Exploring AI’s Impact on Cancer Drug Development and a Microcap’s Bid to Lead It

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The Promise of AI in Cancer Treatment
In oncology today, treatment often feels like a gamble. Doctors typically follow standard protocols and prescribe therapies based on broad categories, hoping for a positive response. But when tumors don’t shrink, and they often don’t, the patient is left dealing with the fallout: months of toxic side effects with no real benefit. This is the costly reality of one-size-fits-all cancer care. Most patients don’t respond to the first drug they’re given, and sometimes not even the second or third.
What if we could predict ahead of time which treatment will actually work? That’s the question artificial intelligence is helping us answer. By analyzing vast amounts of clinical and molecular data, AI can identify patterns that humans can't easily detect. These insights can lead to more personalized treatments, higher response rates, and less time wasted on therapies that were never likely to help in the first place.
Beyond individual care, the impact could be even broader. Drug development in oncology is notoriously slow and expensive. It takes years, often more than a decade, and billions of dollars to bring a new therapy to market. The majority of experimental cancer drugs fail somewhere along the way, usually in late-stage trials. In many cases, these failures happen not because the drug didn’t work at all, but because it didn’t work for the majority of patients. There may have been a subgroup of responders, but the overall data masked the signal.
AI is beginning to change that. By predicting who is most likely to respond to a given treatment, it gives researchers a chance to design smarter trials, narrow the target population, and improve the odds of success. This shift from treating cancer as a monolithic disease to one defined by its molecular diversity marks one of the most significant transformations in oncology in decades.
The AI Opportunity in Drug Development
Now imagine running a clinical trial virtually, testing a drug across thousands of simulated patients with different tumor types and genetic profiles. This is where machine learning comes in. Trained on historical data from past trials and patient records, these algorithms can highlight which compounds are worth pursuing and which are likely to fail. They can even identify new applications for old drugs that were shelved years ago.
Instead of testing 100 drug candidates in the lab, a pharmaceutical company might narrow its focus to just five, based on AI predictions. That saves time, reduces cost, and cuts the odds of failure. It also means fewer patients exposed to treatments unlikely to help them.
The use of three-dimensional tumor models further strengthens these predictions. These models simulate how real human tumors behave, something traditional two-dimensional cell cultures can't do. Combined with AI, they allow for more realistic and reliable testing before a drug ever reaches clinical trials.
This evolution is already reshaping how the pharmaceutical industry approaches early-stage development. Companies are building data science teams, forming partnerships with AI firms, and adjusting trial designs to take advantage of these technologies. Regulators are also beginning to embrace the potential of adaptive trials and biomarker-based approvals, especially in areas like oncology where the need is urgent.
A Microcap Contender: Predictive Oncology
One small company operating at this intersection of AI and oncology is Predictive Oncology Inc. (NASDAQ: POAI). Based in Pittsburgh, this microcap firm is trying to bring precision to drug discovery using a combination of artificial intelligence, lab modeling, and a vast repository of real patient tumor samples.
At the center of its platform is PEDAL, an AI engine that predicts how a specific tumor might respond to a given compound. The system is trained on extensive drug response data and draws on one of the largest live tumor biobanks in the industry, containing over 150,000 samples across more than 130 cancer types.
The company also operates TumorGenesis, a system that recreates tumor environments using 3D cultures. Unlike flat lab-grown cells, these cultures mimic the biological conditions of actual tumors, offering more accurate insights into how a drug might perform in the human body.
By combining PEDAL with TumorGenesis, Predictive Oncology offers a kind of early-phase testing ground that mimics real-world treatment. A pharmaceutical partner could run a new compound through PEDAL to see which cancer types are most likely to respond. Then, the same compound could be tested on live tumor cultures that mirror those predictions. This helps drug developers prioritize where to focus and which trials to run, cutting down on both time and uncertainty.
The company has already begun applying this model. In one case, it used PEDAL to analyze previously abandoned drug candidates. The AI identified several that might work in ovarian and colon cancers, giving new life to compounds once considered dead ends. These insights were then confirmed in lab studies using live tumor models.
Predictive has also partnered with Labcorp, providing 3D liver organoids for drug metabolism and toxicity studies. It has collaborated with research hospitals and equipment manufacturers to automate and validate its lab processes. These partnerships show how the platform can extend beyond just predicting drug efficacy to supporting a broader range of pharmaceutical R&D needs.
Momentum and Milestones
Predictive Oncology remains in the early stages of its commercial journey, but the foundation it has built is beginning to take shape. While revenue to date has been modest and primarily tied to legacy assets, the company has sharpened its focus on its core AI and tumor modeling platform. That shift has included divesting non-core operations and streamlining its cost structure to align with long-term goals in oncology and pharmaceutical R&D.
In late 2024, the company began exploring strategic alternatives to accelerate growth and unlock value from its platform. This led to a proposed merger with Renovaro Biosciences, a California-based biotech working in multi-omics and immunotherapy. The combination, still pending as of mid-2025, is aimed at creating a more integrated AI-driven drug discovery engine, uniting Renovaro’s data platforms with Predictive’s tumor biobank and predictive models.
To support near-term execution, Predictive also secured a $10 million equity facility with Yorkville Advisors. This funding mechanism offers the company flexibility as it builds toward stronger commercial traction. Taken together with the sale of its Skyline Medical division, these moves reflect a focused effort to align the business with its long-term vision in AI-enabled oncology.
The path ahead will require continued validation and partnership. Like any early-stage innovator in a competitive space, Predictive will need to show that its platform delivers value to pharma customers, whether by de-risking early-stage programs, shortening timelines, or surfacing promising new therapeutic directions. But with a differentiated platform, recent collaboration wins, and a clear strategic direction, the company is positioning itself to be part of a much larger transformation in how cancer drugs are discovered and developed.
The Bet on AI in Oncology
The idea that AI can guide us toward better cancer treatments is happening now, in real labs and on real datasets. The tools are improving. The appetite for smarter, more efficient drug development is growing. And the stakes could not be higher.
Predictive Oncology is not the only company chasing this opportunity. But it has quietly assembled the ingredients to play a role in the story: a massive biobank, a functioning AI engine, and validated lab models that mimic real human tumors.
It's still early, and the risks are substantial. But for investors looking at the long arc of AI in healthcare and the slow transformation of oncology into a more precise, data-driven discipline POAI offers an intriguing speculative play.
This is a company trying to become essential to the future of cancer drug development. Whether it succeeds will depend on execution, timing, and its ability to translate science into business. But the broader trend is clear: medicine is moving toward prediction and personalization, and AI is helping lead the way.
This content is for informational purposes only and does not constitute financial, investment, or medical advice. Always do your own research before making any investment decisions.
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Disclosure: This article is editorial and not sponsored by any companies mentioned. The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of NeuralCapital.ai.