What is drug repurposing?

Drug repurposing, also known as drug repositioning, is emerging as a low-cost, low-risk, highly efficient means of capturing value in the drug discovery process. It involves finding new pharmacological or therapeutic indications for existing drugs that have already been approved by regulatory agencies. Not only does this circumvent the exorbitant costs and resources required to develop a new drug from scratch, it also means that, in an instance when an existing drug is found to have useful applications for other diseases, patients are able to access the benefits of effective care much faster than with traditional drug discovery processes. 

Over the last five years, drug repurposing has gained significant momentum, with the drug repurposing market estimated to grow to a value of $46 851.5 million by 2028, expanding at a CAGR of 5.4% year on year. There are approximately 10 000 known diseases, but only 2500 are paired with FDA-approved drugs. This presents a major opportunity for pharmaceutical organizations, particularly in the realm of rare diseases, diseases in children and pregnant women, and neglected tropical diseases – many of which could be addressed by existing drugs.

In this article, we’ll take a closer look at drug repurposing, and how it has the potential to deliver lifesaving care to patients across the globe, while delivering major returns to the pharmaceutical industry.

A doctor saves his own life

In the early 2010s, Dr. David Fajgenbaum, at the time a medical student at the University of Pennsylvania, became gravely ill with idiopathic multicentric Castleman disease. The disease is characterized by deadly cytokine storms – flares of immune-signaling cells which cause the immune system to attack the body. Determined to save his own life, Falgenbaum set out to find a new treatment approach, examining his own medical records. He noticed that a protein called vascular endothelial growth factor (VEGF) was spiking at 10 times the normal level. He consulted with his doctor and request a prescription for Sirolimus, an approved immunosuppressant usually prescribed to transplant patients. Years later, he remains relapse-free.

Since then, Fajgenbaum has founded a number of organizations dedicated to finding treatments for the 7000 rare diseases affecting over 400 million people around the world, using repurposed drugs.

Blockbusters-in-waiting

A number of repurposed drugs have gone on to become blockbusters in the pharmaceutical market. Most famously, Sildenafil, originally developed to treat angina pectoris and hypertension, was repurposed into Viagra, one of Pfizer’s most successful drugs of all time. Other examples include Minoxidil, which led to Rogaine, and Wellbutrin, which delivered the smoking cessation drug Zyban. 

The advantages of drug repurposing

Repurposing a drug has two main advantages over the traditional drug development process: faster time to market and significantly lower cost of development. The average drug development process takes approximately 10-16 years on average, as opposed to 3-12 years for a repurposed drug – a saving of about 5-7 years of average R&D time. It only costs about $1.6 billion to develop a new drug using a drug repositioning strategy, as opposed to the average $12 billion it costs to develop a new drug from scratch. Chances of success and approval are also much higher – 45% of the failure rate of traditional new drug discovery processes can be attributed to safety and toxicity concerns, a factor not relevant in already-approved drugs targeted for repurposing. Overall, about 30% of repurposed drugs make it through to patients – 20% more than new drugs. 

How AI is accelerating drug repurposing efforts

Researchers are increasingly deploying AI’s ability to process immense data sets and discover and examine complex relationships between data to identify and predict the success of repurposing candidates. In one recent example, researchers used AI to try and identify existing medications that may be effective for the treatment of COVID-19. In just 48 hours, the system had identified baricitinib, a rheumatoid arthritis drug, as the most likely candidate. The drug went on to gain FDA approval for the treatment of adult COVID-19.

Accelerating R&D through AI-enabled insights management

Drug repurposing is one example of how researchers use existing data to create major value in the drug discovery process. 

For R&D teams throughout the pharmaceutical industry, the need to monitor the ever-increasing volume of data emerging across the industry every day is mission-critical to research success. 

By monitoring millions of data points in real-time, many of which are invisible to traditional search methods, Similari’s AI-enabled insights management platform allows you to offload up to 90% of your manual data monitoring time, keeping you absolutely up to date with all the latest developments in your specific arena of inquiry, and allowing you to optimize decision-making, resource allocation and research efforts across the board. 

Find out how Similari will change the way you work by trying a free demo today.

A major leap for vaccine technology

The COVID-19 pandemic had a paradoxical effect on global vaccine progress. On the one hand, the crisis saw the world falling behind on immunizations against diseases other than COVID-19 – so much so that “The Big Catch-Up” was the theme of this year’s World Immunization Week. On the other hand, the unprecedented levels of firepower aimed at developing appropriate vaccines to curb the pandemic slingshotted vaccine tech years ahead and into what analysts are calling its “golden era”. Since then, there have been some fascinating and fast-moving developments in the world of vaccines. In this article, we will explore four of the latest vaccine innovations which are expected to have a major impact on public health in the near future.

1. Shelf-Stable Malaria Vaccines

For a century, researchers have been trying to find a viable vaccine for malaria. This year, two are showing major promise. The R21/Matrix-M and RTS,S vaccines are both showing efficacy levels of up to 80% in small children between the ages of 5 and 17 months, and up to 75% efficacy in adults – a huge improvement on the only vaccine currently approved for malaria, Mosquirix, which only delivers a 56% efficacy rate after four doses. 

What’s more, both R21/Matrix-M and RTS,S are shelf-stable with a long shelf-life. Neither requires sub-zero temperatures for storage and transportation, and both are able to withstand temperatures of up to 104°F for up to two weeks – key features to overcome the infrastructure and distribution challenges common in more remote areas of Africa.

Ghana and Nigera have both approved the new vaccines – the first approvals in what’s expected to be a comprehensive roll-out.

2. Microarray Patches

As the world’s experience of the COVID-19 pandemic proved, vaccine accessibility is an urgent humanitarian concern. One of the technologies with the potential to revolutionize vaccine accessibility is the microarray patch. According to Birgitte Giersing of the World Health Organization’s Immunization Department, last-mile costs are responsible for more than half the cost of a single child’s vaccination. On top of this, the costs of transportation, appropriate storage, mixing and administration by a professional are prohibitive for many lower-income communities. 

Microarray patches solve many of these problems, providing a cost-effective, simple and easy-to-distribute method of delivering vaccines to even the most remote areas. The small, coin-sized patches deliver dry vaccine via the skin painlessly, either through small needles on the patch or through a soluble formula that dissolves as the patch is held to the skin for a period of time. Microarray patches do not need to be kept at cold temperatures, do not require mixing and can be administered by anyone, removing the need for trained healthcare professionals to run vaccine administration programs. 

Currently, there are microarray patch vaccines in development for measles and rubella. 

3. Personalized Cancer Vaccines

mRNA vaccines have been hailed as the next frontier for vaccine innovation. While researchers have been working on mRNA tech for decades, the pandemic brought about an estimated 15 years’ worth of progress in a matter of 12 months. One of the most exciting potentials unleashed by this wave of progress has been the possibility of personalized cancer vaccines. According to Dr. Paul Burton, Chief Medical Officer of Moderna, the firm hopes to offer “personalized cancer vaccines against multiple different tumor types to people around the world” by the end of the decade. 

4. Maternal Vaccination

Maternal vaccination is emerging as a viable way to tackle infant mortality and morbidity, particularly in addressing RSV, Group B strep, herpes simplex and cytomegalovirus, which are common risk factors for newborns. Pfizer is currently developing a groundbreaking vaccine against RSV in infants. Through passive immunization, antibodies are passed from the mother to the fetus, delivering a 70-80% efficacy for up to 6 months after birth. 

The latest innovations, as they happen

From vaccines to biologics, drug discovery and beyond – no matter your arena of research, you can keep absolutely up to date with the latest developments and innovations using the powerful AI-enabled Similari insights management platform. 

By monitoring millions of data points in real-time, many of which are invisible to traditional search methods, Similari collates and presents a dynamic feed of all the latest news, clinical trial results, press releases, M&A activity and much more, allowing you to offload up to 90% of your manual data monitoring time. 

Find out how Similari is revolutionizing the way R&D teams work by trying a free demo today.

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.

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.

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.

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.

An insurance policy for the life sciences sector

Mitigating risk in the R&D lifecycle

With over 450 000 clinical trials currently in process, the volume of data in play is far too large for error-free human monitoring. In a sector that shifts at a phenomenal rate, without the correct business, competitor and innovation intelligence solution, life science organizations open themselves up to significant risk in a number of critical areas.

Patent thickets, research waste, sunk costs, IP risk, dead ends, trial redundancy, reputational and financial risk, as well as the risk of unnecessarily compromising the health of duplicative trial participants are all immediate dangers without a clear, moment-to-moment view of emerging data within the field.

To avoid throwing precious resources at a dead-end and to retain ethical fidelity, many life sciences organizations are investing in AI-enabled business intelligence solutions as a type of insurance policy to help mitigate these risks. 

Here’s why the right AI-enabled business intelligence solution is critical for R&D teams in the life sciences sector, protecting both organizations and patients from major risk.  

The Ethics of duplicative trials

A 2021 cross-sectional study of clinical trials in mainland China evaluating statins in patients with coronary artery disease investigated the effects of redundant trials on patients. The study revealed that, among 2577 eligible trials conducted, 79.4% of them were considered redundant. In these redundant trials, over 100 000 patients were treated in control groups without statins, resulting in an extra 3000 Major Adverse Cardiovascular Events (MACEs), and nearly 600 deaths.

Avoiding redundancy in clinical trials is a question of ethics, and while most pharmaceutical R&D organizations make sincere efforts to get this right, sufficient monitoring of all ongoing trials is next to impossible without the assistance of an AI-enabled business intelligence solution.

Staying ahead when competition is fierce

Setting up and executing a clinical trial is enormously resource- and time-intensive, and R&D teams face a constant threat of being beaten to the punch by a competitor muscling in on their targets. To mitigate these risks, extensive, granular competitive intelligence is crucial. With an AI-enabled BI solution, researchers have access to moment-to-moment updates on all news, publications, press releases and other relevant information to help them keep tabs on competitor activity, identify viable opportunities before others do, identify threats and risks before they become a problem, and forecast expenses more accurately for better planning and budgeting.

Avoiding IP risk

AI-enabled business intelligence solutions provide insurance against IP risk by providing a comprehensive view of existing patents and prior art findings – a mammoth and high-stakes task with traditional manual search methods alone.

By expediting the FTO analysis and allowing for comprehensive proactive data monitoring, AI-enabled business intelligence solutions keep R&D organizations ahead of both existing and emerging IP risks.

Mitigate risk with the right BI solution

In order to mitigate risk in every step of the R&D process, life sciences organizations need to invest in a comprehensive BI solution for competitor, innovation and business intelligence. An AI-enabled BI solution like Similari allows you to monitor millions of existing and emerging data points in your chosen arena, delivering critical insights which will help you protect your operation from dire financial, innovation and reputational risk, while streamlining your research processes and offloading up to 80% of manual data monitoring time. 

Get in touch with our team today to find out how Similari stands as the insurance you need against redundancy, dead ends, sunk costs, IP risks and more. 

 

You don’t know what you don’t know 

The life sciences IP and patent landscape shifts constantly and rapidly, and to keep up, research teams have historically dedicated enormous amounts of time and valuable resources to manually monitoring emerging developments in the field. The stakes are high – missing a patent thicket, failing to identify potential research duplication or losing track of competitor activity can render costly trials and R&D efforts instantly redundant, no matter how far along they are, or how much money has been sunk into them to date. 

To mitigate these risks, and to help apprehend the staggering amount of data emerging every day in their arenas of enquiry, researchers are turning to AI-enabled insight management software to augment their research efforts. 

Here’s why the right insight management software is mission-critical in the life sciences sector.

What is insight management software?

AI-enabled insight management software is able to monitor millions of disparate data points, many of which are invisible to traditional search engines, moment-to-moment, as the data emerges. Where traditional search analytics platforms leave it to the researcher to curate results, decide what is and isn’t relevant, and manage multiple disparate data sources, insights management software does this heavy lifting for you, extracting and interpreting data to deliver actionable insights.

In short, where traditional search methods favor the broad view, insight management software goes deep, learning from the user over time and adapting to suit their needs. 

With the right insights management platform, research-related processes which historically take weeks or months are now completed in minutes and seconds.

What problems do insight management software platforms solve?

AI-enabled insights management software platforms address a number of key challenges in the life sciences arena.

Big data is only getting bigger

By 2025, the amount of data created, captured, copied and consumed globally is forecast to reach 180 zettabytes. A zettabyte is equal to one trillion gigabytes.

The life sciences sector is responsible for a good portion of that data. Quite simply, the sheer volume of data life sciences researchers need to monitor is far too big for human researchers to apprehend without AI assistance.

Finding the gap

According to Deloitte, 91% of life sciences organizations plan to invest heavily in R&D in 2023, which means the sector is set to experience a significant amount of movement in the near future. The search for true white spaces in the midst of a flurry of R&D activity is a top challenge for researchers. By analyzing trial data, patents, publications, press releases and other emerging data in real-time, insight management software can equip R&D teams with the information they need to make informed strategic decisions around viable targets and resource allocation.

Relieving the skills shortage

Talent availability is currently not keeping pace with growth in the life sciences industry. According to recent research by Randstad, it takes an average of 105 days to fill a position in the US life sciences industry, with only 12 available candidates for every open position. A shortage of data talent in particular is hitting the sector hard. Through the efficiencies insight management software delivers, researchers are freed up to apply their time and skills to more important areas of focus, and fewer data experts are required to cover data monitoring requirements. 

Reap the benefits of AI-enabled insights management with Similari

With a powerful AI-enabled insights management platform like Similari, you can offload up to 80% of your data monitoring time, while gaining access to the competitor and IP intelligence you need to drive effective strategic planning and maximize research performance. 

Similari not only monitors millions of emerging and dynamic data points in the arena of your choice, it also analyzes and presents them to you as actionable insights in seconds via the intuitive Similari dashboard.

Find out how Similari can drastically augment your research and competitor intelligence capabilities by booking a free demo with our team today. We’ll take you through the platform and show you just how much value it can add to your day-to-day operations.

 

Shaping the terrain of the life sciences field

Deloitte’s recently released 2023 Global Life Sciences Outlook highlights some top trends expected to shape the life sciences industry this year. In this article, we’ll take a look at three key takeaways from the report, and how they’re expected to impact the terrain of the life sciences field in the immediate future.

R&D is firmly on the agenda

According to Deloitte’s recent survey of C-suite executives in the life sciences industry, research and development is top of the agenda this year. 

95% of respondents indicated that their organizations would be focusing on the development of innovative products, with 91% planning to invest heavily in R&D innovation and 87% indicating that they will be investing in digital innovation.

This focus can be understood as a response to the increasing pressures to deliver sustainable ROI amidst financial challenges and significant shifts in the market, reimbursement practices and regulation. 

AI is playing an increasing role in drug development 

AI is being deployed into drug development processes to expedite target searches, delivering new candidate medicines in months, not years. The report predicts that AI’s ability to monitor, mine and analyze vast tracts of data too big to be handled by human researchers is going to transform R&D at every level.

Already, researchers are using AI rapidly identify new compounds for development, and analyze data from patient monitoring, previous trial results and patient histories to drastically reduce R&D time and time to market. It is also being used to collect and interpret increasingly important RWE data and reduce research waste and duplication by identifying innovation white spaces. 

Deloitte projects that AI will be used to reshape clinical trials. Phase 3 trials came in at an average of 3.5 years in 2022. AI-enabled “intelligent trials” have the potential to drastically improve trial and regulatory evaluation times by analyzing more data, more quickly, while minimizing the risk of human error. Deloitte predicts that, by 2030, life sciences organizations in collaboration with the academic community will be using AI-controlled simulations for drug discovery much more, leading to cheaper drug discovery processes, more affordable studies, and better quality medicines. 

Digital transformation is picking up steam

Historically, the life sciences sector has lagged when it comes to digital transformation. But the COVID-19 pandemic changed that, forcing organizations to digitize their operations almost overnight. 

49% of surveyed biopharma professionals reported using Cloud AI daily, with a third reporting daily use of AI in their work. 

Many life sciences companies are using Software as a Service (SaaS) platforms to optimize important operations, such as tracking clinical trial participants and data monitoring. Analytics and insight management software is being leveraged to optimize resource allocation and improve decision-making during the R&D phase.

That said, research shows only 20% of biopharma companies are “digitally maturing”, according to Deloitte, indicating a long road ahead, and a need for digital transformation to shift to top priority if life sciences companies hope to remain competitive and included in the sector’s new wave of advancement.

Everything, everywhere, as it emerges

It’s an evolving and dynamic moment for the reinvigorated life sciences sector. Competition is fierce, and innovation is unfolding at an astonishing rate. To remain competitive, life sciences organizations are looking for every opportunity to capture value in their drug development pipelines. One way they can achieve this is through better insight management.

With an insights management platform like Similari, R&D teams can augment their innovation capabilities like never before. By monitoring millions of deep data points as they emerge, Similari is able to equip human researchers with a comprehensive, moment-to-moment picture of the field, including all the latest data from publications, press releases, clinical trials and more, allowing them to forecast and plan more accurately, and giving them access to data invisible to traditional search methods. 

With the actional insights Similari extracts, R&D teams are equipped to make better decisions around resource allocation, trial design, and viable target identification, while avoiding duplication, trial waste and patent thickets.

Find out how Similari can help you transform your R&D processes and offload 80% of your manual data monitoring time by booking a quick demo with our team today. 

About a third of patients drop out of clinical trials before they’re complete. That’s an uncomfortable statistic in an industry where millions of dollars are on the line for each and every trial. It’s a familiar problem in the world of pharmaceutical research, but what’s causing it?

Simply put: the patient experience isn’t always what it should be. And many pharmaceutical companies make the mistake of forgetting that the true focus of a trial isn’t a compound, or even the results at the end – but the human participants themselves. 

As Patient-Focused Drug Development (PFDD) gains momentum, pharmaceutical companies and CROs are increasingly open to technological methods that can improve the patient experience. Voice response technology is one of the tools that have the potential to improve patient engagement, and enrich the data that trials generate in the process.

How it works

Interactive Voice Response (IVR) technology uses automated telephone systems to collect patient data using pre-recorded messages or text-to-speech technology. This enables patients to report any symptoms they experience, and to disclose whether they have adhered to protocols. Researchers can then collect and analyze all of that data to build a more complete and more accurate picture of an ongoing trial. 

Here, we’re exploring some of the opportunities this presents for clinical trials of the future.

Listening to patients makes them more engaged

Enrolment and retention challenges have dogged the pharmaceutical industry for a long time. The Center for Information and Study of Clinical Trial Participation estimates that half of clinical trials don’t secure enough participants to begin with. For those who manage to recruit enough, the average dropout rate is 30%.

Facing these grim odds, it’s in the interests of any pharmaceutical company to boost patient engagement. Using voice response technology, patients are able to engage in the process in an active way, increasing the likelihood that they’ll see the process through to the end.

Automating away human inaccuracy for more accurate results

Like any complex and labor-intensive process, human error plays a role in clinical trial research. Voice response technology can reduce the effects of human error by automating the process of collecting patient data and storing it appropriately. Once patients provide their input verbally, NLP systems can analyze it and store it in a way that allows researchers and clinicians to retrieve information and critical insights.

This also saves time and cost, by reducing the need for patient interviews and surveys. Patients can provide data in real-time, instead of waiting for researchers to provide the opportunity. 

Why actively listening makes commercial sense

Failing to listen to patients can skew the results of clinical trials in ways that may be hard to quantify, but often cause significant commercial problems down the line. For example, a drug’s side effects may be deemed medically or technically acceptable, but if patients feel strongly about those side effects, the drug may see limited adoption in the market. Conversely, a drug may be discarded even though patients are willing to tolerate rare adverse events. 

In both cases, simply asking patients could have led to better decision making and better commercial outcomes. And IVR makes it possible to get those answers, early enough for it to make a difference. 

Embracing the bright future of clinical trial research with AI-based insights

The number of registered clinical trials continues to balloon, with over 400,000 registered studies ongoing globally as of March 2023. There’s no shortage of pharmaceutical innovation going on – but for researchers, that’s a double-edged sword. Understanding all of that data is now firmly beyond the capacity of human-only teams. And as that data becomes even richer and more comprehensive – thanks to technologies like IVR – there’s an urgent need to augment innovation capabilities to keep pace.

At Similari, we keep our finger on the pulse of the pharmaceutical industry, automatically surveying millions of data points as they emerge. Similari extracts actionable insights for the human researchers who depend on them to identify white spaces, establish budget guardrails, and design effective trials. 

Discover how Similari is transforming innovation and clinical trial research through a live demo with our team.