May 5, 2022 PAO-04-022-CL-14
10 years at the University of Pennsylvania, where the core science was developed. In 2015, we decided to take the company out of the university and transfer the technology into Anima's R&D center to build our drug discovery platform. At that time, mRNA was not yet such a hot area and was rather viewed as an area with promise that had never been delivered.
tRNA molecules are labeled with red and green fluorescent colors and are transfected into the cells. From time to time, a such a pair of labeled tRNAs end up sitting in a ribosome, and then a light pulse is generated due to a quantum physics phenomena called FRET, which stands for fluorescence resonance energy transfer, where energy jumps from one fluorescent tRNA to another if they are close enough, as happens in the ribosome. Modifications of this idea enabled us to develop technology to monitor the mRNA translation for individual proteins by selecting a unique pair of tRNAs that is “preferred” by the protein as it repeats in high frequency as the protein is made by ribosomes. With where, when, and how much of different proteins are made in the cell in real time, essentially visualizing the most fundamental process of life.
mRNA Lightning platform hundreds of thousands of molecules in a high-throughput manner, discovering compounds that affect the light. A compound that increases the light is increasing the translation of the protein by ribosomes, while one that decreases the light is decreasing it. Such compounds are “hits” discovered by the screening system that have to be further analyzed for their activity and selectivity.
The system generates approximately 50 million images per screen, essentially a huge database with information on how all these small molecules impact mRNA translation. Coupled to this screening system is a high-powered, cloud-based image analysis software with artificial intelligence (AI) that analyzes the images and identifies compounds that have an impact on mRNA translation. The software is also designed to determine the most likely mechanism of action (MOA) of the compound, which is historically a big challenge with phenotypic screening approaches. They key idea behind this somewhat similar to how facial recognition is performed by software. Compounds with a similar mechanism of action would result in “similar” images, so a system can essentially be trained to recognize those patterns. When seeing the resulting images for a particular compound, it calculates its similarity to the images of all previously seen MOAs, a process that Anima calls “MOA Fingerprinting.” It then recommends those MOAs for further investigation through a specific set of biology experiments.
many diseases, the targets are very elusive and not well understood.
That is certainly true in the mRNA space. Applying target-based approach, it is possible to focus on the mRNA molecule itself or the proteins involved in regulating mRNA biology through various MOAs. These mechanisms could be modifications of the mRNA molecule, proteins binding to it (RNA-binding proteins; RBPs), splicing factors, or proteins affecting its relocalization or controlling the availability of tRNA to the ribosomes, among many other possibilities –– none of which, interestingly, have yet been validated to have a therapeutic effect.
A target-based approach against the mRNA molecule itself involves designing molecules that bind to mRNA. Quite a few companies are trying this method. However, even if a small molecule directly binds to the mRNA, it is not known what effect that will have. There is no clear validation of the effect of interference with mRNA in that manner for most diseases. Importantly, even if one could find such a compound, selectivity and safety would be potential issues if the mRNA is expressed in multiple tissues. In such a case, the molecule would have undesirable systemic side effects.
Because of this problem, over the last few years, more companies have migrated to targeting different MOAs, with some focused on splicing factors or RBPs, although, as said, these mechanisms have yet to be validated. It is like applying a target-based approach to something that is not even clearly affecting the disease, which means these companies are hoping to find a molecule that will bind and then also have the desired downstream effect. Since all of these experiments are performed in vitro or in silico, there is a long way to go before their use can be proven in the live biology of the disease.
Anima Biotech is — as far as we know — the only company applying phenotypic screening to the discovery of small molecule mRNA drugs. This work is being performed in a high-scale, fully automated biology lab built by the company. The lab runs 24/7, systematically screening hundreds of thousands of molecules to determine their impact around the mRNA, on the mRNA, on the translation of mRNA, on the localization of the mRNA — on anything related to mRNA, really.
The images generated from the phenotypic screens are not general phenotypic screening images at the cellular level but are truly visualizing the mRNA and what’s happening to it. We see the translation of the mRNA, where it is, how much there is, and so on. We see if the mRNA is moving around. We see if a compound has the effect of reducing the amount of mRNA or eliminating it altogether, which would typically mean that it is a transcription inhibitor. By not seeing the mRNA in the image when simultaneously visualizing the transcription site being “shut down,” it is possible to identify MOAs that are upstream of the mRNA. With this phenotypic screening approach, Anima is therefore able to discover molecules at any point within the entire mRNA life cycle, leading to multiple disease intervention or modification strategies.
With phenotypic screening, the endpoint is much more relevant than the information that can be collected in an in vitro mechanistic study. With Anima’s system, the endpoint can be a decrease or increase in protein production or something very different that is relevant to the disease. It isn’t computational. It is live biology.
In the mRNA Lightning platform, this capability is then combined with the ability to analyze the millions of images for the active compounds to determine the MOAs and the molecular targets, which has traditionally been perhaps the greatest challenge in this kind of phenotypic screening. Anima’s AI-based software is trained by running through thousands of compounds and capturing millions of images of what the compounds are doing. In less than a second, each is analyzed and compared to previously known families of effects, a process we refer to as fingerprinting of the MOA. For instance, the software has been trained to recognize images showing that mRNA has been moved around by a compound, that mRNA has disappeared altogether, and so on. Each MOA has its own fingerprint, visible in how it affects the image.
With all of this information, it is possible to create a decision tree. When you look at the biology around mRNA regulation across its life cycle, there are different groups of proteins that do different things, and different MOAs appear differently in the images collected by our system. As a result, it is able to suggest the most likely MOAs for each active molecule and recommend a series of live biology experiments to further validate the hypothesis.
The process then systematically runs back to the wet lab, where confirmation of the MOA is established. This technology has already been proven to work. In Anima’s two leading programs for lung fibrosis and oncology, for the c-myc oncogene, which is a transcription factor that is constitutively and aberrantly expressed in over 70% of human cancers, the mRNA Lightning platform has enabled us to identify six different molecules and their associated MOAs and molecular targets in less than 12 months.
The platform is a gamechanger for phenotypic screening, because it enables us to identify compounds that modulate mRNA biology in a disease-relevant MOA but also to know how they actually achieve their effects. It also makes it possible to identify multiple compounds with different targets and MOAs, affording multiple options for progressing the drug discovery project. Some could have better selectivity, less toxicity, or greater solubility than others.
For pharma partners, Anima’s ability to rapidly and systematically identify small molecule compounds that modulate mRNA biology to positively affect a disease while elucidating their MOAs, can provide a tangible competitive advantage. Anima currently has 18 programs in our pipeline and have been able to advance them at unprecedented speed and rate of success.
compounds modulate mRNA biology. The understanding of mRNA biology remains limited because commercial applications are still quite new. There have been many publications conceptually demonstrating different MOAs regarding the synthesis, transport, and modification of mRNA. Through Anima's programs and with partners, we have uncovered concrete examples to support these conjectures — and new MOAs as well.
For example, mRNA relocalization could be a means for a compound to substantially affect synthesis of a given protein, particularly if the location of the mRNA is relevant to the translation, and for many cells it is a way for them to stage and decide on when to actually translate a protein. mRNAs can be “parked” and waiting, then brought to the ribosomes, which then begin translating them. However, first a carrier needs to transfer the mRNA to the ribosomes.
form connective tissue in order to build skin, tendons, and bones. It is also involved in wound healing, during which the body must synthesize collagen much faster than normal. The normal transcription process takes 24 hours, which isn’t sufficient for the rapid response needed to heal wounds.
Instead, the mRNA for collagen has to already be present in cells in large quantities, waiting where ribosomes cannot reach it. That means it is bound to an RNA binding protein (RBP). Once an activating signal has been given, the RBP must be removed, and the ribosomes can translate the mRNA immediately. That is what enables such rapid translation.
In general, mechanisms for controlling the increase or decrease of a particular protein’s production on a “moment’s notice” must be very selective and efficient. With collagen, the molecule that removes the RBP from the mRNA must only remove that specific RBP and not affect any other RBPs, or the translation of other proteins will also be increased. Fortunately, with mRNA translation, selectivity is almost built in because it is a very fast process, and fast processes that react to signals on demand must be very selective. Consequently, mRNA biology is a great place to find selective drugs, because the mechanisms that the cells use are inherently selective.
structure of the mRNA, and very hostile chemistry is involved in the binding of small molecules to mRNA. There are also significant unknowns with respect to the consequences of successful binding.
With Anima Biotech’s mRNA Lightning platform, it is now possible to understand both the targets and the MOAs of small molecule mRNA drugs and identify those that are selective and produce only the desired effects. The platform can identify compounds that can either decrease or increase selectivity or even inhibit a mutated variant while leaving the functional protein unaffected. Manipulating mRNA biology affords an opportunity to approach the broadest range of disease conditions. It is not limited to a therapeutic area and is a “two-way street” approach that can handle either over- or underexpression of a protein and even mutations. The mRNA Lightning platform is applicable to the discovery of small molecule drugs for almost every possible disease.
Anima Biotech is focused on areas wherethe company has a strategic and sustainable competitive advantage. One of those areas is fibrosis, for which the buildup of collagen I in an uncontrolled manner is the hallmark. It isn’t possible to directly target type I collagen because that would affect all of the collagen in the body. It also isn’t possible to knock down the mRNA, because the same problem would result.
Anima has identified small molecules that selectively inhibit collagen I by working only in the lungs, with no side effects in other tissues. That is possible because Anima's compounds are targeting mRNA biology, not the mRNA itself. The platform is capable of identifying MOAs that are specific to a particular tissue: tissue-selective type I collagen translation inhibitors. The company expects to be able to replicate this strategy in fibrosis of the kidney and the liver. It is a completely different strategy, with a novel disease-relevant MOA for most fibrotic diseases and thus a strong area for Anima.
In oncology, Anima has programs targeting the c-myc and Kirsten rat sarcoma viral oncogene homolog (KRAS) oncogenes, which are two famous targets that for a long time have been considered to be undruggable. Here again, Anima was able to identify selective compounds that control the translation of these proteins.
For, c-myc, the company has three very different selective compounds entering preclinical development, each with tumor-dependent efficacy. Thus, the team has initiated three programs from one phenotypic screen and established a strategic way to apply the platform and create assets that are very powerful and unique.
With KRAS, based on the biology of KRAS translation regulation within cells, Anima developed a strategy completely opposite to everyone else in the industry. There are numerous mutations, but most companies are targeting only a specific one. Using the mRNA Lightning platform, the company has found a way to control the translation of KRAS mRNA in a mutation-independent manner. This is another example of how the power of the platform has been applied to a high-value target using a strategy no one else is capable of employing.
Anima also has a program against the Tau protein in Alzheimer’s disease, another high-value target. In Alzheimer’s patients, tau protein slowly aggregates in the brain. Using the mRNA Lightning platform, we are able to visualize the translation of Tau and identify compounds that impact that translation. In this case again, exploring the translation of the mRNA rather than the activity of the protein is valuable and very differentiated.
Anima’s pipeline is among the most advanced in the mRNA-targeting small molecule drugs space. The company is currently in preclinical testing in multiple programs. It has achieved that progress despite being largely a boot-strapped company. Anima had only one round of investment in 2018, through which we raised money from private investors. Since then, Anima has built itself up to be a powerhouse in mRNA small molecule drugs by partnering. Those partnerships are with big pharmaceutical companies like Eli Lilly and Takeda, and the company is always working to establish more.
What Anima has found is that the science speaks for itself. Of course, the mRNA space — and AI — are very interesting fields in their own rights. The intersection of these two hot areas is very newsworthy. As a result, the company is riding this wave right now, and it is a good place to be. The future looks very bright for the whole field, not just for Anima. Many companies will be successful. Many will also fail because of the nature of mRNA biology. That is why Anima is so well positioned and differentiated — our mRNA Lightning platform has the underlying advantage of an unbiased phenotypic screening approach offering “multiple shots on goal” to de-risk our projects, and it combines with Anima's unique AI image analysis to identify the MOA of discovered compounds. With this unique strategy, the company can explore many options to run a large number of programs with different partners.
Yochi Slonim is a serial entrepreneur with a successful track record in software and biotech. As Co-founder and CEO of Anima, he is driving the company's vision, strategy, fundraising and partnering efforts. Prior to Anima, Yochi built several companies from startup through product development, marketing and sales, all the way to successful IPOs and large M&A exits.