TED’s Audacious Project, a collaborative funding effort designed to help solve grand-scale social impact challenges, will award the $70 million toward “Engineering the Microbiome with CRISPR to Improve our Climate and Health.”

Doudna and Jill Banfield, PhD, both of the Innovative Genomics Institute (IGI) at the University of California (UC), Berkeley, are leading the initiative. It was Banfield who first introduced Doudna to the CRISPR field in bacteria at UC Berkeley’s Free Speech Movement Café in 2006. Banfield pioneered the field of community sequencing or metagenomics, while Doudna shared the 2020 Nobel Prize in Chemistry with her former collaborator, Emmanuelle Charpentier, PhD.

Doudna and Banfield have led a research group that has developed a method of adding or modifying genes within a community of many different species simultaneously, enabling “community editing” of microbes—a technology capable of both editing and tracking edited microbes within a natural community, such as in the gut. The group detailed how they precisely edited genes directly within complex microbiomes, including model systems that replicated natural soil and infant gut microbiomes, in “Species- and site-specific genome editing in complex bacterial communities,” a paper published in January 2022 in Nature Microbiology.

Now, Doudna’s focus on CRISPR and Banfield’s on microbial communities are being combined through the research effort, which aims to apply precision genome editing to microbiomes. The researchers aim to develop a precision microbiome editing platform, and create a new class of products capable of treating and preventing human diseases, as well as reducing greenhouse gas emissions.

“We need to understand microbiomes and there’s a huge knowledge gap even in the simplest microbiomes, such as those that are associated with the human infant and infant calf. We really just don’t understand how they work,” Banfield told GEN Edge. “The power that we are pursuing is to use the editing tools to manipulate organisms in the context of microbial communities, to begin to explore in some detail how organisms interact with each other and how they’re interdependent.”

That approach, she explained, differs from the historic approach to studying microbes growing by themselves, based on pure culture research.

“Our first stage is to begin to explore what functions microbes possess and how they play out. We can do that by using CRISPR-Cas to edit organisms that have not been brought into pure culture,” Banfield continued. “With those tools, we can provide a roadmap or a hypothesis that can be explored in the laboratory, in microbial communities prior to being deployed in actual real-life circumstances.”

Targeting childhood asthma

The IGI—whose priorities include advancing genome engineering to cure disease and advancing food security—will collaborate with two other UC campuses, UC San Francisco (UCSF) and UC Davis. At all three sites, researchers have identified several health challenges they will work on during the seven years of the research initiative.

One challenge is studying whether an inflammatory compound identified by Susan Lynch, PhD, director of the Benioff Center for Microbiome Medicine at UCSF, might be an effective target for treating asthma in children. The compound is produced by gut bacteria in children who develop asthma, and past research by Lynch and colleagues has pinpointed which bacteria produce the greatest concentration of the compound.

Researchers will carry out an extensive analysis of fecal samples collected from children up to two years old to study more potential targets. The research has the potential to be applied beyond asthma, Brad Ringeisen, PhD, IGI’s executive director, told GEN Edge.

“Our approach might be based on the molecule that she’s looked at, and that specific gene and that specific microbe. But it might also be much more expansive. We’re really trying to expand to almost any disease of inflammation,” Ringeisen said.

“We’re really focusing on ways to try to reduce that inflammatory response, which should then reduce the likelihood of developing asthma. But I think it has applications to other diseases of inflammation—things like cardiovascular disease, that are known to be associated with inflammatory markers in the circulatory system as well,” Ringeisen said. “It starts with the characterization of the fecal samples, and then we’ll be able to start going to mice studies, and then ultimately to humans.”

Between the fecal sample characterization and mice studies, Banfield emphasized, the microbial communities will be grown in the lab and tested there.

“One of the three pillars of the initiative is to be able to cultivate realistic human and calf microbiomes in the laboratory, such that they’re stable and representative,” Banfield said. “And then, we can test the editing tools in the context of those communities and also test the consequences of the edits, so we know how the system works in greater resolution before transitioning into organoids, in mice, and ultimately humans or animals.”

Researchers are hoping to expand their studies into calves by year four, then move beyond. If researchers succeed in finding targets, Ringeisen said, “we’re planning to try to start a human trial in year six or year seven.”

Metagenomics and CRISPR

“You can think of the metagenomics component almost as providing a blueprint or a roadmap, and then the CRISPR toolkit being the laser targeted device that’s able to go in and alter either a specific microbe or a specific gene in the microbe,” Ringeisen said. “One doesn’t work without the other. You need both of them combined.”

Over half of human-driven methane emissions are microbial in origin, and the tools needed to deal with problematic microbes at a global scale are lacking or limited: Antibiotics kill both beneficial and harmful bacteria. Probiotics have only shown a limited impact. Fecal transplants have raised concerns over safety and acceptance, though they have shown some promise in specific areas.

Researchers reason, therefore, that animal agriculture is key to addressing greenhouse gas emissions, 15% of which come from livestock, making them a key toward a solution. UC Davis researchers Matthias Hess, PhD, and Ermias Kebreab, PhD, the co-principal investigators heading the livestock methane emissions portion of the research initiative, have focused on dietary interventions aimed at reducing greenhouse gas emissions from livestock.

One such intervention, red seaweed, has led to significant decreases in methane emissions by lowering the expression of methane-producing genes in specific microbes in the cow’s gut.

But providing enough seaweed for daily consumption by the global cattle population—reported at between 942.6 million head (U.S. Department of Agriculture) and 1.5 billion head (World Animal Foundation)—would most likely not work, especially for the grazing cattle that account for most of the world’s livestock. Researchers reason that early intervention with CRISPR, however, could reduce the methane emitted by cows and do so for nearly every calf.

“We’re trying to intervene when the calf is born,” Ringeisen said. “The goal is to use these CRISPR tools and these characterization tools to find a stable microbiome that is a low methane microbiome, and then that that would hopefully set the calf to a lifetime of reduced emissions.”

“Our partners at UC Davis innovated these feed supplements. But now we’re turning their attention to these CRISPR tools and characterization tools to come up with new strategies, which I think are going to ultimately solve the larger problem which the feed supplements can’t.”

Global reach

The research group says it will disseminate the data it collects and the tools it develops to the global research community, and work with global nonprofits to help scale its advances. One global nonprofit the group is working with is the Consortium of International Agricultural Research Centers (CGIAR), a global research partnership with which the researchers have agreed to distribute its technologies to low- and middle-income farmers and ranchers.

Ringeisen said IGI’s public impact team will work to disseminate new technologies developed by the researchers through models that enable companies to profit in the most developed countries but provide the technologies at cost or lower in developing countries.

IGI will soon go public with a task force’s findings on how to broaden access to genomic therapeutics and interventions based on its technologies—and do so affordably. “We’re trying to figure out ways to make these technologies more accessible, so that it can help more people in the world,” Ringeisen said.

Researchers hope to avoid with their technologies the years of legal wrangling that has surrounded the development of CRISPR-Cas9, which has been the subject of a bitter legal battle. Last year, the Patent Trial and Appeal Board (PTAB) of the U.S. Patent and Trademark Office (USPTO) sided against UC,  Charpentier, and the University of Vienna, issuing a decision maintaining that the Broad Institute had priority in the invention of a single RNA CRISPR-Cas9 system that functions in eukaryotic cells.

Beyond legal and logistical issues, an even more basic challenge for researchers is much less discovering what microbes exist than it is learning how they interact in community with other organisms.

“We don’t really understand how to read the genome to predict what they’re doing in complete detail,” Banfield acknowledged. “We can predict a lot of the functionalities based on 100 years of laboratory bench work in biochemistry and genetics. But the part that’s minimal and out of view is the set of genes that are involved in interactions with and amongst organisms, because they can’t be probed by pure culture experiments.”

“It’s not so much about discovering new organisms, because we’ve gone through that phase,” Banfield added. “It’s about discovering how they work in the context of their communities, and how that leads to manifestations of disease or undesirable ecosystem impacts such as greenhouse gas emissions.”

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1. Financing deals: Slower pace to continue

The slower pace of private and public financing and the lower values of deals seen in 2022 will continue into this year. This observation was shared with GEN by Subin Baral, partner and global life sciences deals leader of Ernst & Young. He added, “The access to capital that all these companies enjoyed back in 2019, 2020, and the early part of 2021—I don’t think we’ll see that in the near term.”

A total of $16.58 billion in venture capital (VC) was invested in 285 biotech deals during the first three quarters of 2022, according to Evaluate Pharma, down from the record-high $24.51 billion in 446 deals of Q1–Q3 2021. In Q3, $3.6 billion was invested in 56 deals, compared with $7.14 billion and 135 deals a year earlier.

According to IPOScoop.com, 22 biopharma companies priced initial public offerings (IPOs) during 2022 (up to December 8), versus 74 in 2021. Also declining were biotech special purpose acquisition company (SPAC) IPOs, with 10 biotech firms, and another 6 that revealed SPAC plans in 2021, completing mergers with blank-check companies.

2. M&A: Deals poised for comeback

The slowdown in private and public financings is one factor expected to drive merger and acquisition (M&A) deals in 2023, Baral remarked. Other factors include loss of exclusivity (longtime top-selling drugs are nearing the end of patent protection) and the $1.2 trillion amassed by 25 biopharma giants to carry out M&A. The latter factor is what Ernst & Young calls “firepower.”

“The good deals are getting done,” Baral pointed out. “They aren’t getting done just because they are cheap. Deals are getting done because they are the right deals to get done.”

PwC predicts that the value and volume of pharmaceutical and life sciences M&A will bounce back in the new year—but won’t be as robust as 2021: “In 2023, we expect M&A to more closely resemble prior years with a total deal value in the $225 billion to $275 billion range across all subsectors.”

According to PwC, during 2022 (up to November 15), deal value reached $137.8 billion (down 49% from 2021) in 266 deals (down 28%). The year’s largest deal at deadline was Amgen’s $27.8 billion purchase of Horizon Therapeutics, announced December 12, followed by Pfizer’s $11.6 billion purchase of Biohaven Pharmaceuticals, completed October 3.

3. Cancer: Big names bet on vaccines

Industry watchers will be paying close attention to data that is expected to be released by developers of cancer vaccines. One is BioNTech, which in September presented promising follow-up data from its ongoing Phase I/II trial (NCT04503278; 2019-004323-20) assessing its wholly owned novel chimeric antigen receptor (CAR) T-cell therapy candidate BNT211 in patients with relapsed or refractory advanced solid tumors. The trial’s estimated primary completion date is in August.

In September, Merck & Co. exercised its option to jointly develop and commercialize Moderna’s messenger RNA (mRNA)-based personalized cancer vaccine, for which Merck agreed to pay Moderna $250 million upfront. Although primary data for the personalized cancer vaccine was expected in Q4 2022, the trial’s primary completion date is expected to be in September 2024.

“We expect additional readouts of cancer vaccines in randomized trials that will further inform whether recent advances in antigen targeting, vaccine potency, and patient selection were sufficient to overcome the historical challenges,” Daina M. Graybosch, PhD, senior managing director, immuno-oncology, SVB Securities, wrote last summer.

4. mRNA: Beyond COVID-19

Biopharma firms will step up efforts to develop more drugs and vaccines based on mRNA, and to broaden these efforts beyond COVID-19. Last spring, GEN reported on companies applying emerging technologies that included Cas13-encoding mRNA, circular mRNA, programmable mRNA, and self-amplifying mRNA.

A self-amplifying mRNA collaboration that was announced last November could generate more than $4 billion for Arcturus Therapeutics, which will partner with CSL to develop vaccines for COVID-19, influenza, pandemic preparedness, and three undisclosed “globally prevalent respiratory infectious diseases.” Last year began with Pfizer and Beam Therapeutics launching an up-to-$1.35 billion mRNA base editing collaboration.

Francois Vigneault, PhD, co-founder and CEO of Shape Therapeutics, which develops programmable RNA therapies using artificial intelligence, acknowledged that a continuing challenge for mRNA is duration of response: “Is an acute, very intense response that’s very short lived useful or not? That’s why I think it’s going to be exciting to see the field evolve.”

mRNA has most successfully and lucratively been applied in COVID-19 vaccines, whose top developers reaped billions during Q1–Q3 2022. Pfizer earned $26.477 billion; Pfizer’s partner BioNTech earned €11.413 billion (about $12 billion); and Moderna earned $13.576 billion. In 2023, and possibly beyond 2023, lawsuits will play out between Pfizer/BioNTech and Moderna to settle disputes over mRNA vaccine patents.

5. Cell and gene therapy: More R&D, fewer deals

Growing regulatory acceptance of cell and gene therapies—26 have been approved by the FDA, 4 in 2022 alone—will spur additional development efforts. Kite Pharma, a Gilead Sciences company, in December committed up to $4 billion-plus toward co-development of Arcellx’s multiple myeloma CAR T-cell therapy CART-ddBCMA.

“There’s a lot of collaboration and partnerships that we see,” Baral noted, “and we’ll continue to get early innovation on cell and gene therapies.”

In gene therapy, Bluebird Bio expects to file in the first quarter for FDA approval of its third gene therapy candidate, lovotibeglogene autotemcel (lovo-cel) for sickle-cell disease, after gaining approvals for the other two in 2022.

According to the Alliance for Regenerative Medicine, developers of cell therapies, cell-based immune-oncology therapies, gene therapies, and tissue engineering therapies raised a total $6.4 billion and oversaw 2,093 active trials during the first half of 2022—less than half the $14.1 billion and 11% below the 2,358 trials of H1 2021.

6. Synthetic biology: Biopharma focus

Synthetic biology, or “synbio,” has disrupted industries as diverse as agriculture, food, and fashion, in addition to the biopharma industry. Applications of synthetic biology focused on drug development and manufacturing will be more of a focus this year for at least one of the segment’s largest companies.

“You can expect to see us move more aggressively into biopharma,” Ginkgo Bioworks’ co-founder and CEO, Jason Kelly, PhD, told GEN. He said that Ginkgo sees opportunities in manufacturing products for adeno-associated viruses (AAVs) and capsids, T cells, and RNA. He added, “Those three categories will see us do a lot more deals.”

Ginkgo completed plenty of deals in 2022, including a partnership with Merck & Co. to engineer up to four enzymes toward active pharmaceutical ingredient manufacturing (up to $144 million in milestones). Ginkgo acquired both Circularis Biotechnologies (which developed a circular RNA and promoter screening platform) and Altar (which developed an adaptive evolution platform), both for undisclosed prices. Ginkgo also bought Zymergen for $300 million. “Zymergen’s platform technology is quite good,” Kelly asserted. “We thought it would be better offered as a service and combined with Ginkgo’s platform technology, so that we can do better genetic engineering for more customers.”

7. Regulation: Pricing and politics

Biopharma stakeholders will be watching the effect of the Inflation Reduction Act on drug pricing beyond Medicare. The law lets Medicare set prices for some small-molecule drugs 9 years after FDA approval, versus 13 years for biologics.

“The price controls that the IRA imposes on medicines offered through Medicare may have the unintended consequence of reducing the number of new therapeutics created to respond to unmet medical needs,” warned Jim Greenwood, Alex Pinson, and Jamie Gregorian of DLA Piper.

Supporters counter that the law benefits patients through price curbs such as the $35/month cap on insulin. “Where patient affordability improves, manufacturers may also realize some volume offsets,” observed Muna Tuna, U.S. market access, pricing, and reimbursement leader, Ernst & Young.

The drug pricing debate is unlikely to be resolved by a Congress divided between the House of Representatives’ narrow Republican majority and the Senate’s slim Democratic majority, shaken when Arizona Senator Kyrsten Sinema announced that she had left the Democratic Party and registered as an independent.

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Depending on whom you ask, we are either in the early stages of, somewhat immersed in, or already fully immersed in Industry 4.0, the fourth industrial revolution. Industry 4.0 incorporates artificial intelligence (AI), machine learning (ML), and big data to enable integrated and autonomous manufacturing systems to operate independently of humans. Like 5G, the metaverse, and genetic engineering, Industry 4.0 is assumed to be a revolution of gargantuan scale. Your opinion as to whether we are at the beginning or in the midst of this transformation is likely to be based on your industry and what part of that industry you work in.

In pharmaceutical development and manufacturing, AI and ML have certainly arrived. Together, they already represent an important aspect of how modern contract development and manufacturing organizations operate. The steady increase in the complexity of manufacturing new medicines and the desire to reduce time to market drive the need for faster development and manufacturing. This pressure is propelling the implementation of AI solutions into many activities related to pharmaceutical development and manufacturing.

AI is not the only answer to these new challenges, but it is often a pivotal part of the solution. In practice, established methods still deserve their place in development and manufacturing, and they can be used to reinforce AI’s support of human intelligence, ingenuity, and innovation. Reaching the ultimate goal of lights-out manufacturing—closer and closer to the vision of AI—will still require tremendous effort and a substantial boost in IT infrastructure.

Nevertheless, we believe the industry is at a critical stage in its digital transformation journey. Today, AI has the potential to shorten operational cycle times while simultaneously increasing quality and/or reducing overall costs and raw material consumption. It provides a solid basis for greater automation and knowledge expansion as an enabler of better, faster decision-making.

Key distinctions, wide-ranging opportunities

At Lonza, we have successfully optimized product quality using computer vision technologies in quality assurance. We are also taking our first steps toward AI-enhanced genetic engineering based on bioinformatics methods. Most recently, we have been developing hybrid approaches leveraging AI, mechanistic models, and traditional statistics for scaling up processes.

It is important to understand the difference between AI and ML. AI is made up of ready-to-use products that leverage human-like pattern recognition or decision-making capabilities to solve individual tasks and perform various activities. ML is a set of mathematical algorithms solving individual tasks by making predictions based on assumptions derived from historical data. ML is a subset of AI, meaning that all ML is AI, but not all AI is based on ML.

At Lonza and across the biotechnology and pharmaceutical spaces, AI, ML, and big data are used routinely in areas such as research, computer-aided drug design, protein profile assessment, the engineering of mammalian expression systems by means of DNA element design, and the prediction of side effects for novel therapy forms. In small-molecule development and manufacturing, ML is used for synthetic route optimization, retrosynthesis, toxicological assessment of new chemical entities, and formulation design. On the other side of the drug development and manufacturing spectrum, ML is employed in developing controlled-release tablets—to assess the hardness, particle size, moisture, and other factors to predict a tablet’s in vitro behavior.

Manufacturing applications

AI and ML are also increasingly being employed in pharmaceutical manufacturing. In an area such as process analytical technology (PAT), spectroscopical methods like Raman are used in combination with an ML algorithm to monitor critical process parameters. When used with a Raman in-line probe, the PAT and ML combination can monitor metabolites and raw material concentrations, which cannot be measured directly through Raman linear regression. Today, we can even find research describing the indirect measurement of pH values using Raman-ML methods. Similar results have been reported using a combination of either Fourier transform infrared spectroscopy or ultraviolet-visible spectroscopy with ML. Even monitoring of Escherichia coli contamination with Raman and ultraviolet-visible spectroscopy was recently published.

These methods allow production process performance to be monitored without taking a manual sample if an in-line spectrometer probe is installed. This has significant advantages, including less variability in analytical test results, fewer verification activities (in the case of a validated system), and more process knowledge, given that we are measuring process performance continuously and can reduce the risk of contamination through the manual sampling process.

Another significant impact that ML is having on pharmaceutical manufacturing is its ability to make predictions based on historical data. This can have a very important impact in an area like predictive maintenance. By combining an ML algorithm with high-frequency sensors and assessing assets for factors such as sound, vibration, or electricity consumption, it is possible to predict the latest possible time for maintenance or repair of an asset. This can reduce costly production equipment maintenance time and increase the asset’s availability. This approach is applicable not only for manufacturing equipment but also for laboratory equipment (such as high-performance liquid chromatography equipment for quality contol) and utility systems (such as heating, ventilation, and air conditioning systems for clean air).

In the commercial manufacturing area, we are focusing on bio operations and processes. We are working on ML algorithms in combination with Raman spectroscopy to give us the ability to monitor glucose levels (or the levels of different metabolites) in bioreactors without taking manual samples. This ability also allows us to accrue valuable process knowledge at the same time.

We are also investigating the potential of AI and ML applications in product technology transfer. We encounter different scales and different equipment setups during technology transfers. The number of process variables and critical quality attributes involved in technology transfers adds another dimension of complexity. AI and ML applications are predestined to predict process performance or critical process steps in such technology transfers, helping to address these complex challenges.

Another area that we are investigating is the use of AI and ML in deviation management and change control applications. Such applications can add significant value as transactional intelligence systems.

AI and ML systems can also be used to assess behavioral challenges and improve training. For example, AI is being used with computer vision technologies to study how people behave in clean rooms. And we are using virtual reality to train unit operations. When virtual reality is in place, neither trainers nor trainees need to be on site. Also, equipment that has yet to be installed can be the subject of training. Finally, routine operations can be handled by workers who need to be off-site.

Future possibilities

Data science, which represents the next revolution in pharmaceutical manufacturing, will become a fundamental technology in all pharmaceutical manufacturing areas. A crucial aspect driving this AI revolution is the collection, management, and utilization of specific process data. In addition, general data in areas such as warehouse conditions or raw materials will play a critical role. These data sets will be the basis for advancing our use of AI applications.

As the manufacturing process itself becomes more and more automated, like a self-driving car, it will be able to react to unpredictable events during operations. There will be significantly more data available, and edge computing will help to process this data, in real time, at the source to steer the process toward a “golden batch.” More process knowledge will be gained through these technologies, and the decade-old vision of a parametric release of the product may well become a reality.

There are currently a wide variety of uses for AI, with many associated positive impacts. The range of positive impacts driven by AI and ML is helping us increase safety, quality, and sustainability while lowering costs. While a continuing increase in AI and ML integration will put new demands on IT infrastructure and employees, the indications are that AI and ML may yield exponential results if employed in a thoughtful way.

Stephan Rosenberger, PhD, is head of digital transformation at Lonza.

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A review of approved drugs from 2020 and 2021 reveals that only 22% are designed against a truly novel target—a gene or protein that is thought to be capable of participating in a disease-modifying drug interaction. The implication, then, is that over three-quarters of recently approved drugs are not designed using new biological insights, despite two decades of mining the human genome.

The completion of the Human Genome Project in 2002 represented groundbreaking findings that significantly elevated our understanding of the human genetic instruction book.¹ The human-focused project also paved the way for the analysis of genomes from other species, showing us that our genome and the other genomes in the animal kingdom are more similar than they are different, especially if the other genomes belong to closely related species, such as our fellow mammals.

Flash forward 20 years. Despite the sequencing of over 30 million people, the top disease killers in the United States and worldwide remain largely unchanged.² Heart disease, cancer, stroke, diabetes, respiratory disease, and renal disease kill over 1.5 million people each year in the United States alone (Table 1). Seven of the top killers in the United States—ischemic heart disease, stroke, respiratory disease, cancer, Alzheimer’s disease, diabetes, and kidney disease—are also top disease killers globally.³ Clearly, our approach of focusing almost exclusively on human data, along with a handful of imperfect model organisms, to drive innovation and support the development of new treatments for these diseases is flawed.

Innovative technologies and approaches are needed to improve our understanding of susceptibility and to inform our efforts to discover novel targets. One approach is to look beyond traditional laboratory rats and mice, which are used to crudely approximate disease in humans, and instead focus on disease avoidance strategies that have evolved in other animals, some of which could hold the secrets to unlocking protective mechanisms that already exist humans but are inactive.

Comparing genomics across species

Evolution in the genome tends to operate on the “if it’s not broken, don’t fix it” principle. The parts of the genome essential for survival and reproduction tend to remain the same over millions of years. When we compare genomes of different species, we see these sequences are very similar (or conserved). Identifying a change in a conserved part of the genome that is specific to one species or a set of species with a given characteristic can reveal the genomic basis of that characteristic.

For example, naturally low biliary phospholipid levels in guinea pigs and horses have been linked to the gene Abcb4 by analyzing common patterns of genetic variation. Similar mutations in the human version of the gene, ABCB4, also result in low biliary phospholipid levels but also severe liver disease.⁴

Comparative genomics also shows that for some diseases, these types of modifications inevitably lie in the part of the genome that does not encode for genes, as genes are generally highly or even entirely similar between species. Therefore, integrating epigenomic data (or understanding how genes are regulated) will improve our understanding of disease and prove essential for genome-based therapeutic discovery in the future.

Natural disease resistance is a phenomenon where species have evolved mechanisms to either prevent a disease entirely or, perhaps more interestingly, respond to the disease after it has started to develop. For example, after aspects of a disease develop in organisms of such species, those organisms may be able to reverse those aspects and return to a state of health.

This can be seen in humans as well, in families that inherit specific genetic mutations that protect them from disease (such as in the case of mutations in the gene PCSK9 resulting in low cholesterol).⁵ There are companies now mining specific human populations for these aberrations. Variant Bio is one such company examining specific populations of humans with extreme traits (including obesity or resistance to certain infectious diseases) to search for new drug targets. Unfortunately, as a whole, humans are just too similar, and there are not enough naturally occurring human populations resistant to the many complex diseases that ail us.

But there are many examples of this when you examine the broader animal kingdom.

Studying disease resistance in other species

The 13-lined ground squirrels and tenrecs naturally resist strokes and heart attacks when they drastically reduce the oxygen supply in their brain and heart during hibernation-like states called torpor. Such an experience would cause inflammation and fibrosis in humans, but these consequences are entirely avoided in these species.

Evolved disease avoidance in mammals is not unique to hibernators. The spiny mouse (Acomys cahirinus) seemingly regenerates skin and multiple organs (including the kidney and spinal cord) without fibrosis or scarring after injury. Out of the 300 species reviewed by Fauna Bio thus far, 51 species have been found to have evolved resistance toward one or more human disease states.

Following genetic clues

Multiple studies now support the assertion that drugs with genetic support are more likely to succeed in clinical trials^6,7 and be approved as therapeutics. However, we can improve our ability to find functional genetic targets by understanding which genes have been highly conserved across evolution, as these genes are more likely to contribute to disease, particularly for the genetic risk of complex diseases (including diseases such as coronary artery disease and type 2 diabetes).⁸

It makes sense that genes that have not changed in hundreds of millions of years often do something meaningful. Improved sequencing technologies have enhanced our understanding of genetic conservation across hundreds of species. Combining this with functional data from species that are using those genes in unique and protective ways provides us the ability to rapidly home in on those genes that are highly functional and can, hopefully, help us prevent or reverse disease in humans.

Scientists, physicians, and even the National Human Genome Research Institute acknowledge that there is much more to do, and that the human genome alone doesn’t hold all the answers we need.⁹ By continuing to innovate, investigate, and build on the available data we have today, we can open up many new potential classes of targets and medicines that will impact some of the biggest known killers of humans today.

Ashley Zehnder, DVM, PhD, is chief executive officer of Fauna Bio.

References

1. National Human Genome Research Institute. Human Genome Project Results. Last updated: November 12, 2018. Accessed December 5, 2022.
2. Crespi S. Looking back at 20 years of human genome sequencing [podcast]. Science. Posted February 4, 2021. Accessed December 5, 2022. DOI: 10.1126/science.abg8636.
3. World Health Organization. The top 10 causes of death. Posted December 9, 2020. Accessed December 5, 2022.
4. Hiller M, Schaar BT, Indjeian VB, et al. A “forward genomics” approach links genotype to phenotype using independent phenotypic losses among related species. Cell Rep. 2012; 2(4): 817–823. DOI: 10.1016/j.celrep.2012.08.032.
5. Benton ML, Abraham A, LaBella AL, et al. The influence of evolutionary history on human health and disease. Nat. Rev. Genet. 2021; 22(5): 269–283. DOI: 10.1038/s41576-020-00305-9.
6. Nelson MR, Tipney H, Painter JL, et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 2015; 47(8): 856–860. DOI: 10.1038/ng.3314.
7. King EA, Davis JW, Degner JF. Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval. PLoS Genet. 2019; 15(12): e1008489. DOI: 10.1371/journal.pgen.1008489.
8. Finucane HK, Bulik-Sullivan B, Gusev A, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 2015; 47(11): 1228–1235. DOI: 10.1038/ng.3404.
9. National Human Genome Research Institute. Comparative Genomics Fact Sheet. Last updated: August 15, 2020. Accessed: December 5, 2022.

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Ten years ago, CRISPR gene editing was introduced to the world by a paper in Science. Just eight years later, the paper’s senior authors, Jennifer A. Doudna and Emmanuelle Charpentier, shared the Nobel Prize for Chemistry. By then, it had already become abundantly clear that the gene editing tool known as CRISPR-Cas9 was transforming researchers’ ability to make precise alterations to genomes.

CRISPR has been used to manipulate the genomes of organisms in humans and animals, among other living organisms. When it comes to drug discovery and development, CRISPR is used to remove or modify DNA in research mice to study disease phenotypes and develop new treatments.

The technology streamlines the quick creation of structural variants through deletions, reproductions, or inversions of genomic regions in animal models, allowing scientists to produce more predictive cell-based models and identify new drug targets in a quicker, more cost-effective way. Sophisticated methodologies allowing for CRISPR screening to occur in the natural microenvironment—in organoid models and even in whole animal models—has exposed genetic dependencies that could not be revealed by in vitro screening efforts.

Besides the ability to create more relevant model systems relatively easily, the factor of time has also proven to be a game changer. Modeling complex human diseases requires complex genetic modifications that used to take months or years to achieve. With CRISPR, however, these modifications can be achieved in weeks. As CRISPR technology continues to evolve, it’s changing how we study animal models in major ways—transgenic mice can be generated faster than ever before and with great precision, and these animal models are instilling the ability to test and predict the efficacy and safety of a drug target early on, optimizing and accelerating the process.

1. Precision modeling that revolutionizes target validation

Animal models generated by CRISPR technology have been crucial in the discovery of new medicines. This technology generates highly customized mice and rats whose DNA has been altered to reflect a humanized immune system, which enables precision modeling in research, as well as target validation and efficacy assessment. Prior to the use of this technology, it could take up to 18 months to produce test animals—it now takes anywhere from one-third to one-half the time to create a founder animal, making it more cost-effective than alternative preclinical animal models.

CRISPR-Cas9 technology has been used to knock out DNA in research mice and rats, meaning the animal gene for a particular target is silenced and the defective gene for human disease is knocked in, introducing the same mutation into the mouse gene as we see in the human population. This allows researchers to focus on therapies for the human target.

A pre-CRISPR example of this approach can be seen in an apolipoprotein E (Apo E) knockout mouse that was manufactured by two independent laboratories in 1992. Apo E wipes away dietary and endogenous lipids and removes cholesterol from peripheral tissues. The deficiency of Apo E generates aggressive atherosclerosis, a form of cardiovascular disease, in the mouse.

The mouse with deficient Apo E showed that restricting cholesterol absorption would minimize both plasma cholesterol and the development of atherosclerosis, giving the researchers confidence to pursue clinical trials for ezetimibe, a drug later approved in 2002 for the treatment of hypercholesterolemia. Clinical trials continue to demonstrate that ezetimibe prevents major cardiovascular events in high-risk patients with preexisting cardiovascular disease.

Today, models incorporating even more precise modifications can be generated with CRISPR technology.

2. Testing efficacy in humanized systems

Predicting the efficacy of treatments is important when planning for costly and time-consuming clinical trials. Animal model systems can emulate human physiology, so these models can help generate reliable data that shows the safety and effectiveness of a treatment.

To evaluate and ensure translation between humans and animal models, CRISPR streamlines the creation of single nucleotide polymorphisms (SNPs), which are the most abundant source of genetic variation in the human genome. Accordingly, CRISPR can facilitate the use of SNPs in humanizing sizable areas of DNA in animal models and creating predictive models of disease. The ability to identify SNPs that contribute to the susceptibility of common diseases provides information that can expedite early diagnosis, prevention, and treatment of human diseases.

Researchers hoping to proceed to clinical trials for conditions such as cardiovascular disease must be able to predict the efficacy of treatments aimed at reducing morbidity and mortality. The data stemming from research with animal model systems is pivotal to efficacy predictions. Treatment efficacy can be highly impacted by genetic variation, and the introduction of disease-associated mutations in a humanized animal model can give efficacy studies a relevant background, thereby enhancing translational value even when not all genetic interactions are known.

CRISPR-Cas9-based screening can also be used to identify targets for combination therapy. For example, synthetic lethality was observed in a CRISPR screening campaign between lenvatinib treatment and the genetic inhibition of epidermal growth factor receptor (EGFR). Efficacy studies applying co-treatment of EGFR inhibitors gefitinib and lenvatinib supported these results with a good translation to advanced hepatocellular carcinoma patients who were unresponsive to first-line drug lenvatinib, showing meaningful clinical responses to the combination therapy.

3. Inspiring confidence in the safety of a given treatment

Safety is imperative in the drug development process. Before studies can move to human targets, the clinical risk profile of a potential new treatment must be predicted.

Data rendered from a reliable animal model instills trust in the technique and the safety of a particular approach. Based on results observed in these models, companies can proceed to the next stages of research and development. CRISPR-Cas9 technology can be employed at various stages for the mitigation of safety risks. Mutating and restoring a putative drug target in an isogenic environment can allow for the discrimination of off-target from on-target responses, and when this approach is applied early on, it can enable a selection of compounds with fewer off-target risks to be taken into safety studies.

Once in vivo safety studies are entered, more accurate mimicking of the complex human system helps to expose risks that may occur when moving to clinical trials, for example, by genetic variation affecting functioning of connected tissues or the immune system.

When it comes to the success of revolutionary CRISPR-based medicines, off-target effects of gene editing are pivotal. The risks of DNA deletions and unintended mutations must be considered and are an important aspect of the safety profile of such treatments. Once this is under control, a CRISPR-Cas9-driven approach can advance the use of stem cells in regenerative medicine applications such as the replacement of dysfunctional tissues or organs.

The evolving CRISPR landscape and the future of animal models

The power of the CRISPR technology lies in the unprecedented ease and control CRISPR brings to genome editing, and this power can be applied across multiple steps in the drug development pipeline. Although the technology has limitations, such as off-target effects or difficulties creating more complex genome modifications, CRISPR-Cas9 promises to advance novel medical applications, potentially treating or even curing conditions ranging from cardiovascular disease to rare inherited blood disorders.

Lieke Geerts, PhD, is a group leader at Charles River Laboratories.

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