Bioinformatics for drug development
Discover drug targets, mechanisms and biomarkers with cutting-edge omics data analysis.
Molecular measurements based on next-generation sequencing (NGS) and mass-spectrometry (MS) are routinely used throughout the drug development process. RNA-sequencing and proteomics, in particular, reveal a more detailed view of pathways and functions in disease models and patients.
We work with customers developing small molecules and biologics as well as gene, cell and biomaterial therapies. Learn more about the stages in drug development that benefit from high-throughput molecular measurements coupled with state-of-the-art bioinformatics, and browse our numerous references!
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NGS and mass-spec data analysis supports drug development at all stages
Basic research into the molecular and cellular biology of a disease is a prerequisite for rational drug discovery. Much of the work our customers outsource to us falls under this category. Read more about our experience in basic research by research areas and data modalities.
Target-based drug development begins at identifying a protein or other biomolecule to use as a target for treatment. The wealth of public, semi-public and proprietary data lends itself to data-driven target discovery. Examples include:
- Identifying causative genetic variants in genome-wide association studies or genetic studies of families with a hereditary disease.
- Identifying genes associated to disease progression events from e.g. tumor RNA-sequencing or proteomic data.
- Identifying genes with disease-specific up- or down-regulation using patient samples or animal models of a disease.
- Identifying signaling pathways activated or inhibited in a disease.
A candidate target can be further studied and validated using gene knock-out models, in vitro or in vivo. Gene expression analysis of such models may reveal both wanted and unwanted downstream effects of a gene knock-out.
After a candidate drug has been identified against the target, mechanism-of-action and off-target analyses can be performed, again using in vitro or in vivo models. Transcriptomics (RNA-seq), proteomics and epigenomics (e.g., ChIP-seq, ATAC-seq) are particularly applicable high-throughput measurements.
Biomarkers, such as as genetic variants, proteins or metabolites, can be instrumental in stratifying patients based on their predicted likelihood of benefitting from the treatment.
Candidate biomarkers can be identified already before a clinical trial (from e.g., biobank data) to identify high-risk patients.
Molecular measurements during and after a clinical trial, on the other hand, can be used to identify biomarkers for treatment response or side effects (including pharmacogenetic markers).
- DNA-seq data analysis
- RNA-seq data analysis
- Single-cell RNA-seq data analysis
- Epigenomic data analysis
- Proteomic and metabolomic data analysis
- Predicting cancer survival with machine learning
- Predicting treatment response with machine learning
- Bioinformatics for cancer research
References and case studies
- Tailored bioinformatics support for a clinical trial (Amazentis)
- Mechanism of action study (MinaTx)
- Transcriptome-based target identification (Barron Biomedical)
- Cancer risk prediction with machine learning (Karolinska Institute)
- Cell therapy biomarker discovery (Kuopio Center for Gene and Cell Therapy)
- Extracellular vesicle-based biomarker discovery (FastEV)
- Pharmacogenomic sequencing analysis (Karolinska Institute)
- Mechanism of action study (Epitherapeutics)
Selected publications from our customers
- Singh, A. et al. (2022). Urolithin A improves muscle strength, exercise performance, and biomarkers of mitochondrial health in a randomized trial in middle-aged adults. Cell reports. Medicine, 3(5), 100633. https://doi.org/10.1016/j.xcrm.2022.100633
- Tusup, M. et al. (2022). Epitranscriptomics modifier pentostatin indirectly triggers Toll-like receptor 3 and can enhance immune infiltration in tumors. Molecular therapy : the journal of the American Society of Gene Therapy, 30(3), 1163–1170. https://doi.org/10.1016/j.ymthe.2021.09.022
- Madonna, G. et al. (2021). Clinical Categorization Algorithm (CLICAL) and Machine Learning Approach (SRF-CLICAL) to Predict Clinical Benefit to Immunotherapy in Metastatic Melanoma Patients: Real-World Evidence from the Istituto Nazionale Tumori IRCCS Fondazione Pascale, Napoli, Italy. Cancers, 13(16), 4164. https://doi.org/10.3390/cancers13164164
- Pernaute-Lau, L. et al. (2021). Pharmacogene Sequencing of a Gabonese Population with Severe Plasmodium falciparum Malaria Reveals Multiple Novel Variants with Putative Relevance for Antimalarial Treatment. Antimicrobial agents and chemotherapy, 65(7), e0027521. https://doi.org/10.1128/AAC.00275-21
- Hussey, G. S. et al. (2020). Lipidomics and RNA sequencing reveal a novel subpopulation of nanovesicle within extracellular matrix biomaterials. Science advances, 6(12), eaay4361. https://doi.org/10.1126/sciadv.aay4361
- Gurvich, O. L. et al. (2020). Transcriptomics uncovers substantial variability associated with alterations in manufacturing processes of macrophage cell therapy products. Scientific reports, 10(1), 14049. https://doi.org/10.1038/s41598-020-70967-2
Selected publications from our team
- Annala, M. et al. (2021). Cabazitaxel versus abiraterone or enzalutamide in poor prognosis metastatic castration-resistant prostate cancer: a multicentre, randomised, open-label, phase II trial. Annals of oncology : official journal of the European Society for Medical Oncology, 32(7), 896–905. https://doi.org/10.1016/j.annonc.2021.03.205
- Viana, J. et al. (2020). Clozapine-induced transcriptional changes in the zebrafish brain. NPJ schizophrenia, 6(1), 3. https://doi.org/10.1038/s41537-019-0092-x
- Mehtonen, J. et al. (2020). Single cell characterization of B-lymphoid differentiation and leukemic cell states during chemotherapy in ETV6-RUNX1-positive pediatric leukemia identifies drug-targetable transcription factor activities. Genome medicine, 12(1), 99. https://doi.org/10.1186/s13073-020-00799-2
- Jylhä, A. et al. (2018). Comparison of iTRAQ and SWATH in a clinical study with multiple time points. Clinical proteomics, 15, 24. https://doi.org/10.1186/s12014-018-9201-5
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