Rethinking precision oncology drug development: AI and tumor biology for smarter therapeutic programs External Journalist 9 minutesmins June 30, 2026 9 minutesmins Share WhatsApp Twitter Linkedin Email Photo credits: Indivumed Newsletter Signup - Under Article / In Page"*" indicates required fieldsX/TwitterThis field is for validation purposes and should be left unchanged.Subscribe to our newsletter to get the latest biotech news!By clicking this I agree to receive Labiotech's newsletter and understand that my personal data will be processed according to the Privacy Policy.*Company name*Job title*Business email* Despite decades of progress in oncology, the majority of cancer drugs that enter clinical trials never reach the market and the patients who need them most. Late-stage failure remains one of the most costly and persistent problems in drug development, and its roots often lie in insufficient understanding of tumor biology and poorly defined patient populations. A growing number of precision oncology companies are trying to change this by anchoring R&D in patient-relevant disease biology from the outset, using AI to integrate complex data and match the right therapeutic modality to the right target in the right patient group.Successful precision oncology programs have demonstrated that, when the right treatment reaches the right patient, outcomes can improve dramatically. In some cancers, this has redefined the standard of care. Yet across many tumor types, this promise remains unrealized. Clinical attrition rates in cancer drug development are among the highest. It is estimated that only one in twenty cancer drug candidates entering phase 1 trials ultimately receives regulatory approval. Then, roughly half of all oncology drug candidates that reach phase 3 trials still fail, and lack of efficacy is consistently the leading cause.These failures are expensive. A single failed late-stage trial can cost hundreds of millions of dollars, and the cumulative toll on patients (delayed access to effective treatments, exposure to ineffective therapies) is harder to quantify but no less significant. The urgency of solving this problem is difficult to overstate. What becomes increasingly clear is that many of these failures are the predictable consequence of decisions made years earlier: targets selected on compromised data not grounded in patient tumor biology, patient populations defined too broadly, and therapeutic modalities chosen without sufficient evidence that they are the right fit for the underlying target.Table of contentsThe roots of clinical failure in early R&DTumors are biologically complex and highly heterogeneous, not only across different cancer types, but within a single indication. This heterogeneity is one of the central reasons why programs that look promising and even perform well in early studies often fail in the clinic: the patient population enrolled in a trial may not reflect the biology the therapy was designed to target.If tumor biology is not characterized using data that genuinely represents how disease behaves in the human body, the consequences compound throughout development. Patient populations may be defined too broadly, enrolling individuals unlikely to respond. A selected target may be relevant to a subset of tumors but not to the majority or may play a role in the primary tumor but not in metastases, where the therapeutic stakes are often highest. A chosen modality may not be the most effective way to intervene on a given target in the biological context.Successful drug development in precision oncology therefore depends on building a deep, accurate understanding of disease biology early and using that understanding to define, from the outset, which patients are most likely to benefit, whether a target is genuinely driving disease progression, and which therapeutic approach is best suited to the task.What better stratification looks like in practiceThere are already examples of what becomes possible when patient stratification is tightly coupled to disease biology. In breast cancer, the disease has been subdivided into biologically distinct subtypes – hormone receptor-positive, HER2-positive, and triple-negative – each with a different molecular basis and a corresponding treatment approach. The resulting development of targeted therapies was transformative precisely because it was matched to a patient population defined by a robust biological marker, a distinction now codified through mandatory HER2 testing before treatment. That biological precision is what made the therapy work – and what made the clinical program succeed.Extending this model to many other solid tumors remains a major challenge, and colorectal cancer (CRC) illustrates the difficulty well. CRC is not a single disease, but a collection of biologically diverse subtypes with distinct molecular drivers and clinical behavior. Broad disease labels are often not sufficient to identify which patients will benefit from a specific therapy. For most CRC subtypes, unmet need remains high, and the precise biological classification needed to drive effective drug development is still unreachable for many programs.Mutation profiles and tumor localization provide important insights, but they often capture only part of what drives an individual tumor. A more complete picture requires integrating additional layers of biology: splice variants, transcriptional programs, immune signatures, and structural genome architecture. It is this multidimensional view of tumor biology, rather than any single biomarker, that enables robust patient stratification and gives therapeutic programs the foundation they need to succeed in the clinic.Building precision oncology programs from the patient outwardIndivumed, a clinically grounded biotech company based in Hamburg, Germany, is building its precision oncology pipeline on precisely this principle: starting as close to the patient as possible and working outward to target selection, patient stratification, and modality choice.“To develop targeted, effective therapies, our R&D team starts as close to the patient as possible. We ask: What is the right target for this disease? Which patient group will truly benefit? And what modality will best deliver on that promise?”At the core of Indivumed’s approach is a high-quality, globally standardized cancer biobank, built to capture tumors in their native state and compare them to healthy tissue from the same individual. Minimal ischemia times (the interval between surgical sample collection and appropriate preservation) combined with standardized operating room collection procedures, ensure data quality and biological comparability across patients.Each sample is supplemented with several hundred preclinical and longitudinal follow-up data points – spanning diagnostic evaluation, therapy response, and disease progression – to truly understand the complexity of each tumor, including available metastatic tissue. This provides a granular, patient-level view of cancer biology that reflects how disease actually behaves over time.Strong biological rationale backed up by multi-omics and clinical dataThis foundation of patient-derived samples supports a deep multi-omics analysis, evaluating DNA, RNA, protein, and post-translational modifications to identify new therapeutic targets. Methods including mutation profiling, single-cell expression validation, signaling pathway analysis, and druggability assessments are employed to characterize patient biology subgroups and evaluate the biological and therapeutic relevance of the potential new candidate in combination with suitable antibody modalities.Integrating multi-omics molecular insights across these data layers with comprehensive clinical data provides a strong biological rationale from the outset – one that can accelerate early R&D, reduce downstream risk, and inform clinical trial design in ways that can improve the probability of success.“Developing novel therapies is only part of the story,” said Woodsmith. “We want to get them to the right patients as quickly as possible.”Validating precision oncology targets and modalities in human-relevant modelsUnderstanding tumor biology is necessary but not sufficient. To reduce risk in the clinic, that understanding must be coupled with rigorous validation of both targets and therapeutic modalities in settings that reflect human disease as closely as possible. Immortalized cell lines and murine models are highly valuable, yet have well-documented limitations in capturing human tumor physiology. Indivumed addresses this by using samples and sample derivatives from its biobank, including various tissue types, patient-derived cell lines and 3D cellular models, to validate new drug targets in an iterative process that keeps human biology at the center. This approach helps rapidly identify a patient subgroup most likely to respond to treatments acting on a given target and builds the translational evidence needed to support clinical development with greater confidence.“Starting the target selection process from patient tissue, adding multi-omics and clinical data, and validating on patient material is the fastest, most efficient way to evaluate new targets and therapies in a human-relevant setting.”Where AI fits in: integrating complexity to inform better decisionsThe biological information generated across molecular, tissue, and clinical levels is substantial, and making sense of it requires tools capable of handling that complexity. The Indivumed team integrates AI across its R&D workflow to help bridge precision oncology research and clinical application, using it to support target identification, patient-target matching, and modality selection.AI tools integrate relevant scientific literature alongside internal data and in combination with computational tools for in silico structural prediction and epitope design, to inform novel target identification and predict promising therapeutic modalities, including bispecific antibodies and antibody-drug conjugates. Data from the iterative target validation process feeds back into these tools to refine patient-target matches, clarify therapeutic mechanisms, and improve the overall probability of success.Or, as Woodsmith puts it, “AI is plugged in across all steps to generate a biological picture that is as cohesive as possible to inform patient and modality selection.”The value of AI here lies not in replacing biological insight, but in making it tractable, enabling the integration of data layers that would otherwise be too complex to synthesize manually, and surfacing patterns that translate into more precise, better-supported therapeutic decisions.Looking ahead: AI in precision oncologyAcross the pharmaceutical industry, AI tools become increasingly important throughout the drug development process – from integration of data layers for target identification and patient stratification to biologics design.For instance, AI models analyzing multi-omics data can uncover patterns among thousands of molecular variables simultaneously, exposing potential targets that traditional analysis would miss.In patient stratification, machine learning techniques can simultaneously define molecularly distinct patient groups from complex biological datasets and reduce the number of molecular measurements required to categorize patients, achieving a number that can be implemented in the clinic. This moves beyond individual biomarkers toward integrated molecular profiles that better predict which patients will benefit from a particular treatment.As algorithms improve and more high-quality biological datasets become available, the potential to accelerate the development timelines and reduce attrition will grow.Indivumed’s approach, anchoring R&D in patient-derived biology, coupling robust patient stratification to target and modality selection, and leveraging AI to integrate the resulting complexity, is designed to address the problem of late-stage clinical failures at its source.“We aim to create a highly efficient loop between patient-derived molecular and clinical data, validated by patient-derived models that enables high-value targets and therapeutic options in months rather than years,” Woodsmith summarized.The success of this strategy is reflected in Indivumed’s pipeline of promising targets for colorectal cancer therapy, each with patient stratification and modality fit built in from the earliest stages of R&D. Pharma companies looking to build on this foundation can access flexible partnering models designed for speed, regulatory confidence, and clinical success.Here’s how you can partner with Indivumed to achieve the right fit across patient, target, and modality in AI-supported precision oncology. Explore other topics: Artificial intelligenceCancerClinical trialDrug developmentomicsPrecision medicine