The next-generation virtual cell: From spatiotemporal transcriptomic modeling to closed-loop target discovery in...

Area of Science:

• Computational biology
• Pharmacology
• Genomics

Background:

• Drug discovery for complex diseases faces high costs and low success rates.
• A paradigm shift is occurring from static models to dynamic virtual cells using multi-omics data.
• Understanding biological context, including spatial heterogeneity and tumor microenvironment, is crucial.

Purpose of the Study:

• To review advancements in next-generation virtual cells for drug discovery.
• To highlight their role in resolving drug mechanisms of action (MoA).
• To explore decoding immune evasion and acquired resistance.

Main Methods:

• Leveraging large-scale single-cell and spatial multi-omics data.
• Utilizing transcriptomic perturbation for high-throughput MoA resolution.
• Integrating in silico predictions with wet-lab validation (organoids, cellular arrays).

Main Results:

• Next-generation virtual cells offer high-throughput resolution of drug MoA.
• They aid in decoding mechanisms of immune evasion and acquired resistance.
• The approach enhances mechanistic interpretability and bridges the transcriptomics-proteomics gap.

Conclusions:

• Next-generation virtual cells are poised to accelerate pharmacological target discovery.
• Integrating computational and experimental validation is key to overcoming translational barriers.
• This technology offers new pathways for precision medicine development.