Advanced Analytics

The conundrum of a precision-based approach to medical product development is that it often starts with the opposite: the analysis of a small dataset with heterogeneous data. Such analyses easily result in significant multivariate problems, which can only be alleviated using advanced analytical and Artificial Intelligence (AI) tools. If your data set is too complex to easily identify the markers that may be useful for selecting patients for a clinical trial, finding characteristics that may expose a patient to potential harm, or if you need a model to find potential new indications for a molecule, we can support your needs: our experts have extensive experience in complex data analytics, modeling and simulation, and innovative statistical applications. They are also the inventors of KEM® (Knowledge, Extraction, Management), a versatile AI data analysis platform allowing, for example, the integration disperse patient attributes to create logical signatures which can be used to map patients to clinical response and outcomes.

Representative Services

  • Discovery of biomarkers from limited/ sparse data sets
  • ‘Big data’ analytics, including e.g. data analysis using combination of large heterogeneous data sources
  • Analysis of complex biomarker data and clinical data sets
  • Evaluation of complex data against clinical safety and efficacy results
  • Identification of optimal drug combination and biomarker interactions for optimized drug – biomarker pairing
  • Analysis of microbiome data and identification of microbial species for lead in drug development
  • Identification of baseline patient characteristics, optimal clinical endpoints, responder-and non-responder populations
  • Statistical analysis for real world evidence (RWE) and health economics and outcome research (HEOR) applications
  • Modeling and simulation allowing integration of preclinical and clinical data for translational medicine
  • Using public databases to validate hypothesis derived from clinical trial

Typical Challenges we Encounter in Advanced Analytics

  • I have multiple clinical trial data regarding same product. Can I perform a meta-analysis and identify optimal indication, companion markers and potential synergistic products?
  • I have Phase 1 dose escalation data in a patient population. Can I derive early efficacy and patient selection hypothesis for design of Phase 2?
  • Can I integrate pre-clinical data to interpret the results?
  • I have data from a 30 patient Phase 2a clinical trial. Can I use the data to generate companion marker hypothesis and design of a Phase 2b trial?
  • Do I have and can I demonstrate a dose-response relationship for my drug when a simple linear relation does not exist?
  • The assessment of the condition of my patients is made through a number of scores that combine questionnaires. How can I introduce a biomarker approach with the questionnaire for optimal patient selection?

Examples of Completed Assignments

  • Discovery of biomarkers for sensitivity assessment and optimization of indication during pre-clinical phase on oncology application
  • Companion diagnostic biomarker discovery using ‘omics data’ against efficacy results from multiple cell lines, integration of results into design of Phase 2 confirmatory trial.
  • Identification of optimal drug combination and biomarker interaction in pre-clinical setting leading to synergistic drug – companion biomarker pairing.
  • Phase 2 immune-oncology trial analysis leading to identification immune markers associated with efficacy; followed by design of Phase 3 clinical trial
  • Analysis of Phase 3 trial data (rheumatology indication) leading to the identification of hyper-responder subpopulation that was used for building case for reimbursement
  • Compilation and analysis of 135 million patients records (real-world data) for the identification of at-risk populations at risk (health economics and outcomes research, HEOR, setting)
  • Identification and validation of biomarkers of therapeutic synergy for combination therapy in melanoma.
  • Enhancement of an ongoing clinical trial with DNA, RNA and microbiota data in Alzheimer’s Disease.
  • Retrospective analysis of 30 patients, identification of inclusion biomarkers and design and submission of a targeted therapy Phase 2 clinical trial for accelerated approval
  • Retrospective meta-analysis of phase III clinical studies and supporting responder (sub-)group evidence for reimbursement discussion with payers in fibromyalgia indication
  • Diagnostic signature detecting response to Hepatitis C treatment

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