Expensive Failures: 3 Drug Development Missteps, and How to Avoid Them

After years of development and billions in R&D spend, the vast majority of drugs never make it to market. Failure is a near certainty, and there’s no shortage of dangerous pitfalls along the way. 

Here are three costly missteps that can derail the process of bridging a new drug to the market.

Drug development misstep 1: Blind spots in strategic planning

By some estimates, 10% of drug development failures result from poor strategic planning. This can include a wide range of problems such as sub-par clinical studies, changes in therapeutic focus or company mergers that make prior research untenable. Given that it generally takes an average of 14 years to bring a drug to market, it’s crucial that companies invest enough time in strategically planning the drug development process, from target identification through to (hopefully) approval. 

Is the projected ROI realistic? 

Investors care about the return on the investment, and they need to see facts and figures to back it up. This is especially true in a tight funding environment. But it is a major mistake to over promise in order to secure investment. That leads to overly ambitious timelines, and underestimated budgets. 

Setting manageable expectations from the outset is key to maintaining healthy investor relationships and suitable budget guardrails. 

Has product market fit been thoroughly assessed?

Before proceeding to the first step in drug discovery, fundamental questions about the project’s viability need to be answered. Companies should only pursue products that are likely to match a medical need in the market. 

Physicians and patients are the people to consider at this stage, before investors even enter the picture. Ideally, the product should be something that will have a significant impact on patient outcomes, with minimal adverse effects. It’s also worth considering whether the proposed product is a “me-too” drug – a drug that closely resembles an existing product – as these often have lower return on investment. 

How likely is the product to qualify for reimbursement after approval?

Reimbursement rates are important for long term profitability. If federal or private insurers are unlikely to reimburse for the product, it may not be worth pursuing. Insurance coverage is often overlooked in the early stages of the drug development process, which is a strategic oversight. To increase the likelihood that they will be reimbursed, companies should build a value proposition that demonstrates the clinical and economic benefits of the drug, before they embark on the discovery process. 

Drug development misstep 2: Not having a clear compliance and approval strategy

Once a drug has made it through clinical research, it needs to clear an FDA review (or comparable authority outside the US). To pass, drug developers must submit extensive documentation on safety and efficacy. FDA approval takes a wide range of factors into account, including:

  • Treatments already available for the target condition:
    Reviewers weigh the risks and benefits of the drug to determine whether it is needed in the market. Risk tolerance varies according to the severity of the disease that the drug treats, and the severity of adverse effects. 
  • Clinical findings:
    The agency expects a minimum of 2 adequately designed trials, and may demand more. To reach a decision, reviewers scrutinize all of these results and weight the benefits and risks for target populations.
  • Risk mitigation measures:
    The agency assesses the risks for consumers, and may make recommendations such as an FDA-approved label. It may also require companies to implement a Risk Management and Mitigation Strategy (REMS).

The complexity of this process calls for specialized teams, which adds another layer of cost, which small to mid-sized pharma companies may struggle to absorb. 

In cases like these, partnerships and alliances can do a great deal to speed up the process and lower the cost. Strategic partnerships can help to expedite approval, by filling gaps in a company’s compliance expertise. Partners with experience navigating the drug approvals process can help with long-term submission planning, and handling questions that health authorities may ask. 

Drug development misstep 3: Failure to keep an eye on the landscape

Even with solid product market fit and an experienced team guiding the process, it’s vital to keep up with developments in the market. To support their own market positioning, drug developers need to stay in the know about what other companies are doing, both rivals and potential partners.

For example, news of a failed clinical trial can help R&D teams to steer the project in a safer direction. On the other hand, a successful trial conducted by another company may eliminate the need to design and run your own. 

All of this information is missed when companies fail to track their competitive landscape before and during the drug development process. This can easily lead to a lack of distinction in the market, hindering market access and reducing the quality of returns. By staying abreast of competitor information in real-time, companies can better identify viable white spaces, and allocate budget and resources accordingly. 

Avoiding costly pitfalls through AI-based insights

With these and even more challenges around every corner, the need for comprehensive and accurate data is more acute than ever. In the world of drug development, what you don’t know can kill you – and there’s simply too much data out there for humans to track and manage without the help of AI.

Similari augments research and business development teams, enabling them to make faster, more accurate decisions through intelligent insights. From identifying innovation white spaces to spotting promising partnership opportunities, Similari helps companies to maximize the value of their drug development process, and steer clear of potentially mission-ending pitfalls. 

Discover the future of drug development today, through a live demo of Similari’s next-generation capabilities.

The Anatomy of a Search Query: How AI Knows What You’re Thinking

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. 

Planning Your 2023 Conference Schedule: So Many Events, So Little Time

In-person conferences are back in full swing, and the event schedule for pharma and related industries is already jam-packed. But with so many events to choose from, businesses need to think strategically. Which events are really worth traveling to and attending in person, and which ones can you afford to piece together through publications and press releases?

Here is our quick guide to the conference scene, and how to refine the list.

Why attending in-person conferences is good business sense

Firstly, conferences provide an opportunity to learn new strategies and gain knowledge from industry experts in real-time. They also offer a unique chance to network with like-minded people: founders, researchers, healthcare executives, and leaders. While we’ve all become accustomed to doing this virtually, being in the room physically has definite advantages when it comes to relationship building.

For startups, conferences are a great way to get up to speed with the latest trends and regulatory requirements in their industry. Startups can also showcase their products at turnkey exhibits, generating exposure to potential customers and investors. Some conferences even offer pre-scheduled one-to-one meetings with investors or strategic buyers.

How to whittle down the list

With dozens of events spread over the entire globe, business leaders need to be selective. It’s simply not possible to be physically present at any more than a handful of in-person events.

Begin by identifying your goals. Are you primarily interested in networking or gaining specific information? If it’s networking, are you hoping to rub shoulders with potential partners, or investors? Each conference will have a different audience, so research this in advance to determine if it matches your goals.

Next, consider the agenda and speaker lineup. Conferences usually advertise a core organizing theme to distinguish themselves from others. It’s important to evaluate this theme in light of your own business needs. For early-stage startups, it makes sense to attend an event that emphasizes strategic partnerships. A telemedicine company may benefit from hearing industry leaders talk about digital transformation and compliance. 

To help you make the most effective choices, we’ve compiled a list of 5 upcoming events that are attracting industry leaders and innovators, each with a distinct focus.

Pharma USA: March 28 – 29, Philadelphia

This event will feature talks from over 100 industry leaders, with over a thousand change makers and innovators in attendance. Major themes for this year’s conference include the value of partnerships, and the primacy of data and insights for innovation and business strategy. 

Global Pharma & Drug Delivery Summit 2023: April 24 – 26, Frankfurt

Just like it says on the label, this event promises to examine the most vital pharma trends and key issues in the drug development process. Its stated aim is to bring together masterminds, including researchers, practitioners and technologists to forge collaborations and learn from the industry leaders in the room. 

BIOMED Israel: 16-18 May, Tel Aviv

The BIOMED Israel Conference and Exhibition is the largest convention in Israel for life sciences, bringing together industry experts, manufacturing sectors, and leading companies throughout the supply chain. The conference program will explore trends and innovations that are shaping the future of the life sciences industry. 

Similari will be attending, and we are especially excited to see conference sessions devoted to discussing AI in biopharma.

BIO International Convention: June 5-8, 2023, Boston

The BIO International Convention is one of the largest global events in the biotechnology industry. Hosted by the Biotechnology Innovation Organization (BIO) that brings together leaders and stakeholders in the biotech industry to exchange knowledge and ideas. It includes keynote presentations, panel discussions, networking events, and an exhibit hall showcasing the latest technologies, products, and services in biotechnology. The convention attracts thousands of attendees from around the world and provides a platform for connecting with potential partners and exploring new business opportunities in the industry.

Similari will be attending, and we are especially excited to see conference sessions devoted to discussing Innovation intelligence biopharma.

CPhI Worldwide: October 24-26, Barcelona

CPHI is a pharmaceutical event that brings together more than 100,000 pharmaceutical professionals annually through exhibitions, conferences, and online communities to network and identify business opportunities. CPHI Worldwide is the world’s leading pharmaceutical exhibition, hosted at a different location every year. The event covers all aspects of the industry and provides a platform for the global pharma community to gather and discuss the latest trends and innovations in the industry.

Similari will be attending, and we are especially excited to see conference sessions devoted to trend detention usign AI in pharma.

Being virtually everywhere at once, with Similari

The reality is that missing out on any large industry event comes with a cost. The value of creating in-person connections and learning directly from expert presenters is hard to quantify. But there is a way to stay in the know, without missing a beat. With Similari, you can access the most relevant insights from top conferences you weren’t able to attend in person – and even the ones you weren’t aware of. 

Similari’s insights mechanism surveys thousands of data points in press releases, M&A announcements, clinical trials and articles, and extracts critical insights for human decision makers to interpret and act upon.

Our next-generation AI empowers you to be (virtually) everywhere you need to be. Get in touch with our team to learn more.

The AI-Enabled Research Toolkit: Google Alerts, OpenAI and Similari

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.

What’s Next for the US Cannabis Market? Key Trends, Challenges, and Opportunities

The legal cannabis industry has emerged from its recent slump and appears ready for massive growth in the near future. It’s been a rough ride getting here, and companies who have weathered the storm now have the opportunity to turn things around.

But what do piecemeal legalization and broad investor pullback mean for businesses? And how should these trends impact the decisions being made right now?

In this article, we’re exploring the case for optimism, pointing out some of the pitfalls, and recommending the best way forward. 

The case for cautious optimism: signs of recovery and opportunities for the future

The US legal cannabis industry took a beating in 2022, with the largest publicly-traded companies reporting collective losses of $550 million. But analysts are now expecting a rebound, with sales projected to increase to $50.7 billion by 2028.

This growth is expected to come largely from the piecemeal, state-by-state legalization that is now taking hold. New states that are now beginning to allow sales: Connecticut and Missouri for adult-use, and Mississippi for medical use. It’s likely that as these regulatory changes open new markets, the industry will eventually right-size itself relative to the level of demand. That could allow cannabis-lifestyle and therapeutic businesses to expand their operations into newly legal markets.

Opportunities for biomedical industries

It’s not just recreational or lifestyle use that benefits from a more relaxed regulatory environment. It also creates more opportunity for biomedical and pharmaceutical companies to investigate cannabis for therapeutic applications. Pfizer’s acquisition of Arena Pharmaceuticals was just one major signal of this increasing interest in cannabinoids. 

Cannabinoids are already used in a wide variety of therapeutic applications spanning pain management, immune diseases and improving patient outcomes in chemo-therapy. Legalization is making it easier for researchers to access and work with cannabis, even though at the federal level, significant obstacles remain. 

But overall, the industry looks ripe for product innovation and accelerated growth. 

Why the cannabis market isn’t fully out of the woods just yet

The legal cannabis industry has to reckon with the same broad investor pullback affecting almost every other industry. Investors are – understandably – demanding more proof of future profitability, which may be especially difficult for operators in mature markets where prices have leveled out. In addition, inflation and rising costs will continue to impact the bottom line. 

With funding and M&A on a downward trajectory that shows no signs of reversing anytime soon, the industry’s best chance lies in strategic partnerships. 

Better together: why partnerships are so crucial for cannabis companies

Partnerships have the potential to act as a force multiplier to differentiate companies, expand their reach, and navigate complex regulatory challenges. 2023 has already been hailed the “Year of Partnerships” by industry leaders due to the recognition that the industry will need to pull together to overcome challenges and make the most of the potential the market is showing. 

Partnering for compliant distribution: MSOs and co-manufacturers 

As we mentioned earlier, legalization is rolling out state by state, but federal prohibitions still make interstate commerce illegal. This is where Multi-State Operators (MSOs) play a pivotal role, enabling companies to expand their presence to other states, without running afoul of federal rules. 

 Co-manufacturing partnerships offer another way for companies to expand without overextending budgets. These deals enable companies to pool resources to enter new territories or start new product lines. This can also help to avoid the costs of setting up new manufacturing operations, leveraging partners’ facilities instead.

Accelerating biotech research through partnerships with authorized manufacturers

Partnerships are also enabling progress in biotech research and the creation of cannabis-based medicines. The recent partnership between biotech company Bioharvest Sciences and Royal Emerald Pharmaceuticals is one example of this type of partnership. Through this alliance, Bioharvest Sciences can more easily sell to research institutions federally, leveraging Royal Emerald Pharmaceutical’s ability to legally produce marijuana in the United States.  

Cut through the noise with AI-enabled insights

To succeed in an increasingly saturated market, cannabis companies will need to take a hard look at operational efficiency, and cut costs aggressively. But even more importantly, they will need to find ways to know what is happening in the market, moment to moment, in order to move in on opportunities as they arise.

Similari empowers companies with the always-on market intelligence they’ll need to make agile decisions in a constantly evolving commercial landscape. Through its next-generation insights mechanism, Similari gives researchers and innovators real-time updates on competitors, technological advances and critical changes to the regulatory environment, adapting to users’ needs and preferences over time. 

Schedule your very own demo with our team to see how Similari brings order out of chaos, with timely, reliable insights. 

Breaking the Bank to Save Lives: the Burgeoning Costs of Drug Development

It’s no secret that drug development costs are ballooning, while the number of products that actually make it market is decreasing. This phenomenon has become a kind of truism in the life sciences – so much so that it has spawned its own “law”. The so-called “Eroom’s Law” states that, adjusted for inflation, the cost of drug production doubles every 9 years.

That may or may not be technically true. But what’s undeniable is that managing the costs of drug development is now a critical priority for the pharmaceutical industry. Advanced AI holds the promise of shortening the timeframe and reducing the overall costs of drug production through in silico modeling. But a long process of research and ideation has to take place before an asset reaches that stage. 

And traditionally, that process has been resource-intensive, both in terms of cost and personnel. In this article, we’re taking a look at how this stage can be optimized and accelerated with AI-enabled search capacities that augment human research teams. By saving time and cost here, companies can move from white space to asset more quickly and more confidently than ever before. 

But what’s really new? Drug development has never been cheap

The high costs of drug development are nothing new. Neither are the factors that go into it: extensive research and development, clinical trials, regulatory approval, and marketing. Drug development has also always required investment in infrastructure and personnel. Back in 2021, the average cost of developing a single asset was estimated at just over $2 billion

But while the fundamental inputs that go into that cost haven’t changed, their relationship to market conditions have. 

Cost up, ROI down: the conundrum for Pharma

According to a Deloitte study, 2023 is putting drug developers between the rock of higher development costs and the hard place of lower sales forecasts. In 2022, costs rose to pre-pandemic levels, with sales dropping almost as low as 2019. Looking beyond 2023, pharmaceutical companies in the United States will also need to be agile enough to adjust their clinical and commercial strategies as the Inflation Reduction Act changes the commercial landscape for prescription drugs.

The perfect storm of scarce talent and tight budgets

Another factor complicating the question of cost is the prevailing state of the market. The economic outlook for 2023 is bleak, and companies across the board are being forced to trim the fat, and find ways to do much more, sometimes with much less. At the same time, employees still hold the upper hand in a historically tight labor market. Skill shortages are being felt everywhere from high-tech to marketing. 

For business leaders, the problem is made even more insoluble by global employee mobility and remote work: highly skilled professionals are able to be selective about who they work for, and they’re confident enough to strike out on their own in search of better conditions. 

Managing costs with artificial intelligence 

These challenges underscore the importance of leveraging AI solutions to manage costs, optimize processes, and make up for personnel shortages. Here’s how AI-enabled research impacts every stage in the development process.

Hit identification: automatically sorting the hits from the misses

The average cumulative cost for this initial stage is around $102 million. AI can help to control this cost by analyzing large datasets of biological and chemical data before it reaches human researchers. They’re then able to focus their efforts on only the most promising targets. This can radically enhance high-throughput screening, by accurately picking the best compounds for analysis.

Progressing hits to leads and optimizing them

Tools like Similari make it possible for researchers to instantly survey the landscape and technical literature to accurately assess the viability of leads. By automating away the bulk of their otherwise manual search time, these tools accelerate the hit-to-lead and lead-optimization phases. And by leveraging proactive analytics, researchers can respond to emergent threats or new findings, to adjust their strategy accordingly. 

Streamlining preclinical development & clinical trials

Always-on market surveillance allows researchers to “fail early” (and cheaply) by knowing the results of prior research. With the volume of clinical data growing exponentially year on year, AI offers a way for human teams to cut through the noise and identify salient insights that can make or break their innovation initiatives. 

Streamlining approvals: the last hurdle 

By the time an asset reaches the approval stage, the cumulative cost will have reached around $3 billion. AI has a crucial role to play in managing these ballooning costs, before, and during the approvals phase.

Before an asset gets here, AI like Similari can help reduce the costs of regulatory approval by predicting the likelihood of approval and identifying potential safety issues early on. This helps companies make better decisions about which drugs to invest in and reduce the risk of costly regulatory failures. 

Companies can also leverage market intelligence to identify potential partners with the requisite expertise and experience to achieve compliance. By entering licensing or partnership agreements with these (usually larger) entities, startups in particular stand to gain a great deal when it comes to expedited approval and go-to-market. 

Breaking free of Eroom’s Law starts with data

The way out of the bind is to improve the quality and the quantity of data available to decision makers. Similari puts thousands of clinical trials, along with millions of patents, articles and press releases at their fingertips. R&D professionals and innovation leaders are leveraging Similari’s next-generation AI capabilities to accelerate drug development, and improve outcomes with accurate and up-to-date insights, moment to moment. 

Using NLP to Address Data Overload in the Life Sciences Industry

Globally, we’re producing more data than ever before. But the events that catalyzed and accelerated that historic increase caught us by surprise, so the methods that we use to handle it are still catching up. None of this is news to anybody, but it’s less obvious why this is a problem.

After all, data is the lifeblood of innovation, and the better informed we are, the higher the quality of our decision making. But if you don’t have the right tools to organize that data and extract insights from it, it’s very much a case of “water, water, everywhere, but not a drop to drink.”

Data overload: too much of a good thing?

What we’ve described is the unfortunate situation that many life sciences businesses find themselves in, and now it has a name: data overload. Companies that rely on old, manual methods, may find themselves overburdened by the volume and velocity of data. So how should companies be leveraging big data, without triggering data overload? 

The answer lies in AI technologies like Natural Language Processing (NLP), that enable teams to digest huge amounts of information, quickly, and distill actionable insights from the data stream. 

In this article, we’re going to explore the key problems that data overload causes, and how solutions like Similari leverage AI and NLP to address these challenges. 

Data overload woes for R&D and innovation teams

Data overload happens when there is too much data to effectively process, analyze, and make decisions from. In the age of “Big Data”, companies urgently need a way to sift this information before they can use it. Ultimately, it’s not about how much data you have, but how you use it to achieve your goals.

Back in 2001, Doug Laney defined Big Data using three essential characteristics:

Volume: the amount of data that needs to be processed and stored

Velocity: the rate of real-time data creation

Variety: this data can be structured, semi-structured or unstructured

And with Big Data now bigger, faster and more varied than ever before, businesses need to guard against risk factors like these:


More clinical trials, publications and drug patents are good news in a sense: they increase the amount of information available. But the sheer volume of available data makes it difficult for businesses to process it, let alone extract insights by selecting the most relevant and actionable information.

This inefficiency arises because traditional search methods are manual and reactive (or historical). They involve sifting through the results of events that have already taken place. But in a dynamic environment (remember: big data is generated in real time), this isn’t enough. What do you do when one set of scientific results supersedes a previous one? AI resolves this problem neatly – we’ll get to that a little later. 

Missed opportunities

In the face of increased volume and velocity of data, companies that rely on manual or database search methods inevitably have to choose between limiting the scope or the depth of their research. When your focus is too narrow, or too shallow, you risk missing out on key market trends and opportunities. But the opposite problem is equally dangerous: choice paralysis is a very real problem, as articulated in the so-called Hick’s Law: an excess of options can actually slow down decision-making. This is crippling for humans, but not for AI (more on this later). 

Poor decisions

Perhaps worse than missed opportunities and decision paralysis, data overload can lead companies to take decisive steps – but in the wrong direction. Fixating on a single data point, or misunderstanding facts in their particular context, can lead to inaccurate conclusions.

Addressing data overload with Natural Language Processing

NLP is an interdisciplinary field that brings together artificial intelligence, linguistics and computer science. It focuses on the interaction between computers and human language. NLP techniques are used to analyze, understand, and generate human language in a way that AI can understand and process. If it’s sophisticated enough, NLP can read and comprehend written texts, extracting the most salient points and distilling insights for the human decision-maker using the system.

NLP alleviates the burden of data overload by automating away mundane, time consuming workflows, allowing data intelligence professionals to focus on making sound decisions. Crucially, it also replaces reactive search with proactive insight: when facts change, an AI-powered system can register the change almost instantaneously. As we discussed earlier, that overcomes one of the most profound weaknesses of traditional methods. 

Key use cases for NLP in the life sciences

Literature mining: R&D teams can use NLP techniques to extract information from large volumes of scientific literature, such as research papers and patents. This can be used to identify new drug targets, understand disease mechanisms, and track the progress of scientific research.

Clinical trial management: NLP can be used to extract information from clinical trial protocols and reports, such as inclusion and exclusion criteria, adverse event reports, and treatment efficacy. This information can be used to select appropriate sites and improve the design of clinical trials. It can also help companies avoid costly and redundant dead ends. 

Drug discovery: NLP can be used to extract information about chemical compounds, proteins and genes to identify new drug targets, and better understand the mechanism of action of existing drugs. And it can get this all done fast – and more accurately – than any human could, by automating key components of the process:

  • Surveying biomedical literature for specific genes related to therapeutic outcomes
  • Identifying white spaces for specific disease targets
  • Searching patent data concerning specific technologies

Further applications for NLP in life sciences research and innovation 

Executive buy-in: R&D and innovation can cut through the noise of data overload and find the precise insights that bolster the business case for each new initiative. Using this information, they can motivate resource allocation to the C Suite based on sound, up-to-date insights. 

Build partnerships: Using NLP techniques, business development and partnership teams can parse scientific literature and extract key information from these documents, such as the names of researchers, institutions, and technologies. All of this data can be used to identify potential partnerships and opportunities for collaboration. 

From data overload to data motherlode: how Similari harmonizes data to supercharge innovation

The proliferation of data isn’t going to slow down – in fact, we can expect to see over 180 zetabytes in 2025, as the curve gets steeper. Next generation AI technologies make it possible to keep up, by translating huge swathes of data into graphs and presentations that make sense for the humans who use them.

Similari empowers scientists, business leaders and data professionals to harmonize data, reveal insights and make the sound, innovative decisions that will shape the future of life sciences.  

Get in touch with our team to learn more, or schedule a demo to see exactly what Similiari can do.

Stronger Together: Leveraging AI to Build Strategic B2B Life Sciences Partnerships

We’re now in the midst of what many have called “The Decade of the Ecosystem”. Partnerships look set to become the most important revenue generation channel – and for many, they already are. Forging strong partnership ecosystems is now a critical priority for life science startups, faced with a tough fundraising climate, and the “triple squeeze” of economic pressure, talent scarcity and disrupted supply.

But before startups can reap the benefits of belonging to these ecosystems, they need to find a way into them, or even build them. The good news is that the same AI capabilities that enable deep competitive intelligence can just as easily be used to identify and pursue high value partnerships. In other words, AI systems like Similari are as good at finding friends, as they are at keeping tabs on foes. For life sciences startups, finding opportunities for partnership or licensing agreements is every bit as critical as knowing what a competitor is doing.

Here, we’re taking a look at exactly why it is so important, especially in the current moment. Then, we’ll outline a use case for startups seeking to strengthen their partnership networks through AI-enabled discovery.   

What’s driving the shift towards partnerships – and why startups should embrace the change

At this year’s J.P Morgan Healthcare Conference, partnerships eclipsed the usual focus on fundraising. Digital health funding in particular is now 48% lower than the 2021 peak. It’s clear that the pandemic-era investment boom is over. For startups, that has spurred a renewed interest in optimizing costs and revising go-to-market strategies.

It’s also driving partnership ecosystems into the spotlight. For a wide range of reasons, this is actually good news. 

Partnerships can remove roadblocks to distribution and sales

Partnerships with established medical device distributors and sales organizations can give startups access to larger customer bases and sales channels. The partnership between Pfizer and BioNTech is a good example of a partnership that amplifies market reach and distribution. In 2020 they announced a partnership to develop, manufacture, and distribute BioNTech’s COVID-19 vaccine. The partnership enabled BioNTech to scale up production quickly, and Pfizer’s distribution capabilities helped to make the vaccine widely available.

Clearing regulatory and compliance hurdles through alliances

Life science startups may partner with companies who know how to navigate complex regulatory environments. They can use these partnerships to achieve compliance and accelerate approvals. This is particularly important for biotech and cannabis startups, who frequently encounter obstacles to FDA approval. Medical device startups, too, can benefit from partnering with more established firms to achieve compliance, and to build recognition and trust. 

Cross-pollinating technical expertise in life sciences partnerships

Partnerships with large companies in the medical device industry can provide startups with access to technical expertise in engineering, manufacturing, and quality control. And sometimes, the flow of expertise is mutual, like GRAIL’s 2021 partnership with GlaxoSmithKline. The partnership combines GRAIL’s expertise in early cancer detection with GSK’s expertise in diagnostics and oncology to develop new products.

“Build-to-buy” partnerships balance the interests of startups and Big Pharma partners 

These startups may also enter build-to-buy partnerships with larger companies. These are partnerships to co-develop and co-commercialize a medical device. The larger company provides funding and resources for the development of the device, while the smaller medical device company provides expertise and intellectual property. In a build-to-buy partnership, the larger company typically has the option to buy the smaller company or its assets once the device is developed and commercialized.

The startup partnerships dilemma: there’s no time (and no money) for traditional partner discovery 

So how should startups proceed with building robust partnerships that coalesce into thriving ecosystems? This is where it gets complicated.

Traditionally, partnership management is a complex business function, with dedicated personnel who seek out and engage prospective partners. Partnerships teams use trade fairs, industry networking events and online partner networks to meet and evaluate prospects. Behind the scenes, they have to invest extensive time and resources in researching each prospect’s business model, reputation and customer base to determine whether they fit their Ideal Partner Profile (IPP). 

Accelerating partnership discovery with a little help from AI

Much of that workflow can (and should) be automated. And while the world of Partner Relationship Management (PRM) is forging ahead with digital transformation, less attention is paid to the initial partner discovery phase, where AI can make a huge impact. Instead of manually searching across a wide range of networking platforms, partner managers can repurpose AI-powered intelligence tools to spot viable partners in any industry. In many cases, they can even rely on the same metrics that R&D teams use to evaluate competitor performance: market reach, customer base and current innovation activities. 

Using Similari, even cash-strapped early phase startups without a partnerships department can tap into a dynamic picture of their space and identify relevant, high quality partnership leads. A new biotech product that you could license, or an upcoming medtech company whose niche expertise could fill non-core competencies that you need – it’s all easily discoverable using Similari’s advanced analytics and proactive monitoring. 

Book a demo to find out why so many life science startups are turning to Similari to help them hone in on partnership and licensing opportunities, and building ecosystems that will weather the storms just over the horizon.  

Innovation intelligence: your go-to glossary

  Demystify the language of innovation intelligence. 


Advanced analytics 

Machine learning, data mining, and statistical analysis can be used to deliver advanced analytics. These make it possible to extract insights and make predictions from complex data. With advanced analytics, businesses can discover hidden patterns and relationships in their data and make more sound decisions as a result. 

Artificial Intelligence (AI) 

This is a broad field that involves creating machines or systems that can perform tasks in a way that usually calls for human intelligence. AI applications can be used for problem-solving, decision-making, and learning. Examples of AI techniques include rule-based systems, expert systems, Natural Language Processing (NLP) and Machine Learning (ML).


Big Data

Big Data refers to extremely large sets of data that are too complex to be analyzed using traditional methods. Big Data can be structured, semi-structured or unstructured and often comes from multiple sources. It requires specialized tools and techniques to process and analyze, such as distributed computing and machine learning.


Biologics are medical products made from living organisms or their products. They include products such as vaccines, gene therapies, and monoclonal antibodies.


Biomarkers are characteristics that can be measured and used as indicators of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. Biomarkers play an important role in clinical trials by providing a way to measure the efficacy of a drug or treatment being tested. They can be used to track the progression of a disease, monitor the response to a treatment, and predict the outcome of a trial. Biomarkers can be either physiological, such as changes in blood pressure or heart rate, or molecular, such as changes in gene expression or protein levels.


Biotechnology is the application of technology to the study of living organisms, cells and biological systems. It involves the manipulation of biological systems to create new products and therapies.

Build-to-buy Partnership

A collaboration between a biotech or pharmaceutical company and a larger company, in which the biotech company is responsible for developing a product or technology, while the other company provides funding and resources. In exchange, the larger company has the option to purchase or license the product or technology once developed.

In this partnership, the biotech company, known as the “builder”, focuses on the R&D and clinical development of the product, while the “buyer” company provides expertise in regulatory affairs, sales, and marketing. This allows the biotech company to bring the product to market more quickly and efficiently, while the buyer company can secure exclusive rights to the product or technology under its own brand.

Smaller biotech companies can leverage these partnerships to bring their products to market, while the bigger companies can secure exclusive rights to new products and technologies to expand their portfolio.


Claim Scope

Claim scope refers to the scope of protection provided by a patent. It defines the boundaries of the protected invention, and is determined by the language of the claims in the patent. The claim scope determines what others are allowed to do without infringing the patent.

Clinical Development

Once a drug or treatment clears the preclinical testing phase, it can proceed to clinical development. This stage is tightly regulated, because it involves human testing. A drug must go through multiple phases of clinical trials to test its safety and efficacy.

Clinical Trial Registry 

A publicly available database that contains information about ongoing and completed clinical trials. It allows researchers, patients, and the public to search for and find information about clinical trials for a specific disease or condition.

Clinical Trial Protocol

A clinical trial protocol is a document that outlines the plan for a clinical trial, including its objectives, design, population, interventions, outcome measures, and procedures for monitoring and reporting results. The protocol serves as a guide for the conduct of the trial and is used to ensure that the trial is conducted consistently and in compliance with regulations. It is reviewed and approved by institutional review boards or ethics committees before the trial begins.

AI-powered clinical trials intelligence can speed up and enhance the process of creating trial protocols, reducing manual search time and refining trial design to ensure success. Similari surveys tens of thousands of trials to surface key strategic insights.

Cloud BI 

This is a type of business intelligence that uses cloud computing to deliver BI capabilities and data to users over the internet. This allows users to access their data and analytics from anywhere and at any time, eliminating the need for on-premises hardware to store data.


Data mining

Before data can be useful, it needs to be mined for insights. Data mining is the process of extracting useful information from large data sets. This is done through algorithms and statistical techniques that identify patterns, trends, and relationships in data. Data mining can be used for a variety of purposes, such as market research, fraud detection, and customer relationship management.

Data Visualization

Data visualization is the process of representing data in a graphical format, such as charts, graphs, and maps. It allows users to quickly understand and interpret large amounts of information. Data visualization is essential to hypothesis testing and decision making.

Deep learning (DL) 

This is a subset of ML that relies on neural networks with multiple layers. The goal of DL is to make systems capable of identifying patterns and making predictions. DP is used for image recognition, speech recognition and natural language processing.

Digital Health Startup

A digital health startup is a company that uses technology, such as software and telecommunication tools, to improve healthcare delivery and patient outcomes. These startups can be focused on various aspects of healthcare such as remote monitoring, electronic medical records, telemedicine, mHealth (mobile health), and health data analysis.

Drug Development 

The drug development process involves several stages, including drug discovery, preclinical development, clinical development, and regulatory approval. It can take several years and requires the coordination of many different teams, including scientists, doctors, and regulatory agencies.

Drug Discovery 

Drug discovery is the process of identifying and developing new drugs to treat disease. The process begins with the discovery of a target, and the development of a drug. The interaction between the target molecule and the drug is then evaluated and tested, before the candidate drug can proceed through preclinical and clinical development. 


FDA approval process

This is a series of steps that a drug must go through before it can be marketed and sold to the public in the United States. It includes preclinical testing, clinical trials, and regulatory review.



Innovation refers to the process of creating new ideas, products, or technologies. To innovate, companies apply creative solutions to problems to develop new products, services, or business models. Innovation can also involve the improvement of existing products, services, or technologies.

Innovation Intelligence 

Innovation intelligence involves gathering and analyzing data to identify new opportunities and white spaces. Companies invest in innovation intelligence to stay ahead of their competitors by identifying new trends and technologies, understanding customer needs and preferences, and pinpointing the most viable opportunities for innovation.

Integrated BI 

When different business intelligence data sources are combined and analyzed together, this is known as integrated BI. By integrating data sources, decision makers can get a more comprehensive view of their business.

Intellectual Property (IP) 

Intellectual property refers to the legal rights that protect intangible creations of human intellect. These include inventions, literary and artistic works, symbols, names, images, and designs. The most common forms of IP include patents, trademarks, and copyrights.



In the life sciences industry, licensing agreements often involve the transfer of rights to a particular drug or technology, such as a patented drug compound or a proprietary manufacturing process. The licensor company may be a biotech or pharmaceutical company that has developed the product or technology, while the licensee company may be a larger pharmaceutical company that has the resources and expertise to bring the product to market.

There are different types of licenses, such as exclusive licenses, in which the licensee is the only company that has the rights to use the product or technology, and non-exclusive licenses, which allows multiple companies to use the product or technology.


Machine learning (ML) 

A subset of AI that uses algorithms and statistical models to enable systems to improve their performance with experience. Machine Learning trains machines to make predictions or take decisions based on data, without being explicitly programmed.


Natural Language Processing (NLP) 

NLP is a branch of AI that deals with the interaction between computer systems and human language. NLP is used to enhance language translation, recognize speech, summarize texts, and analyze sentiment.


If an invention would be obvious to anyone with ordinary skill in the relevant field, then it does not fulfill the requirement of non-obviousness. If an invention is not novel or would be obvious to someone with ordinary skill in the field, it is not eligible for a patent.


If an invention is novel, it is new and has not been previously disclosed or made available to the public. For any invention to be patented, it must be novel and not obvious to someone with ordinary skill in the field.



To be patentable, an invention must be eligible for a patent. To satisfy that requirement, it must be novel, non-obvious and useful. It must also fall into one of the categories of inventions that are eligible for a patent, such as a process, machine, manufacture, or composition of matter.

Patent Portfolio

A collection of patents and patent applications owned or controlled by an individual or organization. A portfolio can include patents and applications in a variety of technology areas and can be used to protect and monetize inventions. Companies often use a patent portfolio to protect their innovations and gain a competitive advantage in the market.

Patent Search

Patent search is the process of searching for existing patents and patent applications to determine the novelty and non-obviousness of an invention. Patent search can be conducted to identify potential patent infringement, to assess the potential for licensing a technology, or to identify potential partners for a joint venture.

As the volume of patent applications continues to mushroom, patent searches that rely on manual or reactive methods cannot deliver the timeous, accurate insights that IP professionals need to navigate around patentability issues. Similari surveys over 150 million patents, automatically generating critical business insights.

Patent Landscape

A patent landscape is a visual representation of a particular field of technology, showing the number of patents and patent applications, the types of claims, the assignees, and other information. Patent landscapes can be used to identify trends, competitive activity, and opportunities in a particular field.

Patent Map

This is a visual representation of a patent portfolio, showing the technology areas covered by the portfolio, the types of claims, the assignees, and other information. Patent maps can be used to identify gaps in a portfolio, to identify potential licensing opportunities, or to identify opportunities for partnerships.

Patent Thicket

A patent thicket refers to a situation where many patents or patent applications cover a particular field of technology, making it difficult for new companies to enter the market or for existing companies to innovate without fear of infringement. 

Phase I Trials 

This is the first step in testing a new drug or treatment in humans. Phase I trials involve a small number of healthy volunteers and are used to determine the safety and investigate the pharmacology of the drug.

Phase II trials 

These trials  involve a larger number of patients with the disease or condition the drug is intended to treat. The goal is to determine the drug’s efficacy and identify potential side effects.

Phase III Trials 

These involve even larger groups of patients than Phase II trials. They help to confirm the drug’s efficacy, monitor side effects, and compare the drug to treatments that already exist on the market.

Phase IV Trials 

After a drug has been approved and marketed, they are subjected to Phase IV Trials. The goal of these trials is to gather additional information about the drug’s safety, efficacy, and optimal use.

Preclinical Development

The stage of drug development that takes place before human testing. It includes laboratory and animal testing of a drug candidate to determine its safety, efficacy, and pharmacokinetics.

Prior Art

Any information that has been publicly disclosed or made available before a patent application is filed is known as prior art. This can include published patents, publications, and other forms of public disclosure. Prior art is used to determine if an invention is novel and non-obvious.


Real-time BI 

This is a type of business intelligence that provides up-to-the-minute data and analytics to users. This allows businesses to make decisions based on the most current data, and to quickly respond to changing conditions.

Redundant Trials

A redundant clinical trial is a study that is conducted when the results of previous trials on the same topic are already available and the new study is unlikely to provide any new or significant information. These trials may be deemed unnecessary or a waste of resources because they do not add to the existing body of knowledge. Redundant clinical trials may also be referred to as “duplicative” or “unnecessary” trials. 

AI-powered intelligence solutions like Similari help to reduce the risk of engaging in redundant trials.

Research and Development (R&D) 

R&D professionals specialize in conceptualizing new ideas, products, or technologies. The goal of R&D is to create new solutions, test them, and get them to market. The research and development process can be greatly enhanced through the adoption of AI-powered market intelligence. This enables R&D professionals to stay abreast of relevant market trends and opportunities, and channel resources towards the most viable projects.


Semantic search

Semantic search uses natural language processing and knowledge graphs to understand the meaning of search queries and return relevant results. It allows users to perform natural language queries and get more accurate and relevant results.


Tech Partnership

These are strategic alliances between two or more companies, organizations, or individuals, in which they collaborate to achieve a shared goal or objective, typically related to technology. Tech partnerships can take many forms, such as joint ventures, licensing agreements, and collaborations to develop new products or services.

Tech partnerships can bring together companies with complementary skills, resources, and expertise to create new products, services, or technologies that would be difficult or impossible for either party to develop alone. They can also help companies to enter new markets, access new customers, or reduce costs.

Text mining 

The process of extracting relevant information from unstructured text data is known as text mining. This relies on AI techniques such as NLP and ML. Text mining is useful for a wide range of applications such as sentiment analysis, topic modeling, and named entity recognition. Text mining enables organizations to glean insights from large amounts of unstructured data.


White Space

These are areas where there is an opportunity to create new products, services, or technologies that can fill a gap in the market. Finding white spaces is a priority for R&D and innovation, because they may offer a chance to gain a competitive advantage or create new revenue streams.

2022 in Review: a Year of Resilience and Adaptation

2022 is now firmly in the rearview mirror, but many of its most important trends are still playing out. Here, we’re picking out of a few of the most significant highlights and lowlights, and discussing what they could mean going forward.

Necessity: the mother of invention? 

One of the prevailing themes of 2022 was “doing more, with less”. From the very beginning, business faced historic talent shortages, exacerbated by “The Big Quit”. Geopolitical destabilization made a bad situation worse, disrupting access to Ukrainian tech talent. All in all, 2022 was a rough year for hiring, and it’s still looking very much an employee’s market in Q1 of 2023.

But it’s not all doom and gloom. Valuable lessons were learned, particularly when it comes to optimizing for innovation. Companies have turned to AI solutions to alleviate the pressure of R&D talent shortages and adapt to a leaner, more agile way of working. Those who have adapted in this way have begun 2023 on a stronger footing, and are better equipped to keep innovating in an uncertain climate.

Turning over a new leaf: sustainability, prioritized across the board

While it has been – and still is – hotly contested, there were major developments in the ESG space last year. 2022’s proxy season saw a record number of ESG proposals, primarily focused on environmental issues, but also social causes.

This renewed focus on sustainability goals was also reflected in M&A activity. IBM’s acquisition of Envizi signaled a commitment to move towards more sustainable business practices. Envizi’s software enables companies to coordinate and consolidate the hundreds of data types needed to analyze and manage their ESG goals. By integrating Envizi with IBM’s asset management, supply chain and environmental intelligence software, companies will now be able to automate much of this workflow and scale their efforts.

The view from life sciences and pharma: M&A down, partnerships up

While M&A deals in life sciences dropped off, licensing partnerships saw a small but significant increase, from $178 to $179 billion, an early harbinger of what is to come in 2023. As funding dries up, and life sciences companies face uncertain economic conditions, collaboration and ecosystem building have become critical priorities.. 

Through 2022, buyers also demonstrated a growing appetite for licensing agreements that spread the risk, rather than outright acquisition. This cautious approach was exemplified by Roche’s strategic collaboration with Poseida Therapeutics, combining Poseida’s novel cell therapy techniques with Roche’s development and commercialization capabilities. 2022 set the tone that 2023 is following: partnership and licensing agreements are key business development priorities that call for next-generation solutions. 

Life sciences companies have yet to make DnA a part of their DNA

But when it comes to adopting those solutions, the life sciences lagged behind other industries throughout 2022. Mckinsey notes that just over half of Digital and Analytics (DnA) leaders in pharmaceutical companies report having implemented digital applications at scale. The situation was even worse for MedTech. By their estimate, full adoption of digital solutions could bring the industry up to $190 billion in gains through the life sciences value chain, from streamlining clinical trials to enhancing drug discovery.

Generative AI and the future of BI

No review of 2022 would be complete without a nod to the rise of generative AI. 2022 was a bumper year for AI, across the board. But Openai’s GPT-3 took center stage towards the end of the year. The potential use cases for business intelligence are still unfolding, but it’s already being used for a variety of applications:

  • Data analysis: GPT-3 can help analysts by quickly summarizing large amounts of data, generating reports, and performing exploratory data analysis.
  • Predictive analytics: GPT-3 can be used to generate forecasts and predictions based on historical data and business trends.

Similari knew all of this before we did (and much more).

From a human point of view, 2022 felt like a whirlwind – even one and a half months later. But for Similari, it’s all been processed neatly and accurately into readily accessible data – data that business leaders can tap into for reliable and actionable insights. 

To find out how Similari helps businesses find certainty in uncertain times, schedule a demo with our team. We’ll guide you through Similari’s key features and show you how intelligent market surveillance and proactive analytics equip companies to tackle the future with confidence.