Shruthi Bharadwaj, Ph.D

Don’t miss the latest episode of The Bio Report podcast, where our CBO Hilary Eaton, PhD joined Daniel Levine for a wide-ranging conversation about the role of #GenerativeAI in drug development, our OpenCRISPR-1 molecule and why we made it open-source. 👉 https://lnkd.in/gkD4iAEt #AI #CRISPR #OpenCRISPR #ProteinDesign #Podcast

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Deep physics. Generative #AI. Big words, with even bigger potential. How can combining them accelerate drug discovery? Matt Truppo sat down with Maximilien Levesque, CEO of AQEMIA, to talk about how we are ramping up our partnership to harness AI to help translate biomedical research into new therapies. 🎥 Check out the full video to learn more about our collaboration 👉 http://spkl.io/6045fJw0T #TransformingCare #CollaborativeWorking

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Yesterday, Odyssey's Chief Data Officer Joseph Mcdonald joined industry leaders from Johnson & Johnson Innovative Medicine, BigHat Biosciences, Sequome, and Samsara BioCapital for a panel discussion on the opportunities and challenges of using #AI and #ML tools for hit identification, multiparameter optimization, and in vivo property prediction to accelerate #DrugDiscovery in small molecules. During the session they were asked for their opinions on the impact of AI/ML in the industry over the next three to five years. Joe shared: “It could be that we will see new ways of monitoring patients, where instead of just using the maximum tolerated dose, we use machine learning to monitor patients more effectively. This allows us to titrate their dose, so treatments are effective and avoid toxicology risks.” Thank you, Biotechnology Innovation Organization, for hosting us and orchestrating an amazing event! #BIO2024

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#AI is the talk everywhere, but to make AI work you need structured, harmonized data, and use a standardized terminology. This is also true for #lifescience process data analytics projects. Only a #datacentric approach ensures that the data is fit for purpose and aligned to standard terminology via an ontology-backed approach. #Ontologies make a difference in #therapeutics drug discovery, development, and manufacturing. Come learn more in tomorrow’s webinar, Wednesday at 9.00AM ET, which details how the L7 and SciBite partnership achieves this. #datastrategy #processorchestration #LLM #artificalintelligence #L7ESP #CENtree

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Breaking [DATA ]Barriers in Drug Discovery with Iambic Therapeutics Enchant ... ... ... Drug discovery 💊 has always been a journey through uncharted territory—one fraught with immense promise but also tremendous uncertainty 😱. The industry has long faced a persistent challenge: how do we reliably predict clinical outcomes using insights from the earliest stages of drug discovery? Enter Enchant. Iambic Therapeutics has introduced a new AI platform [another one again ... you might say] that takes on this critical problem. Most platforms rely heavily on either vast clinical datasets (which are often scarce) or purely experimental methods (which can be slow and resource-intensive). Enchant flips this script. What makes Enchant unique? ✨ Multi-modal Integration: Enchant merges diverse data streams—preclinical and limited clinical data—into a cohesive model [I worked on similar projects - and that's HARD!]. This approach doesn’t just extrapolate; it predicts clinical properties like pharmacokinetics with precision. ✨ Physics-Informed AI: By weaving physics-based models into their AI, Iambic opens doors to previously untapped chemical structures and novel therapeutic mechanisms. ✨ Fast Iteration: With high-throughput experimentation capabilities, Enchant combines design, synthesis, and testing in a weekly cadence—enabling faster decision-making and fewer dead ends. [That's the lab-in-the-loop approach - all successful AI companies are going in that direction!] The Impact: Enchant isn’t just about efficiency. It’s about reducing risk—clinical, financial, and operational. It ensures that the compounds progressing to trials are optimized for success, effectively bridging the ‘data wall’ between lab and clinic. How does this stand out from the competition? While others lean heavily on siloed approaches—either preclinical data or AI models trained on narrow datasets—Enchant thrives at the intersection. It integrates data like no other platform, blending the best of both worlds: human-driven insights and AI’s computational power. This is more than just innovation; it’s a blueprint for transforming how we approach drug discovery. ______ 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!

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My friends in biopharma are increasingly worried about the headwinds to the industry’s traditional business model. The impact is real - constant organizational transformation and layoffs. We predicted the challenge in our book - Redefining Innovation, and suggested pathways to new business models. However, we also understand the inertia and the inability to change. Overall, we foresee the industry becoming more like the utilities- regulated and price capped. As individuals passionate about the industry, it is time to rethink how we will disrupt and innovate ourselves, for our own viability. That in turn might be the impetus for large scale organizational transformation the industry needs now.

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Sanofi and OpenAI have come together to introduce a powerful AI-based platform aimed at tackling one of the most pressing issues in clinical research: reducing timelines. This collaboration combines OpenAI’s expertise in advanced machine learning and Sanofi’s experience in drug development to create a tool designed to streamline trial phases, ultimately aiming to improve efficiency and accelerate access to new therapies. By addressing the complexities and time demands of clinical trials, Sanofi and OpenAI are advancing a new approach in biopharma. This initiative underscores a broader industry movement towards integrating AI to enhance trial precision, decision-making, and patient outcomes. With the support of industry giants and thought leaders, this project highlights the growing importance of AI in making drug development more efficient, offering an exciting prospect for the future of clinical research. #Biotech #ClinicalTrials #AIinHealthcare #Sanofi #OpenAI Mira Murati Formation Bio Linhao Zhang Lionel Bascles Michael Quigley

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Komodo CEO Arif Nathoo, and COTA CEO Miruna Sasu discuss how AI and technology combine to unlock new opportunities for Life Sciences research across all teams and functions. Watch as Arif shares more on how #AI can open more insights and opportunities from RWD: https://lnkd.in/gpwgVye3

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Congratulations to #JLABS @ Shanghai Alumni and #QuickFireChallenge Awardee Qure.ai, a medical imaging AI healthcare firm, on closing $65M in their Series D round. This was led by Lightspeed and 360 ONE Asset, with contributions from Merck Global Health Innovation Fund and Kae Capital. The round also included participation from existing investors, including Novo Holdings, HealthQuad, and TeamFund. The funds will help to expedite expansion into the US market and other geographies, increase investment into foundational AI models and enable complementary med-tech company acquisitions. Founded in 2016, Qure.ai is on a mission to make healthcare more accessible and equitable globally. Qure.ai solutions are deployed in over 90 countries across 3000+ sites, powering the efficient identification and management of critical diseases. Early this year, the company obtained two major clearances from the United States Food and Drug Administration: a breakthrough device designation for its tuberculosis screening AI solution, qSpot-TB, and a 510(k) clearance for the lung nodule on the qXR AI-powered chest X-ray software. Well done team! #healthtech #ai #lungcancer #tuberculosis #stroke

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Generative AI has the potential to reduce and even eliminate many of the incidental complexities once inherent in scientific R&D. On the pharmaphorum podcast 🎙, Enthought COO Michael Connell discusses the what the technology can realistically do now and in the near future in drug discovery and life science research. "The basic affordance [of generative AI] over the longer term that's really going to be important is that—for the first time we can actually have an operational, usable model of the logic of biology." Listen to the podcast: https://bit.ly/3ZakwTL   #ai #genai #llm #drugdiscovery #scienceandtechnology #randd #enthought #podcast

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📺 Catch our conversation with Roger Li, MD, of Moffitt Cancer Center, on the final results of #CORE001, which investigated #cretostimogene plus #pembrolizumab for #BCG-unresponsive #NMIBC with #CIS. 👉 Find the Full Video Here:

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Our CEO, {{linkedin_mention(urn:li:person:MG8_4hSnpb|Avi Veidman)}}, was interviewed by {{linkedin_mention(urn:li:person:vgKi_zBgFc|Brian K. Buntz)}} for {{linkedin_mention(urn:li:organization:9334323|Drug Discovery & Development)}}! Avi shares how Nucleai's AI-driven spatial biomarkers aim to accelerate drug development: “We’re helping find the right drug for patients while making clinical trials shorter and more likely to succeed for pharma companies, potentially saving years and billions of dollars in drug development.” Learn how Nucleai is leading the way as the first company with an algorithm in active clinical trials. Read more: https://lnkd.in/gpUFUe2Z #SpatialBiology #AI #Oncology #SpatialBiomarkers #Biomarkers #ClinicalTrials #DigitalPathology #PrecisionMedicine #CDx

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It is time to check in on the happenings in the #AI world of clinical research. These three articles truly caught my eye in the past 2 weeks. ❓Which one do you find the most interesting? 🕐We start with a brilliant article by Bakul Patel published in 𝘕𝘌𝘑𝘔 𝘈𝘐 that discusses the regulatory challenges posed by clinical AI, particularly focusing on the USA. It differentiates between pregenerative artificial intelligence (PGAI) and generative artificial intelligence (GAI), highlighting the unique challenges each presents. PGAI includes clinical decision support and machine learning applications that have been regulated by the FDA. GAI, exemplified by large language models (LLMs), poses unprecedented regulatory challenges due to its multipurpose nature and evolving capabilities. The article suggests that new regulatory paradigms may be needed for GAI, potentially treating it more like human clinical intelligence, requiring ongoing training, evaluation, and supervision. 𝑳𝒊𝒏𝒌: https://shorturl.at/oFLHi 🕑Cambridge University researchers Peter Tino and Christopher Chen have developed an AI tool that can predict with 82% accuracy whether individuals with mild cognitive impairment will progress to Alzheimer's disease within three years. This tool outperforms current clinical diagnostic methods, potentially reducing the need for invasive and expensive tests while improving early intervention opportunities. It uses routinely collected, non-invasive, and low-cost patient data, including cognitive tests and structural MRI scans. While incredibly promising, the effectiveness of the tool relies on the quality and representativeness of the data used to train the AI model. Any biases or limitations in the training data could affect its performance. 𝑳𝒊𝒏𝒌: https://shorturl.at/LuWrn 🕒To conclude on a sobering note from Josh Au Yeung, the article from 𝘕𝘢𝘵𝘶𝘳𝘦 𝘔𝘦𝘥𝘪𝘤𝘪𝘯𝘦 evaluates the limitations and potential of LLMs in clinical decision-making. It highlights that while LLMs have shown impressive performance on medical licensing exams, they struggle significantly in real-world clinical settings. The study used a curated dataset based on the Medical Information Mart for Intensive Care (MIMIC-IV) database, which includes 2,400 real patient cases with four common abdominal pathologies. The findings reveal that current LLMs do not accurately diagnose patients across all pathologies, fail to adhere to diagnostic and treatment guidelines, and cannot interpret laboratory results correctly. The article concludes that LLMs are not yet ready for autonomous clinical decision-making and require extensive clinician supervision. 𝑳𝒊𝒏𝒌: https://shorturl.at/q05bK ~~~ Hi! I'm Elena Sinclair, and I help small biotech companies establish agile and compliant biomarker and biospecimen logistics and vendor oversight. If you want to streamline your operations and site training, 🛎️send me a DM to chat! #clinicalreasearch #clinicaltrials

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Latest blog on approaches to participant recruitment in #RWE! Patients are at the heart of our real-world evidence research. But what to do if you craft a fabulous study design, in collaboration with patients and KOLs, and then start to look at the part of the design concerning the recruitment pathways?! ❓ How to do it? ❓ What are the options? ❓ What do you need to think about? Have a read to see the possible approaches to participant recruitment in real-world evidence research. #patientcenteredresearch #patientrecruitment

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Our Director of Data Science, Tom deLaubenfels, PhD, featured in Applied Clinical Trials Magazine, shares insights on the evolving landscape of objective cough monitoring in clinical trials! Here are the highlights: Objective monitoring provides deeper insights into treatment efficacy, going beyond traditional methods to measure outcomes accurately. Challenges such as demonstrating treatment efficacy and overcoming logistical hurdles are pivotal in advancing this technology. Integrating lung sound monitoring alongside cough assessment enriches clinical evaluations, offering a comprehensive view of treatment impact.   Read Tom’s full interview for a deeper look into the future of objective cough monitoring in respiratory trials: https://bit.ly/3ScZ4sV   #clinicaltrials #respiratoryhealth #datascience #healthcareinnovation

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AI is making waves in clinical trials, from predicting patient treatment response to creating digital trial participants, and beyond. Allison DeAngelis sheds light on how companies like QuantHealth are utilizing AI to improve the clinical trial process in an insightful new STAT article. https://lnkd.in/e9dDKiZr

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I'm honored to have done this interview with The Evidence Base®, Mahalo nui loa for having me! Check this out to see why I am so passionate about the importance and power of CODefs for used #RealWorldData to generate meaningful #RealWorldEvidence!

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In #NeurodegenerativeDisease news, specifically on an experimental treatment for #ALS, with this new #strategic partnership is penned between Dewpoint Therapeutics and Mitsubishi Tanabe Pharma America. ⚛️ The asset is the former's drug, designed "to address the accumulation of a protein into toxic structures that lead to neuronal dysfunction in people with ALS." 🤝 The alliance would grant MTP exclusive rights to license, upon achieving certain developmental milestones. ⚕️ This would add to MTP's existing US-marketed ALS treatment, #Radicava https://lnkd.in/evayrVv4' #Dewpoint #Mitsubishi #Pharma #Biopharma #Healthcare

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