Author: Dylan Arslanian

In 2019, an HIV vaccine trial, Mosaico, began. There was of course much pinning on it. Without treatment, the dangerous human immunodeficiency virus wreaks havoc on those it infects and broader communities. An article in Nature notes that advances in care over the past three decades have transformed HIV from a deadly disease to a manageable chronic condition. This is because of the amazing research and development, and drug discovery that has taken place to treat this complex condition. Nevertheless, substantial gaps in care remain and HIV continues to be a major public health threat. The best course? Preventative vaccines. 

But Mosaico failed. The vaccine meant to target a variety of HIV subtypes didn’t invoke the necessary immune response to neutralize the antibodies powerful enough to fight the infection. All was not lost. Dr Anthony Fauci asserted that despite Mosaico’s disappointing outcome, it did show that any further vaccine should rouse the body to produce broadly neutralizing antibodies. 

As biotech entrepreneurs, we have to confront wily adversaries such as HIV and consider the most innovative and thoughtful ways to tackle them. It’s really challenging to get the preventative treatment right. Healthcare experts, such as Fauci, lament that while there are working treatments, a patient must take them for the rest of their lives. In resource-constrained public health systems, this is not ideal. 

The silver-lining: why life-sciences companies need to appreciate failure

Scientific Learning: 

Vaccine trials, whether successful or not, provide valuable scientific data that can improve our understanding of the virus and the immune response. Each trial contributes to the growing body of knowledge in HIV research, potentially uncovering new avenues for vaccine development.

Targeting a Complex Virus: 

HIV is a highly mutable virus, which means it can change its genetic makeup rapidly, making it challenging to design an effective vaccine. By conducting trials like Mosaico, researchers can assess the efficacy of different vaccine candidates and refine their approaches.

Vaccine Development Strategies:

The failure of a vaccine trial provides insights into which strategies might not work and prompts researchers to reevaluate their hypotheses. It can lead to the refinement of future vaccine candidates, guiding scientists towards more promising avenues.

Ethical Considerations: 

Vaccine trials involve extensive testing on human volunteers. While a failure may be disheartening, it is essential to ensure the safety and efficacy of potential vaccines before wider deployment to avoid potential harm.

Future Trials: 

The data and lessons learned from a trial like Mosaico can inform the design of future trials. Researchers can adapt their methodologies and focus on different aspects to increase the chances of success in subsequent attempts.

Biotech entrepreneurs can take advantage of drug discovery funding

Fauci noted that the field is “going to continue to pursue very active research in that area.” For biotech companies, this signals opportunities. It’s not only vaccines that are being trialed. Indeed, the field will always require a variety of treatment and prevention options. From long–acting injectables to oral pre-exposure prophylaxis, the drug discovery landscape is a biotech entrepreneur’s oyster. 

To stay abreast of drug discovery, new and ongoing clinical trials, choose Similari

The world of life-sciences is in constant flux and motion. New discoveries are made while others are paused or halted altogether. With Similari, you can get to grips with this information and so much more.  Similari’s AI-enabled insights management platform brings news and developments directly to you by monitoring millions of data points in your chosen arena of inquiry, many of which are invisible to traditional search methods. Reach out today for a free demo, and find out how Similari can help you keep absolutely up-to-date with the latest life sciences news and developments.

Big data is changing the trial game

As big data analysis capabilities have increased over the last five years, so have the possibilities for radically improving efficiency and success rates in clinical trials. Digital transformation at the level of general healthcare, including the prevalence of digitized records, the proliferation of data-capturing wearables market and other sources of patient-related information mean that researchers now have access to a range of invaluable data collected during patients’ normal course of care outside of the clinical trial setting. 

This Real World Data (RWD) presents a wealth of information about drug and treatment efficacy, and it’s changing the way clinical trials are designed and executed. Here’s why.

What is Real World Data?

The term Real World Data refers to health-related data generated during the course of a patient’s routine care or post-treatment life that relates to their health status. This data comes from sources outside of the clinical trial itself, such as healthcare records, insurance claims and billing activity, medication order records, or from the patient’s own data generation via wearables or other at-home medical devices. 

From Real World Data, researchers can synthesize Real World Evidence (RWE), which is the clinical evidence generated by the analysis of RWD that reflects the safety, efficiency, benefits and risks associated with the drug under testing.

What are the sources of Real World Data?

Real World Data can be sourced from any reliable point of origin outside of the clinical trial itself, many of which appear fairly unconventional. RWD sources include:

• Clinical sources, including EHR records, pathology/histology data, discharge and progress reports. 
• Medication sources, including order records, Point of Sale data, concomitant therapies and prescription refills. 
• Claims, including medical and prescription drug claims, and other treatment usage data. 
• Family history, including historical data on health conditions relating to the patient’s extended history, smoking in the household and alcohol use. 
• Mobile health, including data gathered by fitness trackers and wearables delivering clinical-grade information on heart rate, PulseOx levels, sleep, calories and more. 
• Social media, including patient support groups, blogs and posts. 
• Environmental sources, including data on air quality, climate factors, community infections, and OHSA data. 

Real World Data applications

Real World Data is being applied in the drug discovery process in a number of important ways. 

Guiding pipeline and portfolio strategy

Pharmaceutical companies can use RWD to guide strategic decision-making, including estimating disease prevalence and incidence and predicting the scope of the potential patient population. Increasingly, researchers are bringing RWD into earlier phases of the product development process to help guide the allocation of resources and maximize the potential for success. 

Optimizing study design

Researchers can use RWD to optimize study design through advanced feasibility determinations, improved cohort recruitment and predictive enrichment, and lowering trial costs through the development of “synthetic” control arms built from pre-gathered RWD. Researchers are also using RWD to make more informed decisions regarding required enrollment sizes,  trial site selection and trial patient diversity. 

Post-launch safety and efficacy surveillance

RWD is emerging as an invaluable data source for post-launch drug safety and efficacy assessments. To assess the safety and efficacy of a drug, researchers need visibility of drug performance and potential associated risks across a longer period of time and against a broader scope of variables than possible during the limited time period of a trial.

How advanced data management is unlocking major value in the drug discovery pipeline

Clinical trials are producing more data than ever before, especially with the increasing use of RWD. The sheer volume of data being generated every day from clinical trials, M&A activity, press releases and publications has become impossible for human researchers to monitor and analyze effectively. 

Increasingly, pharmaceutical R&D teams are turning to AI-enabled data monitoring and insights management platforms to help them keep on top of the impenetrable data volume flooding their areas of research. Similari is one such platform. 

By monitoring millions of data points in a defined arena of inquiry, Similari is able to collate key insights which help inform data-driven decisions, identify true white spaces and avoid research duplication and dead ends. 

Find out how Similari can augment your R&D capabilities 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.

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.

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.

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. 

The word “Google” became a verb in 2006, when it found its way into the Merriam-Webster dictionary. But like all shifts in language, it had caught on long before official recognition caught up to everyday usage.

That happened because search engines like Google had caused a seismic shift in the way humans access information. The algorithms read linking behavior between pages to enable information retrieval on an unprecedented scale. And it was all wrapped up in a streamlined, easy-to-use interface that gave users (even non-technical ones) what they wanted, fast: lists of websites semantically linked to their search query. 

Bad news for public libraries, but amazing for almost everything else. It’s become so ubiquitous that most of us struggle to imagine the “before time”.  

Why you might not be Googling for much longer (or at least not in the same way)

None of this is news to anyone, so why am I talking about it? Simple: we’re now at another inflection point, heralded by the arrival of powerful new technology. Generating lists of websites ranked according to relevance is useful, but it’s just the starting point. The user still needs to sift, evaluate, analyze and synthesize that information.

And as Google itself has acknowledged, there is now a pressing need to go further. Lists of websites are not enough: users want “deeper insights and understanding.” In this post, we’re taking a look at how generative AI is changing how we search, paving the way to richer insights and better decision making. 

But first, a brief diversion on semantic analysis. 

Semantics: building search queries, word by word

Every word has what is known as a semantic domain, a range of other words that connect to a shared substrate. For example, the word “vehicle” is part of a semantic domain that includes words like car, plane, ship, and many more. “Vehicle” can also signify a means of achieving something, particularly in medical and scientific applications. 

Search engines operate by identifying pages that contain keywords in the user’s search query. But one of the conventional limitations of keyword-based search is that it lacks the human intuition to situate the right part of the semantic domain. So, “vehicle”, taken out of context, can recall the entire semantic domain of “things that travel”, and the entirely different domain of “biological component that delivers drugs”. That’s too broad to be useful.

But there are many ways to refine a search query to get narrower and more relevant results:

• Quotation marks around a phrase to fetch exact matches of that phrase: “the costs of drug discovery” will fetch only results that contain those words and in that order.
• Hyphens to exclude certain results: if you want to get results about unicorn companies, but don’t want to deal with pages that talk about magical creatures, you can use: unicorn -creature 
• If you need information in a specific format, you can ask Google to only return results in that format, as in: tech talent shortage filetype: pdf

From keyword search to generative AI: bridging the human/machine gap

Behaviors like this simulate the context-driven decision-making that we take for granted when we communicate with other humans using a natural language. And they are behaviors that generative AI can now automate. 

Generative AI reads search queries using NLP algorithms that interpret language in the way that we do as humans – but on a much larger scale. This allows it to understand the intent behind a user’s query, and create content that matches it. In other words, generative AI makes that crucial step that search engines couldn’t make – interpreting the information on web pages for the human user. And, much like search engines did in the early 21st century, generative AI like ChatGPT combines this new power with a user-friendly interface that people love to engage with.

Why it isn’t quite over yet for search engines

As others have noted, search engines still have the edge over chatbots when it comes to crawling the web to find up-to-date information. Chatbots are trained on large but static data sets. For researchers in particular, this is a major limitation. 

And much like search engines, the output of generative AI is only as good as the input it receives. In other words, it takes knowledge and skill to create an effective prompt for the AI to use. We can all expect to hear a lot more about prompt engineering as an in-demand competency as the AI revolution continues to unfold. 

What’s needed is a way to leverage the real-time information gathering of a search engine, with the intuition and nuance of generative AI and NLP.

Similari: AI-based insights for the future

By leveraging the latest advances in AI, ML and NLP, Similari equips researchers and business development professionals with continually up-to-date insights on their industry and their competitors – all without the need for complex human-led prompt engineering.

Because it learns the habits and preferences of human users over time, Similari can refine searches by identifying the most relevant terms from a semantic domain, and tailoring results to specific business needs. 

Get in touch to learn how Similari combines the intelligence and power of an AI chatbot, with the flexibility and 360-degree line of sight of a search engine. 

In a sense, R&D professionals are spoilt for choice when it comes to automated search tools. There is now a wide (and growing) range of solutions that can streamline the process of monitoring technical data, and extracting insights from it. 

Here, we’re unpacking three solutions that augment human search capabilities: Google Alerts, ChatGPT3 and, of course, Similari, to find out where their strengths and weaknesses lie. 

Google Alerts: your trusty research sidekick for the last 20 years 

Google Alerts can be a powerful tool for gathering information about new and emerging trends, technologies, and ideas that impact innovation strategy. 

By simply signing in and setting some parameters, users can track industry keywords, flag key trends and stay up to date with the news in their field. They can even keep tabs on competitors, provided the information makes it into the news cycle (more on this later).

And it’s all served to them, daily, weekly, or however they prefer, via email. All in all, it’s a useful tool that can help innovators to at least keep up with the curve, even if staying ahead of it remains just out of reach, for reasons we’re about to explore. 

Everyone has a blindspot (even Google)

For all its (completely free) benefits, there are some limitations to keep in mind when it comes to using Google Alerts for innovation intelligence.

Available sources: vast, but limited in important ways

Sources are limited to pages indexed by Google, and within that, mostly news sources (recall what we said earlier about competitors). That may be enough for certain use cases, but it leaves a lot of relevant information out of the frame, especially when it comes to scientific and technical literature. 

Not everything worth knowing makes it into the news – and if it isn’t there, it won’t make it into a daily Google Alert.

The brute facts aren’t enough on their own

Users have noted long standing challenges like false positives (unrelated content that matches the keywords) or false negatives (relevant content that does not contain the exact keywords). 

Additionally, Google Alerts does not offer much customization, so users can’t combine multiple searches into a single alert. And because it’s email-only, there’s no easy way to combine all emails and news stories into a single source. Reporting on the facts, and analyzing their significance – that’s left up to researchers to do the old-fashioned way.

From data to insight: the missing step

Perhaps most importantly, Google Alerts can’t automate that crucial step from data to insight – and insights are what fuels innovation. The work of figuring out the story the data is telling remains with the user. In other words, it’s a valuable tool for exploration, but not exploitation.

ChatGPT3 and the future of AI-enabled research

Meanwhile, at this very moment, the internet’s favorite chatbot is having human-like conversations with millions of users. What are they talking about? In short, everything, including a wide range of business use cases: automating marketing & sales, debugging code, and most importantly for our purposes – R&D. 

But it’s not just the impressive NLP that has the whole world abuzz. It’s also the extraordinary ease of use that ChatGPT3 provides. The user simply gives an input, asks a question, and sits back while the generative AI works its magic. Unlike email-based alerts, this experience is conversational. The user can ask follow-up questions or demand justification. 

Scientists and researchers can curate their own corpus of technical information and feed it to the system, and leverage its computing power through that simple interface. 

ChatGPT’s limits are harder to find, but they exist

Since it appeared in 2022, ChatGPT has been used by millions of people, and it’s received intense scrutiny in the process. This has provided useful input for the platform itself, while pointing up its limitations. 

The problem of data (again): historical and reactive 

As we saw with Google Alerts, historical data can only take you so far. ChatGPT has limited visibility past 2021. And while that could change in the future, its text-based output is best suited to providing a historical account of past data.

Innovators seek uncommon knowledge, not the common ground

ChatGPT is adept at drawing on billions of data points to come to a single answer. It can tell users, usually with great accuracy, where the common ground lies in a specific scientific dispute. It can even point you to its sources (if you ask nicely). This is all extremely impressive. But it’s not what researchers need to help them innovate, because innovation is about challenging the status quo, and finding new ways to interpret the data. 

And even with a private corpus of texts to work with, ChatGPT’s output is always text-based. That’s ideal for marketing, or generating source code, but not for scientific research, where data visualization plays a vital role in decision making. 

Similari: insights at your fingertips

We would be remiss to not tackle the question of where Similari fits in this picture. In short, Similari allows researchers to go deep where other tools favor breadth. In a matter of minutes, researchers can configure Similari to start watching, learning and generating deep insights about their specific field of inquiry. 

Picking up where search analytics leaves off: an insight mechanism that learns over time

Traditional search analytics platforms excel at finding data and presenting it in a predefined way. But they leave the heavy lifting to the researcher or analyst who must spend time curating that information, deciding what is and isn’t relevant, and juggling multiple disparate data sources. Similari replaces that workflow with a unified, live feed of up-to-date insights.

And over time, Similari learns from the human user, absorbing their preferences and imitating their behaviors – all without explicit instruction, thanks to its sophisticated underlying ML framework. That’s how Similari is able to slash manual data monitoring time by 80% or more, while actually enhancing research outcomes and fuelling innovation. 

Harnessing the power of AI to know everything, all the time

Augmenting human capabilities with AI is now a priority shared by businesses in almost every industry. And as the market matures, it’s providing more and more targeted solutions for highly specified business needs. Similari is one of them. And, for innovation professionals at least, it’s one of the most effective. 

To learn more about how Similari enhances and streamlines research for R&D and innovation teams in the life sciences and beyond, schedule a demo with our team. We’re ready to show you everything you could be missing with traditional search analytics – and much more besides.