FALS Therapeutics: Prompting a Blueprint for a Biotech Discovering and Developing Disease-Modifying Therapies for Amyotrophic Lateral Sclerosis Using an Agentic AI Framework

The high cost and complexity of early-stage drug discovery create profound barriers for raredisease programs with limited funding. Sporadic amyotrophic lateral sclerosis (sALS) exemplifies this challenge: despite affecting 90-95% of the ~32, 000 prevalent ALS patients in the United States, no approved disease-modifying therapy exists specifically for sALS. Here we demonstrate FALS Therapeutics, a conceptual virtual biotech blueprint built and directed entirely through iterative prompting of Claude Sonnet 4. 6 (Anthropic), a commercially available agentic large language model accessed through the claude. ai conversational interface without custom software infrastructure, MCP servers, or fine-tuning. A CEO agent received a 5M seed mandate and instantiated four AI direct-report agents: Head of Commercial, Head of Research, Head of CMC, and Head of Clinical Development, forming a Development Committee (DC) for stage-gate governance. The virtual organization completed four sequential work-streams: commercial landscape analysis, research master plan including target selection and molecule generation decision, clinical development master planning including a patient enrichment modeling strategy and combination trial proposal, and CMC manufacturing planning across four drug modalities. The blueprint was created by the AI agent via planning documents and interactive dashboards, presentation decks, figures, tables and code. Key outputs include an ~791-812M global ALS market characterization, a 12-target ranked biological long-list with TBK1 identified as a novel Tier 1 target not in the initial mandate, a fully open-source AI tool stack saving ~302, 000/year in licensing costs, a Phase 1-3 clinical development plan with a four-layer patient enrichment model (FALS-PEM) estimated to reduce Phase 2b sample sizes by 40-60%, a Phase 2a and 2b combo trial of two assets in the pipeline proposed by the AI agent without prompting, and a consolidated CMC budget of ~35. 5M across all modalities from seed to NDA. A critical design feature - the iterative gap-identification and gap-closure loop directed by the CEO agent by prompts from expert human-in-the-loop - produced 18 governance documents without additional human specification. A systematic reference verification audit - the primary methodological contribution - flagged seven categories of AI-generated claims requiring caution and identified and corrected three errors introduced by AI hallucination. This work extends earlier published work of AI agents in drug discovery 9, 10, 11 by demonstrating the lower bound of what expert-guided prompting of an unmodified conversational AI, without bespoke agent infrastructure, can achieve. It can generate the full portfolio of pre-IND planning documents for a rare-disease biotech, compressing months of work into days at minimal cost but expert review and continuous feedback via prompting is still crucial for ensuring scientific and technical validity.