As we turn a new leaf for 2017, the calendars may have changed for pharmaceutical companies, but the threats and challenges that existed in 2016 are still very much on the minds of pharmaceutical leaders across the world.
We have seen the UK government hit Pfizer with the largest fine ever after it was found that they had overcharged the NHS by upwards of 2,600% over the sale of an anti-epilepsy drug whilst Actavis is currently waiting to hear if it will face similar punishment for allegedly increasing the price for hydrocortisone tablets by 12,000%. There is also the threat that Donald Trump’s promise to repeal Obamacare could have a significant impact on pharmaceutical companies, given that a report from the Robert Wood Johnson Foundation and the Urban Institute, claims that its repeal would lead to 24 million people losing their health insurance. That would lead to significant decrease in the number of drugs sold so profits are likely to decline as a result. Similarly with Brexit, the price of certain drugs are likely to increase within the UK, meaning that people are going to struggle to afford them and therefore profits will decrease.
This combination of close monitoring of prices by the biggest countries in the world and potential challenging business environments means that action needs to be taken, but luckily the spread of big data throughout the industry has allowed for several innovations that could be the saviour of many pharmaceutical companies in the next 12 months.
Predictive modeling as a concept has been around for a long time, but with increasing computing power and database size, the pharmaceutical industry has some significant opportunities to use it in the coming 12 months.
Molecular modeling has had a number of largely unsuccessful iterations in the past, but 2017 may be the time where it can really gain traction given the developments in the area. The ability to identify which ingredients are going to work together and which are going to mix and kill people is essential, but something that has always left a significant margin of error given the huge variety of people and diseases that they could be used on. With the acceleration in both the amount of data available to pharmaceutical companies and the speed in which it can be analyzed, it is possible for pharma companies to theorise and either reject or move forward with drugs considerably faster. In fact, Mckinsey have estimated that these better informed decisions could generate up to $100 billion in value for pharma companies.
The vast majority of the costs involved in manufacturing drugs is in the discovery phase, where successful products often need to also offset the costs from unsuccessful experiments elsewhere in the company. By modelling drugs and predicting their successes or flaws, this is likely to significantly reduce costs, both for the company in the discovery stage and for the consumer who won’t need to bear the cost of all the failed drugs before it.
One of the elements that makes big data ‘big’ is that it gathers together huge varieties of data, not simply about the drugs being produced directly, but about anything that could impact the company.
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