For those not familiar, the IO market currently has 6 PD-1/PD-L1 inhibitors vying for dominance over the oncology market. For those familiar with the ‘IO race’, you’ll know that Merck’s Keytruda has become the clear front runner, leading in terms of market share and revenue ($11.1 Bn, 2019), followed by BMS’s Opdivo ($8 Bn, 2019), Roche’s Tecentriq ($1.9 Bn, 2019), and AstraZeneca’s Imfinizi ($1.5 Bn, 2019), respectively (even further behind is Pfizer/Merck KGgA’s Bavnecio and Sanofi’s Libtayo) (3).

Now, while these agents are certainly not identical in terms of their efficacy or safety profiles, they are generally considered to be much more similar than different. Several meta-analyses suggest that at best, minor differences may exist between PD-1s and PD-L1s, and between the individual agents themselves, however the data has been contradictory between studies to date. Until direct head-to-head comparisons exist (and for the purposes of this article) we posit that the decisions made by these companies, and not the intrinsic feature of the agents, are the major contributing reasons for the disparities in the success of these agents.

This leaves us with an ideal scenario for not only exploring the question of “which company”, but also “why” one company succeeds over the others when given the same (or in this case, similar) products.

In considering the success of Keytruda over other PD-1/PD-L1 agents, we identified three macro level trends that seem to be setting the front runner apart from the rest of the PD-1/PD-L1 pack*:

Breadth of clinical program (or scale of investment)

In looking at comparisons between the leader, Keytruda and other IO agents, one difference becomes clear; the number of previous & ongoing clinical trials, and the number of approved indications is greater for Keytruda. In other words, Merck’s decision to pursue a mammoth number of shots on goal, investing heavily in clinical development, is likely a significant driver of their success. We see this trend across the market, with the number of trials & approved indications being correlated to revenue (the more indications, and greater number of clinical trials, the greater the revenue) (2), (3).

With 22 discrete indications in the US (as of March 2020), Keytruda dwarfs the other PD1 / PD-L1 agents with only Opdivo coming close at 15 US approvals (1). Similarly, Keytruda lists 1,037 active or planned trials compared to Opvido’s 920. (2)

Relative timing to enter a market

While this may not come as a surprise, timing of market entry is a major driver of success in the PD-1/PD-L1 race. Being the first PD-1/PD-L1 to an indication has shown to be a straightforward way to success. Examples of this can be seen in a number of indications, including Keytruda’s pivotal victory in Non-Small-Cell Lung Cancer (NSCLC).

After initially lagging behind Opdivo as the second IO in 2nd line NSCLC, Keytruda was able to become the first approved IO in front line NSCLC, the biggest market in oncology, which became a major turning point for the brand (we’ll discuss this example further in the next section).

Perhaps an even clearer example of the value of timing can be seen in Imfinzi, who’s dominance in Stage 3 lung cancer was born out of a decision to move into early cancer well ahead of the competition – this move has proved to be one of their greatest successes to date.

Conversely, when a PD-1 or PD-L1 enters an indication as a follower (without having a differentiated population or data set) it has tended to struggle against incumbents. Tecentric, Imfinzi and Bavencio have all suffered this fate in a handful of indications, often coming in behind Keytruda and Opdivo.

Increasing the probability of clinical trial success

The third trend relates to increasing the probability of success of pivotal clinical trials. Time and time again we see examples of one agent succeeding when others failed. Upon further analysis, it appears many of these differences can be attributed to differences in clinical trial design and execution. Merck appears to have mastered this, specifically designing and executing clinical trials in ways that maximize their potential to show benefit and thus gain a regulatory nod.

There are several examples where Merck’s choice of biomarker, patient selection criteria and combination partner maximized their probability of clinical trial success, while the competitors pursued greater commercial opportunity in larger populations and in-house combinations. Perhaps the single greatest example came a few years ago when Keytruda became the first front line PD-1 agent in NSCLC.

For those not familiar, at the time, Opdivo was indicated for all 2nd line patients regardless of PD-L1 biomarker status, while Keytruda had taken a conservative approach, only gaining approval in patients with high levels of PD-L1, decreasing their addressable population relative to Opdivo, but increasing the benefit patients derive. As the two sought indications in front line NSCLC, Merck continued with their conservative approach, seeking a monotherapy indication in patients with high levels of PD-L1 (PD-L1> 50%), while BMS, seeking to target a larger population, studied Opdivo in patients with only modest levels of PD-L1 (PD-L1> 1%). Ultimately, Keytruda was successful in showing statistically significant benefit, while Opdivo failed in their broader population.

In addition to this, Merck decided to combine Keytruda with the already proven standard of care, chemotherapy for patients with both high and low levels of PD-L1 while BMS tested their in-house combination (Opdivo & Yervoy), which was unproven in NSCLC, but if successful, would provide two sources of revenue. Again, Keytruda, in its more conservative approach, came out the victors while Opdivo’s novel approach failed.

While we can’t fully quantify how much these strategic choices have led to Merck’s success, we can see how they’ve likely played a key role in driving their dominance.

Of course, some clinical difference may be helping Keytruda perform better, but until we know for sure, we can only look at the decisions and strategies these companies have employed in answering the question posed 12 years ago; “which company would do better when given the same (or in this case, similar) products”? The answer thus far has been Merck.

(1) Cancer research institute (2) Clinicaltrials.gov (3) Globaldata