediatric protocols in young adults, have improved survival in clinical trials. Here, we examine population level survival in Germany and the United States (US) to gain insight into the extent to which changes in clinical trials have translated into better survival on the population level.
Esteban Gonzalez Burchard and colleagues explore how making medical research more diverse would aid not only social justice but scientific quality and clinical effectiveness, too.
In the scientific literature, spin refers to reporting practices that distort the interpretation of results and mislead readers so that results are viewed in a more favourable light. The presence of spin in biomedical research can negatively impact the development of further studies, clinical practice, and health policies. This systematic review aims to explore the nature and prevalence of spin in the biomedical literature. We searched MEDLINE, PreMEDLINE, Embase, Scopus, and hand searched reference lists for all reports that included the measurement of spin in the biomedical literature for at least 1 outcome. Two independent coders extracted data on the characteristics of reports and their included studies and all spin-related outcomes. Results were grouped inductively into themes by spin-related outcome and are presented as a narrative synthesis. We used meta-analyses to analyse the association of spin with industry sponsorship of research. We included 35 reports, which investigated spin in clinical trials, observational studies, diagnostic accuracy studies, systematic reviews, and meta-analyses. The nature of spin varied according to study design. The highest (but also greatest) variability in the prevalence of spin was present in trials. Some of the common practices used to spin results included detracting from statistically nonsignificant results and inappropriately using causal language. Source of funding was hypothesised by a few authors to be a factor associated with spin; however, results were inconclusive, possibly due to the heterogeneity of the included papers. Further research is needed to assess the impact of spin on readers' decision-making. Editors and peer reviewers should be familiar with the prevalence and manifestations of spin in their area of research in order to ensure accurate interpretation and dissemination of research.
The US continues to lead the world in research and development (R&D) expenditures, but there is concern that stagnation in federal support for biomedical research in the US could undermine the leading role the US has played in biomedical and clinical research discoveries. As a readout of research output in the US compared with other countries, assessment of original research articles published by US-based authors in ten clinical and basic science journals during 2000 to 2015 showed a steady decline of articles in high-ranking journals or no significant change in mid-ranking journals. In contrast, publication output originating from China-based investigators, in both high- and mid-ranking journals, has steadily increased commensurate with significant growth in R&D expenditures. These observations support the current concerns of stagnant and year-to-year uncertainty in US federal funding of biomedical research.
Data from clinical trials, including participant-level data, are being shared by sponsors and investigators more widely than ever before. Some sponsors have voluntarily offered data to researchers,(1),(2) some journals now require authors to agree to share the data underlying the studies they publish,(3) the Office of Science and Technology Policy has directed federal agencies to expand public access to data from federally funded projects,(4) and the European Medicines Agency (EMA) and U.S. Food and Drug Administration (FDA) have proposed the expansion of access to data submitted in regulatory applications.(5),(6) Sharing participant-level data may bring exciting benefits for scientific . . .
Despite recent efforts to enforce policies requiring the sharing of data underlying clinical findings, current policies of biomedical journals remain largely heterogeneous. As this heterogeneity does not optimally serve the cause of data sharing, a first step towards better harmonization would be the requirement of a data sharing statement for all clinical studies and not simply for randomized studies. Although the publication of a data sharing statement does not imply that all data is made readily available, such a policy would swiftly implement a cultural change in the definition of scientific outputs. Currently, a scientific output only corresponds to a study report published in a medical journal, while in the near future it might consist of all materials described in the manuscript, including all relevant raw data. When such a cultural shift has been achieved, the logical conclusion would be for biomedical journals to require authors to make all data fully available without restriction as a condition for publication.
Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may be useful for biomarker identification and drug discovery, the bulk of it remains underutilized. Deep neural networks (DNNs) are efficient algorithms based on the use of compositional layers of neurons, with advantages well matched to the challenges -omics data presents. While achieving state-of-the-art results and even surpassing human accuracy in many challenging tasks, the adoption of deep learning in biomedicine has been comparatively slow. Here, we discuss key features of deep learning that may give this approach an edge over other machine learning methods. We then consider limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility.
Optical sensors for ultrasound detection provide high sensitivity and bandwidth, essential for photoacoustic imaging in clinical diagnostics and biomedical research. Implementing plasmonic metamaterials in a non-resonant regime facilitates sub-nanosecond, highly sensitive detectors while eliminating cumbersome optical alignment necessary for resonant sensors.