PharmaTrace

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PharmaTrace offers an advanced predictive analysis solution that addresses the current crisis of pharmaceutical shortages, which significantly affects patient care and the ability of healthcare providers to deliver essential treatments. It serves as a critical asset for healthcare executives and administrators, providing them with actionable insights to make informed decisions on drug ordering and supply patterns. The tool’s descriptive and predictive capabilities allow users to negotiate better prices, stabilize supply fluctuations, and improve the overall ordering process. Implementing this technology can lead to substantial financial savings and operational improvements in managing drug supplies.

How PharmaTrace can help you:

Predict drug shortages and allow for proactive supply chain management.
Provide data-driven insights for better decision-making in drug procurement.
Enable negotiation of more favorable drug prices with suppliers.
Reduce supply variations and ensure a consistent supply of necessary medications.
Streamline and optimize the ordering process for pharmaceuticals.

Why choose PharmaTrace: Key features

Advanced analytics to manage and predict drug supply needs.
Tools to analyze and reduce spending within pharmaceutical management.
Potential to save significant costs, with hospitals saving almost $10 million annually.
Offers descriptive insights and predictive analytics for smarter purchasing strategies.

Who should choose PharmaTrace:

Healthcare organizations and hospitals seeking to reduce spending on pharmaceuticals.
Healthcare executives and administrators tasked with procurement and supply chain management.
Any organization experiencing challenges with drug shortages and supply fluctuations.
Providers who prioritize efficient, data-driven decision-making to support patient care.

About PharmaTrace

Website

https://www.pharmatrace.io/chronic-diseases-ai

Release Date

February 2024

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Drug Discovery Informatics Market Size Set to Surpass USD 6.8 Billion by 2031 with 10.9% CAGR as AI Revolutionizes Data Analysis in Drug Development | Transparency Market Research, Inc.

Wilmington, Delaware, United States, Transparency Market Research Inc. - , Nov. 04, 2024 (GLOBE NEWSWIRE) -- The global drug discovery informatics market (약물 발견 정보학 시장) is estimated to flourish at a CAGR of 10.9% from 2023 to 2031. Transparency Market Research projects that the overall sales revenue for drug discovery informatics is estimated to reach US$ 6.8 billion by the end of 2031. Quantum computing, although in its nascent stage, holds immense potential. Its ability to process complex computations exponentially faster than traditional computers could revolutionize molecular simulations and accelerate drug discovery timelines significantly. This technology's full integration into informatics systems remains a work in progress. The advent of blockchain technology showcases promise in ensuring data integrity, transparency, and security within drug discovery processes. Implementing blockchain could streamline data sharing among researchers, pharmaceutical companies, and regulatory bodies, enhancing collaboration while safeguarding sensitive information. Download Sample PDF of the Report: https://www.transparencymarketresearch.com/sample/sample.php?flag=S&rep_id=33713 The rise of interdisciplinary approaches, such as the integration of omics data (genomics, proteomics, metabolomics), with informatics tools promises a more holistic understanding of disease mechanisms. This fusion facilitates the identification of novel drug targets and the development of precise therapeutics tailored to individual patient profiles, advancing the era of personalized medicine. Drug Discovery Informatics Market: Competitive Landscape The drug discovery informatics market navigates a dynamic landscape shaped by technological advancements, competition, and evolving research needs. Key players like Schrödinger, Genedata, and Certara drive innovation, offering robust informatics platforms. Emerging contenders such as Collaborative Drug Discovery and OpenEye Scientific contribute disruptive solutions. Cloud-based platforms and AI-driven tools from companies like IBM and Dassault Systèmes further intensify competition, revolutionizing data analysis and accelerating drug discovery. Consolidation and strategic collaborations, witnessed among major players, emphasize expanding portfolios and global market presence. With increasing demand for efficient solutions, the competitive landscape continually evolves, fostering innovation to expedite drug development and meet healthcare challenges. Some prominent manufacturers are as follows: Charles River Laboratories International Inc.Thermo Fisher ScientificPerkinElmer Inc.Biogen Inc.Boehringer Ingelheim GmbHInsilico Medicine Inc.International Business Machines CorporationSchrödinger LLCCuria Global, Inc.Certara USA Key Findings of the Market Report Discovery informatics spearheads the drug discovery informatics market, driving innovation and efficiency in early-stage drug research and development.In-house informatics leads the drug discovery informatics market, empowering companies with tailored solutions and enhanced control over research processes.Molecular modeling emerges as the leading function segment in the drug discovery informatics market due to its predictive modeling capabilities. Drug Discovery Informatics Market Growth Drivers & Trends Increasing integration of artificial intelligence and machine learning algorithms accelerates data analysis, enhancing efficiency in drug discovery processes.Adoption of cloud-based informatics platforms facilitates collaboration, scalability, and real-time data accessibility, driving operational agility.Growing emphasis on personalized medicine fuels demand for informatics tools tailored to individual patient profiles and treatment modalities.Leveraging big data analytics transforms data into actionable insights, optimizing decision-making and shortening drug development timelines.Collaborations between pharmaceutical companies and technology providers foster innovation, enabling the development of novel informatics solutions to address evolving research needs. Unlock Growth Potential in Your Industry! Download PDF Brochure: https://www.transparencymarketresearch.com/sample/sample.php?flag=S&rep_id=33713 Global Drug Discovery Informatics Market: Regional Profile North America stands as a frontrunner, driven by technological advancements, robust infrastructure, and substantial investments in R&D. The presence of key players like Schrödinger and Certara fuels innovation, fostering a thriving market for informatics solutions.In Europe, particularly in countries like Germany and the UK, a strong emphasis on research excellence and collaborations between academia and industry defines the landscape. Companies like Genedata lead with sophisticated informatics platforms, leveraging Europe's scientific expertise to advance drug discovery.The Asia Pacific region emerges as a burgeoning hub, witnessing rapid growth due to increasing investments in healthcare infrastructure and rising demand for novel therapeutics. Countries like China and India foster innovation, propelled by government initiatives and a burgeoning biotech sector. While the market is evolving, companies such as WuXi AppTec and Infosys contribute significantly, amplifying the region's prominence in the global drug discovery informatics arena. Product Portfolio Biogen Inc. pioneers in neuroscience, offering innovative therapies for neurological and neurodegenerative diseases like multiple sclerosis and Alzheimer's. With a focus on groundbreaking research, Biogen aims to redefine treatment approaches, improving lives worldwide through advanced biotechnology and pharmaceutical solutions.Boehringer Ingelheim GmbH excels in healthcare, dedicated to enhancing patients' lives. Focused on pharmaceuticals, the company delivers high-quality products in various therapeutic areas like respiratory, cardiovascular, and oncology, emphasizing innovation and patient-centricity in its global reach.Insilico Medicine, Inc. specializes in AI-driven drug discovery and development. Combining deep learning algorithms and bioinformatics, Insilico accelerates drug development, striving to address unmet medical needs efficiently. By revolutionizing traditional approaches, it aims to bring novel therapeutics to market faster, transforming healthcare landscapes globally. Drug Discovery Informatics Market: Key Segments By Product Discovery InformaticsDevelopment Informatics By Mode In-house InformaticsOutsourced Informatics By Function Sequencing and Target Data AnalysisDockingLead Generation InformaticsIdentification and Validation InformaticsMolecular ModelingOthers By End User Pharmaceutical and Biotechnology CompaniesContract Research OrganizationsOthers By Region North AmericaLatin AmericaAsia PacificEuropeMiddle East & Africa Buy this Premium Research Report - https://www.transparencymarketresearch.com/checkout.php?rep_id=33713&ltype=S More Trending Reports by Transparency Market Research - Stem Cell Manufacturing Market - The global stem cell manufacturing market size (줄기세포 제조 시장) is anticipated to touch value of USD 26.6 Billion by 2033. 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Our experienced team of Analysts, Researchers, and Consultants use proprietary data sources and various tools & techniques to gather and analyses information. Our data repository is continuously updated and revised by a team of research experts, so that it always reflects the latest trends and information. With a broad research and analysis capability, Transparency Market Research employs rigorous primary and secondary research techniques in developing distinctive data sets and research material for business reports. Transparency Market Research Inc. CORPORATE HEADQUARTER DOWNTOWN, 1000 N. West Street, Suite 1200, Wilmington, Delaware 19801 USA Tel: +1-518-618-1030 USA - Canada Toll Free: 866-552-3453 Website: https://www.transparencymarketresearch.com Email: sales@transparencymarketresearch.com Follow Us: LinkedIn| Twitter| Blog | YouTube Market News and Data brought to you by Benzinga APIs

Benzinga

Mon, 4 Nov, 4:03 PM UTC

Dozee Harness AI for Personalised Patient Care

In many Indian hospitals, diligent monitoring of patients is largely focused on those in ICUs, leaving the ones in general wards vulnerable to health deterioration owing to lack of effective monitoring. Regional hospitals, often short of staff, struggle to adequately monitor patients, increasing the risk of unnoticed clinical decline. So, how can hospitals bridge this gap, by meticulously tracking vital signs such as heart rate, respiratory rate, and blood pressure even in general wards? Enter Dozee, India's first AI-based contactless remote patient monitoring system. Dozee recently published a study in Frontiers in Medical Technology detailing the impact of its AI-powered Early Warning System (EWS). Conducted at King George's Medical University (KGMU) in Lucknow, the study reveals how Dozee's technology can predict health deterioration up to 16 hours in advance, creating a critical window for healthcare providers to intervene and potentially save lives. With approximately 1.9 million patients in Indian hospitals' general wards relying on periodic manual checks, Dozee, the pioneer of the AI-driven technology in the country offers a transformative solution. The innovation promises to bring round-the-clock, affordable monitoring to 95% of hospital beds. The system is said to track metrics like alert sensitivity, specificity, response time, and healthcare activity. As per the study, it could save 21 lakh lives annually and reduce healthcare costs by INR 6,400 crore. Dr. Jean-Louis Teboul, world-renowned intensivist and critical care expert at France's Paris-Saclay University, while emphasising the global implications of the study, said, "What we have achieved in India has the potential to reshape healthcare globally. The challenges may differ, but the need for equitable, timely, and affordable care remains universal." The product is currently functional across 280 hospitals including Apollo, Ramaiah, Sparsh, and Medicover Hospital in Bengaluru. Across the world, technology is being leveraged in a similar yet distinct way. At the University of Florida Health Center in the US, doctors are using AI to monitor patients closely. The insights gained are encouraging the development of advanced algorithms designed to deliver real-time healthcare recommendations. For instance, a patient awaiting a lung transplant at the Health Center is placed in a high-tech ICU equipped with sensors and cameras that are monitoring crucial details like vital signs, facial expressions and movements. This is how AI continuously analyses over 350 gigabytes of patient data, detecting subtle cues like a patient's grimace, which might indicate distress. Funded by the US health department's National Institutes of Health, this project aims to ease the burden on healthcare providers by allowing AI to interpret actions and present data in an accessible format. Meanwhile, digital consultancy Netguru is changing the game with tools and resources that accelerate digital transformation. Their work empowers organisations to innovate, offering solutions that reimagine traditional processes. One such innovation is AI-assisted patient scheduling. With an aim to tackle the frustration of long wait times and disorganised appointment systems, these algorithms analyse patient data to find the ideal time slots for each individual. This technology also allows hospitals to optimise resources, creating a smoother and more responsive flow of care. The AI healthcare market, valued at USD 11 billion in 2021, is expected to grow to USD 187 billion by 2030, according to Statista. This increase signals a transformative shift in the way hospitals, medical providers, pharmaceutical companies, and other healthcare stakeholders operate. However, for organisations developing AI solutions, accessing diverse data sources remains a challenge and an expensive one. Speaking at Cypher 2024, India's Biggest AI Conference, Wadhwani AI CEO Shekar Sivasubramanian pointed out, "Collecting data like X-rays and MRIs needed for health-related AI innovations is costly. An X-ray copy costs INR 100, while taking a photograph of the X-ray is free." By capturing X-rays as photographs, innovators can cut down on expenses without compromising on the data's utility, making data collection an affordable resource for AI-driven healthcare solutions. As a nonprofit AI institute dedicated to social impact, Wadhwani AI collaborates closely with India's Central TB Division (CTD) to support the National Tuberculosis Elimination Programme's AI readiness. The institute uses AI to interpret LPA blood test results, which identify drug resistance in tuberculosis patients. Each LPA strip shows a unique pattern of dark and light bands, indicating specific regions of the TB bacterium's genome and the patient's drug-resistance profile. Implementing monitoring systems like Dozee-EWS in general wards has shown promising results, boosting patient safety through timely alerts that allow for early detection of health deterioration. This system also provides substantial time savings, enabling healthcare providers to optimise their workflow and spend more time on direct patient care. Even so, there is a need for advanced monitoring technologies to elevate clinical outcomes and efficiency, particularly in resource-constrained hospitals. Going forward, the study must focus on expanding these findings across varied healthcare settings, integrating additional vital signs and maximising the clinical value of these systems.

Analytics India Magazine

Tue, 29 Oct, 8:01 AM UTC

How A.I. Is Disrupting Pharma: The High-Stakes Race to Revolutionize Drug Discovery

As the FDA prepares to regulate A.I. in drug development, biotech startups and pharma giants are racing to harness its potential for faster, more precise drug discovery. For better or worse, A.I. in the pharmaceutical industry has reached a fever pitch -- a point of no return. Ever cautious, the Food and Drug Administration (FDA) has stepped in to regulate the burgeoning use of A.I. in drug discovery and development, vowing to "promote innovation and protect patient safety." But while the agency ponders policy, companies like Immunai have already charged ahead. The biotech company recently inked an $18 million deal with AstraZeneca, which aims to harness Immunai's A.I.-powered immune system model to optimize clinical trials. Startups like Insilico Medicine and Recursion Pharmaceuticals tout A.I. as their secret weapon in the hunt for new drugs, though skeptics whisper that their claims smack of A.I. washing. Sign Up For Our Daily Newsletter Sign Up Thank you for signing up! By clicking submit, you agree to our <a href="http://observermedia.com/terms">terms of service</a> and acknowledge we may use your information to send you emails, product samples, and promotions on this website and other properties. You can opt out anytime. See all of our newsletters "There is a spectrum of opinions on whether A.I. is going to solve problems on its own in drug development, versus whether it's not going to be relevant and feasible at all. I think I sit somewhere in the middle," Raviv Pryluk, co-founder and CEO of PhaseV, tells Observer. Pryluk, who previously served as SVP of operations and analytics at Immunai, struck out on his own in 2023 to launch a company focused on using A.I. and machine learning to streamline clinical trials. "It can bring a tremendous amount of value, but only when done carefully," he cautions. The FDA claims that since 1995, it has received over 300 submissions for drugs and biological products containing A.I. components. Yet the complexity -- and promise -- of A.I. has grown exponentially, says Dave Latshaw II, formerly the head of A.I. drug development at Johnson & Johnson. "It's a bit different these days, primarily through the scale and quality of data that we have access to," Latshaw tells Observer. In practice, A.I. has already reshaped clinical trials, from pairing patients with studies to targeting specific populations. But there's a catch: while narrowing down patient types might yield more accurate results, it also restricts the commercial appeal of a drug. "They want to find the exact type of person their drug will be useful for but, at the same time, doing that limits the scope of the total population it will be useful for and limits the total commercial impact," Pryluk says. Latshaw, who left Johnson & Johnson in 2020 to co-found BioPhy, sees an ongoing tension between technological advancement and commercial priorities. He launched BioPhy to address the inefficiencies he couldn't touch within J&J's bureaucratic walls. For him, the real promise of A.I. is in refining the entire drug discovery pipeline, including clinical trial optimization. Mergers and acquisitions also present a tantalizing opportunity. Pharmaceutical giants often rely on acquisitions to bolster their portfolios, and A.I. can sift through troves of pre-clinical and clinical data to spotlight the most promising drugs. "Machine learning and A.I. can help run over the pre-clinical and clinical data and all the available literature in order to make better decisions on whether to acquire this drug versus that drug," Pryluk notes. A.I. could also help diversify clinical studies, a longstanding challenge in the industry. Since 1993, the National Institutes of Health (NIH) has mandated the inclusion of women in research, but gender gaps -- particularly in preclinical animal studies -- remain a stubborn problem. Researchers could tailor studies using advanced methods like causal machine learning to ensure that findings apply across demographics. "What if we can use more sophisticated and granular methods like causal machine learning, for example, and ask causal questions on whether the results that we've seen on subsets of women patients actually can tell us something, and then we can expand the population?" Pryluk asks. "I think this type of method can bring more women, more people from minorities, to participate in clinical trials." Pryluk believes that precision medicine -- treating each patient as a unique case -- will be the industry's next seismic shift. Of course, there's still plenty of skepticism. Every A.I. application in pharma has to survive a gauntlet of risk-benefit analysis. Data sharing, cybersecurity, inscrutable algorithms and biases pose perennial challenges. For instance, genetic data -- the lifeblood of many A.I. models -- raises significant privacy concerns. Just look at the ongoing debates around platforms like 23andMe. Steven Aviv, CTO of Pentavere, sees data trust as paramount. Pentavere's DARWEN A.I. sifts through unstructured healthcare data to identify patients for specific treatments. "Pharmaceutical customers tell us that trust in the data for these applications is imperative," Aviv tells Observer. That's why Pentavere leans on Databricks' Data Intelligence Platform, he says. The secure framework is designed to handle massive data volumes without compromising compliance. Latshaw, too, emphasizes transparency in A.I. solutions. Interpretability is key, he says, noting that researchers risk misinterpreting A.I.'s outputs without it. "There's always a motivation for publishing the next even moderately state-of-the-art method for something, but sometimes you have to ask if the question you're addressing in the research is actually the right question," Latshaw cautions. "Just because you can predict the structure of the protein doesn't mean you can accurately predict what its function is going to be and how that impacts the disease." Pryluk is blunt about the stakes. "We are not in a game. We are talking about patients. People are dying and it needs to be practical," he says. Despite the inevitable setbacks, Latshaw is bullish on A.I.'s long-term impact. "There's going to be failures in the space as there always are when people push the envelope," he says. "But there's going to be an incredible amount of learning from that, and the byproduct is going to be higher productivity in discovering new molecules." As the FDA debates regulation, it's inviting input from industry stakeholders. Latshaw reminds us that the agency's goal is patient protection, not industry obstruction. "If they see the industry wanting to use new technology, it's their responsibility to make sure it's used in a way that doesn't impact that overall goal," he says. "The strength of that regulation will depend largely on how much of an impact the technology will have on the end recipient." In Latshaw's view, the payoff could be transformative: over the next 5-10 years, he expects a boom in viable drug candidates reaching clinical trials. "What that means for the companies is they're going to be able to bring a lot more drugs to market, but also get that many more drugs to the patients that need them," he says.

Observer

Tue, 12 Nov, 2:32 PM UTC

AI in clinical trials presents a data-driven model | Insights | Bloomberg Professional Services

This analysis is by Bloomberg Intelligence Industry Analyst Andrew Galler and Senior Associate Analyst Jack Maltby. It appeared first on the Bloomberg Terminal. Clinical trials represent a significant portion of the drug development cycle, accounting for over half of the total time and cost, but the overall process has remained little changed in recent decades. We think the integration of artificial intelligence should allow drug developers to take a more data-driven, quantitative approach to clinical trial conduct that can drive efficiencies without negatively affecting a program's probability of success. Expanding eligibility without sacrificing efficacy Aside from disease-specific criteria, most clinical trials have sets of inclusion-exclusion criteria that's largely driven by heuristics. These include lab measures, such as liver enzymes and white blood cell counts, that are rarely expected to significantly impact efficacy but rather exclude patients with separate, underlying conditions. We think by training AI on prior clinical trial datasets, drug developers can take a data-driven approach to enrollment criteria without sacrificing efficacy, potentially increasing the speed of enrollment, which is the longest part of most trials. In one such data-driven approach for non-small cell lung cancer trials, the model was able to double the eligible trial population with no estimated impact on efficacy. Indeed, some models even boosted predicted efficacy.

Bloomberg Business

Fri, 15 Nov, 8:09 PM UTC

Top Pharma Companies Using Generative AI

Generative AI is already a sub-branch of artificial intelligence involved in the invention of new varieties of such content, either visual, textual or even molecular. The changes right at the very end, by Generative AI, are too many and across a wide array of industries. The power of generative AI lies in deep delivery for quicker drug discovery, optimum clinical trials, and hence personalized medicine for better health outcomes. Still, further on what the shared article elucidates is how some of the world-leading pharma companies are using generative AI to attain such transformational benefits. Also, there are . Pharma businesses need to be aware of the main cybersecurity dangers in 2024 since they could cost them money and tarnish their reputations. Pharmaceutical organizations may be shielded from dangerous actors with the aid of cyber security solutions, and they can feel secure knowing that their data is safe. Innovation is evolving extremely rapidly throughout every sector. The with its operating base and stringent regulatory climate. The industry must find ways to keep these improvements up to the mark to guarantee that products are still suitable to produce. There have been a growing number of indications that the use of this AI branch in pharmaceutical research and development is being taken up by these top ultra-competitors in the niche. Increasingly, pharma and biotech organizations are embracing more effective, automated processes that integrate data-driven decisions and utilize predictive analytics tools. The next development of this approach to deal with cutting-edge data analytics fuses machine learning and AI. Thus, the and innovations. This paves way forto invest hugely in and machine learning with the hope that these technologies will make the drug discovery process faster and cheaper.

Analytics Insight

Mon, 15 Jul, 8:03 PM UTC

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