The emergent state of drug development:

Synthetic biology in the 21st century

Drug development is emerging with a new twist. One of the greatest discoveries of the human genome project was the discovery that our genome changes – not just at conception. If someone were to sample my DNA today and again in one year, scientists recently discovered that my genetics would look totally different.

Due to this discovery, scientists have been racing to develop and reduce the costs of DNA sequencers. The pricing function of these devices resembles the pricing of computers in the early 1980’s, when computers were the size of a small room and cost more than $500,000 to own. Today, less than 40 years later, computers are the size of your palm and cost only hundreds of dollars.

The best thing, however, that this new genetic revolution has introduced is the onset of “big data.” More specifically, this emergent paradigm shift has introduced large, muti-national collaborative efforts to collect, store, and share insights into this massive wave of genetic information. As insights emerge, further data emerges. As data emerges, discoveries occur. Those discoveries result in further funding, some from governments, but mostly from philanthropists, charities, and private research organizations. Educational institutions also have a vested interest in preparing and providing additional data to assist in this collaborative effort.

Examples include:

  • Ensembl
  • Genbank
  • Drugbank.ca
  • Blast
  • ChEMBL
  • PharmGKB
  • PubChem
  • BindingDB

 

Thankfully, this data is available for development of novel applications. For my purposes, I wish to discover ways of computing drug interactions. These interactions can be predicted by evaluating enzyme interactions, drug metabolism, binding site interactions, and associated properties with those binding sites (e.g. PKI/binding affinity, diffusion coefficients, etc.). Thus, we can obtain a deterministic, differential model for computing the net effects of complex drug interactions in patients taking multiple medications. This will help providers determine whether or not it is safe to prescribe a particular medication for a patient with complicated medical histories. It will also help providers prevent dangerous adverse events by helping them determine when a particular drug may result in a “tipping point” from insignificantly contributing to an adverse event to putting the patient into danger. As data emerges, we will see the paradigm shift away from typical drug discovery toward a protein and enzyme driven approach. I will share more on this later.

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