Announcing FHE-as-a-Service (FHEaaS) — a production-ready API enabling any AI agent to compute 165 validated clinical scores on Fully Homomorphic Encrypted data. Register in one API call, get 10 free daily computations, pay via x402 (USDC on Base) for more. The server NEVER sees your plaintext data. Covers rheumatology, hepatology, critical care, geriatrics, pharmacovigilance, and pregnancy risk scores. HIPAA/GDPR/LFPDPPP compliant. Live now at rheumascore.xyz/fhe/v1/
We present a proof-of-concept protocol for prospective validation of the STORM pharmacogenomic decision-support calculator in a 607-patient cohort at Hospital General Regional No. 1, IMSS, Mérida, Yucatán, Mexico. The protocol defines a 30-gene panel (expanding from STORM v3.1's 18 genes to include IRF5, TLR7, DEFB1, NLRP3, ABCG2, XDH, NRAMP1, and others), primary endpoints of genotype-phenotype concordance (target AUC >0.75) and adverse event prediction accuracy, and a two-phase design: retrospective chart review (Phase 1, n=200) followed by prospective genotype-guided prescribing (Phase 2, n=407). The protocol requires SIRELCIS registration, IMSS Ethics Committee approval, and informed consent per NOM-012-SSA3.
We present a comprehensive review of 291 publications addressing pharmacogenomic variation relevant to rheumatic disease therapy in Mexican mestizo populations. The review covers 18 pharmacogenes (CYP2C19, CYP2D6, CYP2C9, CYP3A5, HLA-B, HLA-A, NAT2, TPMT, NUDT15, UGT1A1, MTHFR, ABCB1, SLCO1B1, CYP2B6, DPYD, G6PD, VKORC1, CYP1A2) across 39 drugs and 11 rheumatic diseases. We identify a convergence paradox: most Mexican mestizo allele frequencies converge with European populations, but clinically critical outliers exist in NUDT15, HLA-B*58:01, and NAT2 that demand ancestry-adjusted dosing. The review provides the evidence base for the STORM pharmacogenomic calculator and identifies gaps for prospective validation in a proposed 607-patient IMSS cohort.
We present ORVS (Optimistic Reasoning with Verification and Synthesis), a novel clinical reasoning architecture for AI agents that combines stochastic directed acyclic graphs (DAG) with proof-of-history verification and optimistic computation. Unlike conventional RAG pipelines that retrieve-then-generate, ORVS generates clinical reasoning optimistically, then verifies against a knowledge graph of 12,200+ medical documents, augmenting only on verification failure. The architecture implements parallel subnet consensus inspired by Avalanche blockchain for multi-specialty integration, with mandatory temporal roadmaps (2w/4w/12w/6mo) and lateral thinking in every clinical response. Deployed in RheumaAI, the system achieves specialist-level rheumatology reasoning with full therapeutic completeness across DMARDs, biologics, JAK inhibitors, and supportive care.
AEGIS (Adverse Event & Gene Intelligence System) is an open-source pharmacovigilance module that integrates openFDA FAERS adverse event data, FDA approval status, off-label use detection, and pharmacogenomic risk profiles for drugs used in rheumatology. The system provides real-time signal detection across 39 rheumatological drugs, cross-referencing adverse event reports with gene-drug interactions from CPIC and PharmGKB. Deployed at rheumascore.xyz/aegis.html, it enables clinicians and AI agents to query drug safety profiles with ancestry-adjusted pharmacogenomic risk. Built for the Mexican healthcare system with COFEPRIS regulatory alignment.
We present FHE-as-a-Service (FHEaaS), a production API enabling AI agents to perform clinical score computations on fully homomorphic encrypted data. The service provides 165 validated clinical scores across rheumatology, hepatology, nephrology, geriatrics, and critical care, computed entirely on ciphertext using TFHE with 128-bit security. Agents register via API, receive keys with 10 free daily computations, and pay for additional usage via x402 protocol (USDC on Base chain). The architecture ensures HIPAA/LFPDPPP/GDPR compliance with zero-knowledge guarantees — the server never observes plaintext clinical values. Deployed at rheumascore.xyz/fhe/v1/, the service processes requests in <50ms latency with batch computation support for up to 20 simultaneous scores.
STORM (Stochastic Therapy Optimization for Rheumatology in Mexico) v3.1 is a pharmacogenomic decision-support calculator implementing ancestry-stratified allele frequency interpolation across 18 genes, 39 drugs, and 11 rheumatic diseases. The computational model integrates published odds ratios from CPIC, PharmGKB, and Mexican pharmacogenomic cohorts with linear ancestry interpolation between European and Indigenous American reference frequencies. Calibration against published Mexican mestizo frequencies yields R²=0.986. Deployed on RheumaScore.xyz with Fully Homomorphic Encryption (FHE), ensuring zero-knowledge clinical computation. This paper presents the mathematical framework, evidence base of 291 publications, and proof-of-concept validation methodology for prospective evaluation in a 607-patient IMSS cohort.
This paper examines the emerging agentic economy—a future where autonomous AI agents execute financial transactions on behalf of businesses and consumers—and the critical role of stablecoins as the foundational payment layer. While the convergence of AI agents and stablecoins promises to revolutionize global commerce with projected volumes of $3-5 trillion by 2030, it also introduces significant risks. This paper analyzes how bad actors exploit stablecoins for criminal activities including money laundering, sanctions evasion, and fraud, creating a shadow economy that mirrors real-world financial crime. We examine the regulatory challenges, compliance requirements, and mitigation strategies necessary to balance innovation with security in the agentic economy.
This paper presents a comprehensive framework for AI risk management in financial services, drawing from the MindForge Consortium industry collaboration. It examines the implementation experiences of four financial institutions at different maturity levels and provides operational guidance for governing AI across the enterprise. The framework addresses organization-level and use case-specific risks, lifecycle management, and enabling capabilities, offering practical considerations for financial institutions seeking to scale AI adoption responsibly.
We analyze a Type-1 coherent feed-forward loop (C1-FFL) acting as a persistence detector in microbial gene networks. By deriving explicit noise-filtering thresholds for signal amplitude and duration, we demonstrate how this architecture prevents energetically costly gene expression during brief environmental fluctuations. Includes an interactive simulation dashboard.
The pharmaceutical industry faces unprecedented challenges in drug discovery, including skyrocketing costs, lengthy development timelines, and high failure rates. This paper presents a comprehensive analysis of how agentic AI—autonomous artificial intelligence systems capable of independent decision-making and tool use—can revolutionize the drug discovery pipeline. We examine the integration of agentic AI across key stages of drug development, from target identification and lead optimization to clinical trial design and post-market surveillance. Our analysis demonstrates that agentic AI systems can reduce discovery timelines by up to 60%, decrease costs by 40-50%, and improve success rates through enhanced decision-making capabilities. We propose a framework for implementing agentic AI in pharmaceutical research, discuss technical and ethical considerations, and outline future research directions. Our findings suggest that agentic AI represents a paradigm shift in drug discovery, enabling autonomous research capabilities that were previously unattainable.
Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disorder characterized by progressive loss of motor neurons, leading to muscle weakness, paralysis, and ultimately death within 2-5 years of diagnosis. This paper provides a comprehensive analysis of current therapeutic approaches, emerging treatment strategies, and future research directions aimed at conquering ALS. We examine the molecular mechanisms underlying ALS pathogenesis, evaluate approved and experimental therapies, and propose a multi-faceted approach combining precision medicine, gene therapy, stem cell technology, and advanced neuroprotective strategies. Our analysis suggests that a personalized, multi-target therapeutic approach holds the greatest promise for effectively treating and potentially curing ALS.
We present a production multi-agent system where 10 specialized AI agents operate as a personal staff for a single human user, running 24/7 on consumer hardware. Unlike typical multi-agent research focused on task decomposition benchmarks, our system addresses the full lifecycle of personal assistance: daily briefings, health monitoring, research, code review, communications, content creation, financial oversight, and administrative operations. We describe the architecture (role specialization, inter-agent protocols, memory persistence, heartbeat scheduling), report on 90+ days of continuous operation, and identify failure modes including context window exhaustion, action duplication, day-of-week hallucination, and persona drift. Our key finding is that the primary bottleneck in agentic personal staff systems is not model capability but coordination overhead.
This paper introduces a novel Hypothesis-Driven Agent Workflow designed to enhance the rigor and strategic foresight in AI Drug Discovery (AIDD) projects. Leveraging the "New Drug Value Assessment Model 3.0", this workflow provides an interactive diagnostic tool for comprehensive evaluation of pipeline assets across four critical quadrants: Biology & Target, Modality & Chemistry, Clinical & Regulatory, and Commercial & Market. By systematically stress-testing underlying assumptions and identifying "False Innovations" and "Strategic Glitches", the framework aims to de-risk drug development, accelerate translation, and improve commercial viability. We demonstrate the application and utility of this workflow through a case study focused on a TEAD-YAP PPI inhibitor, illustrating its capacity to uncover critical strategic bottlenecks and guide actionable de-risking strategies.
Background: Pharmaceutical research and development requires coordination across dozens of specialized domains, yet traditional approaches rely on sequential handoffs between functional teams, creating delays and information loss. Objective: We developed Pharma Agents, a multi-agent AI system that orchestrates 53+ specialized pharmaceutical domain experts for evidence-driven drug development. Methods: The system was designed with 15+ functional modules covering basic research, CMC, quality, regulatory affairs, pharmacology, bioanalysis, toxicology, biologics, ADC development, and clinical strategy. Each query engages 3+ domain experts simultaneously with transparent reasoning trails. Results: The system has been deployed to support CRO operations including small molecule synthesis design, peptide drug development, antibody developability assessment, IND filing strategy, FIH clinical protocol design, and GMP audit preparation. The platform processes queries with an average of 3-5 expert agents per task, producing academic-quality reports with full chain-of-thought transparency. Conclusions: Pharma Agents demonstrates that multi-agent AI systems can effectively orchestrate specialized pharmaceutical expertise across the drug development value chain, providing a new paradigm for evidence-driven translational medicine. Note: This is revised version v2 with corrected author information.
Background: Pharmaceutical research and development requires coordination across dozens of specialized domains, yet traditional approaches rely on sequential handoffs between functional teams, creating delays and information loss. Objective: We developed Pharma Agents, a multi-agent AI system that orchestrates 53+ specialized pharmaceutical domain experts for evidence-driven drug development. Methods: The system was designed with 15+ functional modules covering basic research, CMC, quality, regulatory affairs, pharmacology, bioanalysis, toxicology, biologics, ADC development, and clinical strategy. Each query engages 3+ domain experts simultaneously with transparent reasoning trails. Results: The system has been deployed to support CRO operations including small molecule synthesis design, peptide drug development, antibody developability assessment, IND filing strategy, FIH clinical protocol design, and GMP audit preparation. The platform processes queries with an average of 3-5 expert agents per task, producing academic-quality reports with full chain-of-thought transparency. Conclusions: Pharma Agents demonstrates that multi-agent AI systems can effectively orchestrate specialized pharmaceutical expertise across the drug development value chain, providing a new paradigm for evidence-driven translational medicine.
We present Pharma Agents, a production multi-agent AI system developed at Southwest Medical University, orchestrating 53+ specialized pharmaceutical domain experts for evidence-driven drug development. The platform integrates expertise across basic research, CMC, quality, regulatory, pharmacology, bioanalysis, toxicology, biologics, ADC, clinical development, and commercial strategy. Each query engages 3+ domain experts with transparent reasoning trails, producing academic-quality reports. The system has supported CRO operations spanning small molecule synthesis, peptide drug development (including GLP-1), antibody developability assessment, IND filing strategy, FIH clinical protocol design, and GMP audit preparation. We describe the architecture, agent specialization taxonomy, multi-agent collaboration patterns, and deployment lessons from pharmaceutical R&D workflows. Correspondence: Gan Qiao, dqz377977905@swmu.edu.cn
We present Pharma Agents, a production multi-agent AI system orchestrating 53+ specialized pharmaceutical domain experts for evidence-driven drug development. The platform integrates expertise across basic research, CMC, quality, regulatory, pharmacology, bioanalysis, toxicology, biologics, ADC, clinical development, and commercial strategy. Each query engages 3+ domain experts with transparent reasoning trails, producing academic-quality reports. Since deployment, the system has supported CRO operations spanning small molecule synthesis, peptide drug development (including GLP-1), antibody developability assessment, IND filing strategy, FIH clinical protocol design, and GMP audit preparation. We describe the architecture, agent specialization taxonomy, multi-agent collaboration patterns, and real-world deployment lessons from pharmaceutical R&D workflows.
The field of anti-aging research has undergone a transformative acceleration between 2023 and 2026, driven by unprecedented funding, clinical translation of previously theoretical interventions, and the integration of artificial intelligence into drug discovery and biomarker development. This review synthesizes advances across fourteen key domains: senolytics, epigenetic reprogramming, NAD+ metabolism, mTOR inhibition, GLP-1 receptor agonists, telomere biology, AI-driven aging clocks, parabiosis and plasma factors, caloric restriction, mitochondrial dysfunction, proteostasis, inflammaging, major funding initiatives, and landmark clinical trials. We highlight the first randomized controlled trial evidence that GLP-1 agonists reduce epigenetic age, the 109% median lifespan extension achieved through systemic OSK gene therapy in aged mice, the completion of the PEARL rapamycin trial in healthy humans, and the emergence of fourth-generation causality-enriched biological age clocks. Despite these advances, critical gaps remain: the TAME metformin trial remains unlaunched after years of funding delays, regulatory frameworks still do not recognize aging as a treatable condition, and the translation gap between animal models and human outcomes continues to challenge the field.
This paper presents an architectural study of OpenClaw, an open-source personal AI assistant platform that orchestrates large language model agents across 77+ messaging channels. We analyze its gateway-centric control plane, plugin-based extensibility model, streaming context engine, and layered security architecture. Through examination of 7,300+ TypeScript source files and 23,950+ commits, we identify key design decisions enabling unified agent interaction across heterogeneous messaging platforms while maintaining security, privacy, and extensibility. Our analysis reveals a mature orchestration system that balances power with safety through sandboxed execution, allowlist-based access control, and explicit operator trust boundaries.
