How to optimize early oncology discovery in precision therapy


Precision medicine involves adapting therapy to the patient’s disease. Here, we examine the important role that sample quality, data, and analysis play in the development of these targeted therapies.

Developing precision therapies requires accurately identifying therapeutic targets from available data. In parallel, the subgroup of patients who would most benefit from therapy also needs to be determined. Because precision medicine is so patient-centred, pre-clinical research for this therapy is challenging.

Jonathan Woodsmith, Vice President of Advanced Analytics at Indivumed Therapeutics
Jonathan Woodsmith, Vice President of Advanced Analytics at Indivumed Therapeutics

“Identifying the right target, in the right tissue, for the right patient group is critical for precision therapy,” said Jonathan Woodsmith, Vice President of Advanced Analytics at German-founded global oncology company Indivumed Therapeutics.

Therapeutic target discovery programs that fail to get these elements right have a lower chance of success, despite the investment of time and money.

The lesson is clear: without optimizing targets for tissue type, and patient subgroups, it is difficult for therapies to advance through preclinical research and clinical trials.

Where we are today: the process of early discovery made possible by technology

The foundation for any precision oncology discovery program is accurate, data-driven cancer imaging. This fosters a clear understanding of the disease and ensures that the right targets are identified.

To produce clinically relevant patient data, the use of high-quality biospecimen is essential, emphasizes Woodsmith.

Biospecimen that is too long outside the body or has been suboptimally aggregated, may undergo morphological or physiological changes. This invariably results in the loss of critical information that can critically alter high-throughput data collection and subsequent disease description, leading to targets being missed or misidentified.

“The process of getting freshly frozen biospecimen collected and processed consistently with high sample integrity is not easy,” admits Woodsmith. “But, in the end it is a sample that accurately reflects the disease process and allows the production of high-quality data.”

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Applying the right analysis to derived data is as important as high sample quality, says Woodsmith. This is because the analysis helps explain the relationship between the patient’s disease and the potential target.

This is where today’s emerging field of multi-omics offers promise, says Woodsmith. Multi-omics combines multiple datasets using different biological analyzes spanning the genome, proteome, microbiome and more, providing an integrated perspective to drive discovery.

In addition, today’s data sets are large – often spanning terabytes of data – and highly complex. These challenges make determining the relevant analysis more complicated, and can cause bottlenecks in early discovery programs.

To help simplify and speed up the discovery process, the use of machine learning (ML) and cloud-based computing tools has grown in popularity in recent years. Woodsmith notes that by addressing the combinatorial complexity of disease with cost-effective data processing, ML and artificial intelligence (AI) algorithms have the potential to reveal efficient ways to select the right targets in the right patients.

Optimizing pre-clinical validation using disease-adjacent models

Once a drug candidate has been identified in silico, in vitro validation of the findings is necessary before the therapy can proceed to clinical trials.

This usually involves using an experimental model to show that the identified target molecule inhibits cancer. Traditionally, such validation is performed using immortalized cancer cell lines derived from tumor cells that can grow and replicate in the laboratory.

However, as preclinical research continues to advance, scientists have realized that the disease proximity of a model – that is, how closely a system captures a patient’s disease – is important.

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“Initial work should be done on data and experimental systems that represent disease in humans as closely as possible. This increases the likelihood that the therapy will be successful further down the line,” explains Woodsmith.

As immortalized cell lines do not fully capture human physiology, they have been supplemented with the use of more disease-adjacent models as technology advances.

Currently, patient-derived cellular models, such as organoids, represent state-of-the-art experimental models for pre-clinical validation. This allows one of the closest recapitulations of the patient’s physiological processes, while also representing the hallmarks of the disease.

Unraveling the complex: pre-clinical AI-based discovery and development tools

Scientists are equipped with more powerful early discovery tools and pre-clinical models today than were available decades ago. Nonetheless, the ability to reliably determine the impact of potential targets on specific patient cancer inhibition remains a major pre-clinical challenge.

Recognizing this, the Indivumed Therapeutics team has channeled efforts to leverage state-of-the-art technology to help reduce uncertainty during drug development and overcome these challenges.

“nRavel® is our adaptive discovery and development platform designed to screen patterns to identify, characterize and validate anesthetic targets,” explains Woodsmith. “This is done by aggregating and analyzing in silico insights, multi-omics data, and related patient information from our proprietary database, in addition to results from in vitro trials.”

Biospecimen and clinical data form the input for nRavel® collected in an international standard way. This was achieved with the help of Indivumed’s global network of partner clinics, after which the biospecimen and data were curated by Indivumed’s trained staff.

“Sample processing, clinical data curation, multi-omics and AI-powered analysis are all done in-house by our team,” said Woodsmith. “In addition, we ensured that the multi-omics and clinical data obtained from our clinical collection matched the cellular model, enabling data consistency from start to finish. This allows us to provide verifiable target identification and validation.”

precision medicine, cancer, nRavel, Indivumed Therapeutics, early oncology discoveries

“Because of nRavel® Home to deep molecular profiles extracted from analysis, it can also be applied to help optimize clinical study designs in downstream development. This is more helpful in determining patient selection criteria to identify the most suitable group for therapy.”

Since 2022, Indivumed has been supported by the European Regional Development Fund (ERDF) in developing an early detection oncology channel using nRavel®.

As part of the project, samples have been collected from 10 types of cancer so far. And efforts are ongoing to identify the most promising actionable targets across a wide range of clinically relevant disease groups.

With many promising in-silico-identified targets, the Indivumed team has started the first validation trials as part of the ERDF project, said Woodsmith. The trials will involve in-vitro validation using patient-derived organoids developed by CellPhenomics, a partner of Indivumed, a company specializing in organoid design.

Looking forward to the future of precision medicine

Ultimately, the quality of the biospecimen, and the resulting data, can make or break the therapeutic potential a drug has to offer, mused Woodsmith.

“The sample lays the groundwork by reflecting on disease. Its quality is the key in determining the success of a precision therapeutic approach,” he continued. “All of the next steps build on that, to help address unmet medical needs as quickly as possible.”

“Sample and data collection techniques, as well as advanced analytics, have advanced rapidly over the last decade,” said Woodsmith. “As a company, we will remain at the forefront of this space to ensure that the most effective precision therapies get to the relevant patient subgroups when they need them.”

Here’s how You can partner with Indivumed Therapeutics for ooptimize your early discovery flow. To hear more from Jonathan Woodsmith about the power of combining multi-omics, clinical data, and advanced analytics, check it out latest movie.

Image courtesy: Indivumed Therapy


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