Article | July 12, 2022
For decades, the pharmaceutical industry has counted on state-of-the-art technologies to ensure the market entry of safe and dependable medications. The recent pandemic has shown how important it is for drug companies to get new drugs and vaccines on the market as soon as possible.
The incorporation of artificial intelligence and machine learning technologies has greatly benefited the consumer healthcare business and the pharmaceutical industry. These technologies have been indispensable in the field of augmented intelligence, where they are used for applications such as disease detection and diagnosis, research and development, drug manufacturing, and others.
How is AI Being Used Across the Pharmaceutical Sector?
AI and ML are finding a plethora of applications across the pharmaceutical sector, starting from managing the process of clinical trial databases to drug discovery and disease diagnosis and treatment. These advanced technologies have further gained immense popularity with the advent of the COVID pandemic and the race to discover effective vaccines.
The top-level uses of AI across the pharmaceutical sector are as follows
Personalized Treatment/ Digital Therapeutics – AI is extensively being used to identify and assist drug developers to provide reliable and accurate insights for developing personalized therapeutics.
Disease Identification/ Suggestive Treatment – With robust assessing abilities, AI is finding applications for the diagnosis of diseases ranging from Covid-19 to oncology to degeneration in the eyes.
Drug Discovery and Manufacturing – AI assists in screening and comparing the predicted success rate of drug compounds based on biological factors with the results of the initial screening process such as rapid RNA and DNA quantification.
Clinical Trials – The technology helps in identifying the most suitable candidate for the clinical trial on the basis of disease conditions, history, and additional attributes covering infection rates, ethnicity, and demographics to study the impact of the drug.
The Way Ahead
With growing applications in the development of novel therapeutic medications, shifting patient inclination toward personalized medicines, and the introduction of advanced medical fields such as gene therapy, AI is estimated to transform the pharmaceutical
Article | September 1, 2021
Applications for AI are as diverse as the industries that employ them, and pharma has identified the particular varieties of AI that are most effective in attaining quicker, more fruitful results across a variety of business activities. In a world where every second counts, pharma and biotech businesses are under pressure to shorten the time to insight and deliver success. As a result, leading organizations quickly realize the potential of artificial intelligence (AI) as a crucial tool for advancing their operations.
Leading pharma and biotech firms have realized the potential of AI and are utilizing it to boost productivity and innovation across the board, from production to drug discovery. Their procedures have significantly benefited from the application of machine learning (ML) and natural language processing (NLP), and the results are only becoming better because AI gets stronger and "smarter" the more data it processes.
Advantages Pharma Industry Can Leverage
Increased effectiveness across the spectrum in the pharmaceutical industry
Drug discovery accelerates
Superior disease surveillance, detection, and prevention
Clinical trials with lower risk
Greater insight into the client
NLP is used to turn clinical trial data that is text-intensive and highly categorized into the data utilized in machine learning (ML) models, allowing the computer system to apply patterns to the data and generate insights. Clinical trial data is structured and enriched, making it possible to analyze and visualize the data for use in successful plans and strategies for clinical trial design, manufacturing, marketing, and other areas. Faster time to insight and improved business outcomes are the end results.
A particularly true principle of machine learning applications is that the outcomes from using AI applications are only as reliable as the data itself. The Pharma Intelligence offering, which combines high-quality, extensive data from the pharmaceutical and biotechnology industries with advanced analytics and AI applications, has assisted customers with high-value products in resolving some of their most difficult key problems, including target prioritization, modalities innovation, competitive benchmarking, clinical trial design and deployment, and more.
Article | August 18, 2021
In developing or evolving a strategy, there are key decision moments. Those are the moments where you are deciding where you need to focus, what you need to excel at to win there, and where and how to allocate resources to get to a point in the future.
At these moments, it is the contest of ideas that matters. Having choices matters. Having a cross-functional team participating in the development of strategy is one way of ensuring that you are going to be more successful at generating choices before you start making choices.
What Is a Cross-functional Team?
A cross-functional team is a collection of individuals with varied skillsets from different areas of a business collaborating to achieve a common goal.
Why Are Cross-functional Teams Essential for Business Success?
Having this diverse set of minds analysing the situation, considering the big picture and the organisation’s capabilities, and the needs of all stakeholders, inspires teams to think about the choices they have differently and more creatively. For example, in a pharmaceutical setting, the medical affairs team brings knowledge of the data, unmet needs, and insight into clinical practice. Access and reimbursement teams identify the right data and take the lead in building that value story to accelerate market access and product uptake. It is incumbent upon commercial to hear their ideas, obtain their perspective and secure their alignment to all strategy decisions.
Cross-functional collaboration can help break down silos. Research suggests that working in silos and not sharing data with team members from other departments can cost a company close to $8,000 per day in wasteful expenses. Time is widely recognised as a scarce resource: we need quick access to accurate and real-time insights to make effective business decisions. Real time insight will come from those closest to the customer, so it is important for cross-functional members from different geographies to participate in the development of strategy. Improved insight is a source of sustainable competitive advantage.
One single version of the truth is what is required for the right narrative to take place. The right narrative will lead to the right decisions. One single version of the truth is more easily achieved by cross-functional team members working closely together.
Better Innovation & Creativity:
Individuals with diverse skillsets often explore a problem in different ways. When different people working in different capacities come together, they think outside the box to significantly improve outcomes. It is a great way to come up with concepts that distinguish companies from their competitors.
Achieving alignment with strategy across functions and geographies:
Today, businesses are moving faster than ever and organisations are seeing possible competitors in areas they never knew existed before. With so much choice about where to focus, you really want your workforce to align around one strategy. Underperformance is inevitable if everybody is off working in ten different directions.
Improving the customer experience:
Creating an effective customer experience is about more than just ensuring your customers receive the products and services they desire in a timely and efficient manner. It’s also about creating touchpoints with real people who can organically evangelise and grow your brand through their social media and offline interactions with friends and family.
Your customers are engaging with multiple communication channels– official websites, social platforms, virtual platforms, medical science liaisons, sales reps, and more. Everyone needs to be aware of, and understand, the moments that matter to your customer and the business along that customer journey and how they contribute to delivering that positive experience.
This is more likely to be achieved with a cross-functional approach to strategy development.
Cross-functional teams are typically small, adaptable, and flexible. Such teams can move faster as they don’t have to wait and rely on other departments or external sources. They can help in tackling any silo mentality and bridge gaps between team members. They can come together to consider new information and/or changes to adapt the strategy if necessary and/or react to any setbacks immediately. They are better placed to make decisions when problem-solving amidst uncertainty.
In summary, the rapidly changing environment and new information requires medical affairs, along with access and reimbursement and commercial, to work together, to ensure that patient’s benefit from the value of new innovative therapies. Companies stand a better chance of creating a winning strategy if it is created by a cross-functional and geographically diverse team. With every team member bringing their abilities and knowledge to the table, the strategy over time can only move from strength to strength. However, if not supported correctly, a cross-functional team working on strategy can be dysfunctional and chaotic, and result in a laborious and time-consuming approach to strategy development.
Digital strategy platforms such as Nmblr offer an inclusive and structured process to facilitate a strategy discussion and allow people to bring forward ideas. They do this by: working against silos – the structure provided, levels the playing field. The guidance provided equips people from different disciplines to contribute to the conversation.
Article | June 2, 2022
Nature Reviews Drug Discovery makes these points well. It goes over historical and recent successes of the phenotypic approach, and discusses some areas that it's opening up for discussion and research. One of these is the long-vexed question of polypharmacology: what do you do when your active compound doesn't seem to have a single target, but rather hits a whole list of stuff at varying degrees of potency? Seen from a pure target-based viewpoint, this is a failure, and you'd better start working on something else. But to be honest, there are a lot of drugs out there (and not all of them ancient legacy compounds by any means) that work this way, even if their developers didn't think so at the time. So it's not to be disparaged on principle, but that said, it's still a difficult area to make progress in because of all the variables. A good enough phenotypic hit, though, makes its own case that it's worthy of further investigation and development, even if it's not "clean" by rigorous target-based standards. But as always, your phenotypic screen had better be a good one. That is, it had really better model the human disease in a useful way, and have a good signal/noise. The authors note that you're much better off with assays that involve a gain-of-function/gain-of-signal readout, as opposed to ones that could read out just through cellular stress or cytotoxicity, which is an invitation to chase your tail.
Another area the paper brings up is searching lower-molecular-weight compounds than are usually screened, down to fragment-sized. There are quite a few useful drugs out there with really low molecular weights - ibuprofen, aspirin, metformin, dimethyl fumarate, lacosamide and more - and any screening program would be happy to have discovered something as useful as those. As the authors note, hits like these in phenotypic screens might be another case of polypharmacology, or they might be hitting pathways whose "tone" we have not understood well (and for which micromolar inhibitors might work out just fine). At any rate, there might be an opportunity for fragment phenotypic screening, and even of covalent fragments (which will call for even more attention to the validity of the underlying screening model, I'd say).
The paper discusses the question of target ID, which for most phenotypic programs feels like a natural progression. Most of us are innately biased towards thinking in terms of drug targets, so when a phenotypic compound emerges we want to know what it's "really" doing. And most of the time, there is such a target in there somewhere, although finding it can be quite a haul. I know of several compounds that have been kicking around for years that are obviously doing something in the assays, but no one has ever been able to pin down quite what that is! This paper makes the case for getting out of a binary mindset for target identification. They point out, correctly, that target ID is a means to an end, and that you do not actually need to identify your target to go on to clinical trials and go to the FDA for approval. I always find it surprising to find how many people are surprised by that, but it's true. You also need to realize that knowing a target may not tell you nearly as much as you would want about a compound's mechanism of action, if your new target lands in the middle of a bunch of not-well-worked-out biology.
There's a good case to be made that modern chemical biology and imaging techniques have made it easier to progress things, even if you're not quite sure how they're working. We can extract huge amounts of information about the cellular effects of a given compound, and if you do a good job of matching this against a closely related structure that's phenotypically inactive, you can make a lot of headway. This doesn't mean that you shouldn't bother trying to find the target - as mentioned, this is a great way to expand the knowledge of the underlying disease, and can lead to other new programs spinning off of the phenotypic effort. But it does mean that you shouldn't freeze in fear if you don't have a target to point to. The FDA wants to see safety and efficacy, and that's what we should want to see, too, for starters.
But as the paper notes at the end, phenotypic screening is going to advance at the pace of good model development. Many of these same chem-bio tools can be brought to bear on this question as well, along with advances in cell culture, organoids, and other new assay technologies. You're not going to be able (realistically) to recapitulate all the features of a human disease, so you will probably find yourself concentrating on certain features that you can make the case for driving a project on. I was very happy to see this paper reference Jack Scannell's paper on translatability (blogged about here), because its point is crucial to the whole phenotypic screening endeavour. If your underlying assay is flawed, there is nothing you can do in any other part of the project to make up for it. A poorly translatable assay is a sign that you should spend your time trying to fix it, or to go do something entirely different instead. It is not a sign that you should just keep on going, because "it's the best thing we've got". If it isn't good enough, it isn't good enough. I don't get to quote A. E. Houseman much around here, but he's right: "The toil of all that be. Helps not the primal fault; It rains into the sea. And still the sea is salt." If you don't fix your assay up front, you are raining into the sea.