Insilico – on a mission to accelerate senescence drug discovery and development by continuously inventing and deploying new AI technologies.
Over the coming weeks, we will be bringing you extracts from 7 company profiles from our Longevity Senotherapeutics Report. Each profile includes an assessment of Longevity Potential, Pre-Clinical and Clinical Studies, Technology/platform analysis, Safety and Risks, Target Market and Success Factors.
We also study each company’s IP, team, UVP, product efficacy, competitive advantage, runway and inflection point, and much more. Here’s the lowdown on Insilico.
To view a full size version of Insilico profile please click here.
Insilico Medicine, founded in 2014, focuses on generative models, reinforcement learning (RL), and other modern machine learning techniques for the generation of new molecular structures with specified parameters, generation of synthetic biological data, target identification, prediction of clinical trial outcomes, and applications in aging research. Since its inception, Insilico Medicine has raised over $310 million from expert biopharmaceutical and technology investors, established R&D centres in 6 countries or regions, nominated preclinical candidates for novel targets for major diseases, published over 130 peer-reviewed scientific papers, applied for over 30 patents, generated millions in revenue from partnerships with pharmaceutical companies and received multiple industry awards.
Alex Zhavoronkov, the founder and CEO of Insilico Medicine, comes from a computer science background specializing in graphics processing unit (GPU) and neuroscience. Since 2004, Alex switched his focus to aging research and drug discovery receiving a Master’s degree at Johns Hopkins University, a PhD from Moscow State University, and managing the regenerative medicine and sequencing and bioinformatics laboratories at the Centre for Paediatric Haematology, Oncology, and Immunology. At the dawn of the deep learning revolution in 2013- 2014, Alex Zhavoronkov with a co-founder, Alex Aliper (Endpoints top 20 under 40 biotechnology executives), refocused on the applications of deep neural networks to target identification, chemistry and clinical trial prediction problems. Currently, the company has a wide geographical presence in multiple countries and regions with headquarters based in Hong Kong and over 160 scientists worldwide.
Insilico’s technology platform, Pharma.AI, uses AI to link together generative chemistry and biology to cover every step of target discovery and expedite the drug discovery process. Traditional drug discovery starts with the testing of thousands of small molecules, followed by further testing and synthesis of hundreds of molecules in order to get to just a few lead-like molecules appropriate for preclinical studies, of which only about one in ten of these molecules pass clinical trials in human patients. Incredibly slow and expensive, the overall process on average often totals over ten years of development and billions of dollars, with each of the processes costing millions of dollars. Further compounding the hurdles in bringing a new drug to market are the massive number of R&D steps involved – each costing millions of dollars – often disconnected and conducted by different companies or different business units in the pharmaceutical ecosystem.
Insilico’s technology platform, Pharma.AI, starts with PandaOmics, a proprietary target discovery system designed to identify therapeutic targets through deep feature selection, causality inference and de novo pathway reconstruction. Then Insilico’s generative chemistry platform for drug discovery, Chemistry42, generates hit compounds from scratch for the chosen target of interest. By using Pharma.AI, Insilico can assess the probability of success for a given clinical or preclinical program. Taken together, this forms a unique ecosystem of AI tools that is able to significantly accelerate the drug discovery process, specifically in the field of chronic age-related diseases.
Insilico’s team firmly believes that AI has transformative potential in the process of discovering and validating new drugs and is confident its platform can make the pharmaceutical drug discovery and development process more efficient. Insilico has multiple milestones for integrating more AI engines into its end-to-end pipeline as well as plans to expand its collaborations with the robotics drug discovery companies and to build its own robotics facility.
Through its AI platform, Insilico has developed a rich pipeline of therapeutic programs including several antifibrotics, anti-cancer, and metabolic disease programs as well as programs targeting CNS diseases, and the basic process of senescence. In addition, Insilco has multiple collaborations with pharmaceutical companies where it is identifying new targets and pathways implicated in senescence or supports novel chemistry generation efforts. In the next few years, Insilico plans to progress more of these programs into human clinical trials.
Insilico’s flagship program is a novel antifibrotic targeting senofibrosis. In the context of senescence, the company developed a target and pathway discovery engine and consequently a multi-step combination therapy. Senescent cells and the SASP have been identified in multiple pathologies of skin, liver and lung and in experimental models. For the first time, using many interconnected deep learning models and other advanced AI approaches, Insilico has managed to link biology and chemistry to build a platform for novel biological target discovery and novel small molecule generation for multiple modalities of senescence-related therapeutics. Insilico proposes a 5R (Rescue, Remove, Replenish, Reinforce, Repeat) strategy for managing cellular senescence (a driving cause of multiple pathologies) by selectively rescuing pre-senescent cells, removing senescent cells, replenishing and reinforcing with new healthy cells and repeating the procedure. The platform itself and the 5R approach related IP is protected.
One of Insilico’s most important key partnerships for senolytic drug discovery is that with Taisho Pharmaceutical from Japan, announced in October of 2020. This collaboration brings together Insilico’s state-of-the-art AI technologies with Taisho’s expertise in drug development. Insilico Medicine will be responsible for early research phase target identification and molecular generation and Taisho will work collaboratively with Insilico in validating the results in various in vitro and in vivo assays.
Insilico’s first published work in senescence-related target discovery dates back to 2016 and consisted of basic experimental validation with a company called BioTime (now AgeX Therapeutics). Insilico also has a long story of collaboration with one of the leading nutraceutical companies in longevity field – Life Extension Foundation with two products on the market since 2017. These collaborations allowed Insilico to scale up its internal capabilities in the development of therapeutics targeting senescence. Currently, Insilico has a variety of internal drug development programs in pre-clinical stage with 3 IND-enabling studies planned in 2022. The major area of those studies is fibrosis. The distinct feature of the majority of the current programs is that they rely on novel molecules and novel targets identified by Insilico’s proprietary AI platform. The platform itself generates licensing deals resulting in a decent revenue and aids in engaging with the leaders of the industry potentiating the future licensing and co-development deals..