DeciBio’s Post

In a significant advancement, scientists have successfully utilized artificial intelligence to design antibodies from scratch, opening new possibilities in the field of protein design and drug development. Through the modified protein design tool, RFdiffusion, this proof-of-concept work not only promises to streamline the creation of antibodies against complex pharmacological targets but also democratize this process. This achievement marks a step forward in the development of effective and personalized antibody drugs, anticipating a future where perhaps we can design treatments with just the push of a button. Learn more about this groundbreaking advancement here 👉🏼 https://lnkd.in/e4qW5_Uf #techtitute #AI #biomedicine

AI in Medicine: Transforming Healthcare in 2024 AI is revolutionizing the fields of diagnostics, drug development, personalized treatment, and gene editing. Here’s a concise overview of the top applications: 1. Disease Diagnosis Diagnosing diseases traditionally requires extensive training and can be time-consuming. AI, especially through Deep Learning, has made significant strides in automating diagnostics. By analyzing digitized data like CT scans and ECGs, AI can quickly and accurately identify conditions such as lung cancer, cardiac issues, and skin lesions. This not only speeds up the process but makes high-quality diagnostics accessible globally at a lower cost. 2. Faster Drug Development AI streamlines the drug development process, which is notoriously lengthy and expensive. Machine Learning enhances efficiency in: • Identifying disease targets • Discovering drug candidates • Accelerating clinical trials • Finding diagnostic biomarkers By analyzing vast datasets, AI helps identify potential drug compounds and suitable trial participants faster, saving time and resources. 3. Personalized Treatment Different patients respond differently to treatments. AI can analyze patient data to predict how individuals will react to specific treatments, enabling personalized care plans. By learning from similar patient cases, AI assists doctors in designing the most effective treatment strategies, improving patient outcomes. 4. Improved Gene Editing Gene editing, particularly with CRISPR-Cas9, allows precise DNA modifications. AI aids in selecting the best guide RNAs to minimize off-target effects, making the editing process safer and more efficient. This accelerates advancements in gene therapy and personalized medicine. The Future of AI in Medicine AI is already enhancing our ability to diagnose diseases, develop drugs, personalize treatments, and edit genes. As medical data becomes more digitized and unified, AI will continue to uncover valuable patterns, leading to more accurate and cost-effective healthcare solutions. #AIinMedicine #HealthcareInnovation #DigitalHealth #MedTech #AI2024 #FutureOfHealthcare #PersonalizedMedicine #GeneEditing

📰 Another valuable resource on single-cell technology for drug discovery and development. The key takeaway is that combining single-cell omics and artificial intelligence techniques empowers us to precisely identify therapeutic targets, predict drug responsiveness, and decipher mechanisms of action (MoA). At QurieGen, we have demonstrated how, by using multi-omic tools we can unravel the MoA of drug molecules, uncover new opportunities for combination treatments, and profile immune cells with unprecedented detail. Together, these advancements are paving the way for transformative breakthroughs in drug discovery and precision medicine.

Antibody discovery platforms have evolved significantly, incorporating advanced technologies to streamline and enhance the process. Traditional Methods include hybridoma technology (immunizing animals to produce Abs, which are then fused with myeloma cells to create hybridomas that cn be screened for desired Abs) and phage display (uses bacteriophages to display antibody fragments on their surfaces, allowing for the selection of antibodies that bind to specific targets). For High-Throughput Screening, label-free BLI/SPR (these technology allows for the rapid screening of large libraries of antibodies by manipulating small volumes of fluids, significantly speeding up the discovery process) and Next-Generation Sequencing (NGS can be used to analyze the genetic sequences of antibody-producing cells, providing insights into the diversity and specificity of antibody responses) are popular. Single B cell workflow is also getting more attention. Today, AI and ML are increasingly being integrated into antibody discovery to predict and optimize antibody structures and functions. Recent advancements in AI have enabled the design of new antibodies from scratch. This approach uses generative AI models to create novel antibody structures that can be tested and optimized in the lab. Human Antibody Platforms are hot topics for the past couple of years. For instance, Transgenic Animals (These animals are genetically modified to produce human antibodies, which can be harvested and used for therapeutic purposes) and Yeast and Bacteriophage Display(These platforms display human antibody fragments, allowing for the selection of high-affinity antibodies). Gator Bio is partnering with many pharmaceutical companies to leverage screening and more accurate antibody development (www.gatorbio.com). We look forward to working with you. #Antibody #Screening #Hybridoma #PhageDisplay #AI #BLI #SPR #ML

🔬 𝗗𝗿𝗶𝘃𝗶𝗻𝗴 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗗𝗿𝘂𝗴 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 𝘄𝗶𝘁𝗵 𝗦𝗶𝗻𝗴𝗹𝗲-𝗖𝗲𝗹𝗹 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 🔬 In the world of pharma tech, the challenge of drug discovery has always been balancing efficacy, safety, and speed. A recent study highlights how 𝘀𝗶𝗻𝗴𝗹𝗲-𝗰𝗲𝗹𝗹 𝗼𝗺𝗶𝗰𝘀 is reshaping this process, offering a more precise lens into cellular behaviors and drug interactions. Using tools like single-cell RNA sequencing (scRNA-seq), single-cell epigenomics, and spatial transcriptomics, single-cell proteomics, researchers are unlocking critical insights into diseases like cancer, neurological disorders, and infectious diseases. These technologies allow us to: • 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗻𝗲𝘄 𝗱𝗿𝘂𝗴 𝘁𝗮𝗿𝗴𝗲𝘁𝘀 by analyzing cellular heterogeneity and molecular mechanisms at unparalleled resolution. • 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝘁𝗵𝗲𝗿𝗮𝗽𝗶𝗲𝘀 by predicting drug responses for specific cell types, improving precision and minimizing side effects. • 𝗥𝗲𝗽𝘂𝗿𝗽𝗼𝘀𝗲 drugs with single-cell data to find novel uses for existing compounds, saving time and resources. What excites us most is the integration of 𝗱𝗲𝗲𝗽 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 with these single-cell methods. By applying AI models, we can predict how drugs will interact with specific cellular subtypes, improving screening processes and uncovering hidden therapeutic opportunities. For example, tools like scGEN and SCAD use advanced machine learning to simulate cellular responses to drugs, accelerating drug discovery timelines. At QurieGen, we are inspired by these breakthroughs. The key takeaway is that combining single-cell omics and artificial intelligence techniques empowers us to precisely identify therapeutic targets, predict drug responsiveness, and decipher mechanisms of action (MoA). At QurieGen, we have demonstrated how by using multi-omic tools we can unravel the MoA of drug molecules, uncover new opportunities for combination treatments, and profile immune cells with unprecedented detail. Together, these advancements are paving the way for transformative breakthroughs in drug discovery and precision medicine. #SingleCellOmics #DrugDiscovery #AIInBiotech #PharmaTech #PrecisionMedicine

In this study, researchers from the University of Toronto present the AI-Guided Ionizable Lipid Engineering (AGILE) platform, a deep learning powered approach to accelerate #LNP development for #mRNA delivery. Ionizable lipid nanoparticles (LNPs) are seeing widespread use in mRNA delivery, notably in SARS-CoV-2 mRNA vaccines. However, the expansion of mRNA therapies beyond COVID-19 is impeded by the absence of LNPs tailored for diverse cell types. #AGILE streamlines ionizable lipid development with efficient library design, in silico lipid screening via deep neural networks, and adaptability to diverse cell lines. Using AGILE, we rapidly design, synthesize, and evaluate ionizable lipids for mRNA delivery, selecting from a vast library. Intriguingly, AGILE reveals cell-specific preferences for ionizable lipids, indicating tailoring for optimal delivery to varying cell types. These highlight AGILE’s potential in expediting the development of customized LNPs, addressing the complex needs of mRNA delivery in clinical practice, thereby broadening the scope and efficacy of mRNA therapies. https://lnkd.in/e2RaE28F #lipids #polymers #lipid #LNPs #lipidnanoparticles #nanoparticles #drugdelivery #DDS #ai #ml #artificialintelligence #machinelearning #biotech #biotechnology #ionizablelipids #cationiclipids

2024 marks the start of a third year of collaboration between Servier and Aitia, the market leader for digital twins and causal AI technology. Fabien Schmidlin Executive Director of Translational Medicine, R&D at Servier, looks at how this technology can contribute to research and its application for the health sector. 👉 Fabien, could you explain what “digital twins” are and how they function in practice? Digital twins are the virtual representation of an organism including human patients, developed based on genetic, molecular, and clinical data collectively referred to as “multi-omics.” These data come from patient registries, as well as data from our clinical trials. Among other factors, they make it possible to simulate a disease’s progression and highlight the causal relationships and molecular connections between the various organs and cells and clinical outcomes. It also becomes possible to simulate gene and protein knockdowns or response to a drug candidate at the individual patient level to help identify novel drug targets and drugs with corresponding patient populations. The technology is based on iterative mathematical models, AI, analytical data and machine learning. The development of this technology is complex, but its application for clinical research is promising. 👉 What is the benefit of using digital twins for Servier? Digital twins offer an opportunity for the teams to expand the range of potential therapeutic targets for developing innovative new treatments. Naturally, our research is focused in priority on the most recommended therapeutic targets according to scientific literature. With AI, research takes on a whole new dimension: the therapeutic target options proposed by a digital twin may be far more varied and even “off the radar” for researchers. This opens up new possibilities and enables us to accelerate the identification process. The technology also helps us identify subgroups of patients who carry specific biomarkers. This information is valuable in terms of offering increasingly personalized therapeutic solutions for patients based on their biological characteristics. 👉 How could this approach be a game-changer for patients? For cancers with needs that are poorly covered or not yet met, the use of digital twins could first of all accelerate research by identifying new therapeutic targets. Additionally, through the identification of biomarkers, we will gain a better understanding of a disease’s biology. We will be able to define subgroups of patients and build more relevant clinical trials, which include from the outset the patient profiles that are most likely to respond positively to a drug candidate. This personalized approach to medicine will enable us to maximize a drug candidate’s chances of success, and therefore ultimately reduce development times for the benefit of patients. #digitaltwins #AI #Aitia #Servier 👉 www.servier.com

Single Cell Genomics & Proteomics: Unlocking Cellular Secrets Single-cell analysis: This technique examines individual cells, revealing their unique characteristics and roles within tissues. Overcoming limitations: Unlike bulk analysis, it prevents data loss from averaging cell types. Technology Powerhouse: Advances like next-generation sequencing and AI are fueling this field. DNA & RNA Together: Studying both genomes and transcripts uncovers how DNA mutations affect gene expression in single cells. Cancer Insights: This helps understand tumor heterogeneity and track how cancer cells evolve under treatment. Drug Resistance: By analyzing individual cells, scientists can identify subclones that resist treatments. Cellular Plasticity: We can explore how cells within subclones adapt to treatment pressures. Vulnerability Hunting: Single-cell analysis may reveal new targets for cancer drugs. Genomic Variations: This approach helps pinpoint genetic differences among cells. Applications Beyond Cancer: Studying cell differentiation, circulating tumor cells, and drug discovery are all areas of exploration.

How AI Accelerates Research and Development (R&D) in Healthcare: Artificial Intelligence (AI) is revolutionizing healthcare research and development (R&D) by speeding up the discovery of treatments, improving clinical trials, and enabling innovative approaches to understanding diseases. AI-powered tools and algorithms can process vast amounts of data, identify patterns, and make predictions far faster and more accurately than traditional methods. AI Applications in Accelerated R&D: 1) Drug Discovery and Development 💊 -Target Identification: AI analyzes biological data to identify potential drug targets, such as specific proteins or genes linked to diseases. -Molecule Screening: AI-powered simulations assess millions of chemical compounds for potential drug candidates, reducing the time needed for experimental testing. -Drug Repurposing: AI identifies existing drugs that may be effective for new diseases, shortening the development timeline. 2) Predictive Modeling for Disease Pathways 🧬 -AI helps researchers model complex biological systems and predict how diseases progress at the molecular and cellular levels. -This accelerates understanding of conditions like cancer, Alzheimer’s, and rare genetic disorders. 3) Faster Clinical Trials 🩺 -Patient Recruitment: AI analyzes electronic health records (EHRs) and demographic data to identify eligible participants faster. -Trial Optimization: AI predicts patient responses to drugs, reducing trial durations by focusing on the most promising candidates. -Real-Time Monitoring: AI-powered tools monitor patient data during trials, identifying anomalies or risks early. 4) Genomics and Personalized Medicine 🔬 -AI processes genomic data to identify mutations, gene expression patterns, and biomarkers associated with diseases. -This enables the development of targeted therapies and personalized treatment plans. Benefits of AI in R&D: -Speed: Reduces drug development timelines from years to months. Efficiency: Processes large datasets with minimal human intervention. -Precision: Identifies drug candidates and targets with higher accuracy. -Scalability: Enables research on a broader range of diseases, including rare conditions. -Innovation: Introduces new ways to understand and tackle complex health challenges.

Blind testing the computational drug discovery: CACHE#2 Challenge identifies molecules for pan-coronavirus treatment 💻 💊 https://lnkd.in/dVqDUycd The nonprofit drug discovery biotech Conscience has announced exciting results from their second CACHE (Critical Assessment of Computational Hit-Finding Experiments) Challenge. In an impressive display of global collaboration and scientific ingenuity, 23 research teams from around the world applied their computational methods to identify small molecule ligands that bind to the RNA groove of the SARS-CoV-2 helicase NSP13, a key target for developing broad-spectrum antiviral treatments. The challenge, sponsored by the U.S. National Institutes of Health, brought together diverse expertise in physics-based and AI-driven computational methods. Participants collectively submitted 1,957 compounds, which underwent rigorous experimental testing. Key Highlights: 🔹 46 compounds of interest from 18 participants advanced to Round 2 based on binding activity. 🔹 Over a dozen chemical series with measurable KD < 150 µM were discovered. 🔹 Five hits received a score above 15 from the independent Hit Evaluation Committee. 🔹 The dataset, including seven promising hits, is now publicly available to advance research. 🔹 The top-performing team, led by Karina dos Santos Machado from universities in Brazil, utilized a combination of open source tools and AI to design the highest number of confirmed active molecules. Other standout teams hailed from the U.S., U.K., Canada, and Ukraine, showcasing the power of global collaboration in driving drug discovery. These promising molecules represent significant progress towards developing a pan-coronavirus treatment that could complement existing therapies and bolster preparedness for future outbreaks. The openly available dataset also provides a valuable resource for training AI drug-design algorithms and accelerating early-stage drug discovery. Congratulations to all the participants for their groundbreaking work! The success of the CACHE#2 Challenge underscores the immense potential of computational methods and open science in addressing critical unmet medical needs. Disclaimer: The opinions and information presented in this post are my own and do not reflect the official position of my employer. #drugdiscovery #drugdesign #computationalmedicine #openscience #pancoronavirus #cadd #CACHE2Challenge