The authors argue that the pharmaceutical industry’s dominant mental model, the linear pipeline in which discovery hands off to preclinical testing, then to phase I, II and III trials, is fundamentally mismatched to the complexity of human biology. In a pipeline, information flows forward and decisions are made at discrete gates with limited opportunity to revisit earlier assumptions. When a candidate fails in phase II, the most common reasons, according to decades of attrition analyses, are not pharmacokinetics or safety but lack of efficacy, meaning the underlying biological hypothesis was wrong, or the wrong patients were enrolled. By that point, hundreds of millions of dollars have been spent, and the evidence that could have corrected the hypothesis has often been discarded rather than systematically captured and fed back into the program.

Shalita and Amit propose reframing drug development as an iterative learning system driven by continuous data feedback, a structure they illustrate most thoroughly with immunotherapies, one of the most transformative and also most unpredictable areas of modern medicine. Cancer immunotherapies, from checkpoint inhibitors to CAR T cells, work by manipulating the immune system itself, and the immune system is exquisitely context-dependent: it differs between tissues, between individuals, between tumor types, and even within a single tumor across space and time. The authors contend that high-dimensional, multimodal molecular readouts, particularly single-cell and spatial profiling of therapeutic perturbations, can serve double duty. They can de-risk development by revealing early whether a drug is engaging its intended biological circuits in human patients, and they can deepen mechanistic understanding of how human immune responses actually work, knowledge that in turn sharpens the next round of therapeutic design.

The technical core of the framework is a feedback loop analogous in structure to reinforcement learning. In reinforcement learning, an agent acts, the environment returns a reward signal, and the agent updates its policy accordingly. In the proposed biomedical analogue, the intervention, whether a small molecule, antibody, cytokine or engineered cell, is the action; the patient’s molecular and clinical response, measured through single-cell sequencing, spatial transcriptomics, and longitudinal clinical assessments, is the feedback signal; and the mechanistic models of disease, sometimes framed as ‘virtual cell’ models, are the policies being updated. Each cycle of treatment and measurement refines the model, and the refined model guides the design of the next intervention. Crucially, the authors emphasize that human data, not animal or cell-line data, must provide the authoritative signal, because the immune circuitry that determines success or failure in patients is precisely what current preclinical systems most often fail to reproduce.

This emphasis on context is a direct response to a long-standing weakness in computational biology. Much effort has gone toward building a universal ‘virtual cell’, a comprehensive model of cellular behavior that would generalize across all conditions. Shalita and Amit argue instead for context-specific virtual cell models: models tuned to a particular disease state, tissue microenvironment, or patient population. The distinction matters because the same molecular circuit can behave oppositely in a healthy tissue and an inflamed tumor. Single-cell atlases such as the Human Cell Atlas and Tabula Sapiens, together with disease-specific atlases of tumors and inflamed tissues, provide the reference substrate, but the framework demands that these references be continuously updated with perturbation data, the readouts of what actually happens when a therapeutic is applied, rather than static snapshots of untreated biology.

The toolkit for generating perturbation data has matured rapidly. Pooled CRISPR screens coupled to single-cell RNA sequencing, first demonstrated in 2016, now allow researchers to map genotype-phenotype relationships at genome scale, and newer methods extend this to spatially resolved, multimodal readouts directly in tissue. Harmonized datasets of single-cell perturbation experiments are becoming shared community resources, and massively multiplexed chemical screens can profile thousands of drug candidates at single-cell resolution. The authors argue that these technologies should not sit solely in discovery labs but should be embedded throughout the development process, including in early-phase clinical trials, where serial biopsies and blood draws can reveal within weeks whether a therapy is reprogramming the intended immune cell states, long before conventional efficacy endpoints mature.

Immunotherapy case studies illustrate the cost of the linear model. Resistance to checkpoint blockade frequently traces to specific tumor or immune cell states, such as loss of interferon-gamma pathway genes or exclusion-prone cancer cell programs, that only single-cell analysis of patient samples could reveal. CAR T cell programs have encountered off-tumor toxicity when single-cell atlases later showed that the nominal tumor antigen was expressed in unexpected normal tissues, and neurotoxicities have been linked to mechanisms invisible to conventional monitoring. Conversely, single-cell analysis of patients’ pretreatment immune landscapes is increasingly able to predict who will respond to bispecific T cell engagers or CAR T therapy, pointing toward patient stratification as a first-class design input rather than a post hoc statistical adjustment. Each of these findings, the authors note, is exactly the kind of feedback signal that could have redirected a development program earlier had the loop been closed in real time.

The framework also carries implications for how development organizations are structured. The authors point to industry analyses showing that the best-performing research and development organizations succeed not by generating more candidates but by making better decisions about which hypotheses to pursue and when to abandon them, echoing the observation that quality of biological rationale, evidenced for example by the strong correlation between human genetic support and drug approval success, beats quantity. An iterative, data-centric system institutionalizes that principle: every trial is also an experiment, every measurement updates the model, and termination of a candidate is not a sunk cost but a data acquisition event that improves the next iteration. The authors draw a parallel to reinforcement learning from human feedback in artificial intelligence, where the model is continually aligned to human judgments; here, the model is aligned to human molecular and clinical reality.

Significant obstacles remain. Single-cell and spatial assays are still expensive and logistically demanding inside clinical trials, computational models are sensitive to batch effects and require rigorous validation, regulatory pathways are built around linear evidence generation, and access to high-quality longitudinal patient samples is uneven. The authors argue these are engineering and policy problems rather than fundamental barriers, and point to ongoing initiatives, from large-scale cell atlasing efforts to public virtual cell challenges, as evidence that the community is assembling the necessary infrastructure. Their perspective is ultimately a call to treat the drug development process itself as a learning system: mechanistically grounded, continuously updated by human data, and designed so that every patient treated generates knowledge that improves the next therapy. If the last several decades have proven that more data alone does not produce more approved drugs, the coming test is whether closing the loop between data and decisions can.

Subject of Research: Data-centric feedback loops that use single-cell and clinical data to iteratively improve immunotherapy development

Article Title: Data-centric feedback loops for next-generation immunotherapy development

Article References: Data-centric feedback loops for next-generation immunotherapy development. (n.d.). https://doi.org/10.1038/s41551-026-01785-6

Image Credits: AI Generated

DOI: 10.1038/s41551-026-01785-6

Keywords: immunotherapy, drug development, single-cell genomics, data-centric frameworks, feedback loops, reinforcement learning, virtual cell models, CAR T cells, checkpoint inhibitors, patient stratification, CRISPR screening, Nature Biomedical Engineering