The Promise & Pressures of Biologics R&D
Generic small molecule drugs take advantage of abbreviated approval pathways and have markedly lower development costs compared to their reference products.
Biologic drugs have increasingly delivered treatments for cancer and rare diseases and continue to show efficacy in other areas, from neurological and metabolic disorders to respiratory and cardiovascular diseases. But along with the incredible potential biologics offer, there is also great pressure to discover and deliver novel treatments quickly and cost-effectively.
Biologics represent a rising share of FDA approvals, with a market size projected to exceed $700 billion by 2030. According to The Antibody Society, more than 150 therapeutic monoclonal antibodies have been approved for use in the United States and Europe since appearing more than 20 years ago.
In 2018, RNA therapy first came on the scene with a drug that treats a genetic disease called hereditary TTR-mediated amyloidosis (hATTR), caused by mutations in the transthyretin (TTR) gene. Then Biogen discovered the wildly successful nusinersen (Spinraza), which treats spinal muscular atrophy (SMA), a rare genetic disease that is fatal if untreated.
Nusinersen produced approximately $1 billion in sales in its first year alone. Most recently it was an RNA-based vaccine that was developed to help fight the global pandemic.
The American Society of Gene and Cell Therapy (ASGCT) reports that as of Q2 2022, 19 gene therapies, 18 RNA therapies, and 59 cell therapies have been approved for clinical use globally and many promising candidates are in clinical trials.
Investment in biologics R&D is strong, with ASGCT reporting that start-ups working in gene, cell, and RNA therapies raised nearly $800 million in Q2 of 2022 alone. Interestingly, a trend toward financing and partnerships has emerged, letting investors and pharmaceutical companies support innovators through license agreements and partnerships without the full commitment of an acquisition.
Even as the investments flow, techbio organizations face a conflicting reality. On one side, there is incredible potential. Biologics offers upsides like favorable safety profiles, longer patents, and relatively low generic competition compared to small molecule drugs.
There is also rising demand for novel treatments for prevalent chronic diseases such as diabetes and obesity, and biologics are poised to deliver. Recent examples include work researchers are doing to:
- Prevent the premature termination of protein expression caused by genetic mutation.
- Inhibit or alter protein expression by modifying RNA, such as with enzymes or small molecule drugs.
- Develop delivery systems for gene therapy and RNA drugs.
- Apply protein degraders to conditions outside oncology.
- Perform gene editing using CRISPR.
- Uncover potential targets and drug candidates using machine learning methods.
Unfortunately, the flipside to the potential is incredible pressure. Development costs for biologics are notoriously high.
And the clock is ticking, as patents approach expiration such as AbbVie’s Humira, which had $21 billion in 2021 sales alone and expires in January 2023. Federal policies have also made biosimilar competition more viable.
In the realm of small molecule drugs, generic competition is well established and fairly straightforward. Manufacturing processes are standardized and generics are essentially chemical equivalents to their originals, with only slight ingredient variation allowed.
Generic small molecule drugs take advantage of abbreviated approval pathways and have markedly lower development costs compared to their reference products. They are often readily substitutable for their primary counterparts at the pharmacy and are priced around 80%-85% less.
The playing field is different with biologics. Creating equivalents is just not as simple.
Biologics are more complex—not only in their larger structures, but also in the complicated processes used to analyze, develop, and manufacture them. In fact, the term generic is not even used for biologics; instead, we have biosimilars or biobetters, which each have their own set of pros and cons.
Biobetters try to improve upon reference biologics, not just emulate them. They are currently considered new drugs and therefore receive patent exclusivity. Although this means no fast-tracked approval process, it does mean developers can avoid waiting for the reference drug’s patent expiration, making it possible for a biobetter to beat a biosimilar to market.
Biobetters are subject to all the rigorous testing and trials required for a new drug. However, developers have a head-start with a known target protein and efficacy, and safety data established for the reference structure. This knowledge can be used to explore things such as structural changes, chemical modifications, or process alterations that could help deliver an alternative that is hopefully more efficacious, better tolerated, safer, easier to administer, or longer-lasting than the original.
And while the call to lower drug prices and improve patient accessibility to novel biologic treatments is louder than ever in the pursuit of biosimilars and biobetters, it all comes down to the data. Researchers not only leverage heaps of data related to a reference biologic, they also need to take into account the data in relation to the volumes of data streaming from their study of new candidates’ pharmacokinetics, pharmacodynamics, safety, purity, potency, immunogenicity, and response.
Some of the biggest hurdles for innovation are siloed data, fragmented workflows, and clumsy business practices. Those are coupled with the fact that biology is complicated.
As the need for biologic drugs increases, innovators are left with no choice but to find new ways that help them work smarter and faster, starting in the earliest days of discovery. If we want to realize the full promise of biologics, we’ll first need to tackle these challenges to reap the benefits.
About the Author
Christian Olsen is a business segment lead at Dotmatics, a leader in R&D scientific software connecting science, data, and decision-making.