Jahnavi Vellanki, Industry Researcher, Brownsburg, IN 46112, USA
Artificial Intelligence and Machine Learning are revolutionizing pharmaceutical lifecycle management, maximizing drug discovery, clinical trials, manufacturing, supply chain, regulatory compliance, and pharmacovigilance. Predictive analytics by leveraging AI improve the selection of drug candidates, reduce late-stage failures, and enhance clinical research capabilities. AI-driven intelligent manufacturing gained the achievement in real-time quality assurance, predictive maintenance, process optimization, and AI-fueled supply chain management streamlines accurate forecasting and inventory management. AI enhances the performance of compliance with regulations using automated documentation, detection of adverse drug reactions, and real-time verification. AI-assisted post-market surveillance deploy the advantage of safety monitoring and risk detection. While these advances are promising, issues of data privacy, algorithmic bias, ethics, and regulatory hurdles continue to hamper progress. Explainable AI (XAI) for transparency, integration of blockchain with AI for secure data handling, and AI-based personalized medicine for patient-tailored treatment are research areas where future efforts are needed. Conquering these challenges will make it possible for AI to lead innovation, efficiency, and ethical development in pharmaceutical lifecycle management.
Artificial Intelligence, Machine Learning, Drug Discovery, Regulatory Compliance, Pharmacovigilance, Personalized Medicine.
Matthew Stanley Leibel, Independent Researcher, Canada
The Grand Computational System proposes a unifying framework in which mass, gravity, time, and consciousness emerge from a quantum information processing substrate. Rather than treating spacetime as a static background and mass as intrinsic, this model views the universe as a dynamic computational engine, where energy is processed through entropy-driven interactions and encoded into spacetime via structured photonic emissions. Key to this theory is an observer-based correction to the Bekenstein Bound, asserting that information is encoded only across the observer-accessible plane, not the full 2D boundary. A case study using a 1 kg steel cube introduces a square root correction to photon emission data, allowing a derivation of Einstein’s equation (E = mc²) [Einstein, 1905] from quantum information principles and thermodynamics. Mass is reconceptualized as structured light, and inertia as resistance to information restructuring. Gravity emerges not as a force, but as a computational constraint that adjusts spacetime curvature to optimize information flow. Black holes are framed as nodes of information restructuring, resolving the information paradox by treating Hawking radiation as an encoded, non-thermal emission. Time arises from quantum update cycles, and its directionality is tied to entropy accumulation. Consciousness is modeled as a non-local interaction between the brain and the quantum information field, with subjective experience emerging from entangled informational states. The theory unifies classical and relativistic physics through information and entropy, offering testable predictions in quantum computing, gravitational anomalies, and consciousness studies.
In essence, this framework recasts reality as a holographic, self-regulating quantum information system governed by entropy, light, and observation.
Matthew Stanley Leibel, Independent Researcher, Canada
This paper introduces OLBOS — the Observable Light-Based Operating System — a unified framework that reconceptualizes mass, light, time, space, and observation as interdependent expressions of a single recursive process. Contemporary physics treats these elements as distinct, often requiring separate domains of theory: general relativity for spacetime, quantum mechanics for light and matter, and thermodynamics for entropy. OLBOS dissolves these boundaries by revealing all structure and experience as the result of light recursively folding through a holographic boundary — a boundary that is the observer. In this model, spacetime is not a passive arena but the reflective surface of observation itself. Light, when projected and reflected through this surface, forms stable recursive loops, giving rise to mass. Time emerges not as an absolute dimension but as the computational rhythm of these recursive updates — the sequencing of state changes. Observation imposes a geometric constraint that filters total structure through dimensional compression, allowing only a square-root projection to be perceived. The classical identity - is reinterpreted here not as a conversion, but as a recursive encoding function, with mass as structured light and energy as its potential unfolding. This framework has profound implications for fundamental physics, the nature of time, blackbody radiation, and the emergence of consciousness — all seen now as recursive operations within a self-observing light-based system.
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