GNQ Insilico, Inc. (“GNQ” or “the Company”), a TechBio platform company, announced the successful development and validation of its AI-powered Drug Assessment Platform prototype for clomiphene citrate dose optimization. The fertility treatment market represents a significant opportunity, with clomiphene citrate being a first-line therapy for ovulatory dysfunction affecting millions of women globally.
This proof-of-concept demonstrates GNQ’s ability to address critical pharmaceutical industry challenges including the high cost of bringing a single drug to market and the significant economic burden from treatment failures that plague current approaches.
Prototype Achievements
GNQ’s platform uses cutting-edge AI and digital twin technology to revolutionize personalized medicine by analyzing the complex interplay of genetic, epigenetic, and environmental factors affecting drug metabolism. The clomiphene optimization prototype, serving as the platform’s first use case, has demonstrated the following capabilities:
- 117,600 digital twins generated through rapid AI-powered virtual patient modeling
- 2,521 clinical scenarios analyzed with +/- 20% precision pharmacokinetic optimization
- Multi-variable analysis across genetic, epigenetic, environmental, and drug interaction factors through proprietary knowledge base
- Proven therapeutic optimization with patient outcome simulations demonstrating a remarkable 128% AUC improvement from suboptimal (461.86 ng·h/mL) to target therapeutic range (1052.01 ng·h/mL)
Core Platform Components
- Proprietary knowledge base consisting of 1,800+ biological entities with 11,000+ relationships (continuously expanding)
- Digital twin engine facilitating scalable virtual patient modeling across therapeutic areas
- AI/ML architecture for advanced pattern recognition and predictive modeling capabilities
- Physiologically-based pharmacokinetic (PBPK) modeling for precision optimization
Future Development
With successful prototype validation, GNQ will now accelerate platform development leveraging the same methodology to capture opportunities across multiple therapeutic areas. GNQ’s multi-revenue-stream approach targets life sciences companies, research organizations and Clinical Research Organizations (CROs) through platform licensing arrangements, formulation-based drug development assessment, clinical trial optimization services, regulatory strategy consulting and data analytics solutions.
“This prototype validates our platform’s transformative ability to integrate genetic, epigenetic, environmental, and drug interaction variables into a unified AI-powered solution,” said Sudhir Saxena, Chief Technology Officer at GNQ. “This proof-of-concept demonstrates how we can empower pharmaceutical partners to develop more effective treatments through precision medicine approaches, significantly improving success rates while reducing development costs across the industry.”
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.