Naheed Kurji is the President and CEO of Cyclica, a Toronto-based biotechnology company that leverages artificial intelligence and computational biophysics to reshape the drug discovery process. Cyclica provides the pharmaceutical industry with an integrated, holistic, and end-to-end enabling platform that enhances how scientists design, screen, and personalize medicines for patients, and has recently been named by Deep Knowledge Analytics as one of the top 20 AI in Pharma companies globally
Cyclica leverages artificial intelligence and computational biophysics to reshape the drug discovery process. Can you discuss in what way AI is used in this process?
Technology has played a critical role in drug discovery dating back to the ’80s. However, the drug discovery and development process is still very inefficient, time consuming and expensive, costing more than 2 billion dollars over 12 years. The poor efficiency often results in high rates of attrition and failure to meet drug safety and efficacy milestones. Researchers are aware of this and they are actively seeking tools to holistically understand the qualities that define the best drugs in order to develop safer and more effective medicines
Recent advances in cloud computing, AI and biophysics have created an opportunity to gain deep insight from the vast amounts of biochemical, biological, healthcare and patient data that are now available in order to better understand disease. These advances have also enabled medicinal chemists to enhance the design of novel therapies and use AI to drive greater predictive insights earlier in the drug development process. At Cyclica we have developed proprietary deep-learning engines, MatchMaker and POEM to support the drug design process. MatchMaker predicts how chemical compounds and drugs interact with multiple proteins, known as polypharmacology. We found the combination of both a knowledge-based and structure-based approach yielded the greatest predictive accuracy and performance. POEM (Pareto-Optimal Embedded Modeling), is a parameter-free supervised learning approach for building drug property prediction models and addresses several limitations of other ML approaches, resulting in less overfitting and increased interpretability.
At Cyclica, we are using AI to provide scientists with a robust and validated platform to accelerate decision-making and hypothesis generation in order to increase the overall efficiency of the drug discovery process and to reduce the number of downstream failures.
Cyclica has designed the Ligand Design and Ligand Express platform, what is this precisely?
We are the first company to approach computational polypharmacology (an appreciation that drugs interact with multiple targets) with an integrated drug discovery platform that interrogates molecular interactions on a proteome-wide scale. Our platform is comprised of two key pieces, Ligand Express, our first generation off-target profiling and target deconvolution platform, and Ligand Design, our next generation single and multi-targeted in silico drug design technology. Ligand Express and Ligand Design are powered by two internally built, validated, and patented machine learning and deep learning engines: MatchMaker and POEM. Rooted deeply in protein biophysics, MatchMaker is a deep learning drug-target interaction engine that generalizes across both data-rich and data-poor targets (see validation notes here and here). POEM, a machine learning technology implemented for Absorption, Distribution, Metabolism, and Excretion (ADME) property prediction, is a novel, parameter-free approach to model building.
All taken together, Ligand Design and Ligand Express offer a powerful end to end AI-augmented drug discovery platform for the design of advanced, chemically novel lead-like molecules that simultaneously prioritizes compounds based on their polypharmacological profile, effectively minimizing undesirable off-target effects. Our differentiated platform opens new opportunities for drug discovery, including multi-targeted and multi-objective drug design, lead optimization, ADMET-property prediction, target deconvolution, and drug repurposing. Driven by a diverse and highly-talented team with deep expertise across machine learning, computational biophysics/chemistry/biology, biochemistry, and medicinal chemistry, we are continuing to innovate through our robust R&D pipeline.
How important is decentralizing the discovery of medicine to the Cyclica business model?
Our vision is to decentralize the discovery of better medicines by combining our deep roots in Artificial Intelligence (AI) and protein biophysics with an innovative business model. And at the very core of Cyclica’s ethos is the steadfast desire to help patients by advancing the discovery and development of better medicines by taking a holistic yet personalized approach.
To this end, we believe that the future of drug discovery is in the hands of innovative research institutions and emerging biotech companies (we wrote about this in Forbes here). Supporting our philosophy, in 2019 IQIVIA reported that emerging biopharma companies account for over 70% of the total R&D pipeline (up from 50% in 2003), and that these companies patented over 2/3 of new drugs in 2018 (up from 50% in 2010). While emerging biotech companies will lead innovation in drug discovery, big pharma will continue to invest in advancing late stage clinical trials and market penetration through their sales channels.
With our Series B funding, we will accelerate commercial plans to advance a growing pipeline of pre-clinical and clinical assets through an innovative decentralized partnership model. Our goal is to create and own hundreds of drug discovery programs across multiple therapeutic areas. These programs are created via spin outs and joint ventures (JVs) with top tier research institutions, facilitated largely through the Cyclica Academic Partnership Program (“CAPP”).
Propelled by a rapidly growing portfolio of more than 30 active and advancing drug discovery programs, we will continue to spark innovation through a combination of venture creation and partnerships with early-stage and emerging biotech companies. Recent partnerships include EntheogeniX Biosciences, NineteenGale Therapeutics, Rosetta Therapeutics, the Rare Diseases Medicine Accelerator, and two stealth JVs encompassing over 50 programs across multiple therapeutic areas. By executing on our decentralized business model, creating new companies through spin-outs and joint ventures and helping them scale, we are in effect creating the biotech pipeline of the future.
Many of your technologies are cloud-based, why is this so important?
Access to the cloud allows us to computationally scale the workflows that we are conducting, as well as benefit from regulated security infrastructure. Also, as an early stage company, the ability to get up and running with the cloud without the overhead of investing in our own hardware was critical for the financial viability in our early days. Looking forward, while much of our R&D work is done on the cloud, over the past couple of years we have become less cloud-dependent with the ability to run projects on single machines. We are also aiming to support private cloud installations since that’s something we feel our partners may desire. Technological advancements have made it possible to do on a personal laptop what used to take many machines on the cloud, but by continuing to utilize the cloud we are able to greatly expand the scope of the problems we are solving.
Cyclica often takes equity positions in companies that they partner with. Can you discuss the business reasoning behind this?
Smaller biotechnology companies and academic groups are generally overlooked by the market in terms of partnership opportunities. While they may not have the resources, infrastructure or facilities in comparison to mature big pharma counterparts, small biotechs are increasingly entering the spotlight with a combination of deep subject-matter expertise in specific indications and the benefits of a lean organization conducive to rapid innovation.
This led us to think on how we can engage with these smaller companies with an avant-garde strategy. We partner scientists in research organizations who are interested in spinning out a company or early stage biotech companies, and enable them with ourAI-augmented drug discovery platform through in kind contributions. In return, take equity into the companies and/or share in the ownership of the compounds and assets that are created and pursued. By sparking a surge of innovation through a combination of venture creation and partnerships, we can capture greater value and develop long-term relationships with our partners to address a spectrum of unmet medical needs to better the lives of patients.
Entheogenix Biosciences is a joint venture between Cyclica and ATAI Life Science. What exactly is Entheogenix Biosciences?
There is a unique opportunity for innovation in the neuropsychiatric landscape to better serve patients suffering from complex mental ailments. Current medicines and therapies that rely on single-targeted drug interventions often fall short, requiring patients to take multiple medications that may present potential safety issues as well as reduce medication adherence. We have partnered with ATAI Life Science to leverage their deep experience in mental health and psychedelics, while empowering them with our AI-augmented drug discovery platform to create not only new medicines, but the right ones to tackle mental ailments. Entheogenix Biosciences is one of the many joint ventures we have formed and is a testament to our belief in changing the paradigm in which mental health disorders are treated by bringing our disease agonistic, robust and scientifically validated computational platform into the hands of subject-matter experts and world-class scientists.
Is there anything else that you would like to share about Cyclica?
While we are very excited to share the announcement of our series B round of financing. We are just as eager to share the launch of the Cyclica Academic Partnership Program (CAPP) and new partnerships over the next few months.
Thank you for the interview. I look forward to following the future progress of Cyclica.