敏感，大规模定量代谢组学的策略

Executive Industry Relevance

This protocol enables sensitive, large-scale quantitative metabolomics for polar metabolites without derivatization, addressing key limitations in current platforms such as low detection limits and slow scan speeds. By coupling amide-based hydrophilic chromatography with Q-Exactive MS capable of positive/negative ion switching, it supports robust detection of 168 targeted polar metabolites and thousands of additional features. The method enhances predictive confidence in target validation and mechanistic de-risking by providing reproducible, quantitative metabolic profiles from cultured cells, with direct applicability to serum and tissue samples.

Strategic Applications in Biopharma R&D

Early Discovery & Target Validation

• Scientific Value: Enables interrogation of therapeutic hypotheses through comprehensive polar metabolite profiling in disease-relevant cellular systems.
• Operational Value: Supports biological de-risking by quantifying pathway-specific metabolites (e.g., glycolysis, TCA cycle, nucleotide synthesis) to validate target engagement.
• Predictive Value: Facilitates portfolio triage by linking metabolic perturbations to mechanism of action and pathway modulation.

Screening & Assay Development

• Scientific Value: Prepares validated biological systems for downstream screening by establishing baseline metabolic states and detecting off-target effects.
• Operational Value: Ensures assay standardization and reproducibility through rigorous mass calibration (<5 ppm error) and CV-based filtering (20% cutoff) for reliable metabolite quantification.
• Scalability: Enables high-throughput analysis via automated sample sequencing with wash/run blanks to minimize carryover and ensure cross-run comparability.

Translational & Preclinical Research

• Translational Continuity: Supports seamless transition from discovery to preclinical validation by enabling consistent metabolite profiling across cultured cells, serum, and tissues.
• Biomarker Alignment: Detects endogenous metabolites (e.g., glutathione, proline, methyl glyoxal) that serve as translational biomarkers for pathway activity and drug response.
• Risk-Adjusted Advancement: Provides quantitative metabolic readouts to inform go/no-go decisions based on on-target pathway modulation and absence of toxic signatures.

Pipeline & Workflow Integration

The method integrates into the discovery continuum from early biology through lead identification to preclinical studies, enabling metabolic phenotyping at each stage to de-risk targets and mechanisms.

• Discovery Biology: Supports hypothesis testing and pathway clarification by quantifying changes in central carbon metabolism and redox metabolites upon perturbation.
• Screening: Delivers assay-ready, reproducible quantitative outputs (peak areas) for compound library screening and SAR analysis.
• Analytics: Generates high-resolution, accurate mass data (<5 ppm error) enabling confident metabolite identification and quantification across positive and negative ionization modes.
• Translational Research: Ensures continuity by applying the same extraction and detection protocol to serum and tissue samples, preserving metabolic fidelity.
• Enterprise Reuse: Establishes a standardized, scalable metabolomics platform reusable across projects and therapeutic areas with minimal reoptimization.

Operational & Enterprise Impact

• Scientific Value: Increases predictive confidence in target validation by reducing mechanistic ambiguity through comprehensive polar metabolite coverage.
• Operational Value: Delivers standardization, reproducibility, and scalability via automated LC-MS workflows and software-based peak alignment.
• Strategic Value: Improves go/no-go decision quality, capital efficiency, and reduces late-stage attrition by identifying off-target metabolic effects early.
• Portfolio Impact: Enables risk-based prioritization through quantitative metabolic signatures linked to mechanism and safety.

Implementation Considerations

• Requires expertise in metabolomics sample preparation, LC-MS operation, and data analysis using commercial software for peak extraction and database matching.
• Dependence on high-resolution MS (Q-Exactive or equivalent) with HESI source, nitrogen gas supply, and temperature-controlled centrifugation and vacuum drying equipment.
• Necessitates cross-team standardization of extraction solvents, cell harvesting timing (80% confluence), and cold methanol quenching to ensure reproducibility.
• Adaptation across model systems requires validation of metabolite recovery and matrix effects, particularly for serum and tissue lysates.
• Practical limitations include ion suppression at high cell densities (>1×10⁷ cells) and the need for manual peak inspection to filter noise-level components in untargeted analysis.

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