Combining RNA Splicing and AI Technologies to Accelerate Drug Discovery and Development

Combining RNA Splicing and AI Technologies to Accelerate Drug Discovery and Development

April 12, 2023PAO-02-23-CL-08

Envisagenics is a techbio company that uses machine learning and advanced AI for the rapid discovery and validation of next-generation drug targets based on RNA splicing errors, which are associated with almost 400 diseases. 

Building Upon Successful Proof of Concept in Spinraza

Envisagenics focuses on oncology, neurodegenerative, and metabolic disorders. We have developed a proprietary target discovery platform to look for RNA-splicing-derived drug targets for antisense oligonucleotides (ASOs) and immunotherapeutics (including antibodies and cell-based therapies). 

The company was spun out of the lab of Adrian Krainer, Ph.D., at Cold Spring Harbor Laboratory, which studies mechanisms of RNA splicing. The lab was involved in the development of a therapeutic for spinal muscular atrophy (SMA), a neuromuscular disorder that is the leading genetic cause of infant deaths. This drug later became Spinraza — developed in collaboration with Ionis and Biogen — the first therapeutic approved by the FDA based on modulating RNA splicing.

The success of Spinraza and the impact that it had on so many children’s lives inspired us to found Envisagenics in 2014. We saw the promise of RNA splicing therapeutics and knew we could leverage AI and machine learning to automate, accelerate, and significantly improve the manual process of drug target discovery across multitudes of indications.

Building a Platform that Improves over Time

Envisagenics is at the forefront of a new era in biopharma — the emerging intersection of advanced AI and RNA splicing-based therapeutics. Through the years, Envisagenics has analyzed thousands of RNA-sequencing data sets to identify and validate quality assets into their discovery pipeline. Their AI-driven platform SpliceCore® continues to become more robust over time as it processes additional sequencing and experimental data. As progress was made with these in silico identified targets, Envisagenics has built out their own laboratory space to translate in silico findings into validated drug target candidates.

Envisagenics is one of the founding members of The Alliance for Artificial Intelligence in Healthcare (AAIH), a coalition of technologists, pharmaceutical companies, and research organizations that have a shared goal of realizing the full potential of AI and machine learning in healthcare. Since their inception, Envisagenics has remained on the cutting edge of AI drug discovery, executing multiple partnerships with biopharma companies that revolve around the drug targets discovered by SpliceCore platform.

The Importance of RNA Splicing

mRNAs are not consecutive coding sequences; they include coding segments (exons) interrupted frequently by noncoding segments (introns). Splicing is an RNA-processing step that removes the introns and connects the coding exons together to generate mature mRNAs. Without robust and accurate splicing, the production of mature mRNA molecules breaks down. 

Splicing is regulated by the spliceosome, a large, dynamic complex comprising over 300 proteins. As the biggest complexes in cells, spliceosomes often fail due to mutations and differences in gene expression. These splicing alterations often lead to splicing errors that can affect many mRNAs throughout the cell, some of which cause diseases. To date, approximately 400 diseases with diverse pathologies have been connected to splicing errors. 

Connecting Splicing Errors to Diseases

Cancers are often associated with spliceosomal failure and splicing errors in general. Many hematopoietic tumors have mutations in the core spliceosome proteins responsible for catalytic activity (cut-and-paste RNA). Solid tumors (breast, lung, colorectal cancer) tend to have altered expression of core, as well as ancillary, splicing proteins. The altered state of the proteins disrupts the splicing activity, creating a vulnerability for discovering novel targets and therapeutic development. Likewise, similar widespread misregulation in RNA processing underlies neurodegenerative and metabolic diseases. Aberrant splicing-derived transcripts in many cases drive disease progression and likely also contribute to the pathophysiology of the diseases.

Many RNA splicing diseases have been identified lately and, until recently, there was no easy way to profile the spliceosome. With mRNA sequencing technology, it is now possible to quickly focus on the spliceosome and look for alterations. As a result, in the last 10-15 years, more evidence of splicing errors has emerged. 

Given that the splicing-based therapeutics sector is still emerging, there are relatively few examples of approved therapies. Spinraza provided a compelling proof of concept and roadmap for further work, including treatments for Duchenne muscular dystrophy (DMD) and other neurodegenerative diseases. More candidates are progressing through clinical trials, many of which are related to neuromuscular disease. In oncology, there are several therapies under development that target the spliceosome itself with the intention of correcting splicing errors, with most currently at the preclinical stage and a few in clinical stages.

Genomic versus Splicing Approaches for Immunotherapy Target Identification

The current approach to identifying immunotherapy targets involves leveraging genomics data. A DNA sequence is evaluated, and variants that potentially encode neoantigens are identified. One of the biggest limitations to this strategy is that not every tumor exhibits multiple mutations that can be leveraged. Breast cancer, prostate cancer, and certain types of leukemia have low tumor mutational burdens, so it can be difficult to identify targetable neoantigens. The tumor types that have low mutation burdens are often rich in splicing errors, and a splicing approach can fill in the gaps and aid in the discovery of novel neoantigens.

If a neoantigen is identified through DNA analysis, it is still necessary to confirm the expression for which those genes are responsible. A mutation in the DNA sequence does not necessarily lead to a real effect on gene expression. Neoantigens found on mRNA are, by definition, expressed at the mRNA level. Therefore, identifying them by using mRNA rather than DNA can save time.

Using AI to Predict Drug Targets

A predictive ensemble approach involves bringing together several algorithms in a voting system. Each unique algorithm trains a different model with different data to answer the same question and proposes the best answer. In the case of Envisagenics’ technology, the goal is to identify optimal drug targets.

Developing bespoke algorithms with very specific questions and training them with adequate data facilitates interpretation of the results. Understanding the reasons behind the prediction is also easier, which is critical in drug discovery. Drug discovery is risky, expensive, and time-consuming, and it is necessary to have a narrative to support the pursuit of drug targets proposed using AI.

The SpliceCore platform from Envisagenics uses RNA sequencing data and outputs drug target candidates for specific modalities. Different modalities have different requirements, so it is often not appropriate to use the same set of algorithms for the identification of any target. For example, antisense therapies must affect splicing regulation, while immunotherapy targets must be expressed on the membrane. 

Regardless of the modality, the incoming data are used to reconstruct the transcriptome, leveraging an exon-centric approach, focusing on smaller exons that contain all of the information needed for target discovery. Fractions of the transcriptome are created first, which allows the software to consider approximately 7,000,000 potential splicing events when constructing the transcriptome, rather than only about 30,000 genes. Consequently, the search base is much larger and enables the identification of pathogenic splicing errors.

In the next stage, a predictive ensemble is employed to seek optimal drug targets. The different algorithms look at different definitions, such as expression in a certain manner, protein stability, localization, antibody accessibility, regulator blocking, and so on. The platform synthesizes all of this information and generates a list of targets that are optimal for the greatest number of individual algorithms. No target is optimal for all predictors, because not all targets are optimally predicted in the same manner.

From the generated list, a pool of targets is selected to take into the lab for validation. Generally, the pool comprises a diverse set of targets to create resilience. Rather than using a funnel approach where a large set of potential targets is continuously narrowed down, Envisagenics uses many different approaches, because algorithms can also test perfectly on paper but fail in practice. The more algorithms included in the ensemble, the better. 

Overall, the platform interrogates approximately 3.5 million hypotheses per hour. This approach is compelling and unique. Ultimately, however, target validation is what matters. In the lab, Envisagenics works to show that these novel targets exist — that they are indeed being expressed — and that they behave as expected in terms of subcellular localization and mechanism, heterogeneity, and other relevant factors.

Continuously Innovating with a Focus on Talent and Science

Envisagenics’ success can be largely attributed to the mission-driven team that has been assembled. Hiring efforts focus first on talent and science. As a result, the team is highly diverse, both professionally and culturally. Everyone is passionate about developing splicing-based therapeutics for patients using AI — but everyone has different ideas and a unique vision of how to get there. By establishing an environment where people are encouraged to voice their opinions, we ensure that everyone is heard, and the myriad talents are fully leveraged. With shared values guided by our mission to help patients in need, we can inspire insight-generating discussions that result in innovative ideas and solutions. 

One of those shared missions is constant innovation. The team at Envisagenics is constantly thinking about how to integrate new algorithms and new data types into the platform. The data science team in particular focuses on creating new ways to identify drug targets, efforts that are shaped by the evolution of data types, developments by other groups reported in the literature, and general technological advances, such as new cloud-computing technology. 

Envisagenics’ Partnerships

Partnerships are a core part of the Envisagenics business model. In general, each research collaboration focuses on a specific disease or disease subtype. Determining which disease indication the company should focus on requires synthesizing many factors. The need in the marketplace is one consideration, as is the feasibility of the disease with respect to the potential for identifying splicing-derived targets, and the ability to validate identified targets in the lab. 

It is equally important to assess the availability of data. The ideal scenario will involve thousands of patients and five or six independent cohorts to evaluate reproducibility and other attributes. Within Envisagenics, a group is dedicated to searching for data and creating separate collaborations based on data. One example is a current collaboration with Cancer Research Horizons, in which Envisagenics is performing research using their patient data.

For biopharma partners tackling relevant disease indications, meanwhile, Envisagenics identifies optimal candidates, and the partner uses its expertise in drug development to take the assets through the clinic. Over the years, the company has worked with a range of biopharma companies, such as Johnson & Johnson and Biogen. Most recently (November 2022), a collaboration was established with Bristol Myers Squibb to leverage Envisagenics’ SpliceCore AI platform for accelerated discovery and development of oncology splicing-derived targets for therapeutic development to expand BMS’ oncology pipeline.

Over the near and intermediate terms, Envisagenics will pursue additional partnerships with biopharma companies in which the SpliceCore platform is leveraged to identify novel targets in combination with the biopharma partner’s expertise to successfully develop therapeutic candidates. In the future, the company intends to advance its internal assets as far as possible. 

Bigger Role for Data and AI Beyond mRNA Splicing

Envisagenics’ expertise lies in leveraging AI to analyze mRNA splicing. The role of big data and AI in biopharma is the next frontier of scientific advancement. Society today is data-driven and, given that biopharma is a place where smart scientists and technology coincide, innovation in the field cannot take place without leveraging the latest developments in big data and AI. 

Many companies, Envisagenics among them, will be looking to eliminate inefficiencies and bottlenecks to speed up drug development. Some will focus on target discovery, others on drug design, and yet others on improving clinical trials. Some of these companies will grow and scale into full-blown biopharmaceutical firms that happen to integrate AI into their processes.

Envisagenics is motivated and inspired by the promise that advanced AI technologies. Using these technologies, we can streamline and optimize the drug discovery process, reducing the cost of medicines, ensuring that treatments are readily available to patients in need. These goals are already in our reach.

Envisagenics secured Series A financing in September 2021, which was led by Red Cell Partners, and which included follow-on investments from Dynamk Capital and investors from Envisagenics’ seed rounds, including Microsoft’s M12, Madrona Venture Group, Third Kind Venture Capital, and Empire State Development’s venture capital arm, New York Ventures. Envisagenics’ investors understand and uphold the company’s goals and have the vision needed to support the development of therapeutics through the combination of classic research and AI.

chat button