Biopharmaceutical companies are increasingly turning to service providers for all aspects of drug development. More often than not, they are looking to contract development and manufacturing organizations (CDMOs) with integrated service offerings across the entire pharmaceutical development cycle, from discovery to commercialization, for APIs and formulated drug products with lifecycle management that can help drug manufacturers meet aggressive development timelines for complex products while realizing greater efficiencies.
The most successful CDMOs have a tradition of innovation, cost-effective operational scale, and the ability to customize platforms to suit customer needs. These services are made possible by teams of scientific experts capable of highly efficient process and analytical development and that can solve technical challenges and bring products to market in the shortest possible time.
While the concept of the contract development and manufacturing organization (CDMO) has been discussed for some years, it has been fully realized in the last few. A flurry of acquisitions in the CMO space, including CMO-CMO and purchases of facilities from sponsor pharmaceutical companies, has occurred, largely with the intention of establishing integrated service capabilities. Some CMOs have sought to expand their global footprint in order to provide local service to their global clients. Others looked to achieve greater cost efficiencies by expanding their capacities. Many, however, participated in the M&A frenzy in order to expand into new service areas — particularly process / analytical development and / or final formulation — and gain access to highly differentiating, advanced technologies.
Effective process development is, in fact, essential for achieving cost-effective, robust biopharmaceutical manufacturing operations. In particular, processes designed with scale-up to commercial volumes in mind enable much smoother technology transfer, reduced manufacturing issues, higher product quality, lower processing costs, and faster time to market. It is therefore not surprising that as pressures from consumers, investors, insurance companies, regulators, and governments to drive down costs and improve product performance have increased, biologic drug manufacturers have turned to integrated CDMOs with advanced process development and scale-up technologies to realize measurable efficiencies and cost savings without compromising patient safety and product quality.
From Expression to Validation
Process development begins with the expression system and continues through to API release, covering all of the upstream and downstream unit operations that lie in between, plus analytical and cleaning method development and validation. True biopharmaceutical CDMOs can support their clients across the entire gamut of process development activities, including cell line development and banking, scale-down, process characterization utilizing design space mapping via design of experiment (DoE) approaches, process optimization, and viral clearance studies and toxicology lot manufacture — in addition to scale-up, tech transfer, GMP manufacture, and validation.
Strategies and procedures for effectively managing client projects from start to finish are also necessary. Many clients prefer to form strategic partnerships with CDMOs that have a culture and processes designed to encourage collaboration with client personnel and across functions (such as process development, manufacturing, and quality assurance) within the CDMO. Dedicated program and project managers that serve as the main points of contact and work closely with clients are often effective at facilitating this much-needed communication. A commitment to innovation and continuous improvement and a stable yet flexible and skilled workforce, including employees with demonstrated technology transfer/scale-up experience, are also invaluable.
Greater Understanding and Control
The increased emphasis of regulatory authorities on quality-by-design (QbD) for risk mitigation has created an even greater need for process development expertise. Meeting these requirements increases the level of work that must be completed during early process development phases, but the increased process understanding enables greater process control. The result is more robust processes with higher yields and productivities, and fewer impurities and process variations. In addition, troubleshooting and problem resolution generally can be achieved more rapidly with enhanced process knowledge. The most successful CDMOs, therefore, have identified strategies for completing process development projects efficiently and effectively while incorporating DoE and QbD approaches that provide increased process understanding and lead to optimal processes.
Scale-Down Models
Strong scale-down models are essential for successful process development, characterization and validation. Development of relevant scale-down models requires both process development experience and equipment representative of large-scale manufacture. Scale-down models, which can mimic and predict large-scale operations, are instrumental for DoE characterization and validation studies, including media stability, generation of Cells at the Limit (CAL) of in vitro Cell Age (IVCA), resin lifetime, resin cleaning and disinfection, and viral clearance. CDMOs with the ability to develop scale-down models that provide good predictions of large-volume process behavior in the lab, plus have the necessary skills for conducting and evaluating scale-down model runs, are in a position to more rapidly develop scalable processes and achieve seamless tech transfer for their clients.
High-Throughput Advances
Scale-down modeling allows for the use of less material, but is often still insufficient for speeding up the process development process given the larger number of runs that must be completed to acquire the desired level of process understanding. State-of-the-art process development laboratories at both CDMOs and sponsor companies have therefore pursued the use of high-throughput systems to increase productivity.
High-throughput development (HTPD) techniques require much smaller quantities of material and more rapidly provide information on a greater number of process parameters. As a result, it is possible to more quickly identify optimum process conditions for the development of more robust processes. HTPD techniques can now be applied to a number of bioprocesses, including clone selection, protein production, and downstream chromatography and viral clearance steps, among others. Many high-throughput technologies incorporate automated systems for sample handling and data analysis and reporting, which further accelerates process development programs. The use of automation also has the benefit of reducing the opportunity for human error and providing more consistent results.
For upstream process development, the most widely used high-throughput systems are based on miniaturized parallel experimental technologies. High-throughput mini- screening systems have been applied for clone screening and selection, while micro- and miniature bioreactors (< 1 to 500mL), designed with the same mixing properties and control software as commercial-scale counterparts, are used for the rapid determination of critical process parameters (CPPs) and the determination of optimal cell-culture conditions. Slightly larger systems (up to 4L) that are designed to operate in parallel are also available for the investigation of process parameters that cannot be evaluated using the very small quantities in microreactors.
High-throughput systems for the development of downstream processes include micro-column and plate-based methods that, for instance, allow the determination of equilibrium constants and binding capacities for different chromatography resins under various elution conditions. The use of process analytical technology (PAT) is also valuable for accelerating the process development process. The data that can be rapidly obtained using HTPD techniques combined with PAT provides much more information about processes than was ever possible before. Once the optimum process conditions are determined and a robust process is developed, PAT can then be used to monitor and control thecommercial-scale process for enhanced pro-cess performance and product quality.
Analytical Improvements
CDMOs with such advanced process development capabilities also generally have advanced analytical capabilities, because state-of-the-art analytical systems are required to evaluate the results generated during high-throughput experimentation. In fact, advanced analytical procedures and state-of-the-art instrumentation are required for not only process characterization, but also raw material testing, product characterization, and impurity identification.
CDMOs should also be aware of new rapid assays and next-generation sequencing technologies for the detection of known and unknown adventitious agents. Assay development, tech transfer, validation, and qualification capabilities are also essential.
Data analysis and Process Modeling
Effectively evaluating all of the data that is generated when implementing a QbD approach and utilizing high-throughput development techniques can be a challenge. Advanced CDMOs have developed data analysis capabilities that enable them to realize the maximum value afforded by that data. For example, techniques such as multivariate data analysis (MVDA) can be used to map the behaviors of CPPs in order to develop process models that can then be used to further explore the process operating space without the need for additional practical experimentation. These models can also be employed as algorithms in combination with PAT to provide model-predictive control (MPC) of the process.
Conclusion
The preference of biologic drug manufacturers for CDMOs that offer integrated services across the entire pharmaceutical development cycle appears to have crystallized in the last few years. The most successful CDMOs are able to provide advanced process development services, including effective scale-down modeling, high-throughput techniques, state-of-the-art analytical capabilities, and effective data analysis and modeling.
