Why Information and Speed Matters in Industry

We all know how competitive markets can be, especially when there are billions of dollars in stake. Both pharmaceutical and biotechnological sectors are indeed very competitive. Companies spend millions (sometimes billions) of dollars to get drug approvals and place a new medicine, drug or treatments in the market and it can take a decade to get the proper approvals. Regulation and approval are always a hurdle, but these are very important steps to evaluate a drug’s safety, toxicity, and the effects in the population in general, thus several clinical trials in different populations have to be done before any approval. We can see this going on with treatments and vaccine development for the coronavirus disease (for more information please check my previous blog posts). However, today in my blog post I will shift gears a bit to discuss about an area that has become very important for pharma companies: Immuno-Oncology (IO) and the drugs associated with cancer treatment. In fact, these drugs in a simple explanation, use the person’s immune system to attack cancer cells and shrink the tumor (sometimes even cure the patients). Thus, several tests and “facts” need to be checked before these types of treatment could be applied. I will discuss today about Business Intelligence (BI) and competitive advantage in life sciences. The example here will be two specific treatments that have been approved by the American Food and Drug Administration (FDA) to treat various cancer type. Immunotherapy combo drugs such as Opdivo or Nivolumab (developed by Bristol-Myers Squibb – BMS – NASDAQ: BMY) and Keytruda or Pembolizumab (developed by Merck – NASDAQ: MRK) have changed the cancer treatment landscape, but it’s becoming increasingly clear from clinical trial results that these potent therapies need to be targeted to the right patient populations to have the best clinical results; something named precision medicine. Both treatments use specific genetic profiles and background of the patient’s immune system to be successful, which is the expression of two proteins by patients’ immune cells: the so-called “check point” proteins PD-1 and PD-L1. These treatments actually block PD-1 with monoclonal antibodies (mAb) helping the patient’s immune system to kill cancer cells. That is the basic concept (for more information on the molecular biology of these treatments check “A molecular and preclinical comparison of the PD-1 targeted T-cell checkpoint inhibitors nivolumab and pembrolizumab”). In that regard, the brand names Keytruda from Merck and Opdivo from BMS mainly function by blocking the PD-1 ligand protein and have several structural similarities. In fact, both were approved in the same timeframe by the FDA in the end of 2014. However, Keytruda (Pembrolizumab) is projected to generate at least U$22.5 billion in revenue by 2025, according to an analysis by Global Data, a data and analytics company. In 2019, Keytruda generated more than U$7 billion in revenue and, based on more than U$5 billion it earned in the first half of 2019 it could hit U$10 billion or more by the end of 2020. Keshalini Sabaratnam, a pharma analyst at Global Data, said Keytruda has developed into Merck’s biggest product since it was first approved by the FDA in 2015. On the other hand, BMS’ Opdivo (Nivolumab), a rival checkpoint inhibitor to Keytruda, is expected to be in fourth place for global revenue and AbbVie and Janssen’s Imbruvica will take the fifth position, according to Global Data’s prediction. In 2018, Opdivo earned U$5.7 billion (for more information check “Keytruda Set to Become World’s Top-Selling Drug, Forecast Shows”). Why is that? Well, trying to understand why this market share change occurred in such a small timeframe (3 years or so) since both treatments were approved by the FDA in about the same timeline, I started digging in the approvals for both treatments since then. The main points I want to discuss in order to answer why Merck’s treatment is “winning” the competition now are: 1) Merck started combining biomarker identification to stratify groups of patients that would respond better to their drug, even in the clinical trials before approval, especially patients with high expression of both PD-1 and PD-L1; 2) big data analytics probably helped Merck to gain more information and faster approvals, so maybe people working for Merck were paying more “attention” to the advancement and developments of “targeted therapies” in immuno-oncology, which means that speed in getting information and data makes a big difference in business intelligence for big pharma and 3) it seems that Merck’s treatment can now be applied to several types of cancer and the one from BMS is a little more “restricted” to specific cancer types in terms of FDA approval. Thus, the take home message here is that we are in need of better tools, especially for life sciences’ companies, to get trustful information faster and use it smarter. There are already some solutions that can help big pharma in the market such as the Elsevier database (NYSE: RELX), Thomson Reuters Corp. database (NYSE: TRI), etc which are bringing revolutionary tools to speed this process. Indeed, sometimes being the first is not enough; keeping track of the information and the evolution happening in the scientific and biomedical fields are the differentiation factor to “win” in such a competitive market.

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