Entrepreneurship

Our TAM, SAM, SOM:

During our search for a product-market fit application of our foundational models, we held extensive discussions with academic leaders in computation biology, bacterial systems, and phage therapies. Currently, the technology is in early stages, and it will take years of R&D to be able to fully expedite the preclinical discovery of phage therapies.

As proven through our wetlab-drylab cycle, we're able to obtain results from our AI models and test them for biological function. Our solution is highly scalable as in vitro results can be used to further fine tune our models, lessening the need for in-depth wet lab validation. Initially, the biggest bottleneck would be investigating the biological function of computationally-generated results.

In our discussion with academics, we have confirmed that our usage of AI models to modify phage sequences/DNA alongside our protein interaction validation is at the cutting edge of synthetic biology. Given the proprietary nature of our competitors' technologies, we’re unable to verify how close our approach is to current methods.

Seed Stage: We aim to undertake the appropriate legal entity and trademark registrations in Canada and the United States. We aim to on board AI scientists and bioinformatic specialists, and develop models to target carbapenem-resistant E. coli. This initial milestone serves as a Proof of Concept, and addresses an urgent need for a treatment involving this strain.

Series A: Our plan to secure $15 million allows us to expand our Proof of Concept to other bacterial strains, aligning with the typical expansion plans for an early-stage biotech company. Given that we've made it to the "Go-to-market" stage at UofT's flagship incubator, we expect to be able to hit this milestone in the next 5-8 years.

Series B: Depending on the success of previous rounds, we aim to raise additional money as needed for the development of additional models tailored to specific bacterial strains. This long-term funding strategy allows us to maintain competitiveness by acquiring necessary R&D resources.

Exit: Our structure and long-term plan is to integrate into a company's existing pipeline. Once our MVP has been validated to work, we aim to exit via a Merger & Acqusition (M&A).

We've crafted an 8-year Gantt chart illustrating more specifics. Please note that this is preliminary, and may change depending on the circumstances

We've also created a Risk Management Chart outlining our potential business risks, contingency plans, and other factors:

We've conducted detailed competitor analysis to understand the competitive landscape. Here's a visual summary (more found below):

We require substantial funds to hire, train, and support AI developers and bioinformatic specialists to develop the initial phase of our models. Additionally, we need to build the computational infrastructure in order to sustainably train and develop our models. We require reagents, equipment, and lab space in order to validate the models' results. We intend to source the reagents and equipment from ThermoFisher and other local suppliers. We intend to obtain lab space through SpinUp, a Toronto-based incubator with lab space for early-stage startups.

We are currently collaborating with key partners to advance our R&D. Our partnership with the Garton Lab at the University of Toronto provides us with dry lab expertise and computational data. Our partnership with the Lan Lab allows us a deep insight into the drylab-wetlab validation cycle, ensuring proper workflows to discern our models' biological accuracies.

Through our incubator, we have solidified relationships with KOLs, mentors, and venture capitalists that have pledged their support in assisting us develop our solution further.

For more specific details, please refer to our business plan below.

Skill Gap Analysis:

SWOT

Artificial intelligence is a cutting-edge technology that has gained great attention in the world for its potential and seemingly endless applications. Using artificial intelligence to design genetic codes has definite concerns in affecting evolution and may potentially be a force contributing to natural selection, in which we see increasing pressure for regulations and legislation on data collection, utilization and generation. Our Human Practices (HP) subteam has done a detailed analysis on the effects of a project on relevant stakeholders across biotech industries, ethical practices, healthcare industries and synthetic biology laboratories alike. In particular, through discussion with bioethicist Dr. Jim Shaw, McGill chemistry professor Dr. Karine Auclair, biosecurity expert Dr. Jonathan Herington and others, Plasmid.AI can incorporate feedback from the leaders in the industry to create a well-poised product.

Our stance is to see artificial intelligence as a neutral tool that could both benefit or damage to society, depending on the intent of its user. With the growing stress from antimicrobial resistance, our team see it as an inevitable advancement to use artificial intelligence to create effective weapons against bacteria, and to save the lives of millions. For more information on our analysis on long-term and short term impacts of our fully developed solution, please visit the Human Practices section of our wiki.