The industry is yet to see a return to pre-pandemic levels in non-COVID-related clinical trials. “The biggest impact has been in Phase 1 trials, suggesting that there will be fewer drug candidates to progress into later phases of development,” Dowdeswell said. “That said, we still see high demand for CDMO services to support biotech companies in early-stage development.”
In the following interview, Dowdeswell provides perspective on which small molecule trends the industry should keep an eye on in 2023. He dishes on everything from machine learning to the increasing complexity of small molecules.
What small molecule trends should pharma companies and manufacturers look out for in 2023?
Dowdeswell: In 2023, I remain highly interested in the ongoing trend of increasingly complex small molecules, as they will be more precisely designed and able to address more unmet clinical needs, such as rare diseases. As compounds become more complex, the number of synthetic steps will increase; in the past 20 years, the number of steps for making small molecule APIs has grown by two-thirds, as has the number of chiral centers. Similarly, average molecular weights continue to rise, and approximately three-quarters have poor solubility. With so many new molecular entities (NMEs) being approved via some form of expedited approval pathway, there is continued high demand for CDMO services and demand to meet ever-shorter timelines. To ensure that manufacturers can meet accelerated timelines for complex small molecules, innovation will be vital, including the development of digital tools that create robust processes.
On the environmental sustainability front, ambitious targets in reducing emissions and waste are driving significant advancements. To help reduce environmental impact, we now embed sustainability into our process design for chemical synthesis. We have initiated projects that look to eliminate toxic materials from processes or implement environmentally friendly replacements. I expect this will become a key area of differentiation in the CDMO industry.
How do you think the rise of biologic therapeutics affects small molecules?
Dowdeswell: Despite the rise in complex biologically derived molecules (biologics) and other factors, there continue to be record numbers of small molecule candidates in the clinical pipeline. These drugs can reach targets that biologics cannot access, for example, infiltrating cells to disrupt the disease processes taking place inside them. They can also be formulated to be taken as an oral dose, a choice many patients prefer due to its accessibility.
For patients, this means that they will be able to have more treatment options. The increased understanding of biologic and small molecule therapeutics will enable them to have access to treatment at earlier stages, potentially increasing their health span and quality of life.
Pharma companies must continue to invest in both proven small molecule solutions and explore the potential of biologics. The fact that small-molecule drugs can be dosed orally is a huge factor to consider when thinking of patients, and these compounds will only become more precise and effective as companies continue to invest in research and development. This is why we continue to build on our capabilities. The resulting facility expansion, staff training and enhanced processes allow us to manufacture even more complex small molecules.
What influence do you think AI and machine learning will have on small molecule trends?
Dowdeswell: With respect to research, which generally impacts manufacturing and business operations, artificial intelligence (AI) and machine learning (ML) can greatly optimize processes and maximize efficiency. At Lonza, we pursue ML to leverage data for optimizing process engineering and formulations to arrive at more robust solutions more efficiently for our clients.
One opportunity for the future of research and manufacturing lies in harnessing rich data, despite these data not being used as effectively as they could be. Therefore, we are working to make use of data to be more predictive of technical outcomes and therefore deliver better results more quickly. This is important, as a large fraction of new chemical entities in the industry are being advanced under accelerated development programs, such as breakthrough therapy designations, where speed is critical to bring new medicines to patients.
In addition, the complex nature of the chemical matter (modality, size, solubilities), as well the need to deliver these new modalities effectively into the body with speed and agility in the development program, adds an extreme need to be predictive and utilize existing data to boost chances of success.
AI and ML will likely be cornerstone technologies in future drug development, and I look forward to seeing how both can help bring success to our customers.
What does it mean for the industry that small molecules are becoming increasingly complex?
Dowdeswell: Within the last twenty years, the complexity of chemical synthesis has almost doubled from eight chemical steps to an average of 14 in 2021. The increase in steps to manufacture an active pharmaceutical ingredient adds to the complexity of the molecule. From a supply chain perspective, it adds multiple layers of nuances in containing, handling and transporting different chemical elements, which means that manufacturers must have a strong command of their value chain to ensure that development timelines are consistently met.
The other aspect is the drug product itself, where most drug candidates in the market have some sort of bioavailability challenge. What this means for the manufacturing and development of small-molecule drugs is that enabling technologies must be employed to solve these challenges. Particle engineering technologies, such as amorphous solid dispersion or micronization, will be critical in ensuring the efficacy of these drugs. We have a suite of experts specializing in this at Lonza, and our team established the global industry standard for small molecule bioavailability enhancement.
Filed Under: clinical trials, Drug Discovery