Our fundamental goals in Johns Hopkins iMIND are to accelerate scientific discovery through integration of basic science with clinical and data sciences, and then to identify biological mechanisms that underlie not just one disease, but several different diagnoses. We believe that these efforts overcome the “valley of death,” the gap between laboratory findings and patient care, in which drug development typically slows, stalls or dies.

 

In essence, our mission is to develop multi-diagnostic interventions that can benefit patients whose different diseases share common manifestations, driven by the same biological mechanisms. Through this dynamic, multi-disciplinary process we are:

  

1.  convening a multi-disciplinary team comprising data scientists, basic scientists, and clinical scientists;

 

2.  selecting a phenotypic pattern that appears in different diseases, such as the cognitive inflexibility common among patients who have Alzheimer’s disease;

 

3.  recruiting patients across clinical departments, such that the cohort enrolled spans disease by sharing a photopic characteristic;

 

4.  performing deep phenotyping on participants, using imaging, genotypic,  molecular, behavioral, neuropsychiatric and other clinical data;

 

5.  employing artificial intelligence and machine learning to mine this data, so that we can:

a.  search neural correlates of the phenotype;                  

b.  stratify patients across diagnoses by this factor

 

 6. generalize a mechanistic hypothesis based on these findings;

 

7.  screen existing agents for the ability to interrupt this mechanism;

 

8.  test the hypothesis and drug candidates in cellular and animal models;

 

9.  partner with pharmaceutical companies, if needed, on drug development and testing when viable drug candidates are identified