Why multi-omics data generation has outpaced mechanistic decision-making in drug development, and our approach to catch up.
CO-AUTHORED BY:
Juozas Gordevičius, PhD. Co-Founder and CSO at VUGENE and Jean Pineault, MSc. Global Head, Life Sciences Practice, HumanFactor Enterprises LLC
Juozas Gordevičius, Founder at VUGENE
Abstract
The capacity to generate multi-omics biological data has been and remains far ahead of the capacity to interpret it. That gap has existed for over a decade, but its consequences have become structurally acute: sequencing now costs under $300 per genome, proteomics runs at industrial throughput, and data volumes compound faster than interpretation infrastructure has evolved. The industry’s response has been linear: more headcount applying the same approach at increasing cost. We characterize the nature of the interpretation gap, survey why existing approaches cannot close it, define the technical architecture needed to do so, and illustrate the operational consequences when that architecture is deployed.
The Problem
Life sciences companies have extraordinary scientists, sophisticated technology, and more data than ever before. But more scientific data does not necessarily produce better decisions. The people closest to the science may recognize an important pattern long before the organization can determine what it means for the business.
Scientific evidence moves across functions – from scientific to clinical, regulatory, commercial, and financial, and ultimately to executives, boards, and investors. At every interface, information can be interpreted differently, challenged, filtered, or stripped of important context. The result is a gap between what the science is telling the organization and what the organization ultimately decides to do.
Why It Matters
The gap shows up directly in program economics:
- Phase II success is now 28%, and the probability of approval from Phase I entry has fallen to an all-time low of 6.7% – failures that trace disproportionately to gaps in mechanistic characterization: the wrong patient population, uncharacterized off-target activity, or a biomarker strategy that arrives too late.
- Interpretation speed sets the pace of the program. A seven-week turnaround versus a seven-month one determines how many design-test-interpret cycles a program can complete before a decision is forced – every extra month burns capital and risks a missed milestone.
- A molecule without a characterized mechanism can’t move forward with confidence – not through preclinical development, not into a clinical protocol, not in front of a regulator.
- Phase II success is now 28%, and the probability of approval from Phase I entry has fallen to an all-time low of 6.7% – failures that trace disproportionately to gaps in mechanistic characterization: the wrong patient population, uncharacterized off-target activity, or a biomarker strategy that arrives too late.
Generating that data is itself demanding work – study design, sample sourcing, quality control, and platform execution all require real expertise. But while that expertise has scaled, interpretation has not kept pace. That is where the bottleneck now sits.
Read The Biology Interpretation Gap here
Cover photo credits: vectorfusionart
Photo credits: Andrej Vasilenko
