Pharma M&A Is on Fire and AI Is the Gasoline

A perfect storm is brewing in pharma M&A. Reduced R&D productivity, comparative advantages of large pharma companies, and the Trump Administration’s tax cuts make M&A more attractive than usual for an already acquisitive industry. To navigate these waters, pharma giants must embrace new AI tools to get the job done right.

On July 27, 2015 Valeant Pharmaceutical’s stock price reached its all-time high. Mike Pearson, then-superstar CEO, had perfected the art of gobbling up smaller pharma companies, slashing costs, and, most importantly, quietly hiking prices in the market. Despite their success, they knew where to stop and masterfully managed the process. The stock price had risen more than four thousand percent. Valeant made 33 significant acquisitions under Mr. Pearson, while spending almost nothing on R&D. Valeant and Mr. Pearson were darlings of Wall Street, and the envy of big pharma CEOs.

The rest of the story is infamous. Over next few quarters, Valeant would lose 96% of its value. Investors with previously impeccable records would lose billions. Mr. Pearson, while never convicted of any crime, would exit amid allegations of fraud, kickbacks, price-gouging and shady accounting practices reminiscent of Enron. Today, Valeant has come to symbolize everything wrong with pharma. Still, its lessons were not lost on the leaders of its larger competitors.

Big Pharma allocates about 20% of revenue to research and development. This has not changed meaningfully over the last two decades. Only one in 5,000 drugs entering preclinical trials actually makes it all the way through the pipeline. As per Tufts Center for the Study of Drug Development, Pharmacos pay a $2.6Bn bill simply to bring a drug to market. The Valeant story merely underscored the fact that Big Pharma’s true strengths lie not in groundbreaking research, but elsewhere.

Decades of diligent marketing has created formidable brands. Brand valuation consultancy Brand Finance estimates the top 10 pharma brands to be worth a whopping $42 bn in 2019. These grew by 11% over the last year in value. Even big pharma’s generic drugs command a price premium in a commoditized market. Add to it the carefully curated relationships with regulators and the well-oiled global sales machine and delivery infrastructure – it becomes clear that buying smaller companies with promising new treatments makes much more sense than investing heavily in R&D.

The Need for Innovation

It is not just greed; it is also fear that is driving a need for change in big pharma.

Pharma pundits are currently predicting that the era of blockbuster drugs is over. Personalized and precision medicine are coming fast; pharma companies will have to massively retool themselves for the coming age in which treatment plans based on generalized demographics aren’t good enough. The math is simple – How can pharma companies continue to invest $2.5Bn for every drug when the market is going to be less than $1Bn? 

These changes are already making an impact. The share of profits coming from outside core big pharma is up from 25% in 2001 to over 50% in 2016, and patents on blockbuster drugs are expiring as the pipeline decreases. At the same time, smaller biotech firms are booming – in 2018 alone, biotech IPOs raised $8 billion, and with initiatives like KASA (Knowledge-aided Assessment and Structured Applications) the FDA is encouraging more and more generics and small drug manufacturers.

Despite all their resources and expertise, pharma giants will not be able to adjust their business models overnight. If plucky biotech startups ripe for scale-up can boost declining pipelines, acquiring tech innovations like AI could be critical to make the whole thing work.

Pharma is already known for being an acquisitive industry, with its top 20 notorious for particularly splashy M&A moves. Pharma giants like Pfizer and BMS have frequented lists of the biggest mergers and acquisitions for decades. Adding fuel to the fire, Trump announced huge tax cuts at the end of 2017, meaning that upwards of $4 tn in overseas cash can be moved back into the US at a much lower rate than before. As of the end of 2018, almost $465Bn had been repatriated. There were 248 pharma and life science deals in 2018 and BMS’s bid for Celgene and Eli Lilly’s $8Bn offer for Loxo Oncology signal just the beginning. Since then, the deal value of mergers & acquisitions has hit an all-time global high at $2.51 tn.

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The Last Wrinkle

M&A has never been easy. A report in the Harvard Business Review says that a dismal 70-90% of deals fail to create shareholder value. The reasons vary from deal to deal, but a common thread seems to be overly optimistic assumptions about how much added revenue will come from combining entities.

The core of the problem lies in the M&A process itself. Before committing to a decision, a seller will want to know exactly what it is buying. Financials, tech & IP, market & competitive positioning, data privacy & security, company culture - each of these has a huge influence on whether the acquisition target will be a strategic fit with the buyer, and an M&A team could spend years investigating nuance. There simply is not time. Once a deal is made, the integration process is just as messy, if not messier, as processes are thrown together or torn apart and institutional knowledge is lost in the churn.

All stages of M&A are ripe for acceleration just as big companies break out their wallets. Just in the nick of time, new technologies stand ready and waiting to enable a real step change in M&A.

Artificial Intelligence to the Rescue

Joint ventures, licensing deals, bolt-on sales and M&A - all of these are complicated no matter the industry. However, M&A teams within pharma have to be especially tough to navigate the regulatory questions, the complex sales environment, and the science itself.

Some pharma giants have already embraced AI for discrete applications. Companies like GSK, Sanofi, and Merck have partnered with healthcare-focused AI startups like Coseer, Exscientia and Numerate for positive results outside of M&A, but there have been disappointments as well. After a well-publicized failure to evaluate oncology data for MD Anderson, IBM has withdrawn its flagship AI, Watson, from drug discovery applications.

To tackle M&A, a new AI paradigm is emerging. To handle high-level due diligence on one hand and granular scientific onboarding on the other, pharma’s M&A superstars need something more comprehensive than ever before - all of this in enough time to actually affect an outcome.

AI solutions have to be able to collate massive amounts of data in any format, including unstructured text like emails, scientific papers, and internal memos. AI must lead to tools that can answer questions in natural language, and provide real insight rather than merely gesturing in the general direction of a possible answer. Tools to make a senior scientist’s life easier as he sifts through piles of trial data late at night to meet a tight deadline.

They are up to the task.

A client of ours was happy to report that “we were able to get done in a day what took a week before Coseer” after adopting NLS-based AI for M&A. He was tired of seeing senior scientists’ time wasted poring through documents instead of working on what they were trained for, what they like - developing life-saving treatments. However, the reality of M&A at pharmacos means that these brilliant minds must divert energy to diligence activities more often than anyone would like.

This is one area where AI is already majorly surpassing expectation. It can summarize information in any format these scientists can. It can generate insights from natural language as well as images to deliver objective insights that may be missed due to one too many late-nights on a deadline.

What can I do with AI in Pharma M&A?

Big pharma is a high-stakes, high-rewards industry with huge revenues and huge costs. M&A has always been a popular growth strategy, but in recent years market forces are aligning to accelerate M&A activity even further. As deal making in pharma gets increasingly complex and competitive, elite M&A teams need the best tools to make the right decisions for their company. NLS-driven AI is uniquely positioned to offload non-value-added work in due diligence, negotiation, and integration by deep-diving into transaction-level detail and unlocking actionable insight, all within tight deadlines.

Ask an AI assistant these questions during due diligence:

• What are the top value drivers right now?
• Where is growth coming from? Which region, segment, or treatment?
• Were there any regulatory red flags that made it through but may pose problems in the future?
• How have margins evolved over time as manufacturing ramped up?

Negotiation

The best insight in the world is useless if it comes too late to affect decision making. With AI, insights and answers can be accessed within a very short time, arming an M&A team with the best possible information to go into negotiations with a full picture.

Ask an AI assistant these questions before going into negotiation:

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• Would a licensing agreement or JV be better suited to this target than an acquisition?
• Does this pursuit warrant a counteroffer?
• Are there any risks we are likely to encounter if we move forward?
• Which key employees/product lines/geographies are critical to maintain if we acquire?

Integration & Building Synergy

With AI, it is possible to get a fair picture of potential risks before signing the deal. However, once the handshake is done, AI can help point out the best next steps forward, like key areas for optimization or overlooked potential synergies.

Ask an AI assistant these questions after signing the deal:

• Where should we renegotiate with customers/suppliers to quickly achieve cost savings or improvements?
• What risks should we prioritize?
• How are customers and stakeholders reacting to the change in ownership?