Ian Dix

Are you a user of #EMBLEBIresources? Please fill in this quick survey to help shape their future strategy. #openData #FAIRdata

4

#WCLC24 is fast approaching and the programme looks very promising! Grab a copy of detailed preview + planner below, delve into the key data expected to be presented at the event (both #clinical- and #artificialintelligence and #machinelearning-related) and get ready for an exciting conference! Some of the highlights to expect at #WCLC24: 🔬 Clinical Breakthroughs in Lung Cancer Treatment: - Phase 3 Study of Ivonescimab vs. Pembrolizumab in PD-L1-positive NSCLC sets new standards in first-line treatment. - Neoadjuvant Durvalumab with CT in resectable NSCLC shows promising efficacy and safety in Neocoast-2 study. - Osimertinib with or without Savolitinib provides a new option for MET aberrant, EGFRm advanced NSCLC in the CTONG 2008 trial. 🔎 Innovative Approaches in Advanced Therapies: - Aumolertinib Maintenance post-chemoradiotherapy in Stage III NSCLC showing promising interim results in the POLESTAR study. - Efficacy of Amivantamab and Lazertinib vs. Osimertinib explored in the MARIPOSA study for EGFR-mutant NSCLC. 🤖 AI/ML Integration in Thoracic Oncology: - AI-driven Predictive Models: Enhancing diagnostic accuracy and treatment personalization, from predicting PD-L1 status to optimizing chemotherapy benefits in EGFR-mutant NSCLC. - AI for Pathologic Assessment: Aligning neoadjuvant chemoimmunotherapy response with improved pathological assessments. 🧬 AI/ML for Early Detection and Prognosis: - AI Sensitivity in Low-Dose CT screening improves early detection of lung cancer, critical for enhancing survival rates. - Pulmonary Nodule Growth Prediction: Advanced AI models using 3D neural networks to forecast nodule changes, aiding in timely clinical interventions. 🔗 Stay tuned for more and like / share this post to see more like it! See you in #SanDiego!!! Reach out to #LucidQuest at info@lqventures.com to find out more about what we do and how we can support your brand to thrive! American Association for Cancer Research AbbVie Association of Cancer Care Centers Akeso Inc. 康方生物（9926.HK）Addario Lung Cancer Medical Institute (ALCMI) Amgen Amoy Diagnostics AstraZeneca Bayer Biocartis BioNTech SE Boehringer Ingelheim Bristol Myers Squibb Caris Life Sciences Catalyst Pharmaceuticals, Inc. CyPath Lung Daiichi Sankyo US DAVA Oncology EMD Serono, Inc. Exact Sciences Flatiron Health Fosun Pharma Foundation Medicine Genentech Genmab Gilead Sciences GLCC Global Lung Cancer Coalition GSK Guardant Health OptoMedic Technologies, Inc. Jazz Pharmaceuticals Johnson & Johnson Eli Lilly and Company Merck Novartis Novocure Pfizer Regeneron Rigel Pharmaceuticals Inc. Takeda #Oncology #CancerResearch #MedicalInnovation #lungcancer

13

3 Comments

🚀 Transforming Drug Discovery: The AI Revolution In the rapidly evolving landscape of pharmaceutical R&D, artificial intelligence (AI) is proving to be a game-changer. A recent published in Drug Discovery Today has highlighted some groundbreaking trends that underscore the potential of AI in revolutionizing drug discovery and development*. Exponential Growth in AI-Driven Discoveries Over the past decade, there has been a significant surge in the number of drugs and vaccines discovered through AI. This growth is not just in numbers but also in the speed with which these discoveries are moving from the lab to clinical trials. AI-native biotech companies, often in collaboration with big pharma, are leading this charge, showcasing an impressive annual growth rate in clinical entries*. Doubling Success Rates One of the most striking findings from the study is the success rate of AI-discovered molecules in Phase I clinical trials, which stands at 80-90%. This is nearly double the historical average*. Such high success rates in early trials are indicative of AI's capability to design molecules with optimal drug-like properties, potentially transforming the economics of drug development. Accelerating Drug Development AI is not only improving success rates but also significantly speeding up the drug discovery process. Traditional methods take approximately 4-6 years to discover new small molecules. With AI, this timeline is dramatically reduced. For instance, a recent example from the industry showed that a drug candidate was developed in just 8 months using AI, compared to the years it would typically take**. The Future is Now As we look to the future, the integration of AI in drug discovery promises not only faster development times and improved success rates but also a potential shift in how we approach complex diseases. With every pharmaceutical giant now investing in AI, the next decade could see a dramatic transformation in how new therapies are developed, tested, and brought to market. Call to Action For those of us in the life sciences sector, the message is clear: embracing AI is no longer optional but a necessity to stay competitive and lead in innovation. The opportunities to improve patient outcomes and reduce development costs are immense and within our grasp. Let's discuss how we can further integrate AI into our strategies to not only keep up with the pace of innovation but to set new benchmarks in drug discovery and development also with tools such as our #AIDDISON solution #Digital #AI #ArtificialIntelligence #DrugDiscovery #DrugDevelopment #Technology #Innovation #creativity #futurism https://lnkd.in/gdrdCNfC * https://lnkd.in/gbaiwB4X ** https://lnkd.in/gfF2kuAk

39

3 Comments

Success requires more than just technical solutions though. We need recognition of the challenge, appropriate funding, and strategic focus. Equally important is the governance framework that ensures public acceptability and ethical use of data. As we move forward, we must maintain transparency in our objectives and be honest about both the challenges and opportunities. The NHS has a unique opportunity to lead in ethical, effective healthcare data utilisation, but this requires careful navigation of technical, cultural, and governance challenges. #nhs #powerofdata #innovation #productivity #ethicaluseofdata #informationgovernance #patientoutcomes #clinicalefficiences #operationaleffectiveness #ai #machinelearning #genai #valuebasedcare

4

An Integrated Data Review Plan streamlines the clinical trial process by eliminating redundant data reviews, establishing clear quality objectives, and minimizing the overall burden of data review. #clinical #clinicaltrials #clinicalresearch #biometrics #statistician #data #healthcare #rbqm #rbm #innovation #sasprogrammers

48

RBQM Silence: Every Speaks, less Follows- Why or Why not? Key Factors! #clinicaltrials #clinicalresearch #rbqm #rbm #data #innovation #healthcare #data #biometrics CuMinds Innovations

39

1 Comment

Key Differences b/w Randomized Clinical Trial (RCT) vs RWE (Real-World Evidence): Learn more about Real World Data: https://lnkd.in/gRGZdJJM Reference: https://lnkd.in/gN2vf6Hp #clinicaltrials #clinicareserach #data #healthcare #innovation #patient #regulatory #india #rct #rwe

20

Five key priorities for what NHS tech and AI really need from the new government if the NHS is realise the benefits of technology and AI 1. Give greater priority to technologies that help with admin and operational tasks 2. Fund the change, not just the tech 3. Get more out of existing technologies like electronic health records 4. Create a dedicated strategy for AI in health care 5. Engage the public and NHS staff more in how technology is changing health care Technology has significant potential to help make the NHS more sustainable, support care staff, and improve patient experience. But unless the next government pays far more attention to how the health and care system can realise these benefits – particularly to how technologies can be successfully implemented and used within the NHS – the chances of technology and AI ‘saving the NHS’ will be slim.

4

Developing Clinical Analytics Solution? Understanding Role-Specific Challenges to Drive Analytics and Technology Solutions! Identifying and addressing the pain points of individuals involved in clinical trials is paramount to the development of effective analytics solutions and technology platforms. By understanding the unique challenges faced by each role, we can create tools that streamline processes, improve efficiency, and enhance decision-making. CuMinds Innovations #clinicaltrials #clinicaldatamanagement #clinicalresearch #data #biometrics #innovation #technology #healthcare #patient

35

1 Comment

🚀 Are #AI Ideas More Creative but Less Practical? 🤔 A recent study found that AI-generated ideas tend to be more creative, but not quite as practical as those from human experts. It got me thinking🙄does this mean AI is pushing boundaries in some ways but falling short in others? 🧠 Over 100 researchers in natural language processing (NLP) were asked to come up with fresh ideas and evaluate both AI and human suggestions. 🔒 The reviews were anonymous, so nobody knew which ideas came from AI or people. 🌟 AI ideas stood out for being more original, but they were seen as a bit harder to put into action. 🧩 The study pointed out some challenges for AI, like trouble evaluating its ideas and sometimes getting stuck in similar patterns. Why does this matter? It’s an important question for anyone interested in AI’s future: Can it help us think in new ways and move science forward? According to this research, it’s definitely contributing fresh ideas, but we might still need that human touch to make them work. What’s your take on this? Can AI bring fresh ideas to the table, or does it need more refining before we can count on it for practical solutions? I’d love to hear your thoughts! 👇 #AI #NLP #Research #AIinAction #TechTalk #AIandHumans

3

Another potential of AI to revolutionise dementia diagnosis and treatment, bringing us one step closer to better patient outcomes worldwide! Dementia poses a significant global healthcare challenge, affecting over 55 million people worldwide and costing an estimated $820 billion annually. The number of cases is expected to almost treble over the next 50 years. The main cause of dementia is Alzheimer’s disease, which accounts for 60-80% of cases. Early detection is crucial as this is when treatments are likely to be most effective, yet early dementia diagnosis and prognosis may not be accurate without the use of invasive or expensive tests. A team led by Cambridge scientists has developed an AI machine learning model capable of predicting the progression of Alzheimer’s disease in individuals with mild memory and thinking problems. This model outperforms existing clinical diagnostic tools in accuracy. "AI models are only as good as the data they are trained on. To ensure ours is viable for healthcare settings, we trained and tested it using routinely-collected data from both research cohorts and actual memory clinic patients. This demonstrates its potential for real-world application.” - Professor Zoe Kourtzi, University of Cambridge

39

Underlines the importance of #artificialintelligence in the transformation of our society. Hype or not. Over time, I am convinced #AI will drive more insights and automate value chains. In the life science area, for sure we will we the benefits both within the industry, but more importantly in the healthcare system ultimately benefiting patients. Will it be a straightforward path? No, for sure not. But we will see the benefit, and the more we collaborate across areas - and in particular public / private partnerships are key here.

66

1 Comment

Great to see this information presented in such a clear and simple way.

3

1 Comment

Data Science in Healthcare: Applications & Challenges! #innovation #data #healthcare #clinicaltrials #clinicalresearch #patient #datascience #dataanalytics #biometrics #dataprivacy

23

Estimating the value of patient data - The process of data transformation drives value creation The value of patient data increases as raw encounter data is curated, standardised, and linked with previous encounter and departmental data, claims (payer) data, to create high quality, longitudinal patient-level data assets. There are a number of well-defined steps associated with the process of transformation of patient data (See Figure): 1./ The first step is data strategy i.e., capturing the right data, that is relevant to the clinical context, and defining how cleaning the raw data and coding will occur in a way that ensures that the data is transformable and analysable. 2./ The second pivotal step is data transformation which is when the raw data is standardised and curated, during which time it and can be linked to other clinically meaningful data assets e.g., annotated departmental data or auxiliary data sets such as from pharmacy, pathology, radiology, lab results and even genomic profiles. 3./ The third step would then be using this data to generate analytical insights, populate reporting solutions ready for internal and external data feeds, and provide the foundation for preparing and releasing data extracts (or access via a trusted research environment #TRE or secure data environment #SDE). Data needs to be hosted in a suitably structured clinical data warehouse that provides the security to store and protect patents data and their privacy, whilst allowing for data to be safely accessed ensuring healthcare professionals have access to the clinical data they require to inform decision making. For further information please see our full report “Realising the value of healthcare data” :- https://lnkd.in/gS6FCuM3 #powerofdata #nhs #clinicalexcellence #innovation #patientoutcomes #realworlddata #patientoutcomes #personalisedmedicine #ai #bigdata #valuation #fairmarketvalue #ey #valueofdata #SDE #TRE

25

2 Comments

🚀 "Knowledge graph with GenAI interface at AstraZeneca": insights from Mathew Woodwark ... ... ... There’s no denying the buzz around AI in drug discovery, but where are we in terms of trust and adoption? 🔍 Early Pipeline? A Definite Yes! At the early stages, AI shows tremendous promise. It’s exciting, and scientists are asking: Can we use it for this? And that? ⚙️ Later Pipeline? Proceed with Caution While there’s enthusiasm, adoption is cautious—checks, balances, and human oversight are crucial. Why? Trust isn’t fully built yet, and challenges like fragmented data sources remain a hurdle. 📊 Knowledge Graphs & Gen AI Interfaces: A Glimpse of the Future One organization tackled this by building a Knowledge Graph for Discovery and layering it with a generative AI interface. The result? It’s incredibly popular, offering rich insights—but only from public domain data. The next step? Integrating internal data to unlock full potential. 💡 The Missing Link: Measuring Impact While these tools are delivering great results, the real question is: How are these outcomes influencing decisions? Tracking this impact will be the game-changer in validating AI’s value in the drug R&D pipeline. 🌟 Big Takeaway: We’re in the phase of cautious optimism—no internal disasters, but also no monumental breakthroughs. The journey to fully embedding AI into R&D pipelines will hinge on trust, rigorous processes, and ensuring data is ready to fuel these models. Link to the video in the comment section 👇 ________ Love ❤️ this post? Add your comments 💭, re-share ♻️ it with your network, follow me 🔔 for more posts like this, and DM me 📩 if you want to deep dive on this topic!

12

1 Comment

The a16z Podcast with Scale AI Alex Wang: Thoughts on the AI Revolution and What’s Missing in Pharma I recently tuned into the A16Z podcast featuring Alex Wang from Scale AI (link in the comment section 👇 ). It’s a must-listen for anyone passionate about data, AI, and the business of building shovels during the gold rush (metaphorically speaking, of course). Alex highlighted three core pillars fueling the AI revolution: 1️⃣ Data 2️⃣ Algorithms 3️⃣ Compute It’s a solid framework, but as I listened, I couldn’t help but think about what’s missing: Infrastructure, or what I’d call the Digital Backbone. In Pharma, for example, I’ve seen organizations with great data and access to state-of-the-art models but unable to make them work. Why? They lack the Digital Backbone or the systems and processes that enable: 📌 Seamless access to data (breaking down silos and ensuring FAIR principles). 📌 Efficient data transfer and integration to where it’s needed. 📌 Scalable pipelines for training and deploying models. Without this foundational layer, the promise of AI remains just that: a promise. The Digital Backbone is what connects all the pieces, operationalizing AI and unlocking its full potential. Infrastructure might not grab headlines like algorithms or compute, but it’s the linchpin. It’s where Pharma (and many industries) are ripe for disruption. Many companies have dominated the headlines when it comes to #Compute (e.g., NVIDIA), #Models (like OpenAI or Meta), and even #Data (though data feels more like it’s everywhere and nowhere at the same time). Ilya Sutskever has mentioned the idea of “peak data,” but I think we’re only scratching the surface: especially since the most valuable data remains locked behind paywalls, buried in corporate silos, or simply inaccessible. And then there’s the #DigitalBackbone: the critical layer that ties all of this together, enabling compute, models, and data to work in harmony and unlock true potential for companies. Unlike the other pillars, I’m not aware of a standout leader in this space. What do you think? Is there a company leading the charge in building this foundational layer, or is this still a wide-open opportunity? ________________ Love ❤️ this post? Add your comments 💭, re-share ♻ it with your network, follow me 🔔 for more posts like this, and DM me 📩 if you want to deep dive on this topic!

10

5 Comments

Just came across this insightful article on the state of the CRO industry on Contract Pharma. It's clear that the industry is rapidly evolving with key trends such as decentralized clinical trials, digital transformation, and real-world evidence coming to the fore. It also highlights the importance of strategic partnerships and collaborations for industry growth. It's an exciting time to be part of this dynamic industry. #CRO #PharmaIndustry #ClinicalTrials #DigitalTransformation

1