Mimicking the Many Facets of Oncology R&D

Oncology has long challenged how pharma companies think about drug development and modeling drug development programs to reflect the true opportunities ahead. In 1970, when looking at the overall oncology landscape, the five-year survival rate was about 50%. Moving forward 40 years, living past the five-year survival rate has gone up to about 70%, a significant improvement.


One explanation for this is that treatment technology has advanced since 1970. However, that is only part of the explanation. Those 50% to 70% of cases don't consider different types of cancer. While there may be significant improvements in some cancer indications, the landscape looks different depending on what is being studied. For example, pancreatic cancer is still below 50%.


When pharma companies discuss modeling oncology medicine opportunities in today's rapidly changing environment, it boils down to how fast they move to market. In oncology, the market move sometimes happens right after the first human trials. If the data looks good and the product is promising, the opportunity to go directly to market is feasible, but conversely requires consideration of questions like:

  • Is there a supplier ready?

  • Are the commercialization plans in place?

  • Is it possible to meet the goal?

"The challenge lies in representing all necessary planning to allow the pharma companies to see all possibilities."

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Blockbuster Opportunities

Innovative oncology therapies present enormous health improvements and market opportunities. With that, the current market is still dominated by a few core blockbuster products that make up a significant chunk of oncology solutions. This group is comprised of only seven products with more than $5 billion in sales. Considering the magnitude of the monetary benefit, most pharma companies aspire to get the jump on a blockbuster and are undoubtedly keen on investigating if these opportunities can be achieved.


Nevertheless, not every product is going to be a blockbuster. What needs to be understood in the pursuit of going beyond the non-blockbuster space into the blockbuster space is how to balance out the potential for the investment if the company ends up in the lower tier of the blockbuster spectra. That essentially means that if it does not reach that elite status, the therapy should be strategically treated as such. If the treatment turns out to be a blockbuster, the pharma company must be equipped and ready to take on that opportunity. A pharma company must be prepared to handle both scenarios; therein lies the modeling challenge.


Unique Drug Development Modeling

To fully understand the uniqueness of oncology medicine development modeling, it’s easiest to break it into three components:

  • Speed of Development: When looking at innovative medicines, pharma companies want to quickly understand what they're targeting, what the market opportunity looks like, and if there is an opportunity for a breakthrough.

  • Oncology is Not a Single Disease: Every oncology tumor type has unique treatment considerations, and a single molecule may lead to indications across multiple tumor types. Thus, dependencies between the different opportunities should be factored in. For example, targeting a molecule for an underserved disease area may lead to breakthroughs for other therapies, and following up on those opportunities is advisable.

  • Fast-Changing Landscape: In recent innovations, the therapy is changing the standard of care at an inconsistent pace compared to other therapeutic areas. Therefore, it is tough to predict the next innovation and how impactful it can become.

What can pharma companies do to negotiate these challenges?


For traditional development paths, activities progress logically through the different phases of development, and companies can use standard business forecasting models to support the roadmap. The problem with doing so is that no pharmaceutical development process is standard according to a preset template, and it does not follow a logical path.


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Mimicking the Many Facets of Oncology R&D

As previously mentioned, oncology can take on many forms of treatment. Since a single molecule may lead to indications across multiple tumor types, pharma companies must use this knowledge and reflect it in their models to better understand the potential opportunities and risks.


At Captario, the development process is represented in what is referred to as Business Process Modeling & Notation, (BPMN). Using BPMN to model the process results in the discovery of several different development paths. This enables pharma companies to see multiple opportunities if they go down one development path, further allowing for consideration of developmental dependencies and probabilities.


Read also: A Quick Read About BPMN

BPMN provides those multiple potential paths allowing pharma companies to play out and simulate different probabilities of pursuing one path or another. The overall value of the opportunity is adjusted by the uncertainty of getting an acceleration or not, and understanding all the potential futures and their likelihoods. Additionally, BPMN modeling provides unbiased outputs; the forecast is based on simulations that provide an output range of estimated results instead of applying estimations within the model itself—something that renders the process contingent on the in-model estimations, creating the risk of information distortion.

For example, imagine that a pharma company has a basket study in oncology that has the potential to look for a signal against a particular tumor type. For this case, the pharma company can model out whether the signal is positive and whether it would want to pursue the development path for that particular tumor type indication. The company could have a potential positive path outcome, a negative path outcome, or multiple positive path outcomes in each case. The strategy is to simulate all the potential paths forward for all indications while considering the uncertainties of whether to pursue specific paths or not; it could be contemplated overall with an understanding of the value of going down each way or even the desire to stage the different paths to accommodate the cost and the impact of the planning exercise. For instance, it’s possible to assess the probability of delayed launch dates due to declined acceleration.

Conclusion

When running the acceleration scenarios to pace with the many facets of oncology, it is advisable to run multiple forecasting iterations and distribute the probability of launching in a model that allows for flexibility, dependencies, and viable data. In doing so, the forecast carries more depth in its analysis output. The assessment of the likelihood of a January 2026 launch versus the likelihood of a July 2026 launch will then mimic the actual relationship between indications instead of providing theoretical estimations. For the same scenario, it's also possible to plot out the potential revenue difference for a base case versus an accelerated case and the probability of getting the desired net present value (NPV).


By playing out different scenarios, modelers can re-run the simulations to compare the overall outcome against other portfolio options and complex drug development paths for a particular asset with the ability to then look at them in a comprehensive portfolio scenario. This increases the chances of having an effective strategy in place should a blockbuster opportunity emerge.


Conclusively, by contrasting one development path against the other, pharma companies can generate a solid base of information on how they want to progress the investment decisions at an individual asset level and make sure that those decisions are taken in the context of the broader portfolio.