EY: What challenges in the life sciences business model are Massive Bio trying to solve?

Kurnaz: Massive Bio is trying to enable health equity for any cancer patient that wants to get access to clinical trials, regardless of where they reside in the world and what their financial status is. Even in highly affluent countries, when you go to a medical oncologist, they will suggest a clinical trial at most 50% of the time, and even then they will only consider clinical trials within their own institutions. Outside the likes of MD Anderson or Memorial Sloan Kettering Cancer Center, this means patients have very few clinical trial options. However, worldwide there are over 14,000 actively-recruiting clinical trials in oncology. Massive Bio is a matchmaker between these 14,000 clinical trials and cancer patients around the globe. Unlocking access to clinical trials for these patients takes pressure out of the system. Because site networks are based on therapeutic area, you cannot create an all-comer patient recruitment company – others have tried and failed in the past. You need to focus on a specific therapeutic area. Oncology is our chosen target because it affects so many people and as a result is both the largest and the fastest-growing market.

EY: How does artificial intelligence (AI) drive your business model?

Kurnaz: At Massive Bio, we view AI in three different components. First, pre-screening patients. Pre-screening a patient manually to enrol them in a clinical trial takes 25 minutes. Across 14,000 trials, that would take a full-time employee more than eight months. We aim to reduce that time to minutes, if not seconds, through the use of large language models which structure clinical and genomic patient information against clinical trial inclusion and exclusion criteria. Time is important to all of us – for a cancer patient, it is ten times more so. Second, patient identification. We use AI to consolidate and streamline the different datasets we use, from payers, diagnostic laboratories, speciality pharmacies and others, and to predict where the next cancer patient is going to appear who fits the characteristics of the trial you are recruiting for. Third, patient and provider engagement. Our Patient Connect application runs on AI-based infrastructure, and allows patients to gain access to trial pre-screening, monitor the process and engage with the app through chatbots. Our Clinical Network application is the first application ever to allow a treating oncologist to pre-screen and refer a patient, through the use of AI, anywhere in the world. It is also the first application augmented with AI for physicians to pre-screen patients independent of the Electronic Medical Record (EMR).

EY: What are the opportunities and challenges of partnerships between pharma players and AI technology companies?

Kurnaz: In terms of what pharma companies do internally, they either have their own AI-related capabilities to make their processes more efficient, or they partner with external vendors to get that capability. In terms of partnerships, we work with pharma companies in two ways. One side is that we provide a lot of proprietary data. Without the right data AI is an engine without the gasoline. We have onboarded 160,000 cancer patients and pharma companies partner with us to access these data and to train their own models based on the data we provide them. The owner of the data, of course, is the patient – we get patient consent to collect and process data, and maintain the right SOPs, processes, communications and transparency to ensure that everything works correctly from a regulatory perspective. The other side is scale. In order to pre-screen those 160,000 patients you need AI to do the work for you. We bring pharma that AI component, so they do not have to develop it internally. This pre-screening capability is fundamental for pharma’s drug development processes but at the same time it lies outside pharma’s core specialisms. If you can bring the data and the scalability, the AI algorithms themselves are the easiest aspect of the problem – typically open-source, off-the-shelf algorithms are enough. AI companies seriously thinking about partnering with pharma need to understand the industry’s expectations. In terms of timing, big pharma processes are incredibly long; if a young company partners with pharma expecting to make rapid progress in three months they are not thinking realistically. Another issue is that innovation is not the same as validation. As a company you may be innovative but pharma thinks in terms of numbers, results, success, and to be credible in front of pharma clients you need to think about how you satisfy those kinds of expectations. Finally you need to think about pricing and finding the right buyer – with a pharma company’s Chief Medical Officer or Head of Therapeutic Area you may be able to negotiate a very different contract than if you approach the Procurement function. All of these factors need to be kept in mind.

EY: How do you see the AI and pharma dealmaking landscape today?

Kurnaz: About 80% of the AI companies who were successfully fundraising in 2020-21 have gone bankrupt or are on the verge because they could not generate the revenue needed to meet investor expectations despite lavish spending. Generating revenue from AI-driven services to pharma is more difficult than going to Mars. There are another 10% of companies now reaching the light at the end of the tunnel: pharma is currently running due diligence on these companies and they will be acquired. The remaining 10% are those like us, the future Amazons of the world. Their importance may not have been understood at the beginning, but their value is going to be realized over time like a fine aged wine. It may take 25 years to develop, but these players will be the ones that last to create the trillion-dollar companies of the future.

EY: What do you see as the biggest challenges for dealmaking and capital allocation in the current business environment?

Chris Sheldon: Our industry has to keep reinventing itself with innovative R&D pipelines. That is how the healthcare innovation ecosystem works to ensure new medicines address patients’ needs. At GSK, we are using  targeted Business Development (BD) as an opportunity to reinvest in the business, seeking potential new medicines which can launch at the backend of this decade or early next decade as we continue to grow our pipeline.

Any single pharma company on the planet can only account for a small amount of global innovation – there is always more going on outside our four walls than inside, it is a simple numbers game. This is true for GSK and all our peer companies. For most big pharma players, more than 50% of commercial products or pipeline assets come from business development in some form. We always look for the intersection of compelling science, patient need, a good financial fit and the right cultural elements as a ripe starting point for industry leading BD. Truly transformational opportunities are rare but when we find them we ruthlessly pursue them with speed and agility.

Public company M&A is challenging because if a company has great data their value often rises very quickly and it becomes very difficult for pharma BD teams to make the risk and reward calculations work. That is likely why we have seen limited public M&A this year. However, as the IPO market cooled down in the last few years, more companies are staying private for longer, advancing and de-risking their programs rather than seeking an early IPO exit. This means more de-risked innovations with clinical data, which is often the sweet spot for pharma M&A.

EY: In terms of potential M&A targets, how much importance do you attach to therapeutic area focus, compared to, for example, targeting new modality platform technologies?

Chris Sheldon: No one company can do everything. It is absolutely critical to scale and build strategic focus in core therapy areas. To scale a business in a given therapy area, you need deep subject-matter expertise; every therapy area is competitive and knowledge of the specific ecosystem and community is so important, from building relationships with key external experts to recruiting patients for clinical trials.

At GSK, we are investing deeply in areas where our pipeline has the greatest potential impact for patients: infectious diseases, HIV, respiratory/immunology and oncology. In all of these areas we are very well positioned to deploy the expertise and global capabilities necessary to create value for patients. We focus our strategy around product area leadership teams (PALTs), working at the intersection of R&D, commercial and medical to ensure a full cross functional representation of a given therapy area.

In today’s multi-modality era, with the rise of advanced modalities such as ADCs, oligonucleotides, T cell-engagers and others, you also need to find the best modality for a given target to ensure the greatest potential for patient benefit. In terms of platform deals we are interested if a platform is the right modality for a specific use case and clearly demonstrated with data. Specifically, a validated program with the right attributes including the ability to address an unmet medical need and disrupt standard of care with best or first in class credentials. The bigger picture is key here: we think in terms of disease area, unmet need, a compelling target with clear disease linkage, and, within that, which modality is right one for this target. Ultimately our goal in pharma is to get great medicines to market, I define those as one with great effect sizes and therefore the best way to achieve that is through a deep therapy area focus.

EY: What about digital technologies and AI? What role do they play as targets, or even within the BD process itself?

Chris Sheldon: Embracing advances in technology is core to our R&D strategy and we think in terms of two technology buckets. Firstly platform technologies, which are the technologies needed to discover, develop and manufacture medicines and vaccines. Secondly, data tech, which is the use of data to deeply understand human biology and disease mechanisms and to drive value-generating performance across our business.

In terms of BD specifically, we are a fundamentally relationship-driven industry where face-to-face debate at medical and scientific meetings, and speaking to investigators and clinicians, is core to our success. We are in the people relationship business, and technology will not replace those relationships, but it is a critical contributor to developing the right relationships. At every stage you need the best possible information to guide your business case with the human element infused to form the best partnerships built on trust and an aligned vision.

EY: How do you find the right deal in 2024? What factors do you look at in terms of possible partners, deal structures, different geographies?

Chris Sheldon: Partnerships for early-stage programmes are typically kept strategically aligned but operationally independent. What I mean is we ensure the partner is not distracted and the entrepreneurial spirit and dedicated focus of smaller companies are maintained. What is important is that either side of the partnership recognises their respective strengths and expertise.For M&A, higher levels of integration are often needed to extract full value from an acquisition, particularly assets needing parallel development for multiple indications where data and knowledge transfer is mission critical. We are also involved in other kinds of partnership, including academic collaborations and joint ventures. We see VC as an important part of the ecosystem for healthy and open debate. We consistently maintain our dialogue with the VC community as we scout the world for innovation.

At present, where we are searching for innovation has changed somewhat and that is leading to different deal structures. For example, China is an important of innovation, and we have seen pharma, biotechs and even VCs forming companies around assets from China. That will increase because the Chinese ecosystem is well-equipped to develop advanced modalities and to generate early clinical datasets which help to inform western development plans. Often the deal model is that the Chinese company retains the rights in China and the acquirer leads everything else, presenting an opportunity for both companies to leverage their strengths.

At GSK we are deal structure agnostic and always open minded however we would probably prefer a structure linking milestones to de-risking events along an asset’s life cycle from discovery to commercialization as value is unwound and risk clearly discharged. However, it is just a fact of life that the seller often dictates structure, not the buyer, and in order to be the preferred partner, you have to be somewhat deal-agnostic – and as I always say, ultimately, every deal is a snowflake.