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      <title>Life Sciences &#038; Healthcare</title>
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      <description>Life Sciences &#038; Healthcare</description>
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      <![CDATA[ How can we make biomanufacturing for life sciences and healthcare more sustainable? ]]>
      </title>
      <link>https://blog--3ds--com.apsulis.fr/industries/life-sciences-healthcare/sustainability-in-biomanufacturing/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/274611</guid>
      <pubDate>Wed, 04 Dec 2024 16:02:30 GMT</pubDate>
      <description>
      <![CDATA[ Learn how digitalization drives sustainability in biomanufacturing and helps companies create new treatments that don’t cost the Eart. 
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      <![CDATA[ 
When a team of Canadian researchers discovered how to isolate, extract, purify and administer insulin in the 1920s, they transformed the treatment of diabetes. Their use of this naturally occurring hormone to create medicine also kick-started the development of biomanufacturing in life sciences and healthcare.



Skip forward a century and biologics – biomanufactured drugs made from natural resources like microorganisms and cells – offer a vast range of personalized and targeted treatments. This is the sector that, in 2020, collaborated to develop, produce and distribute effective vaccines to combat COVID-19 in a matter of months – a process that would normally take years.



Today, biomanufacturers are innovating at pace, and growing their production, supply and distribution capacity so they can meet unmet medical needs and an increasing demand from an aging global population. But, currently Life Sciences and Healthcare therapy development and production activities generate 4.4% of global emissions, this means these companies cannot continue on this path – they must produce more with less resources.



Pressure on sustainability performance is coming from all sides. Measures that were once secondary concerns – like reducing waste, energy consumption and emissions – are now a top priority for business leaders. Companies face intense scrutiny from investors and customers when it comes to advancing sustainability in biomanufacturing while reducing costs. And they must also prove their performance against increasingly stringent regulations.



A closer look at biologics



Biologics excel in targeting the proteins, cells or pathways involved in a disease’s progression. Examples include monoclonal antibodies, which are designed to neutralize harmful proteins in the body. Then there are cytokines, which modify immune system responses to fight off or suppress a specific condition. Meanwhile vaccines prevent diseases from developing by stimulating the immune system to fight certain pathogens.



This targeting ability makes biologics effective for treating chronic diseases including autoimmune disorders, cancer, diabetes, inflammatory bowel disease and rheumatoid arthritis. They are also less likely to cause side effects than conventional drugs, which act more broadly across the body.



However, the size and complexity of the molecules involved means that biologics are more complex, time-consuming and costly to manufacture than conventional drugs, which are made from smaller, simpler molecules. The procedure typically begins when a gene, which encodes the desired protein, is transferred into “production cells” such as E. coli bacteria. These cells are then cultivated in a bioreactor under carefully controlled conditions such as temperature, pH and nutrient supply. When the cells are ready, they are processed to release the protein. This is then refined to make sure it meets the purity, potency and safety standards required.



These processes use significant natural resources and energy while producing emissions and waste. But biomanufacturers need to be sure that any measures they take to improve sustainability will not adversely affect the quality of their product, but must be also cognizant of the waste they create. To minimize that risk, sustainability must be designed into these delicate procedures, not bolted afterwards.



Where can biomanufacturers make sustainability gains?



To make meaningful sustainability gains, biomanufacturers must adopt a new paradigm and factor in sustainability concerns from the very beginning of drug development.&nbsp; From the ideation of the drug to the biologic process to facility development on through delivery, each stage in the development process can include sustainability indicators that impact the whole.



Let’s look at a few:




Product development. On average, 80% of any product’s environmental impact is made during the design phase. If biologic developers identify which manufacturing methods will yield the best results for their recipe and the environment, they can make a big dent in that figure.



Supply chain and logistics. These activities typically account for 60-80% of a pharmaceutical company’s greenhouse gas emissions. Those percentages come down when key performance indicators (KPIs) for sustainability are factored into supply chain design from the start.



Continual process improvement. By monitoring environmental performance alongside critical parameters – such as temperature, which is crucial in biologic production – it’s possible to identify small changes that will improve on the process &nbsp;without risking the product’s quality.




What challenges are slowing progress toward sustainability in biomanufacturing?



Knowing where to look is one thing. Finding the information can be quite another, especially since pharmaceutical companies tend to have volumes of scientific data across separate systems and departments. This disjointed view makes it difficult to build a common sustainability strategy across the organization or the product lifecycle. As a result, measures often focus on individual business units, which limits their success.



Factoring in third-party providers such as suppliers and logistics organizations throws up more obstacles. When decision-makers don’t have all the information available in one place, they can’t make informed decisions that balance sustainability alongside KPIs like time and cost.



And what about those opportunities for improvement that emerge during manufacturing? Without a holistic view, based on all the data involved, it’s impossible to know where to look, or what effect any decisions will have further down the line.



Digitalization can drive sustainability in biomanufacturing



Unifying data in one place – the 3DEXPERIENCE platform – has helped Dassault Systèmes customers across Life Sciences &amp; Healthcare and other industries to put sustainability performance in the context of other critical KPIs. The platform allows the creation of virtual twins – holistic digital models of real-world products, systems and processes. By using a virtual twin to structure and contextualize data it allows to see how different KPIs relate to each other across the product lifecycle, biomanufacturers can target new measures wherever they are needed,



It’s important for biomanufacturers to target areas where the biggest sustainability gains can be achieved. They can use a recipe lifecycle management solution, for instance, to see how different processes all along the product lifecycle – such as an energy-intensive or high-emission manufacturing method or some aspects of the supply chain– will affect a product’s environmental impact. Then they can make the appropriate changes to bring those figures down.



Solutions are also available to support the optimization of supply and demand planning. For example, a built-in algorithm can be used to show supply chain planners which suppliers perform best across KPIs like sustainability, reliability and cost, so they can choose the one that strikes the best balance between all those priorities.



Once the biologic enters production, monitoring processes and equipment in real time is essential to identify ongoing opportunities for improvement. This is what the &nbsp;Continued Process Verification toolset does. Its algorithm helps companies understand the different patterns at play in areas like energy consumption and emissions, as well as production parameters – so users can see where to make changes that will improve sustainability while staying within production parameters.



Sustaining the journey



There has always been a sound business incentive for improving sustainability. Minimizing waste and reducing energy consumption often go hand in hand with increased efficiency, as well as potentially attracting more customers and investors. What’s been missing until recently is the joined-up view that shows how and where sustainability can be optimized in balance with performance and quality KPIs across the product lifecycle.



Technologies like those discussed above are bringing that view to life, supported by powerful algorithms. We’re just starting to see how artificial intelligence, with its complex data analysis capabilities, can play a pivotal role in enabling biomanufacturers to thread sustainability across their business. In this intensely innovative industry, it will be exciting to see how companies work to achieve that.



Check out our new whitepaper, &#8220;Innovating for a More Sustinable Future: Leveraging Greener Practices in Biomanufacturing&#8221; for an even deeper dive into sustainability in biomanufacturing, including how advanced  digital tools like AI and virtual twins allow companies to improve efficiency and reduce emissions, waste, energy, and water usage while ensuring quality and safety.




Download the whitepaper today





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      <title>
      <![CDATA[ The Shape of Water ]]>
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      <link>https://blog--3ds--com.apsulis.fr/brands/biovia/the-shape-of-water/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/273202</guid>
      <pubDate>Fri, 22 Nov 2024 01:16:25 GMT</pubDate>
      <description>
      <![CDATA[ Discover how BIOVIA Solvation Chemistry and COSMOtherm uncover the hydrophobic effect of water, a phenomenon fundamental to life and industry.
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      </description>
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      <![CDATA[ 
Understanding the Hydrophobic Effect



Although it seems quite abstract, the interactions of molecules and atoms on a microscopic level often have profound effects on the macroscopic properties of materials and life itself. BIOVIA Solvation Chemistry provides the scientific link to understand these connections between microscopic, molecular interactions and industry-relevant experimental properties of liquids such as solubilities, vapor pressures, partition coefficients and many more. Exemplarily, in this blog post, we want to discuss the anomalous hydrophobic effect of water on a molecular level and its implications on industrial applications and life on Earth in general.



The phenomenon known as the hydrophobic effect, originating from the Greek words ύδωρ (ydor, water) and φόβος (phobos, fear), describes how molecules that “fear” water, tend to come together to avoid interacting with it. This effect explains, why oil and water do not mix but form separate phases, or why some compounds are better soluble in water than others.



It is not just dry textbook knowledge, but a concept fundamental to the existence of life on our planet, serving as a key driver behind biological processes such as the formation of cells and the folding of proteins in active or inactive structures. Proteins are essential building blocks of our body, consisting of chains of amino acids. In many cases, proteins only function properly when they are in a certain folded state, a state that is typically achieved within a specific range of temperatures. Among other factors, increased temperatures can lead to the denaturation of proteins, rendering them inactive. This process can be easily observed when e.g. boiling an egg, where egg albumen turns into a white, opaque substance upon denaturation of proteins (mostly ovalbumin) above 60°C. Also at lower temperatures, proteins can undergo reversible unfolding, a process known as cold denaturation. This behavior is directly linked to the temperature sensitivity of the hydrophobic effect and in order to understand it, we need to investigate the hydrophobicity of water itself.



The Surprising Hydrophobicity of Water



It might sound surprising, but water itself can be hydrophobic, at least to a certain degree.



Water molecules can be arranged in specific shapes or clusters. Here, molecules are connected by, what chemists call, a hydrogen bond. In water, this special type of bond occurs between a hydrogen atom and the oxygen atom of another water molecule. Since water has two hydrogen atoms and each oxygen atom can accommodate two hydrogen-bonds, complex networks can be formed, that stabilize different shapes of water clusters. Interestingly, the surface of these clusters has different properties than the surface of individual water molecules.



Relative screening charge density profile of the surface of a single water molecule (blue, structure top left) and a cluster of 20 molecules (orange, structure top right). Clearly the peak of charge neutral area in the center of the orange curve is visible, that is absent for isolated water molecules.







The picture shows the charge density surface of water and a cluster of water molecules connected by hydrogen bonds, easily computed with BIOVIA Turbomole Blue and red areas denote surface areas with large positive or negative screening charge densities, whereas green areas denote small screening charge densities close to zero. Generally, as a direct consequence of Coulomb’s law, opposing charges attract each other. Therefore, compounds with large positive or negative screening charge densities, prefer to be in contact with compounds of matching opposing screening charge densities. Compounds with a lot of nonpolar surface area (green) prefer other nonpolar compounds. As can be seen, water itself has quite a lot of positive and negative screening charge densities (blue, red). In contrast to this, the cluster structure, has more neutral area with small screening charge densities. For this reason, these clusters partly behave like a nonpolar hydrophobic substance.



Therefore, at low temperatures, when these clusters become more stable, surface properties of water change. Water itself becomes increasingly hydrophobic and in turn, hydrophobic molecules become more soluble in water. When the temperature increases, the clusters break apart and the solubility of hydrophobic molecules decreases. At even higher temperatures, other thermodynamic effects increase the solubility again, leading to a minimum in solubility, that is typically found somewhere in the range of 20 to 80 °C, i.e. near room and body temperature.



The accurate assessment of this temperature dependence is of importance in many applications, ranging from the solubility of additives in oil processing or carbon capture applications, to computation of partition coefficients of active pharmaceutical ingredients.



Advancing Innovation with BIOVIA COSMOtherm



BIOVIA COSMOtherm allows to simulate the temperature dependent hydrophobic effect of water in an efficient and accurate way, as is shown in a recent publication by M. P. Andersson and M. Richter



Aqueous solubilities of hexanol and benzaldehyde over a wide temperature range are shown. Experimental solubilities (blue curves) show a minimum around 320 K (hexanol) and 290 K (benzaldehyde) due to the hydrophobic effect of water. COSMOtherm FINE 2023 is able to recover this minima with high accuracy (325 K for hexanol and 290 K for benzaldehyde).







The picture shows the temperature dependent solubility of hexanol and benzaldehyde in water. Since both compounds are rather hydrophobic, the solubility is generally quite small, but simulation and experiment clearly show a minimum of solubility around 290 K (17 °C) for benzaldehyde and 320 K (47 °C) for hexanol. With BIOVIA COSMOtherm the temperature dependent solubility of any compound in water can be easily assessed out-of-the-box, including anomalies like the hydrophobic effect of water. This ability allows performing large-scale in-silico screenings of molecular properties with high accuracy that complement experimental efforts, speed-up innovation and reduce time-to-market for our customers.  



Learn more about BIOVIA COSMO RS.







References




Andersson, M.P. &amp; Richter, M. (2024). Comment on: The shape of water &#8211; how cluster formation explains the hydrophobic effect. J. Mol. Liq., 409, 125465 https://doi.org/10.1016/j.molliq.2024.125465








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      <title>
      <![CDATA[ Interpretable Machine Learning in Pipeline Pilot ]]>
      </title>
      <link>https://blog--3ds--com.apsulis.fr/brands/biovia/interpretable-machine-learning-in-pipeline-pilot/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/272613</guid>
      <pubDate>Fri, 15 Nov 2024 17:21:04 GMT</pubDate>
      <description>
      <![CDATA[ Explore how interpretable machine learning models like SISSO are advancing scientific insight in chemistry
 ]]>
      </description>
      <content:encoded>
      <![CDATA[ 
Introduction



The majority of machine learning algorithms applied in chemistry and biology are black box models used to make predictions on given target properties.1–3



The model receives input features and generates an output but the inner workings of how the model arrived at the output is unknown or extremely difficult to understand due to the complexity of the models. Therefore, extracting meaningful scientific insights from these models has proven to be a challenge.1,4



Interpretable ML models that offer predictive capabilities combined with interpretable physical equations are gaining traction in many areas of science.1,2,5,6



The goal here is to have what is termed a glass-box model where simple physical equations relate the input features to the target properties. In this way relationships in the data can be understood and improved scientific insight can be gained from the model.







Figure 1: Schematic of Black-box models and Interpretable glass-box models



Sure Independence Screening and Sparsifying Operator – SISSO



Among the methods developed for interpretable machine learning the Sure Independence Screening and Sparsifying Operator – SISSO methodology has been widely applied in heterogeneous catalysis and organic chemistry.7–11 SISSO is part of the symbolic regression class of models and can be used to find mathematical functions to predict the target property.



In simple terms SISSO consists of 2 parts:




Creation of a large feature space by combining the feature columns or descriptors with user selected operators (e.g., multiplication, division, ln, sqrt etc.).



Using sure-independence screening (SIS) to select the descriptors with highest correlation to the target property. Finally applying regularization (ℓ0) to select low-dimension linear models with the lowest error.




With this approach, the aim is to use SISSO to find interpretable equations, that make scientific sense, from a range of input features. The input columns or descriptors can be experimental and/or those obtained from molecular modelling studies, including those conducted in BIOVIA Materials Studio® or BIOVIA Turbomole®.







Figure 2: Example of the types of inputs that have been used with SISSO



The original SISSO code was implemented in FORTRAN12 and does not contain direct Python support. However, a newer C++ implementation (SISSO++) has been released by the NOMAD Laboratory which has native Python integration.13,14



Let’s say I wanted to apply the SISSO algorithm to some chemistry datasets in order to expand my scientific insight. How could I go about deploying this ML method as part of my data science pipelines?



The answer is to use BIOVIA Pipeline Pilot15 &nbsp;to wrap the Python code and extend access to this glass box models.



SISSO++ integration with Pipeline Pilot



Using the strong integration between BIOVIA Pipeline Pilot and Python there are a range of options one can take to incorporate Python code into existing data pipelines.16



In this example we are going to use the Jupyter Notebook components to handle the Python portion and use the native PLP components to read, write and clean data ready for input.



We will take two sets of data, one published by bp17 and one published by Sigman and co-workers.10 The bp data covers the use of benzaldehyde promoters in H-ZSM-5 dehydration of methanol to dimethyl ether (DME) and the Sigman data a diastereoselective Rh catalysed C-H insertion.



Both datasets are small (22 rows and 84 rows) by the standards of most AI methods but reflect the realistic acquisition of small high-quality datasets typically found in industry and academia.



Inside the Python Jupyter Notebook component, SISSO++ can be set up by selecting the operators, target column and the desired train/test split. In addition, hyperparameters can be set and the calculation type can be toggled between regression and classification.



We apply the model to the bp dataset where the target property is DME STY (space time yield – a measure of catalytic performance) and the 10 descriptor columns are density functional theory (DFT) derived features for the organic promoter aldehydes (other reaction parameters are kept constant).



We obtain an interpretable equation that provides scientific insight and outputs are displayed using the Pipeline Pilot reporting components. The SISSO++ model is comparable to the reported model and makes chemical sense as it relates steric and electronic features of the aldehyde promoter to catalytic performance.







Figure 3: Output of SISSO++ regression model for bp dataset run through BIOVIA Pipeline Pilot



One potential limitation of the SISSO++ code is that it can become computationally expensive with datasets that contain a large number of features. To that end, the Materials AI team at BIOVIA along with Felix Hanke (formerly at BIOVIA) developed a BIOVIA Pipeline Pilot native version of SISSO++ for regression problems.



Native SISSO++ in Pipeline Pilot



By using the parallelisation and simplicity of BIOVIA Pipeline Pilot we can increase the speed of finding interpretable equations for scientific datasets and run the models without any coding expertise.



The new protocol applies the same SISSO++ methodology whereby a vast number of features are generated and then parsed to give the best performing equations, but this is performed in a different way inside Pipeline Pilot.







Figure 4: Native SISSO protocol in BIOVIA Pipeline Pilot



The resulting output is comparable to the SISSO++ Python package but simplifies usage for the scientist as you do not need to interact with any code. In fact, the protocol can be run through the Pipeline Pilot Web Port with users selecting parameters through drop-down menus making it ideal for scientists with no coding experience.



In this example we show the output for the dataset from Sigman and co-workers10 where the target is ΔΔG‡ (a measure of diastereoselectivity) and there are 19 DFT derived chemical descriptors. Again, we obtain an interpretable equation for the data that is comparable to the reported model which related steric and electronic properties of the catalyst/ligand to the diastereoselectivity.



Due to the ability of Pipeline Pilot to handle large amounts of data effectively obtaining models with larger datasets (&gt;50 billion generated features) is also possible.







Figure 5: Example of BIOVIA Pipeline Pilot Web Port being used to run native SISSO algorithm.



Conclusion



The simple integration of Python in BIOVIA Pipeline Pilot enables us to incorporate SISSO++ and other Python packages easily into new and existing data pipelines.



We can also make use of the flexibility and speed of BIOVIA Pipeline Pilot to incorporate new methods for interpretable machine learning into data science workflows. In this way, BIOVIA Pipeline Pilot can be used to help scientists gain meaningful scientific insights from predictive models. With BIOVIA Pipeline Pilot these types of models can be deployed in a low/no-code environment to aid understanding and further innovation in scientific challenges.



References



(1)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Esterhuizen, J. A.; Goldsmith, B. R.; Linic, S. Interpretable Machine Learning for Knowledge Generation in Heterogeneous Catalysis. Nat Catal 2022, 5.



(2)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Azodi, C. B.; Tang, J.; Shiu, S.-H. Opening the Black Box: Interpretable Machine Learning for Geneticists. Trends in Genetics 2020, 36 (6), 442–455.



(3)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Jiménez-Luna, J.; Grisoni, F.; Schneider, G. Drug Discovery with Explainable Artificial Intelligence. Nature Machine Intelligence. Nature Research October 1, 2020, pp 573–584.



(4)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Molnar, C. Interpretable Machine Learning. A Guide for Making Black Box Models Explainable; https://christophm.github.io/interpretable-ml-book/., 2019, accessed 04/11/2025



(5)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; La Cava, W. G.; Lee, P. C.; Ajmal, I.; Ding, X.; Solanki, P.; Cohen, J. B.; Moore, J. H.; Herman, D. S. A Flexible Symbolic Regression Method for Constructing Interpretable Clinical Prediction Models. NPJ Digit Med 2023, 6 (1).



(6)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Rudin, C. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nat Mach Intell 2019, 1 (5), 206–215.



(7)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Foppa, L.; Rüther, F.; Geske, M.; Koch, G.; Girgsdies, F.; Kube, P.; Carey, S. J.; Hävecker, M.; Timpe, O.; Tarasov, A. V.; Scheffler, M.; Rosowski, F.; Schlögl, R.; Trunschke, A. Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation. J Am Chem Soc 2023, 145 (6), 3427–3442.



(8)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Miyazaki, R.; Belthle, K. S.; Tüysüz, H.; Foppa, L.; Scheffler, M. Materials Genes of CO 2 Hydrogenation on Supported Cobalt Catalysts: An Artificial Intelligence Approach Integrating Theoretical and Experimental Data. J Am Chem Soc 2024, 146 (8), 5433–5444.



(9)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Wang, J.; Xie, H.; Wang, Y.; Ouyang, R. Distilling Accurate Descriptors from Multi-Source Experimental Data for Discovering Highly Active Perovskite OER Catalysts. J Am Chem Soc 2023, 145 (20), 11457–11465.



(10)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Souza, L. W.; Miller, B. R.; Cammarota, R. C.; Lo, A.; Lopez, I.; Shiue, Y.-S.; Bergstrom, B. D.; Dishman, S. N.; Fettinger, J. C.; Sigman, M. S.; Shaw, J. T. Deconvoluting Nonlinear Catalyst–Substrate Effects in the Intramolecular Dirhodium-Catalyzed C–H Insertion of Donor/Donor Carbenes Using Data Science Tools. ACS Catal 2023, 104–115.



(11)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Park, J.; Oh, J.; Kim, J. S.; Shin, J. H.; Jeon, N.; Chang, H.; Yun, Y. Catalyst Discovery for Propane Dehydrogenation through Interpretable Machine Learning: Leveraging Laboratory-Scale Database and Atomic Properties. ACS Sustain Chem Eng 2024, 12 (28), 10376–10386.



(12)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Ouyang, R.; Curtarolo, S.; Ahmetcik, E.; Scheffler, M.; Ghiringhelli, L. M. SISSO: A Compressed-Sensing Method for Identifying the Best Low-Dimensional Descriptor in an Immensity of Offered Candidates. Phys Rev Mater 2018, 2 (8), 1–12.



(13)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Purcell, T. A. R.; Scheffler, M.; Carbogno, C.; Ghiringhelli, L. M. SISSO++: A C++ Implementation of the Sure-Independence Screening and Sparsifying Operator Approach. J Open Source Softw 2022, 7 (71), 3960.



(14)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Purcell, T. A. R.; Scheffler, M.; Ghiringhelli, L. M. Recent Advances in the SISSO Method and Their Implementation in the SISSO++ Code. J Chem Phys 2023, 159 (11), 114110.



(15)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Pipeline Pilot. https://www.3ds.com/products-services/biovia/products/data-science/pipeline-pilot/.



(16)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Pipeline Pilot | Integration of Python and Jupyter Notebook. https://www.youtube.com/watch?v=1sFaA7Fj0oM, accessed on 18/09/2024.



(17)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Yang, Z.; Dennis-Smither, B. J.; Buda, C.; Easey, A.; Jackson, F.; Price, G. A.; Sainty, N.; Tan, X.; Xu, Z.; Sunley, G. J. Aromatic Aldehydes as Tuneable and Ppm Level Potent Promoters for Zeolite Catalysed Methanol Dehydration to DME. Catal Sci Technol 2023, 13 (12), 3590–3605.







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      <title>
      <![CDATA[ 11 real ways AI is transforming healthcare ]]>
      </title>
      <link>https://blog--3ds--com.apsulis.fr/industries/life-sciences-healthcare/ai-in-healthcare-transforming-industry/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/271934</guid>
      <pubDate>Fri, 01 Nov 2024 17:11:46 GMT</pubDate>
      <description>
      <![CDATA[ From earlier diagnosis and tailored treatments to precision surgery and remote monitoring, AI in healthcare is unlocking groundbreaking ways to improve patient outcomes and save more lives.
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      <![CDATA[ 
Healthcare is on course to become more personalized and effective than ever, thanks to advances around artificial intelligence (AI). In a recent report, the World Health Organization outlined the transformative potential of AI in global health, citing the many ways it will change drug development, administration, diagnosis, treatment, and patient care. If governed and implemented effectively, WHO said, AI will not only improve access to higher quality services for all, but also address workforce shortages and reduce health system costs in the process.



In the not-too-distant future, then, we can expect to see treatment plans that are tailored precisely to each patient to determine the most successful interventions, taking into account everything from genetics and medical history to lifestyle choices. Because of AI’s ability to mine vast amounts of data, medical professionals will be given the insights they need to accurately detect, diagnose and develop bespoke treatments for critical health issues and diseases to save more lives. In clinics, they’ll be empowered with real-time support to improve decision-making. In surgery, smart robot systems (like F.MED’s microsurgery robot) will assist with intricate procedures and perform minimally invasive techniques, resulting in better outcomes and faster recovery times. Through AI-powered remote monitoring, patients will receive ongoing care and therapy from the comfort of their own homes. And by streamlining processes and easing administrative burdens, AI will free up healthcare professionals to focus on what truly matters – patient care.



In this new AI-driven world, healthcare systems will become more proactive and deliver patient-centric experiences that reach more people. Medical breakthroughs will be reached faster, and treatments and therapies will dramatically improve patient outcomes. And, through personalized insights and tools for wellness management, more people will be empowered to live healthier lives. With all this in mind, here are some of the remarkable ways AI in the healthcare industry is already making a difference on a global level:



How is AI being used in healthcare?




Research and development: AI is transforming product and drug discovery. In silico compound screening accelerates the development process with foundational chemistry models that can map millions of chemical compounds by structure and function – it works like generative AI content tools, but instead of words it predicts the next part in the molecular structure. Deep learning algorithms also assist in virtual screening by analyzing large datasets of chemical compounds to determine how different drugs might interact with specific proteins or molecules. From here, scientists can focus on the most promising drug candidates. Additionally, generative models, paired with detailed data analysis, can help identify existing drugs that might be suitable for use in other, new therapeutic applications.



Clinical trials: AI is proving promising when it comes to optimizing clinical trials, helping to identify the most suitable and eligible participants faster and predict trial outcomes to reduce the time and cost of clinical research. Biomarkers help to assemble diverse and representative populations while digital medical writing assistants, powered by natural language generation, streamline regulatory filings and reporting, and analyze vast datasets quickly, uncovering key insights and patterns to accelerate decision-making. By boosting efficiency across the entire clinical development process, pharmaceutical companies could achieve 50% cost reductions from streamlined clinical trial processes and auto-drafting trial documents; faster clinical trials by more than 12 months; and at least a 20% increase in net present value.



Manufacturing: Pharmaceutical companies like GlaxoSmithKline and Sanofi have embraced AI to enhance the efficiency and reliability of drug production. AI models support predictive maintenance, which allows them to fix and replace equipment before it disrupts manufacturing, and optimize stock management to avoid waste. Using AI and machine learning tools to analyze quality control issues (also known as deviations) at its production sites and automate the review process for minor deviations, Sanofi has reduced closure times by 60%, resulting in shorter cycle times and enhanced quality and reliability across the supply chain.



Safety and quality regulations: AI is enhancing safety and quality in healthcare and pharmaceuticals by automating tasks like compliance checks and reporting, and streamlining the regulatory submission process by generating all required documents, tracking changes, and verifying data. In drug manufacturing settings, AI can detect anomalies in production data to resolve potential issues quickly and reduce the risk of non-compliance. And for ongoing drug safety, AI is being used to analyze clinical data, proactively identify potential adverse effects and safety risks, and continually monitor information from healthcare providers and patients.



Commercialization: McKinsey estimates that AI could generate US$60 billion to US$110 billion a year in economic value for the pharmaceutical and medical-product industries by accelerating the process of identifying compounds for possible new drugs, accelerating their development and approval, and improving the way they are marketed. In particular, marketers can use AI’s advanced search and data analysis capabilities to extract deeper insights from customer research, information on physicians, as well as updates on policy changes, legal developments, and formulary considerations. With all this information, they can get a better understanding of the markets they’re targeting and fine tune their campaign strategies. 



Consultation: Now, before seeing a medical professional, patients might instead interact with an AI virtual assistant, which gathers their medical records, assesses symptoms, and triages them based on their condition. From here, doctors benefit from more focused consultations, backed by valuable insights to help with diagnosis and treatment. They can also take advantage of natural language processing (NLP) tools, which relieve the administrative burden and save them valuable time by transcribing and summarizing all clinical engagements​.



Diagnostics: By training AI algorithms to analyze medical images and detect patterns from symptoms and other factors, it’s becoming possible to better identify things like cancerous lesions and tumors and detect diseases earlier, with greater accuracy. For example, the American Cancer Society reported that many mammograms produce false positives, leading to one in two healthy women being misinformed about having cancer. However, it discovered that AI can review and interpret mammograms 30 times faster, with 99% accuracy, significantly reducing the number of unnecessary biopsies.



Medical decision-making: AI is helping medical professionals diagnose medical conditions, build the most effective treatment plans and better predict patient outcomes by consolidating and drawing insights from all manner of data including medical records, lab results and imaging data. Machine learning models are proving particularly valuable for helping to detect serious conditions like sepsis, meningitis and heart disease, which might get overlooked in initial consultations​. AI is also helping to build a holistic 360-degree patient view, sometimes called patient 360 records, by drawing together data from electronic health records, lab results, wearable devices and more.



Treatment: In the UK alone, more than 14,000 hospital beds are occupied every day by patients who are well enough to be discharged. Busy hospitals and bed shortages have a significant impact on patient safety. AI is helping by predicting patient admission rates, identifying seasonal peaks, and optimizing everything from staffing and resource allocation to bed management. NLP tools generate discharge summaries in a fraction of the time, reducing the burden on healthcare staff and expediting the overall discharge process. AI is also helping to improve communication between different hospital departments – vital for effectively coordinating care in complex cases that involve multiple specialists. 



Follow-up: AI is transforming patient follow-up care through effective remote monitoring and personalized patient experiences. AI is now capable of doing things like sending personalized follow-up messages and medication reminders to reduce readmission rates, as well as flagging abnormalities to help with timely interventions. Developments like personalized diabetes management tools help to keep track of patients and make sure they’re adhering to their treatment plans, making recommendations based on real-time patient data.



Health assessment/self-monitoring: The rise of consumer wearables and medical devices, enhanced by AI technology, is revolutionizing the management of chronic illnesses including heart disease. Through better monitoring, healthcare professionals can keep a closer eye on patient symptoms and identify potentially life-threatening episodes earlier when they’re more treatable. Smart watches now include features like heart rate monitoring and use AI algorithms to analyze heart rhythms, helping users detect irregularities such as atrial fibrillation. 




Dassault Systèmes and the future of AI in healthcare



Much like in our everyday lives and across most industries, AI is set to become an integral part of the global healthcare system. More than 70% of healthcare organizations are already testing and implementing AI capabilities to improve the patient experience and streamline operations. Many expect AI, combined with other technological advances, to reshape the industry entirely – driving the shift to a new era of efficient, personalized and proactive care.



In many ways, this vision aligns with Dassault Systèmes’ own mission to drive innovation and efficiency in life sciences and healthcare – a future where patients become consumers, in control of their healthcare, and an industry where virtual twin experiences are the catalyst for sustainable innovation and efficient healthcare systems.



Through its virtual twin capabilities and powerful AI tools, Dassault Systèmes strives to empower healthcare organizations, pharmaceutical companies and consumers with new, data-driven ways of visualizing, predicting and managing responses to treatments and interventions. This approach involves the creation of dynamic, highly detailed digital replicas (virtual twins) of patients, considering their individual anatomy, genetics, and real-world medical data. In clinical trials, virtual twins will replace traditional placebo groups, using synthetic patient data to speed up research and broaden access to innovative therapies. And when it comes scaling up precision medicine production, virtual twins and AI will make it possible to manufacture new biologics efficiently and deliver them globally.



“Just imagine… if you’re able to understand, represent, test and predict what can’t be seen – from the way a drug affects a disease to the outcome of a surgical intervention,” said Claire Biot, our VP of Life Sciences and Healthcare, introducing Dassault Systèmes’ vision for the industry earlier this year. “That’s really what we are trying to achieve and we want to position the virtual twin as a way to propose a platform for medical practice excellence and value-based care.”
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      <![CDATA[ 7 innovative approaches to cancer treatment ]]>
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      <link>https://blog--3ds--com.apsulis.fr/industries/life-sciences-healthcare/7-innovative-approaches-to-cancer-treatment/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/270543</guid>
      <pubDate>Fri, 11 Oct 2024 16:02:34 GMT</pubDate>
      <description>
      <![CDATA[ In honor of Breast Cancer Awareness Month, we’re spotlighting seven innovative approaches to cancer treatment that hold the potential to change the future of breast cancer.
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      <![CDATA[ 
With the changing leaves and a wave of pink ribbons, October stands as Breast Cancer Awareness Month, a global campaign dedicated to promoting the importance of early detection and supporting those affected by the disease.



Breast cancer is a significant health concern, with 1 in 12 women being diagnosed in their lifetime. In 2022 alone, the World Health Organization reported 2.3 million women were diagnosed with breast cancer and 670,000 died globally. Given these numbers, it’s likely that many of us know someone personally affected by this disease.



Breast cancer occurs when malignant cells develop in the breast tissues, affecting both women and men. With various types making it a complex disease, it remains the most common cancer among women. According to the American Cancer Society, when breast cancer is detected in its earliest, localized stages, the five-year relative survival rate is an impressive 99%. Advances in early detection and treatment methods have significantly boosted survival rates in recent years. Today, in the United States, there are over 4 million breast cancer survivors, a testament to these medical advancements.



In raising awareness of breast cancer this October, we’re highlighting seven innovative approaches to cancer research and treatment that have the potential to change the future of breast cancer and its treatment options.



1.  LATTICE MEDICAL







French startup LATTICE MEDICAL, has developed MATTISSE, a bio-resorbable tissue engineering chamber. With a 3D-printed implant, they offer natural breast regeneration in a single surgery. The implant will affix to the patient&#8217;s tissue and regenerate what was lost in the mastectomy in three to six months and then biodegrade within 18-24 months.&nbsp;



MATTISSE demonstrates how a revolution in reconstructive breast surgery for breast cancer patients is possible. By using the patient’s own tissue and enabling natural reconstruction, the surgery adapts to the individual&#8217;s anatomy, moving away from the traditional one-size-fits-all approach. Current techniques can often necessitate multiple painful surgeries, making MATTISSE a more comfortable and promising option for those worried about reconstructive treatment.&nbsp;



A member of the 3DEXPERIENCE Lab startup accelerator, LATTICE MEDICAL uses the 3DEXPERIENCE platform for everything from 3D product modeling to biomechanical simulations to scaling production processes.&nbsp;







2. VORTHEx virtual radiotherapy 



The H. Hartmann Institute of Radiotherapy and Radiosurgery in France, in collaboration with the 3DEXPERIENCE Lab, developed a novel solution, VORTHEx. This solution uses virtual twins to simulate radiotherapy in the virtual world before it happens in the real one in an effort to relieve the anxiety and distress that patients experience from radiotherapy procedures.&nbsp;



For women (or men) battling breast cancer, an experience like VORTHEx would be a significant step toward alleviating the anxiety of facing radiotherapy procedures. VORTHEx shows the entire procedure, including the room, the Accuray Cyberknife® robot arm and the patient&#8217;s position. A patient care team member is present for the virtual experience, answering any questions related to the simulation. 



By providing a clear understanding of the procedure and what to expect, VORTHEx empowers patients – and their families – with knowledge and reduces their stress and anxiety. This not only improves their overall experience but also enhances their chances of overcoming breast cancer with a positive mindset, giving them a sense of control over their treatment journey.&nbsp;











3. The MEDITWIN Project



Over the next five years, Dassault Systèmes will be at the forefront of the MEDITWIN initiative, an ambitious project that brings together 14 world-class partners and financial support from the French government to develop virtual twins for medical practice and make healthcare safer and more accessible for all. The MEDITWIN initiative will open up new possibilities for breast cancer research and treatment.&nbsp;



Using MEDITWIN, doctors can create virtual twins of individual patients. One promising area where MEDITWIN will have a significant impact is oncology. These virtual twins are scientifically accurate digital replicas that simulate the patient&#8217;s organs, metabolism and cancerous tumors. The MEDITWIN project is an innovative initiative that combines French science and technology to develop these personalized virtual twins for medical practice. Doctors will be able to simulate future scenarios for patients, offering hope for more accurate diagnosis and personalized treatment options.



4. IASO Showcase



The IASO showcase is a demonstration created by Dassault Systèmes to highlight the capabilities of our 3DEXPERIENCE platform in transforming the patient experience, especially in oncology. It covers all stages of a combination product&#8217;s lifecycle, from research and discovery to commercialization. The showcase includes features like drug product development, device design, clinical development, and manufacturing excellence.&nbsp;



The IASO showcase demonstrates how Dassault Systèmes’ solutions and the 3DEXPERIENCE platform can deliver to stakeholders challenged with bringing innovation to the market through combination products and transforming the patient experience. It demonstrates the potential to make a real impact in oncology and breast cancer care, instilling confidence in the future of treatment.











5. CDR-Life Collaboration



In March, Dassault Systèmes announced a collaboration with CDR-Life, a Swiss biotherapeutic company, to accelerate their development of next-generation highly tumor-selective immunotherapies using CDR-Life’s proprietary M-gager® platform. Highly tumor-selective immunotherapies are advanced cancer treatments that specifically target tumor cells using the immune system while minimizing harm to healthy tissues.



For a disease as common as breast cancer, next-gen immunotherapies could be huge. This is because they empower the immune system to eradicate malignant cells with unparalleled specificity. As a result, working together and improving the stability of antibody-based T-cell engagers can lead to high tumor cell killing potency, longer duration of effect, and lower risk of immune-relation adverse effects.&nbsp;



This collaboration will lead to more successful treatment of cancer and can make a huge impact for those undergoing breast cancer treatment and reducing negative effects or complicated treatment.



6. BIOVIA&#8217;s Generative Therapeutics Design 



As companies implement AI to improve research and treatment, BIOVIA&#8217;s Generative Therapeutics Design (GTD) is making significant strides in cancer treatment by using AI and machine learning to design and optimize new therapeutic compounds. This technology helps create drugs that meet specific target product profiles (TPPs), which are essential for effective cancer treatment.&nbsp;



The AI-driven approach has already led to the successful design and experimental validation of several compounds, some of which are now moving towards clinical testing.&nbsp;



This innovative method, driven by AI, is set to accelerate the development of high-quality, effective cancer therapies. As cancer therapy is crucial to treating breast cancer, the potential of AI to enhance treatments is a significant development in the field.



7. EORTC and Medidata Partnership



On September 11, Medidata, a Dassault Systèmes brand, and the European Organisation for Research and Treatment of Cancer (EORTC) announced a four-year extension of their partnership, a move that promises significant benefits for the field of oncology research. This partnership will allow EORTC to further increase patient access to oncology trials, make trial participation easier, and help to deliver new treatments to the market faster. EORTC leverages 13 Medidata solutions which enable their researchers to access and manage all clinical data in a single place.&nbsp;



This partnership will help EORTC researchers shorten study timelines and deliver safer oncology trials for patients. It also allows further research with MedidataAI, which could lead to significant improvements in cancer care.&nbsp;



These seven innovative approaches to cancer research and treatment have the potential to revolutionize the future of breast cancer care. By enhancing treatment options, expanding research opportunities, and improving the patient experience for those fighting breast cancer and millions of families worldwide, we are making significant strides toward beating this disease. While there are 4 million survivors today, we hope this number grows exponentially as we pave the way to a brighter, cancer-free future.



Read More 




A Decade of Transformation, Generations of Impact



Development of Innovative Drugs Using Materials Studio



Entering a new age of medical innovation



What is precision medicine?



How has AI improved your life today?





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      <title>
      <![CDATA[ A Decade of Transformation, Generations of Impact ]]>
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      <link>https://blog--3ds--com.apsulis.fr/industries/life-sciences-healthcare/10th-virtual-human-twin-experience-symposium/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/270485</guid>
      <pubDate>Thu, 10 Oct 2024 14:53:17 GMT</pubDate>
      <description>
      <![CDATA[ Announcing the 10th Virtual Human Twin Experience Symposium October 30-13 as part of Dassault Systemes’ 2024 Science Week in Paris. 
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      <![CDATA[ 
Ten years ago, we embarked on an ambitious journey to revolutionize healthcare through the power of virtual twin experiences. What began as a focused effort to model the human heart has blossomed into a global movement uniting over 1,000 experts across disciplines and continents.



Today, I am excited to announce the 10th annual Virtual Human Twin Experience Symposium, with this year’s theme &#8220;A Decade of Transformation, Generations of Impact.&#8221; A hybrid event, our symposium will take place from October 30-31 as part of Dassault Systemes’ 2024 Science Week events.



Register now to attend the Virtual Human Twin Experience Symposium online or in-person.



This landmark event is more than a milestone; it&#8217;s a celebration of a decade filled with innovation, collaboration, and significant strides toward a future where virtual human twins enhance every facet of healthcare. It will be an unprecedented convergence of multi-disciplinary minds dedicated to shaping the future of healthcare. It will be the largest gathering of its type on the planet, with 40 speakers, over 8 hours of networking, and interactive experiences in our technology playground. The symposium promises to be a dynamic forum for innovation and collaboration.



After a decade of effort, it is now clear that our unique approach, combined with your individual and collective works, have not only advanced the field of cardiac medicine but more importantly have laid the groundwork to consolidate humanity’s understanding of the entire human body. If you can only attend one conference this year, I suggest this is the one. It will no doubt change your work … and just might change your life.



Each year, we strive to craft an impactful agenda driven by the key themes that are most relevant. This year is no exception, so let me share my perspective.



Day 1



Updating a Decade of Innovation



The pre-symposium will begin with the Living Heart Colloquium, featuring pioneering work emerging from the Living Heart Model R&amp;D team. We&#8217;ll revisit the roots of the Living Heart Project and showcase our latest advancements, including the next-generation four-chamber heart model. This 2.5-hour session will be a rich testament to how far we&#8217;ve come and sets the stage for the groundbreaking discussions to follow.



Seating is limited and will be first-come-first served, so arrive early. But don’t worry, the recording will be available for future reference.



Following lunch, the Symposium formally kicks off with my opening remarks, in my capacity as the Master of Ceremonies throughout the event. If I do my job you will learn everything you need to navigate the two days.



Inspiring Keynotes and Visionary Insights



In the first session, we will be honored by thought leaders who have been instrumental in shaping the landscape of virtual healthcare. Key discussions will focus on:




The transformative impact of virtual twins across all aspects of healthcare.



Why virtual human twins are essential for advancing medical research and patient care.



Visionary insights on using the virtual world to create real human experiences for a more sustainable world with quality and accessible healthcare for all.




From Virtual Vision to Real



The kickoff visionary session will conclude with a talk that truly captures the spirit of the Living Heart Project: one focused on actual vision. The Living Eye Project team will provide an update on their progress in creating a virtual eye model capable of solving common causes of blindness or simply vision loss, demonstrating the expansion of virtual twin technology into ophthalmology and its potential to revolutionize eye care.



Our first networking break will provide the opportunity to explore the playground or one-on-one conversations with the speakers and other attendees.



Showcasing Success and Charting the Future



When we come back, in keeping with my philosophy that, “To know where you are going, you must know where you have been,” a unique highlight of this year’s symposium will be our panel discussion, &#8220;Journey of the Living Heart Project: Lessons Learned and Future Perspectives,&#8221; featuring speakers who have helped to shape the project from the start. This session will mark the 10-year journey, sharing insights and setting the course for the next decade.



After that time of reflection, we&#8217;ll get technical and delve into applications showcasing progress of virtual human twins across various organ systems. Through compelling case studies, we&#8217;ll demonstrate how virtual modeling is teaming with AI to improve the robustness and speed of virtual twin models, advance clinical research and expanding into new medical frontiers:




AI Methods for Standardizing Virtual Human Modeling



Virtual Twins in Clinical Research &amp; Medical Training



The COVID-19-inspired Living Lung Project




These presentations will mark the progress and remaining challenges of the technology, guiding our mission to model the full human body.



Reflections on a Decade of Innovation



The agenda concludes with my personal reflections on our 10-year journey and the milestones we achieved that have brought us to this pivotal point. In particular, I will bring the patient voice that has only been implicit in the past, into the foreground.



The day will end with a cocktail reception and the traditional exchange of ideas amongst the 300+ people who have kept them bottled up during the day.



Day 2



Global Collaboration for Generations of Impact



Day 2 will be our look to the future. As we do, international collaboration becomes ever more critical. We will kick off the day with a Vision for a Global Community of the Virtual Twin for Humans, emphasizing the commitment to coordinate a collective effort in advancing this technology.



Next will be the invited keynote, one you will not want to miss. This session will provide glimpse into the future of precision medicine. It will open your eyes to the impact virtual twins are already having on the most challenging cases in pediatric cardiology, including an emotional, &nbsp;firsthand account of the patient’s perspective. Already, the team is supporting multiple hospitals around the world. This is the future and, as you will see, it looks very bright.



Following the networking break in the technology playground, the next session will feature efforts emerging from the EU that will transform virtual twin technology from science to practice, highlighting collaborative projects and initiatives that illustrate global efforts in developing a comprehensive virtual human twin:




EDITH &#8211; European Virtual Human Twin Project, illustrating Europe&#8217;s collaborative efforts in developing a comprehensive virtual human twin.



A representative from the European Commission will share the EU&#8217;s broad vision for virtual human twins.



MediTwin Project ambitions will be illustrated by three sub-project leaders, showcasing specific projects and their contributions to the overarching goal of modeling the human body.




We’ll then enjoy a taste of Paris cuisine on a long lunch break providing plenty of time for discussions, side meetings and project planning. Many of tomorrow’s breakthroughs will no doubt find their start on the napkins sketches that emerge.



Bridging Innovation and Implementation



Now we get down to business. A hallmark of this community has been a central focus on regulatory acceptance of the technology from the start. We recognized that understanding the regulatory and ethical landscape is essential for realizing the potential of this technology for translating innovation to clinical practice. Notably, several recent landmark publications from the FDA have signaled a change is on the horizon.



As compared to previous years, the impact of generative AI will be a common thread throughout the discussion. Two panels will address these considerations, one from the researchers perspective and the other from the regulators perspective, featuring voices from the FDA, clinical practice, leading universities, and industry experts:




Clinical Applications: Regulatory &amp; Ethical Considerations



Regulatory Applications: ENRICHMENT In Silico Clinical Trial Playbook




On the heels of the publication of the in silico clinical trial regulatory foundation from the FDA, this session promises to deliver actionable content that will guide industry and clinical R&amp;D. We can all agree that defining pathways to lower the barriers to delivering innovations to the patients that need them will be welcome.



Educating the Next Generation



The future of healthcare will be determined by the next generation of scientists, engineers and healthcare professionals. To change current practice in industry we must first change current practice in education. Our next session, Educational Applications: Driving Interdisciplinary Collaborations takes this head-on and will feature detailed discussions aimed at inspiring and equipping future leaders with the tools and knowledge to continue this vital work.



Toward a Comprehensive Model of the Human Body



We&#8217;ll conclude the symposium with a session bookending the day with a talk that will help guide our aspiration of creating a fully integrated virtual human twin:




Future Vision: Towards a Comprehensive Model of the Human Body




This presentation will outline the steps needed to achieve a comprehensive model, setting the stage for generations of future impact.



&nbsp;I will close the afternoon session by reflecting on the project&#8217;s impact over the past decade, and more importantly, I will set the stage for Day 2 by summarizing the insights shared by the project community regarding our ambitions as we advance toward realizing the virtual human twin in the coming decade.



Join Us in Shaping the Future



This symposium is not just another event—it&#8217;s our call to action. An invitation to take part in a network that has created the fabric behind hundreds of activities that are redefining what&#8217;s possible in healthcare, As we celebrate a decade of transformation, we are also laying the foundation for generations of future impact.



I invite each of you—researchers, clinicians, educators, industry leaders, and policymakers—to join us at this landmark symposium. Together, we will push the boundaries of science and technology, turning visionary ideas into life-changing realities.



Let&#8217;s make the next decade even more transformative than the last.



Register for the Virtual Human Twin Experience Symposium today and be a part of this exciting journey toward a healthier future for all.
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      <title>
      <![CDATA[ Biotherapeutics: What Do We Make Next? ]]>
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      <link>https://blog--3ds--com.apsulis.fr/brands/biovia/biotherapeutics-what-do-we-make-next/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/270366</guid>
      <pubDate>Wed, 09 Oct 2024 18:39:22 GMT</pubDate>
      <description>
      <![CDATA[ For years, computational methods for small molecule drug design have offered numerous algorithms and methodologies to help generate new ideas and guide the iterative process of lead design and…
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      <![CDATA[ 
The question of what should we make next has challenged the world of drug discovery for decades. For years, computational methods for small molecule drug design have offered numerous algorithms and methodologies to help generate new ideas and guide the iterative process of lead design and optimization. For a particular drug target, these methods help to identify high-quality candidates that may eventually advance to clinical development with less experiments and time in the lab. From the early days of combinatorial chemistry and bioisosteric replacement to ligand-, fragment- and structure-based design, there have been many tools, leveraging numerous algorithms that suit your project constraints and design criteria.&nbsp; More recently, AI and machine learning algorithms have been popular in allowing researchers to rapidly explore more ideas in the chemical space and propose novel structures that a medicinal chemist may not have considered trying out when looking for new drugs.



Until recently, the computational design tools for biotherapeutics seemed to require more expertise, and to be more sparse and application-specific compared to the tools that exist for small molecule therapeutics. Of course, there are computational design algorithms available such as homology modeling, protein-protein docking and combinatorial scanning mutagenesis for general protein modeling and binder design, which are used in biotherapeutics lead discovery and optimization. For designing certain types of biological therapies, such as monoclonal antibodies, there are methods such as affinity maturation, humanization and immunogenicity prediction algorithms. However, to help answer directly what variation of our biotherapeutic we should make and test next, two recent AI methods, RFDiffusion and ProteinMPNN, have totally changed the nature of biotherapeutics discovery. These tools have the potential change the way we design biotherapeutics by helping to identify novel candidates that the computational and molecular biologists may not have considered.



Generating Proteins with AI: RFDiffusion and ProteinMPNN



RFDiffusion is a cutting-edge generative AI algorithm that can &#8220;diffuse&#8221; a collection of amino acids into a protein structure. The diffusion process starts with a random, noisy collection of atoms and, through a series of controlled refinements the algorithm makes adjustments to the structure to reduce the noise and move closer to a biologically realistic and functional protein structure. One common analogy for the diffusion process is developing a photo from a blurry image; iterative processing steps can take an initial grainy image and refine the detail and clarity to produce a final clear picture.



RFDiffusion can be utilized for a number of different biotherapeutic design challenges, such as engineering a biologic that can bind to a viral protein to neutralize the virus. With antibody structures or other protein-protein systems, RFDiffusion can be used to design new protein scaffolds that may improve binding affinities or enhance the stability of the binding partners. RFDiffusion can be also used to generate enzyme therapeutics that may break down a specific substrate to treat metabolic disorders. Beyond biotherapeutics, RFDiffusion has potential to help design proteins for industrial and biotechnological applications such as making enzymes that catalyze specific chemical reactions or proteins that suit very specific conditions including low or high temperature, pH, etc.



ProteinMPNN is a state-of-the-art neural network that can predict one or more probable protein sequences given a protein structure. This algorithm has been published with success in one of the most critical aspects of protein sequence design – generating sequences that fold into a stable protein/peptide with propensity to crystallize, facilitating the structure determination of these proteins. ProteinMPNN can be used in conjunction with RFDiffusion to generate new protein designs such as new enzymes or antibodies that can be further evaluated for desired properties such as stability, activity, affinity, and specificity. One of the strengths of ProteinMPNN is its ability to generate multiple sequence variants. This ability is invaluable as different variants provide more options to test and identify candidates with the best performance in terms of efficacy, safety, and manufacturability. Just as significantly, these variants also provide alternative leads when candidates encounter unforeseen issues in protein optimization, during protein expression, or ADMET challenges such as solubility and immunogenicity.



Together, RFDiffusion and ProteinMPNN significantly expand the biological space that can be explored in silico before biologists need to commit to expensive and time-consuming physical experimentation.  They have the potential to open up exciting avenues for more intelligent, model- and data-driven workflows driving innovation in biotherapeutic design.



Generating Proteins with RFDiffusion and ProteinMPNN in Discovery Studio Simulation



In BIOVIA Discovery Studio Simulation, a new Generate Protein Scaffolds protocol now provides easy access to RFDiffusion workflows, the first of which is motif scaffolding. Users can start with a specific part of an existing protein (the motif) and design a complete new protein scaffold that incorporates this motif. This approach allows precise control over the functional regions of the protein, as well as control over the protein scaffold design, via different model weights that suit particular proteins and complexes.



Figure 1- Discovery Studio Simulation users now have access to motif scaffolding with RFDiffusion.



A second new protocol, Generate Protein Sequences, allows users access to not only ProteinMPNN, where they can easily define sequence residues for design, but also to LigandMPNN and SolubleMPNN models. LigandMPNN is an extension to ProteinMPNN that is able to consider protein, small-molecule, nucleic acid, and metal ion ligands as additional context for designing sequences, with the potential to improve the chemical properties of the designed sequences. SolubleMPNN could be a better model to use when protein solubility is part of your design criteria. Users can determine the degree of sequence diversity and confidence desired, as part of the generative design, and have the ability to control the bias of particular amino acids.



Figure 2- Discovery Studio Simulation users can now generate new sequences using ProteinMPNN models and use AlphaFold/OpenFold to generate their 3D structures for further applications.&nbsp;







These two significant new enhancements are exciting additions to the biotherapeutics and protein design tools in Discovery Studio Simulation in the 3DEXPERIENCE® Cloud, which already includes AlphaFold and OpenFold AI structure prediction. They expand the ever-growing arsenal of powerful AI tools for molecular modelers and biologists to help answer the question of “what to make and test next” and accelerate the rational design of biologics. In combination with the existing physics-based methods in Discovery Studio Simulation, users can rapidly explore many more possibilities in silico before arriving at the final handful of candidates that are ready to become a successful commercial biotherapeutic or a biological to be used in agriculture, food and beverage, or environmental industries.



Nobel Prizes in Chemistry and Physics



This year’s Nobel Prizes in Chemistry and Physics celebrate how AI is pushing the boundaries of scientific research. John J. Hopfield and Geoffrey E. Hinton were awarded the Nobel Prize in Physics for their foundational discoveries in machine learning with artificial neural networks, while David Baker, Demis Hassabis, and John Jumper received the Nobel Prize in Chemistry for breakthroughs in computational protein design and protein structure prediction.



At BIOVIA, we are proud to be part of this AI revolution. By integrating AlphaFold2, OpenFold, RFDiffusion, and the ProteinMPNN family of models into our platform, we empower researchers with cutting-edge tools for protein structure prediction and protein design.



Watch the video to learn more how Discovery Studio Simulation now helps users generate novel biologics with RFDiffusion and LigandMPNN models.
















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      <![CDATA[ The microsurgery assistance robot upskilling surgeons and saving lives ]]>
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      <link>https://blog--3ds--com.apsulis.fr/industries/life-sciences-healthcare/the-microsurgery-assistance-robot-upskilling-surgeons-and-saving-lives/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/269583</guid>
      <pubDate>Wed, 25 Sep 2024 05:01:00 GMT</pubDate>
      <description>
      <![CDATA[ Japanese medical device manufacturer F.MED is using the 3DEXPERIENCE platform on the cloud to develop its microsurgery assistance robot system and get it certified for use in hospitals.
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      <![CDATA[ 
A medical robot that began life as a research project at Kyushu University in Japan is on track to help transform the way that surgeons perform delicate microsurgical procedures. Developed by medical device manufacturer F.MED, the microsurgery support robot was born from an idea to tackle the shortage of professionals trained in microsurgery and speed up the time it takes for surgeons to acquire new skills.



Microsurgery involves repairing or reconstructing tiny structures in the body such as blood vessels and nerves that are less than a few millimeters in diameter. Surgeons typically use specialized microscopes and instrumentation to perform these complex procedures. With F.MED’s advanced robot, they will be able to do the surgery remotely, operating a robotic arm from a large monitor, where they can view every detail up close. Rather than holding the fine instruments themselves, the surgeons guide the movement of the robot arm with controls for enhanced precision and dexterity. Not only does this approach overcome the human limitations of performing such delicate microsurgical procedures, particularly in terms of stabilizing hand tremors, it should also help to make microsurgery more widely available in hospitals.



“By creating an environment where microsurgery can be done in many hospitals using robots, we can improve the quality of life of patients and help them return to society,” said Keita Shimomura, CEO of F.MED.



F.MED’s microsurgery assistance robot system has already been successfully tested in collaboration with specialists at Kyushu University and the Japanese Society of Plastic Surgery. The company is now working on a third-generation prototype before it plans to launch the robot to market.



All product development is managed using the 3DEXPERIENCE platform on the cloud. Engineers use CATIA and SOLIDWORKS to design the robot’s different components and integrate them within the wider assembly – which involves more than 10,000 interconnected objects, as well as electrical wiring and piping.



Being able to design each part within the context of the larger 3D model helps to resolve integration issues earlier and speed up development time. And thanks to SIMULIA on the platform, the team appreciates being able to carry out all design and structural analysis processes in one place without having to switch between different applications.



As F.MED gears up to launch its robot on the global market, it values the ability to manage all product data centrally. This is crucial for maintaining full traceability of the change history of the 3D model, handling the approval process and getting the robot certified.



Discover more about why the 3DEXPERIENCE platform on the cloud is the perfect fit for F.MED and how MODSIM supports the company to bring its advanced microsurgery assistance robot system to life.
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      <![CDATA[ Meet the life sciences executive drawing inspiration from the past to create a more equitable future   ]]>
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      <link>https://blog--3ds--com.apsulis.fr/industries/life-sciences-healthcare/claire-biot-profile/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/269556</guid>
      <pubDate>Tue, 24 Sep 2024 05:14:00 GMT</pubDate>
      <description>
      <![CDATA[ Get to know Claire Biot, the vice president of the Life Sciences and Healthcare Industry here at Dassault Systèmes. 
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      <![CDATA[ 
In the era of Elon Musk, Steve Jobs and Stephen Hawking, names like Marie Curie and Louis Pasteur may not be top of mind when you think of innovators changing society. But, their impact remains immeasurable still today. Curie discovered polonium and radium and championed the use of radiation in medicine, earning a Nobel Prize in 1903 for her work. Pasteur, a French chemist, pharmacist, and microbiologist, is renowned for his research in vaccines and is credited with developing the first rabies vaccine in 1885.



A century ago, Curie and Pasteur could not have imagined the profound impact their work would have on the world—from the widespread use of X-rays in healthcare to the development of vaccines for treating and preventing diseases. They also could not have foreseen the inspiration they would provide to generations of innovators — scientists, medical professionals, and researchers — leading to even more groundbreaking work today. However, that inspiration is precisely what Claire Biot found in Curie and Pasteur.



“Pasteur was not only doing outstanding academic research but cared a lot about the applications of his findings to the real world, which is why we have the rabies vaccine,” said Biot. “This is what we would call translational medicine today, and it’s an important driver for me: not only doing exciting science, which is intellectually stimulating, but also improving the real world, which speaks to my heart.”



When it comes to Curie, it’s her resilience and passion for innovation that inspired Biot.



“Marie Curie had a challenging childhood, it was very difficult for her to get access to advanced education, but she never gave up, she found her way through,” Biot said. “She taught us courage and resilience. And she had a brilliant mind. Creativity and serendipity fueled her solid scientific foundation.”



Biot added: “When I’m thinking about these 2 great scientists, this also brings to my mind Katalin Kariko, very recent Nobel Laureate in 2023 for her work on mRNA vaccines within Academia but also at BioNTech. She comes with the same dedication to real world application of her academic research than Pasteur and with the same difficult childhood, courage and resilience than Marie Curie!”



As Dassault Systèmes&#8217; Vice President of the Life Sciences &amp; Healthcare Industry, Biot has a relentless curiosity for the unknown. Her career has focused on breaking down silos in healthcare and fostering collaboration to improve the lives of both patients and medical professionals.



The power of virtual twins – and collaboration



“Every minute counts” is how Biot felt when the pandemic hit in 2020, a year after she’d joined Dassault Systèmes, and it only fueled her passion for collaboration and curiosity to solve some of the world’s most pressing challenges. “For me, healthcare has always been this very stimulating sector where the purpose and need are obvious, and the way to get there is intellectually stimulating,” said Biot. “I would say that COVID made it even more obvious, because the world stopped until diagnostic tests and vaccines were made available.”



The impact of the coronavirus was felt around the world, and drivers in healthcare were vastly accelerated. From the move toward patient autonomy to access to life-saving vaccines and the shift toward clinical trials taking place in patients&#8217; homes or via telehealth – the industry changed significantly. Biot and Dassault Systèmes led many of the changes – from fully digitizing most vaccine trials in record time, to repurposing ventilators for multiple patients at once. For Biot, the commitment to supporting Dassault Systèmes customers during the pandemic was paramount.



Navigating the pandemic was something Biot was thrust into, but since joining Dassault Systèmes, her primary focus has been on the development of virtual twins—a scientifically accurate digital duplicate of a real-world object—to help advocate for the future of personalized healthcare.



&#8220;When I was in school, I had a teacher ask me if I cared about curing patients or if I cared more about understanding why they are sick,” said Biot. “They are interconnected, but I realized that I cared more about understanding why they are sick so others could help cure them. That’s part of the reason I’m at Dassault Systèmes.&#8221;



Biot’s interest in virtual twins blossomed when she joined Dassault Systèmes. &#8220;The first example I had of virtual twins was actually with car manufacturers,” Biot explained. “They use virtual twins to simulate crash scenarios. It saves time, it saves resources, and it allows them to explore more possibilities. And in healthcare, that’s what we want to achieve.”



Virtual twins in healthcare hold vast potential, and Dassault Systèmes is leading the innovation wave with The Living Heart Project, which Biot points to as a prime example of the power virtual twins can have in diagnosis and treatment.



The Living Heart Project aims to create realistic digital simulations of the human heart to improve cardiovascular care and research. The project leverages the 3DEXPERIENCE platform to model and simulate the heart&#8217;s behavior, enabling medical professionals to study its function and test treatments in a virtual environment.



For Biot, the collaborative focus of The Living Heart Project – which has been replicated for other organs, like the brain and eyes – is where the magic happens.



&#8220;Collaboration is key. Take cardiac disease, for example. It’s a disease, so there are doctors, surgeons, and cardiologists who are going to look after the patient. Radiologists are involved too, because they need to take MRI or CT scans to help evaluate the disease,” Biot explained. “But at the same time, your heart is a pump, and it’s also about fluid dynamics and electromechanics, and you need someone knowledgeable in math. Some doctors may need more support to fully understand the calculations. The point being, there are a lot of different disciplines that can now collaborate on a virtual twin to help find the right treatment.&#8221;



Biot&#8217;s projects are not just focused on assisting doctors and medical professionals with treatment options and diagnoses; they also aim to improve the patient experience. One such project is VORTHEx, a collaboration between the Hartmann Institute of Radiology and Dassault Systèmes, designed to help patients undergoing cancer treatments. The primary goal of the project is to help patients feel more comfortable with their treatment by providing a virtual experience that reconstructs all the technical and protocol components of the treatment in 3D.



“As soon as patients learn they will undergo radiotherapy, they can use a VR headset to explore the treatment room and the control room,” Biot said. “This immersive experience educates patients about the equipment and simulates the sensations of the treatment, including the robot&#8217;s movements and its loud sound.” &nbsp;



All of this innovation helps improve the patient experience, as many have anxiety when it comes to treatment.



“I may be an executive working in healthcare, but I’ve also been a patient,” Biot said. “Being a patient in a siloed environment like healthcare and without much knowledge of the technology being used can be intimidating.” &nbsp;



Fulfilling her curiosity in healthcare



Biot’s path in life can be defined by an innate curiosity to learn about the unknown. Her path to building a more equitable healthcare system didn’t start when she joined Dassault Systèmes; it began much earlier in her life, and really exploded when she attended École Polytechnique, a leading French institution that combines top-level research, academics, and innovation.



“As the name would suggest, the focus was on “Poly-techniques”, which means multiple disciplines,” she said. “They want you to explore different areas because they believe that a good brain is one that has the ability to stretch from one discipline to the next, and that real innovation takes place at the boundaries of those different disciplines.”



Biot didn’t have healthcare on her mind at first – when she began her studies, she wanted to become an astrophysicist. “I thought it was super cool to understand why the planet Earth arose. What happened at the Big Bang? What more could we find out? What other planets are out there?”



But the “aha moment” in her life – the realization of what she wanted to focus on – took place when she attended a life sciences class.



“I realized I wanted to build my career around innovation in life sciences and healthcare. It’s very exciting and fast-paced, from the discovery of DNA to genomics sequencing, to vaccines, and so much more,” said Biot. “The space changes so quickly. Life sciences today are not what they were 10 years ago, or what they will be 20 years from now, and I find that very stimulating from an intellectual standpoint. Plus it’s highly connected to applications to the daily life – the real world – finding new cures for patients. So it speaks to my heart.”



Giving back and looking ahead



Today, Biot is continuing to channel her passion and inspiring future generations, much like Curie and Pasteur did for her.Biot is an executive sponsor of Rise Up, a leadership development program at Dassault Systèmes.



Each year, about 100 Dassault Systèmes employees are selected to join Rise Up. They come from all over the globe and work in different functions within the organization. “It’s a 10-month program where they go through self-assessment, define their own leadership style, receive mentorship, and more. It’s incredibly exciting to help foster that each year,&#8221; Biot explained.



It&#8217;s not just the mentorship Biot enjoys, but the self-reflection too.



&#8220;I love the notion of giving back more and more and really enjoy guiding young leaders. You learn so much,” she said. “It seems like you look at yourself in the mirror when you listen to them to some extent. It gives you this notion that you’re not alone, and that you have a cohort of people who have the same goals.&#8221;



As for what’s next for Biot? It&#8217;s all about staying curious and learning.&nbsp;She currently sits on the board of Mauna Kea Technologies, which provides real-time visualization of cellular structures, at the microscopic scale, enabling physicians to make informed decisions across various medical specialties.



&#8220;It’s a very exciting project for me,” Biot said. “I support them with their go-to-market strategy and thinking through how to embed more AI systems into their functionality because they collect so many images. It gives me a different perspective on business models.&#8221;



Biot has dedicated her life’s work to improving healthcare, both for patients and medical staff. &#8220;If I had to boil my hope for the world into one sentence, it would be to achieve sustainable healthcare for all, with enjoyable working conditions for healthcare professionals.&#8221;



Biot isn’t the only one helping reshape the healthcare industry. Check out some other humans driving progress.
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      <![CDATA[ Intern Spotlight: An Investigation into New DFTB + Parameterisation Techniques ]]>
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      <link>https://blog--3ds--com.apsulis.fr/brands/biovia/intern-spotlight-an-investigation-into-new-dftb-parameterisation-techniques/</link>
      <guid>https://blog--3ds--com.apsulis.fr/guid/269284</guid>
      <pubDate>Wed, 18 Sep 2024 10:32:49 GMT</pubDate>
      <description>
      <![CDATA[ Watch this video to learn about Fred Kirk’s summer internship on the BIOVIA Quantum Mechanics team at Dassault Systemes.
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      <![CDATA[ 
Fred Kirk recently completed a two-month summer internship as a software engineering intern on the BIOVIA Quantum Mechanics team. During these two months he worked on research and development projects specifically focused on improving Density Functional Tight Binding (DFTB+) parameterization techniques.



Fred&#8217;s primary project centered on using sparse Gaussian process regression to fit empirical splines, enhancing the accuracy and efficiency of a DFT (Density Functional Theory) approximation. In addition to his technical contributions, Fred brought fresh perspectives and creative problem-solving skills to the team, making him an invaluable part of our intern program this year.




What started out as quite an intimidating project quickly became a genuine interest. With endless support from the many brilliant minds in the office, my task steadily progressed. By the end, I had an outcome that I was truly proud of.




Curious to learn more about Fred’s project and his experience working with the BIOVIA team? Watch the video below, where Fred shares insights into his research and the broader impact of his work on the industry.







If you are interested in exploring the challenges and rewards of a career in scientific software development at Dassault Systèmes, visit this website:



 Be the Next Game Changer | Careers &#8211; Dassault Systèmes (3ds.com)
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