This month’s figure: 90
As in the 90% shortening in vaccine development time made possible by a digital approach.
STORY OF THE MONTH
In this new ODDO BHF Artificial Intelligence monthly flash, we delve into how artificial intelligence and machine learning (AI/ML) are revolutionising the process of drug development and manufacturing.
The many benefits of AI/ML in drug development
Only 12% of drugs that undergo clinical trials end up being approved for commercial launch. The average development time of a drug throughout all its cycles lasts eight years on average. This is why the pharmaceutical industry has always been structured along long cycles and why returns on capital employed have been hampered by low success rates during development phases. And yet, we expect artificial intelligence and machine learning to move this industry into a new era. Three categories of AI/ML benefits have already been found: 1) identifying innovative therapies; 2) shortening drug development times; and 3) raising the likelihood of success of molecules in clinical trial phases. In particular, machine learning algorithms will provide major benefits: 1) in setting the right dosage of the tested drug so as to find the right balance between safety and efficacy; and 2) in putting together patient cohorts for clinical trials that offer the desired combination of characteristics (in terms of phenotypes and genotypes).
Easier to be born a “digitalised platform” than to become one...
Based on these findings, the world’s 10 largest pharmaceutical groups have all made small bolt-on acquisitions of artificial intelligence start-ups. These digital nuggets have been incorporated into the R&D workflows that had been built up in recent decades in “analogue” mode. Unsurprisingly, integration has had rather uneven successes, as pharma majors have run into the following obstacles: 1) a lack of know-how in machine learning techniques among senior drug developers; and 2) significant losses of speed in the development process, as entire swaths of the chain are still in analogue mode (unlike fully digitalised companies).
As a result, higher success rates from using artificial intelligence in drug development are now found among companies that, from the start, built fully digitalised development models based on artificial intelligence and its derivatives. The most famous example of this strategy is currently Moderna, thanks to its breakthrough mRNA 1273 Covid vaccine and the resulting surge in economic and stock-market value in just a few years. There are several other companies, both listed and non-listed, that have placed artificial intelligence at the heart of their strategy. These include: 1) Recursion Pharmaceuticals, which will apply machine learning algorithms to proprietary series of biological and chemical data in developing new therapies; and 2) Kronos Bio, which will use highly elaborate computational models to attack cancerous targets that had until now withstood all forms of treatment.
Moderna offers the most complete example thus far
“We developed a Covid vaccine in two months. This was 90% faster than the normal 20-month development time”. This is how Stéphane Bancel describes the process that began with identifying a SARS coronavirus and lasted until the start of phase 1 clinical trials in the first quarter of 2020. An accomplishment like this is not due to chance or to the individual skills of ingenious researchers, but rather to Moderna’s development of a suite of software and algorithmic tools that have digitalised the entire drug development value chain (from messenger RNA sequencing to the study of its chemical and geometric properties to its manufacturing).
Messenger RNA is a programmable therapy that digitalises, i.e., sequences human scourges, and offers innovative solutions to them in record time.
Through its fully digitalised approach and its 10 years of experience in this technology, Moderna is its flagship representative. In addition to its Covid-19 vaccine, Moderna’s pipeline contains various programmes at the clinical or pre-clinical testing stage, like other prophylactic (flu-type) vaccines, cancer vaccines, and drugs that fight some rare diseases. We are confident that this pipeline of indications that are treatable using messenger RNA will continue to expand in future years. To accompany its growth, Moderna possesses production capacities in Massachusetts, all of which are also fully digitalised and headed by the former head of manufacturing at Novartis . The road ahead will nonetheless be long and bumpy. While messenger RNA offers precious advantages (such as being a programmable, i.e., sequencing therapy), its potential therapeutic use is constrained by: 1) how long its effect lasts (for messenger RNA to lead to a drug it must generate proteins, and this effect may last only a short time); 2) the immunity system’s reaction to the insertion of messenger RNA into a cell is not always what is desired by researchers working in these areas; and 3) the mode of delivery and genetic modification produced by messenger RNA is lipide nanoparticles (LNPs), which have the advantage of non-toxicity (unlike other approaches) but also the drawback of less effective transport faculties of the transforming gene than other transport technologies (such as virus-based ones).
ODDO BHF ARTIFICIAL INTELLIGENCE
• A new way of managing investments: The power of artificial intelligence (AI) combined with a proven quantitative model that captures the performance of the best global listed Artificial Intelligence companies.
• A robust theme: With its structural growth engines, we expect AI to grow faster than other economic sectors. Companies able to seize the opportunity are likely to create value in the long run.
• Integration of artificial intelligence in the investment process: This unique approach allows us to analyse more than 4 m pieces of data each day and to quickly detect new trends in the theme and shifts in market sentiment, while capturing the growth of under-the-radar small and mid caps on a global scale.
• An experienced team whose talents play off one another: Brice Prunas, a manager with more than 20 years of experience as a financial analyst specialising in technologies. Maxence Radjabi, a manager with three years of experience in investing with a bent towards quantitative analysis.
