Artificial intelligence (AI)-enabled data collection and management can be a game changer for life sciences companies in the drug development process. Once the stuff of science fiction, AI has made the leap to practical reality. Yet, to date, most life sciences companies have only scratched the surface of AI's potential. One area that holds particular promise: digital data flow automation for clinical trials. With the power of AI, companies can rapidly digitize clinical-trial processes so they can complete studies faster.

Leading Fortune 1000 companies have been organizing and analyzing their data to gain business insights for years. Traditional industries with a long history of capturing and managing their data, notably financial services, have decades of experience mining data to better service and grow their customer relationships and manage risk. During the past decade, as data volumes grew, and as new sources of data emerged, Big Data initiatives captured C-suite attention. Today, Fortune 1000 companies are managing exponentially greater volumes and sources of data, while leveraging emerging technology capabilities including machine learning and AI to gain ever more granular insights into market opportunities. Companies are leveraging computers to do the work that they once relied on humans to perform.

We invest in early-stage life sciences companies often when they are no more than an idea. We are fanatical about helping the rare inventor who is compelled to build their own enduring business. If you or someone you know has a great idea or company in life sciences, Axial would be excited to get to know you and possibly invest in your vision and company . We are excited to be in business with you - email us at info@axialvc.com Artificial intelligence (AI) has the potential to transform many parts of life sciences from preclinical drug development and healthcare to synthetic biology and diagnostics. Basic research in AI has made major strides over the last 5 years, and when combined with biologists working with data as much as they work on the bench, the technology is changing how biology is studied and engineered. As a result, AI-first companies in life sciences at first may not look like traditional companies. Whereas, traditional life sciences companies usually have a core set of IP or are based on a biological hypothesis, AI life sciences companies often look like R&D shops or services companies at first.

COVID-19 ushered in major shifts in the way that healthcare professionals (HCPs) interact with life sciences companies, but how have emerging biopharma companies adapted to this new reality? The core challenges small- to mid-sized (SMB) companies face -- limited resources and overburdened staff tasked with a wide span of responsibilities -- have not changed. Yet suddenly, there is growing demand for advanced technology solutions that empower leaner commercial teams to reach more HCPs efficiently. The life sciences industry has been trying to find the right ratio of sales reps to HCPs for decades. Today, there are about 60,000 pharmaceutical sales reps in the U.S., down from 100,000 in the mid-2000s.

Artificial intelligence (AI) has revolutionized many technology areas. As a few examples, it has already been instrumental in improving and enabling voice recognition algorithms, digital assistants, advertisement recommendation engines and financial trading applications.[1] Significant investment is being made for further development of this promising new technology, with R&D spending on AI predicted to reach $57.6 billion by the end of 2021.[2] Along with these R&D efforts, companies are also trying to protect and monetize their AI inventions, in some cases opting to seek patent protection. From 2002 to 2018, the number of AI patent applications filed with the United States Patent and Trademark Office (USPTO) more than doubled, from 30,000 to 60,000.[3] These R&D efforts are no longer limited to software companies.

With all the challenges the COVID-19 pandemic has added to the life-sciences field's already formidable pile, professionals in the industry are increasingly turning to advanced technology to help keep their R&D work going. One of those technologies of growing interest is artificial intelligence (AI). Outsourcing-Pharma (OSP) recently spoke with Rohit Vashisht (RV), CEO and cofounder of WhizAI, a technology company offering AI solutions specifically targeted toward life-sciences applications, about the technology, and how its use is evolving in the industry. RV: AI means different things to different people. Some believe it is an elixir that will cure everything that's wrong with software and take human productivity to the next level; on the other hand, some think it is all hype and will never work.

Key quote "The life sciences industry has only begun to scratch the surface of AI's potential but the good news is biopharma and life sciences leaders see the potential and are willing to make the investments necessary to realize what's possible. They should be cautious, though, and carefully plan and strategize so those investments are used wisely and result in the desired outcomes. By spending time on a solid strategy, putting the building blocks in place for success and leveraging relationships with relevant partners, AI can help transform the life sciences industry as we know it and get the necessary products to market more quickly." Why this matters From R&D to manufacturing, supply chain to commercial functions, AI is beginning to have an impact on increasing efficiencies across the biopharma value chain, especially as a result of the COVID-19 pandemic. In addition, increased remote work environments helped life sciences leaders realize how effective digital solutions can be in helping their businesses run smoothly, transforming mindsets and enabling executives to lean into a future grounded in digitization, data and AI.

Over the last few decades, the stream of data available to life sciences companies has grown from a trickle to a tidal wave: genetic and genomic portraits of individual patients, metabolomic and proteomic profiles, real-world data from wearables measuring everything from heart rate variability to blood glucose levels, detailed patient clinical histories from electronic health records. The total volume of health data in the world is expected to soar to 2,314 exabytes by 2020, 15 times what it was in 2013. By some estimates, if this data were stored in a stack of tablet computers, the stack would reach 82,000 miles high. Data analysis has flourished, too. Alongside classical statistics, powerful artificial intelligence technologies have emerged that can manipulate massive numbers of inputs and curate data stored in non-standard formats--take, for instance, the more than 700 different ways researchers have historically recorded gender in clinical trials.

For a new compound to make it from initial discovery through development, testing and clinical trials to finally earn regulatory approval can take a decade or more. Nine out of 10 promising drug candidates fail somewhere along the way. As a result, on average, it costs life sciences companies $2.6 billion to introduce a single new prescription drug. This is much more than just a challenge for life sciences companies. Streamlining drug development is an urgent issue for human health more broadly.

Models and data analytics not only recap what is already occurring between water and plants across expansive rows of corn, they can actually predict what will come in the hours, days and weeks ahead. Reprinted with permission from Iteris, Inc.: For thousands of years, the survival of farmers' crops -- and finances -- have been inextricably linked to the weather. Whether growing corn on the plains of Nebraska or wheat in the mountains of southeastern Turkey, Mother Nature's seemingly unpredictable temperament has forced farmers to find innovative ways of better understanding soil moisture, rainfall, and crop health throughout the ages. One fact that doesn't change, however, is that crops need water. And, whether they get it from natural rainfall or modern irrigation methods, crops need it consistently at key growing points to remain healthy and abundant.