Discussing the highlights from this year's DCAT week, Vertommen delved into emerging trends, the evolving role of Contract Development and Manufacturing Organizations (CDMOs), and the integration of innovative technologies like artificial intelligence (AI) and machine learning (ML) into drug development processes. Here's a closer look at Vertommen's perspectives on these critical topics.
OSP: What is your take on news and views from this year's DCAT Week? Any trends that stand out?
DCAT provides a unique opportunity to meet the C-Suite level management of our large pharma as well as our smaller biotech customers, who represent the vast majority of our customers.
There are four main takeaways from my meetings with large pharma companies. First, large pharma companies have a strong interest in securing process optimization and manufacturing capacity at CDMOs for recently acquired products due to increased merger & acquisition (M&A) activity at these large pharma companies. For many of the acquired small molecule-based products, the manufacturing process needs further optimization, and hence, development and manufacturing capacity are needed to perform this process optimization.
Second, sales projections for the acquired products are often far higher than what would have been the case if the product had remained in the hands of the acquired biotech company. As a result, increased manufacturing capacity at CDMOs in a relatively short time frame is often needed, which may be challenging to accommodate. Furthermore, in view of the strong M&A activity in the ADC field, many customers are looking for additional manufacturing capacity for the payload-linker portion of their ADCs. Third, sustainability is clearly gaining importance in the CDMO relationship, moving from words to action. Sustainability is becoming a decision criterion in awarding new programs, and sustainability engagements are becoming part of the contractual framework between CDMOs and larger pharma companies.
Lastly, funding remained a key discussion topic during our interactions with our large community of smaller biotech customers. While there were some positive funding signals mentioned, many of them had or will have to make hard choices ranging from reducing R&D spending, focusing on the one commercial asset they have, to selling assets or simply ceasing activities.
OSP: What is your view on the current state of the pharmaceutical CDMO sector? How does the sector approach patient-centricity, recruitment, and trials?
Today, CDMOs are viewed as key collaborators with biotech and pharmaceutical companies to bring their innovative, complex therapies to market. As these industries evolve, CDMOs will continue to focus on increasing flexibility in manufacturing, with investments focused on digitalization and Industry 4.0. We continue to invest in our capacity, capability, and expertise to develop a wide range of products that meet the needs of drug developers.
In addition to our focus on flexibility and capacity, we are also creating patient-centric solutions by meeting the demand for more flexible formulation options, especially for populations with specific needs, such as children and older adults. For example, Lonza's multiparticulate (MP) technology addresses this challenge by enabling the design of patient-friendly dosage forms. MP formulations are made up of multiple, small drug-containing particles that together make up a dose. This allows for a tailored approach that involves identifying the target population's comprehensive needs to inform the design of drug products that provide the best overall benefit-to-risk profile. Moreover, multi-particulates allow flexible adjustment of the dose for each patient in a clinical trial, which is important for trials involving a pediatric population.
OSP: What are future trends?
Like all industries today, artificial intelligence (AI), machine learning (ML), and robotics are driving the pharmaceutical industry forward. These new technologies can help address the growing demand to develop drugs and therapies faster and more efficiently, thus reducing customer timelines and costs. We are integrating digital technologies into the drug development and manufacturing journey. For example, we are implementing ML algorithms and AI into our processes to navigate the complexity and speed requirements of manufacturing novel treatments and accelerate the synthesis of APIs.
In small molecule development and manufacturing, ML is used for synthetic route optimization, retrosynthesis, toxicological assessment of new chemical entities, and formulation design. In drug product development and manufacture, ML is employed in developing controlled-release tablets to assess the hardness, particle size, moisture, and other factors that predict a tablet’s in vitro behavior.
From the DCAT presentations and discussions, it became clear that data availability to train AI-based systems is the key to success. In that respect, the vast databases related to material sourcing that CDMOs have may provide a unique selling point for AI-based route-scouting software that is being developed.
OSP: Any comments you would like to add on any recent Lonza-related news specific to small molecules?
We're excited about the expansion of our spray-drying services for biologics targeted at respiratory delivery. This reflects the significant shift in the pulmonary drug landscape, with biologics now comprising nearly a third of the pipeline. Traditionally, protein-based therapies for respiratory diseases have relied on injections. This offering leverages dry powder inhalers, potentially offering a more patient-friendly and efficient delivery method.
We believe this expansion strengthens Lonza's position as a leader in spray-drying technology. By providing manufacturing capabilities from early-stage clinical trials to commercial production, we aim to become a valuable partner for companies developing these innovative respiratory therapies.
