- 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
- 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?
- 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
“KEM Clinical services were instrumental in the outcome of a discussion between my former [Pharma] Company and a large Pharmaceutical organization during the M&A process. “
“Opus Three assisted our company over a period of 4 years in determining path to market, product priorities, and regulatory strategy. Their extensive and up-to-date knowledge of the global regulatory environment and the clinical evidentiary demands was excellent, and enabled the company to successfully navigate the changing healthcare landscape as a young technology-based company.”
“Opus Three provided sure-footed guidance for a plan to obtain FDA approval for a new infectious disease diagnostic, and the clinical studies required to compile the necessary data.”
“Dr. Frueh has provided our company with extremely useful guidance in exploring the commercialization of a Genetic Test for Depression. His pragmatic detailed strategic advice has been coupled with magnanimous personal network connections, at the highest levels of this sector.”