By David Bienvenue, Ph.D., senior director of protein science, Bonum Therapeutics
I joined Good Therapeutics (now Bonum Therapeutics) in the summer of 2021. This was a marvelous opportunity to develop a new class of regulated cytokine therapeutics for cancer and autoimmune diseases. My prime directive was to build a team focused on protein production and characterization, with an aim to select stable “manufacturable” leads to move into clinical development.
As director of protein science, first at Good and now holding the same position at Bonum, I face considerations similar to those weighing on most biotech startups regarding the balance between building out the internal group of scientific expertise versus delegating work to external vendors.
At the time I was hired, Good was three years into its Series A financing, with a solid foundation in place — an experienced management team, skilled research staff, funding, and proof-of-concept data showing that regulated cytokines could be engineered with the desired biological activity. While a small internal team had begun producing protein and acquiring analytical instrumentation to assess the quality of both “home-made” and purchased materials, we initially custom ordered most proteins used to develop the technology from vendors. Once we had promising early data in hand, we began adding staff and key equipment and shifting responsibility for production of candidates to the internal team.
But the question remained: how far do we go, given our resources and time constraints?
When Is The Right Time To Set Up Protein Production In-House?
Whether to build out a protein group or rely on vendors is not a binary decision. A hybrid approach that adjusts the ratio of the two sources as the company matures is arguably the best solution for many young companies. This is the strategy used first by Good Therapeutics and which we continue to apply at Bonum.
Like most startups, at Good the initial focus was on obtaining proof of mechanism for our novel technologies. Achieving that goal is critical to ensure further funding and expansion of the organization. It doesn’t make sense to build out a large protein team during that period. Experiments can be performed using a combination of common reagents and custom orders to obtain that key initial data in a timely manner.
The vendor-centric approach was and remains a great approach for getting a new company off the ground. Commercial organizations have a dizzying array of “off the shelf” products to support drug research. Proteins of therapeutic interest, along with their ligands, can be purchased with different tags, expressed by different host cell lines, including variants from other species like mice and non-human primates. Vendors can be enlisted to make custom proteins for anything else not readily available.
Importantly, for early preclinical research, vendors should be selected that can provide quality control data so the user can confidently use the reagents in their experiments. At a minimum, analytical SEC, protein concentration, estimated MW, and endotoxin concentration should be available. Ideally, having someone on staff who is knowledgeable in protein quality/analytics can be helpful to manage these functions and assess the QC data being provided.
The alternative to purchasing reagents is to build internal resources for protein production and analysis. This can require significant investment of capital — a major step for any startup company to undertake. In addition, experienced personnel will need to be hired to support these functions. While these challenges argue for sticking with the vendor option, adding these capabilities in-house generates significant long-term benefits. Moreover, a robust market exists for used scientific equipment that can be obtained at a discount. If paired with a service contract, refurbished instrumentation is a great option in the early days of building an organization.
It’s a matter of deciding when, and to what extent, a young company wants to commit to this approach. This can be a data-driven decision. Consider boosting internal protein capabilities at the point the company makes progress on its pipeline and obtains promising in vitro biological function. Initially, the focus could be on expanding protein analysis rather than production. It is important to confirm the quality of the reagents being used at this stage, as the data may be the basis for raising initial Series A funding. It would be devastating if that data resulted from false readouts due to poor reagent quality.
The progression toward in vivo experimentation warrants even more significant investment in internal protein production equipment and staff, with timing dependent on the composition of the therapeutic. Vendors can rapidly produce monoclonal antibodies using platform methods to support mouse in vivo experimentation. However, generic production methods may fall short for more complex molecules like multi-specific mAbs and fusion proteins. Additional purification steps may be required to achieve the purity suitable for animal experimentation. More intensive protein analytics may be needed to verify that the desired product is isolated from product-related contaminants.
By adopting a hybrid approach, a company can split responsibility for production and analytics between the drug innovator and the vendor. This is the case at Bonum - while our team now produces the lion’s share of new candidates on site, we retain the option to outsource to avoid slowing progress on critical programs. The goal here is to build the internal team capabilities and capacity at the right level and time, and leverage vendor partners when it makes sense – all to avoid impacting the rate of testing of new molecules.
Protein Science’s Role In Clinical Lead Selection
In addition to supporting the company’s research, observing protein behavior during production and storage can give critical insight into biophysical differences between potential leads. Precipitation and other signs of product heterogeneity are worth noting. This information may not be relayed from the vendor in standard batch documentation but can be captured in a lab notebook by an observant staff scientist. These notes represent the start of a dossier on promising binding domains. Multiple instances of bad behavior can be justification to deprioritize or optimize these sequences, and if they are identified early enough won’t impact the IND timeline.
The protein science team can interrogate other aspects of developability besides what is noted during production. Some methods of evaluating drug candidates for ideal biophysical characteristics don’t require large amounts of protein or specialized equipment. These can be run in parallel to protein production by the same staff members. Biological activity is paramount, but potency can be weighed with other characteristics like stability, solubility, and various measures of unwanted “sticky” behavior. This data can be used to select a lead with reduced development risk prior to transfer to a contract manufacturer for cGMP manufacturing. Lastly, this data can help your company’s efforts to partner/out-license the program or technology if that is part of the business strategy. In the absence of process development and large-scale production data, characterization data generated internally and by CROs can provide supportive data for these discussions.
At Bonum, we now have a great team of scientists focused on protein production and analytics, supported by reliable vendors when suitable. Like many biotech companies, our needs will inevitably change over time, as we move our most promising regulated cytokine therapeutics toward the clinic.
About The Author:
David Bienvenue, Ph.D., joined Good Therapeutics in 2021 and then transitioned to Bonum Therapeutics after Good's acquisition by Roche in 2022. He previously served as vice president of protein sciences, analytical and process development, at Aptevo Therapeutics; director of protein sciences at VLST Corporation; and purification development lead at Dendreon Corporation.