IV. Facilitating Advanced Drug Development and Evaluation Using CRISPR/Cas9 Technology:

A Bigger Picture

Chi-Ping Day, Ph.D.Staff Scientist

Laboratory of Cancer Biology and GeneticsNational Cancer Institute

NIH, Bethesda, MD

Drug Development as a Process of Disease Modeling

• Drug development is about finding an intervention to correct an error in system dynamics.

• The process is part of modeling a disease

David Goldstein, Comp. Physiol. Review, 2015

Modeling Cancer

Thomas, Van Dyke, Merlino, and Day. 2016 (Under revision in Cancer Resaerch)

Drug Development

1. Identifying the system where the dysfunction causes the disease 2. Generating hypothesis for target identification3. Target validation by testing the hypothesis4. Outcome evaluation 5. System reconfiguration

System Identification• Defining the scope of the question

Physiology, Immunology, Etiology, etc.

• Where does the dysfunction occur? How cells transform? Why they are allowed to transform?

• Supportive evidences: epidemiological association analyses of genetics preclinical models clinical studies

Generating hypothesis for target identification

• Hypothesizing the driving factors of the system• Building a model that can replicate essential behavior of the system in the

defined question• A “zoom in” process to identify the driving factor(s) as therapeutic target(s)• Confirming the targets by adjusting other driving factors in the model

Validation of the Target by Testing the Model

• Preclinical studies model the biological procedures• Clinical studies model the patient cohorts (analogy in polling process)• Testing the target in preclinical and clinical studies• Statistical power- always ask biostatisticians and bioinformaticians before

planning any study!!!!!

Outcome Evaluation

• Which clinical variable is the endpoint of the study compatible with?• Is the outcome “translatable” to clinical situation? • Scaling consideration?• Shor-term and long-term effect? (Is fast track always better?)

Improvement of the Model

• Identification of the gap between model and the system in the question• Finding new driving factors from the model testing• Finding irrelevant factors that should be removed• Again, statistical power• Designing new model for next round of modeling

Biological Version of Heisenberg's Uncertainty Principle

• Exploring the system will definitely disrupting it, generating biased results

Insertion of a gene in the genome Overexpression of a gene in the cells

Vector effect Confounding effects from off-targets in studied system

Placebo effect Social and population effects

…etc

Advantages of CRIPSR in Disease Modeling

Reduction of the confounding effects • Specific gene editing• Transient expression of the effectors

Versatility in Target identification and validation• Screening strategy by sgRNA library• Multiple gene edits• Reversal of the edited genes

Issues that Remain to Be Confounding Factors

• Off-targets• Potential DNA damage• Immune response• Efficiency