The impact of patent thickets on R&D innovation

Patent thickets – good, bad or simply ugly?

Patent thickets have long been a point of controversy in the life sciences industry. By design, patents are intended to incentivize the R&D innovation and capital investment needed to bring new drugs to market with the promise of exclusive rewards downstream. But a wave of patent legislation passed in the US in the 80s broadened the scope of patentable subject matter and strengthened patent rights significantly, opening the door for a new, arguably anticompetitive practice often employed by bigger pharmaceutical companies to protect their R&D investments by preventing generics or biosimilars from entering the market for years beyond the original drugs’ initial patent coverage period. 

Ethicists claim that patent thickets drive up drug prices, slow down research and ultimately impact patients’ ability to access care. IP advocates assert that patent thickets are simply a natural product of the competition required to drive the innovation that makes treatments available in the first place. But how do patent thickets really impact R&D in the life sciences?

What is a patent thicket?

A patent thicket is a dense knot of often overlapping patents carefully constructed to protect a particular product or technology by inhibiting or preventing generics or biosimilars produced by competitors from entering the market. Patent thickets often include patents which cover similar or the same subject matter. To quote one patent researchers, branded pharmaceutical companies’ patent portfolios often look like the “many-headed hydra from Greek mythology”. 

In order to develop generic or biosimilar drugs, competitor pharmaceutical companies need to either wait for the patents which cover the original drug to expire, or they need to challenge each patent by arguing that the subject matter they cover is not novel or obvious.

The denser the patent thicket surrounding a drug, the more difficult and costly it is to break through it. As a result, patent thickets are an effective deterrent against the development of competitor generics or biosimilars.

Humira approaches the patent cliff

Patent thickets have re-entered the conversation this year as one of the most famous patent thicket cases – the blockbuster drug Humira – approaches key patent expiration in 2023. Humira, made by AbbVie, is the world’s top drug by sales, and it has long been surrounded by controversy due to the sheer number of overlapping patents that protect it – 257 in total, with protections that will run into the next decade, including a patent that covers the “firing button” of the Humira pen device.

To give you an idea of the density of the patent thickets which surround America’s top drugs, 584 additional patent applications were filed for the top five drugs in the US (Humira, Enbrel, Keytruda, Revlimid and Imbruvica) after initial FDA approval, gaining in one case, an additional 28 years of patent protection beyond the key patent expiration.

Thickets like these have come under increasing scrutiny, particularly in the US, where the public pays higher prices for drugs than anywhere else in the world. Last year, a bipartisan group of US Senators formally requested that the US Patent and Trademark Office make decisive moves to clear out patent thickets they believe inflate drug prices and stifle healthy competition, and combating high drug prices has been firmly on the agenda for the Biden administration. In 2021, the President passed an Executive Order that clearly pointed to the misuse of patents to “inhibit or delay generic drugs and biosimilars” as a key driver of drug price inflation. 

The problem with upstream patents

One of the primary concerns around patent thickets is that many are constructed around basic upstream research technologies, making it increasingly difficult to conduct effective biomedical research without risking patent infringement. 

One famous case of upstream patents restricting research was the “golden rice” development process. Scientists developing the vitamin A-elevated genetically modified crop apparenetly came up against over 70 different patents hindering their ability to conduct the foundational research required to take the project to completion. In this case, the relevant patent holders agreed to license the technology needed for free (primarily because the project was in aid of a humanitarian cause, not for-profit). 

However, while upstream patents covering basic research technologies are troubling in principle, there is no clear evidence to suggest that upstream patents have any major impact on biomedical R&D efforts, specifically in academia. 

Avoiding patent thickets with Similari

For researchers and R&D teams, patent thickets present an expensive dead end to R&D efforts. But due to their complexity, they can be difficult to avoid without a clear overview of the patent landscape for the field of research you’re working in. As a result, many researchers spend hours every week manually monitoring data for patent news and competitor intelligence to ensure that resources aren’t being directed toward protected targets. 

The AI-enabled Similari insights management platform allows researchers and R&D teams to offload up to 90% of this manual data monitoring time, providing you with a moment-by-moment crystal-clear picture of your defined arena of inquiry. By monitoring millions of deep data points across your field, Similari is able to deliver actionable insights and the latest patent and clinical trial news, much of which is invisible to traditional search methods. 

Find out how Similari can help you navigate and avoid patent thickets by trying a free demo today.

How AI is being scaled across the biopharma industry

AI in the life sciences

While AI has been around for the better part of the last decade, the last few years have marked a Rubicon moment for the technology as it reaches the point where it is finally fit for purpose and broader application across the life sciences industry. Across the sector, AI is being deployed in a number of exciting ways to drive better drug discovery and help lower the costs and risks involved in the R&D process. 

From in silico testing to identifying viable drug targets, here are some of the ways in which AI is being scaled across the biopharma industry.

More effective drug discovery processes

One of the most promising use cases for AI is its application in increasingly effective and accelerated drug discovery processes.

It’s estimated that the chemical space includes up to 1023—1060  drug-like compounds. The sheer scale of possible compounds is intractable to human beings. AI’s generative and predictive capacities mean it can be put to work identifying novel drug candidates in this vast chemical space by predicting hit and lead compounds and pharmaceutical outcomes, while also predicting the potential bioactivity, toxicity and physicochemical properties of a target compound. 

AI can also be used to screen vast molecule libraries, looking for appropriate compounds to best pair with a particular target with a known molecular structure. In 2021, Exscientia and Evotec released the first AI-detected anti-cancer drug candidate using this method, shortening the drug discovery period to 8 months from five to six years. 

DL-enabled de novo drug design uses AI to generate novel lead compound candidates using AI, allowing researchers to hone in on active agents with desirable properties faster, and at far less of a cost. A recent compound to fight fibrosis was created using this method in just 21 days, 15 times faster than traditional discovery processes.

Repurposing existing drugs

AI can also be deployed to analyze datasets to identify new indications for existing drugs, matching them with rare diseases, for example. During the COVID-19 pandemic, AI algorithms were deployed to screen for effective treatments for the virus, identifying the best candidate (a rheumatoid arthritis drug) in less than 48 hours. The drug went on to gain FDA approval for use in adults hospitalized with COVID.

Optimizing preclinical and clinical trial design

AI-enabled data management can streamline, optimize and accelerate clinical and preclinical trials by helping researchers with more effective trial design, big data analysis, and participant selection. Its potential to facilitate remote trials through the collection and analysis of data from wearable devices is particularly exciting, as it would address some key challenges, including patient attrition, the cost of administering trial sites and patient diversity. The overall result – faster, better trials, carried out at a fraction of the cost.

In silico testing

AI enables accurate molecular simulations to test candidate compounds, run on computers. In silico testing, as it’s called, eliminates the need for the physical testing of candidate compounds. This method is cheaper, faster and more accurate than traditional chemical testing. 

AI-enabled insights management solutions

AI-enabled insights management platforms allow researchers to monitor millions of emerging data points related to breaking developments in their field of research. This helps R&D teams avoid research duplication, dead-ends and patent thickets, while identifying true R&D white spaces. In a field as fast-moving and dynamic as biopharma, R&D teams spend hours manually monitoring data from clinical trials, press releases, M&A news, publications and other sources in order to make sound strategic R&D decisions. AI allows them to automate this process. 

Add the AI-enabled Similari platform to your R&D toolkit

The AI-powered Similari insights management platform is an invaluable tool for life sciences R&D teams. By constantly monitoring millions of deep data points across your chosen arena of inquiry, Similari equips you with a comprehensive, moment-by-moment snapshot of your field, as it develops day to day. With Similari, you can offload up to 90% of your manual data monitoring time while accessing insights invisible to traditional search methods.

Find out how Similari’s AI-powered platform can augment your research capabilities like never before by trying a free demo today.

6 tech trends advancing biotech and pharma

Unlocking the power of big data 

The life science sector, a historically slow adopter of digital transformation, has taken enormous strides toward digital maturity over the last few years, primarily in response to the unprecedented demands of the COVID-19 pandemic. 2023 sees a host of exciting technologies, and the enormous potential for progress they represent, rolling out into common usage across the industry. Some of these technologies are not new, but rather newly useful, due to new data analytics technologies which help turn the data they provide into actionable insights and efficiencies. Others have been in the pipeline for years, finally reaching the inflection point where they’re fit for purpose to effect real change. 

In this article, we take a closer look at 6 tech trends currently advancing biotech and pharma, and the ways in which they’re set to change drug discovery and patient care forever. 

Wearable tech

The increasing accuracy and capabilities of wearable technologies for collecting health data have them playing a growing role in clinical trials going forward. It’s estimated that 70% of clinical trials will include some kind of wearable tech data collection by 2025. Not only does wearable tech allow closer monitoring and automated collection of patient health data, it also allows for offsite or remote trials, cutting the costs associated with administering trial sites, which can range from 35-65% of the total cost of a trial, and lowering the rates of patient attrition. The use of wearable tech in collecting invaluable Real-world Evidence for post-trial studies is also becoming increasingly important, helping life sciences organizations optimize their processes and more accurately analyze their products’ efficacy and safety once it goes to market. 

Blockchain

Blockchain technology is being deployed to increase visibility across the pharmaceutical supply chain and secure its fidelity by improving privacy and security at critical touchpoints. This includes secure tracking and tracing of products, more efficient product recall capabilities, combating drug counterfeiting and securing patient and trial data. Blockchain is also helpful in the R&D process, by facilitating robust IP and data-sharing protection, particularly for dispersed research teams. 

Artificial Intelligence (AI)

Analysts predict that 2023 will mark an inflection point for the use of AI and ML in the biopharma industry, as the products available have reached a point where they are finally fit for purpose. AI can cut down both the time and the significant cost it takes to bring a new molecule to market as it’s deployed throughout the drug discovery lifecycle, from identifying the most viable targets and predicting small molecule structures to analyzing the increasingly large amount of data generated throughout the clinical trial process. According to one report, the AI healthcare market will be worth at least $31.3 billion by 2025, growing an astonishing 41.5% per year until then. 

AI-enabled Insights Management Platforms

Researchers, R&D teams and other key stakeholders in the life sciences sector are using AI-enabled insights management platforms to carry out key data monitoring tasks. With AI-enabled insights management, researchers are able to offload up to 90% of manual data monitoring time, while accessing deeper insights, as they emerge. These platforms monitor millions of data points across the industry, including the latest trial data, press releases, patent news, emerging startups, competitor activity and more. This information is then used to identify true white spaces for R&D efforts, inform strategic decision-making and avoid research duplication.

Data Analytics

Forbes projects that the life sciences industry will increase the amount spent on data analytics by 27%, to $1.2 billion by 2030. According to Forbes, digital analytics technologies are set to deliver more effective clinical trials, better risk assessments, improved forecasting and optimized R&D processes, reducing drug development costs by at least 15%.

Digital Health

Many of the biggest players in the pharmaceutical industry have made significant investments in digital health this year, including giants like Merck, Pfizer and Novartis. Digital health involves using digital tools to monitor and improve patient care, including wearable medical devices, mobile apps, software as a medical device (SaMD) and health informatics. The digital health market is set to hit $42 billion by 2027.

For AI-enabled insights management, choose Similari

The powerful AI-enabled Similari platform allows you to put your data monitoring functions on autopilot. By constantly monitoring millions of deep data points across your chosen arena of inquiry, Similari equips you with a comprehensive, moment-by-moment snapshot of your field, as it develops day to day. Spot potential partnerships, identify white spaces, streamline research processes and avoid costly R&D dead ends with the right insights, delivered to your feed as they happen.

Find out how Similari can advance your R&D efforts by trying a free demo today.

How partnerships are driving the life sciences sector

The power of collaboration

After the remarkable collaboration efforts that emerged in response to the COVID-19 pandemic, the pharmaceutical industry has entered a new age of partnerships, leveraging extensive collaboration to become more flexible and agile, particularly in early R&D efforts. In fact, over 50% of late-stage pharmaceutical projects originate from collaboration.

Partnerships are not only good for innovation, they are also good for mitigating the increasing inefficiency of R&D across the pharmaceutical industry. As the cost and complexity of the drug discovery process increases year by year, life sciences organizations are having to find ways to maximize their R&D ROI in order to stay competitive. But what does this look like for the sector?

Eroom’s Law and the R&D efficiency problem

As a metric, R&D efficiency is difficult to measure, particularly as R&D processes become more and more complex. It’s generally taken as the ratio between input and output, with input referring to the overall cost of an R&D effort, and output referring to the number of granted patents, patent applications, approved New Molecular Entities (NMEs) and publications that result from that effort. 

Recent academic research shows that there has been a significant decrease in pharmaceutical R&D efficiency in the last few decades. Although the capital investment in and cost of pharmaceutical R&D has consistently increased, the number of approved drugs has consistently decreased – a phenomenon researchers have termed “Eroom’s Law” (Moore’s law, backwards). In fact, the number of new drugs approved by the FDA per billion dollars spent has halved every nine years since 1950. And while total R&D spending in the pharmaceutical industry is set to hit USD 230 billion in 2026 (one of the largest R&D budgets across all industries) the sector’s growth rate is set to drop or plateau – a direct result of R&D inefficiency. 

A move toward more open partnership models to drive innovation

So how are life sciences organizations working to counteract the effects of Eroom’s law? Many are turning towards more open partnership models to drive innovation and build more efficient R&D processes. A recent survey showed that 65% of the top 20 pharmaceutical companies are engaged in some form of open innovation. By outsourcing more aspects of the R&D lifecycle, pharmaceutical organizations are able to plug gaps in their portfolios, mitigate risk and overheads, access skills and technologies driven by innovative emerging startups and leverage more cost-effective R&D processes to maximize their return on R&D investments.

Open innovation models take a number of forms, not all of them new. For example, over 20 000 licensing agreements were in place in 2023, with that number expected to rise steadily as the sector stabilizes after the COVID-19 pandemic era. Through licensing agreements, life sciences can fill pipelines and access innovative discoveries and technologies without the significant costs associated with M&A.  

Outsourcing R&D processes to Contract Research Organizations (CROs) is an increasingly popular strategy, too, particularly in the clinical phase. The CRO market has experienced unbelievable growth since 2015, expecting to exceed $60 billion by 2024. 

Public-Private Partnerships (PPPs) are another open innovation model available to life sciences organizations. In these instances, R&D activities are funded through public funds or charities, and usually focused on more niche targets where R&D activity is less active.

Crowdsourcing initiatives are another example of an open innovation model that pharmaceutical companies are increasingly adopting to drive R&D processes. For example, the EteRNA platform gamifies the design of RNAs by asking the public to solve shape-based puzzles. These desktop experiments are intended to verify the prediction of how RNA molecules fold. 

Partnering with emerging startups to fast-track R&D

According to a recent IQVIA report, emerging biopharma companies were responsible for 65% of all new molecules in the industry’s 2022 R&D pipeline. 42% of all new products filed with the FDA came from emerging companies. In 2021, only 18.6% of new active substances were launched by the 10 biggest biopharma companies. In reality, it’s emerging companies and startups who are driving the biggest share of innovation in the biopharma and pharmaceutical industries. 

Partnering with emerging startups is a solid strategy for accelerating R&D efforts, one which Big Pharma is increasingly adopting – 90% of all deal activity in 2021 involved emerging startups. Research shows that emerging companies are consistently responsible for new products with the highest sales, as long as larger companies launch those products. 

That said, 62% of deals involving emerging companies did not involve larger organizations in 2021, indicating an increase in partnerships between startups to drive innovation, too.

Spotting potential partnerships first

In the current life sciences landscape, partnerships are proving to be the most productive approach to mitigating R&D inefficiency and driving innovation. As emerging companies and more open partnership models continue to drive innovation in the sector, it follows that collaboration with innovative emerging startups is a solid strategy for accelerating R&D efforts. But how do you spot these partnership opportunities before your competitors do?

You’ll find them first in your Similari feed.

Similari’s AI-enabled insights management platform gives you a real-time overview of all emerging and rapidly growing startups in your arena. From the dashboard, you’ll get key insights into who is currently leading your arena of inquiry, who’s growing the fastest, and who is working on what, and when.

Never miss a potential partnership opportunity again. Get in touch today, and we’ll show you how Similari gives you a major edge when it comes to monitoring even the smallest movements in your field.