Addressing Rare Disease Treatments with Biosimilars and Orphan Drugs
Bio Spectrum|August 2024
Approximately 10,000 rare diseases (RDS) impact about 400 million people worldwide, with 30 million in the United States alone.
Addressing Rare Disease Treatments with Biosimilars and Orphan Drugs

In Europe, the European Medicines Agency estimates that up to 36 million people are diagnosed with an RD. However, approximately 5 per cent of RDs have US FDAapproved treatment options, while up to 15 per cent have at least one drug that exhibits potential for treatment, diagnosis, or prevention. The growing number of unaddressed RD needs is a major catalyst for R&D. There is a need for novel medicine to treat RDs that currently have limited therapeutic choices. Let's explore further.

Governments across the world have regulatory incentives to support orphan drug (OD) development. It is more appealing for pharmaceutical firms to engage in R&D for RDS because of these incentives, which include prolonged exclusivity, tax benefits, and simplified and expedited regulatory procedures and approvals.

Recent advancements in precision medicine and informatics, such as big data analytics, multi-omics, nanomedicine, gene-editing techniques, and nextgeneration diagnostics, have created opportunities to develop specific and individualised therapies for RDs. The convergence of cancer and RDs is becoming evident. Precision oncology and tailored medicine for rare tumours are emerging as prominent themes in the discipline, facilitating the OD industry's expansion.

There are multiple challenges restraining the development and adoption of orphan drugs. Owing to low awareness of RDs, many patients go undetected for extended periods. Apart from diagnosis, both prognostics and therapy are seeing significant gaps that must be filled. Challenges in prognosis assessment are due to the absence of reliable parameters to measure improvement and/or biomarkers as well as a lack of knowledge of underlying pathophysiological pathways.

Furthermore, a limited patient sample size prevents the derivation of statistically significant parameters.

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