Amit U Sinha

Reagene Biosciences Pvt Ltd (RBS) and Oncoseek Bio Pvt LtD (OSB) Join hands in Drug Discovery/Development services (Major Focus: Oncology). We are pleased to integrate our strengths in 3D bioprinting, spheroid, tumoroid, organoid models for improved clinical success of drug discovery. Over the last five years, RBS developed 3D bioprinted multi-cellular models, mimicking human disease pathophysiology, emulating Multi Organs-On-a-Chip concept using human cells from different organs communicating with each other. Similarly, OSB developed Tumor Spheroids using patient biopsy samples. Staggering 97% clinical failures in oncology drug development is a major issue in bringing novel therapeutics into the market. To date, R&D in oncology is diversified from small molecules to biologics monoclonal/bispecific/multi specific antibodies, antibody drug conjugates, cell, gene and immuno therapies, all have been explored with limited clinical success Clinical failures account for both toxicity (hepatotoxicity, cardiotoxicity etc), and poor efficacy. In some cases, efficacy limited to sub-populations subjected to genetic and epigenetic regulations of target biology. We know cancer is a multi step (initiation, promotion, progression & metastasis) process with multiple genetic and epigenetic insults of a normal healthy cell resulting in clonal expansion to cancers. As described above both small molecule and large molecule targeted therapeutics, have been explored with some clinical successes but mostly clinical failures. Many reasons contribute to clinical failures but notably due to two major reasons 1) Differences in mechanistic biology of multi-step cancer process is different from animals to humans and within human sub populations. So, a successful preclinical trial, therefore, may not translate to successful clinical outcome. 2) Drug disposition is different from animals to humans (notably drug metabolism, drug transport and tissue distribution). These differences contribute for both efficacy and safety related failures. Thus, there is an urgent need to develop human relevant models for improving clinical success rates. We at RBS developed 3D bioprinted human surrogate models to mimic human drug disposition whereas OSB developed tumor spheroids/organoid models from patient derived tumor biopsies. Combining these two technologies can help: A) Evaluate efficacy, ADME and toxicity of new experimental therapeutics; B) Help discover novel biomarkers those can be applied in combined phase 1/2 trials; C) Help design INFORMED clinical trials for successful outcomes. Our RBS and OSB partnership with integrated human surrogate models can be leveraged by many companies around the world in discovering novel therapeutics. Please contact us for your drug discovery and early development needs in Oncology therapeutics space. Dr. Subrahmanyam Vangala -RBS Dr. Suresh Poosala -OSB References; https://lnkd.in/g4PrjBFQ https://lnkd.in/gMX5gJ6e

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We are excited to back Morphing Machines. As Dr. Ranjani, CTO and co-founder says: “For the first time in India and globally, we have a processor that is truly runtime reconfigurable, catering to emerging workloads.” While Nvidia’s growth and high valuation are driven by robust demand for “infra-compute” in high-performance GPUs, spurred by Gen AI, there is a distinct need for innovative solutions tailored to a new wave of reactive applications. Enter Morphing Machines. It addresses this demand with its unique product designed for next-gen telecom, autonomous vehicles, collaborative robots, avionics, and online data analytics. This product is specifically engineered to handle heterogeneous workloads and diverse tasks with varying performance demands simultaneously, ensuring a high level of scalability. As we move from an institutionally-backed research team at IISC to productization, Golden Sparrow is collaborating with Speciale Invest and others to support Deepak Shapeti, who joined as CEO three years ago after earning his Masters at Stanford. He brings not only technical expertise but also the business acumen necessary to guide the product to commercial success. Venkat Raju Afreed Faizan Anand Rao Ritik Singh Michael Marmor

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Happy Varamahalaxmi LinkedIn fam 🙏🏻 ✨Just as the eight forms of Lakshmi embody different aspects of prosperity, biotech startups can draw valuable insights. 🧬From Adi Lakshmi's emphasis on foundational research to Vijaya Lakshmi's celebration of success, each form offers guidance. 🪙Like Dhana Lakshmi, financial sustainability is crucial. 🌾Dhanya Lakshmi reminds us of the industry's role in agriculture. 📈 Gaja Lakshmi emphasizes scaling operations. 🔬Santana Lakshmi inspires innovation and new beginnings. 🦸🏻‍♀️Veera Lakshmi instills the courage to overcome challenges. 📚 Vidya Lakshmi highlights the importance of continuous learning. 💫By embodying the spirit of Ashtalakshmi, biotech startups can navigate the complex landscape and achieve remarkable success. #Varamahalaxmi #Biotechnology #Startup #Ashtalakshmi #Innovation #Success #Growth

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Platform drug discovery delivering. Converting data gathering around complex novel biology, into a clinical candidate to test in humans, at scale. The Recursion/Exscientia pipeline is growing & moving forwards for patients! Love it. $RXRX $EXAI

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Join Elucidata on September 12th for an insightful webinar exploring the power of single-cell data! Speaker- Krutika Gaonkar will dive into key challenges like integrating multiple datasets and adding biological context to enhance analysis. Learn how re-analyzing single-cell data can provide high-quality, AI-ready results! Don’t miss out! #Webinar #Data #DeepLearningModels #AI

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Colleagues, a few years ago I sat on a panel to discuss the 'future of chemistry', alongside Nobel Laureate Ben Feringa and Bowen Liu, a super bright doctoral candidate from Stanford. I came to the conclusion, happily, that emerging scientists like Bowen, who have adapted an amazing multi-disciplinary skillset from science and technology, really are the future of chemistry (and life science). I've followed Bowen as his career has progressed and he has definitely reinforced my thinking. I found this recent posting of his to be really interesting, as he discusses with Vijay Pande and Surya Ganguli an area very closely aligned with my interests. Check it out!

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Key Takeaway from the 41st Biotech CEO's Meet at IBioM Indian Biotech MSME May 25th Meeting: 𝗞𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 📋 The complex regulatory process in India for biotech startups includes obtaining various licenses for R&D, import, distribution, and manufacturing. 💡 Experience in navigating regulatory approvals can significantly reduce the time and roadblocks in getting market approval for biotech products. 🧬 Hiring the best talent in biotech often means looking abroad, as the ecosystem abroad helps individuals to be better and more successful. 🧬 The future of biotech is moving towards integrating biology with engineering, creating a need for new skill sets in the industry. 💼 The integration of data analytics and AI in the healthcare industry is evolving rapidly, creating opportunities for biotechnologists and data analysts. 🧬 Game changers like AI, bioinformatics, and mRNA technology are entering the biotech industry, paving the way for innovative solutions to existing problems. 🌱 Agri bio Tech is going to get revolutionized using AIML, potentially alerting the supply chain or farmers based on satellite pictures of the soil. 💼 The collaboration between academia and industry in developing specific training programs can be a game-changer for both sides, addressing the needs of startups and the requirements of the industry. Watch the full live stream recording at https://lnkd.in/gMFU__jR

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Wow - 2 ex-Meta employees launch EvolutionaryScale landing $142m to advance AI in biology. EvolutionaryScale has introduced ESM3, a cutting-edge language model for biology that predicts protein sequences, structures, and functions. This model can simulate millions of years of evolution, generating new proteins with potential applications in medicine, research, and sustainability. ESM3 represents a significant advancement in AI's ability to design and engineer biological systems. There will be comparisons with AlphaFold3, intrigued where this goes... https://lnkd.in/dhtQDtsA #AIinDrugDiscovery #genomics #proteomics

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https://lnkd.in/gezNxetm The is the 1st free access multi-omics data portal for Indian patients. It contains DNA, RNA, and protein data together with clinical data! This dataset could be a very useful for translational research!

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This will be an interesting workshop. Since many early stage investigators, Ph.D and Post Docs would like to learn the leading edge technology #spatialtranscriptomics from #GeoMx DSP NanoString Technologies, Inc.. Numerous projects have been completed. What works, what does not work, how to anayse the data, how to trouble shoot and other necessary skills could be learnt in this workshop. Learn from the experienced scientists at theraCUES and get training certificate also. I am sure it will be helpful for career. #precisionmedicine #careers #phd #postdocs

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This article is inspired by comments from Tony Czarnik and Stefan Ivanov regarding my previous blog post at https://lnkd.in/gFPhXfKW. To clarify, I am writing from the perspective of an AI believer or practitioner, acknowledging the critical viewpoint that claims "AI has shown little in terms of actual achievements in drug discovery." However, I believe it is not sufficient to simply agree with this stance without further elaboration. My focus here is on higher-level concepts rather than practical examples. Here are three key takeaways: 1. Need for Comprehensive Data Collection The current drug discovery process often lacks systematic documentation and relies heavily on subjective reporting, which hampers the ability to draw robust conclusions. Scientists, particularly chemists and biologists, should prioritize automating the collection of extensive data and metadata to create a rich database for analysis. 2. Integration of Mathematical and Data-Driven Approaches Two primary strategies for improving drug discovery are mathematical modeling and data-driven methods. While traditional scientific practices lean toward mathematical modeling for deeper understanding, advancements in AI highlight the effectiveness of data-driven approaches in addressing existing challenges and providing new insights into complex biological processes. 3. Transformative Role of AI AI technology plays a crucial role in drug discovery by enabling scientists to embrace data-driven methodologies that enhance problem-solving capabilities and deepen understanding of biological systems. Combining both mathematical models and data-driven techniques can lead to a more robust framework for innovation in the pharmaceutical industry.

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We’re excited to introduce Chai Discovery and release Chai-1, a new multi-modal foundation model for molecular structure prediction that performs at the state-of-the-art across a variety of tasks relevant to drug discovery. Chai-1 enables unified prediction of proteins, small molecules, DNA, RNA, covalent modifications, and more. https://lnkd.in/e4xGQw-2 The model is available for free via a web interface, including for commercial applications such as drug discovery. We are also releasing the model weights and inference code as a software library for non-commercial use, as well as a technical report covering the results. We tested Chai-1 across a large number of benchmarks, and found that the model achieves a 77% success rate on the PoseBusters benchmark (vs. 76% by AlphaFold3), as well as an Cα LDDT of 0.849 on the CASP15 protein monomer structure prediction set (vs. 0.801 by ESM3-98B). Unlike many existing structure prediction tools which require multiple sequence alignments (MSAs), Chai-1 can be run in single sequence mode without MSAs while preserving most of its performance. Chai-1 is the first model that’s able to predict multimer structures using single-sequences alone (without MSA search) at AlphaFold-Multimer level quality. In addition to its frontier modeling capabilities directly from sequences, Chai-1 can be prompted with new data, e.g. restraints derived from the lab, which boost performance by double-digit percentage points. Using even a handful of contacts or pocket residues (potentially derived from lab experiments) doubles antibody-antigen structure prediction accuracy. Our team comes from pioneering research and applied AI companies such as OpenAI, AI at Meta, Stripe, and Google. The majority of the team has been Head of AI at leading drug discovery companies, and has collectively helped advance over a dozen drug programs. Chai-1 is the result of a few months of intense work, and yet we are only at the starting line. Our broader mission at Chai Discovery is to transform biology from science into engineering. To that end, we'll be building further AI foundation models that predict and reprogram interactions between biochemical molecules, the fundamental building blocks of life. We’ll have more to share on this soon. We are grateful for the partnership of Dimension, Thrive Capital, OpenAI, Conviction, Neo, Lachy Groom, and Amplify Partners, as well as Greg Brockman, Blake Byers, Fred Ehrsam, Julia Hartz, Kevin Hartz, William Gaybrick, David Frankel, R. Martin Chavez, and many others. Read more about Chai Discovery in Bloomberg: https://lnkd.in/eFn3MzPw

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Thoughtful and insightful podcast on H5N1 and related topics, Arnab Chatterjee. Thanks for taking the time to share your wisdom, especially the part about needing more and better antivirals. https://lnkd.in/eMB-mta8

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A few thoughts: - It’s important for management to have clarity as to what are they trying to achieve with AI. The "signal-to-noise" ratio is too degraded with so much conversation and not enough substance. - Boards are jumping to figure what their job is in being stewards of the AI adoption - but won't happen without the former being clear. - Our data shows that half of 2,500 chief executive officers surveyed in April said their businesses are hiring for generative AI-related roles that didn’t exist in 2023. If companies try to "buy" skills for jobs not defined 2 years ago - they'll overpay and likely be disappointed. Talent development is more important that talent acquisition, grounded on the intent and clear use for AI. https://lnkd.in/eW9zNsM5

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Unveiling the Journey of Dr. Chandrashekaran Siddamadappa: From Humble Beginnings to Biotech Brilliance!🚀 Deciding to do something is a battle half-won! Join Disha Prasad in deep diving into an enriching #CEOSecrets interview with Dr. Chandrashekaran Siddamadappa, Chairman and MD, Vipragen Biosciences Pvt Ltd.🎙️ In this captivating discussion, listen to the story of resilience, determination, and unwavering passion that propelled Dr. Chandra from humble origins to the pinnacle of success in the biotech industry. 🔍Explore how childhood experiences, educational insights, and work environments shaped his entrepreneurial journey. 🌱Discover the pivotal role of mentors, teachers, and supportive networks in overcoming challenges and seizing opportunities. 🔥Learn why Dr. Chandra believes age is no barrier to dreaming big and achieving greatness. But here's the twist: How do you balance luck and hard work in your entrepreneurial endeavors? And what strategies do you employ to manage the overwhelming influx of information in today's fast-paced world? Join the conversation, share your insights, and embark on a journey of inspiration and innovation!💡 Let's ignite curiosity and spark meaningful discussions! 🚀🌟 #Entrepreneurship #SuccessStory #Leadership #Biotech #Innovation #CEOInterview #Business #Startups #Inspiration #YoungLeadership #CEOSecrets #LinkedInVideo #ThoughtLeadership #India #Insights #CEO

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CRISPR and AI are redefining gene editing. Profluent and Stanford University are leading the charge with their latest breakthrough - AI-designed CRISPR models. No more time-consuming search for organisms. No more slow, resource-draining methods. Welcome to the era of AI-driven CRISPR research. AI models, steeped in protein and genomic sequences, can decode genetic patterns with ease. They create CRISPR designs that surpass traditional tools, offering precise edits and significantly reduced side effects. Meet OpenCRISPR-1. An AI-designed, error-minimizing, gene-editing powerhouse. Better yet, it's open-source. Now everyone can explore this technology, free of charge. The future of precision medicine is here. https://lnkd.in/dxt7vJBg #genomesequencing #biomarkers #aiindrugdiscovery

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https://lnkd.in/gVJEBQFX Genomics workflows process petabyte-scale datasets on large pools of compute resources. In this blog post, we discuss how life science organizations can use Amazon Web Services (AWS) to run transcriptomic sequencing data analysis using public datasets. This allows users to quickly test research hypotheses against larger datasets in support of clinical diagnostics. We use AWS HealthOmics and AWS Step Functions to orchestrate the entire lifecycle of preparing and analyzing sequence data and remove the associated heavy lifting. #aws #genomics

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Hey LinkedIn Fam - Help out Narwhal Bio! As part of the BioTools Innovator competition, Narwhal has put out a 1 min video about what we're building. The video will be part of a number of factors the BTI team of of advisors will use to rank the companies. Please go to Youtube and "Like" the video as exposure counts! https://lnkd.in/gc6zFqGf Please also "Like" this post below to keep it at the top of people's feeds! Thanks everyone!

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https://lnkd.in/gGSJf9aw Genomic language models are a new and exciting field in the application of large language models to challenges in genomics. In this blog post and open source project, we show you how you can pre-train a genomics language model, HyenaDNA, using your genomic data in the Amazon Web Services (AWS) Cloud. Here, we use AWS HealthOmics storage as a convenient and cost-effective omic data store and Amazon Sagemaker as a fully managed machine learning (ML) service to train and deploy the model. #genomics #aws #healthomics #sagemaker

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