Scientists Flip the Drug Discovery Pipeline to Put Human Biology First in Cardiovascular Research

Cardiovascular medicine is quietly undergoing a structural rethink, and the change begins with the order of operations. For most of the past half century, the discovery pipeline that produced heart disease therapies ran in one direction: researchers would identify a promising molecule or target in cells or animals, develop a drug against it, and only then move into human testing, where the vast majority of candidates failed. A new perspective published in Experimental & Molecular Medicine argues that this linear, ‘pipeline-first’ model has exhausted much of its productive capacity, and that the field should reverse its direction — starting with richly characterised human data and working backwards toward mechanisms, targets and therapies. The approach, described as a ‘human-first’ multi-omics strategy, aims to make human biology the starting point of discovery rather than its final examination.

The scale of the problem motivating this reversal is stark. Despite decades of progress, cardiovascular disease remains the leading cause of death worldwide, and the attrition rate in cardiometabolic drug development remains punishingly high. Many agents that showed dramatic effects in animal models of atherosclerosis, heart failure or hypertension produced negligible benefit, or unacceptable toxicity, when they reached human trials. The authors of the new work contend that this is not a failure of chemistry or clinical execution but a failure of translational assumptions: animal models, however refined, cannot fully reproduce human cardiovascular physiology, human genetic variation, or the decades-long natural history of diseases that unfold over a lifetime in people.

The human-first proposal inverts the classical sequence. Rather than beginning with a hypothesis generated in a model system, investigators begin with comprehensive molecular measurements drawn directly from human populations — genomes, transcriptomes, proteomes, metabolomes, epigenomes and, increasingly, single-cell profiles of human cardiovascular tissue. These layers of ‘omics’ data are then integrated computationally to reveal which genes, pathways and cell types are genuinely perturbed in human disease. Only after such human-grounded signals are identified does the work move toward experimental validation, drug targeting and therapeutic design. In effect, the clinic and the population study become the source of hypotheses, and the laboratory becomes the venue for testing them.

Each omics layer contributes a different kind of evidence. Genome-wide association studies have already delivered hundreds of genetic loci linked to myocardial infarction, atrial fibrillation, cardiomyopathy and related traits, but most of these associations point to non-coding regions of the genome whose function is unknown. Transcriptomics converts those static genetic signals into dynamic statements about which genes are actively expressed in diseased hearts and vessels. Proteomics captures the actual effector molecules of biology — the proteins that drugs must engage — while metabolomics offers a real-time readout of cellular chemistry and environmental influence, including diet, microbiome activity and medication effects. Epigenomic profiling explains how risk is encoded not only in DNA sequence but in the regulation of gene activity across a lifetime.

The real power, the authors argue, emerges from integration. No single omics layer is sufficient, because each is noisy, incomplete and context-dependent, but convergent evidence across layers can separate true disease biology from statistical artifact. If a genetic variant associated with coronary artery disease falls in a regulatory region that is active specifically in human vascular smooth muscle cells, and if the gene it controls shows altered expression and altered protein abundance in diseased tissue, and if metabolites in the same pathway track with disease severity, the case for that pathway’s causal involvement becomes far stronger than any single measurement could provide. Multi-omics integration is thus a way of triangulating on human disease mechanisms with a confidence that single-technology studies rarely achieve.

Recent technological advances have made this vision practical in a way it was not a decade ago. Single-cell RNA sequencing can now resolve the cellular composition of human heart tissue cell by cell, revealing disease-specific states in cardiomyocytes, fibroblasts, endothelial cells and immune cells that bulk measurements average away. Spatial transcriptomics preserves the anatomical context of gene expression, showing not just which cells are involved but where they sit within the architecture of a plaque or an infarcted wall. Long-read sequencing is closing gaps in genome annotation. Mass spectrometry has pushed proteomics toward near-comprehensive coverage of the human proteome. Meanwhile, large biobanks linked to electronic health records — containing hundreds of thousands of participants with genetic data and longitudinal clinical outcomes — provide the population-scale foundation on which human-first discovery depends.

Human pluripotent stem cell technologies supply the experimental counterpart to these population resources. Induced pluripotent stem cell-derived cardiomyocytes and vascular cells allow investigators to model an individual’s genetic background in a dish, testing how specific risk variants alter cell behaviour under controlled conditions. When combined with CRISPR-based gene editing, these systems permit precise causal tests: take a human variant identified through population omics, introduce or correct it in human cells, and observe the consequences. This closes the loop of the reversed pipeline, in which human observation generates the hypothesis and human-derived experimental systems validate it before any animal model or clinical trial is considered.

The therapeutic implications are already visible in recent cardiovascular successes that followed this logic in reverse. PCSK9 inhibitors emerged from human genetics — people with loss-of-function variants in the gene had low LDL cholesterol and reduced heart attack risk — rather than from animal screening. The discovery that elevated lipoprotein(a) is causally linked to aortic stenosis and coronary disease came from human cohort genetics, and drugs targeting that protein are now in late-stage trials. Angiotensin-related pathways, inflammation-driven residual risk identified through human trial data with canakinumab, and genetic validation of targets for heart failure all illustrate the same principle: targets grounded in human evidence have repeatedly outperformed targets chosen on model-organism grounds alone.

The authors are careful to note the substantial challenges that remain. Multi-omics datasets are expensive, and most existing data come from populations of European ancestry, raising urgent questions about equitable generalisation. Integrating heterogeneous data types requires statistical and computational methods that are still maturing, and correlational signals at population scale do not automatically establish causation. Human tissue, particularly healthy and early-disease cardiac tissue, is difficult to obtain, and much of what is available comes from end-stage disease or organ donors, limiting the view of how cardiovascular disease begins. Privacy and consent frameworks for deeply characterised human data remain an active area of policy development. The human-first approach, in other words, demands infrastructure — biobanks, computational platforms, tissue networks and diverse cohorts — as much as it demands new biology.

Even so, the strategic argument is compelling and timely. The pharmaceutical industry has invested heavily in human genetics and real-world data precisely because traditional pipelines have underdelivered in cardiometabolic disease. Academic consortia assembling multi-omics atlases of the human heart and vasculature are generating public resources that any laboratory can interrogate. Artificial intelligence and machine learning, trained on integrated human datasets, are beginning to predict gene function, prioritize drug targets and identify patient subgroups that classical diagnostics lumped together. The human-first framework unifies these developments into a coherent discovery philosophy: measure human biology comprehensively, infer mechanism from the data, validate in human-derived systems, and only then build the therapeutic. If the approach continues to deliver, the pipeline that once flowed from bench to bedside may increasingly be understood as flowing the other way — with the patient, and the population, at its source.

Subject of Research: A human-first multi-omics strategy for cardiovascular disease discovery

Article Title: Reversing the pipeline: a ‘human-first’ multi-omics approach to cardiovascular discovery

Article References: Reversing the pipeline: a ‘human-first’ multi-omics approach to cardiovascular discovery. (n.d.). https://doi.org/10.1038/s12276-026-01835-8

Image Credits: AI Generated

DOI: 10.1038/s12276-026-01835-8

Keywords: cardiovascular disease, multi-omics, human genetics, drug discovery, genomics, proteomics, metabolomics, single-cell sequencing, precision medicine, translational research, Reversing, pipeline