We are focused on Precision Medicine as an approach to discovering and developing potential treatments that can deliver superior outcomes for patients, by integrating clinical and molecular data to understand the biological basis of disease, the pharmacology of our drug candidates and the appropriate patient population to treat. Precision medicine efforts have the potential to lead to better matching of drug targets with selected patient populations that may experience clinical benefit.
We are interested in establishing collaborations to develop and access:
Patient cohorts
- Large-scale datasets with high quality longitudinal clinical (e.g., electronic medical record)
- Molecular, imaging and other phenotypic data appropriately consented, preferably with broadly consented biospecimens (e.g., whole blood serum/plasma, saliva, tissue, PBMCs, stool, etc.)
- Cohorts with the potential to recall patients based on genotype or phenotypefor follow up studies
Systems Biology/Pharmacology
- Databases with high quality data on treatment and disease outcomes associated with genetic, as well as molecular (metabolomic, proteomic transcriptomic, epigenetic, clinical chemistry markers) or functional measures, in particular with associated imaging data
- Databases of searchable expression quantitative trait loci (eQTLs), protein quantitative trail loci (pQTLs) across tissues
- Disease biology guided combination therapy design platforms
- Systems biology approaches and proven in silico tools to evaluate pharmacological perturbation and to elucidate mechanisms of in vivo toxicity
- Mining of data for correlation and understanding of causality
Breakthrough diagnostic technologies that are highly quantitative, require minimal specimen/ tissue, can offer quick turnaround time and can be multiplexed. This will include but is not limited to:
- Near-patient Point-of-Care technologies
- Next Generation Sequencing technologies that can use multiple specimen matrices, including tissue and biofluids
- Circulating tumor cells
- Circulating cell-free nucleic acids
- Antigen receptor sequencing
- The above would ideally be paired with capabilities and footprint for distribution in global markets, regulatory and reimbursement strategies, and commercialization capabilities
In vivo imaging technologies (including MRI, PET, CT, optical imaging technologies, imaging agents, genetically encoded tags, ultrasound, etc.) with particular interest in
- Imaging agents for small and large molecule compound distribution studies
- Imaging agents monitoring physiology mechanisms and disease
- Analytical tools and technologies
Biospecimen Analysis
- Circulating tumor cell and cell free nucleic acid quantification and analysis
- High dimensional single cell analysis platforms
- High dimensional IHC/IF for tissue digital image analyses (cancer, safety)
- Advanced ADME-related genotyping
- Extracellular vesicle, exosome analysis
- 3D cell models for safety and efficacy assessment that ideally incorporate genetic diversity
- Skin tape strip, sebum analysis
- High dimensional flow cytometry
- Emerging “omic” analysis (e.g., phosphoproteome, autoantibody profiling, microbiome in addition to proteomics, metabolomics)
Physiological Biomarkers
- Technologies that have the potential to add enhanced precision to pre-clinical studies
- EEG-based biomarkers
Induced pluripotent stem cell (iPSC) resources and technologies to generate iPSCs that may be used to enable Precision Medicine strategies
- Validated cell differentiation protocols
- PSCs derived from sub populations with specific genotypic/phenotypic data
- Technology that can create iPSCs in a rapid and reproducible fashion without insertional approaches
Biospecimen collection/stabilization technologies:
- Novel sample collection approaches that allow frequent (at home) sample collection with appropriate stabilization (e.g., dried blood spots, swabs)
Remote Patient wearable technologies:
- Novel actigraphy and other wearables that allow frequent at home collection of data regarding relevant physiological states or biomarkers
Advanced computational biology approaches/platforms:
- Integration of high-dimensional data across various platforms in combination with traditional clinical readouts for the predictive modeling of patient response or disease progression
- AI approaches to gaining disease insight, target selection and/or patient populations likely to respond to potential treatment
- Microbiome, including virome characterization