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Synthetic biology has been recognized as one of the most promising technologies that may impact our future and I, for one, believe that its potential applications are remarkable and incredibly diverse. In this series of articles I report the results of the work that I carried out as part of my internship at Cambridge Consultants.
One of the objectives of my research was to identify a rational method for the identification of drug classes that could benefit the most from a synbio-based manufacturing process. To identify these drugs I examined the price information of 150 generic drugs (used as a proxy for manufacturing costs, hence hurdle in manufacture) and other characteristics such as molecular size, number of chiral centres, and whether the drugs belonged to a specific chemical compound class. This analysis allowed me to understand whether there was a relationship between the price and the aforementioned characteristics.
What resulted from the analysis?
Without beating around the bush, the analysis didn’t show clear patterns and couldn’t be used to unequivocally
identify opportunity areas for specific suitable compound classes. Take a look at the graph below (Figure 1): it is apparent that there is no clear pattern in the relationship between the price of the drugs and their molecular size. It was therefore not possible to use the graph to isolate classes of drugs which may particularly benefit from the implementation of novel synbio-technologies.
The situation does not improve when looking at the graph showing the relationship between the price and the number of chiral centres in each drug (Figure 2). The hope was to verify the hypothesis that a positive relation between number of chiral centres and the price of a drug exists. Such a relationship would have indicated that drugs with higher chirality have higher manufacturing costs and hurdles and thus may particularly benefit from synthetic biology. Again, no correlation existed and thus the analysis could not be exploited to find opportunity areas.
Interestingly, it was more useful to simply analyse the drugs’ prices on the basis of their belonging to a specific compound class. The analysis, which included the minimum, the maximum, and the average price of 16 drug classes, revealed that a substantial difference in price exists among them. More specifically, classes such as Alkaloids, Polyketides, Carboxylic Acid Derivatives and Steroids seemed to be the most expensive, therefore representing potential targets for synthetic biology. Another class, metallic/non-metallic mixes, appears to be on average relatively expensive, however it was not included in my analysis since it includes non-organic compounds (Figure 3).
Although the overall rational analysis did not yet identify a clear target compound class for synbio, this last analysis showed some interesting patterns that could be used to lay the foundation for future research. It is possible that the use of average price as an indication of complexity and cost of manufacture was not an appropriate proxy – this also could benefit from further research and analysis.
So it all comes down to talking with people.
The best way to understand this niche yet attention-drawing field is perhaps to gather opinions of experts from within the drug industry and academia. These experts can help understand the relevant issues of this field, such as the needs of drug manufacturing, the potential drivers for the adoptions of synthetic biology, as well as the challenges thereof.
In the next and final article of this series, I will report what resulted from the interviews with experts from the industry and academia, and conclude my opinions on the application of synthetic biology to pharmaceutical manufacturing.