When Transcriptomic Foundation Models Scale: Domain-Focused Pretraining for Drug Development in Immunology and Inflammation

Recent work has reported two failure modes of transcriptomic foundation models: validation loss plateaus beyond 100M parameters, and consistent underperformance against simple linear baselines on clinically relevant tasks. We show that both findings invert under a specific pretraining recipe. We introduce EVA-RNA, a transformer pretrained on 545k human and mouse samples spanning bulk RNA-seq, microarray, and pseudobulked single-cell data, scoped to immunology and inflammation, one of the largest therapeutic areas in clinical development. EVA-RNA exhibits clean power-law scaling from 7M to 300M parameters, with no plateau emerging within our scale range. On a benchmark co-designed with immunologists and drug development experts, EVA-RNA outperforms existing foundation models on every task category, spanning drug discovery, preclinical-to-clinical translation and patient stratification. Mechanistically, EVA-RNA learns species-invariant representations in which orthologous genes progressively align across layers without supervision. We interpret these results as evidence that scope and data composition are sufficient, with conventional architecture and knowledge-informed gene embeddings, to achieve clinical utility and scaling in I&I. We also release EVA-RNA 60M model weights to support continued investigation.