Preclinical contract research organizations (CROs) play a pivotal role in guiding clients' therapies through complex preclinical stages of drug development1. Clients place immense trust and financial investment in CROs, expecting rigorous, cutting-edge in vivo study designs that maximize the potential of their therapies. As the scrutiny of experimental design grows, CROs must demonstrate expertise and reliability to build enduring client relationships. This white paper examines the challenges faced by CROs, highlights the impact of innovative solutions such as ModernVivo's AI-powered approaches to in vivo study design, and explores how improving accuracy in in vivo study design can enhance client trust in CROs.
The Importance of Client Trust for CROs
Regardless of a CRO's reputation, clients must place a fundamental level of trust in the CRO to execute experiments to the highest standards, thoroughly exploring every avenue to give their novel therapy the best possible chance of success. Although clients outsource many essential tasks to CROs, this does not imply a lack of understanding of good experimental design on their part, rather, CROs are often utilized as a collaborative partner to help fast-track studies, provide access to advanced technologies, and offer expertise on the later stages of clinical trials. Considering the significant investment and high stakes involved in engaging with a CRO for preclinical work, clients are likely to closely scrutinize experimental designs to ensure they are receiving value for their money and to confirm that the CRO is competent and capable. Effective preclinical experimental design enables CROs to demonstrate to their clients that they fully understand their goals and know the best strategies to achieve them. It also reassures clients that they can trust the CRO to manage the later-stages of clinical trials, especially those a more discovery-focused client may not be deeply familiar with.
The longitudinal success of a CRO is based on reputation and trust, and expectations for quality and efficiency continue to rise for both biotech companies and CROs. ModernVivo offers leading AI-driven solutions for in vivo study design, allowing CROs to instantly scan entire literature databases to identify the optimal parameters for in vivo work. ModernVivo empowers CROs not only to convey authority and expertise but also to back it up with reliable data in a timeframe that meets client expectations. Just as automated platforms have revolutionized high-throughput screening, ModernVivo transforms study design with its innovative approach, enabling faster and more precise study development. CROs can no longer afford to overlook opportunities for increased efficiency in any area of preclinical work, making ModernVivo an essential tool for modern research.
Challenges Faced by Preclinical CROs in Gaining Client Confidence
There are many challenges faced by preclinical CROs in demonstrating expertise to clients and earning their trust. These challenges represent a significant barrier to obtaining new contracts, building long-term client relationships, and forging a reputation as a reliable partner for preclinical research.
Variability in In Vivo Study Results
Variability in in vivo study results represents a significant barrier for CROs who want to demonstrate reliability and consistency in their approaches and capacity to deliver results. Variation is inherent to biological science, and this is perhaps most apparent in in vivo work, where animals are chosen specifically to represent the biological complexity of humans2. However, this similarity also increases the chances of variation between individuals which can reduce study reproducibility if not properly accounted for. This reproducibility problem is now widely recognized in the field of biological research. A well-known paper found that results from just 23 of 53 prominent cancer biology papers could be repeated by independent researchers3. Although there are numerous potential sources of variability in in vivo experimentation, responsibility often falls on the researchers conducting the experiments, in this case, the CRO4. While some variability is beyond investigator control, CROs must do everything they can to ensure that variables are controlled wherever possible. This comes down to a detailed and thorough experimental design.
Limitations of Traditional Study Design
Study design often suffers from an overreliance on outdated protocols, which may produce worse results than more modern but less cited methods. Academic laboratories are particularly prone to relying on inherited protocols, but CROs have an increased obligation to ensure they are performing the optimal in vivo experiments based on current gold standards, not based on what has been used previously. Approaching individual projects using one-size-fits-all methods may be seen as a red flag by research-savvy clients who will almost certainly be familiar with the latest developments in their field and have a good grasp of the available in vivo models.
The rise of modern tools for in vivo study design, coupled with a drive towards increased transparency in preclinical research, ensures that unjustifiable rigidity and a reluctance to adopt contemporary approaches will not go unnoticed by current and prospective clients5. Ultimately, poorly designed experiments result in poor outcomes, and when clients are selecting a partner, this is one of the first aspects they are likely to evaluate. Thus, CROs must keep a finger on the pulse of emerging models and standards to maintain client trust.
Competitive Landscape and Client Expectations for Innovation
CROs must strike a balance between reliability and innovation to set themselves apart in what is an increasingly competitive area. There are many options available to CROs for performing an experiment, which can sometimes be more of a hindrance than an advantage. The abundance of options makes it virtually impossible to gain a full grasp of the experimental design landscape, making it hard to gauge client expectations. Additionally, adopting a new approach solely for the sake of innovation can backfire, potentially earning a CRO a reputation for recklessness and poor design choices.
On the other hand, being overly cautious might leave some clients feeling that they are not maximizing the value of their investment. It could also result in key parameters—that might provide valuable insights for advancing to in-human trials—being overlooked. Being innovative while ensuring success requires a broad and deep understanding of the literature, and the reward for striking this balance will be a reputation for forward-mindedness coupled with research excellence.
The Role of Accurate Preclinical In Vivo Study Design in Developing Customer Trust
Good study design communicates research excellence to clients, however, it is ultimately the results that will earn a CRO the trust of clients. Accurate in vivo study design is crucial for avoiding errors, meeting timelines, and establishing reproducibility.
Preclinical Drug Development Overview
The preclinical drug development process involves establishing a drug's safety, efficacy, and feasibility before progressing to human trials6. The process begins with target identification and validation, followed by the optimization of lead compounds or other therapeutic modalities, such as biologics or cell-based therapies. In vitro and in vivo studies are conducted to evaluate biological activity, pharmacodynamics, and pharmacokinetics, while toxicology tests assess acute, chronic, and reproductive toxicity7. Pharmacokinetic and pharmacodynamic studies further explore absorption, distribution, metabolism, and dose-response relationships8. Once these parameters are satisfactorily addressed, manufacturing processes are scaled up in compliance with Good Manufacturing Practices (GMP). Preclinical data are then submitted to regulatory agencies as part of an Investigational New Drug (IND) application, which reviews the drug's safety, efficacy, and manufacturing protocols. Upon IND approval, the drug proceeds to clinical trials for testing in humans. Each of these stages is filled with potential pitfalls that poorly designed studies will immediately fall into.
Drug development involves several critical stages, each with distinct objectives and focus areas. The process begins with Target Identification and Validation, where biological targets are identified and confirmed. This stage emphasizes the disease relevance and druggability of the target. Following this, Lead Optimization focuses on refining lead compounds or other therapeutic modalities, such as biologics or cell-based therapies, with the goal of improving efficacy and safety. Next, in the In Vitro Studies phase, researchers evaluate biological activity and conduct preliminary safety assessments in controlled laboratory environments. This stage also involves assessing pharmacodynamic and pharmacokinetic properties. Subsequently, this phase entails testing the drug in animal models to further understand its behavior and safety. During this stage, pharmacodynamics, pharmacokinetics, and toxicity are thoroughly evaluated. Toxicology Testing is then conducted to perform detailed safety assessments, which aim to identify potential adverse effects. This includes studies on acute, chronic, and reproductive toxicity. Finally, the Pharmacokinetics and Pharmacodynamics stage involves studying the absorption, distribution, metabolism, and dose-response relationships of the drug. This step is crucial for dose optimization and understanding the systemic effects of the drug in the body.
How Precision in Experimental Design Reduces Variability and Errors
It is important to choose every study parameter carefully and with a specific outcome in mind. This can be challenging for CROs to achieve with short timelines and when information is scattered and buried in the literature databases. However, understanding the role of each parameter and the ramifications of different options is essential for reducing variability and errors9. Errors can arise in areas where protocols are unclear or when sections are copied from preexisting protocols that may not be fully compatible with the current study. Careful experimental design minimizes the impact of biological variability on study results. A well-planned approach ensures the selection of the most suitable model and the most effective readouts to answer the research question accurately. Sample size is another critical area where inadequate rigor can cause problems. Insufficient sample sizes may result in a lack of statistical power to draw meaningful conclusions, while excessive sample sizes can waste resources and negatively impact animal welfare10.
Establishing Robust Reproducibility and Replicability
Many factors can enhance the reproducibility and replicability of research, and failure to replicate preexisting research may make some clients question a CRO’s reliability. To improve in these areas, CROs can focus on establishing blinding and randomization of different groups and ensuring that there is a clearly defined hypothesis and set of goals before the start of the experiment11,12. Including adequate control groups enables researchers to draw more reliable conclusions from their findings and directly supports the ability of others to replicate the results effectively13.
Meeting Regulatory Requirements and Timelines
Delays in preclinical experiments can be extremely costly, often requiring the repetition of experiments or a complete reevaluation of the experimental design, leading to further investments of time and resources. Prolonged timelines can often mean that animals age out of viability for a given experiment, causing further delays and expense. Regulatory requirements are constantly shifting, and the cost of delays demands a greater focus on resource allocation14. CROs must carefully consider the specifics of their experimental design and stay aligned with the regulatory landscape to maintain compliance. Failure to adapt can result in delayed research timelines, directly impacting client success and harming the CRO's reputation.
Modernizing In Vivo Study Design to Achieve Preclinical Research Excellence
Fortunately, many modern tools are emerging that remove significant sources of risk from experimental design, making it more simple for CROs to perform robust studies that align with client expectations.
Adoption of Cutting-edge Technologies
AI and machine learning are helping to change the face of experimental design for preclinical in vivo work and for clinical trials as a whole15. While many of these tools are just emerging, other areas are already available for researchers to use. These tools are being applied in several areas, helping refine the approach to in vivo experimental design. For instance, there is a move towards using AI to perform virtual screening of compounds for testing in in vivo toxicology studies, which simplifies and refines in vivo experimental setups16. This also facilitates dose optimization, further reducing the need for more complex study designs. AI can potentially be used to help predict the pharmacodynamics and pharmacokinetics of different therapeutics17. This is incredibly valuable for simulating the effect of newer biologics-based treatments that interact with complex biological systems such as the immune system18.
AI platforms, like ModernVivo, can also be used to perform literature reviews, which summarize the available approaches to answer different experimental questions. This means that CROs can quickly pinpoint the optimal parameters for a research project without manually reviewing large literature databases.
The concept of AI agents has recently gained attention in experimental planning19. These agents have the potential to integrate new and existing data with in silico simulations, enabling them to design experiments and guide discovery efforts toward areas with the greatest potential for high-impact breakthroughs. Importantly, these agents could operate with direct input from human creativity and expertise and parallel AI tools to optimize decision-making processes, ensuring that experimental strategies are not only innovative but also efficient and targeted.
AI agents can perform advanced data analytics by drawing from large and diverse datasets to derive faster insights than human operators. For instance, AI can integrate data from single-cell omics experiments, microscopy, spatial biology, viability experiments, and more to gain a broader understanding of cellular responses to specific stimuli. This has direct implications for study design as it can give researchers a clearer picture of the variables that may affect their results and highlight future avenues for research (Fig. 1).
Benefits of Precision and Modern In Vivo Experimental Design for Preclinical CRO Customers
Precision in vivo experimental design using modern tools has many benefits for preclinical CRO customers.
Confidence in Research Outcomes
Researchers may not always achieve the desired results from experiments, but a well-designed experiment remains valuable, no matter the outcome, by giving researchers sufficient information to support downstream decision-making. In vivo tools enhance researchers' confidence in results by ensuring experiments are properly designed to minimize confounding variables. Pursuing clinical research is an inherently risky and costly process, and failure of trials in phase III may be due to a lack of robustness at the preclinical stage20,21.
Well-designed studies provide CRO clients with stronger evidence to convince investors that their potential therapy has a higher likelihood of yielding a return on investment. Conversely, if the results indicate the therapy should not proceed to clinical trials, researchers can use the data to refine the design or formulation of their next therapeutic candidate. This approach not only avoids investing in a therapy likely to fail in human trials but also equips researchers with valuable insights to increase their chances of success in future trials21. Poor experimental design can leave researchers uncertain about how to proceed with a therapy—or whether to proceed at all. This uncertainty can trap them in a preclinical experimental loop, where they repeatedly second-guess their methods and results.
Faster Timelines for Project Completion
Precision in vivo design helps researchers stay on schedule with project timelines. Well-designed experiments require minimal adjustments, ensuring an efficient path to achieving optimal results. Furthermore, advanced AI-assisted tools not only lead to more accurate design with a greater chance of success, but they also enable a more streamlined design process. This means that researchers don’t need to spend months devising an experimental protocol, which may not draw from the full wealth of the available literature. AI tools, like the ModernVivo platform, allow researchers to quickly pull the most relevant information from a wide variety of sources, immediately outpacing manual methods of design. Better design allows researchers to align more closely with industry trends for innovation. Using the latest and most advanced in vivo models conveys professionalism and authority to reviewers. It may also make it easier for regulators to understand and get on board with the protocols used and gives the therapy the best chance of success.
Best Practices for CROs to Implement Precision and Modernization
With so many options for AI-assisted solutions on the market, it can be difficult to know how best to implement these tools. While AI makes processes more efficient, it’s important for CROs to establish systematic approaches for experimental design, personnel training, and client communication to ensure these new tools can be used to their full potential.
Establishing a Systematic Approach to Experimental Design
Experimental design can be overwhelming given the number of different parameters that need to be carefully considered and selected within restrictive barriers such as budgets and time constraints. CROs must balance the need for thoroughness and accuracy with the practical limitations of available resources, all while ensuring compliance with ethical standards and regulatory requirements. To achieve clarity during design, it’s important to take a systematic approach.
An important first step can be to achieve alignment with the sponsor on a concise hypothesis to be tested22. Generating a clear hypothesis makes all subsequent decisions far more straightforward and is important when using AI-driven tools, which may return less usable results where the research question is not well-defined. After the hypothesis is agreed upon, identifying the best model to address the hypothesis should become the primary aim, and other study parameters should be selected to facilitate this model23.
Training for Preclinical Researchers
AI tools can be used to establish hypotheses and identify optimal models and parameters for the experiment19. However, it is important that the people generating this information and using these tools have received the proper training to use them effectively. Most AI tools are designed to be user-friendly, but collaborating with their providers can help ensure a smoother transition. It is also important to communicate clearly as to why these approaches are being implemented and how this will impact day-to-day operations for researchers.
Client Engagement and Feedback
AI-based tools are becoming more widely used; however, hesitance to implement these tools is understandable, especially for high-stakes applications like preclinical in vivo experiments. AI suffers from the “black box” problem where users don’t know how AI generates its outputs, leading to concerns about transparency, reliability, and accountability24. This lack of interpretability can make researchers wary of trusting AI-generated recommendations, especially regarding critical decisions about safety, efficacy, and compliance. The use of AI can be an even greater cause for concern because of the added degree of separation, as is the case when CRO researchers use AI to design experiments for a client. Being transparent about how the AI is used and allowing for questions and feedback are essential for helping clients to understand and accept modern approaches. Highlighting how AI addresses specific client concerns, such as regulatory compliance or animal welfare, and showcasing its strengths, such as employing innovative methodologies, can be effective strategies for gaining customer support.
The process begins with establishing a robust hypothesis, which simplifies experimental design, aligns stakeholder expectations, and streamlines the use of AI tools. Training follows, ensuring effective utilization of these tools, addressing concerns about AI implementation, and enabling faster adoption and project timelines. Finally, client engagement is crucial for clarifying the experimental approach, highlighting the benefits of AI, mitigating concerns about the "black box" nature of AI, and fostering confidence in its implementation.
ModernVivo: Your Partner in In Vivo Experimental Design
ModernVivo is redefining the landscape of in vivo experimental design, empowering preclinical CROs to build and maintain the trust of their clients. By embracing precision-driven and modernized study design, CROs can enhance client confidence, mitigate variability, and align with stringent regulatory standards, all while accelerating project timelines. As the preclinical research landscape evolves, leveraging cutting-edge tools like AI-driven design platforms is no longer optional but essential for staying competitive. ModernVivo’s commitment to scientific rigor and client satisfaction positions it as a vital partner in advancing preclinical research excellence. With ModernVivo, CROs can drive transformative breakthroughs, reinforcing trust with clients and paving the way for a future of unparalleled scientific progress.
Get in touch with one of ModernVivo’s experts today to learn how our platform can minimize delays in in vivo experimental design and help you convey authority to your clients.
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AI Disclosure: Some of this content was generated with assistance from AI tools for copywriting.
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