Bridging the cancer drug development gap: AI-driven precision oncology rewrites pharma's playbook

Cancer drug development has never moved faster. Yet, for too many patients, the next treatment decision still begins with uncertainty. That is not a failure of effort. It is a failure of evidence.

Pharmaceutical companies have invested decades building more targeted therapies, more refined trials, and more sophisticated molecular data. Science has advanced. But the fundamental problem has not changed. When physicians stand in front of patients whose cancer has progressed, the most important question remains unanswered: which drug will actually work for this patient’s tumour?

Beyond genomics: Bridging the gap between molecular insight and real treatment outcomes

Genomic profiling has brought oncology far. Actionable mutations have been identified. Targeted therapies have transformed outcomes across several cancer types. But genomics was never designed to answer that question. Molecular profiles provide a map of potential vulnerabilities. They do not confirm response. That distinction is not academic. It shapes every treatment decision and every clinical trial.

The dominant model in oncology drug development is built on averages. Trials are designed around population-level evidence. Drugs advance based on aggregate response rates. That model has produced important advances. But population-based evidence was never designed to answer the most important clinical question: which drug will actually work for this specific patient’s tumour?

The limits of population-based evidence

Two patients with the same diagnosis, the same mutation, and the same treatment history can respond to the same drug in completely different ways. That variability is not a statistical anomaly. It is the biology of cancer. Tumours evolve under therapeutic pressure, adapt to their microenvironment, and develop resistance through mechanisms that molecular sequencing alone does not always capture. Analyses have shown that only a small proportion of patients with advanced cancer ultimately benefit from genome-informed treatment strategies. The problem is not a shortage of drugs. It is the inability to determine with confidence which drug will produce a meaningful response in a specific patient.

Physicians at the front lines of oncology have watched this play out for years. Patients exhaust their options while drugs that might have worked for them never complete development. That is not a pipeline abstraction. It is a clinical reality with a name and a face. When eligibility criteria are built on molecular markers that do not reliably predict how a tumour actually responds, the right patients get missed, and the wrong ones get enrolled. Trials fail. Timelines stretch. The signal that looked promising early collapses under the weight of a population that was never truly defined. The industry keeps refining the process. What it has not confronted is whether the process itself is the problem.

Introducing functional precision oncology

Functional precision oncology introduces a different kind of evidence. Instead of inferring response from molecular features, it measures response directly. Patient-derived tumour samples are tested ex vivo against panels of approved drugs and combinations, generating a quantitative profile of sensitivity and resistance grounded in the actual behaviour of that patient’s tumour. When integrated with genomic and transcriptomic data, these results create a more complete picture of what the disease is doing and how it responds. Critically, that data can be generated within days of biopsy receipt. Speed is not a convenience. It is a clinical and commercial requirement. Data that arrives after a development decision has already been made has limited value to anyone.

This is not an incremental refinement of genomics. It is a complementary layer that addresses what genomics was not designed to answer. The shift is from predicting what might work to validating what does. Molecular data identifies the candidates. Functional data confirms the response. Physicians receive the information they need at the point of the treatment decision, before a line of therapy begins. The same principle applies in development. Functional evidence generated before a clinical commitment changes the quality of the decision. It moves development teams from inference to confirmation before capital is committed, not after it has been spent.

From prediction to validation in treatment decisions

Each patient evaluated through a functional precision platform generates structured data on how their tumour responds across multiple drugs and combinations. Individually, that data informs a treatment decision. Aggregated across patients, it becomes something more. These datasets reveal patterns of drug sensitivity and resistance that are difficult to capture through conventional trials, shaping trial design with real biological evidence, rather than hypothesis alone. Functional precision data also introduces a prospective, patient-level dimension to biomarker discovery, tracing observed response back to molecular context in ways that retrospective trial analysis rarely can.

From individual insight to data intelligence

Some of the most important decisions functional precision data informs are not about which drug to advance, but which ones deserve a second look. Functional precision data not only informs new programmes, but it can also recover stalled ones. A drug that failed to advance because a single biomarker did not predict response is not necessarily a lost asset. When functional testing reveals the true responder population, programmes with no apparent path forward can be stratified, redirected, and advanced. That is a direct answer to one of the most costly problems in oncology drug development: programmes terminated too early on incomplete evidence.

The role of AI in translating complexity into action

None of this is possible without artificial intelligence (AI). Functional testing generates enormous volume. A single patient’s tumour evaluated across hundreds of drugs and concentrations produces thousands of data points. Machine learning systems integrate those results with genomic findings, prior treatment history, and published evidence, translating complex biological data into structured information that physicians can act on. Automation ensures the testing environment remains consistent across samples, sites, and operators.

The result is not AI replacing clinical judgement. AI-provided data makes clinical judgement more precise. That is the point: functional precision oncology gives physicians the data they need to make better decisions about each patient’s care. For pharma, that same capability supports more precise trial enrolment, reduces population heterogeneity, and delivers biological evidence of drug activity before late-stage investment is committed.

Cancer remains a leading cause of mortality globally, with more than 600,000 Americans dying from the disease each year. For patients with advanced disease, each decision is immediate, and its consequences are real. The pharmaceutical industry has absorbed the cost of that uncertainty for decades, in the form of trial failures, narrow approvals, and drugs that work in populations, but not reliably in individuals.

The future of cancer drug development

The drugs that will define the next era of oncology will not be selected based on averages. They will be validated on the biology of individual patients. That means incorporating functional response data at the front of the development process, not as a fallback after a programme has already struggled. It means reaching earlier, higher-confidence Go/No-Go decisions before timelines are dictated by signals that should have surfaced sooner. It means treating functional validation not as a research exercise, but as a decision support tool, one that gives development teams the same clarity that physicians need at the point of care.

Pharma has both the opportunity and the incentive to lead that shift. The question is whether the industry moves deliberately toward that model or waits until the cost of the current one becomes impossible to justify.

About the author

Dr Maggie Fader is CMO at First Ascent Biomedical. She is a physician leader specialising in oncology and precision medicine. Her work focuses on integrating functional tumour response data with advanced analytics to support physician decision-making. She leads efforts to operationalise functional precision oncology within clinical workflows and improve how therapy decisions are made for patients with complex cancers.