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AI transforms cancer drug development from trial-and-error to targeted selection

AI transforms cancer drug development from trial-and-error to targeted selection

New Capabilities
By Newzino Staff | |

Machine learning systems now identify which patients will respond to immunotherapy before treatment begins

January 13th, 2026: AstraZeneca Acquires Modella AI to Scale Oncology AI

Overview

For decades, cancer drug trials have failed at a rate exceeding 95%—burning through $50-60 billion annually on treatments tested in patients who were never likely to respond. On April 17, 2025, researchers from AstraZeneca and Tempus AI published results in Cancer Cell showing that the Predictive Biomarker Modeling Framework (PBMF)—a machine learning system using contrastive learning—can identify, from existing clinical data, which cancer patients will survive longer on immunotherapy versus chemotherapy. Applied retrospectively to completed phase 3 trials, the system improved survival outcomes by 15% compared to traditional patient selection.

Now nearly 10 months later, this approach gains regulatory momentum with the European Society for Medical Oncology (ESMO) releasing in December 2025 the first guidance on validation requirements for AI-based biomarkers (EBAI), establishing standards for clinical implementation and building trust among clinicians and regulators. The implications extend beyond individual drugs: widespread adoption of AI-driven biomarker discovery could fundamentally alter cancer drug economics by enabling precise patient selection before trials begin, potentially rescuing failed therapies through identified responsive subgroups.

Key Indicators

15%
Survival Risk Improvement
Patients identified by the PBMF biomarker showed 15% better survival outcomes than those in the original trial design
95%
Oncology Trial Failure Rate
Traditional cancer drug development fails at an exceptionally high rate, largely due to poor patient selection
$200M
AstraZeneca-Tempus Partnership Value
Combined data licensing and model development fees for building the largest multimodal oncology foundation model
8M+
Tempus Patient Records
De-identified patient records including clinical notes, genomics, imaging, and transcriptomics available for AI training

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People Involved

Etai Jacob
Etai Jacob
Senior Director, Head of Data Science and AI, Early Oncology at AstraZeneca (Lead researcher on PBMF development)
Eric Lefkofsky
Eric Lefkofsky
Founder and CEO, Tempus AI (Leading expansion of AI precision medicine platform)
GA
Gustavo Arango-Argoty
Lead author, PBMF research paper (AstraZeneca researcher)

Organizations Involved

AstraZeneca
AstraZeneca
Pharmaceutical company
Status: Developing AI-powered oncology drug development infrastructure

British-Swedish pharmaceutical giant investing heavily in AI integration across oncology research and development.

Tempus AI
Tempus AI
Health Technology Company
Status: Providing data infrastructure for AI-driven precision medicine

Chicago-based company that built the largest clinical and molecular database in oncology, now powering AI drug development.

U.S. Food and Drug Administration
U.S. Food and Drug Administration
Federal Regulatory Agency
Status: Adapting approval pathways for AI-driven precision medicine

Federal agency responsible for approving drugs and companion diagnostics, increasingly accepting AI-derived biomarkers in regulatory submissions.

Timeline

  1. AstraZeneca Acquires Modella AI to Scale Oncology AI

    Industry

    AstraZeneca acquired Boston-based Modella AI to embed multimodal foundation models and AI agents across its global oncology R&D organization.

  2. ESMO Releases First Guidance on AI-Based Biomarkers

    Regulatory

    European Society for Medical Oncology published EBAI framework defining validation requirements and clinical implementation standards for AI-generated biomarkers in oncology, addressing gaps in trust and regulatory acceptance.

  3. Cancer Cell Publishes PBMF Demonstration of AI-Improved Trial Outcomes

    Research

    Peer-reviewed publication showed the Predictive Biomarker Modeling Framework improved survival outcomes by 15% when applied retrospectively to immunotherapy trials for lung, kidney, and bladder cancers.

  4. Tempus AI Goes Public on Nasdaq

    Industry

    Tempus began trading under ticker TEM after raising $1.05 billion in private funding, providing public market validation for AI-driven precision medicine.

  5. AstraZeneca Commits $200M to Oncology AI Partnership

    Industry

    AstraZeneca, Tempus, and Pathos AI announced agreements to build the largest multimodal foundation model in oncology, trained on Tempus's database of 8 million patient records.

  6. PBMF Research Posted as Preprint

    Research

    AstraZeneca and Tempus researchers posted their Predictive Biomarker Modeling Framework study to medRxiv, showing AI could identify predictive biomarkers in immunotherapy trials.

  7. Watson for Oncology Revealed to Recommend Unsafe Treatments

    Investigation

    Internal documents showed IBM's Watson recommended treatments with black box warnings for patients who should never receive them, trained on synthetic cases rather than real patient data.

  8. FDA Approves First Pan-Tumor Genomic Profiling Test

    Regulatory

    FoundationOne CDx became the first FDA-approved comprehensive genomic profiling test for all solid tumors, analyzing 324 cancer genes.

  9. Tempus Founded After Founder's Personal Cancer Experience

    Industry

    Eric Lefkofsky founded Tempus after his wife's breast cancer diagnosis, recognizing that treatment decisions relied on inadequate data infrastructure.

  10. IBM Launches Watson for Oncology

    Industry

    IBM partnered with Memorial Sloan Kettering to develop Watson for Oncology, promising AI-powered treatment recommendations for cancer patients.

  11. FDA Approves First Biomarker-Targeted Cancer Drug

    Regulatory

    The FDA approved trastuzumab (Herceptin) for HER2-positive breast cancer alongside the HercepTest companion diagnostic, establishing the template for precision oncology.

Scenarios

1

AI Biomarker Discovery Becomes Standard for Oncology Drug Approval

Discussed by: CAS scientific trends analysis, BioSpace precision oncology coverage, ARK Invest research

FDA and other regulators formally incorporate AI-derived biomarkers into companion diagnostic requirements, making retrospective biomarker optimization a standard step before phase 3 trials. Drug development programs that integrate AI patient selection achieve the documented 2x improvement in approval likelihood, fundamentally changing oncology economics.

2

Pharmaceutical Industry Consolidates Around Data-Rich AI Platforms

Discussed by: Pharmaceutical Technology, Fierce Biotech, industry analysts covering AstraZeneca-Tempus partnership

The demonstrated value of large clinical datasets for AI training creates a winner-take-most dynamic. Companies like Tempus with 8+ million patient records become essential infrastructure partners. Smaller biotechs without data access struggle to compete in precision oncology, leading to increased partnerships or acquisitions.

3

AI Biomarker Systems Face Watson-Like Scrutiny Over Real-World Performance

Discussed by: STAT News, oncology clinical practice commentators, AI ethics researchers

As AI-derived biomarkers move from retrospective validation to prospective use, gaps emerge between research performance and clinical reality. Regulators or researchers identify cases where AI recommendations underperform physician judgment or fail to generalize across patient populations, triggering reassessment of the technology's readiness.

4

AI Rescues Failed Drugs by Identifying Responsive Subpopulations

Discussed by: Frontiers in Oncology, Cancer Cell editorial commentary, drug development strategists

Pharmaceutical companies systematically apply AI biomarker frameworks to previously failed compounds, identifying patient subgroups where the drugs actually work. Several therapeutics return to clinical development with AI-optimized patient selection, creating a new category of 'rescued' precision medicines.

Historical Context

Herceptin and HER2 Testing (1998)

September 1998

What Happened

The FDA approved trastuzumab (Herceptin) for metastatic breast cancer alongside the HercepTest to identify patients whose tumors overexpress the HER2 protein. This was the first cancer drug approved with a required companion diagnostic, establishing the template for precision oncology that matches treatments to molecular targets rather than tumor location.

Outcome

Short Term

Herceptin transformed outcomes for the approximately 20% of breast cancer patients with HER2-positive tumors, who previously faced the worst prognoses.

Long Term

The drug-diagnostic co-development model became standard practice. By 2023, approximately 50% of new oncology drug approvals included companion diagnostic or biomarker requirements.

Why It's Relevant Today

The PBMF research extends this paradigm from single-gene testing to AI-discovered multi-factor biomarkers. Where HER2 testing identifies one known protein, PBMF discovers novel biomarker signatures from complex clinical and genomic data—potentially expanding precision medicine to cancers without obvious molecular targets.

IBM Watson for Oncology Failure (2012-2023)

2012-2023

What Happened

IBM invested an estimated $4 billion developing Watson for Oncology, promising AI-powered treatment recommendations. The system was trained on synthetic patient cases and the opinions of specialists at Memorial Sloan Kettering rather than real-world outcomes data. In 2018, internal documents revealed Watson had recommended unsafe treatments, including drugs with black box warnings for patients who should never receive them.

Outcome

Short Term

Only a few dozen hospitals adopted the system. Foreign physicians complained recommendations were biased toward American treatment practices and failed to align with local guidelines.

Long Term

IBM sold Watson Health assets in 2022, effectively ending its AI healthcare ambitions. The failure became a cautionary tale about AI systems trained on expert opinion rather than outcome data.

Why It's Relevant Today

The PBMF approach directly addresses Watson's core failure. Rather than encoding physician opinions, PBMF uses contrastive learning on real patient outcomes to discover which biomarkers actually predict treatment response. The 15% survival improvement came from retrospective validation on completed trials—exactly the outcome-based evidence Watson lacked.

FoundationOne CDx FDA Approval (2017)

November 2017

What Happened

Foundation Medicine's FoundationOne CDx became the first FDA-approved comprehensive genomic profiling test for all solid tumors, analyzing 324 cancer genes in a single test. The approval came through a novel parallel review process with FDA and the Centers for Medicare and Medicaid Services, establishing national coverage for genomic testing in cancer.

Outcome

Short Term

Comprehensive genomic profiling became accessible to cancer patients nationwide with insurance coverage, enabling identification of rare mutations and trial eligibility.

Long Term

By December 2025, Foundation Medicine achieved 100 approved companion diagnostic indications, demonstrating regulatory acceptance of multi-gene panels as standard of care.

Why It's Relevant Today

FoundationOne showed regulators would accept complex genomic data for treatment decisions. PBMF builds on this foundation by using AI to extract predictive patterns from even larger datasets combining genomics, clinical notes, imaging, and treatment outcomes—moving from 'what mutations does this tumor have' to 'which patients will respond to this drug.'

14 Sources:
+8
Cancer Cell PubMed Tempus medRxiv Pharmaceutical Technology CAS BioSpace Modella AI STAT News Foundation Medicine Drugs.com Wikipedia ESMO Daily Reporter PubMed