Category: Pharma

The impact of AI and data analytics in pharma research with futuristic elements.

The Impact of AI and Data Analytics in Pharma Research

November 11, 2025

The pharmaceutical industry is on the cusp of a seismic transformation. No longer simply buzzwords, AI and data analytics have emerged as mission-critical technologies powering the next wave of data-driven drug discovery and pharma R&D efficiency. From predicting disease progression to identifying molecular drug targets, AI transforms pharmaceutical research. Data analytics for pharma is rapidly evolving as companies adopt AI for pharmaceutical analytics to improve drug development and patient outcomes. AI analytics for pharma is transforming how companies optimize drug development and patient outcomes, making data analytics in the pharma industry more powerful and predictive than ever. Understanding the challenges AI foundation models pose for therapeutic development in biopharma is essential for addressing the broader challenges biotech companies face when adopting AI for drug discovery. Effective data strategies in AI-driven drug discovery can create a significant competitive advantage in pharma, especially when combined with the power of advanced analytics in pharma to accelerate decision-making and innovation. In 2025, the challenges in pharma investment in AI platforms are increasingly linked to the impact of reducing AI partnerships on pharma innovation, creating strategic uncertainty for companies aiming to accelerate drug discovery and development. In 2025, the growing challenges in pharma investment in AI platforms are raising concerns about the impact of reducing AI partnerships on pharma innovation. In this blog, we explore how AI and analytics are revolutionizing pharma, highlighting recent breakthroughs and tools, and providing expert perspectives on what’s next. Generative AI and pharma are increasingly shaping the breakthroughs highlighted in recent pharma AI news. Data analytics in pharma R&D is being rapidly transformed by AI for pharmaceutical analytics, enabling deeper insights and faster decision-making throughout the drug development lifecycle. AI in Pharma R&D: Redefining the Research Life Cycle Conventionally, R&D in Pharma has been a lengthy process with high investments. This paradigm changes with AI by: Accelerating compound screening: Machine learning models analyze large chemical libraries much faster than classical wet-lab approaches. By integrating AI customer insights for pharma with advanced data analytics for pharma, companies can better understand patient needs and optimize commercial strategies. Predictive modeling by AI can anticipate the behavior of molecules in biological systems, hence reducing the need for expensive clinical trials. Recent pharma data analytics news often highlights growing challenges in pharma AI data strategies for drug discovery, especially around data quality, integration, and regulatory compliance. Optimizing clinical trials: Advanced algorithms help in the design of trials, improved cohort recruitment, and early detection of side effects. Dr. Anjali Mehra, Chief Data Scientist at BioSynthAI, said, AI isn’t replacing scientists; it’s making scientists more efficient by transforming billions of data points into actionable insights in seconds. Data-Driven Drug Discovery: The Power of Predictive Analytics Data-driven drug discovery currently enables pharma companies to accelerate innovation: It helps analyze complex genomic data sets to find new targets for therapy. Real-world data: RWD involves mining data from patient health records, wearables, and even social data to spot patterns and risks. Digital twins: simulated models of human organs are tested with drugs, reducing the need to conduct trials on live ones. Visual Insight: Below is the comparative chart for 2025, depicting the reduction of time in varied R&D stages due to AI integration: R&D Phase Avg Time (Pre-AI) Avg Time (With AI Tools 2024) Time Reduction Target Identification 2 years 6 months 75% Compound Screening 1.5 years 4 months 78% Clinical Trial Design 1 year 3 months 70% New Pharma AI Tools in 2025 You Should Know Some of the most sophisticated pharma AI tools at the forefront of the race in 2025 include: DeepMind’s AlphaFold 3 Predicts protein structures with unprecedented accuracy. Insilico Medicine’s Pharma.AI It automates the complete drug discovery pipeline. BenchSci It uses machine learning to decode scientific experiments and suggest the best pathways. Atomwise Deep learning-based drug design based on structure. BioSymphony Indian startup that makes use of AI to synthesize generative compounds with higher efficacy. Ready to Leverage AI for Your Pharma Innovation? Partner with INT Global to develop and deploy AI and data analytics solutions tailored towards your pharma enterprise’s needs. Benefits of AI in Pharma: What Makes It a Game Changer? Shorter development cycles mean faster drugs to market. Lower R&D costs = Higher ROI. Better targeting leads directly to better treatment outcomes. Better reporting and safety profiling are a result of regulatory compliance. Challenges & Ethical Considerations Despite the benefits, challenges like Data Privacy and Patient Consent Algorithmic bias  Integration with legacy systems  Regulatory uncertainty Clearing these hurdles will necessitate sheer AI governance, multidisciplinary collaboration, and continuous regulatory evolution. By combining data analytics in pharma R&D with AI-driven customer insights, pharmaceutical companies can accelerate innovation while delivering more personalized healthcare solutions. Generative AI continues to reshape drug discovery, a trend highlighted in recent pharma AI news reporting breakthroughs in molecule design and clinical trial optimization. Future of AI in Pharma: What Lies Ahead? The future is promising and fast-evolving: AI-driven personalized medicine will become the norm. Quantum computing and AI will boost simulation speeds. Collaborative AI models across pharma giants will improve global research. Innovate Smarter with AI & Data-Driven Strategies The integration of AI and data analytics is no longer optional—it’s the cornerstone of modern pharma research. Whether you’re a biotech startup or an established pharmaceutical leader, the time to act is now. 👉 Transform your pharma R&D with INT Global. Let’s build AI solutions that save lives. 🔗 Get in Touch with Our Pharma AI Experts Frequently Asked Questions Q1. How is AI used in pharma R&D today? AI is used to analyze chemical compounds, design clinical trials, predict drug efficacy, and more. It helps speed up research, lower costs, and improve accuracy. Q2. What are some of the best pharma AI tools in 2024? Top tools include AlphaFold 3, Pharma.AI, Atomwise, and BenchSci. Q3. Is AI replacing human scientists in pharma? No. AI is an augmentative tool that enhances human decision-making, not a replacement. Q4. What challenges do pharma companies face with AI adoption? Major challenges include data

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Digital Twins and Virtual Simulations in Drug Development

April 22, 2024

Pharmaceutical research, particularly for drug development, is being revolutionized significantly through technological innovations like virtual simulations and digital twins. The basic definition for any digital twin can be a virtual structure that is modeled on the real-world counterpart with the same internal links and processes. There must also be a mechanism for continual data inputs and updates into the virtual system from the real one. Let us learn more about how it can transform drug development and delivery. Digital twin pharma solutions leverage digital twins in pharma to simulate drug development, optimize manufacturing processes, and improve patient-specific treatment outcomes. The India digital twin market, particularly in the pharma digital twin segment, is experiencing rapid growth as pharmaceutical companies adopt advanced simulation and real-time modeling technologies to optimize manufacturing, ensure regulatory compliance, and accelerate drug development. Twins Pharma is transforming manufacturing medicine by leveraging digital twins to improve efficiency, quality, and innovation. Pharma digital twins are increasingly being applied to drug formulation simulation in Brazil to reduce development time and improve manufacturing robustness. Novartis digital twin technology to advance research and development in the digital twin pharma ecosystem. Pharmacokinetics in computer applications is increasingly being enhanced by firms using digital twins in drug discovery. Companies using digital twins in pharmaceutical research are accelerating drug discovery, proving that digital twins in pharma can significantly enhance predictive modeling and clinical decision-making. India’s digital twin market is growing rapidly, and several companies are using digital twins in pharmaceutical research to improve drug development efficiency and patient-specific treatment simulations. Digital pharmacological twins leverage digital twin technology in pharma to simulate patient-specific drug responses with high precision. Drug discovery simulation and digital twins for GRC together enable organizations to model complex biological and governance systems, predict outcomes, and improve decision-making while reducing risk and development time. A digital twin simulation for pharma can significantly enhance drug development efficiency, as demonstrated by Pfizer’s advanced digital twin initiatives. By adopting digital twin simulation for pharma, companies can enhance R&D efficiency, while advanced drug formulation simulation in Brazil is accelerating the development of safer and more effective medicines. A pharmaceutical digital twin is becoming an essential tool as the use of digital twin technology in the pharmaceutical industry continues to grow. How Digital Twins Make For a Compelling Case Omics data has proliferated swiftly these days, containing valuable inputs on biomolecular activities in cells. Drug development and delivery involve several aspects, like the physiology of patients, drug attributes, and delivery systems. Conventional methods are often based on the trial-and-error framework, which may be lengthy and costly. The development of digital or virtual models that simulate drug behavioral attributes within the body and forecast safety and effectiveness can be the solution that pharmaceutical research has needed all this while. What firms are using digital twins in drug discovery? Companies using digital twins in drug discovery Digital twins have immense future potential since they can enhance both development and outcomes for patients. The global market for this technology is anyway forecasted to witness 58.9% of compound growth annually, touching $48.2 billion by the year 2026, as per MarketsandMarkets. Digital twins will lower the time-to-market for pharmaceutical companies while enhancing development alike. It will enable higher optimization of dosages and formations prior to clinical trials as well. Digital twins may be developed with data from several sources, including wearables, imaging technologies, electronic health records, and the like. They may help in simulating drug behavior in our bodies, enabling a better understanding of possible side effects. Pharmaceutical researchers can thus tweak the administration of drugs and their dosages likewise. It may enable patients to get more personalized and better treatments, while the industry saves resources and time on the identification of any issues/hurdles before they actually happen in the physical world. To cite a few instances, AstraZeneca has already tied up with Insilico Medicine for leveraging digital twin technologies across its drug candidate identification and drug development procedures. Pfizer has entered into a similar partnership with BioLingus for digital twins to optimization biologics formulation and delivery. Roche has itself made an investment in Physiomics for tapping digital twins while Novartis is using this technology in partnership with Lattice AI. Even Sanofi has made an investment in a startup using digital twin technology, namely Owkin, for faster drug development and discovery alike. What Digital Twins Promise Digital twins can be helpful in predicting drug distribution, absorption, metabolism, and even excretion. They may leverage patient-based data including physiology, genetics, lifestyle aspects, and more. Digital twins may help researchers find future issues and optimize their formulations accordingly. They may also help simulate drug delivery systems including nanocarriers or implantable devices, enabling optimization of drug dosages and release rates accordingly. The benefits are already being harnessed by several players in the sector. Ansys has been collaborating with researchers in Oklahoma State for creating a digital twin to boost drug delivery. They are enabling this via simulated lung models. The collaboration has unearthed the fact how 20% of several drugs achieved their desired targets. Hence, the digital twins are empowering them to redesign everything from drug composition attributes to particle sizes, leading to 90% growth in delivery-related efficiency. Drug delivery robots are another area where digital twins may come in handy. Researchers can simulate robot behavior in the body, enabling better function and design optimization. It may enhance precision in drug delivery while reducing human error-related risks alongside. In a nutshell, digital twins for drug development and delivery promise the following advantages for pharmaceutical research and manufacturing: What are the Challenges for Digital Twins? Some key challenges in the implantation of digital twins for drug delivery and development are the following: These are some of the potential challenges that pharmaceutical companies may encounter in terms of harnessing the benefits offered by digital twins. How VR and other Technologies Contribute Virtual reality (VR) can be a major game-changer for drug design, delivery, and development. VR can help in visualizing molecule interaction while also simulating molecular dynamics. It may help in understanding

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overall equipment efficiency

What are the common pitfalls of improving Overall Equipment Efficiency and how to avoid them?

February 22, 2024

Most enterprises, be it in biopharmaceuticals or other industries will naturally strive to enhance their overall equipment effectiveness/efficiency (OEE) in a bid to stay competitive in a fast-changing global environment. However, there are several OEE optimisation mistakes that are avoidable for Biopharma and other industrial players. Knowledge of the right OEE fundamentals is also necessary for proper implementation and optimisation alike. Here’s taking a closer look. OEE fundamentals at a glance Here are some key points on overall equipment effectiveness/efficiency (OEE) that are worth noting. Now that you have a basic grasp of the OEE fundamentals, it is time to look at how you can avoid common mistakes within the fold of your optimisation efforts. OEE Optimization- Mistakes to Avoid Let us look at a few common overall equipment effectiveness optimisation mistakes that Biopharma companies often end up making. As can be seen, while striving to enhance OEE is always desirable, it is important to set realistic benchmarks and look at surrounding issues that your system may not always help you detect. Avoiding these mistakes will undoubtedly be beneficial for Biopharma companies in the long run. FAQs What risks are associated with neglecting the impact of external factors, such as supply chain disruptions, on OEE in the Biopharma industry? There are several risks that are associated with neglecting the sheer impact of external factors on OEE like disruptions in the supply chain. Biopharma players can face risks like improper forecasting and risk management, inventory management woes, higher loss ratios, poor delivery of requirements, and even quality drops. How can a lack of standardized metrics and benchmarks hinder OEE improvement efforts in the Biopharma industry? The absence of standardised benchmarks and metrics will naturally bog down OEE improvement initiatives in the Biopharma industry. There will be no clarity on what to measure and fix with most companies calculating OEE in the wrong way as a result. What risks are associated with setting unrealistic OEE improvement goals in the Biopharma sector? There are associated risks for Biopharma companies setting OEE improvement goals that are unrealistic. These include insufficient visibility, decisions based on wrong or limited analytics, not accounting for actual issues in the calculation, and focusing on the wrong metrics at the outset. How does Equipment Utilisation impact time-to-market for biopharmaceutical products? Equipment utilisation has a huge impact on the time-to-market threshold for biopharmaceutical products. Proper utilisation and productivity will help combat unplanned downtime and sudden disruptions, while enabling smoother delivery as per targets without frequent changeovers or higher occurrences of rejects. Faster time-to-market is a necessity for staying competitive in the current scenario and suitably utilizing equipment is necessary for this purpose. How does a lack of scalability in OEE improvement solutions pose challenges for growing Biopharma companies? Non-scalable solutions for OEE improvement may pose various challenges for growing Biopharma entities. They may be bogged down by issues like improper risk management and higher loss ratios along with poor inventory management and real-time performance tracking.

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How Equipment Utilisation can improve Manufacturing Analytics & OEE in Biopharma

How Equipment Utilisation can improve Manufacturing Analytics & OEE in Biopharma?

January 29, 2024

Biopharma enterprises are steadily turning their attention towards manufacturing analytics and OEE best practices in sync with equipment utilisation and enhancing overall productivity. In an ideal scenario, a Biopharma entity will be functioning at 100% in the available time due to overall equipment effectiveness. This will be based on the validated performance of the machines in question along with 100% quality product outputs. How OEE Matters When it comes to ensuring overall equipment effectiveness (OEE), embracing smart manufacturing is the need of the hour for Biopharma players. With OEE in place, manufacturers can examine the effectiveness levels of their manufacturing operations and overall execution. Root causes behind several problems can be swiftly identified while prompt action can also be taken for improving processes. Machine sensors and subsequent manufacturing analytics can enable the collection of OEE information on a real-time basis, thereby offering higher efficiency in tracking process-linked downtime and making instant improvements in terms of machine performance and efficiency levels. OEE is the ratio of the utilisation of the pharmaceutical manufacturing facility in comparison to the total output that it was tailored for. It is a productivity measurement that helps calculate the efficiency of equipment that helps manufacture finished products through taking three fundamental aspects into account. These include the following:  • Availability- It indicates uptime, i.e. the total availability of time for the manufacturing facility. This does not account for sudden downtimes (unscheduled) and maintenance. Hence, this is the planned time of production that may be assigned for production. • Performance- It is the final output of the plant in the time that it is functioning as compared to the maximum possible output that it may have obtained at its validated speed. Losses can be in the form of slow cycles which indicate how equipment functions slower in comparison to the validated speed. Another issue is micro stops or when equipment stops working for a small duration. There are more sub-segments for all these aspects in a bid to zero in on the reasons behind downtimes. It helps in planning on improving these parameters or doing away with issues as much as possible. The calculation of OEE in Biopharma manufacturing is necessary for ensuring overall profitability. Manufacturing analytics and OEE data will help in decision-making across aspects like manufacturing infrastructure and operator training investments, lean manufacturing value, ROI calculation, profit gains from procedural and infrastructural improvements, lowering efforts for production performance follow-ups, investment comparisons via ROI data, digitisation of plants and obtaining OEE data quickly. It also helps in deciding how to scale up and secure production output and enable total control over machine performance and operations. Smart Manufacturing Technologies Worth Embracing When it comes to smart manufacturing for boosting equipment utilisation, along with machine analytics and insights, here are some aspects worth noting carefully. • Automation is indispensable for enabling better control. This also depends on data analytics. Data gathered at each development stage should be examined to keep quality and production controls in check. Suitable analytical frameworks are crucial for better automation and opens up newer productivity gains via AI, IoT and ML. • Purview of Data Analytics- Analytics has a crucial role to play across several Biopharma spheres including biosimilars, platform processes, process intensification, advanced therapies, personalised medicines, CDMOs, and of course, defining the artificial intelligence (AI) strategy. • Relying on Artificial Intelligence- Usage of AI in the Biopharma industry has now become mainstream. More companies are embracing automated AI-backed procedures that thrive on data-based analytics and insights for decision-making and make use of predictive analytics too. AI is being used to make manufacturing highly efficient while scaling up equipment utilisation levels alongside. How It Adds Up For Biopharma Companies Biopharma companies are already facing challenges in terms of staying more competitive in the current scenario. Several pharmaceutical players may not be able to reap the rewards of high-selling patent-safeguarded drugs with higher margins and sales volumes. Generic drugs are already taking up the lion’s share of prescriptions written globally (a whopping 85% in the US alone). As per The Economist, drugs valued up to $170 billion in yearly revenues are not be in the patent-protected category and will face competition from multiple generic versions. To stay competitive in this fast-changing industry landscape, Biopharma enterprises have to utilise equipment more effectively in a bid to scale up production and cut down on wastage and unnecessary costs/overheads alike. Compliance and quality challenges also have to be tackled more effectively in order to mitigate root causes/issues. Manufacturing analytics should be a firm point of focus in this case. ERP systems can gather raw data (material-based), while the MES (manufacturing execution system) will have details of execution of particular batch manufacturing processes. The key operating parameters will also be stored in specific data management tools while the laboratory management system will have product quality-based data. Incident management systems can gather adverse events and other occurrences alongside. All this data can be made silo-free and consolidated with easy viewing for a more holistic picture with proper manufacturing analytics and techniques like multivariate analysis and other tools. This will automatically contribute towards superior equipment utilisation as well. FAQs Are there specific best practices for implementing Equipment Utilisation strategies in Biopharma manufacturing? Some of the best practices that can be ensured include data consolidation for a holistic view and also focusing on manufacturing analytics for data gathering and insights regarding equipment productivity, condition, and other parameters. It will also help in coming up with predictive analytics-based strategies on maximising equipment utilisation. What key performance indicators (KPIs) should Biopharma companies monitor alongside OEE to gauge overall manufacturing efficiency? Some of the KPIs (key performance indicators) that Biopharma companies may track along with OEE to assess manufacturing efficiency include customer rejects, downtimes, lead time to customer, inventory turns, and equipment maintenance. How does a focus on Equipment Utilisation align with sustainability goals in Biopharma manufacturing? Focusing on equipment utilisation aligns perfectly with sustainability goals in Biopharma production. Operating equipment at its maximum capacity with minimal

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The Impact of AI and Data Analytics in Pharma Research

The Impact of AI and Data Analytics in Pharma Research

January 3, 2024

AI in pharma research has the potential to be a veritable game-changer for the entire sector. Data analytics in pharmaceuticals along with other innovations like data-driven research and AI/machine learning in pharma have made it comparatively easier to develop new drugs and tackle emerging diseases. Biopharma research remains expensive and lengthy although AI can play a vital role in enabling higher probabilities of success and boosting productivity.  How AI and Data Analytics are Indispensable for Pharma Research Here are a few ways in which AI in pharma research can be indispensable for the industry soon. AI in pharma research will enable the creation of feedback loops for further refining the predictive abilities and stability of AI algorithms. They will also inform experimental design functions accordingly. Through analytics and data science tools, pharma can capture the entire value of the present portfolio and create mechanisms and IP for driving research in the future. AI-drug discovery is already taking place with several companies building their pipelines. Biopharma entities are also developing top-down and executive strategies where AI-backed discovery can be a vital indicator and enabler of performance in the future. Automated image analysis or lead optimisation will be bolstered along with the collection of experimental data in a reusable manner, automated screening algorithms linking molecular descriptions with hits or desired outputs, blueprinting, enabling better testing and learning solutions for product delivery and designing new screening protocols. AI is already transforming the research space through the application of machine learning and data science to huge data sets, enabling swifter discoveries of newer molecules. It enables cross-referencing of published scientific literature with alternate sources of data (clinical trial data, conference abstracts, public databases, and unpublished data) to surface therapies that are promising. Medicines can be delivered in months at times instead of several years as a result. AI can also help lower clinical trial costs and cycle times while enhancing overall clinical development outcomes considerably. ML and AI are already being used for automatically generating study protocols while NLP (natural language processing) is being used to scale up manual tasks. AI algorithms can also ensure continual clinical data cleaning, coding, aggregation, management, and storage. Through automation and centralisation of intakes for adverse event reports backed by AI-backed technologies like NLP and OCR (optical character recognition), case documentation workloads are considerably reduced for expediting investigative processes. These are only a few of the widespread benefits that data analytics, AI, and ML can bring to the table for life sciences and pharmaceutical companies, especially in terms of research and development. FAQs What role will AI play in optimising clinical trials and research methodologies, and how is this expected to impact the pharmaceutical industry in 2024? AI will play a huge role in the optimisation of research methodologies and clinical trials in the future. This will have a positive impact on the pharmaceutical industry in 2024 and beyond since AI will optimise patient recruitment, predict the efficacy of treatments, automate data analysis, and boost safety tracking. It will also accelerate trial procedures while lowering costs and enhancing data quality. This will lead to more personalised and successful clinical trials. How will integrating AI and data analytics accelerate drug discovery processes within the pharmaceutical industry in the upcoming year? Drug discovery processes within the pharmaceutical industry can be accelerated in the upcoming year through the integration of data analytics and AI. This will be possible through the prediction of drug-target interactions, evaluation of the safety and efficacy of drugs that are repurposed, and identification of newer options for treatments. Potential biomarkers can be identified while researchers can easily analyse big data sets and design new molecules while forecasting the efficacy levels of potential drug candidates accordingly.

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Life Sciences & Healthcare News Wrap

Healthcare & Life Sciences News Wrap | Weekly Snippet | December

December 1, 2023

✅Fem-tech, AI in Mental Health on Investor Radar Next Year The fem-tech sector focuses on meeting women’s healthcare needs and is projected to reach $60 billion in the next four years. And, artificial intelligence is being integrated into digital mental health services to address the shortage of mental health professionals. https://www.financialexpress.com/business/industry-fem-tech-ai-in-mental-health-on-investor-radar-next-year-3318500/ ✅Rising Chronic Disease Rates Boost Global Digital Health Market The market size of digital health is expected to reach USD 651,924 Million. It can be seen to grow at a CAGR of 17.92% by 2030. https://finance.yahoo.com/news/digital-health-market-size-expected-174000895.html ✅Automation, Burnout Drive Healthcare Tech Integrations Healthcare facing disruption with virtual visits, demand for rapid access to patient data, and the rise of AI. Managing this data influx requires extensive collaboration, often involving external entities like labs and physicians, handling a multifaceted challenge for the industry. https://www.businesswireindia.com/healthcare-leaders-cite-automation-worker-burnout-as-top-drivers-of-technology-integrations-mgma-and-laserfiche-study-finds-87662.html ✅Pharma’s path to Net Zero: Targeting Scope 3 emissions With a target of net zero for 2050, pharmaceutical companies need to tackle Scope 3 emissions that generate the majority of their carbon footprint. Scope 3 emissions encompass raw material production, distribution, product use, and end-of-life disposal, making them a complex part of the industry’s carbon footprint. https://www.pharmaceutical-technology.com/features/pharmas-path-to-net-zero-targeting-scope-3-emissions/?cf-view #DigitalSuccess #newswrap #lifescience

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Top Data Analytics Trends in Pharma to Look Out For in 2024

Top Data Analytics Trends in Pharma to Look Out For in 2024

November 14, 2023

There are numerous data analytics trends in pharma that have disrupted the sector steadily in recent years. The industry itself is seeing a major integration of things like blockchain, Industry 4.0 techniques, and AI (artificial intelligence) among other game-changers.  Along with pharma data analysis and the usage of real-world data for collecting patient experiences, blockchain for secure transactions, and even managing patient records, there are several use cases worth considering in this regard. There is also a steady emphasis on offering augmented, virtual, and mixed-reality solutions throughout the industry spectrum. Here are a few pharmaceutical industry trends worth noting from a data analytics standpoint.  Pharma Analytics 2024 Trends  Here are some of the top data analytics trends in pharma that are worth noting.  These are some of the top data analytics trends in pharma that deserve to be noted in the current scenario. Data analysis and insights are completely changing the game for pharmaceutical companies in terms of enabling benefits throughout the entire spectrum.  FAQs How is the utilization of big data and advanced analytics improving drug discovery and development processes? Big data analytics works to reduce the costs and time of clinical trials. Through the usage of machine learning (ML) algorithms, pharmaceutical companies can easily identify sub-groups of patients which are more likely to respond to specific treatments. Researchers can also design more targeted and smaller trials that will succeed more. Data sets can be integrated with big data from diverse sources. Through this analysis, researchers can easily identify drug indicators, newer targets, and drug response biomarkers with lower risks.  What role does artificial intelligence play in optimizing pharmaceutical research and manufacturing operations? AI-based algorithms may optimize and analyze drug candidates by taking several aspects into account. These include pharmacokinetics, safety, and efficacy levels. It enables researchers to fine-tune specific therapeutic molecules to boost overall effectiveness while lowering side effects simultaneously. Predictive maintenance is also used through artificial intelligence (AI) throughout the manufacturing process. It may be applied to production data for enhancing maintenance planning and the prediction of failures.  What challenges and opportunities are associated with data analytics in pharmaceuticals, and how can companies stay competitive in this evolving landscape? Data engineering and analysis come with various challenges including the management of data from diverse sources while also sticking to stringent regulatory requirements and safeguarding the privacy levels of patients. There are varied challenges relating to data quality along with data silos, governance, and integration. These can be overcome through master data management platforms which ensure more reliable and accurate data that helps companies build their competitive advantages accordingly. 

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5 healthcare companies that have transformed digitally

5 Healthcare Companies that have Transformed Digitally

October 31, 2023

As the world seeks more efficient, patient-centric, and accessible healthcare solutions, companies have emerged as pioneers, pushing the boundaries of what’s possible in this critical sector. In this blog, we will explore 5 healthcare companies that have explored the power of digital transformation to reimagine the way we approach medical care, manage health data, and deliver life-saving treatments. From telemedicine breakthroughs to cutting-edge health data analytics, these organisations are at the forefront of a digital healthcare revolution that is changing lives, improving patient outcomes, and shaping the future of the industry. Join us as we delve into their inspiring stories and discover how they’re leading the charge towards a healthier and more connected world. Grail: Grail, a cancer detection company developed a blood test that can detect over 50 types of cancer early, when they are most treatable. The company’s test uses ML to analyse DNA fragments circulating in the blood, known as circulating tumour DNA (ctDNA). Grail’s test is still in the early stages of development, but it has the potential to transform cancer detection and treatment. Tempus: Tempus, a data-driven precision medicine company, helps clinicians make better decisions for their patients with cancer. The company’s platform collects and analyses genomic and clinical data from millions of patients to identify new patterns and insights. It then uses this data to develop personalised treatment plans for each patient. The platform is used by clinicians at over 800 cancer centres around the world. Meddo: Meddo, a digital healthcare platform, provides telemedicine services and other healthcare solutions to hospitals and clinics. The company’s platform offers a variety of features, including online doctor consultations, patient record management, and e-pharmacy services. Meddo’s platform is used by over 1,000 hospitals and clinics in India. Practo: Practo, a digital healthcare platform, connects patients with doctors and other healthcare providers. The company’s platform offers a variety of services, including online doctor consultations, appointment booking, and medical record management. Practo has over 150,000 doctors and 20,000 clinics on its platform, and it serves over 100 million patients each year. PharmEasy: PharmEasy, an online pharmacy delivers medicines and other healthcare products to customers’ doorsteps. The company’s platform offers a wide range of products, including prescription medicines, over-the-counter medications, and personal care products. PharmEasy also offers a variety of services, such as online doctor consultations and diagnostic testing. The digital transformation of the healthcare industry is rapidly changing the way that healthcare is delivered and consumed. The healthcare companies listed above and many more like them are leading the way in this transformation, and they are making a significant impact on the health and well-being of people in India and all across the world.

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Harnessing CRM to drive innovation, and strong customer experiences in Life Sciences

Harnessing CRM to drive innovation, and strong customer experiences in Life Sciences

October 5, 2023

CRM has been delineated by Gartner as specific systems or technologies which facilitate wider engagement with customers within the overall business strategy. These usually cover four key segments, namely marketing, sales, customer support/service, and digital commerce. For the life sciences industry, which encompasses the biotechnology, pharmaceutical, diagnostics, and medical device sectors, CRM is mostly helpful for field and sales systems, since they usually depend on on-the-ground engagement models.  These are systems which enable more support for automating sales and engagement activities, account management, monitoring experts and personnel, and also deliver educational content. They are also helpful for nurturing and finalizing HCP (healthcare provider) contracts or agreements with other healthcare organizations. CRM systems are also helpful for maintaining regulatory compliance. Hence, it is quite clear that life sciences entities are leveraging CRM (customer relationship management) software and platforms for various purposes. They are using cloud-based solutions for managing customer relationships better, along with managing their interactions with patients, physicians, and other stakeholders. Companies are steadily widening their customer base while boosting compliance and revenue streams. They are also gaining invaluable insights while foraying into newer markets and products.  How life sciences companies are benefiting from CRM Here are some of the ways in which life sciences companies are driving further innovation and revamping customer experiences with CRM solutions.  In the healthcare space too, CRM solutions enable better patient engagement and EHR (electronic health records) capabilities along with systems for managing claims better. At the same time, they offer more visibility into the entire journey throughout the healthcare system. Here are some more ways in which CRM can be a major boon for life sciences and healthcare companies.   Why and how CRM is a game-changer for life sciences companies CRM solutions play a vital role in streamlining and automating workflows. They can integrate multiple entities and systems while combating redundancies simultaneously. They are also helpful in enabling better customer engagement for life sciences companies, as illustrated above. CRM platforms are not just scalable but also flexible. They can evolve in sync with life sciences companies with changing customer and market needs. Cloud-based architecture also goes a long way towards enabling better integration with systems and applications, along with seamless customisation.  CRM platforms also enable better channels for communication, facilitating collaboration across multiple functions and teams alike. Building a centralized platform leads to better coordination and boosts sales teams while also adhering to compliance requirements. Gaps in communication are removed while CRM solutions also enhance customer relationships and overall satisfaction with quicker response timelines. The full visibility and control gained by life sciences entities help them achieve better outcomes while maintaining their competitive advantage and enabling higher innovation simultaneously. 

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Life Science & Pharma News Wrap | Weekly Snippet

Life Science & Pharma News Wrap | Weekly Snippet | August

September 1, 2023

✅ Lantern Pharma is boosting the clinical development of Immune Checkpoint Inhibitors with enhanced AI capabilities on the RADR® platform that includes the creation and testing of molecular signatures of ICI response. https://www.businesswire.com/news/home/20230828513669/en/Lantern-Pharma-Expands-AI-Capabilities-of-RADR%C2%AE-Platform-to-Accelerate-the-Clinical-Development-of-Immune-Checkpoint-Inhibitors ✅ Pharma-focused decision analysis software provider Aily Labs supercharges pharma analysis tools with a whopping $21M Series A funding that will go towards expanding Aily Labs’ U.S. presence and honing its use of generative AI. https://www.axios.com/pro/health-tech-deals/2023/08/24/aily-labs-raises-21m-series-a-pharma-intelligence-ai ✅ Trinity Life Sciences gets recognised in the prestigious Gartner Hype Cycle under sections like AI (Artificial Intelligence) in Commercial Operations,  Advanced Decision Support for Sales, and more. https://finance.yahoo.com/news/trinity-life-sciences-recognized-gartner-123700571.html ✅ Parexel and Partex have formed a powerful alliance to harness the potential of AI and Big Data, enhancing drug discovery & development for biopharma customers worldwide.

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