In the quest to transform disease, it is essential to target any novel biology uncovered. Once a target is selected, researchers now have a toolbox of traditional and innovative molecules they can use.
From small molecules and monoclonal antibodies to messenger RNA and cell therapy, the opportunities to create new medicines for any targets have never been greater.
AstraZeneca has a toolbox of 18 types of molecules it can select from as the company looks to create the next generation of medicines. They combine their chemistry skills and technologies with those of other leading companies in highly specialised fields, as they work towards addressing the unmet medical needs of patients.
AstraZeneca is already leveraging the rapid advancements in science and technology to create new therapeutics for disease mechanisms which were previously considered difficult, if not impossible, to target. Additionally, they are working on advanced delivery technologies. Each new insight and every new piece of information brings them closer to creating better, more advanced, and more effective treatments for patients.
Two areas of advancement are cell therapy and using Artificial Intelligence (AI) to speed small molecule discovery.
Cell therapy is a promising, rapidly advancing field with the potential to transform medicine across disease areas with significant therapeutic need. Using cell therapy to halt and reverse disease, restore damaged organs, and ultimately cure many life-threatening conditions is now a realistic goal. Important advances in the understanding of disease biology – as well as major innovations in gene editing, protein engineering, and cell culture technology – have created a highly fertile scientific environment in which cell therapy research is flourishing.
AI is another area that is helping to discover new medicines. It has great potential to increase the quality and reduce the time it takes to discover a potential drug candidate.
It currently takes several years of detailed scientific research to discover a potential drug candidate, with scientists having to synthesise and test thousands of molecules in order to achieve the right drug properties.
AI is transforming this lengthy process – enabling scientists to rapidly generate novel ideas for molecules and to make and rank these ideas by using predictions based on large data sets.
Having identified promising molecules, the next step is to synthesise the molecules in the lab. AI is starting to help here, too – the science of synthesis prediction is rapidly evolving, and scientists will soon be able to use AI to help deduce the best way to make a molecule in the shortest time.
Half of AstraZeneca’s small molecule projects apply AI approaches, and the first AI-derived molecules have already entered their pipeline.
AI is a key component in the chemistry lab of tomorrow – not only for discovering and making new drugs, but for controlling automation to speed up the repeated cycles of generating, analysing and testing high-quality compounds.