Are Traditional Preclinical Models Limiting Your Research? Leading Experts Explain Why Human-Based Models Matter

Academic institutions face significant challenges in translating biomedical discoveries to clinical success. Two leading researchers explain why traditional preclinical models may be systematically misleading clinical predictions and how human-based alternatives can improve translational outcomes in academic drug discovery.

Dr. James L. McGrath, the William R. Kenan, Jr. Professor of Biomedical Engineering at the University of Rochester, has identified what he believes will be the most transformative change in healthcare research. "The most significant way that human-based models will impact health care," he states, "is by eliminating first-in-human trials that are destined to fail because the drug's toxicity is not evident in animal models."

It's a sobering assessment from one of academia's leading voices in biomedical engineering. Dr. McGrath is pointing to a fundamental flaw in how academic research approaches drug development: a flaw that extends far beyond individual laboratories and into the very foundations of how universities train researchers and allocate resources.

The problem runs deeper than just animal models, argues Dr. Mukhtar Ahmed, a biotech and biopharma executive with extensive experience in drug development and regulatory strategy. Dr. Ahmed emphasizes that academia's traditional reliance on reductionist approaches is actively undermining the translation of promising research into effective therapies.

"Human-based NAMs, such as iPSC-derived cardiomyocytes, capture the integrated physiology that reductionist assays like hERG alone miss, avoiding false attrition and rescuing viable therapies," Dr. Ahmed explains.

"As these models gain validation across organ systems, they not only improve predictive accuracy over animal studies but also mitigate the costly burden of late-stage trial failures."

Understanding the Academic-Clinical Translation Gap

The implications of these expert observations highlight significant challenges for academic institutions. Consider the typical trajectory of academic drug discovery research: a promising compound is identified, characterized using standard animal models and reductionist assays, published in high-impact journals, and eventually licensed to industry for clinical development. Often, these compounds fail in human trials for toxicity reasons that were not evident in the original academic studies.

This pattern represents more than scientific disappointment: it represents a disconnect between preclinical findings and clinical outcomes that undermines the translational promise of publicly funded research. When Dr. McGrath talks about eliminating "first-in-human trials that are destined to fail," he's describing a predictive validity challenge that affects both patient safety and research efficiency.

The human impact is clear: patients participate in clinical trials based on preclinical data that may not fully reflect human biology. There are also academic implications, as research funding from taxpayer-supported agencies may sometimes go to studies with limited translational potential when traditional models are relied on exclusively.

Limitations of Current Preclinical Research Paradigms

Dr. Ahmed's perspective reveals why this translation challenge persists despite decades of awareness. The academic research ecosystem has been structured around reductionist principles that, while mechanistically informative, may fail to capture the integrated biological responses that determine clinical efficacy and safety.

Take cardiotoxicity screening, one of the most critical safety assessments in drug development. For decades, academic laboratories have relied on the hERG channel assay: a reductionist approach that examines cardiac potassium channels in isolation. While mechanistically elegant and suitable for high-throughput screening, this approach generates data that appears scientifically rigorous but may lack predictive value for integrated cardiac responses.

As Dr. Ahmed notes, this reductionist framework misses "the integrated physiology" that actually determines cardiac safety in humans. A compound might demonstrate acceptable hERG channel data while still causing dangerous arrhythmias through complex multi-channel interactions that only manifest in integrated cardiac tissue models.

This gap between reductionist testing and physiological integration may contribute to why cardiotoxicity remains a significant cause of drug development failures, despite extensive preclinical screening. Academic researchers, following established protocols, generate mechanistic data that meets publication standards but may provide limited clinical predictive value.

The challenge extends beyond individual assay limitations. Current academic training emphasizes reductionist experimental design, statistical analysis of isolated variables, and mechanistic hypothesis testing: approaches that excel at understanding biological processes but may struggle with predicting complex, multi-system clinical responses.

Implementing Human-Based Models in Academic Research Programs

The transition to human-relevant models requires academic institutions to complement mechanistic studies with platforms that capture integrated physiological responses. This doesn't mean abandoning reductionist research: mechanistic studies remain crucial for understanding biological processes. Rather, it means strategically integrating human-based models that can bridge mechanistic insights to clinical predictions.

The technological infrastructure for this transition exists. Platforms utilizing iPSC-derived tissues, such as cardiomyocytes, hepatocytes, and neuronal models, can provide the integrated physiology that traditional approaches may miss. These systems offer academic laboratories access to human-relevant data while maintaining the experimental rigor required for peer review and grant funding.

Advanced 3D Cardiac Models: A Case Study in Translational Integration

Modern cardiac safety assessment exemplifies how human-based models can address traditional limitations. Innovative platforms like 4DCell’s' SmartHeart technology demonstrate the practical implementation of integrated cardiac models in academic research settings. The system enables formation of beating cardiac tissue rings using iPSC-derived cardiomyocytes in a controlled 3D microenvironment, providing researchers with physiologically relevant cardiac responses that traditional 2D culture and animal models may miss.

This approach represents a significant advancement over conventional hERG channel screening. Where traditional assays examine isolated potassium channel function, integrated cardiac tissue models capture the complex multi-channel interactions, contractile function, and electrophysiological responses that determine actual cardiotoxic potential. Academic laboratories using such platforms can generate data that demonstrates both mechanistic understanding and clinical predictive value: addressing the fundamental translation challenge Dr. McGrath and Dr. Ahmed identify.

The practical advantages for academic researchers are compelling: these systems require minimal cell numbers, provide high-resolution imaging capabilities for detailed analysis, and deliver reproducible data suitable for peer review and regulatory consideration. For cardiotoxicity assessment specifically, studies have shown that compounds known to induce cardiac toxicity (such as doxorubicin, verapamil, and quinidine) demonstrate different response profiles in 3D cardiac models compared to traditional 2D cultures, suggesting improved predictive accuracy for human responses.

For academic researchers, human-based platforms offer several potential strategic advantages:

• Enhanced Grant Competitiveness: NIH review panels increasingly recognize applicationsdemonstrating translational relevance. Human-based models may strengthen grant applications by demonstrating human relevance.

• Industry Partnership Opportunities: Pharmaceutical companies seek academic collaborators with human model expertise. Research programs utilizing these platforms may develop stronger industry collaborations.

• Publication Impact: Journals increasingly recognize the translational value of human-based model data. Studies incorporating these approaches may receive broader scientific interest.

• Student Career Preparation: Graduate students and postdocs trained on human-relevant platforms develop skills that are valuable for biotech and pharmaceutical careers.

More critically, human-based models address the fundamental scientific responsibility Dr. McGrath and Ahmed identify. By generating data that better predicts human responses, academic researchers can help ensure their work contributes to successful therapeutic development rather than clinical trial failures.

Methodological Considerations for Human-Based Model Implementation

Successful integration of human-based models requires careful attention to validation protocols, standardization procedures, and quality control metrics. Academic laboratories must establish benchmarks that satisfy both peer review standards and regulatory acceptance criteria.

• Validation Requirements: Human-based models should demonstrate reproducibility across multiple laboratories, correlation with clinical data where available, and appropriate sensitivity/specificity metrics. The FDA's New Alternative Methods Program provides guidance for validation studies that academic researchers can incorporate into grant applications.

• Standardization Protocols: Inter-laboratory variation represents a challenge for human-based models. Academic institutions should establish standardized protocols for cell culture conditions, differentiation procedures, and assay readouts. International guidelines provide frameworks that academic laboratories can adapt for research applications.

• Quality Control Metrics: Human-based models require robust quality control procedures to ensure consistent performance. Academic laboratories should implement appropriate quality control measures, establish acceptance criteria for cell batch quality, and maintain detailed documentation.

  
  

Practical Implementation Considerations

The transition to advanced 3D models requires strategic planning. Modern platforms such as organ-in-a-well® systems provide academic laboratories with scalable solutions that can accommodate both small-scale mechanistic studies and higher-throughput screening applications.

These systems typically integrate with standard laboratory equipment and imaging systems, minimizing infrastructure requirements while maximizing experimental capabilities.

Quality control becomes particularly important with 3D tissue models. Platforms that incorporate multiple tissue structures per well (such as systems with 9 cardiac rings per well) provide internal controls and statistical power while ensuring data reliability. This design approach addresses reproducibility concerns that have historically limited academic adoption of complex tissue models.

Curriculum Reform

Preparing Graduate Students for Translational Success

A critical aspect of this transition involves graduate education and postdoctoral training. Academic institutions currently produce biomedical researchers who are expert in animal models and reductionist assays but may be less prepared for the integrated, human-relevant approaches that industry increasingly utilizes.

This training ga p may perpetuate translation challenges. Graduate students learn experimental design principles using methods that may provide limited clinical predictive value. When they transition to industry positions, they often must learn drug development approaches using human-relevant models and integrated experimental methods.

Industry positions increasingly require experience with human-based models, yet graduate programs may provide limited hands-on training with these technologies. This creates both individual career challenges and industry recruitment difficulties.

NIH Initiatives and Funding Opportunities for Alternative Models

The transition to human-based models aligns with evolving federal funding priorities. The NIH has emphasized the importance of translational research and human-relevant models through multiple funding mechanisms and strategic initiatives.

NCATS Funding Programs: The National Center for Advancing Translational Sciences has allocated substantial resources specifically for alternative model development and validation. Academic researchers can access these funds through several mechanisms:

• Tissue Chip for Drug Screening Program: Multi-year funding for organ-on-chip technology development [https://ncats.nih.gov/research/research-activities/tissue-chip/projects]

• Small Business Innovation Research (SBIR): Funding to develop innovative technologies, including human-based platforms [https://www.sbir.gov/topics/11357]

  
  

Regulatory Science Initiatives

FDA's initiatives explicitly call for human-based alternative methods. Academic researchers contributing to regulatory science can access funding through FDA's various programs. The funding landscape appears to be evolving toward human-relevant approaches. Academic institutions that anticipate this transition may position themselves advantageously for future funding opportunities.

Strategic Implementation

A Framework for Academic Institutions

Successful adoption of human-based models requires institutional leadership and systematic implementation strategies. Based on successful program examples, academic institutions might consider the following framework:

Phase 1: Infrastructure Development

1. Establish shared core facilities equipped with human-based model platforms

2. Recruit faculty with expertise in human model development and validation

3. Develop partnerships with technology providers and pharmaceutical companies

4. Create internal funding mechanisms to support pilot studies

Phase 2: Training Integration

1. Integrate human-based model training into graduate curricula

2. Establish postdoctoral fellowship programs focused on translational research

3. Develop continuing education programs for existing faculty

4. Create industry exchange programs for students and postdocs

   
   

Phase 3: Research Program Transformation

1. Prioritize grant applications incorporating human-based approaches

2. Establish translational research centers focused on human-relevant discovery

3. Develop industry partnership programs for collaborative research

4. Create technology transfer mechanisms for human model innovations

   
   

Performance Considerations

Institutions should establish metrics to track progress:

1. Industry partnership development and licensing activity

2. Graduate student placement in biotech/pharmaceutical careers

3. Grant funding success for translational research

4. Publication outcomes and citation patterns

5. Clinical translation outcomes of research programs

   
   

Technical Implementation Guide for Academic Laboratories

Academic laboratories considering human-based model adoption should understand the practical requirements for successful implementation:

Equipment and Infrastructure

• Cell culture facilities with specialized equipment for iPSC maintenance and differentiation

• Analytical instrumentation for physiological measurements (patch clamp, calcium imaging, contractility analysis)

• Data management systems for complex multi-parameter datasets

• Quality control equipment for batch testing and validation

  
  

Modern organ-in-a-well platforms offer particular advantages for academic implementation. Systems designed with multiple tissue structures per well provide robust statistical analysis while maintaining compatibility with standard laboratory equipment.

High-resolution imaging capabilities enable detailed mechanistic studies while generating physiologically relevant integrated responses. Recent advances in anchoring technology have achieved reproducibility rates exceeding 95% for organoid formation, addressing traditional concerns about 3D model consistency.

A Strategic Opportunity for Academic Excellence

Dr. McGrath and Dr. Ahmed's observations point to both challenges and opportunities for academic biomedical research. The challenge is clear: traditional approaches may generate research that fails to translate to human benefit, potentially wasting resources and ultimately affecting patients who volunteer for clinical trials based on preclinical data of limited human relevance.

But the opportunity is significant. Academic institutions that embrace human-based models and integrated approaches may produce research with superior translational potential, attract enhanced industry partnerships, and train students who possess valuable skills for biotech and pharmaceutical careers.

The technological infrastructure exists. Advanced platforms such as organ-in-a-well systems provide academic researchers with practical tools for implementing human-relevant studies. These systems offer the integrated physiology that traditional approaches may miss while maintaining the experimental rigor required for academic research. The scientific rationale is compelling. Regulatory agencies support the development of alternative methods. Industry partnerships are available for academic institutions with relevant expertise.

What's required is institutional leadership to drive systematic adoption of human-relevant research approaches. As Dr. Ahmed notes, these models are demonstrating validation across organ systems. The question for academic leaders is whether their institutions will participate in this evolution or continue relying exclusively on traditional approaches.

The ultimate goal is eliminating clinical trials destined to fail: protecting patients from unsuccessful experiments while ensuring that academic research delivers on its promise of improving human health. For academic biomedical research, this represents both a critique of current limitations and a roadmap for potential improvement. The translation challenge demands institutional response. The technology enables that response. The question is whether academic leaders will embrace this opportunity or continue with approaches that may inadequately serve the patients research is meant to help.

About the Experts

Dr. James L. McGrath is the William R. Kenan Jr. Professor of Biomedical Engineering at University of Rochester, leading the Nanomembrane Research Group with over 20 years of faculty experience. An internationally recognized expert, McGrath has been leading his highly interdisciplinary, multi-institutional research team since 2007, developing ultrathin silicon 'nanomembrane' technologies. He served as the first director of the BME graduate program for over 10 years, is a co-founder and past president of SiMPore Inc., and was elected as a Fellow of the American Institute for Medical and Biological Engineering (AIMBE) in 2015.

Dr. Mukhtar Ahmed, PhD (University of Toronto, Pharmaceutical Sciences), serves as Director of Project & Portfolio Strategy with extensive experience bridging academic research and biopharmaceutical development. His academic training under Professor Ian Macara at Vanderbilt University focused on quantitative methods in cell biology, targeted proteomics, and single molecule membrane biophysics to study polarized membrane trafficking in mammalian epithelial systems. Dr. Ahmed's research has been recognized by the Canadian Institutes of Health Research and the Natural Sciences and Engineering Research Council of Canada.

Disclaimer: The framework and expert insights presented here discuss human-based models in a general context. References are not intended to endorse or highlight any specific commercial products. While 4DCell supports and applies these approaches, the experts cited are commenting on the broader field rather than any specific product.