On paper, biosimilars have great potential to cut development costs, expand patient access to biologics, and improve affordability. The practical reality is that these goals are difficult to achieve.
Technology isn’t the biggest problem, say experts. Intellectual property challenges, added costs to characterize reference products, and uncertainty around what constitutes acceptable similarity are the stumbling blocks. That said, the biopharmaceutical industry is pushing forward aggressively to overcome the difficulties, and it’s making substantive progress.
Quality by design (QbD) approaches can reduce biosimilar development risk. Single-use technologies can cut costs. Modern bioprocessing methods produce higher titers than when originator products first reached the market. The ability to characterize large complex biologics, such as monocolonal antibodies (mAb), has improved. And the recent draft guidance from the FDA is further clarifying the regulatory landscape.
“We are all happy to have guidance documents,” says Joerg Windisch, Ph.D., head of global technical development for Sandoz Biopharmaceuticals, a player in biosimilar and innovator biologics. “They are very science-based and give FDA flexibility. We like many things about them.”
For example, the draft guidance allows global development, sparing biosimilar developers the need to conduct two clinical studies, one against the European product and one against the U.S. reference product. “We were very worried about that,” Dr. Windisch admits.
One way to help reduce development uncertainty is to use QbD concepts. “In this context, you design the manufacturing process for the biosimilar to produce a molecule that is very much the same as the original molecule,” notes Dr. Windisch.
The first step, of course, is to define the target by characterizing the reference product. This is an expensive, time-consuming process that involves buying multiple batches of the reference product and profiling the critical quality attributes (CQAs) with an arsenal of modern analytical tools.
A nagging issue is the range of variability in CQAs common to originator products including changes in the variation range over time. The next step is designing a process to produce a biosimilar that will inevitably use a different cell line, more advanced technology, and more sensitive analytics.
Sandoz employs a systematic approach, using design-of-experiment (DOE) methodologies and hundreds of experiments to characterize products and processes. Various critical attributes—basic and acidic variance, structural variation, PK/PD activities, immunogenicity, functionality, etc.—are measured and used in building mathematical models to predict process performance and product quality.
Sandoz’ work with rituximab is a good example, notes Dr. Windisch. Rituximab works largely by inducing ADCC (antibody-dependent cell-mediated cytotoxicity), which in turn is controlled by two different glycosylations on rituximab. Sandoz developed a process model that accurately predicts how much of either glycosylation occurs in response to even small changes in the process.
“We have a mechanistic model in that we have an algorithm of how different process parameters (pH, temp, addition of sugar precursors, etc.) systematically influence both of these glycosylation parameters, and we can forecast if we run a process a certain way what levels of ADCC response we will get,” he says.