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Page 746 Lyons et al. Cancer Drug Resist 2021;4:745-54 https://dx.doi.org/10.20517/cdr.2021.37

Keywords: Antibody-drug conjugate, tumor, preclinical, organoid, CRISPR, preclinical imaging, theranostic

INTRODUCTION
In recent years, monoclonal antibodies have become a hugely promising class of anti-cancer therapeutic
molecules, given their high affinity and precise specificity for any number of tumor-specific target antigens.
As an effective anti-cancer treatment, antibodies have been used to directly and specifically block cell
[1]
surface receptor signaling (e.g., trastuzumab and Her2 receptor-positive breast cancer ). Of broader appeal,
however, antibodies can be readily modified to carry a variety of treatment or imaging payloads and target
them to practically any tumor-specific cell surface antigen. This effectively concentrates the antibody-drug
conjugate (ADC) payload to sites of tumor development, significantly reducing possible treatment-related
cytotoxicity in other organs or improving the ability to non-invasively image tumors above background
[2]
noise .
The current development pipeline used to translate any promising cancer agent from the bench to the
bedside is both high risk and prohibitively expensive in terms of both financial and human cost. The overall
percent failure rate of taking any new molecule into the clinic has been reported to exceed 95% . The
[3]
[4]
average total development cost to FDA approval has recently been quoted at 2.8 billion USD . Recent
analysis has suggested that poorly predictive preclinical assays and models are a key contributing factor to
[5]
this multifaceted problem . In addition to the possible misunderstanding of mechanisms of targeting and
therapeutic effect, misleading preclinical data also mean that critical “go, not go” decisions are not made
until late in the drug development process after considerable research dollars have been spent and people
have been enrolled on clinical trials with a weak premise.

As with the clinical development of any anti-cancer agent, certain aspects of ADC performance must first be
rigorously evaluated in an experimental preclinical setting. At a minimum, in addition to showing safety,
the target tumor antigen should be identified and then rigorously tested to show that in vivo accumulation
of the ADC is antigen-specific and not the result of off-target interactions or leaky tumor vasculature and
[6]
the EPR effect (enhanced perfusion and retention) . Given that most ADCs in clinical development
recognize and bind to human antigens, IHC staining of frozen human tissue microarrays will most likely be
preferable over in vivo mouse models to predict where appreciable levels of the ADC may accumulate in the
human body other than tumor sites. However, the relationship between ADC and target antigen in the
context of whole-body physiology and measurements of therapeutic effect and ADC biodistribution can
now be interrogated to much higher experimental standards.

We present here several recent advances in preclinical research that stand to significantly raise the rigor by
which candidate ADC molecules and anti-cancer drugs can be assessed prior to clinical application. These
include the ability to efficiently establish more representative in vitro and in vivo tumor models from
patient-derived material (matching normal, tumor, and metastatic tumor organoid cell lines), the ability to
use CRISPR or inducible transgene technology to specifically manipulate the expression of antigen, and
advances in non-invasive imaging that allow dynamic tracking of the ADC molecule or resulting treatment
effects. Essentially, these advances greatly improve the quality of experimental control, such that the
comparisons of ADC accumulation or therapeutic efficacy can be readily made between matched pairs of
normal and tumor cells or between matched tumors that only differ in antigen expression. Imaging further
permits many of these effects to be seen in the same individual subject dynamically over time, reducing the
need for large experimental cohorts. Imaging also enables the standardization of ADC administration based
on measured and not assumed tumor parameters, greatly improving the quality of data.