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.
Variety Of Data Collected And Analysis
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.
In normal circumstances this could be from weather conditions in sub-Saharan Africa which increase the number of mosquitoes, thereby triggering pharma companies to increase manufacture of malaria drugs and mosquito repellants. In 2017 this is not likely to be any different, especially as we have seen the mosquito-borne Zika virus making headlines around the world, but it will also need to look at more societal data too.
Going back to the potential repeal of Obamacare, pharma companies will need to know the potential impact this may have. This needs to take a huge number of variables into a predictable algorithm. In order to accurately predict sales they will not only need to look at the numbers of people insured and basic rates of disease, but then factor in people who will lose insurance, how that will impact their health (fewer check ups means more dangerous illnesses could progress further before diagnosis) and how much people can afford to pay (which will then be linked to average wages, which then need to be subdivided into geographies and demographics). Factoring all of these constantly changing data points into a workable model will certainly be a struggle.
Given the lack of clarity surrounding major political decisions (a Trump Presidency and Brexit), it is an environment in which planning will be difficult. The use of data, potentially even in real-time, will be essential for pharma looking to maximize their potential in the coming year.
The last 2 years have not been good for perceptions of pharma companies, which essentially started because of Martin Shkreli, who GQ referred to as ‘the worst person of 2015’ and who Seth Myers labelled ‘a real slappable prick’. This came after his company, Turing Pharmaceuticals, bought the out-of-patent drug Daraprim and increased the price by 5000%. It was picked up by every major media outlet and was even used as a campaigning point by several major politicians, including presidential nominee Hillary Clinton, and brought the term ‘price gouging’ to the national lexicon. Following this came the cases brought against Actavis and Pfizer by the NHS in the UK, which saw them overcharging the public healthcare system by 12,000% and 2,600% respectively. Essentially, the price of drugs is now an international sore point and every price change is strictly analyzed by millions of people.
This makes the job of leaders within the pharma industry much more difficult, because in the face of potential loss of customers they cannot be seen to significantly increase pricing, given how controversial it is to do so in the current atmosphere. With this option essentially off the table, pharma leaders need to look at other ways of saving money and making their businesses more efficient, which will require empirical approaches through data. Having the ability to accurately see trends, cost centres, and department performance will make this considerably easier.
At present there is little known about the internal data of many pharma companies, given that they are naturally, and understandably, secretive. However, when the challenges of the coming year begin to really manifest, we are likely to see those doing it properly and those who aren’t.