In this DeciBio Q&A we spoke with Kamraan Shariff, a key thought leader in corporate development with extensive experience in precision medicine. Kamraan shares the value of corporate development, as well as symptoms of underdevelopment, and how to strategically address fundamental disconnects of a company’s end-state vision and the path they forge to achieve it. He provides a high-level overview of the corporate development process both generally and specifically relevant to precision medicine, alluding to the ever growing need for accessible data and analytics to make evidence-backed strategies for commercial success.

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Hi Kamraan, thank you for joining us. To start, could you please share a bit about yourself and what you’re working on now?

I started out as an investment banker, in London as well as in Asia. I was then part of GE’s Corporate Development / M&A function for about a decade supporting the entire GE portfolio. I then transitioned over to GE Ventures focused on our Healthcare & Life Sciences venture building activity, during which time I was so intrigued with the level of innovation occurring globally that it felt like it was the right time to move out of big corporate life and dive into the startup & growth-stage ecosystem for Healthcare & Life Sciences. 

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I then worked for a few years in corporate development (CorpDev) and C-suite roles for private equity and venture capital-backed companies, mostly in the omics and data analytics space focused on advancing precision medicine. Since the beginning of 2024, I have pivoted into  CorpDev consulting, either as a fractional member of the C-Suite or as a consultant to these companies. A lot of these companies don’t have a fully-fledged internal CorpDev function so I come in and help with strategy development and implementation, capital raising preparedness, licensing, and essentially anything that falls into the gap between what inorganic initiatives the strategy consultants recommend and how to translate that to a valuation inflection point to support financing, M&A or an exit

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You see a lot of Series A to Series B companies in your role; are there any common pain points or unmet needs that consistently appear when you work with precision medicine companies of that size?

I see a few common themes; companies spun out of academia often assume that the company’s thesis resonates with the market rather than going out and validating it. This testing should be done under stealth or immediately post-launch of the company–but it often isn’t–so you end up with a ‘product looking for an application’. 

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For companies that are more platform-based and pursue data and analytics, or an assay platform that can span multiple therapeutic areas, selecting the correct first indication is crucial. I’ve often seen that these companies don’t, because it hasn’t been correlated with market sizing and the scale and complexity of evidence generation requirements for regulatory, reimbursement or both. When you’re early on in an initial product conception or academic spin-out, market sizing will really help with prioritization. Listening to and understanding the voice of the customer is also important to ensure your UI / UX is exactly what the user needs. 

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Another key pain point is companies’ end-to-end evidence generation strategy. Once you select that first indication, what’s the evidence generation that you need to get there? How much is it going to cost? Is this factored into your funding plan? I’ve seen companies go commercial too early and allocate funds to scale commercial resources when you really needed it in R&D to generate this critical evidence. Once you have this dossier for reimbursement and regulatory submissions you’re likely to scale revenue much quicker than if you went commercial too soon. And management needs to be brave and persuasive in how they articulate this capital allocation strategy to their investors and board!

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I have also observed a consistent lack of a Build / Buy / Partner strategy. There’s a “not invented here” syndrome that plays into this, and so companies can fall into this mindset that you have to build it yourself. In reality, especially for VC-backed companies who are very focused on that next milestone to secure the next round of funding, licensing partnerships can be a more effective way to build your product roadmap and build up your technology stack. This is particularly relevant to precision medicine where you have such a fragmented ecosystem of siloed point solutions.

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These companies can also lack an “end-state vision”, or an ultimate goal that they’re working towards. What does this company look like at the point of exit and what is your proposed liquidity event? Essentially, it is a balancing act between having a clear and validated end-state vision and executing on a series of clearly defined R&D and business milestones to achieve along this pathway to support capital raising at progressively higher pre-money valuations. 

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When should companies start thinking about this Build / Buy / Partner approach? If you’re a founder or CEO of a series A company, how do you go about developing this plan?

It comes back to this idea of an end-state vision; what’s your product roadmap to get you there? And it all starts with asking some fundamental questions: Is there a need for differentiation from an IP perspective? Is rapid speed to market required? Is the market uncertainty high for this capability? And likewise are there acquisition targets or partners available? 

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There also needs to be an underpinning of evidence to all of this. Understanding the cost to build, the time to incremental commercial revenue, opportunity cost impacting other projects in the roadmap, and so on. Once you’ve got this data-driven decision matrix aligned across build vs buy vs partner outcomes, CorpDev gets to work and we start looking for targets for either partnerships or M&A. No matter what you do though, you need to make sure that your proposed strategy is aligned with the company’s current financial metrics and ultimately with the risk profile of your investors. 

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Honing in on the “Build” part of this strategy, when does it make sense to build out these internal capabilities?

Firstly, consider if this is a strategic competence that you need to own and what is the associated patent landscape. If there’s critical IP that you need to own in the space, then that rules out partnerships. Secondly, if you can genuinely do it faster, better, cheaper than anyone else, it makes sense to pursue internal product development. Finally, make sure that there’s clear alignment between all of the leadership team that this is something you should do. Product teams tend to want to build everything, and CorpDev teams are obviously biased towards M&A, but there needs to be clear alignment across the leadership team. 

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You’ve outlined a really methodical approach to this planning, but is there ever a world in which you look at what’s already available in the market and opportunities to seize?

Absolutely, I think you need to be running both in parallel. It’s essential to any CorpDev function to be constantly in the market, looking at targets, having conversations, and just getting out there looking for collaborations. You don’t know until you start where these conversations might lead. But if you did it that way, you’d still need to go through this more methodical approach because that ultimately is what anchors your diligence and provides an internal benchmark to support valuation analysis. Ultimately, both this conversational and methodical approach should land you in the same place, with a clear goal in mind and a validated and evidence-based means to get there. 

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How should companies determine the optimal path between pursuing the next financing round, partnerships, M&A, or exit?

For financing, you need to understand your end-state vision and the key inflection points that will get you there. If you can get there on a standalone basis by scaling product development, business development, or sales teams then that’s great and your use of proceeds from the targeted capital raise should be defined accordingly! The second question is what is the optimal mix of investors in the cap table; should you be just targeting financial investors or is there merit in considering strategic investors who could assist with de-risking commercial channels or R&D activities?

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For partnerships, it is important to make a distinction upfront between those that will always remain partnerships and those that might be a “try before you buy” scenario, i.e. a partnership deal that could evolve into an acquisition. With the latter, however, you need to be careful that a ‘try before you buy’ partnership does not limit exit flexibility. This usually happens when your revenues and product roadmap are tied to their commercial channels so any valuation uplift is tied to the partner. But it is important to point out that well-structured partnerships can derive a meaningful uplift to the financing process so they are by no means mutually exclusive strategic pathways.

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There’s also an often-overlooked opportunity for a ‘merger of equals’. If you can actually combine two companies that have complementary products and/or commercial channels to provide a holistic turnkey solution that is more effectively embedded into workflows…which is a particular issue in Precision Medicine…then perhaps this is an effective means of accelerating both product development as well as commercial efficiency But for these deals to happen, both sets of investors need to have an aligned view on relative valuation and resulting governance of the merged entity!

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For the future of precision medicine, what role do you see data playing? 

I fundamentally believe that what we need for the advancement of precision therapeutics and diagnostics is a convergence of data, services, and analytics. Data by itself is relatively low margin, analytics without data results in not being able to efficiently train AI models. You also need services to ensure sticky adoption. What we have today are data assets that sit in their silos whereas what we need is the aggregation of data assets on a global basis that are brought together under a common data model to get statistical power for Pharma’s drug discovery efforts. We need a “data lake” that ideally has diversity by ethnicity, age, disease areas, and so on, paired with genomics and other omics datasets as the key differentiator. And there is an emerging need for this aggregated data offering in therapeutic areas other than Oncology. So for the future of precision medicine, the foundational requirement is high-quality data at scale with the diversity metrics and modalities I mentioned earlier and then on top of this data lake, you build your analytical tools and services, which will be particularly important for the broader biotech ecosystem. Even if you have some of these capabilities internally, Pharma companies would still value an RWD vendor doing the heavy lifting on data aggregation, integration, and harmonization. Overall, what we’re building is an efficient two-sided marketplace between data providers and data subscribers. 

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If you get increasingly escalating value from diverse, high-quality, multimodal datasets in the form of sophisticated insights, what does that mean for the industry going forward? Are we going to see a consolidation of data assets, or will it remain relatively fragmented like it is today?

We are already starting to see management teams of these individual data assets realize that they need to collaborate with each other to provide the aggregate statistical power and data harmonization that big Pharma requires, along with a services capability and some level of analytics. So there is no doubt that this has to happen. The key question in my mind is under what deal structure and with what mix of investor types…strategic, venture / growth capital, sovereign wealth funds? Is there a business case for outright acquisition of data partners vs a more hub and spoke partnership and revenue share model?  And what are the use cases for the dataset? Is topline generated purely from target discovery or expanded downstream into clinical development applications…trial design, trial enrolment, etc.? And to your question about sophisticated insights, the data platform will need to prove that their analytical tools are better and trained on larger datasets than Pharma’s internal tools. How this all plays out is going to be very interesting!

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