Asimov

The F.D.A. recently approved Aucatzyl, a CD19 CAR-T therapy for B-ALL leukemia in adults. It joins a handful of other approved CAR-T therapies designed to treat various forms of cancers, including multiple myeloma. But it’s worth looking at Aucatzyl, in particular, because it has some unique aspects… But first: CAR-T therapy stands for chimeric antigen receptor T cell therapy. It’s a form of immunotherapy that modifies a patient's own T cells (a type of white blood cell) to detect and attack cancer cells. How it works, basically, is that researchers take T cells from a patient, engineer them to express receptors specific to cancer cell surface proteins, and then reinfuse those cells into the patient’s bloodstream. Once inside the body, the engineered T cells grab onto cancer cells and destroy them using cytotoxic mechanisms (like punching holes in the cancer cell membrane using perforins). In the case of B-ALL, the T cell receptor that gets expressed binds CD19 — a protein commonly found on the surface of B cells, including cancerous ones. There are existing CAR-T therapies targeting this protein — including Tecartus — but Aucatzyl’s approach is distinct. Like many other cell therapies, Aucatzyl uses a lentiviral vector to deliver a CAR-encoding gene into a patient’s T cells. But the CAR protein that it carries was designed to have a high dissociation rate. In other words, when a T cell engineered to express this “special” CAR grabs onto a cancer cell,? it delivers its cytotoxic payload and then disengages from the B cell more rapidly than other immunotherapies. This rapid release allows the T cell to move on and attack multiple cancer cells in succession — a phenomenon known as "serial killing” — which enhances its tumor-killing efficiency. In a clinical trial with 65 patients, 42% achieved complete remission within three months of receiving Aucatzyl, with a median remission duration of 14.1 months. Severe cases of cytokine release syndrome — a common and potentially dangerous side effect of CAR-T therapies — were reported in just 3% of patients, which is lower than other therapies. Unfortunately, all cell therapies are complex and expensive to manufacture. If we want these therapies to reach more patients, we’ll need to find ways to slash costs and make them at larger scales. At Asimov, we’re building a platform, called LV Edge, that combines wet-lab and computational tools to make lentivirus manufacturing and payload design much easier — and cell therapies cheaper. Learn more: https://lnkd.in/eiJKeQPH

We built a codon optimizer that boosts expression of clinically-relevant transgenes up to 7x. We’ve compared our algorithm to five other codon optimizers available on the market, and results suggest that our algorithm is consistently superior across genes of interest. But what is a codon optimizer? And why does any of this matter? Generally, a codon optimizer is a software tool that alters the DNA or RNA sequence of a gene by adjusting its codons — the three-nucleotide units encoding amino acids — to match the preferred codon usage in a specific organism. Consider, for example, the amino acid leucine. This amino acid can be encoded by several different codons (including CTT, CTC, and TTA), so codon optimization tools begin by swapping out unpopular codons and replacing them with more widely-used variants. If scientists want to take a gene from, say, a plant, and insert it into a human cell, then they’d use codon optimization to adjust the plant gene’s codons to match the human cell’s preferences, thus enhancing protein translation and boosting expression. In other words, codon optimization makes it easier for a cell to ‘read’ a gene and convert it into proteins. But this is, admittedly, a simplistic explanation. Modern codon optimizers do a lot more than just swap out “unpopular” codons. Some algorithms also check mRNA folding patterns to make sure the gene, once transcribed, won’t fold into weird structures that impede translation. Our codon optimizer does all of these things and more. Our algorithm accounts for the entire lifecycle of a gene. When designing an AAV vector, for example, it considers not only “unpopular” codons, but also ensures the gene will be faithfully packaged into the vector, its mRNA is not prone to rapid degradation, and that it will effectively utilize the host cell’s gene expression machinery. It’s not easy to select sequences that are compatible with all these bottlenecks, so we augment our optimizer with in-house knowledge of AAV biology. To test out our codon optimizer, we did an experiment. Briefly, we took two clinically-relevant payloads — Luxturna and Zolgensma — and tagged of them with a fluorescent reporter protein. These sequences were either altered using our codon optimizer, or left intact. We packaged these payloads into AAVs and then transduced HEK293T cells with them. Finally, we studied protein expression levels using both microscopy and flow cytometry. The data are shown below (cells “glowing green” is a good thing, as is shifting purple peaks to the right.) We are still validating this tool across more conditions, but all of our data so far suggests that these results are transferable across different cell lines and different cell types (HEK293, HEK293T, T-cells). This codon optimizer is part of our AAV Edge platform. Learn more: https://lnkd.in/ePA3mg5s

At least 350+ lentiviral-based gene therapies are in clinical trials. Several have already garnered F.D.A. approval, including a gene therapy for beta-thalassemia, multiple therapies for blood cancers, and another for a rare genetic disease — called CALD — that affects brain development in young boys. But what are lentiviruses, and where did they come from? Lentiviruses carry RNA genomes, which are reverse transcribed into DNA and then integrated into a host’s genome after infection. They’re widely used for gene therapies because they are really good at transferring genetic material into cells, and because they can transduce nonproliferating, or slowly proliferating, cells. Lentiviruses can also carry about 10,000 nucleotides of genetic information, more than twice as much as AAV. This means that larger genes, or payloads, can be inserted into cells. Lentiviruses have only been used clinically for a couple decades, but they have ancient origins. The term “lentivirus” itself derives from the Greek word for lenti-, meaning “slow,” because they were first discovered circulating as “a slowly progressive disorder” amongst sheep flocks in Iceland in the 1930s. A long period of time, ranging from several months to years, often elapses between the initial infection of lentivirus and the onset of a disease. The first detailed description of a lentivirus-based disease was recorded as early as 1843, in France, when researchers noticed a form of infectious anemia in horses. But although lentiviruses are ancient — at least 7 million years old — most of the progress in adapting them to cure diseases has happened in the last two decades. The first lentivirus used to make a cell therapy was actually derived from HIV, and it was tested in a clinical trial for the first time in 2003, with a total of 65 patients. In that trial, the lentivirus was used to introduce a ‘genetic payload’ into T-cells to treat — of all things — an HIV infection. None of the patients had an adverse event as a result of the lentivirus, even after 8 years of monitoring. At Asimov, we’re building a platform, called LV Edge, that combines wet-lab and computational tools to make lentivirus manufacturing and payload design much easier. https://lnkd.in/eiJKeQPH

Today we announced that RevOpsis Therapeutics is licensing our CHO Edge platform to develop and commercialize their first-in-class modular, trispecific biologic molecule for retinal vascular diseases. We’re really excited about this. CHO Edge is an integrated system that enables companies to optimize antibody expression, typically achieving titers of 7-11 g/L. It includes four modules: GMP-banked cell lines, a hyperactive transposase, a library of over 1,000 characterized genetic parts for vector design, and advanced AI and biophysics models. We’re so confident in CHO Edge that if you work with us on cell line development, we guarantee a titer of at least 5 g/L for mAbs, or it’s free. https://lnkd.in/ecrmbRKa

We're always interested in the history of the tools we work with. And few tools are more ubiquitous in the biopharma industry than CHO cells, which make ~70 percent of all F.D.A. approved biologics sold on the market. The history of these cells are particularly fascinating, as it involves both a smuggler and the Chinese Civil War. The story goes like this. In 1948, as China's civil war entered its climax, a truck navigated the perilous roads from Peking to Nanking. Inside, a nondescript crate held twenty Chinese hamsters — ten males and ten females — each nestled in its own wood-shaving-lined compartment. The hamsters were a gift from Dr. H.C. Hu, a Chinese physician, to Dr. Robert Briggs Watson, an American doctor working for the Rockefeller Foundation. Watson was retrieving the hamsters for his friend Victor Schwentker, a rodent breeder in upstate New York. Chinese scientists had been studying these hamsters, native to northern China and Mongolia, since at least the 1910s. The hamsters have short gestation periods and natural resistance to human viruses — traits that make them ideal for scientific research. Schwentker wanted to get his hands on some. But with Mao's communist forces advancing, he knew that acquiring these animals would soon become impossible. On December 6, the hamsters arrived at Watson's doorstep in Nanking, a city on the verge of evacuation. The Yangtze River was all that separated the capital from Mao's forces. Despite suffering from dysentery, Watson was preparing to flee. Against the counsel of both his Chinese colleagues and the American Embassy, he loaded the hamsters into a station wagon and drove eleven hours east to Shanghai. The hamsters left China aboard one of the last Pan-Am flights. Watson was later accused of "war crimes" by Mao’s Chinese Germ Warfare Commission and tried in absentia for allegedly conspiring with Chinese nationalists to carry out a biological attack. Dr. Hu faced similar charges and was sent to a detention camp for six months. Upon arrival in San Francisco, the hamsters were shipped to Schwentker in New York. Schwentker was able to domesticate and breed the hamsters, establishing the first colony outside of China by 1950. In 1957, a geneticist named Theodore Puck began seeking robust mammalian cell lines for genetic research. He obtained a single adult female Chinese hamster, extracted an ovary cell, and cultivated it in vitro, thus creating the first CHO cell line. Puck’s cells were both resilient and easy to work with. They grew quickly and could be maintained indefinitely, which was a big improvement for researchers struggling with short-lived mammalian cell cultures. And these are the contours of how CHO became such a dominant cell line for the pharmaceutical industry. We learned about this tale via an excellent 2015 article, called “A Brief History of CHO Cells.” Highly recommend. https://lnkd.in/exjVqS55

What does Asimov do? A brief response. We create tools and software to more reliably engineer cells, thus bolstering humanity’s ability to design living systems and enabling biotechnologies with outsized societal benefit. Biological organisms are typically engineered, today, using iterative trial-and-error. Our goal is to push biotechnology into a true engineering discipline, where experimental outcomes are predictable ahead of time. Our work centers around a concept called genetic design. This as the process of intentionally modifying an organism's DNA using advanced techniques such as characterized parts, modeling, and multi-omics analysis. Genetic design is distinct from traditional genetic engineering in that it focuses on forward design driven by biophysical understanding and model-guided predictions. One of the ways we’re using genetic design is by engineering mammalian cells to make medicines. In other words, we’re making bio-tools, such as expression platforms and engineered cell lines, as well as software tools, including metabolic simulators, codon optimizers and signal peptide predictors, to help our customers engineer cells to make antibodies, AAV, and lentivirus. Let's use antibodies, a type of protein used to make many of the most popular medicines (including Humira for rheumatoid arthritis and Keytruda for cancer), as an example. Many pharmaceutical companies make antibodies using Chinese Hamster Ovary cells, or CHO, but the problem is that this process is unpredictable and has to be tweaked for every new antibody. We use our wet-lab and software tools to optimize and engineer these cells, thus coaxing them to make greater amounts of antibodies with fast timelines and good quality attributes. We also have product offerings for lentivirus and AAV manufacturing. Our basic “tech stack” is the same, regardless of application. We have teams working on optimizing cells and production processes for individual molecules and other teams building computational models — based on biophysical insights or transformer-based AI models — to predict aspects of how an engineered cell will behave before we make it. Another team collects large amounts of data to measure the function of myriad biological processes, and then works with computational biology teams to improve their models. We strive to make every experiment into a data point so that nothing goes to waste. Many of the models and tools we develop for various products are also bundled together and made available through Kernel, our browser-based software for genetic design. You can think of it as the “hub” or “vault” for all of the tools we’re building. So that’s the gist. We make wet-lab and computational tools to engineer cells in more predictable ways. While engineering those cells, we collect a large amount of data and build models to understand how they work. This research, in turn, bolsters Kernel and makes it easier for everyone to design biology. https://www.asimov.com/

Our AAV Edge platform is an AI-powered suite of tools for gene therapy design and manufacturing. Come learn more about it on November 12 at the Genetic Engineering & Biotechnology News webinar! The webinar will cover a lot of issues that may be of interest to those in the gene therapy space, including: - Our in-house transformer-based model to design tissue-specific promoters, resulting in a >200-fold dynamic range in expression between on-target vs. off-target tissues. - A sequence optimization algorithm that boosts expression for clinically relevant transgenes up to 7-fold. - How silencing of transgene expression in the production cell line can reduce cellular stress and toxicity during the manufacturing process. - How our platform achieves unconcentrated titers of up to 1E12 viral genomes per milliliter (vg/mL) across multiple serotypes. Sign up here (it’s free): https://lnkd.in/eGgxUF8K

We're really excited about this codon optimization tool that we built in-house, which boosts clinical transgene expression up to 7x. These microscopy images were made by transducing HEK293T cells with AAVs containing 'normal' or 'optimized' gene sequences.

New blog! This one is about the synthetic biology + computational tools we're building to improve gene therapy manufacturing. ? Our newest launch, called AAV Edge, includes payload design tools, such as tissue-specific promoters?that are only active in specific tissues with minimal off-targeting to the liver (>200-fold increase in expression target vs. off-target tissue), a sequence optimization algorithm that boosts clinical transgene expression up to 7x, and an RNA interference-based circuit to silence the payload during manufacturing to reduce cellular stress and toxicity, thus leading to higher-quality AAVs. ? It also includes manufacturing tools, such as a clonal, suspension-adapted HEK293 cell line, an optimized two-plasmid system that makes it easier to tune the expression of various AAV components, and?data-driven, statistical, and mechanistic models to understand and optimize AAV production across scales and bioreactor configurations. ? Learn more about how this works and why we think it matters in the blog. We'll have a lot more to say about the experiments behind each of these tools in forthcoming articles, too! https://lnkd.in/e_Pbwrvg

This schematic shows how lentiviruses are made in the lab. We made it for a blog, but perhaps it'll also be useful for students and educators. Also, our LV Edge system uses stable cell lines and software design tools to achieve lentiviral titers of 1E9 TU/mL for therapeutic transgenes! Blog: https://lnkd.in/e5CnS6fy