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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.

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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.