Rethinking Drug Development: The Role of Model-Informed Approaches in Advancing Cell and Gene Therapies Without Animal Testing
By Johannes Stanta, PhD, Global Scientific Director, Celerion Inc.
As drug development continues to evolve, few areas are advancing as disruptively as cell and gene therapies (CGTs). These highly complex and often personalized treatments are redefining what is possible in modern medicine. But while the science has leapt forward, many of the tools and assumptions behind therapeutic development have not. In particular, our continued reliance on animal models is becoming an increasingly obvious limitation.
That is where Model-Informed Drug Development (MIDD) comes in, and why now is the time to reexamine how we generate, interpret, and act on data throughout the development process.
Why Animal Models Fall Short for CGTs
CGTs operate through fundamentally different mechanisms than small molecules and biologics. Many of these therapies are intended for one-time administration, with long-lasting effects driven by genome editing, transgene expression, or cellular persistence. They do not follow classical dose-response relationships, do not exhibit linear clearance, and often behave in ways that are highly patient-specific and immune-mediated.
Despite this, drug developers and regulators continue to apply drug development frameworks designed for small molecules, relying on assumptions about repeated dosing, systemic pharmacokinetics, and linear modelling. This disconnect between therapeutic modality and development model often results in inefficiencies, suboptimal trial designs, and an overreliance on animal models that offer limited predictive value. Some regulatory agencies are beginning to acknowledge this gap. The FDA Modernization Act 2.0 and the agency’s 2024 roadmap to reduce animal testing both signal a shift toward more human-relevant, model-based approaches.
The Role of MIDD in CGT Development
MIDD integrates quantitative modelling, simulation, and data-driven decision-making across the product lifecycle. In the context of CGTs, this includes modelling the expansion, contraction, and persistence of engineered cells, predicting vector biodistribution across human tissues using physiologically based pharmacokinetic (PBPK) models, and understanding complex interactions between the therapy, disease pathways, and host immune responses through Quantitative Systems Pharmacology (QSP).
These tools make it possible to simulate hypothetical scenarios, define safe and effective first-in-human doses, and design more informative early-phase trials …well before administering a dose to a volunteer. As a result, MIDD reduces risk, improve administration of effective doses and decreases dependence on translation of animal-based safety and efficacy studies.
Bioanalysis: The Unsung Hero
Models are only as good as the data that inform them. Bioanalysis plays a crucial role in enabling model-informed development. Whether measuring vector DNA by PCR, quantifying cell expansion through flow cytometry, or assessing an expressed protein via ligand-binding assays, high-quality bioanalytical data are the foundation of any meaningful model.
Advanced bioanalytical platforms such as LC-MS/MS, immunoassays, digital droplet PCR, spectral flow cytometry and in vitrofunctional assays are not merely supportive tools, they are essential for the development and application of MIDD. Moreover, these human-relevant technologies are central to the FDA’s strategy for reducing animal testing. In the development of CGTs, mechanism-based bioanalysis is no longer optional. It is a scientific and regulatory necessity.
Looking Ahead: AI, NAMs, and a New Development Ecosystem
MIDD does not exist in isolation. It is now embedded in a broader framework that includes artificial intelligence (AI), New Approach Methodologies (NAMs), and growing regulatory support for model-based submissions. AI and machine learning are already being used to identify pharmacodynamic endpoints, biomarkers, stratify patients, generate virtual populations, and simulate clinical outcomes. At the same time, NAMs (including organ-on-chip systems and in vitro immune models) are producing more human-relevant preclinical data than traditional animal models.
When combined, these tools offer a smarter and more responsive way to develop CGTs. They support a more predictive understanding of efficacy and safety, reduce dependence on animal models that are often poorly translatable, and improve development timelines by avoiding lengthy and costly primate studies that yield limited actionable insights. In vitro methods can generate targeted, mechanistic data that feed directly into model frameworks, making them not only faster and more cost-effective but also better suited to the biology of CGTs.
Yet, development budgets are still heavily weighted toward animal testing, not because it delivers superior science, but because it remains deeply embedded in the regulatory process. As MIDD and NAMs continue to mature and regulatory standards emerge, this logic will be reversed. The focus will shift to the tools that provide the most decision-relevant, human-specific data.
The MIDD-driven ecosystem is also inherently more compatible with personalized medicine. CGTs are frequently developed for narrow patient populations or even individual patients. Traditional animal models are not equipped to handle this level of variability, whereas MIDD allows developers to model population-level and individual responses with far greater precision.
Final Thoughts
As someone working at the intersection of CGTs, bioanalysis, and model-based development, I have seen firsthand how these approaches are converging—and how rapidly expectations are changing across the industry. What was once aspirational is now an operational reality.
If we want to unlock the full potential of CGTs, we must move beyond legacy frameworks. MIDD, informed by robust analytics and bioanalytical data, offers a clearer, more efficient, and more ethical path forward. This is not only better science—it is better drug development.
If you are asking similar questions or actively working to reduce reliance on animal models through smarter, model-based development strategies, we would love to connect.