2024 predictions: Experts comment on AI, ML and automation

2024 predictions, road forward

DDW’s Megan Thomas spoke to experts from the drug discovery industry about their predictions on what 2024 holds for our sector. This is part of a series of predictions based on different themes. Here, experts weigh in on the impact of artificial intelligence (AI), machine learning (ML) and automation in the sector.

Updesh Dosanjh, Practice Leader for Pharmacovigilance Technology Solutions, IQVIA

“In 2024, the traditional pain points of the pharmacovigilance (PV) space will not disappear. Case volumes will continue to grow while the number of individuals working in the field will not follow suit. The PV model is broken, and in 2024, companies will need to assess how automation can fix operations. While this automation is necessary, there is hesitation to implement technologies and tools such as generative AI and ML. Before incorporating advanced automation, organisations must take a holistic look at their processes. Adding automation to an inadequate process will help mitigate issues in the short term but will be detrimental for long-term operations. The PV space is resistant to change, especially because validating new systems can take months. However, organisations need to proactively make a shift, or this change will be forced upon them.

“In the upcoming year, we will see the expansion of new, more complex markets and updated requirements from regulators. The balance between monitoring for these adjustments and maintaining the growing volume of cases will be tenuous. Without fixing the current, broken PV models, the continuous cycle of more complexity in the market, which then pushes organisations to incorporate automation into insufficient processes, will massively hinder PV operations. This will become more evident in the upcoming year, and organisations will be forced to reimagine their models.”

Cameron Ross, SVP, Generative AI, Elsevier

“AI has the potential to dramatically shorten one of the most costly areas of early drug development – small molecule discovery. It’s no surprise that interest in AI for this area is surging, since early phase drug discovery normally accounts for 42% of the cost of the entire pipeline. We’re already seeing biotechs making breakthroughs with AI in this area, with more than 150 small molecule drugs discovered and 15 already in clinical trials. In 2024, we can expect another wave of innovative new drugs to be developed using the technology, given that the AI-fuelled small molecule discovery pipeline is growing by almost 40% annually.”

Leslie Orne, President and CEO, Trinity Life Sciences

“AI and ML will play an increasingly important role in the clinical development and commercialisation of cell and gene therapies, which are typically targeted to small patient populations. Ensuring sufficient and timely patient enrollment in a rapidly growing number of cell and gene therapy clinical trials is becoming a bottleneck to advancing potentially life-saving therapies. Patient finding is one of the most exciting applications of AIML because it can identify and engage the right patients, removing the clinical development barriers and advancing the likelihood that patients who are on-label for approved cell and gene therapies are aware of and have access to transformative treatments.”

Tom Booth, Senior Technical Delivery Analyst, TrakCel

“AI presents a massive opportunity within CGT with tight timelines demanding quick but accurate decisions. Accuracy often hinges on fast analysis of vast data sets, which is where digital solutions excel, but predictive AI has the potential to extend into pattern identification that could present users with better, more efficient options. The challenge is harnessing the power of AI within a highly regulated industry. I believe the uses that leverage user skillset e.g. flagging potential rare disease cases to physicians or modelling scenario outcomes to planners will be amongst the first uses. AI continues to learn and whilst validity and trust remain a barrier, AI use will inevitably grow as it becomes clearer how to navigate these challenges.”

Matt Todd, Head of Architecture, Ori Biotech

“We expect AI and data analytics to support pivotal advances in cell and gene therapy (CGT) manufacturing . Dynamic supply chain management is expected to help optimize the delivery of patient-specific therapies, utilizing scenario-based forecasting and digital tracking. The emergence of low-code and no-code software engineering will start to make CGT application development easier, enabling more rapid, innovative solutions. Concurrently, the rise in unstructured data will be met with AI-driven analytics, offering deeper insights. Looking even further into the future, Industry 4.0 technologies like IoT, digital twins, and robotics are expected to enhance productivity and safety in manufacturing, with cybersecurity becoming crucial. Collectively, these developments are expected to enhance patient access to life-saving treatments, marking a significant advancement in healthcare, with the aims of improving patient outcomes and reducing costs.”

Kishen Chahwala, PhD, Business Development Manager, Enhanc3D Genomics

“AI is transforming the way we fight cancer by unlocking the secrets of large and complex datasets. Not only can it help find new targets, design new drugs, predict drug effects, and optimize trials, but AI can also help diagnose and treat cancer patients by using their unique genomic and clinical data. It is becoming more broadly acknowledged that AI is set to transform one of the most promising yet under-utilised fields of omics – 3D genomics. At Enhanc3D Genomics we’re leveraging 3D genomics, using AI and machine learning, to identify targets and biomarkers that other omics methods would miss. This has great utility in de-risking the drug discovery pipeline, by not only identifying potential targets but also enabling patient stratification and optimisation of clinical trial cohorts. This will ultimately save time and money in drug development. The AI revolution may have come about faster than many anticipated, but I’m thrilled to see how it will accelerate the development of cancer therapeutics.”

Ben Holland, Co-Founder and CTO, Antiverse

“AI and ML now touches 10% of discovery projects, and we predict that this number will continue to rise in 2024. We’ll also see increases in both the scope and performance of AI systems – that is, AI being applied to more aspects of a project and with improved results.

“While the use of lab automation tools and software will increase in some areas such as screening and data generation, we expect that automation will decrease in others where in silico predictions allow focus on high-skill areas.

“As AI for drug discovery matures, we’ll see targets considered undruggable becoming feasible targets, in part due to increased applications of high-throughput testing. Developability prediction is often used in antibody development to reduce wet lab experiments and accelerate the drug discovery process. We anticipate developability prediction becoming widespread and somewhat commoditised, but with a continually rising bar, it will remain an important technology.

“Commercially, the industry will see more big partnerships, mergers, and acquisitions, and hopefully many more new companies will start to join in on the action, with a rise in the number of hybrid projects, where researchers are combining AI and wet lab experimentation to get the best possible results.”

Fiona Maini, Senior Director Global Compliance and Strategy at Medidata

“AI is dominating conversations across the drug discovery process. One of the main concerns surrounding the rapid expansion of AI within any healthcare setting – and particularly the clinical research space – is the implementation of appropriate regulation to ensure patient safety and that data privacy is respected. In 2024, AI will continue to be used across the life sciences sector in new and innovative ways and the industry will start to see the introduction of new regulations to ensure the responsible integration of AI-based tools. In particular, the industry will be closely following the forthcoming EU AI Act – planned to come into effect over the next years – and the impact this will have on the life sciences sector. The key challenge for all regulators next year is ensuring that they can keep up with the progress of the technology.”

Michael De Jong, Vice President, Global Head, Pharmacovigilance Technology Solutions, IQVIA

“In the upcoming year, organisations will widely attempt to integrate generative AI in pharmaceutical safety processes. It is unlikely that this technology will be fully implemented in the space in 2024, but organisations will gain immense knowledge of its capabilities and how it can be used to improve pharmacovigilance process in the future. While automation and more intelligent forms of automation such as generative AI have the potential to disrupt the industry, there needs to be a deeper understanding of the impact and change it will bring. Safety departments must be able to trust the accuracy of the technology, but its potential biases and inaccuracies have created a major barrier. The transformational use of generative AI in pharmacovigilance will come, but not just yet.

“One issue many organisations will grapple with in 2024 is the overwhelming amount of data inaccuracies in safety databases, despite having quality checks. With the increased volume of cases that require manual review, organisations must work to remedy the current human model, which is filled with incorrect data. These inaccuracies can lead to potential patient risk. More and more organisations will turn to technological solutions that can help reduce these data errors, reduce costs, and enable the pharmacovigilance function to focus more on risk management and less on the transactional activities of bringing data into a system and pushing it out to regulators and partners.”

Megan ThomasAbout the author

Megan Thomas is Multimedia Editor on DDW. She has worked across a range of B2C and B2B publications, and was a digital editor at the British Medical Association. She has an undergraduate degree in Print Journalism, Film & Media and a master’s degree in Creative Writing.

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