Creative Proteomics' drug response analysis solutions provide integrated multi-omics analytics to help researchers uncover molecular mechanisms, identify predictive biomarkers, and generate actionable insights, enabling faster, data-driven decisions in compound evaluation and drug development.
What is Drug Response
Drug response research systematically studies how biological samples—such as cell lines, organoids, or experimental model systems—react to a chemical compound. The focus is on measuring the changes at multiple biological levels, including genomics, transcriptomics, proteomics, metabolomics, and lipidomics. This comprehensive view allows scientists to identify key molecular drivers that influence sensitivity or resistance to compounds, understand mode-of-action pathways, and guide compound optimisation.
Rather than relying solely on phenotypic observations, modern drug response research combines quantitative molecular data with advanced computational modelling. This approach supports:
- Sensitivity analysis: Determining the concentration range at which compounds elicit measurable biological effects.
- Resistance mechanism studies: Identifying molecular alterations that reduce compound efficacy.
- Mode-of-action mapping: Uncovering pathways and networks engaged after compound exposure.
- Off-target effect profiling: Detecting unintended molecular interactions.
Technical Workflow for Multi-Omics Drug Response Analysis
Technical Advantages of Our Drug Response Analysis Service
- Comprehensive Data Dimensions: Integrating proteomic and metabolomic datasets alongside genomic and transcriptomic data to capture a complete molecular landscape.
- Proprietary AI Ensemble Learning Algorithm: Enhances model stability and predictive accuracy through multi-model integration, reducing overfitting and improving robustness.
- Multi-Modal Data Integration: Seamlessly combines experimental data from various omics layers with metadata from experimental design, enabling deeper biological interpretation.
- End-to-End Service Model: We offer a streamlined workflow under a unified quality management framework from experimental design to advanced statistical modelling.
Multi-Omics Analysis Service We Provide
- Whole-Exome Sequencing (WES): Detects single-nucleotide variants (SNVs) and small insertions/deletions (indels) associated with drug response.
- RNA Sequencing (RNA-seq): Captures transcriptome-wide changes to reveal active molecular pathways.
- Untargeted Metabolomics: Profiles diverse metabolites to identify biochemical pathway alterations linked to compound exposure.
Recommend integrative analysis of multi-omics:
Transcriptomics + Metabolomics
Transcriptomics + Proteomics + Metabolomics
Data Analysis and Interpretation
- Characterization of Differential Response Features: Identifying and profiling molecular and biochemical features that distinguish distinct response groups, highlighting variations across genomic, transcriptomic, proteomic, metabolomic, and microbiome layers.
- Risk Factor Analysis: Systematic evaluation of factors associated with altered response outcomes, including genetic variants, expression patterns, metabolic signatures, and microbial community composition.
- Correlation Analysis of Key Factors: Quantitative assessment of relationships between different biological variables, revealing potential regulatory networks and interdependent pathways that influence compound response.
- Feature Weighting and Combination Selection: Application of advanced statistical and machine learning algorithms to assign weights to individual features and identify optimal feature combinations for predictive modeling.
- Biomarker Model Comparison and Evaluation: Construction and benchmarking of multiple biomarker-based models, assessing performance metrics such as sensitivity, specificity, and predictive power to determine the most robust and generalizable model.
Application for Multi-Omics Drug Response Analysis
- Investigating molecular mechanisms underlying variable compound responses.
- Identifying molecular signatures linked to sensitivity or resistance.
- Supporting compound optimization and lead prioritization in R&D pipelines.
- Evaluating the biological impact of novel chemical entities in preclinical models.
- Discovering biomarkers for response classification in research models.
Sample Requirements (Recommended)
| Sample Type | Recommended Amount | Storage Condition | Notes |
|---|---|---|---|
| Whole Blood | ≥ 5 mL | EDTA or heparin tube, -80 °C | Avoid repeated freeze–thaw cycles; process within 2 hours after collection. |
| Serum | ≥ 500 µL | -80 °C | Centrifuge within 2 hours of collection; avoid hemolysis. |
| Plasma | ≥ 500 µL | -80 °C | Use EDTA or citrate anticoagulant; avoid repeated freeze–thaw cycles. |
| Fresh Frozen Tissue | ≥ 50 mg | Snap-frozen in liquid nitrogen, store at -80 °C | Avoid thawing before analysis; ensure minimal ischemic time. |
| FFPE Tissue | ≥ 5 sections (5–10 µm thick) | Room temperature (dry) | Use non-decalcified samples; avoid over-fixation. |
| Cell Pellet | ≥ 1 × 10⁶ cells | -80 °C or liquid nitrogen | Wash with PBS before freezing; avoid residual medium. |
| Microbiome Samples | ≥ 200 mg (stool) or swab | -80 °C | Use sterile collection tools; freeze immediately after collection. |
| Urine | ≥ 1 mL | -80 °C | Centrifuge before freezing to remove debris. |
Why Choose Creative Proteomics
- Integrated Multi-Omics Expertise – Extensive experience in genomics, transcriptomics, proteomics, and metabolomics under one roof.
- Advanced Instrumentation – Cutting-edge LC-MS/MS platforms, next-generation sequencers, and high-throughput data processing systems.
- AI-Driven Analysis – Proprietary algorithms enhance predictive accuracy and robustness.
- Flexible Service Models – Fully customizable workflows to meet unique research requirements.
- Data Security and Confidentiality – Strict protocols to ensure data integrity and project privacy.
References
- Chen C, et al. Applications of multi‐omics analysis in human diseases. MedComm, 2023, 4(4): e315.
- Du P, et al. Advances in integrated multi-omics analysis for drug-target identification. Biomolecules, 2024, 14(6): 692.
- Jiang W, et al. Network-based multi-omics integrative analysis methods in drug discovery: a systematic review. BioData Mining, 2025, 18(1): 27.
Integrative Multi-Omics and Drug-Response Characterization of Chinese Prostate Cancer Primary Cell Models
Journal: Signal Transduction and Targeted Therapy
Impact factor: 52.7
Published: 2023
DOI: 10.1038/s41392-023-01393-9
Backgrounds
Prostate cancer (PCa) is the second most prevalent malignancy among men globally. Despite advances in treatment, the molecular mechanisms underlying drug responses remain poorly understood, particularly in Asian populations. Traditional models often fail to capture the genetic and phenotypic diversity of PCa. This study aimed to establish a comprehensive resource of Chinese PCa primary cell models to investigate the molecular determinants of drug response using a multi-omics approach.
Materials & Methods
Developed 35 primary cell lines from Chinese PCa patients, including 25 tumour and 10 benign prostatic hyperplasia (BPH) samples.
| Omics / Assay | Samples analyzed (n) | Key quantitative results (data) | Notes / interpretation |
|---|---|---|---|
| Whole-Exome Sequencing (WES) | 23 samples (WES performed) | 1,329 somatic variation events identified. Overlap with TCGA: 84.6% overlapping mutated genes; 15.6% unique mutations. | WES used to profile somatic variants; highlights ethnic/tumour-specific mutational differences. |
| RNA-seq (transcriptome) | 25 primary cells (tumour + BPH) | 17,558 genes with TPM > 1. | Transcriptome measured across 25 samples; largest dynamic range among omics layers. |
| Global proteomics (TMT LC-MS/MS) | All samples (batches; internal reference pooled) | 7,062 protein groups (FDR 1%). tumour proteins: 6,848; BPH proteins: 6,656. | High reproducibility (internal reference correlation >0.97). Proteome depth lower than transcriptome. |
| Cell-surface proteomics (biotinylation enrichment) | Paired primary samples (n = 26 for surface proteome analyses) | 2,644 cell-surface proteins identified (combined). Per-sample range: 1,842–2,330 surface proteins. ~56% annotated as plasma membrane/membrane-related. | Surfaceome enrichment enabled membrane protein profiling despite low abundance/hydrophobicity. |
| Differential expression (BPH vs tumour) | Proteome / Surface proteome / Transcriptome | Proteome DEPs: 122 proteins (p<0.05, FC>1.2); Surface DEPs: 115 proteins (p<0.05, FC>1.2); Transcriptome DEGs: 637 genes (Wald test, p<0.05, FC>1.5). | Shows the numbers of significantly changed features at each molecular layer. |
Results
- Genetic Alterations: The WES data revealed distinct mutational landscapes between tumour and BPH cell lines, with a higher frequency of gene mutations associated with aggressive PCa subtypes.
- Protein Expression Profiles: Proteomic analyses identified over 7,000 proteins, with differential expression patterns observed between tumour and BPH cells.
- Surface Protein Markers: Cell-surface proteomics highlighted potential biomarkers that could be targeted for therapeutic intervention.
- Drug Sensitivity: The study identified crizotinib as a selective compound for malignant PCa primary cells, with enhanced efficacy observed when combined with the inhibition of Anterior Gradient 2 (AGR2).
- Pharmacoproteomic Insights: 14,372 significant protein-drug correlations were established, providing a comprehensive map of potential therapeutic targets.
Fig. 1 Multi-omics landscape of PCMR.
Fig 2. Integrative multi-omics analysis of PCMR.
Conclusions
This study presents a robust multi-omics resource for understanding the molecular basis of drug response in PCa. Integrating genomic, transcriptomic, proteomic, and pharmacological data offers valuable insights into potential therapeutic strategies. The findings underscore the importance of considering ethnic-specific factors in drug development and highlight the utility of multi-omics approaches in precision oncology research.
What computational tools are commonly used in drug response analysis?
Machine learning, network pharmacology, and predictive modeling are widely applied to integrate omics datasets, identify drug–target interactions, and forecast biological responses to compounds.
What types of data are generated in drug response studies?
Typical datasets include gene expression profiles, protein abundance and modification states, metabolite concentrations, and pathway activation signatures, providing a holistic view of biological changes.
How does genetic variability influence drug response?
Genetic polymorphisms can alter drug-metabolizing enzymes, transporters, and receptors, resulting in variable responses. Studying these differences helps identify biomarkers and predict inter-individual variability in drug efficacy or side effects.
Why is a multi-omics approach critical in drug response studies?
Drug effects are not confined to a single molecular layer. By integrating genomics, transcriptomics, proteomics, and metabolomics, researchers can capture a systems-level view of drug action, revealing mechanisms that would remain hidden in single-omics studies.
Demo 1: Microscaled proteogenomic methods for precision oncology
Fig 3. Microscaled proteogenomics of the DP1 clinical trial (Satpathy S, et al., 2020).
Demo 2: Multi-omic machine learning predictor of breast cancer therapy response
Fig 4. Transcriptomic features associated with response to neoadjuvant therapy (Sammut S J, et al., 2021).