Adapted from an article originally published January 16, 2019 in The Medicine Maker themedicinemaker.com.
The biopharma industry offers big rewards – but it poses big risks, too. Rather than fearing the unknown, the concept of optionality can help you better manage it.
But what if you were able to quantify and, therefore, better manage this uncertainty? This may seem like a big ask, but a tool does exist to help businesses make better decisions in a highly uncertain environment: optionality. Essentially, optionality is the concept of keeping one’s options open so that you have multiple pathways you can take instead of committing to just one – retaining flexibility versus an “all or nothing” proposition.
But despite the high uncertainty biopharma faces, it is surprising how many people involved in the field, and especially in manufacturing, have little or no real understanding of how to best assess investments in the space, and how to apply optional approaches to help them do that.
Blockbuster or big flop?
Think of the traditional process of developing a new product. If you are sitting on a new idea and you need to invest to make it a reality, you have no idea yet if your drug is going to be the next big blockbuster or a “white elephant”. Over time, as you develop the drug, the risk of failure lessens. Following this classical path, you have phase I trials that give you safety data, then phase II for post-escalation safety data, and finally phase III, which will tell you how effective the drug is. These are clear milestones to reach as you derisk your investment and gain a clearer understanding of whether you’re going to be successful.
This is where optionality comes in – as you’re going through this process, you need to consider the manufacturing dilemmas – how to secure the appropriate manufacturing capacity for a drug that is still in development and may fail clinical trials (option to abandon)? Do you build, buy or partner (outsource to CMO)? How much capacity do you secure given uncertain demand? What flexibility in the design or contracts do you need to consider if demand is higher than anticipated (or you expand internationally sooner than originally anticipated) or is slower to ramp up (option to expand or option to contract)?
Not considering the full spectrum of options can lead to negative consequences. Take Tesla – they have a very popular car but they can’t produce the numbers they want at the quantity and quality needed because their manufacturing strategy doesn’t align with the huge demand they are facing. Conversely, there used to be a leader in the cell therapeutics field called Dendreon, which made some of the first cell therapies and garnered a lot of interest from the investor community.
The company had a lot of funding, and built a relatively large manufacturing hub to begin manufacture of what they anticipated would be a blockbuster. But although it did get past phases I-III, the efficacy data and reimbursement weren’t where they thought they would be, and subsequently the company went bankrupt and was sold. So on one hand, you want your manufacturing capabilities up and running to meet high demand. On the other, you don’t want to invest too much too early. Optionality is how you can find the “sweet spot” to be prepared exactly when (and if) you need to be.
When it comes to introducing an optionality approach, you need an internal champion – someone who is really enthusiastic about the idea and will help guide their colleagues and company towards it. The key is to become more comfortable with uncertainty. People always talk about risk, because that’s what they’re afraid of – but risk is just one part of the uncertainty equation – the downside. There’s an upside too; when you are facing risk, you are also facing opportunity. The best and worst case scenarios must both be considered, so it’s not all doom and gloom!
When companies have this explained to them, they tend to be very receptive. A large part of the approach, especially in larger organizations, is bringing different departments together – marketing, new product development, R&D, clinical manufacturing and more – and taking them through this thought process, finding out what flexibilities they have, what their lead times are, and what the budget for certain things is. You need to get people talking and thinking about these issues, as well as about the different scenarios that could happen – almost like a strategic planning exercise with uncertainty as your underling theme.
Next, you can look at how you can use optionality for the specific scenario you’re facing. For example, you’re going into a new market and you decide to spend $100,000 on market research to better understand price variability, which will take six months – this is your first investment. Then you consider your next move: should you pull back, invest in a pilot, or accelerate the investment and go for full market penetration? You need to lay out the options you have along the way and try to better understand what the key decision points along your path are, and what opportunities you have to either halt your investment or accelerate it depending on the knowledge and feedback you are gaining.
You can do this using a combination of financial options analysis combined with decision analytics to quantify your options – this is where things get technical, but it can all be done in a spreadsheet. Quantifying uncertainty can be done using, amongst other tools, Monte Carlo simulation. You can then apply Real Options Analysis to value the flexibility that decision makers have to course correct their investment decisions as uncertainty resolves itself over time. In other words, at key decision milestones, managers should stop and ask, “Okay, what have I found out? Has some of the uncertainty that I had on day zero resolved itself, and if so can I rerun my model and come up with more precise predictions about the future now?”
Keeping options open
Once you have these processes in place, you can take the optional approach and apply it to almost anything – manufacturing, entering uncertain or emerging markets, choosing vendors, considering partnerships, R&D portfolio management… the list goes on. You can even use it in business development and deal negotiation to quantify the risks and opportunities of different decisions to choose the best deal. The sky is the limit!
Will the ballroom concept take over the bioprocess world?
There is no denying the appeal of the ballroom approach in emerging markets. Considering only the facility infrastructure, both the initial investment and the ongoing operating expenses are considerably lower than for a traditional cleanroom facility. The facility will require a smaller footprint, less engineering, lower construction costs, and less time to completion. The environmental impact is also much lower when cleanroom standards are limited to the seed train and post-filtration operations. And while it may sound trivial, removing the need for gowning leads to lower labor costs and greater efficiency. These advantages will tip the balance towards ballroom or dance floor implementations in emerging markets, as in Amgen’s Singapore facility.
Ultimately, optionality isn’t about spreadsheets and software – it’s a way of thinking about problems. It’s not the way managers and leaders are traditionally taught to think, which is unfortunate because it’s a valuable concept for biopharma to embrace. Everyone is familiar with uncertainty, but many people choose to ignore the things they consider unquantifiable and uncontrollable. But the tools are out there – and if you use them correctly, you can approach risk and reward in a much more structured way to make the very best decisions for your business. There is no way to eliminate risk from pharma, but one thing is certain: you can account for it.
Firman Ghouze is the Director of Commercial Strategy at Cytiva. He is responsible for developing Cytiva's commercial strategy and partnerships in the bioprocess space. He has worked in the biomanufacturing industry, encompassing both protein and cell therapeutics, for the last ten years.
Uriel Kusiatin is CFO of Provista Diagnostics Inc. He previously worked as a consultant developing and applying methodologies for assessing investment decisions in the life sciences using financial analysis and strategic decision-making techniques.