Pharmaceutical companies are abandoning traditional animal models and immortalized cell lines in favor of human-derived organoids and induced pluripotent stem cells (iPSCs), which more accurately predict whether drugs will succeed in actual patients. This shift addresses a persistent industry challenge: many compounds show promise in preclinical testing but fail during expensive late-stage clinical trials, wasting billions in development costs and delaying treatments for patients.

Why Are Drug Companies Moving Away From Traditional Lab Models?

For decades, pharmaceutical researchers relied on immortalized cell lines (cells that can divide indefinitely in a lab) and animal models to test drug safety and efficacy. However, these systems poorly reflect the complexity of human disease. Many neurological disorders, cancers, and rare diseases involve interactions between multiple cell types, genetic factors, and tissue-specific processes that conventional models simply cannot recreate.

iPSCs are created by reprogramming adult cells, such as skin or blood cells, into stem cells capable of developing into different tissue types. This allows researchers to generate disease-relevant models that retain patient-specific genetic information, enabling scientists to study how diseases develop differently across individuals and investigate why patients may respond differently to the same treatment.

Organoids take this approach further by enabling researchers to recreate aspects of tissue organization and cellular behavior seen in organs such as the brain, liver, and intestine. These three-dimensional structures more closely mimic how cells interact in the human body, providing a more physiologically relevant testing ground for new therapies.

What's the Main Challenge Holding Back Adoption?

Despite growing interest, reproducibility remains the biggest barrier to wider adoption of these advanced models. Generating complex biological systems is only part of the puzzle; researchers also need systems that produce reliable results across different experiments, laboratories, and large-scale discovery programs.

Variability can occur at multiple stages, including donor selection, cell reprogramming, differentiation, and organoid development. Even small differences in workflow or handling can affect model behavior, creating problems for data-driven research programs where consistency across datasets is essential.

"Reproducibility remains a challenge for researchers working with these types of models, particularly as they become more and more complex," said Steve Smith, CEO of iXCells Biotechnologies.

Steve Smith, CEO, iXCells Biotechnologies

How Are Companies Scaling These Models for Modern Drug Discovery?

To address reproducibility and scale challenges, pharmaceutical companies are investing in standardization, automation, and quality control methods. Standardized workflows and automated cell culture systems can reduce variability during differentiation and organoid generation, while standardized quality control methods improve consistency between batches.

More reproducible models may also reduce experimental noise, helping researchers compare datasets more reliably and make earlier decisions with greater confidence. As drug discovery becomes increasingly data-intensive, researchers need access not only to biologically relevant models but also to large numbers of consistent models capable of supporting high-throughput research.

Disease heterogeneity, the biological differences seen between patients with the same condition, influences disease progression, drug response, and treatment outcomes. By generating large numbers of patient-derived models, researchers can begin to investigate those differences systematically.

"The industry increasingly needs not just high-quality models, but thousands of consistent, well-characterised models to support modern, data-driven science," Smith explained.

Steve Smith, CEO, iXCells Biotechnologies

Steps to Implementing Human-Derived Models in Drug Discovery

• Establish Standardized Workflows: Create consistent protocols for donor selection, cell reprogramming, and differentiation to ensure reproducibility across experiments and laboratories.
• Invest in Automation Technology: Deploy automated systems for cell culture, differentiation, and organoid generation to reduce human error and variability between batches.
• Implement Quality Control Methods: Use standardized evaluation criteria including morphology assessment, genetic stability testing, and pluripotency markers to verify model consistency.
• Build Large Model Banks: Generate thousands of well-characterized, patient-derived models to capture disease heterogeneity and support data-intensive discovery programs.
• Integrate Computational Analysis: Combine traditional laboratory methods with computational tools to analyze large biological datasets generated from advanced organoid and iPSC systems.

Regulatory support is accelerating this transition. The U.S. Food and Drug Administration (FDA) and the National Institutes of Health (NIH) have launched initiatives focused on improving translational research tools, signaling that human-derived models are becoming the industry standard rather than an experimental approach.

The stakes are high. Drug discovery now relies far more heavily on human-relevant models as researchers seek ways to improve clinical translation and reduce costly late-stage failures. Companies that successfully scale these technologies stand to dramatically improve their success rates, reduce development timelines, and ultimately bring more effective treatments to patients faster.