GNQ Insilico’s AI-Driven Digital Twins Platform Shows Promising Results in Simulating Effects of a Drug on Synthesized Digital Twins
GNQ Insilico, Inc. (“GNQ” or “the Company”), a cutting-edge TechBio platform company, demonstrated promising results in synthesizing digital twins human patients, and simulating the effects of an infertility drug on these digital replicas using its proprietary AI-driven platform.
Applications of Digital Twins in Drug Discovery and Development
In the healthcare industry, digital twins are an emerging technology that has the potential to advance patient care and personalized medicine. Medical digital twins are computer-based virtual models of living and non-living entities which can range from an individual human patient to organs, tissue cells, neural networks, micro-environments, or entire populations. Rather than 3D models, medical digital twins are dynamic virtual replicas of real-life entities and processes, continually interacting with and adapting to real-time data and predicting corresponding future scenarios within a complex system, using AI and quantum computer technologies.
Medical digital twins have the potential to significantly improve the drug discovery and drug development process by improving the efficiency, efficacy and outcome of clinical trials. Currently, the average new drug experiences a 90% failure rate1 during clinical trials, while the average cost to bring a new drug to market is estimated at between $161 million – $1.8 billion (fully capitalized costs inclusive of failures)2. The average timeframe for bringing a typical new drug to market, from discovery to FDA approval, is between 10 – 15 years3.
Significant improvements in drug discovery and development can be made possible through “in silico” drug simulations using digital twins, by mimicking how a drug will interact with an individual patient’s unique biology, down to the cellular level. This could assist pharmaceutical companies in better designing and optimizing clinical trial protocols by enabling them to more accurately predict how these drug compounds will behave prior to human trials, thereby reducing costs and failure rates.
GNQ Insilico Platform Prototype
GNQ Insilico simulated the pharmacokinetics and pharmacodynamics of an existing infertility treatment on thousands of digital twins, spanning diverse genetic backgrounds and health profiles, that were synthesized using its platform. GNQ’s AI optimizer then analyzed the simulated outcomes to identify optimal dosing strategies tailored to each digital twin’s characteristics, accounting for factors like genetics, epigenetics, and environmental exposures.
Sudhir Saxena, CTO of GNQ Insilico commented: “Human clinical trials are often hindered by variability in how patients respond to drugs. Our AI-driven digital twins platform will enable us to better optimize the trial design for precise patient subpopulations, before ever running an expensive clinical trial.”
Two of GNQ’s team members, in collaboration with other technologists from leading organizations, also co-authored a recently published paper on a related subject, which illustrates how quantum computing may be leveraged to optimize clinical trial design. To learn more, read the paper: ‘Towards Quantum Computing for Clinical Trial Design and Optimization: A Perspective on New Opportunities and Challenges‘.
About GNQ Insilico Inc.
GNQ Insilico Inc. is a leading TechBio firm that leverages exponential technologies for precision medicine at a personalized level. Founded on the principle that personalized medicine requires a deep understanding of biological complexity, The Company has developed revolutionary platforms that enable truly personalized therapeutic approaches. The Company’s comprehensive digital twin technology and advanced AI-driven assessment capabilities are setting new standards for precision medicine implementation and drug development optimization.
The Company’s multidisciplinary team combines expertise in genomics, systems biology, artificial intelligence, clinical medicine, and pharmaceutical development to deliver solutions that bridge the gap between complex biological data and actionable clinical insights.
Media Contact:
Ghezali Warsi
Business Development
GNQ Insilico Inc.
Email: info@gnq.ai
Company Website: http://www.gnq.ai
Forward-Looking Statements
This press release contains forward-looking statements regarding GNQ’s business prospects, technology capabilities, and market opportunities. These statements are based on current expectations and assumptions and are subject to risks and uncertainties that could cause actual results to differ materially from those projected.
1 Sun, D., Gao, W., Hu, H., & Zhou, S. (2022). Why 90% of clinical drug development fails and how to improve it? Acta Pharmaceutica Sinica B, 12(7), 3049-3062. https://doi.org/10.1016/j.apsb.2022.02.002
2 Morgan, S., Grootendorst, P., Lexchin, J., Cunningham, C., & Greyson, D. (2011). The cost of drug development: A systematic review. Health Policy, 100(1), 4-17. https://doi.org/10.1016/j.healthpol.2010.12.002.
3 Sertkaya, A., Birkenbach, A., Berlind, A., & Eyraud, J., Eastern Research Group, Inc. (2014). Examination of Clinical Trial Costs and Barriers for Drug Development. Assistant Secretary of Planning and Evaluation (ASPE). https://aspe.hhs.gov/reports/examination-clinical-trial-costs-barriers-drug-development-0
