L.E.K. Consulting is an independent global strategy consultancy which works closely with business leaders to seize competitive advantage and amplify growth. The insights its shares with clients are the catalysts that reshape business trajectory – uncovering opportunities and empowering them to master moments of truth.
“We take a highly analytical approach to solving our clients’ problems, taking the time to gather the right data and let the facts inform our thinking,” says Anne Dhulesia, a Partner in L.E.K.’s European Life Sciences practice. “Experience and expertise play a part too, but we believe that every client and every challenge deserves a clean sheet and a fresh take.”
It’s this different mindset and approach that sets L.E.K. Consulting apart and helps clients to navigate the most advantageous path forward.
Here, Stephen Roper, Partner, Global Healthcare and Life Sciences, and Anne Dhulesia, a Partner in L.E.K.’s European Life Sciences practice, discuss AI and biopharma.
Please introduce yourself and your role.
“I am a Partner at L.E.K. Consulting. I work within our global Healthcare and Life Sciences practice and am based in our London office. I spend my time working with clients within the biopharma ecosystem – this includes biopharma companies themselves as well as companies that support them with services or tools. I assist companies primarily with their growth strategies or with M&A activity. Like many in our team, I am a scientist by training and have a PhD in Developmental Genetics.”
What led you to your interest in AI applications within biopharma?
“The challenge of dealing with enormous and ever increasing volumes of scientific and medical data is one that fascinates me. Even if a researcher focuses on a very narrow portion of that data – for example a single human protein or disease – it is a daunting task to keep on top of the growing body of data and to draw conclusions across such a potentially disparate set of insights. AI has the potential to accelerate this data ingestion and interpretation process massively.”
How do you see AI being used in biopharma?
“There are a wide range of use cases for AI in biopharma. These span from the initial stages of discovering drug targets, through to identifying the most promising patients to recruit into clinical trials. One aspect I would like to mention in particular is the usage of natural language processing (NLP) and its potential to assist with some of the challenges I outlined above. The approach has tremendous potential to generate novel insights more rapidly than a person may be able to do so. For example, having interrogated the published scientific literature it may be able to suggest a new potential drug target that can be tested in a disease, or link an existing drug candidate with a new target disease.”
What do the next 12 months hold for the space?
“I am excited to see what impact the new large language models can have in this space. We have seen some companies indicating that they are applying these models and I am hopeful that these approaches can further assist with generating highly valuable, novel insights which biopharma companies can pursue in developing the next generation of drugs.”
Please introduce yourself and your role.
“I am Anne Dhulesia, a Partner in L.E.K.’s European Life Sciences practice. Myself and my colleagues advise Life Sciences clients on some of their key strategic questions. My clients include both investors, typically private equity / growth equity or venture capitalists, as well as corporates. These can be either biopharmaceutical companies that are in charge of developing and /or commercialising drugs, or companies that work for biopharma companies, providing them with products or services.
“Before joining L.E.K., I was a scientist at heart: I studied chemistry in my undergraduate and master studies and then biophysics during my PhD.”
What led you to be interested in AI and big data in R&D?
“Being a scientist by training I have a specific affinity for innovation in Life Sciences, and more specifically in R&D. Scientific discovery underpins advances in treatment options by targeting new mechanisms, or by discovering new ways of improving clinical performance of existing drug classes.
“My biopharma clients’ ultimate aim is to improve patient outcomes, to decrease disease burden. To do so they invest significant resources in R&D: the top 10 pharma companies spend up to US$15bn in R&D per year representing around 15-25% of their revenues.
“It became clear to us and our clients that leveraging the power of data, for example through AI, would provide ways to optimise the deployment of that very significant R&D capital and get novel medicines faster to patients.”
How do you use AI and Big Data in pharmaceutical R&D?
“Over the last decade, pharmaceutical R&D has increasingly leveraged the power of big data and AI, as more and more relevant data was being generated, made accessible, and cleaned in such a way that it could be analysed and serve as training datasets for AI algorithms.
“Use cases span the whole R&D value chain, from very early stage or fundamental research to the final stages of the drug development process (i.e. during clinical trials). Depending on the use case, the type of data that is being used differs and what AI brings also varies.
“Let me give you a few example use cases:
- Early on in the drug discovery process, data from academic scientific publications can be mined by AI tools to find new links that were not necessarily thought to exist – these links allow to discover new biological pathways which novel drugs can target.
- Databases documenting structural physical properties of existing pharmaceutical compounds can be interrogated to find new potential applications for existing drugs; this is how, during the pandemic, UK-based company BenevolentAI found that the molecule baricitinib could have therapeutic potential for COVID-19.
- Finally, the right patients can be selected for the right drugs, using some specific biomarkers, allowing the development of more precise and more powerful therapies, for precisely defined patient segments. To do so, pharma companies need to understand which types of patients respond – and which don’t. They do so by running algorithms on data accumulated in patient trials.
“As these examples demonstrate, AI has the potential to speed up drug development, bring drugs with enhanced clinical profiles to patients and potentially make that process less costly.”
What type of work does L.E.K. do in the life sciences space?
“For investors in life sciences: we help you understand where the field of AI / big data is going. We help get to the bottom of trends driving demand, of what customers need, and we help differentiate between the various players that are out there (there are many!).
“For corporates – in R&D and beyond: we help you see how you could use AI / big data in your own organisation, in which types of programmes, for which use cases. We also help you manage change within your organisation – to ensure that your organisation is ready to embrace AI and big data.”
What do the next 12 months hold for AI and big data in pharmaceutical R&D?
“We are only at the start of what AI and big data can bring in that field. Over the next 12 months I expect that we, collectively, as an industry, will help showcase how the use of AI and big data can help accelerate specific steps along the R&D value chain. We will not see a revolution in how drug R&D is performed, but have some proof that these techniques can accelerate discovery and development, allow us to explore new areas and be used reliably along other methods. Through the accumulation of use cases, it will help establish that AI and big data should always be at least considered as part of any pharmaceutical R&D process, as currently more established methods are.
“I also expect that new partnerships between AI / big data companies and biopharma players will help demonstrate the sustained interest of the pharma industry in leveraging data. We have seen some notable deals over the last 2-3 years: e.g., In 2022, Sanofi partnered with Exscientia to develop up to 15 small molecules across oncology and immunology; in February 2023, Takeda acquired an experimental autoimmune disease drug from Nimbus Therapeutics, a drug discovery company based on computational chemistry and AI algorithm; in May 2023, Eli Lilly entered a partnership with XtalPi to help identifying potential drug candidates based on artificial intelligence- and robotics-powered technologies. These deals provided some much-needed validation. I expect we will see more such partnerships over the next few years as big pharma continues to build their R&D engine.”
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