Regulatory Science Virtual Symposium: “Innovation to Translation: Role of Genomics in Medical Product Development:” Session 6: Applied Genomics and Target Identification (2021)

We all share the same genome, but small changes can make a big difference!

• 1. Genotype (genetic make-up) impacts one’s phenotype (external appearance)
     1. Both impact health of an individual
  2. “Unlucky” genes vs “lucky” genes
     1. Identifying disease genes is fundamental because it provides for the opportunity to discover and develop new medicines associated with “lucky” genes

Very luck (modifier) genes!

1. A scientific paper from Nature Biotechnology revealed that an analysis of 589,306 genomes identifies individuals resilient to severe Mendelian childhood diseases

Innovation in DNA Sequencing technology is driving a revolution in Translation

1. Comprehensive
   1. Unbiased whole genomes sequencing
   2. Databases populated from larger number of people
2. Non-invasive
   1. Easy to obtain DNA
3. Continuous improvement on all metrics
   1. Decrease cost
   2. Decrease cycle-time
   3. Increase quality
4. Genomics can impact drug and development at all stages in a myriad of ways
   1. Diagnosis
   2. Target identification
   3. Prognosis
   4. Stratification

Huntington’s Disease: What I work on and a good example!

1. George Huntington – family doctor on Long Island
   1. Publishes his one and only paper in 1872, describing the movement disorder as chorea
2. Huntington’s Disease (HD)
   1. A progressive brain disorder caused by a single defective gene
3. Publishes in his one and only paper in 1872
4. HD is a hereditary disease
5. Autosomal dominant
   1. Inheritable from a parent with HD
   2. No recessive genes/carriers

The Hunt Begins to Clone the Causal Gene

1. Monogenetic with 100% penetrance
2. Relatively rare disease (1:10,000)
3. Linkage analysis finds the marker in 1983
4. Full gene sequence in 1993

Big deal, you know the gene, so what?

1. HD is not caused by a point mutation
2. Enables genetic testing for diagnostic purposes
3. Family planning
4. Animal models of HD

Directly enable discovery of HTT lowering drugs

1. HTT refers to Huntingtin-Lowering Therapies

Development of pharmacodynamics biomarkers

Back to HD disease

1. Inverse correlation between size and onset
   1. Smaller suggests later onset and vice versa

Back to the patients and families and their genetics

1. (CAG) nucleotide accounts for 66% of age of onset but other factors can come into play
2. The likelihood of “onset” is also heritable

Genomic Wide Association Studies (GWAS): Can be used to identify causal genes which modify disease onset and rate of progression

1. Massive collaborate effort to identify the genetic factors that modify the clinical onset of HD

HD patients continue to provide the keys

1. Identifying “bad” accelerator variants “good” genes
2. Numerous genes associated with DNA
   1. Somatic instability theory refers to when DNA repair is flawed, repeating sections of the DNA code can become unstable
   2. For HD, HD genes will gain more CAGs in certain cells of the brain and body

Modulating rates of Somatic Instability

Stratify (prodromal) Clinical Trial Populations: (CAG)_n and Modifiers

1. CAG refers to a cytosine-adenine-guanine trinucleotide repeat expansion in the huntington gene, HTT

A Few Take Aways

1. The revolution in genomics is having a profound impact on drug discovery
   1. Diagnosis
   2. Prognosis
   3. Stratification
   4. Target identification
   5. Biomarkers
   6. Therapeutic candidates
2. Additional examples beyond HD across multiple therapeutic areas include PCKS9 à Repatha (Evolocumab) and Praluent (Alirocumab)

Accompanying text created by Annie Ly | Graduate Student, Regulatory Science, USC School of Pharmacy lyannie@usc.edu and Emily Donahue | Undergraduate Student, Pharmacology and Drug Development, USC School of Pharmacy emilydon@usc.edu