Next-generation sequencing (NGS) has been an invaluable tool to understand both health and disease in many different organisms. However, for the most part, genomes are static replications of the original germline coding that fail to reflect how an organism is responding to environmental change.

In other words, while DNA studies offer an incredible window to improve our understanding of biology, they are only a starting point. Downstream from DNA, epigenomics, transcriptomics, spatial omics, proteomics, and many other omics are providing more comprehensive and granular information to expand our knowledge and improve clinical care.

Multiomics can help us understand a tissue’s full dynamic range, showcasing how cells respond to disease, diet, aging, toxins, temperature, salinity, pH, and many other factors. Over the past decade, new omics techniques have emerged to characterize biology (right down to single cells), and many of these technologies are integrally connected to NGS. At the same time, prices have plummeted. Sequencing a whole genome now costs as little as $200, and that price should continue to drop.

This confluence of powerful new workflows and reduced sequencing costs should have a profound impact on biomedical research. With the cost barrier lessened, life scientists can dive even deeper into biological complexity.

Addressing tumor heterogeneity

Researchers and oncologists have known for years that tumor genomes are heterogeneous, one of the many factors that make cancers so clinically challenging. Sequencing biopsied tissue from only one tumor region, or at best a handful of regions, will not provide the most complete picture of the driving mutations in that tumor, or of the low-prevalence variations that could gain ascendence in response to treatments.¹

Low-cost genomic sequencing, combined with less invasive biopsies, will help clinicians more thoroughly sample tumors to create detailed genomic maps. Genetic information from multiple tumor regions will provide more complete information on each patient’s cancer. In turn, advanced informatics will analyze this data to better predict the cancer’s trajectory.

These capabilities are evolving rapidly, and single-cell analyses are playing a major role. New technologies are producing single-cell whole genome and transcriptome data, and they are revealing features such as heterozygous alleles, which are key to calling cancer mutations.²

As sequencing becomes more economical, the barrier will no longer be the cost of sequencing 50 or 100 tumor sites, but rather the difficulty of obtaining that much diverse tumor material. Researchers are working on this problem as well, investigating new techniques to sequence cells from fine-needle biopsies.³ Producing complete genomic and transcriptomic readouts from single cells will advance these efforts.

These analyses could provide a wealth of data about variations throughout a patient’s tumor. Currently, a lot of guesswork goes into understanding tumor heterogeneity—a minor allele could be 1% of a tumor or 10%. More comprehensive sampling and analysis could help change that.

On the clinical level, this could help oncologists look around corners—seeing where the cancer is now and predicting where it’s likely to go. From there, they can develop more comprehensive plans to address the most abundant clones, as well as the more obscure ones that could cause trouble later.

Equally important, reduced costs mean clinicians can bring more tools into the mix to assess each patient’s cancer. Even three years ago, sequencing a tumor genome would have cost thousands of dollars. In the next few years, assuming a $200 genome, laboratories will be able to conduct transcriptomic, proteomic, and possibly even single-cell analyses for $1,000 or less. These capabilities will also advance bench research, as investigators will have more detailed information about tumor microenvironments, cell signaling pathways, and many other elements. Ultimately, spatially resolved genomic/transcriptomic sequencing could help us better manage tumor heterogeneity in the clinic.

The new virology

NGS played an enormous role in mitigating the SARS-CoV-2 pandemic—from sequencing the viral genome(s) to creating diagnostic tests to developing mRNA vaccines in record time.⁴ But now that COVID-19 is mostly contained, scientists must scan the horizon for the next viruses that could jump from animals into people. This may be one of the most difficult problems researchers and governments face. There are millions of viruses with the potential to infect humans, and those are only the ones we know about.

The Global Virome Project is working to sequence all viruses, a task that’s possible only through more accessible sequencing.⁵ Other efforts that focus on coronaviruses have found many that are significantly different genomically than the ones in current databases, highlighting potential future threats.⁶ On the other side of the coin, the Human Immunome Project, an international group being led by Vanderbilt University Medical Center, is working to identify all the genes and other molecules associated with human immunity.⁷

Again, inexpensive sequencing will accelerate these efforts, as researchers can stretch their resources. In addition, technologies that support single-cell studies, such as droplet microfluidics, will also play a significant role.⁸

Another emerging technology, descended from NGS, is multiplex assay technology. It can identify millions of antibody epitopes and comprehensively determine an individual’s pathogen exposures. This offers enormous advantages when assessing patient exposures and determining treatment plans.⁹

This could be particularly helpful to understanding autoimmune responses, as well as coinfections. We saw during COVID-19 that people infected with SARS-CoV-2 sometimes had respiratory syncytial virus disease and/or influenza as well. Having a broader read on pathogen exposures could, again, improve care.

The importance of understanding pathogenic risks was illustrated by a recent study, sponsored by the Bill and Melinda Gates Foundation, to reassess biological samples from COVID-19 and identify co-infections.¹⁰ The researchers ran broader genomic panels to look for different bacterial and viral pathogens, antimicrobial resistance genes, and other markers.

The study essentially produced a pathogen weather map, showing which microbes were most active in certain parts of the country at specific times. This comprehensive approach offers new public health opportunities in advanced and emerging nations around the world.

Beyond human biology

Genomics discussions tend to be human-centric, but there’s a lot more we can learn in plants and animals. Veterinary medicine is exploding, and many of the omics advances discussed here could easily be applied to that arena.

Agriculture is another area where low-cost genomic sequencing is likely to have a major impact. Agribusiness is already using low-cost genotyping to better understand herds and crops and select for beneficial traits in both. Low-cost NGS and other omics technologies will likely help advance these studies even further.

At the Salk Institute, researchers are using a variety of omics techniques to develop crop plants and wetland plants that are better at sequestering carbon. Most plants do a good job of removing carbon from the atmosphere, but they also give it back when they die or shed leaves. In its plant research, the Salk Institute seeks to create roots that store carbon for decades or longer, which could have a positive impact on our devolving climate.¹¹

These transitions will not be entirely seamless. There is an ongoing evolutionary struggle between omics (and other technologies that produce massive datasets) and the informatics acumen needed to analyze all that information. Machine learning and other computational approaches will have to keep pace to ensure knowledge from single-cell studies and other data-intensive applications is not wasted. The hard part will be transitioning these techniques into the clinic. No clinician has time to personally crunch this much data. We will need systems that provide actionable information in a hurry.

The Human Genome Project was completed a generation ago, and it has spawned a wide array of useful applications. NGS has become the power source for this ecosystem, helping life scientists branch off into even more granular studies. As sequencing prices continue to drop, we will see even more creative applications to dissect biology and improve care, with each application contributing to a more comprehensive view.

Gary Schroth, PhD, is a distinguished scientist, emerging applications, at Illumina.

References

1. Zhu L, Jiang M, Wang H, et al. A narrative review of tumor heterogeneity and challenges to tumor drug therapy. Ann. Transl. Med. 2021; 9(16): 1351. DOI: 10.21037/atm-21-1948.
2. BioSkryb Genomics. Accurate Single-Cell Genomic Analysis Holds the Key to Understanding Cancer Heterogeneity. Accessed November 17, 2023.
3. Xia Y, Gawad C. Bringing precision oncology to cellular resolution with single-cell genomics. Clin. Exp. Metastasis 2022; 39(1): 79–83. DOI: 10.1007/s10585-021-10129-4.
4. MacDonald A. NGS During the COVID-19 Pandemic and Beyond. Technology Networks. Accessed November 17, 2023.
5. Global Virome Project.
6. Ruiz-Aravena M, McKee C, Gamble A, et al. Ecology, evolution and spillover of coronaviruses from bats. Nat. Rev. Microbiol. 2022; 20: 299–314. DOI: 10.1038/s41579-021-00652-2.
7. Human Immunome Project.
8. Jing W, Han HS. Droplet Microfluidics for High-Resolution Virology. Anal. Chem. 2022; 94(23): 8085–8100. DOI: 10.1021/acs.analchem.2c00615.
9. Ibsen KN, Daugherty PS. Prediction of antibody structural epitopes via random peptide library screening and next generation sequencing. J. Immunol. Methods 2017; 451: 28–36. DOI: 10.1016/j.jim.2017.08.004.
10. Illumina. Aegis writes a new chapter on pathogen surveillance. Accessed November 17, 2023.
11. Harnessing Plants Initiative. Salk Institute for Biological Studies.

The post Inexpensive Sequencing Is Enabling the Age of Multiomics appeared first on GEN - Genetic Engineering and Biotechnology News.

The traditional view of human genetics is that we are born with a genome that provides a predictive model of our risks of developing different diseases. With a few exceptions, such as cancer, genetic testing has focused on identifying the genetic variations we inherited from our parents.

Recent studies have shown that the genomes in each of our cells are not static but are continuously changing because of the somatic mutations that occur as we develop and age. To more fully understand how those genetic changes contribute to human diseases, the models currently used to study genetic risk must be updated.

Mapping cellular evolution across our lifespan

Our cells rapidly divide and specialize after conception, transforming from a single cell into 37 trillion specialized cells that carefully orchestrate tissue function and health. This process, though, comes at a cost. Every cell division introduces uncorrected copying errors that leave permanent changes in the genomes of each of our cells. Furthermore, studies of human tissue have found that each cell in our body has about 100 new genetic changes when we are born. Since we possess trillions of cells when we are born, we begin our lives with enormous cellular genetic diversity that we have just begun characterizing.

We develop ever more genetic diversity as we age and become exposed to environmental mutagens, such as ultraviolet light and chemical mutagens. These factors, paired with the rate of cell division, impact the rate of somatic mutation acquisition across our tissues. Although single-cell genome sequencing is still young, most studies in the field have found that each of our cells acquires tens of new somatic mutations in cellular contexts each year of our lives.

With the development of more accurate methods of single-cell sequencing, we now know that those somatic mutations are present at higher levels than previously anticipated. The question has become: Are those mutations just passengers of life with little consequence, or do they contribute to the formation of human diseases?

Associating somatic changes with human diseases

With the capacity to accurately measure somatic mutations, we can now look at the roles these mutations play in a range of human diseases. It has been known that somatic mutations are associated with various skin conditions, which have been easy to identify based on distinct patterns that could be directly visualized by dermatologists. The development of next-generation sequencing has enabled us to identify causal somatic mutations for some of these conditions, such as Sturge-Weber Syndrome.

More recently, somatic mutations have been found in diseases of other tissues. For example, there have been extensive studies of clonal hematopoiesis of indeterminate potential after the identification of somatic mutations normally associated with the development of acute myelogenous leukemia in blood cells of elderly patients. Although these individuals have an elevated risk of developing leukemia, the greater concern is the increased risk of coronary artery and liver disease. This realization that somatic mutations in one tissue can have far-reaching consequences has spurred interest in studying these mutations in other disease contexts.

Somatic mutations also have been associated with both developmental anomalies, such as cortical dysplasia, as well as degenerative diseases associated with aging, such as Alzheimer’s. It has also been found that benign growths, such as vascular malformations, have specific somatic mutations that contribute to their formation. Further, other studies have shown that mutations in cells of the adaptive immune system can increase our risk of developing serious infections as we age.

This is just the beginning of our understanding of somatic mutations in human disease. The recent development of single-cell methods to identify these changes more accurately has spurred new interest in the field—we have only just begun to study somatic mutations in tissues and diseases on a large scale.

Discovering new views of cancer genomics

We have known for more than 40 years that somatic mutations contribute to cancer formation, but our understanding mostly comes from studies with tissue-level resolution. With single-cell sequencing tools, we now estimate that while each normal cell contains hundreds of somatic mutations, each cancer cell has from thousands to tens of thousands of these somatic changes. Further, those mutations differ across the tens to hundreds of billions of cells in each tumor.

This finding is transforming our view of cancer. Instead of a tissue with a limited number of genetic changes, we see a tissue composed of dynamic groups of related cells that also each harbor unique genetic changes and continuously co-evolve.

With further studies of cancer genomes in the single-cell context, we will develop a deeper understanding of the biology of cancer formation while providing new insights into clinically relevant outcomes—from high-resolution biomarkers of treatment resistance to new therapeutic targets to overcome that resistance.

Exploring implications of somatic mutation context

An important insight from early studies of somatic mosaicism is that most mutations do not cause human disease. Studies of known cancer genes in skin have found numerous small premalignant clones in each square centimeter of skin. However, the vast majority of those clones will never progress to cancer. This further supports the concept that a specific somatic mutation must occur in a specific cell type to result in the development of a disease.

In the context of human skin, for example, cancer-associated mutations may need to occur in a stem-like cell with the intrinsic capacity for self-renewal and other abilities that are required for cancer formation. This presents an opportunity to study somatic mutations in premalignant tissues to better predict which clones are most likely to transform to cancer so that we can intervene earlier and enable better patient outcomes.

Advancing single-cell solutions

Single-cell sequencing has enabled advancements in the field of somatic genetics over the past decade by providing the most quantifiable and accurate measurements of those mutations. Being able to accurately quantify the number, location, and signatures of somatic mutations in single cells has significant benefits over bulk tissue-based measurements that provide only a list of mutations without the cellular context. Moreover, methods that combine accurate mutation and phenotypic information from the same cells will enable scientists to begin studying how specific somatic mutations interact with that cellular context to produce normal and disease-associated cellular phenotypes.

We recently developed primary template-directed amplification (PTA), a method that can amplify the genomes of single cells much more accurately. It is allowing us to accurately call somatic mutations in almost the entire genome of a single cell for the first time. Furthermore, PTA in combination with whole transcriptome sequencing of the same cell allows us to study single-cell genomes in distinct cellular contexts.

These and other new methods are being supported by a new NIH Consortium, Somatic Mosaicism across Human Tissues (SMaHT), which aims to systematically study somatic mutations in a range of human tissues. These tools will undoubtedly be applied to study human diseases, including premalignant and malignant tissues.

With the accelerated adoption of more accurate single-cell technologies to study our evolving cellular genomes, scientists may soon conclude that, in addition to germline predisposition, somatic mutations are major drivers of human diseases that occur throughout our lives.

Charles Gawad, MD, PhD, is an associate professor of pediatrics at Stanford University. Gawad is also a co-founder of BioSkryb Genomics, where he is a member of the scientific advisory board and the board of directors.

The post Our Continuously Changing Cellular Genomes appeared first on GEN - Genetic Engineering and Biotechnology News.

Picture a world where our genomes are sequenced at birth, genetic errors corrected, and continuous monitoring of vital signs allows for tailored dietary and lifestyle adjustments to preempt diseases. Should illness arise, personalized drugs, precisely crafted to meet an individual’s unique needs, physiology, and phenotypes, would be deployed.

This may sound like a page from a science fiction novel, yet certain hard facts suggest otherwise:

•  The cost of full genome sequencing has plummeted from $2.7 billion in the early 2000s to under $1,000 today,¹ paving the way for genome sequencing to become as routine as standard newborn blood screenings.
•  In vivo base editing via infusion can
  effectively reduce high cholesterol levels,² marking a significant leap toward safe and targeted gene therapy.
•  Despite the ethical controversies surrounding Theranos,³ the concept of regular blood biomarker monitoring persists. Daniel Ek, Spotify’s CEO, launched Neko Health, promising artificial intelligence (AI)-backed full-body scans to aid in the early detection of skin conditions, cancer, cardiovascular diseases, and more.⁴
•  The Personalized Medicine Coalition reports that in 2022, personalized medicines constituted 34% of all new drug approvals by the U.S. Food and Drug Administration.⁵
•  Prominent technology entrepreneurs such as Bryan Johnson are engaging in a “Rejuvenation Olympics,” vying to decelerate their biological aging.⁶

These advancements are intrinsically linked to significant technological progress, from increased computing power to enhanced data storage capabilities, propelling AI to the forefront of today’s reality.

Assessing the clinical progress of AI-designed drugs

AI-designed drugs have already progressed to clinical trials.⁷ Their rapid advancement through discovery and preclinical stages is particularly striking. However, the true test for AI-designed drugs lies in their clinical performance, which, to date, has been underwhelming. In 2022, BenevolentAI’s drug for atopic dermatitis failed to outperform a placebo in a Phase IIa trial,⁸ and Exscientia’s cancer drug candidate EXS-21546 fell short in early clinical phases this year.⁹

We must remember, however, that countless AI-generated drugs in preclinical stages haven’t yet reached clinical testing. Drawing conclusions from a limited set of examples is premature. Traditional drug development methods might have led to similar outcomes, but AI’s advantage lies in identifying potential failures earlier and more cost-effectively.

Reimagining drug R&D with the help of data and technology

For many years, the pharmaceutical sector treated data as expendable, focusing solely on wet lab experiments to answer scientific questions, and often disregarding the data once the query was resolved. Only recently has the industry begun to recognize the value of this data, seeing it as a potential gold mine. However, extracting valuable insights from this computational ore has proven challenging.

There’s a noticeable cultural shift within the industry as companies start to see themselves as data companies driving a transformation that emphasizes the reuse of diverse and historical data to shape predictions and inform decision-making.¹⁰

Digital-native pharmaceutical firms, often referred to as techbio firms, attribute their early successes to several factors, the most crucial being their inception as technology companies without the burden of legacy data or technology. Techbio firms are advantaged because they lack data silos and have data architectures designed to handle massive data volumes and run data-intensive applications.

Starting in silico—but then seeking proof in the wet lab

The conventional drug R&D process, while well established, is fraught with challenges. It typically involves selecting a target in the body, designing a molecule to interact with it, synthesizing the molecule, and then testing its safety and efficacy. This method is notably slow and costly and has a high failure rate. Many compounds that show promise in the lab often fail during clinical trials due to efficacy or safety issues. AI is redefining this process by addressing critical failure points such as helping scientists in selecting the right target and designing safe and effective molecules.¹¹

In target identification, AI is playing an increasingly vital role. The sheer volume of scientific literature is beyond human capacity to fully comprehend, but AI can mine extensive scientific databases. It extracts and analyzes data to predict promising drug targets, uncovering connections that might elude human researchers.

AI is also revolutionizing the design of drug molecules. The largest compound libraries for high-throughput screening contain, at most, around 10 million compounds. This pales in comparison to the estimated 10³³ possible drug-like compounds.¹² Given the complex nature of molecular interactions within the human body, AI’s proficiency in sifting through countless possibilities to identify the most promising molecules is invaluable.¹¹

However, it’s important to recognize that AI is not a panacea. The validation of drug efficacy and safety still relies on traditional experiments and human clinical trials. The role of AI in drug development is not to replace these fundamental steps but to enhance the efficiency and focus of the drug R&D process. Where numerous experiments might have been necessary previously, AI can guide researchers to the most promising experiments, significantly cutting down the time and cost involved.¹¹

Impacting the (dreadful) pharma metrics

Launching a drug is often likened to a Sisyphean undertaking,¹³ yet the potential of AI in accelerating drug R&D makes the significant investments and digital transformation efforts in the pharmaceutical industry seem worthwhile. In an industry where spending over $6.5 billion, failing 95% of the time, and taking an average of 12 years to release a single drug is the norm, the promise of AI is particularly compelling.¹² Initial forays into AI-assisted drug development have shown that it’s possible to cut development time by a factor of 15 and to reduce costs by 70% of what they would be without AI.²⁴

An additional, crucial benefit of AI in drug development is the reduction of animal use in preclinical research. The research community is dedicated to achieving the 3Rs (Replacement, Reduction, and Refinement in the use of animals). Virtual Control Groups have already been shown to potentially reduce animal use by 25% by substituting control group animals with preexisting randomized data sets.¹⁴

Finally, AI’s ability to invent entirely novel small-molecule drugs¹¹ and create new proteins with specific therapeutic properties is nothing short of revolutionary.¹⁵ AI-generated proteins surpass the limitations of 3 billion years of natural evolution, showcasing the boundless potential that generative AI offers drug discovery.¹⁵

Navigating the hype—and reality—of AI in pharma

AI, while transformative, is not a silver bullet but a significant addition to the pharmaceutical industry’s toolkit. It’s crucial to balance hype with realistic expectations and learning from past experiences.

IBM Watson’s healthcare journey exemplifies this cautionary tale. Lauded for its potential to revolutionize oncology care with advanced insights and patient care, the reality fell short. Winning at “Jeopardy!” is a far cry from matching the right patients to clinical trials or predicting cancer progression and treatment outcomes.¹⁶

Novartis faced similar challenges with its ambitious Data42 project. Positioned as a major digital and data initiative, the goal was to mine 2 million patient-years of data for novel drug–disease correlations.¹⁷ However, the project faced scale-backs after substantial investment due to cost-cutting measures and complexities in achieving its vision.^18,19

In contrast, Google DeepMind represents a successful integration of AI into life sciences with AlphaFold. This AI system can accurately predict protein structures, a task traditionally demanding extensive, time-consuming experiments like X-ray protein crystallography. AlphaFold’s ability to predict over 200 million protein structures, using existing data from the Protein Data Bank, has significant implications for structure-based drug design and showcases the potential of AI when applied effectively.^20,21

Charting a future path in AI-driven drug R&D

As we envision the future of medicine, reshaped by data and digital technologies, we’re not just contemplating a shift in methods, we’re foreseeing a transformation that benefits the entire healthcare ecosystem. Pharmaceutical companies will optimize drug R&D processes, reducing risks, costs, and animal testing requirements while accelerating market entry. The critical question now is not whether to adopt data and AI-driven R&D—that’s unequivocally beneficial—but rather how to implement it effectively while navigating the potential pitfalls encountered when transitioning from traditional to digital-first R&D models.

Key steps for organizations to succeed in this transition include:

1. Cultivating a data-centric organizational culture: Overcoming the misconception that AI will replace jobs is crucial. In the pharmaceutical industry, AI augments rather than replaces human expertise. This is also the case in other fields. In pathology, the traditional role of practitioners is expected to be enhanced by AI—even though the best practitioners are sometimes outperformed by the technology.^22,23

2. Adapting business models to the data-driven era: Pharmaceutical companies might need to forge new partnerships for data access or technology acquisition. Contract research organizations should consider evolving their business models to accommodate AI-led programs, potentially shifting from traditional fee structures to milestone-based agreements, thereby reducing engagement risks.^24,25

3. Establishing a circular data economy: Emphasizing the non-disposability of data and advocating for their reuse creates a virtuous cycle. Ensuring that all data adhere to FAIR principles (that is, to findability, accessibility, interoperability, and reusability) is a fundamental part of this approach.

Finally, the potential of generative AI cannot be overlooked. The ability to design novel proteins and structures, for scientists without data science expertise, is poised to revolutionize drug R&D.¹⁵ The challenge lies not just in the technology itself, but in ensuring its widespread adoption and trust within the scientific community. Although this journey is bound to be tortuous, it is well to start with a decisive stride toward a future where AI and human intelligence synergize to redefine healthcare.

Thibault Géoui, PhD, is head of science analytics at Charles River Laboratories.

References

1. Jennings K. How Human Genome Sequencing Went From $1 Billion A Pop To Under $1,000. Forbes. October 28, 2020.
2. Kaiser J. Base editing, a new form of gene therapy, sharply lowers bad cholesterol in clinical trial. Science. November 12, 2023.
3. Carreyrou J. Bad Blood: Secrets and Lies in a Silicon Valley Startup. Picador; 2018.
4. Sawers P. Daniel Ek’s Neko Health raises $65M for preventative healthcare through full-body scans. TechCrunch. July 5, 2023.
5. Personalized Medicine Coalition. A New Vision for Health.
6. Kim W. Inside the very strange, very expensive race to “de-age”. Vox. September 26, 2023.
7. Arnold C. Inside the nascent industry of AI-designed drugs. Nat. Med. 2023; 29: 1292–1295.
8. Matsuyama K. (2023, Nov 13). Race for First Drug Discovered by AI Nears Key Milestone. Bloomberg. November 13, 2023.
9. Armstrong A. AI drug hunter Exscientia chops down ‘rapidly emerging pipeline’ to focus on 2 main oncology programs. Fierce Biotech. October 3, 2023.
10. Ferrero E, Brachat S, Jenkins JL, et al. Ten simple rules to power drug discovery with data science. PLoS Comput. Biol. 2020; 16(8): e1008126. DOI: 10.1371/journal.pcbi.1008126.
11. Heaven WD. AI is dreaming up drugs that no one has ever seen. Now we’ve got to see if they work. MIT Technology Review. February 15, 2023.
12. Polishchuk PG, Madzhidov TI, Varnek A. Estimation of the size of drug-like chemical space based on GDB-17 data. J. Comput. Aided Mol. Des. 2013; 27: 675–679. DOI: 10.1007/s10822-013-9672-4.
13. Schuhmacher A, Hinder M, von Stegmann Und Stein A, et al. Analysis of pharma R&D productivity—a new perspective needed. Drug Discov. Today 2023; 28(10): 103726. DOI: 10.1016/j.drudis.2023.103726.
14. Steger-Hartmann T, Kreuchwig A, Vaas L, et al. Introducing the concept of virtual control groups into preclinical toxicology testing. ALTEX 2020; 37(3): 343–349. DOI: 10.14573/altex.2001311.
15. Ingraham JB, Baranov M, Costello Z, et al. Illuminating protein space with a programmable generative model. Nature 2023; 623: 1070–1078. DOI: 10.1038/s41586-023-06728-8.
16. O’Leary L. How IBM’s Watson Went from the Future of Health Care to Sold Off for Parts. Slate. January 31, 2022.
17. Mijuk G. The data42 program shows Novartis’ intent to go big on data and digital. Novartis. March 11, 2020.
18. Alich H. Novartis is cutting back on its AI project to save money. Handelszeitung. August 7, 2023.
19. Michel, S. Novartis scales back AI project Data42.
20. Silver D, Huang A, Maddison C, et al. Mastering the game of Go with deep neural networks and tree search. Nature 2016; 529: 484–489. DOI: 10.1038/nature16961.
21. Heaven WD. This is the reason Demis Hassabis started DeepMind. MIT Technology Review. February 23, 2022.
22. Miller F. Will AI take my job? The Times. November 8, 2023.
23. 23.Baxi V, Edwards R, Montalto M, et al. Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod. Pathol. 2022; 35: 23–32. DOI: 10.1038/s41379-021-00919-2.

24. Goodier C. Accelerating drug discovery & development. Deloitte. https://www2.deloitte.com/uk/en/pages/deloitte-analytics/articles/accelerating-drug-discovery-and-development.html
25. Logica.

The post Synthetic Dreams: Data and AI Catalyze Drug Innovation appeared first on GEN - Genetic Engineering and Biotechnology News.

GEN: What are some of the larger trends you are predicting for the cell and gene therapy industry for 2024?

Glover: There is a great deal of excitement in the gene therapy industry. We are seeing a rise in gene editing therapeutics with the approval of the world’s first CRISPR-based gene editing therapy. (The therapy, Casgevy, aims to cure sickle cell disease and transfusion-dependent b-thalassemia.) Despite this and other recent scientific advancements, there continue to be challenges around cost and access. We must develop improved manufacturing practices for viral vector–based products if we want to help reduce the costs of gene therapies. The industry also needs to work with payers to improve access and distribution of these novel therapeutics.

In early 2023, Peter Marks, MD, head of the FDA’s Center for Biologics Evaluation and Research (CBER) outlined a four-point plan to help safely accelerate approval processes. One way is to use new research efforts to improve manufacturing. As an industry, we must collaborate with academic researchers, early-stage firms, and commercial-stage biotechnology companies to ensure we are developing the right technologies and solutions that will accelerate the development, approval, and adoption of these life-changing therapies.

Westberg: Over the last several years, some autologous chimeric antigen receptor (CAR) T-cell therapies have moved from third-line treatments to second-line treatments. It’s further proof of the science and benefits of the therapeutics. We will continue seeing increased levels of scrutiny and reviews of long-term data which are all part of the standard regulatory review process. The push to develop allogeneic cell therapies and cell therapies for the treatment of solid tumors will continue in 2024 and beyond. Simultaneously, we must continue working toward standardizing and industrializing the manufacturing process. There is a lot of work to be done, but the solutions and technologies that are being researched and developed have the potential to transform global health.

GEN: What are the greatest challenges facing your customers?

Westberg: A key challenge for the entire cell therapy industry is scale-up and industrialization of processes. While automating processes that are overly dependent on manual labor is an important part of this, scaleup requires bespoke equipment, software, and reagents. Cell therapies are highly complex therapeutics with an equally complex manufacturing process. Standardizing and industrializing processes will likely help alleviate some of the pressure around cost and access.

Glover: The field must also continue working to understand the body’s response to in vivo viral vectors. Over the last several years, there have been several adverse events. We are starting to better understand how the body reacts and how we modify treatment regimens to achieve better outcomes, but there is still quite a bit of work to be done.

GEN: What is the greatest opportunity for cell and gene therapy drug developers?

Westberg: For cell therapies, there are three big opportunities in the near future. The first is for approved therapies to be prescribed earlier in the treatment plan, the second is to develop a CAR T-cell therapy for solid tumor cancers, and the third is to develop an allogeneic “off the shelf” therapy. Much work is being done globally for the development of a solid tumor therapy and for an allogeneic therapy. We are hopeful that the research being done today will lead to approved therapies in the next few years. In the meantime, we must continue working to automate processes so that therapies can be manufactured at scale.

Glover: Gene therapies have enormous potential to revolutionize global healthcare. For the first time, we are able to treat the cause of the disease and not the symptoms. Upgrading improved manufacturing techniques that will enable drug developers to commercially manufacture these therapies is an enormous challenge and opportunity. To do that successfully, we must leverage the many years of knowledge gained from manufacturing monoclonal antibodies and recombinant proteins. There are many lessons that can be applied. One area that we are very focused on at Cytiva is improving the biology with stable cell lines and high-density cell culture for adeno-associated virus manufacturing.

GEN: Historically, the biopharma industry has been slow to adopt digital solutions. How will greater adoption of automation and digital solutions impact the larger cell and gene therapy industry?

Glover: Automation is effective only if you have a well-defined and -characterized process. This is where the focus should be for gene therapy. Once we have done that, many automation solutions already developed for monoclonal antibody processes can be used.

Making sure the process is stable and robust requires stable cell lines and high-density cell culture. You can’t have a robust manufacturing process without robust materials supporting that process.

Westberg: Increased adoption of automation and digital solutions is necessary to accelerate the manufacture and adoption of cell therapies. It comes back to automating parts of the workflow that are overly dependent on manual labor and having bespoke equipment, software, and reagents. It requires collaboration across the entire ecosystem. No one company or academic institution is going to solve this problem. We must work together to develop the tools and technologies that will accelerate the industry.

GEN: What lessons from the pandemic can be applied to the cell and gene  therapy industry?

Glover: We learned that when the scientific community, governments, academic researchers, and the life sciences industry come together we can solve problems faster. We know advancing and accelerating the development of cell and gene therapies is going to require more regulatory flexibility. We saw how well that worked with the development of COVID-19 vaccines, both mRNA- and viral vector–based vaccines. Now, the industry is building out strong mRNA capabilities, and there are many clinical trials in development for mRNA-based therapies.

However, despite this success during the COVID-19 pandemic, the industry is less resilient than it was two years ago. According to Cytiva’s Biopharma Resilience Index, a survey of more than 1,200 biopharma and pharma executives across 22 countries, collaboration has fallen in the last two years. The three most challenging R&D partners to find are 1) patient group associations, 2) companies from other industries, and 3) other pharma/biopharma companies.

Additionally, more than half of respondents believe regulatory agencies are not good at ensuring availability of specialized pathways for cell and gene therapies. If we truly want to move the industry forward and deliver for patients who need medicines the most, we must collaborate and be more flexible when it comes to regulatory pathways.

GEN: How can the industry better harness the power of collaboration?

Westberg: We know that one organization or company can’t do it alone. We must rely on the greater power of the industry itself. One example involving allogeneic cell therapies is the Cytiva–Bayer collaboration. By leveraging Cytiva’s expertise in developing tools and technologies for the manufacture of therapies, and Bayer’s deep capabilities in drug development, we are working to create a modular end-to-end manufacturing platform for allogeneic cell therapies. With the science moving so quickly, we are working to make sure that the manufacturing processes keep pace with innovation.

Another great example of collaboration was with AstraZeneca, Oxford University, and Pall Life Sciences. (Pall is now part of Cytiva.) Oxford University and AstraZeneca relied on the expertise of Pall to help develop a scalable manufacturing process in just eight weeks instead of five years. When the organizations delivered an effective COVID-19 vaccine at record speed, it was a real testament to their ability to harness their respective strengths.

As I think ahead to 2024 and beyond, I believe we will need to see more industry collaborations to truly move the cell and gene therapy industry forward.

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