LB-001

GNQ Insilico's Quantum-enhanced and AI-powered Platform to Accelerate Development of MindLab's Novel Pain Management Therapy PLEASANTON, CA, UNITED STATES, October 30, 2025 / EINPresswire.com / -- GNQ Insilico Inc., a pioneer in quantum-enhanced AI drug development, is pleased to announce a strategic partnership with MindLab, LLC to deploy its newly launched Drug Assessment Platform (DAP) to accelerate the development of MLB-001, MindLab’s lead therapeutic candidate designed to transform the treatment of acute pain and rare-disease conditions. The partnership represents the first major commercial engagement for GNQ's recently launched DAP platform and underscores the growing demand for AI-driven modeling solutions that can optimize dosing, predict efficacy and safety outcomes, and strengthen regulatory readiness. MindLab took the first place prize at the RESI Boston Conference held this June, standing out among many promising early-stage life science companies. MindLab will leverage GNQ’s advanced digital-twin technology and AI-driven modeling capabilities to optimize dosing strategies, inform clinical trial design, and strengthen regulatory readiness for the next phase of MindLab’s development. The collaboration will generate predictive modeling of efficacy, safety, and abuse liability—including the euphoria-driven drug misuse potential. MindLab’s proprietary next-generation pain therapeutic is designed to deliver morphine-level analgesia with approximately a 50% lower opioid dose and a longer duration of action, while reducing the euphoria that drives drug abuse and addiction. TRANSFORMING DRUG DEVELOPMENT THROUGH QUANTUM INFUSED CAUSAL AI GNQ's Drug Assessment Platform combines artificial intelligence, quantum computing principles, and sophisticated biological modeling to create digital twins that simulate drug behavior across diverse patient populations. For MindLab's MLB-001 program, this technology will provide critical insights to: • Accelerate Development Timelines by reducing preclinical testing requirements through predictive modeling • Enhance Safety Profiles through early identification of potential safety signals before clinical trials • Optimize Clinical Trial Design with informed patient selection and dosing strategies • Support Regulatory Strategy by providing FDA-aligned quantitative predictions for efficacy versus respiratory depression risk, long-term efficacy, maintenance and physical dependence assessment The platform's ability to model complex drug interactions and predict individual patient responses addresses a critical need in developing controlled substances, where regulatory scrutiny and safety concerns demand unprecedented precision. STRATEGIC ADVANTAGES FROM NOVEL PAIN THERAPIES The partnership positions MindLab to leverage several key advantages: Regulatory Differentiation: The FDA increasingly favors precision medicine approaches for controlled substances. GNQ's personalized risk assessment modeling could support differentiated labeling and enhance approval prospects through patient-specific stratification strategies. Commercial Value Creation: Beyond development acceleration, the insights generated will enable companion diagnostics for patient selection, risk management tools for prescribers, and differentiated market positioning versus standard opioid therapies. Clinical Optimization: Real-time safety monitoring algorithms and adaptive dosing recommendations will maximize the probability of Phase 1 and Phase 2 trial success while minimizing patient risk. INDUSTRY LEADERSHIP PERSPECTIVES "MindLab's decision to adopt our Drug Assessment Platform validates our vision of transforming drug development through quantum enhancement causal inference" said Sudhir Saxena, Chief Technology Officer of GNQ Insilico. "The opioid crisis demands innovation not just in molecular design, but in how we predict and optimize therapeutic outcomes across diverse patient populations. Our digital twin technology will provide MindLab with unprecedented visibility into MLB-001's therapeutic window, allowing them to design safer, more effective pain management solutions with confidence. This partnership demonstrates how AI and quantum-enhanced modeling can compress development timelines while simultaneously improving patient safety outcomes." "Our partnership with GNQ Insilico represents a powerful convergence of neuroscience and AI" said Larry Raoul James, JD, MBA, Founder and CEO of MindLab. “By integrating predictive modeling with our preclinical data, we’re advancing a smarter, faster path to clinical validation and, ultimately, providing new options for patients who live with pain every day.” ABOUT MINDLAB PHARMA, LLC The company is a pharmaceutical development firm focused on creating next-generation pain management therapies that meet the urgent need for effective analgesia with improved safety and reduced abuse potential in acute and rare-disease pain conditions. The company’s lead program, MLB-001, represents a novel approach to opioid therapy through precision combination pharmacology, designed to deliver morphine-level pain relief at approximately 50% lower opioid dose with a longer duration of action and reduced euphoria-driven misuse potential. MindLab is headquartered in New York City, NY. For more information, visit www.mindlabpharma.com or contact info@mindlabpharma.com ABOUT GNQ INSILICO INC. The company is a California-based TechBio firm pioneering the integration of artificial intelligence, quantum computing, and advanced biological modeling for drug development. The company's Drug Assessment Platform (DAP) leverages digital twin technology to predict drug efficacy, safety, and patient response with unprecedented accuracy, enabling pharmaceutical companies to accelerate development timelines, reduce costs, and improve clinical outcomes. GNQ Insilico is headquartered in Pleasanton, CA. For more information, visit www.gnq.ai Rehan Huda GNQ Insilico Inc. info@gnq.ai Legal Disclaimer: EIN Presswire provides this news content "as is" without warranty of any kind. We do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author above.