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McGill University’s spinout TissueTinker is exploring a new bioprinting approach that could improve the way cancer drugs are tested in preclinical settings.

Co-founded by Benjamin Ringler, Madison Santos, and Isabelle Dummer, the startup recently received a Develop award from the McGill Innovation Fund (MIF) to advance its miniature tumor model platform. 

Designed as a human-relevant alternative to 2D cultures and animal testing, the miniature models aim to reduce the 90% failure rate of cancer drugs after preclinical testing by better capturing tumor complexity and improving predictability early on.

“Because the testing environment more readily simulates the human body, researchers can better assess and understand whether or not their drug works before reaching clinical trial stages,” Ringler detailed. “This is key for drug progression and curbing financial waste in the industry.”

Miniature models offer customization edge

TissueTinker’s platform centers on bioprinting tumor models at a scale of around 300 µm, a size the team considers optimal for balancing biological relevance with resource efficiency. 

Using bioink made from living cells, the models are constructed to include both healthy and cancerous tissue types, positioned with spatial precision. This structure enables the replication of key physiological features, such as hypoxic cores, that influence how tumors grow and respond to treatment.

The platform’s design allows researchers to adjust both the structure and cell composition of each tumor model, depending on the specific biological question being studied. This adaptability makes it possible to replicate a wide range of tumor conditions, offering more targeted insights into how treatments behave under different physiological scenarios.

This approach gains added relevance under updated  US Food and Drug Administration (FDA) guidelines, which now allow drug developers to use human-based models in place of animal testing during preclinical research. By offering a method that reflects the complexity of human tumours more accurately, TissueTinker provides a practical option within this shifting regulatory landscape.

Backed by support from the MIF, the team has refined both the technical and strategic dimensions of the platform. In addition to funding, the program provided mentorship that helped the founders focus on long-term development. They are now working to expand their tumor model library and plan to license the platform to pharmaceutical companies and research institutions.

Rethinking drug testing with bioprinted tumors

With cancer responsible for 10 million deaths in 2020 and cases expected to surpass 28 million by 2040 as referenced by McGill, many are seeking more efficient approaches to drug development.

Previously, Edinburgh-based tumor 3D printing specialist Carcinotech and bioprinting firm CELLINK partnered to advance cancer drug development by creating standardized protocols for bioprinted tumor models built from cancer cell lines. These models were designed to replicate the physiological makeup of specific cancer types, incorporating five key cell types in accurate ratios to improve testing relevance. 

Developed for use with CELLINK’s BIO CELLX system, the protocols were expected to enable automated and reproducible 3D cell culture workflows, streamlining drug screening processes. The partnership built on earlier work combining Carcinotech’s expertise in tumor modeling with CELLINK’s bioinks and bioprinting technology to enhance precision in preclinical research.

In 2021, researchers at the University of Stuttgart and Robert Bosch Hospital developed a 3D printed tissue platform designed to improve cancer drug testing while reducing the need for animal experiments. 

As part of a €3.8 million initiative funded by the state of Baden-Württemberg, the team used bioprinting and simulation data to create skin-like microfluidic structures that more closely mimic tumor behavior in the human body. Their approach combined ex-vivo, de-novo, and in-silico strategies, producing modular, nutrient-loaded cell structures that can be assembled like “lego bricks” to simulate realistic tumors and better predict drug distribution outcomes.

What 3D printing trends should you watch out for in 2025?

How is the future of 3D printing shaping up?

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Featured image shows McGill Innovation Fund team TissueTinker is reimagining how we test cancer therapies with customizable, human-relevant bioprinted tumor models that replicate human tissue. Photo via McGill University.

Drug discovery efforts based on DNA-encoded chemical libraries are inadvertently overlooking numerous potential drug candidates, new research shows. 

Each molecule in a DNA-encoded chemical library is tagged with a unique DNA sequence that acts like a barcode. Such libraries have revolutionised early drug discovery by allowing researchers to screen millions, if not billions, of compounds simultaneously. And the resulting datasets are often used to train machine learning models that seek out promising drug candidates.

 Keen to understand how reliable data linked to DNA-encoded chemical libraries actually is, Raphael Franzini, from the University of Utah in the US, and colleagues investigated a library with over 58,000 compounds designed to target enzymes involved in DNA repair and cancer. When they synthesised and tested 33 molecules that screens had dismissed, they discovered that these compounds were often just as effective as those flagged as promising. In particular, various screens nearly missed compounds that were structurally similar to olaparib, an approved cancer drug. 

‘We found that DNA-encoded library data often labels good molecules as bad molecules,’ explains Franzini. 

The problem appears to lie with the DNA barcodes themselves. When the team compared molecules with and without these tags, they found that the DNA reduced molecules’ activity. The effect was even more pronounced when molecules were tested against targets they were not originally designed for. 

Laura Guasch, a computational chemist at pharmaceutical company Roche, Switzerland, describes the findings as ‘a highly relevant contribution’. She says the study ‘raises crucial awareness regarding how these numerous false negatives can impair the increasingly popular machine learning algorithms used in this domain.’

 ‘False negatives introduce substantial noise and bias into training datasets, causing machine learning models to learn misleading patterns or ignore valid chemotypes,’ comments Srinivas Chamakuri, an assistant professor at Baylor College of Medicine’s Center for Drug Discovery in the US. 

Franzini and colleagues demonstrated that even when machine learning models appeared to perform well, they were actually just recognising recurring structural fragments rather than developing genuine predictive capabilities. 

‘A primary implication of this study is the significant risk that current drug discovery programs might be overlooking potential drug candidates due to high rates of false negatives,’ notes Guasch. 

The researchers found that removing unreliable data from the training sets and focusing only on confirmed active compounds dramatically improved models’ ability to identify promising drugs. This suggests that current machine learning approaches in drug discovery may need fundamental changes to account for the inherent biases in screening data.

While the use of radiation bridging therapy (BT) in chimeric antigen receptor (CAR) T-cell therapy for blood cancer is expanding, plenty of unanswered questions remain on topics such as ideal timing and doses, a radiologist cautioned hematologist colleagues.

The lack of guidelines has immediate clinical implications, said John P. Plastaras, MD, PhD, professor of radiation oncology at the Hospital of the University of Pennsylvania, Philadelphia, in a presentation at 18th International Conference on Malignant Lymphoma (ICML) 2025 in Lugano, Switzerland.

“This actually just came up the other day when one of our medical colleagues said, ‘I’m really worried about this patient. They’re ready for CAR T cell, but I think you need to radiate this area. Can you do it a week after [therapy]?’ The answer is, ‘We don’t know.’”

On the other hand, clinicians now have clarity about safety and interaction with CAR T-cell therapy, he noted, and data is coming in rapidly.

Here are some questions and answers about radiation BT:

What is BT in CAR T-cell therapy?

BT refers to treatment that provides a “bridge” for patients between the components of CAR T-cell therapy.

As a 2024 report about BT in hematologic cancer explained, the treatment “is delivered after leukapheresis for CAR T-cells” — the process in which white cells are removed from a patient’s blood, which is then returned to the body — “has been completed and before lymphodepleting chemotherapy and CAR T-cell infusion.”

The report said “patients who receive BT are predominantly those with a higher disease burden and rapidly progressive disease. These patients tend to have worse overall outcomes, likely related to their aggressive underlying disease.”

Where does radiation fit into BT?

According to the 2024 report, “combination chemoimmunotherapy has typically been the form of BT that is used most often.” Targeted therapy is another option, the report said, although data is from “very small sample sizes.”

And then there’s radiation, which the report said is useful “particularly in patients with limited sites of disease or patients who are at risk for structural complications such as airway compromise or renal dysfunction.”

What do we know about radiation’s efficacy?

The first oral report on bridging radiation in CAR T-cell therapy only appeared in 2018, Plastaras said, followed by the first published report in 2019. Despite this fairly short time period, “we are certainly seeing a lot of new data,” Plastaras said.

He highlighted the newly released International Lymphoma Radiation Oncology Group (ILROG) study of radiation BT in conjunction with CAR T-cell therapy for relapsed/refractory B-cell lymphomas. The retrospective study of 172 patients at 10 institutions treated from 2018 to 2020 showed that 1- and 2-year progression-free survival (PFS) rate was 43% (95% CI, 36-51) and overall survival rate was 38% (95% CI, 30-45).

In a multivariable model, comprehensive radiation BT was linked to superior PFS than focal therapy (hazard ratio, 0.38; 95% CI, 0.22-0.63; P < .001).

“Comprehensive radiation was a very strong predictor for improved PFS, but we did not see was a huge dose effect,” said Plastaras, who coauthored the study.

What about toxicity?

Questions about other clinical matters were resolved prior to 2022, he said, when CAR T-cell therapy was used primarily in third line and later settings.

“Does radiation cause excess toxicities?” he asked. “A lot of the single-institution studies answered that, and I think most medical oncologists and hematologists are okay with this idea that radiation isn’t causing a lot of excess toxicities.”

As for whether radiation interferes with the effectiveness of CAR T-cell therapy, “the data to this point have demonstrated that probably not,” he said. “We’ve probably put that one to bed.”

What do we know about treatment timing?

“The timing question is still quite open,” Plastaras said. “How much time should there be between radiation and lympho-depleting chemotherapy? Is it better to put the radiation very close to the CAR T-cell [therapy] so this priming effect might happen, or can that happen weeks in advance? We don’t know the answers to those.”

According to Plastaras, researchers are still trying to understand the role radiation the consolidation period after CAR T-cell therapy. “If we wait for day-30 PET [scan], is that OK? Do we need to wait longer? Are we going to mess up the lymph nodes that have CAR T-cells floating around in them?”

What about doses and imaging?

There’s also a lack of insight into technical questions about radiation dose and fractionation. “The [radiation] volume question is one of key importance. Do we just do gross disease? Do we treat all the other small spots out there, and importantly, do we treat regional nodes or not? We get these questions all the time.”

The role of imaging is also unclear, he said, in terms of timing during and after bridging radiation therapy and after CAR T-cell therapy.

What do we need to learn about now?

Looking forward, Plastaras outlined what he called “version 2.0” questions for the evolving field: Can radiation rebulking decrease CAR-T cell toxicities? Will very low dose “priming” radiation affect outcomes? 

He highlighted other questions: Can radiation be part of a combined modality approach in limited stage relapsed/refractory disease? Should central nervous system lymphoma be treated differently? 

When will we get new guidelines?

According to Plastaras, Memorial Sloan Kettering Cancer Center Radiology Oncologist Brandon Imber,MD, MA, in New York City, is leading a new ILROG guideline project with the intention of publishing details in the journal Blood. “This is a work in progress,” Plastaras said. “Our target is 2025 to at least get something submitted.”

Plastaras had no disclosures.