# drug development

OpenAI has acquired Orchestra Bio, a platform that helps biopharma teams coordinate drug development, and is combining its team and technology with Rosalind, OpenAI's AI models and tools for life sciences research. A new medicine takes roughly 10 to 15 years on average from target discovery to US regulatory approval, and teams must orchestrate programs across internal departments and external partners, often relying on disconnected systems. The stated aim is to build agents that connect scientific findings with the studies, decisions and next steps needed to advance a potential medicine.

OpenAI has acquired Orchestra Bio, a platform that helps biopharma teams coordinate drug development, and is combining its team and technology with Rosalind, OpenAI's AI models and tools for life sciences research. A new medicine takes roughly 10 to 15 years on average from target discovery to US regulatory approval, and teams must orchestrate programs across internal departments and external partners, often relying on disconnected systems. The stated aim is to build agents that connect scientific findings with the studies, decisions and next steps needed to advance a potential medicine.

This work introduces the Virtual Biotech, an organization of AI agents modeled on a drug-development company with agentic divisions spanning target discovery, safety assessment, modality selection, and clinical development, and demonstrates its utility at three drug-development decision points: over 37,000 agents annotated outcomes from 55,984 trials and found that drugs targeting cell-type-specific genes were 48% more likely to reach market with 32% fewer adverse events; it integrated multimodal evidence to propose a therapeutic strategy in lung cancer; and it analyzed a terminated ulcerative colitis trial and inferred potential mechanisms of failure.

This work introduces the Virtual Biotech, an organization of AI agents modeled on a drug-development company with agentic divisions spanning target discovery, safety assessment, modality selection, and clinical development, and demonstrates its utility at three drug-development decision points: over 37,000 agents annotated outcomes from 55,984 trials and found that drugs targeting cell-type-specific genes were 48% more likely to reach market with 32% fewer adverse events; it integrated multimodal evidence to propose a therapeutic strategy in lung cancer; and it analyzed a terminated ulcerative colitis trial and inferred potential mechanisms of failure.

This review proposes and organizes a framework of emerging clinical trial models for central nervous system tumors, including master protocol designs, Bayesian adaptive frameworks, trials as active discovery platforms embedding longitudinal tissue sampling, window-of-opportunity designs and multi-omic profiling, and decentralized models with artificial intelligence tools, arguing that trials should be reimagined as dynamic, biologically integrated, learning-based systems rather than static tests of individual agents in order to accelerate therapeutic progress in neuro-oncology.

This review proposes and organizes a framework of emerging clinical trial models for central nervous system tumors, including master protocol designs, Bayesian adaptive frameworks, trials as active discovery platforms embedding longitudinal tissue sampling, window-of-opportunity designs and multi-omic profiling, and decentralized models with artificial intelligence tools, arguing that trials should be reimagined as dynamic, biologically integrated, learning-based systems rather than static tests of individual agents in order to accelerate therapeutic progress in neuro-oncology.

This review proposes reframing multidrug resistance (MDR) from conventional single-gene mechanism models toward systemic adaptive resistance as a complex and evolving biological phenomenon, and systematically reviews classical resistance mechanisms (drug uptake/efflux, compensatory pathway activation, apoptosis evasion), non-genetic resistance mechanisms (drug-tolerant persisters, DTPs; epithelial-mesenchymal transition, EMT; cancer stem cell, CSC, plasticity and related factors), how interactions among these mechanisms contribute to tumor persistence and MDR evolution, and translational barriers together with emerging pharmacological strategies including longitudinal molecular monitoring and artificial intelligence (AI)-assisted drug resistance prediction.

This review proposes reframing multidrug resistance (MDR) from conventional single-gene mechanism models toward systemic adaptive resistance as a complex and evolving biological phenomenon, and systematically reviews classical resistance mechanisms (drug uptake/efflux, compensatory pathway activation, apoptosis evasion), non-genetic resistance mechanisms (drug-tolerant persisters, DTPs; epithelial-mesenchymal transition, EMT; cancer stem cell, CSC, plasticity and related factors), how interactions among these mechanisms contribute to tumor persistence and MDR evolution, and translational barriers together with emerging pharmacological strategies including longitudinal molecular monitoring and artificial intelligence (AI)-assisted drug resistance prediction.