Introduction
In recent years, the pharmaceutical and biotechnology industries have increasingly turned to complex in vitro models (CIVMs) to improve the predictability of preclinical studies. These models offer a more physiologically relevant environment for cells compared to traditional 2D cell cultures and avoid the species-translation issues of animal models. Because of this, their potential use in Investigational New Drug (IND) submissions to the U.S. Food and Drug Administration (FDA) has become a topic of significant interest.
What are Complex In Vitro Models?
In vitro models are laboratory-based systems that use isolated cells, tissues, or biological molecules to study biological processes and drug effects outside of a living organism. CIVMs take it one step further—they are advanced laboratory cell culture tools designed to simulate the structure and function of human tissues and organs. The IQ MPS—an affiliate of the International Consortium for Innovation and Quality in Drug Development—classifies CIVMs into three major categories:
- Static Models – Traditional in vitro cell culture systems that lack dynamic environmental factors found in vivo, such as fluid flow or mechanical forces. They include 2D cell cultures, such as transwells and co-cultures, and 3D models, such as spheroids or organoids.
- Static MPS Models – A subset of microphysiological systems (MPS) that incorporate advanced engineering features such as electrical sensors but lack dynamic environmental features such as continuous fluid flow or mechanical forces.
- Dynamic MPS Models – Advanced platforms designed to replicate the functional and mechanical aspects of human tissues and organs by integrating dynamic environmental conditions such as fluid flow, mechanical forces, and tissue-tissue interactions. The most prominent example is Organ-Chips.
Each model aims to close the gap between conventional 2D in vitro testing and human biology. By doing so, they can offer improved predictability over traditional preclinical methods.
Challenges in Preclinical Drug Development
In the preclinical stages of drug development, researchers assess the pharmacokinetics (PK) and pharmacodynamics (PD) of a drug candidate to evaluate its safety and efficacy. They rely on two main types of models to do so:
2D Cell Cultures: These static models are simple and cost effective, but they lack the biological complexity of humans, meaning they often fail predict human response.
Animal Models: Animals have long been integral to preclinical drug testing, largely because their biological complexity is similar to that of humans. However, there are still significant differences between human and animal physiology, leading to inaccurate predictions of drug responses in humans.
The limitations of these model types contribute to the high failure rate of drug candidates in clinical trials, with some estimates suggesting that up to 90% of drugs fail in clinical stages due to unforeseen toxicity or lack of efficacy1.
How Complex In Vitro Models Enhance Preclinical Studies
Since CIVMs are designed to recreate the microphysiological environments of human organs, they address many of the limitations of conventional 2D cell culture and animal models:
1. Physiological Relevance
CIVMs are designed to more accurately mimic human tissue structure and function. For example, a key feature of Organ-Chips is that researchers can easily control and finely tune the mechanical forces cells experience. When Organ-Chips are placed under media flow and cyclic mechanical strain, cells experience the mechanical forces they would in the body—such as peristalsis in the intestines, breathing in lungs, and blood flow through vessels. All of these features combined—multicellular complexity, cell-cell interactions, tissue-specific ECM, and mechanical forces—result in more in vivo-relevant gene expression, morphology, and functionality than is possible with conventional cell culture methods.
2. Better Prediction of Human Responses
Because CIVMs can replicate human organ systems with high fidelity, they can often offer better predictive value compared to animal models. In fact, a 2022 survey conducted by the Linus Group on behalf of Emulate found that researchers who have used Organ-Chips in their experiments overwhelmingly agree—70% of experienced users rated the technology as more predictive than animal models, and an additional 21% said the technology is similarly predictive.
3. Reduced Animal Use
In recent years, there has been growing pressure to reduce the use of animals in preclinical testing. CIVMs offer a viable alternative, allowing researchers to perform toxicity and efficacy testing without relying solely on animal models. This not only allows researchers to avoid lengthy and rigid animal experiments, but it also aligns with the FDA’s ongoing efforts to promote alternatives to animal testing, as outlined in their Advancing Alternative Methods (AAM) initiative2.
CIVMs in IND Submissions to the FDA
The FDA requires extensive safety and efficacy data before approving an Investigational New Drug (IND) application, which is the first step towards clinical trials. CIVMs offer several ways to enhance the quality of the data submitted, potentially reducing the risk of delays or rejections.
1. Safety Assessment
The primary focus of an IND submission is to determine the safety of a drug candidate; CIVMs can provide highly predictive toxicology data. For example, Liver-Chips are increasingly used to study drug metabolism and liver toxicity, helping to identify adverse effects that might not be evident in standard in vitro assays.
In a landmark study published in Communications Medicine, Emulate researchers showed that the Liver-Chip S1 outperformed conventional animal and hepatic spheroid models in predicting drug-induced liver injury (DILI), correctly identifying 87% of a set of 18 drugs that caused DILI in humans, despite passing through animal testing. Applied across the pharmaceutical development pipeline, widespread adoption of Organ-Chips in preclinical testing could create productivity gains of up to $3 billion through its increased predictive power. Perhaps most importantly, however, is that the Liver-Chip could have prevented 242 deaths in the clinic due to its predictive power in safety assessments.
2. Pharmacokinetics and Pharmacodynamics
In addition to safety, PK and PD data are essential components of an IND submission. Scientists can create CIVMs that simulate multiple organ systems to assess how a drug is absorbed, distributed, metabolized, and excreted (ADME) in the human body. For instance, a Kidney-Chip model can be used to predict renal clearance and drug-drug interactions, while a gut-on-chip system can study drug absorption3.
These models can offer more accurate data on drug behavior in humans compared to animal models, leading to more confident dosing strategies in early-phase clinical trials.
3. Improved Disease Modeling
CIVMs can also be used to model specific disease states more accurately than animal models. For instance, researchers can create disease-specific Organ-Chip models (e.g., Lung-Chips for studying cystic fibrosis or asthma) to test how drug candidates perform in a human-relevant disease environment. This can provide additional support for an IND submission, demonstrating that a drug is not only safe but also effective in a human-specific disease context.
4. Regulatory Acceptance and FDA Guidelines
The FDA has shown increasing interest in the use of CIVMs for IND submissions, particularly in the context of toxicology and disease modeling. While these models are not yet required in IND submissions, they are becoming an accepted method to supplement traditional data. In 2020, the FDA launched the Innovative Science and Technology Approaches for New Drugs (ISTAND) pilot program, which aims to qualify novel approaches like Organ-Chips for regulatory use. In September 2024, the Liver-Chip S1 became the first Organ-Chip model to be accepted into the program, marking a significant step forward in the regulatory acceptance of CIVMs.
Drug developers must work closely with the FDA during the pre-IND phase to ensure that any data generated using CIVMs aligns with regulatory expectations. The agency’s guidance on the use of alternative methods is evolving, and incorporating CIVMs could improve the chances of a successful IND application.
Real-World Examples of CIVMs in Drug Development
Several high-profile pharmaceutical companies are already leveraging CIVMs to improve their drug development pipelines. Take for example Moderna:
Samantha Atkins, PhD, is a scientist in the Investigative Pathology division at Moderna. Her goal is to de-risk their lipid nanoparticle (LNP) candidates to make them safer before they progress into NHP studies. To increase the efficiency of her research program, Dr. Atkins has started using the Emulate human Liver-Chip to screen for LNP-mediated toxicity instead of relying solely on NHPs.
In a simple cost analysis, Dr. Atkins found that she was able to screen 35 novel LNPs in the Liver-Chip during a course of experiments that took 18 months at a total cost of $325,000. If she were to screen the same number of LNPs using traditional NHP studies, it would have cost Moderna over $5,000,000 and taken over 60 months to complete.
Simply by incorporating Liver-Chips into her workflow, she is able to down-select LNPs over 4x faster and at a fraction of the cost of NHP studies.
Another pharmaceutical company using CIVMs to improve their R&D programs is GlaxoSmithKline. Dr. Josie McAuliffe, the Lab Head of Cell Biology & In Vitro Models, has started incorporating Lymph Node-Chips into her preclinical assessment of vaccines to bridge gaps between in vitro models, animal studies, and clinical outcomes. Dr. McAuliffe’s goals are to test the chip’s capacity with RNA vaccines of varying effectiveness to evaluate the dynamic range of the chip, as well as to better correlate in vitro model outcomes with clinical efficacy to improve the overall translation of vaccine candidates.
These real-world applications demonstrate how CIVMs are making their way into the regulatory landscape, offering valuable data to support IND submissions.
Conclusion
CIVMs represent a significant leap forward in drug development. By providing more physiologically relevant data, reducing reliance on animal models, and offering better predictions of human responses, CIVMs are poised to play an increasingly important role in IND submissions to the FDA. As the regulatory landscape continues to evolve, companies that adopt these models early may have a competitive advantage, reducing the risk of late-stage clinical failures and improving the overall efficiency of their drug development process.
By integrating CIVMs into their preclinical programs, pharmaceutical and biotechnology companies can enhance the quality of their IND submissions, ultimately leading to safer, more effective drugs reaching the market faster.
Continue Learning:
Sources:
- Sun D, Gao W, Hu H, Zhou S. Why 90% of clinical drug development fails and how to improve it? Acta Pharm Sin B. 2022 Jul;12(7):3049-3062. doi: 10.1016/j.apsb.2022.02.002. Epub 2022 Feb 11. PMID: 35865092; PMCID: PMC9293739.
- Commissioner, Office of the. “Advancing Alternative Methods at FDA.” FDA, 5 Jan. 2022, www.fda.gov/science-research/about-science-research-fda/advancing-alternative-methods-fda.
- Center for Drug Evaluation and Research. “Drug Development and Drug Interactions.” U.S. Food and Drug Administration, 2019, www.fda.gov/drugs/drug-interactions-labeling/drug-interactions-relevant-regulatory-guidance-and-policy-documents.
