How New Platforms Are Shaping the Future of Preclinical Research

Since Congress has passed the FDA Modernization Act as part of the FY2023 Omnibus Appropriations Bill to be signed into law by President Biden, I thought that this was an amazing time to understand how we got here and what can be achieved in terms of drug development.

“Just as we owe a debt of gratitude to the medical pioneers who paved the way for modern medicine, we should also recognize the role that animal testing has played in helping us get where we are today.”

Preclinical research is an important step in drug development. They assess the efficacy of a drug candidate and aid in the identification of potential side effects prior to the start of clinical trials. Preclinical research also assists us in understanding how different drugs interact with one another, which can result in unexpected interactions that cause serious illness or death. Before getting deeper into preclinical drug development, I’d be good to look back and see the highlights from the last century and before:

• 1890, Charles-Edouard Brown-Séquard successfully tested a drug on dogs before putting it through clinical trials with humans.
• In the 1930s, anti-bacterial drug trials began
• In the 1940s, penicillin and streptomycin become available for therapeutic use in humans as a result of animal testing.
• In the 1950s, many new drugs were approved as a result of animal testing, including steroids, tranquilizers, and sedatives.
• In the 1960s, there were research breakthroughs in areas such as ulcers and hypertension

You might be surprised to learn that animal testing on television has been going on since the 1960s. Researchers were working to understand and treat diseases such as ulcers, hypertension, heart disease, and cancer at the time. Animal research, in fact, helped us understand how these diseases worked so that we could prevent them.

But it wasn’t just doctors who used animals in their research; scientists also investigated how environmental factors such as pollution or radiation exposure might affect people. They needed reliable animal models that could reflect what would happen if similar tests were performed on humans in order to do this effectively (and ethically).

• In 1985, The FDA approved the use of AZT to treat AIDS after extensive animal testing demonstrated efficacy in delaying disease progression.

AZT is a nucleoside analog that inhibits the reverse transcription of HIV and prevents its integration into human DNA. It was discovered by Jerome Horowitz at the University of California, San Francisco (UCSF) who named it Compound S in his lab notebook.

Without a doubt, preclinical research is an essential component of the drug development process, playing an important role in the development of new treatments. Scientists gained a better understanding of how a drug might interact with the body by conducting preclinical research, allowing them to make more informed decisions about whether or not to proceed with human trials. Preclinical research findings can also be used to inform regulatory decisions and help determine the optimal dose of a potential new drug. Preclinical research is thus an indispensable part of the drug development process, providing essential data that can help bring new treatments to the market. Nevertheless, in the last decade or so, it has been hampered by a lack of models that accurately reflect the complex physiology and pathophysiology of humans. New pre-clinical research technologies are changing this paradigm and enabling more reliable predictions of human outcomes from animal models, allowing for faster development of new therapies for unmet medical needs.

Some examples of these new revolutionary technologies that are attempting to disrupt the status quo of pre-clinical drug development are provided below. These new advancements have the potential to completely revolutionize the drug development process, from artificial intelligence-based models that can accurately predict drug efficacies and toxicity profiles to organ-on-a-chip platforms that can replicate human physiology.

Living chips: organs on a chip

The pharmaceutical industry has long sought a productive and efficient drug discovery research and development framework. However, the current in vitro two-dimensional (2D) or three-dimensional (3D) cell culture and in vivo animal experimentation platforms are insufficient for an efficient and accurate preclinical evaluation of drug efficacy and toxicity before clinical trials in human subjects can be approved. To this day, animal studies are the gold standard for preclinical drug validation in pharmaceutical development; however, the accuracy and reproducibility of testing results obtained from animal studies are compromised in humans due to species differences between the animal and human systems.

Organ chips are a new technology that is rapidly growing in popularity. The idea behind organ chips is that you can use them to study the human body, which is more complex than any computer model or animal can offer. While single-organ chips are designed to mimic individual organ functions, multi-organ chips that integrate multiple organ units, such as the gut compartment for drug absorption, a liver compartment for drug metabolism, and a kidney compartment for drug elimination, in a single chip have recently become popular to allow for more comprehensive studies Pires de Mello et al. developed a heart-liver-skin three-organ system to study the effects of acute and chronic drug exposure on both heart and liver functions. A four-organ chip with sequentially connected intestine, liver, skin, and kidney compartments, as well as stable homeostasis across different organ compartments, was also developed for testing the systemic toxicity of drug candidates. A more advanced version, dubbed “Body-on-a-Chip” or “Human-on-a-Chip,” is currently being developed to mimic the physiology of the entire human body using a single platform for drug pharmacokinetic and pharmacodynamic analyses. Organ chips are also cheap and easy to make, so they’re a great tool for scientists who want to study how drugs work in the human body. By using these devices instead of animals or people (which would cost millions), companies have been able to save time and money while still getting accurate results from their tests.

What is the investment status of the startups in the field?

• Orlando-based Hesperos was founded in 2015 and now is focused on creating customized organ-on-a-chip systems. They raised a total of $5.9M through multiple grants from the National Institutes of Health (NIH) and the National Center for Advancing Translational Sciences (NCATS).
• Quris is an Israel-based artificial intelligence (AI)-driven company, founded in 2019. After two funding rounds, with the last seed round in 2022, they raised a total $28M from multiple investors.
• Founded in 2013, Emulate is a Boston-based company developing organ-on-a-chip systems for various research and drug discovery purposes. By now, they raised a total $224.3M from multiple investors, with the last $82M Series E round led by Northpond Ventures.
• Mimetas is a Dutch company based in Leiden, which specializes in developing organ-on-a-chip models for drug development. Founded in 2013, they raised $32.4M from multiple investors.
• NETRI, is a French organs-on-chip start-up, in 2022 they closed a Series A fundraising round of €8 million from its historical investors (including private Business Angels and Polygone SA), new private investors (including Altana Investissements and Netangels), a new pharmaceutical industrial investor NeoVacs, the Banque Publique d’Investissement (BPI) and its banking partners (including the Place de l’Innovation du CIC).

Computational platforms for pre-clinical studies

AI is reshaping entire industries, including health care (IBM Watson Health and Google’s DeepMind Health). Unsurprisingly, the biopharmaceutical industry recognizes the potential value of AI and has expressed a strong interest in implementing AI-driven discovery platforms in the hopes of streamlining R&D efforts, reducing discovery timelines and costs, and increasing efficiency.

AI technologies used in drug discovery today have their roots in earlier ML and cheminformatics ideas. For instance, ML has been used for a long time to create QSAR models and expert systems to predict toxicity. But recently, big data, advanced analytics, GPU acceleration, cloud computing, algorithm development, and the availability of AI toolkits have made these techniques more popular.

Opportunities for the application of AI techniques in drug discovery

The discovery in the late 1990s that poor pharmacokinetics of drug candidates were a major cause of clinical attrition prompted a paradigm shift in the pharmaceutical industry. In silico ADMET modeling is intended to help project teams design and select novel compounds with superior ADMET properties, as well as direct experimental resources to the most promising compounds, reducing the overall number of compounds that must be synthesized and profiled. Pharmaceutical companies have used many global in silico ADMET models in their discovery pipelines over the years.

AI has sparked a surge of interest and investment in the biopharmaceutical industry. Although supporters of AI technology believe it will usher in a new era of AI-driven drug discovery, critics argue that most of the promises are merely tantalizing and aspirational. However, most experts agree that reality will most likely fall somewhere in the middle. Despite several notable advances demonstrating the impact of AI in preclinical drug discovery projects, it is unclear how far we are from an era of AI-driven drug discovery. AI in preclinical drug discovery is currently in the ‘peak of inflated expectations phase of Gartner’s hype cycle. As a result, it is critical to separate hyperbole from reality and set realistic expectations.

What is the investment status of the startups in the field?

Conclusion

New technologies are expanding research opportunities in this field while also having the potential to make our lives easier. Organ chips, for example, could allow us to study drugs without sacrificing animals — and with fewer side effects. Computational platforms can analyze data from multiple sources at the same time, allowing them to better predict how drugs will affect humans prior to clinical trials. These tools may not be available at your local university lab just yet (or ever), but we’re excited about how they may change the way we approach preclinical studies!

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See you in two weeks — Adrian