Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project

Bie Verbist, Günter Klambauer, Liesbet Vervoort, Willem Talloen, Ziv Shkedy, Olivier Thas, Andreas Bender, Hinrich W. H. Göhlmann, Sepp Hochreiter

Drug Discovery Today · 2015 · 99 citations · 40 references

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TL;DR

The pharmaceutical industry faces declining R&D efficiency, largely because late‑stage failures arise from safety issues or undiscovered side‑effects. The study investigates whether gene expression profiling can de‑risk early‑phase drug development by detecting compound effects across disease areas, targets, and scaffolds. The authors analyzed gene expression data to identify these biological effects. In eight projects, gene expression profiling proved informative, supporting go/no‑go decisions and detecting adverse effects, demonstrating its value in early‑stage drug discovery.

Abstract

The pharmaceutical industry is faced with steadily declining R&D efficiency which results in fewer drugs reaching the market despite increased investment. A major cause for this low efficiency is the failure of drug candidates in late-stage development owing to safety issues or previously undiscovered side-effects. We analyzed to what extent gene expression data can help to de-risk drug development in early phases by detecting the biological effects of compounds across disease areas, targets and scaffolds. For eight drug discovery projects within a global pharmaceutical company, gene expression data were informative and able to support go/no-go decisions. Our studies show that gene expression profiling can detect adverse effects of compounds, and is a valuable tool in early-stage drug discovery decision making.

References

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