Drug Discovery (Chapter 2)
Over the years, research and discovery teams have deployed numerous techniques to identify “trial-worthy” compounds. Some discovery was pure serendipity (e.g. penicillin) while others were the result of decades of research in molecular pathways (e.g. lovastatin). Some drug discovery techniques have included:
- Combing the rain forests and oceans to find bacteria, plants, and animals with interesting biologic properties (e.g. paclitaxel from pacific yew trees).
- Viewing a target molecule using X-ray crystallography and designing a compound that stimulates or inhibits the target (e.g. pralatrexate).
- Synthesizing a product (usually a protein) that the body does not make or does not make in sufficient quantities (e.g. insulin).
Initial drug candidates are developed using these techniques above, and then medicinal chemists create libraries of molecules and select a lead candidate to move forward into development.
Personalized Medicine Implications
As personalized medicines are only effective for a subset of patients with a particular genomic profile, drug discovery units must invest heavily to obtain genomic data from large numbers of patients with and without a particular clinical condition (e.g. diabetes, MS, colorectal cancer, etc.). Discovery teams will then sift through this genomic profile data to identify the percent of patients with a particular clinical condition and a particular genomic profile. If discovery teams are able to screen large numbers of patient genomes, they could identify new targets that may not have been previously identified, particularly if the target genomic profile is relatively low frequency (e.g. only 5% of all lung cancer patients have ALK positive tumors thereby making them eligible to receive crizotinib, a personalized medicine designed for this small minority of lung cancer patients).
Before any drugs are designed to address a specific genomic profile, the development costs should be compared to the potential number of patients and the lifetime revenue per patient and then risk adjusted to see if such an investment is warranted. Unlike conventional medicines, the development of personalized medicines needs to add additional time and money associated with genomic testing. For example, if only 5% of people with a particular condition have the target genomic profile, then 100 clinical trial candidates will need to be tested to identify 5 patients meeting the genomic enrollment criteria. Depending on the incidence and prevalence of the condition and the percentage of patients with the target genomic profile, it might not make sense to move a discovery program forward.
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