How investors in the biotech sector can benefit from artificial intelligence’s increasing importance in drug discovery and development purposes
Artificial intelligence and machine-learning algorithms are disruptive technologies that are having a twofold impact on the drug discovery process. First, the technology can be applied to save time and money during the process of identifying molecular targets for drug development purposes. Second, thanks to the availability of a massive amount of data, clinical trial design and patient selection can be approached in new ways that likewise save time and money and, importantly, also lower the risk of trial failure significantly.
In addition, artificial intelligence tools can improve the depth of the due-diligence element of the stock selection process for biotech investors by allowing them to synthesize a broader set of data. Artificial intelligence will not replace portfolio managers, but it can provide them with additional insights for fundamental investment research purposes that makes it much easier for them to screen and select the right stocks within the given investment universe.
Integrated data
As an example, in recent years we have integrated huge volumes of datasets into the business’s own IT infrastructure, covering a wide range of data types. This pool of data contains patient-derived data, insurance claims, fundamental information about drugs, news, published studies, reports and scientific literature, as well as information gathered through the portfolio management team’s international network of contacts.
The insights gained through the analysis of this data are just as extensive as the sources from which it is collected. Examples include characterising the commercial potential of a drug candidate based on the mechanism of action, levels of competition and quantifying the epidemiology and unmet medical needs of patients. The data is also used to understand patient journey and flow in the medical system by looking at reimbursement patterns within insurance companies, the ultimate payers for marketed treatments.
Advanced analytics will undoubtedly lead to significant medical progress – especially in oncology. This is an area where large amounts of patient and real-world data have been amassed, which facilitates the process of identifying compounds with the best efficacy profile, as well as patients being most receptive to a drug, leading to a product with the greatest market potential.
There are a number of companies developing novel drug candidates in every therapeutic area – in particular neurology/psychiatry and auto-immune diseases, in order to segment better heterogeneous patient population using AI-discovered biomarkers and drug targets.
Biotech companies using AI
The number of companies that are fully focused on computational biotech – that is, using AI and machine-learning for drug discovery or development, has been growing since about 2015, and most are still reliant on venture capital funding. Many computational biotech companies have focused their research and development activities on neurology and cancer. As for the types of drug classes studied, traditional small molecule drugs clearly predominate, followed by biologics (antibodies and peptides). Small molecules are appealing, as they are more easily developed as well as because more AI tools and research is available about them.
The coming years will reveal just how promising and viable drug candidates developed with the help of AI tools actually are. A growing number of product candidates developed with the help of AI are entering Phase II testing yet the majority of therapeutics (42%) being developed using artificial intelligence technology are in the preclinical stage of development.
Investors should be closely monitoring developments in the field of computational biotech during the coming years – indeed two of the companies in our own portfolio are pursuing AI approaches in their drug discovery and development programmes.
Relay Therapeutics has three cancer drugs in advanced clinical studies targeting pathogenic proteins that were previously not viable molecular targets for the development of new therapeutics. It recently published very convincing clinical data on its lead FGFR2 inhibitor product candidate. For its part, Black Diamond Therapeutics, is deploying machine-learning technology to develop novel precision cancer therapies designed to work across a number of tumour types.
We see tremendous potential in the use of artificial intelligence to support both investment decisions and to develop novel drugs. To that end, our own portfolio management team has significantly expanded its data science capabilities and recently recruited two additional specialists.
Dr Samuel Croset is a data scientist at BB Biotech, a €2.7bn investment company specialising in the biotech sector
