Skip to main content

Main menu

  • Home
  • Content
    • Current
    • Ahead of print
    • Past Issues
    • JNM Supplement
    • SNMMI Annual Meeting Abstracts
  • Subscriptions
    • Subscribers
    • Institutional and Non-member
    • Rates
    • Corporate & Special Sales
    • Journal Claims
  • Authors
    • Submit to JNM
    • Information for Authors
    • Assignment of Copyright
    • AQARA requirements
  • Info
    • Continuing Education
    • Reviewers
    • Permissions
    • Advertisers
  • About
    • About Us
    • Editorial Board
    • Contact Information
  • More
    • Alerts
    • Feedback
    • Help
    • SNMMI Journals
  • SNMMI
    • JNM
    • JNMT
    • SNMMI Journals
    • SNMMI

User menu

  • Subscribe
  • My alerts
  • Log in
  • My Cart

Search

  • Advanced search
Journal of Nuclear Medicine
  • SNMMI
    • JNM
    • JNMT
    • SNMMI Journals
    • SNMMI
  • Subscribe
  • My alerts
  • Log in
  • My Cart
Journal of Nuclear Medicine

Advanced Search

  • Home
  • Content
    • Current
    • Ahead of print
    • Past Issues
    • JNM Supplement
    • SNMMI Annual Meeting Abstracts
  • Subscriptions
    • Subscribers
    • Institutional and Non-member
    • Rates
    • Corporate & Special Sales
    • Journal Claims
  • Authors
    • Submit to JNM
    • Information for Authors
    • Assignment of Copyright
    • AQARA requirements
  • Info
    • Continuing Education
    • Reviewers
    • Permissions
    • Advertisers
  • About
    • About Us
    • Editorial Board
    • Contact Information
  • More
    • Alerts
    • Feedback
    • Help
    • SNMMI Journals
  • Visit JNM on Facebook
  • Join JNM on LinkedIn
  • Follow JNM on Twitter
  • Subscribe to our RSS feeds
Research ArticleRadiobiology/Dosimetry

“Standing by” for Bystander Effects: Dual-Isotope Imaging of Antibody–Drug Conjugate and Payload Distribution

Cornelius Cilliers and Greg M. Thurber
Journal of Nuclear Medicine September 2018, 59 (9) 1459-1460; DOI: https://doi.org/10.2967/jnumed.118.213389
Cornelius Cilliers
1Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan; and
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Greg M. Thurber
1Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan; and
2Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Info & Metrics
  • PDF
Loading

See the associated article on page 1461.

Antibody–drug conjugates (ADCs) are a promising class of therapeutics for the molecular targeting of cancer. There is a significant pharmaceutical investment in this area, with more than 70 ADCs in various stages of clinical trials and 4 clinical approvals to date. Although there have been several late-stage clinical failures, encouragingly, 2 of the approvals came last year, thus brightening the prospect of additional progress. ADCs combine the specificity of antibody therapy against a tumor-associated antigen with a potent cytotoxic small-molecule payload. Despite large investments into ADC therapy, quantifying the distribution of the cytotoxic payload in the tumor with high spatiotemporal resolution has remained a major challenge. In this issue of The Journal of Nuclear Medicine, Ilovich et al. present a quantitative dual-isotope autoradiographic method for separately tracking the distribution of both the antibody and the payload portions of an ADC by repeated imaging after “standing by” until one of the isotopes has decayed (1).

A central mechanism of action for ADCs is the delivery of the cytotoxic payload to cancer cells. This delivery is a multistep process consisting of circulation in the blood, extravasation into and interstitial transport through tumor tissue, binding the target antigen, internalization into the tumor cell, payload release, and payload transport to the therapeutic target (typically microtubules or DNA). In the lysosome, the payload is released either from a cleavable linker or as a linker-payload adduct after complete protein degradation. Some payloads are capable of bystander killing by diffusing into nearby cells to exert their pharmacologic effect, whereas others cannot exit the cell in appreciable amounts. The ability of a payload to exhibit bystander effects depends on the physicochemical properties of the released payload. Bystander payloads that are small and lipophilic, such as monomethyl auristatin E (MMAE) and pyrrolobenzodiazepine, are able to permeate out of ADC-targeted cells, diffuse farther into the tumor tissue, and permeate into bystander cells untargeted by the ADC. Nonbystander payloads, such as lysine-emtansine (lysine-DM1) and monomethyl auristatin F, are often larger and more hydrophilic, preventing them from crossing cell membranes and confining their distribution to cells directly targeted by the ADC. In the clinical setting, in which tumors are a heterogeneous mixture of antigen-positive and -negative cells, bystander payloads are able to diffuse out of the ADC-targeted antigen-positive cells to reach and kill antigen-negative cells, albeit indiscriminately. To complicate delivery further, ADCs exhibit a heterogeneous, perivascular distribution in tumors, and there are limited imaging data available showing both ADC disposition and the resulting payload tumor distribution. Despite the significant investment into ADC therapeutics, there is a fundamental lack of knowledge of the relationship between the heterogeneous antibody distribution, the resulting payload distribution, and how both drive efficacy.

To fill this gap in knowledge, several in vitro, in vivo, and computational methods have been used to study bystander effects. Mosaic models, in which antigen-positive and -negative cells are mixed or cocultured, are commonly used for testing bystander efficacy both in vitro and in vivo (2–4). However, in these in vitro models, there are no ADC or payload transport limitations, meaning all antigen-positive cells are exposed to ADC, and antigen-negative cells are exposed to released payload in the culture medium. In vivo ADC distribution with clinically relevant doses is highly heterogeneous and often leaves significant portions of the tumor untargeted by the ADC because of limited tissue penetration (5). Although bystander payloads show better responses in these mosaic models, it is also unclear how far the released payload can diffuse into the tissue. When ex vivo techniques such as tumor tissue homogenization are used, the average payload concentration in the tumor can be measured through liquid chromatography-mass spectrometry (2,6). Although informative, this technique lacks tissue- or cellular-level data, making it difficult to discern whether cell killing is from direct targeting, bystander effects, or other mechanisms. Taken together, the in vitro and in vivo data suggest bystander effects are important for targeting antigen-negative cells; however, there is limited work quantifying the relative impact of direct payload delivery, bystander effects, and payload physicochemical properties that affect their transport and distribution in tumor tissue. Quantifying payload penetration into the tumor will allow for strategies to match payload potency and distribution, ensuring all tumor cells receive therapeutic amounts of payload (7).

There are computational methods of estimating small-molecule delivery in tumor tissue (8), and these principles can be incorporated into ADC tissue models to provide precise predictions of tissue, cellular, and subcellular payload distribution. Theoretically, bystander payloads with optimal physicochemical properties can distribute homogeneously throughout the tumor (9). However, there are currently a lack of available experimental payload distribution data to validate or refute these computational predictions. The high potency of ADC payloads makes their distribution challenging to study, because they are often present in minute quantities within the tissue. The experimental work presented here (1) provides a critical step in this direction.

In their study, Ilovich et al. present a dual-isotope cryoimaging quantitative autoradiography (CIQA) methodology to independently track the tumoral distribution of both antibody and payload of an ADC (1). To our knowledge, this study is the first to visualize both ADC delivery and payload bystander effects at tissue-scale spatial resolution. Alley et al. used a similar strategy to track payload and ADC; although, their study focused on total-organ uptake rather than intratumor distribution (10). The CIQA technique consists of labeling the antibody with a residualizing, short half-life, γ-emitting 111In label and a tritiated MMAE payload. The 3H β-decay signal is shielded with foil, whereas the 111In signal of the antibody is imaged through autoradiography. After greater than ten 111In half-lives, when 111In radioactivity is diminished, the shielding is removed, and 3H β-decay signal from the payload is imaged. Once imaged, the autoradiographs are aligned, and colocalization between antibody and payload is measured. 111In is a residualizing label, meaning it remains trapped within the cells in which the antibody is degraded, whereas the 3H-labeled MMAE payload can diffuse between cells. At early times (1 h after injection), the tumor sections showed colocalization between the antibody and payload. By 24 h, the payload distribution started to deviate from the antibody, indicating that he released MMAE payload diffused into neighboring bystander cells. By 96 h after ADC administration, the payload diffused even farther into the tumor and diverged from the antibody distribution, showing only 0.8% colocalization between the payload and antibody signal versus 15% in antigen-negative tumors. The images showing the diverging distribution of ADC and payload provide the first direct visualization of the bystander effect. Although the images showing the distribution of ADC and payload are compelling, the authors did not include the colocalization analysis for the early times, so it is difficult to determine the impact of the processing/alignment steps on the maximum expected colocalization with intact ADC. However, the higher colocalization in antigen-negative tumors is consistent with the lack of antigen-mediated cleavage of the payload or more diffuse uptake of ADC throughout the antigen-negative tumors through nonspecific macropinocytosis. These results are also an important reminder that macroscopic imaging in the clinical setting, due to practical PET scanner resolution and fundamental positron diffusion distances, does not elucidate the microscopic heterogeneity within these lesions.

Although the CIQA methodology appears promising, there are several considerations for future work and additional questions surrounding payload distribution that remain to be answered. It will be interesting to see how sensitive the technique is to capturing ADC and payload distribution in antigen-positive tumors with lower expression, or when the ADC dose is low. This may be relevant for higher-potency payloads administered at small doses. The distance the ADC traverses into the tumor (a dynamic saturation front often called the binding-site barrier) is in part a function of antigen expression and antibody internalization rate. For example, the divergence of the payload signal from ADC signal in tumors with lower antigen expression may be reduced due to better antibody penetration. Staining interleaving histology sections may provide more detailed tumor structure and help quantify uptake in the immune infiltrate and other noncancer cells. These and other adaptations of the CIQA method should help elucidate (or rule out) the impact of heterogeneous payload delivery on efficacy for various ADC carriers and payloads to improve ADC design.

In summary, the CIQA methodology has the potential to significantly improve our understanding of the link between antibody/payload distribution and overall efficacy of ADCs. This technique provides tissue-scale visualization of the distribution of both the ADC and the payload in a relevant tumor microenvironment. Despite the promise of this approach, much work remains to be done. We anticipate this method will provide critical data to optimize payload physicochemical properties and improve tumor distribution. Additionally, Ilovich et al. have outlined methods for improving the CIQA method by using a less time-consuming and more cost-effective 67Ga isotope on the antibody. We await the insights the CIQA methodology will provide on the distribution of both bystander and nonbystander payloads to help bridge the knowledge gap between tumor payload distribution and ADC efficacy.

DISCLOSURE

Greg M. Thurber has advising/consulting relationships with Abbvie, Advanced Proteome Therapeutics, Bristol-Myers Squibb, Crescendo Biologics, Eli Lilly and Company, Immunogen, Nodus Therapeutics, Roche/Genentech, and Takeda Pharmaceuticals. No other potential conflicts of interest relevant to this article exist.

Footnotes

  • Published online Jul. 12, 2018.

  • © 2018 by the Society of Nuclear Medicine and Molecular Imaging.

REFERENCES

  1. 1.↵
    1. Ilovich O,
    2. Qutaish M,
    3. Hesterman J,
    4. et al
    . Dual-isotope cryo-imaging quantitative autoradiography: investigating antibody–drug conjugate distribution and payload delivery through imaging. J Nucl Med. 2018;59:1461–1466.
    OpenUrl
  2. 2.↵
    1. Li F,
    2. Emmerton KK,
    3. Jonas M,
    4. et al
    . Intracellular released payload influences potency and bystander-killing effects of antibody-drug conjugates in preclinical models. Cancer Res. 2016;76:2710–2719.
    OpenUrlAbstract/FREE Full Text
  3. 3.
    1. Singh AP,
    2. Sharma S,
    3. Shah DK
    . Quantitative characterization of in vitro bystander effect of antibody-drug conjugates. J Pharmacokinet Pharmacodyn. 2016;43:567–582.
    OpenUrl
  4. 4.↵
    1. Miller ML,
    2. Fishkin NE,
    3. Li W,
    4. et al
    . A new class of antibody-drug conjugates with potent DNA alkylating activity. Mol Cancer Ther. 2016;15:1870–1878.
    OpenUrlAbstract/FREE Full Text
  5. 5.↵
    1. Cilliers C,
    2. Menezes B,
    3. Nessler I,
    4. Linderman J,
    5. Thurber GM
    . Improved tumor penetration and single-cell targeting of antibody–drug conjugates increases anticancer efficacy and host survival. Cancer Res. 2018;78:758–768.
    OpenUrlAbstract/FREE Full Text
  6. 6.↵
    1. Erickson HK,
    2. Lewis Phillips GD,
    3. Leipold DD,
    4. et al
    . The effect of different linkers on target cell catabolism and pharmacokinetics/pharmacodynamics of trastuzumab maytansinoid conjugates. Mol Cancer Ther. 2012;11:1133–1142.
    OpenUrlAbstract/FREE Full Text
  7. 7.↵
    1. Minchinton AI,
    2. Tannock IF
    . Drug penetration in solid tumours. Nat Rev Cancer. 2006;6:583–592.
    OpenUrlCrossRefPubMed
  8. 8.↵
    1. Poulin P,
    2. Chen YH,
    3. Ding X,
    4. et al
    . Prediction of drug distribution in subcutaneous xenografts of human tumor cell lines and healthy tissues in mouse: application of the tissue composition-based model to antineoplastic drugs. J Pharm Sci. 2015;104:1508–1521.
    OpenUrl
  9. 9.↵
    1. Khera E,
    2. Cilliers C,
    3. Bhatnagar S,
    4. Thurber GM
    . Computational transport analysis of antibody-drug conjugate bystander effects and payload tumoral distribution: implications for therapy. Mol Syst Des Eng. 2018;3:73–88.
    OpenUrl
  10. 10.↵
    1. Alley SC,
    2. Zhang X,
    3. Okeley NM,
    4. et al
    . The pharmacologic basis for antibody-auristatin conjugate activity. J Pharmacol Exp Ther. 2009;330:932–938.
    OpenUrlAbstract/FREE Full Text
  • Received for publication May 15, 2018.
  • Accepted for publication July 2, 2018.
PreviousNext
Back to top

In this issue

Journal of Nuclear Medicine: 59 (9)
Journal of Nuclear Medicine
Vol. 59, Issue 9
September 1, 2018
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Index by author
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on Journal of Nuclear Medicine.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
“Standing by” for Bystander Effects: Dual-Isotope Imaging of Antibody–Drug Conjugate and Payload Distribution
(Your Name) has sent you a message from Journal of Nuclear Medicine
(Your Name) thought you would like to see the Journal of Nuclear Medicine web site.
Citation Tools
“Standing by” for Bystander Effects: Dual-Isotope Imaging of Antibody–Drug Conjugate and Payload Distribution
Cornelius Cilliers, Greg M. Thurber
Journal of Nuclear Medicine Sep 2018, 59 (9) 1459-1460; DOI: 10.2967/jnumed.118.213389

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
“Standing by” for Bystander Effects: Dual-Isotope Imaging of Antibody–Drug Conjugate and Payload Distribution
Cornelius Cilliers, Greg M. Thurber
Journal of Nuclear Medicine Sep 2018, 59 (9) 1459-1460; DOI: 10.2967/jnumed.118.213389
Twitter logo Facebook logo LinkedIn logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One
Bookmark this article

Jump to section

  • Article
    • DISCLOSURE
    • Footnotes
    • REFERENCES
  • Info & Metrics
  • PDF

Related Articles

  • Dual-Isotope Cryoimaging Quantitative Autoradiography: Investigating Antibody–Drug Conjugate Distribution and Payload Delivery Through Imaging
  • This Month in JNM
  • PubMed
  • Google Scholar

Cited By...

  • No citing articles found.
  • Google Scholar

More in this TOC Section

Radiobiology/Dosimetry

  • Feasibility of Single-Time-Point Dosimetry for Radiopharmaceutical Therapies
  • γ-Tocotrienol–Loaded Liposomes for Radioprotection from Hematopoietic Side Effects Caused by Radiotherapeutic Drugs
  • Dose–Effect Relationships of 166Ho Radioembolization in Colorectal Cancer
Show more Radiobiology/Dosimetry

Basic

  • Dopamine D1 Receptor Agonist PET Tracer Development: Assessment in Nonhuman Primates
  • Optical Navigation of the Drop-In γ-Probe as a Means to Strengthen the Connection Between Robot-Assisted and Radioguided Surgery
  • Synthesis and Preclinical Evaluation of a 68Ga-Labeled Adnectin, 68Ga-BMS-986192, as a PET Agent for Imaging PD-L1 Expression
Show more Basic

Similar Articles

SNMMI

© 2023 Journal of Nuclear Medicine

Powered by HighWire