In the year 2000, every one of the world’s top-10 best-selling drugs was a compound targeting a common and chronic indication – treatments such as statins for high cholesterol, proton pump inhibitors for stomach ulcers, and selective serotonin reuptake inhibitors for depression. Two decades later, that same top-10 chart looks very different, with several of the drugs having started their commercial lives as orphan drugs (including Keytruda, Opdivo, Revlimid and Imbruvica). Although these drugs in the top-10 are all technically ‘partial orphan drugs’ (having expanded beyond orphan diseases since receiving their first market authorization), their target patient groups are still very narrow compared to the average 20th century drug.
New oncology drugs typically cost more than $100,000 per year of treatment, meaning it only takes 10,000 patients to push them over the billion-dollar sales bar.
This evolution towards narrower indications has been the result of several converging forces. Firstly, government incentives have made the orphan route more attractive for pharma by lowering requirements for the clinical data package, shortening approval times, and providing time-limited market exclusivity for new drugs. Secondly, smaller patient groups with common underlying pathologies increase the odds of clinical trial success and obtaining regulatory approval. Thirdly, the lifesaving nature of some of these drugs currently on the best-seller list allows them to command high enough prices to compensate for the small patient populations. New oncology drugs typically cost more than $100,000 per year of treatment, meaning it only takes 10,000 patients to push them over the billion-dollar sales bar.
The oncology model for blockbuster drugs
It’s no coincidence that the ‘orphan’ blockbuster drugs in the bestseller list are all cancer drugs: the oncology drug market presents a prime example of this ‘small patient group’ strategy at work. Advances in our understanding of the molecular basis of cancer, and corresponding expansion of sequencing-based diagnostic toolkits, have allowed us to dissect cancer into dozens of smaller sub-indications that share the same causative mutations and molecular drivers of tumorigenesis. Consequently, there is now an extensive armamentarium of cancer drugs that selectively target oncogenes (such as BRAF, BRCA1/2, EGFR, and RET). Some of these drugs were granted approval based on the molecular targets they modulate, irrespective of the organ in which the cancer occurs. All of them command high prices for the companies that developed them.
With fewer patients and lower product prices, how will biotech companies be able to obtain a return on their investment in drug development?
Many pharma and biotech players are currently hard at work replicating this ‘oncology model’ in other indications, including in neurodegeneration, heart failure, diabetes, and arthritis. As our understanding evolves, it is becoming increasingly clear that these diseases are in fact mosaics of sub-indications lumped together based on common symptoms, each of them having very diverse genetic and molecular causes. To move beyond mere symptom alleviation, therapies need to act on the underlying mechanisms, which tend to vary from patient to patient. As we’ve seen in oncology, the synergy between high-precision patient diagnosis and deployment of mechanistically targeted drugs leads to better disease-modifying therapies. Though the patient groups are smaller, they are also more homogeneous, enabling more effective targeted therapies.
There is however an important caveat to replicating the oncology model in these other large indications: the per-patient drug prices will have to drop significantly. Unlike cancer therapies, treatments for many of these chronic diseases need to be given over a longer period of time. Additionally, many of these indications are not imminently life-threatening, which translates to lower QALY (Quality-Adjusted Life-Year) benchmark scores – the measure commonly used to determine the cost and reimbursement policies for new drugs. And last but not least, some of these indications are so large that they threaten to economically sink healthcare systems if their price tags are not adjusted to the volume of need. While oncology can serve as an excellent model for future therapies in chronic indications, the drug pricing will need to be dramatically different.
How much do you charge for a lifesaving drug? Read this article to find out!
Obstacles still in the way
This conclusion beggars the question: with fewer patients and lower product prices, how will biotech companies be able to obtain a return on their investment in drug development?
One solution is to drastically cut the cost of drug development, which can be done at three levels: drug discovery and candidate development, drug manufacturing, and clinical trials. Drug discovery is already being sped up and having its failure rate lowered thanks to the increased availability of information on protein structures, combined with AI-aided virtual drug design. Lowering the costs of preclinical and clinical drug manufacturing is a more serious challenge, particularly for antibodies, nucleic acids and cell- and gene therapies, though progress is also being made in this space. By far the biggest challenge in the coming decades will be lowering the cost of clinical trials. Even here though, solutions may be found through a combination of improved trials designs, better stratification, further digitalization of read-outs, and the use of AI guidance.
Read this article to find out more about how we can reduce the high cost of clinical trials.
If these hurdles are overcome, then we will likely see drug development trends return to what they were at the start of the century, with pharma and biotech again focused on broad impact, common indications.