The development of novel human biotherapeutics and vaccines requires analytical methods to ensure quality, safety, and efficacy.  These analytical methods must have high precision, linearity, accuracy, and specificity to meet the regulatory requirements for human clinical testing and commercialization.  As new types of complex biotherapeutics become increasingly popular, analytics is being challenged to adequately characterize larger and more diverse products, such as cell therapies, antibody-drug conjugates, bispecific antibodies, viral-like particles, and lipid nanoparticles.  All of these types of complex therapies have their own unique characteristics that require non-standard, purpose-built analytical platforms. 

The key step in biotherapeutic analytics is to define the critical quality attributes (CQAs) that directly influence safety, efficacy, quality, and stability.  For complex therapies, this process is not straightforward and requires a strong understanding of the biology and chemistry of the product to distinguish acceptable and unacceptable variances.  Due to their biologic origin, complex biotherapeutics nearly always possess much higher heterogeneity than traditional drugs, making it necessary to define the ranges of CQAs that show good function, as well as identifying any variants that may impact biological activity and pharmacodynamics in patients.  Some CQAs can be evaluated using established analytical methods, while others require the development of fit-for-purpose assays.  In the latter case, a close collaboration between the therapeutic developer and regulatory agencies is necessary to gain acceptance of these new analytical methods. 

Analytics is often overlooked early in therapeutic development, especially by smaller companies with tight budgetary and time constraints.  Furthermore, the time required to develop and validate analytical platforms is routinely underestimated and is a major cause of delays when moving therapies from the discovery phase into the clinic.  Establishing a solid understanding of the properties of a biotherapeutic early in development can dramatically reduce risk later in the drug development process, when it becomes more expensive to fix any identified issues.  Ideally, CQAs, the assays to evaluate them, and suitable reference standards should be roughly defined during the lead selection stage in drug development, so that several variants of the same product can be compared to choose one with the best activity, stability, and manufacturability.  As the product is further refined, the analytical methods can similarly continue to be refined.

There are many opportunities and areas of growth in analytical method development, including the following: 

1. Automation: Automation of sample preparation and analysis can greatly increase the throughput and speed of analytical assays.  Although it can be challenging to implement, automation makes it feasible to screen a large number of variants and formulations, thus increasing the odds of finding a variant with ideal properties.  In turn, this shortens development timelines.  Partnering high-throughput analytical methods with machine learning to analyze data quickly and accurately is also growing in popularity.  In some cases, molecular simulations can define and limit the test space to increase the probability of success.   
2. Multiplexed assays: As biotherapeutics become more complex and their CQAs increase in number, the ability to determine multiple CQAs in a single assay is growing in importance.  Mass spectrometry methods including multi-attribute monitoring of purified proteins with automated peak detection, are being increasingly accepted by regulatory agencies.  Proteomic techniques, originally used only for discovery work, are now being applied to cell therapies to quantify multiple proteins in a single analysis.  Multiplexed assays are also becoming increasingly common for immunoassays and cell binding assays, including bead-based ELISA-style assays, flow cytometry, and mass cytometry. 
3. In-process analytics: Optimization of biomanufacturing processes would benefit greatly from real-time analytical monitoring of CQAs during production and test runs.  This data provides insights into how various bioreactor conditions, such as temperature, metabolites, pH, and cell viability, affect the CQAs of the final product.  To be useful, in-process analytical assays must be robust and provide results in short timelines, with limited sample, while also being simple enough for non-expert users to implement.   Additional challenges include the direct integration of analytic instrumentation and data workflows into a biomanufacturing suite, with sampling methods capable of drawing samples that accurately reflect the full bioreactor without fouling of the sampling port.
4. In vivoanalysis:  A detailed understanding of the behavior of biotherapeutics in the body during treatment, while challenging, is invaluable and has the potential to identify critical stability or activity issues during pre-clinical testing when there is still the opportunity to address any identified risks.  Ideally, analytics aimed at analysis of biotherapeutics in vivo should be relatively non-invasive.  Preclinical and clinical imaging systems have been growing in sophistication and use, with excellent future prospects.  These systems can demonstrate targeting of biotherapeutics by showing their distribution throughout the body and, with some clever biological experimental design, these systems can also explore several other CQAs in vivo, including stability and activity.  Blood is another feasible biofluid that can be used for in vivo analysis.  Microfluidics is an area with growing innovation in recent years, much of it aimed at blood analysis to enrich for bioanalytes of interest.  These bioanalytes could be the biotherapeutic itself, with subsequent analytics to identify in vivo stability liabilities and unexpected biotransformations.  Alternatively, the bioanalytes may be biomarkers that can identify a responding patient population or provide early indications of treatment success or toxicity. 

Analytical development in biotherapeutic development is a growth area with many opportunities for creative solutions to complex problems.  All of the areas highlighted above are particularly challenging to advance due to their inherent interdisciplinary nature, which requires close collaboration between biologists, chemists, physicists, computer scientists, and engineers.  Investment in biotherapeutic analytics can save time and money, while directly improving the probability of success of new classes of therapies for the improvement of human health.