Pullan's Pieces #131                                     
Pullan's Pieces #131
October 2017
BD News and Analysis for  Biotech and Pharma
Dear --FNAME--,
A fun diverse issue!  

1.  Orphan drug spending

2.   Infographic:  Deals with AI, computational and digital

3.  Patterns in drug company IPOs
4.  BIG Paper on cancer driver mutations

Love to hear from you about what you like or don't.    And hope to see you in Berlin at BioEurope!



Orphan drug spending in 2016 - IMS Report

    IMS has a report on Orphan drug spending.  http://www.imshealth.com/en_US/thought-leadership/quintilesims-institute/reports/orphan-drugs-in-the-united-states   This figure shows the orphan drug act has increased orphan drug spending (another figure shows a similar curve for approvals for orphan drugs year by year).  

        Only a tiny percentage of the sales of orphan drugs are at the sky-high costs per patient because these are for tiny numbers of patients.  Many of the orphan drugs are for cancer patients as cancer is really many different indications.    
Infographic:  Deals with AI, Digital and Computational
IPOs for drug companies since 2012

This is the world of drug company IPOs since 2012, in a great interactive graphic by Evolution Bioscience.   http://www.biotechandmoney.com/evolution-bioscience-ipo-data-visualisation?

I think it gives you a great feel for the impact of Asia in big IPOs but the dominance of the US in number of IPOs.  

    I was surprised to see autoimmune ahead of cancer in dollars raised in IPOs by TA.  No surprise that small molecules are most dollars as they are so common.  Virtually no IPOs have the lead drug at preclinical stage (it is an almost invisible slice between the dark blue for registration and the middle blue for Phase 1.   And orphan drugs are more heavily represented than proportional to their current sales.  Great graphic analysis!  
1-10 driver mutations per cancer- Major Paper
    I think the Cell paper http://www.cell.com/cell/fulltext/S0092-8674(17)31136-4   by Martin Corena et al, may change our thinking on cancers.  It is an elegant statistical analysis of the ratio of mutations that make a difference in coding protein sequence (non-synonymous) to those that result in the same amino acid sequence (synonymous).  By looking at the excess of non-synonymous mutations maintained by positive selection in tumors, compared to synonmyous mutations in the same genes, they identified driver cancer genes (compared to those that are only carried along randomly, passenger genes).  

    Nearly 99% of all gene mutations are tolerated and escape negative selection.  The immune system does not remove these! Negative selection does not play a big role in tumors.  


        There are an estimated 1-10 driver mutations per tumor.  And half the coding mutations are in genes that are not known cancer genes (see the green ones above). In 2b above you can see that an excess of missense non-synonymous mutations occurs in oncogenes (red) and an excess of  non-synonymous truncations occur in tumor suppressors (blue).  
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