**4. Discussion**

The multifactorial nature of immunogenicity requires that an integrated preclinical risk assessment should be a key element of biotherapeutics development. As T cell-dependent responses are major drivers of immunogenicity, in vitro T cell assays are frequently used as tools to identify and measure CD4+ T cell-dependent responses to biotherapeutics. The DC:CD4+ T cell restimulation assay described here assesses the propensity of a biotherapeutic to trigger a CD4+ T cell response that may result in B cell activation and ADA production. This assay plays a key role in our integrated approach to therapeutic protein immunogenicity risk estimation, which could accelerate drug development.

While a number of assays probing T cell activation in the context of immunogenicity have been published in recent years [6], we believe that the DC:CD4+ T cell restimulation assay described here provides a more comprehensive insight into the role of dendritic cells (taken here as the archetypal APC) in the context of their activation of T cells [17]. The immune response follows a three-signal rule for activation (TCR:MHC/peptide interactions, costimulatory interactions such as via CD28, and cytokine production); the assay published herein captures the interplay of all three signals [18]. In addition, the number of preexisting T cells specific to biotherapeutics is very low, ranging between 1 out of 108 (e.g., trastuzumab and etanercept) and 1 out of 107 T cells (e.g., rituximab) [9], but the assay format of the DC:CD4+ T cell restimulation assay allows screening of more CD4+ T cells than in a classical PBMC-based assay. We also believe that the re-stimulation step increases the likelihood of capturing a sustained T cell response [19].

A key part of validating the DC:CD4+ T cell restimulation assay was assessing repeatability and reliability, and its potential to categorize biotherapeutics according to their risk of inducing an unwanted immune response in the clinic. To this end, we carried out a detailed analysis of 24 biotherapeutics with various levels of clinically-detected ADA rates as a proxy for immunogenicity risk. Our robust assessment comprised several levels of repetitions, including repeated assessment of some compounds in multiple assay screens, to provide insights into both the potential and inevitable limitations of the assay. Variance component analysis showed that the primary factors governing the experimental setup, i.e., the screen and the batched donor processing within each screen, did not have a major systematic impact on the readouts. Notable variability arose, however, when compounds were re-tested in another screen, presumably related to subtle variation in compound preparation or the used production batches. Nonetheless, the compound batch effect is not specific to this assay. It is likely that the handling and storage of the sample plays a role here, influencing post-translational modifications and aggregation. Additionally, non-product related factors (e.g., DNA and host cell protein contaminations) have an impact on the risk of immunogenicity and could also influence the assay readout. General donor specific inducibility and the donor specific response to individual compounds also explained parts of the signal variability. However, there was a rather substantial residual unexplained variance, which should caution the user with regard to overinterpretation of individual readouts. Notably, quantification of SI changes in a strict sense were not directly informative, as even clinically-tested compounds with low immunogenic risks (for example, bevacizumab) resulted in a few positive readouts. We presume that additional insights might be gained by fundamental and costly changes of the lab protocol, i.e., performing replicated measurements in different experimental batches for every condition. In our

experience, donor cohorts of 30 individuals per screen offer a reasonable tradeoff between the cost, timelines, and statistical power of the assay. While not specifically discussed in the manuscript, we found it important to test all compounds using a panel of donors that showed an HLA-DRB1 allele frequency that broadly reflects the world population. It has been demonstrated that certain HLA alleles were associated with an increased immunogenic response towards certain biopharmaceuticals [20–25]. Nevertheless, in the context of use of this preclinical assay carried out in 30 donors, we primarily investigated whether compounds may be at risk of inducing an enhanced immunogenic response in a general population. As each screen usually comprised different sets of donors, an arbitrary selection of pre-typed donors with respect to their allelic HLA-DRB1 composition enabled a higher comparability of the data in the long term.

While tempting, it is problematic to compare SI values measured in a DC:CD4+ T cell restimulation assay with actual ADA rates in the clinic, although it is one of the few available measures directly related to clinical immunogenicity. Assays used to measure ADA in clinics are based on different methodologies sensitive to sample handling, the timing of sample collection, concomitant medications utilized in the study, and the underlying nature of the treated disease [26]. Furthermore, while we believe we have used the most recent information available on the FDA database, most labels may not be updated on a regular basis: in a recent review, Borrega et al. showed that 57% (39/69) of the biological drugs authorized before 2012 did not have updated summaries of product characteristics, especially in the immunogenicity section [27]. In our study, we collected the ADA rates of the 24 assessed marketed compounds as a starting point to build a database to benchmark newly developed immunogenicity estimation methods and to have a retrospective and comprehensive overview of the immunogenicity of marketed antibodies. We used the available data to create two categories of compounds, at high (≥20% reported ADA rate) and low (<20% ADA rate) risk for immunogenicity, on which we calibrated the assay's linear mixed model. Thus, this binary high/low risk paradigm is the most reasonable for implementation in preclinical risk evaluation for therapeutics. To facilitate this process, we found it essential to add in the panel a few standard compounds (at minimum, a negative control, such as bevacizumab, and a positive control, such as KLH; any additional comparators also provide useful comparisons) to help set precise boundaries of low and high risk of immunogenicity while mitigating intrinsic donor variability. Accordingly, in this context of use, one of the most useful applications of the DC: CD4+ T cell restimulation assay is to provide a relative ranking for compounds with similar amino acid sequences and mode of action, or compounds that have different formulation or have been produced in different batches.

While assays measuring T cell activation in response to novel biopharmaceuticals are not yet required by regulatory agencies, there is added value in presenting the results of such assays as part of the risk assessment submitted in the Integrated Summary of Immunogenicity [28]. A current challenge is that none of the published assays is considered to be fully validated. We here propose a new assay format that captures the interaction between DCs and CD4+ T cells by monitoring the production of IFN-γ by CD4+ T cells in response to biotherapeutics processed by DCs. We tested the predictive power of this assay vs. clinical ADA rate by assessing 24 marketed antibodies, which resulted in 83% accuracy. Predicting the actual rate of ADA-positive patients in a clinical setting with a single in vitro assay is unlikely to be possible, given the myriad contributing factors. However, the DC:CD4+ T cell restimulation assay can help flag potentially immunogenic biopharmaceuticals in preclinical drug development, allowing for selection or de-immunization before a clinical trial starts, improving both patient safety and the cost of pharmaceuticals. Implementation of this assay as part of a comprehensive risk assessment has the potential to provide a more robust and informative immunogenicity risk assessment in early drug development.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/pharmaceutics14122672/s1, Figure S1: Heatmap showing the relative distribution of the HLA-DRB1 supertype frequencies among the donor cohorts per screen of the DC:CD4+ T cell restimulation assay.

**Author Contributions:** Conceptualization, M.S., G.S., L.C.F., K.B.-F., T.P.H., N.S. and C.M.-D.; Formal analysis, M.S., G.S., L.C.F., A.D. and C.M.-D.; Investigation, M.S., G.S., L.C.F., F.C., J.H., K.H., C.M.L., N.S. and C.M.-D.; Writing—original draft, M.S., G.S., L.C.F., A.D. and C.M.-D.; Writing—review & editing, F.C., J.H., K.H., C.M.L., K.B.-F., O.R., T.P.H. and N.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the local Research Ethics Committee (reference number: 21/LO/0474).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Data are contained within the article or Supplementary Materials.

**Conflicts of Interest:** M.S., G.S., L.C.F., K.H., C.M.L., A.D., K.B.F., T.P.H. and C.M.D. are employees of F. Hoffmann. La Roche (Switzerland); F.C., J.H. and N.S. are employees of Lonza Biologics (United Kingdom). The company had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.


**Surface Engineering & Coating Technologies for Corrosion and Tribocorrosion Resistance**

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Research funding, too, could be increasingly pulled into a block grant which could work reasonably for research training and research infrastructure, although it makes less sense for research projects, as much research project funding is multi-institutional. And research quality seems to beneft from competition and peer review. So, the most useful reset in that area would almost certainly be full funding of research projects in countries where this doesn't occur, as it would pull some of the biggest crosssubsidies out of the system at university and national levels while introducing more transparency into the system and making it easier to estimate the true cost of proposed new activities. For other countries, a more fexible deployment of resources where education and research are conjoined rather than compartmentalised might be a better way of governing activities.

### *National Accreditation and Quality Control*

Several countries have introduced national systems of university accreditation and quality control, often managed by a national oversight body running formal accreditation on a multi-year cycle. This process, along with agreed data collection and input from bodies such as the professions carrying out accreditations of academic units and undergraduate courses in their specialty areas, could make the need to nudge behaviour through marginal funding schemes largely redundant and thereby reduce externally imposed red tape. Currently, too many different and not always aligned accountability regimes operate in parallel, which makes little collective sense.

### Place

Universities have a signifcant physical presence. While some universities, particularly in Europe, are spread throughout what is often a university city, in other countries they are more likely to be contained on campuses that are often interesting architecturally with a variety of sometimes beautiful buildings and spaces between them.

How do universities use the space going forward, especially if their core business is increasingly online? Rethinking the space and the opportunities around it is another reset possibility. Should outdoors be used more for pedagogical purposes at a time when social distancing is the norm? Should community use of the space be encouraged as a way of tightening the links between communities and their universities? The building boom of the last few decades was a testimony of its time, but in the more resilient and resource-effcient era that is now evolving, universities must search for smarter ways of locating and organising their physical presence.

### People

Universities are large employers, and are sometimes the largest employer in town. The university's role as an employer of choice is another point for a possible reset.

Is it time to think more about the role of the academic of the future? Should more fexible working structures be encouraged? We could possibly reset to situations where academics might choose if they are going to do research, and then contribute to their salary through research funds, while others may choose only to teach. Of course, to a considerable extent this is the system in the USA—and in Sweden among our countries under study—where it works well. Can it be tried elsewhere? Or should universities introduce more fexible ways of organising academic work—not in the form of a gig economy labour market of hourly contracts, but rather as a fexible way of combining engagement in different settings, possibly some parts in academia, others in civil society or public or private organisations. The current system is both too rigid and expensive to carry its own costs, and the conditions for insiders and outsiders too different to be legitimate in the future.

Above, we raised the possibility of specialist education companies offering specifc subjects. People who are currently academics might prefer to work for these frms. Might there be research analogues of the same?

# Image Reset—COVID Highlighted the Contribution of Universities

One of the few good things about COVID was that it improved the perception of universities among the general public. There were many highprofle examples of governments turning to specialist units in universities for help with understanding the virus and developing tests and vaccines. Iconic in this regard was the standing ovation the Centre Court crowd at Wimbledon gave for Oxford University's Dame Sarah Gilbert, who was instrumental in developing the AstraZeneca vaccine. But there were many more examples of university institutes providing virological, epidemiological and public health advice. A lot of this came from medical faculties, which underlined the long-term, close symbiotic links between universities and local and national health systems.

But COVID also highlighted how international research links between universities are an important component of a modern economy, often proving the fastest and cheapest reference point for frms and governments seeking to make connections with pockets of specialist expertise worldwide. This is particularly illustrated in books describing the emergence of the COVID pandemic and the role leading infectious diseases researchers played in working out the scale and seriousness of what was happening (e.g. *Spike* by Jeremy Farrar with Anjana Ahuja, Profle, 2021).

### Incentives Are a Must

No attempts at major resets of university operations are going to be successful without paying attention to the incentives for those who must create and sustain the change, mainly universities and their staff, particularly academic staff. In order to achieve new ways of working that produce higher-quality student and research outcomes but at lower and hopefully reducing costs over time, the resets must work for all. As is often pointed out, those who work in universities are very smart and arguably could be earning more in other jobs, but choose university work because they like the environment.

The creativity and effciency with which universities as institutions and academic staff as individuals responded to the COVID challenge shows that the talent needed to bring about major change quickly in the system is available. Removing perverse incentives could be a way to make that change happen.

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# References

#### Part one

© The Author(s) 2022 177

M. Benner et al., *Crisis Response in Higher Education*, https://doi.org/10.1007/978-3-030-97837-2

#### Part TWo

Elliot Major, L., & Machin, S. (2018). *Social mobility. And its enemies*. Pelican.

# inDex

#### **A**

Adaptation, 47 Allocation of resources, 16 Alumni, 162 Anti-university sentiment, 28 Attitudes, 18 Australia, 19 Authority, 12

### **B**

Brexit, 28

### **C**

The civic role of the university, 28 Collaborative research, 170 Communication, 122 Community, 118 Conference, 101 COVID pandemic, 3

COVID stories, 37 Crisis management, 106

## **D**

Dawkins, J.S., 57 Decision-making, 16 Digital divide, 130 Digital learning, 47

### **E**

Education, 5 Engagement, 13 Entrepreneurship, 13

### **G**

General public, 175 Google Meet, 93 Governance, 4

Government policy, 170–173 Governments, 10 Groups, 17

### **H**

Higher education studies, 6

### **I**

The idea of a university, 14 Individuals, 17 Innovation, 151 Instabilities, 11 International students, 60, 144

**K** King's College London, 33

### **L**

Leadership, 49 Lockdown, 95 Long-term fnancial sustainability, 28

### **M**

Mental health, 76 Mission groups, 25 Motivation, 14

# **N**

National fagship universities, 42 Nation-building, 52

### **O**

Online, 122 Online and blended education, 37

### **P**

People, 16 PhD students, 167 Place, 17 Power, 12 Process, 16 Public universities, 54

### **R**

Research, 5 Research commercialisation, 61 Research funding, 167 Rupture, 7

### **S**

Service, 4 Societal inequalities, 71 Societal interests, 7 Stakeholders, 13 Strategy, 15 Structure of power, 12 Students, 18 Student unions, 74 *The Swedish strategy*, 91

### **T**

Teams, 93 Transformations, 3 Travel, 92

### **U**

United Kingdom (UK), 19 United States (USA), 4 Universities UK, 27 University accreditation, 173 University campus, 99 University colleges, 53

University leaders, 135–157 University missions, 3

**W** World Health Organization (WHO), 77

**V** Volunteer, 70 **Z** Zoom, 75