Background: In Brazil, studies that map electronic healthcare databases in order to assess their suitability for use in pharmacoepidemiologic research are lacking. We aimed to identify, catalogue, and characterize Brazilian data sources for Drug Utilization Research (DUR).Methods: The present study is part of the project entitled, “Publicly Available Data Sources for Drug Utilization Research in Latin American (LatAm) Countries.” A network of Brazilian health experts was assembled to map secondary administrative data from healthcare organizations that might provide information related to medication use. A multi-phase approach including internet search of institutional government websites, traditional bibliographic databases, and experts’ input was used for mapping the data sources. The reviewers searched, screened and selected the data sources independently; disagreements were resolved by consensus. Data sources were grouped into the following categories: 1) automated databases; 2) Electronic Medical Records (EMR); 3) national surveys or datasets; 4) adverse event reporting systems; and 5) others. Each data source was characterized by accessibility, geographic granularity, setting, type of data (aggregate or individual-level), and years of coverage. We also searched for publications related to each data source.Results: A total of 62 data sources were identified and screened; 38 met the eligibility criteria for inclusion and were fully characterized. We grouped 23 (60%) as automated databases, four (11%) as adverse event reporting systems, four (11%) as EMRs, three (8%) as national surveys or datasets, and four (11%) as other types. Eighteen (47%) were classified as publicly and conveniently accessible online; providing information at national level. Most of them offered more than 5 years of comprehensive data coverage, and presented data at both the individual and aggregated levels. No information about population coverage was found. Drug coding is not uniform; each data source has its own coding system, depending on the purpose of the data. At least one scientific publication was found for each publicly available data source.Conclusions: There are several types of data sources for DUR in Brazil, but a uniform system for drug classification and data quality evaluation does not exist. The extent of population covered by year is unknown. Our comprehensive and structured inventory reveals a need for full characterization of these data sources.

Through the Medicines and Resources Information Project (GIP), the National Health Care Institute has an independent, reliable and representative information system that contains data on the use of medicines and resources in the Netherlands. The Zorginstituut uses this data to map the developments in the use of medicines and aids and the associated costs.

Since 2004, the data files of the GIP have been made accessible via www.gipdatabank.nl . The GIP database is a unique public data source with detailed figures on the use of medicines and aids in the Netherlands over the past five years. Here you will find detailed information about the volume (number of dispensations and number of standard daily doses), the associated costs and the number of users of medicines and aids.

The data files of the GIP are based on the claim data for pharmaceutical care (including diet and food) and medical aids, from 19 health insurers (risk-bearing labels). This concerns medicines and medical aids that have been prescribed extramurally by the general practitioner or the specialist, and subsequently dispensed by a pharmacist, dispensing general practitioner or supplier of medical aids. This concerns medicines and medical aids that are reimbursed by the health insurer on the basis of the Health Insurance Act (basic insurance).

In 2023, research and development spending in the pharmaceutical industry exceeded 300 billion U.S. dollars globally. For comparison, R&D expenditures totaled 137 billion dollars in 2012. Pharmaceutical R&D includes all steps from the initial research of disease processes, the compound testing over pre-clinical, and all clinical trial stages. At a certain point in the process – mostly during the pre-clinical phase – a governmental authority is involved to overview, regulate, and ultimately approve the drug. In the United States, the Food and Drug Administration is the principal agency associated with processes. The pressure to innovate In comparison to other industries, pharmaceutical companies are more driven by the imperative to manufacture innovative products, and thus to spend significant amounts on research and development. This is largely due to the time-limited patent protection of drugs and the following threat of sales erosion through generic and biosimilar competition. Two major effects of patent expirations for the pharma industry are a specific high R&D intensity and a growing focus on specialty drugs to diversify their product portfolio. The latest trends For the last several years, major developments in pharmaceutical research and development have begun to change the R&D landscape. A growing number of drug manufacturers are outsourcing large parts of R&D, mostly to clinical research organizations (also contract research organizations), with the main aim to reduce costs. Another important development is the use of big data in clinical research. Thus, a predictive modeling is possible which uses clinical and molecular data to develop safer and more efficient drugs. Particularly, real-time or real-world evidence (RWE) is becoming a greater interest. This makes cooperation with technology companies necessary and includes data gathered from various sources, even that of social media.

Objectives: To develop and pilot a tool to measure and improve pharmaceutical companies' clinical trial data sharing policies and practices. Design: Cross sectional descriptive analysis. Setting: Large pharmaceutical companies with novel drugs approved by the US Food and Drug Administration in 2015. Data sources: Data sharing measures were adapted from 10 prominent data sharing guidelines from expert bodies and refined through a multi-stakeholder deliberative process engaging patients, industry, academics, regulators, and others. Data sharing practices and policies were assessed using data from ClinicalTrials.gov, Drugs@FDA, corporate websites, data sharing platforms and registries (eg, the Yale Open Data Access (YODA) Project and Clinical Study Data Request (CSDR)), and personal communication with drug companies. Main outcome measures: Company level, multicomponent measure of accessibility of participant level clinical trial data (eg, analysis ready dataset and metadata); drug and trial level measures of registration, results reporting, and publication; company level overall transparency rankings; and feasibility of the measures and ranking tool to improve company data sharing policies and practices. Results: Only 25% of large pharmaceutical companies fully met the data sharing measure. The median company data sharing score was 63% (interquartile range 58-85%). Given feedback and a chance to improve their policies to meet this measure, three companies made amendments, raising the percentage of companies in full compliance to 33% and the median company data sharing score to 80% (73-100%). The most common reasons companies did not initially satisfy the data sharing measure were failure to share data by the specified deadline (75%) and failure to report the number and outcome of their data requests. Across new drug applications, a median of 100% (interquartile range 91-100%) of trials in patients were registered, 65% (36-96%) reported results, 45% (30-84%) were published, and 95% (69-100%) were publicly available in some form by six months after FDA drug approval. When examining results on the drug level, less than half (42%) of reviewed drugs had results for all their new drug applications trials in patients publicly available in some form by six months after FDA approval. Conclusions: It was feasible to develop a tool to measure data sharing policies and practices among large companies and have an impact in improving company practices. Among large companies, 25% made participant level trial data accessible to external investigators for new drug approvals in accordance with the current study's measures; this proportion improved to 33% after applying the ranking tool. Other measures of trial transparency were higher. Some companies, however, have substantial room for improvement on transparency and data sharing of clinical trials.

Success.ai’s Pharma Data for the Global Pharmaceutical Industry provides a robust dataset tailored for businesses looking to connect with pharmaceutical companies, decision-makers, and key stakeholders worldwide. Covering pharmaceutical manufacturers, research organizations, biotech firms, and distributors, this dataset offers verified SIC codes, firmographic details, and contact information for executives and operational leads.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures your outreach, market research, and business development strategies are driven by reliable, continuously updated, and AI-validated data. Supported by our Best Price Guarantee, this solution is essential for navigating the competitive global pharmaceutical landscape.

Why Choose Success.ai’s Pharma Data?

1. Verified Contact Data for Precision Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of pharmaceutical executives, R&D leads, compliance officers, and procurement managers.
   • AI-driven validation ensures 99% accuracy, optimizing your campaigns and improving communication efficiency.

2. Comprehensive Coverage of the Global Pharmaceutical Sector

   • Includes profiles of pharmaceutical companies, biotech firms, contract manufacturing organizations (CMOs), and distributors across North America, Europe, Asia, and other major markets.
   • Gain insights into regional pharmaceutical trends, product pipelines, and market dynamics unique to global markets.

3. Continuously Updated Datasets

   • Real-time updates reflect leadership changes, mergers, product launches, and regulatory compliance shifts.
   • Stay aligned with the fast-paced pharmaceutical industry to capitalize on emerging opportunities and maintain relevance.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible use of data and compliance with legal standards.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with decision-makers, R&D specialists, and operational leaders in the pharmaceutical industry worldwide.
• 30M Company Profiles: Access detailed firmographic data, including company sizes, revenue ranges, and geographic footprints.
• Verified SIC Codes: Understand industry classifications and product specializations to refine your targeting strategies.
• Leadership Contact Details: Connect with CEOs, COOs, medical directors, and regulatory managers influencing pharmaceutical operations.

Key Features of the Dataset:

1. Decision-Maker Profiles in Pharmaceuticals

   • Identify and engage with professionals overseeing R&D, clinical trials, supply chains, and regulatory compliance.
   • Target leaders responsible for drug development, vendor selection, and market entry strategies.

2. Advanced Filters for Precision Targeting

   • Filter companies by industry segment (biotech, generic pharmaceuticals, vaccines), geographic location, or revenue size.
   • Tailor campaigns to align with specific needs such as drug pipeline acceleration, production scaling, or market expansion.

3. SIC Codes and Firmographic Insights

   • Access verified SIC codes and detailed company profiles to understand market focus, operational scale, and specialization areas.
   • Use firmographic data to prioritize high-value targets and align product offerings with market demands.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes with pharmaceutical stakeholders.

Strategic Use Cases:

1. Sales and Lead Generation

   • Offer technology solutions, research tools, or contract services to pharmaceutical manufacturers, R&D facilities, and distribution networks.
   • Build relationships with procurement teams and compliance officers responsible for vendor approvals and operational excellence.

2. Market Research and Product Development

   • Analyze global pharmaceutical trends, drug approval patterns, and regulatory frameworks to guide product innovation and market entry strategies.
   • Identify high-growth regions and emerging therapeutic areas to focus your resources effectively.

3. Partnership and Supply Chain Development

   • Connect with pharmaceutical companies seeking contract manufacturing, raw material sourcing, or distribution partnerships.
   • Foster alliances that streamline production, ensure quality, and accelerate time-to-market.

4. Regulatory Compliance and Risk Mitigation

   • Engage with regulatory officers and compliance managers overseeing adherence to local and international pharmaceutical standards.
   • Present solutions for documentation, reporting, and risk management to ensure compliance and operational efficiency.

Why Choose Success.ai?

1. Best Price Guarantee
   ...

Background: In Brazil, studies that map electronic healthcare databases in order to assess their suitability for use in pharmacoepidemiologic research are lacking. We aimed to identify, catalogue, and characterize Brazilian data sources for Drug Utilization Research (DUR).Methods: The present study is part of the project entitled, “Publicly Available Data Sources for Drug Utilization Research in Latin American (LatAm) Countries.” A network of Brazilian health experts was assembled to map secondary administrative data from healthcare organizations that might provide information related to medication use. A multi-phase approach including internet search of institutional government websites, traditional bibliographic databases, and experts’ input was used for mapping the data sources. The reviewers searched, screened and selected the data sources independently; disagreements were resolved by consensus. Data sources were grouped into the following categories: 1) automated databases; 2) Electronic Medical Records (EMR); 3) national surveys or datasets; 4) adverse event reporting systems; and 5) others. Each data source was characterized by accessibility, geographic granularity, setting, type of data (aggregate or individual-level), and years of coverage. We also searched for publications related to each data source.Results: A total of 62 data sources were identified and screened; 38 met the eligibility criteria for inclusion and were fully characterized. We grouped 23 (60%) as automated databases, four (11%) as adverse event reporting systems, four (11%) as EMRs, three (8%) as national surveys or datasets, and four (11%) as other types. Eighteen (47%) were classified as publicly and conveniently accessible online; providing information at national level. Most of them offered more than 5 years of comprehensive data coverage, and presented data at both the individual and aggregated levels. No information about population coverage was found. Drug coding is not uniform; each data source has its own coding system, depending on the purpose of the data. At least one scientific publication was found for each publicly available data source.Conclusions: There are several types of data sources for DUR in Brazil, but a uniform system for drug classification and data quality evaluation does not exist. The extent of population covered by year is unknown. Our comprehensive and structured inventory reveals a need for full characterization of these data sources.

Healthcare Provider/Professional Data contains the data of individual providers and facilities, including their information about opening hours, insurance networks, specialties, NPI, etcetera. In addition to discovering data sources, merging data, running analytics, and receiving decision-making guidance, the bigger problem is responding to marketplace business and patient care demands in a timely manner. Pharmacy contains the location details of pharmacies and has attributes such as addresses, opening hours, facilities, etcetera.

A. Usecase/Applications possible with the data:

a. Provider network data systems (PNDS) - The primary goal of the PNDS is to collect data needed to evaluate provider networks, which include physicians, hospitals, labs, home health agencies, durable medical equipment providers, and so on, for all types of Health Insurers. Such information can be used to:

b. Find health care providers in my network - Use this directory to easily find other providers in my network.

c. Comprehensive services assessment - Determine whether insurers have contracted with a sufficient number of primary care practitioners, clinical specialists, and service facilities (hospitals, labs, etc.) within the insurer's service area.

d. Capacity analysis - Calculate the potential capacity of a managed care plan’s primary care providers.

e. Locate pharmacies in your local areas.

f. Support Employee Benefits Decisions - Having access to network data can help you make better decisions about which providers to use for Employee Medical Benefits.

g. Know about the facilities available across different pharmacies.

How does it work?

• Analyze sample data
• Customize parameters to suit your needs
• Add to your projects
• Contact support for further customization

Knowledge Management in Pharmaceutical Market size was valued at USD 1.5 Billion in 2023 and is projected to reach USD 2.63 Billion by 2030, growing at a CAGR of 8.00% during the forecast period 2024-2031.

Global Knowledge Management in Pharmaceutical Market Drivers

The market drivers for the Knowledge Management in Pharmaceutical Market can be influenced by various factors. These may include:

• Regulatory Compliance: Businesses in the pharmaceutical industry work in a highly regulated setting. Clinical trial data, medication development records, and regulatory submissions are just a few examples of the vital material that effective knowledge management systems organize and manage to help guarantee regulatory compliance.
• Rapid Technological Advancements: With the introduction of artificial intelligence (AI), big data analytics, and machine learning, the pharmaceutical sector is constantly changing. Knowledge management systems make it easier to integrate and use these technologies to boost innovation, expedite workflows, and enhance decision-making.
• Growing Complexity of Drug Development: The processes involved in developing new drugs are getting more intricate and expensive. Pharmaceutical businesses can expedite drug discovery, development, and commercialization by managing and utilizing large amounts of scientific and clinical data through the use of knowledge management.
• Globalization and Collaboration: When working on drug development initiatives, pharmaceutical corporations frequently collaborate with research groups, universities, and other business partners. Knowledge management systems make collaborative research more efficient by offering a central location for knowledge exchange and access, encouraging creativity, and boosting productivity.
• Growing Significance of Personalized Medicine: Pharmaceutical companies must gather, examine, and handle vast amounts of patient data, genetic data, and clinical results as a result of the shift to personalized medicine. Knowledge management systems facilitate the amalgamation of heterogeneous data sources to bolster personalized medical endeavors, including the identification of biomarkers, patient classification, and the creation of tailored therapeutic approaches. The pharmaceutical sector is characterized by intense competition and cost pressures, as companies strive to get novel drugs to the market in a timely and economical manner. From discovery to commercialization, knowledge management supports pharmaceutical companies in maximizing resources, reducing risks, and enhancing operational effectiveness.
• Risk management and patient safety: It is crucial to guarantee the security and effectiveness of pharmaceutical products. Pharmaceutical businesses use knowledge management systems to monitor post-market surveillance data to maintain patient safety and to identify, assess, and minimize risks related to medication research and manufacturing processes.
• Demand for Evidence-Based Decision Making: In the areas of drug development, regulatory approval, and patient care, stakeholders in the healthcare industry, such as payers, regulators, healthcare providers, and patients, are calling for more and more evidence-based decision-making. Pharmaceutical businesses can produce, evaluate, and share scientific evidence to help educated decisions and enhance patient outcomes by using knowledge management systems.

AI for Pharma and Biotech Market Outlook

The AI for Pharma and Biotech market is experiencing a transformative phase with an anticipated market size of approximately USD 7.5 billion in 2023, projected to soar to USD 25 billion by 2032, exhibiting a robust compound annual growth rate (CAGR) of 14.5%. This remarkable growth can be attributed to several factors, including the increasing need for precision medicine, a surge in healthcare data, and the continuous innovation in AI algorithms and technologies. The integration of artificial intelligence in the pharmaceutical and biotech sectors is revolutionizing the way new drugs are discovered, clinical trials are conducted, and personalized treatment plans are developed, thus driving the market forward.

One of the key growth factors for the AI for Pharma and Biotech market is the rising demand for precision medicine. Precision medicine, which involves tailoring medical treatments to the individual characteristics of each patient, benefits tremendously from AI technologies. By analyzing vast datasets from various sources such as genomic data, electronic health records, and clinical trial data, AI can identify patterns and correlations that are not apparent to human researchers. This enables the development of more effective treatment protocols and drug formulations, leading to improved patient outcomes and reduced healthcare costs. Furthermore, the increasing prevalence of chronic diseases and the need for targeted therapies are accelerating the adoption of AI in the biotech and pharmaceutical sectors.

Another significant driver of market growth is the exponential increase in healthcare data. With the digitization of healthcare systems and the advent of wearable technology, there is an unprecedented amount of data being generated daily. AI technologies, particularly machine learning and data analytics, are essential tools for making sense of this data deluge. These technologies can process and analyze data at a speed and accuracy far beyond human capabilities, providing valuable insights that drive innovations in drug discovery, diagnostics, and patient care. The ability to predict disease outbreaks, optimize clinical trial processes, and streamline drug manufacturing operations are just a few examples of how AI is enhancing the efficiency and effectiveness of the pharma and biotech industries.

In addition to data-driven innovation, the continuous advancement of AI algorithms and technologies also plays a critical role in market growth. Machine learning and deep learning algorithms are becoming increasingly sophisticated, enabling more accurate predictions and faster processing of complex datasets. This technological evolution is supported by the growing investments in AI research and development from both public and private sectors. As AI technologies become more advanced and accessible, their integration into pharmaceutical and biotech processes becomes more seamless, further accelerating market expansion. Companies are increasingly recognizing the potential of AI to not only improve existing processes but also to create new business opportunities and revenue streams.

From a regional perspective, North America currently holds the largest share of the AI for Pharma and Biotech market, driven by the presence of major pharmaceutical companies, a strong technological infrastructure, and significant investments in research and development. Europe follows closely, with increasing government initiatives supporting AI integration in healthcare and a robust biotech industry. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by the rapid pace of digital transformation, increasing healthcare expenditure, and expanding biotech sector. Meanwhile, Latin America and the Middle East & Africa are emerging markets with growing potential, as governments and private entities in these regions increasingly focus on digital healthcare solutions.

Component Analysis

The AI for Pharma and Biotech market is broadly segmented by components, comprising software, hardware, and services. Within this triad, software emerges as a pivotal element, as it forms the backbone of AI applications in drug discovery, clinical trials, and patient management. The software segment is experiencing significant growth due to the increasing adoption of AI platforms and solutions in the pharmaceutical and biotech industries. Advanced algorithms, data analytics tools, and machine learning frameworks are being employed to analyze complex biological data, streamline research processes, and enhance decision-making capabilities. The demand for customized

Pharma Knowledge Management Software Market size was valued at USD 1.23 Billion in 2023 and is projected to reach USD 7.5 Billion by 2031, growing at a CAGR of 15.24% during the forecast period 2024-2031.

Regulatory Compliance: The pharmaceutical industry is highly regulated. Knowledge management software helps organizations comply with FDA, EMA, and other regulatory requirements by ensuring standardized processes and providing comprehensive audit trails.
Data Integration and Accessibility: The need for seamless integration of vast amounts of data from various sources (clinical trials, research data, market analytics) drives the demand for robust knowledge management solutions that ensure data is accessible and easily retrievable.
Innovation and R&D Efficiency: Pharmaceutical companies rely on cutting-edge research and development. Knowledge management software enhances R&D efficiency by facilitating better collaboration, information sharing, and knowledge retention, enabling quicker innovation cycles.
Cost Reduction: By improving the organization, storage, and retrieval of knowledge, these systems help in reducing operational costs, eliminating redundant efforts, and speeding up decision-making processes.
Enhanced Collaboration: Modern knowledge management systems support better collaboration among teams, departments, and even across organizations by providing platforms for sharing insights, research findings, and developments.
Big Data and AI Integration: The growing role of big data and artificial intelligence in drug discovery and development necessitates efficient knowledge management systems to handle and analyze large datasets effectively.
Globalization of the Pharma Industry: The global scope of pharmaceutical operations requires robust knowledge management systems to handle information dissemination across different geographic locations, ensuring standard practices and knowledge sharing worldwide.
Patient-Centric Care: Trends towards personalized medicine and patient-centric care require comprehensive data management systems to manage patient data, research findings, and treatment protocols efficiently.

During a survey, more than 70 percent of responding consumers who used the internet for medical research stated they began their online research for medical information on search engines such as Google or Bing. Medical information websites, such as WebMD or Healthline, ranked second, mentioned by roughly half of respondents.

The global pharmaceutical market has experienced significant growth in recent years. For 2023, the total global pharmaceutical market was estimated at around 1.6 trillion U.S. dollars. This is an increase of over 100 billion dollars compared to 2022.

Global pharmaceutical markets

Globally, the United States is by far the leading market for pharmaceuticals, followed by other developed countries and emerging markets. Emerging markets can include middle and low-income countries such as Brazil, India, Russia, Colombia and Egypt, to name a few. Despite increasing revenues globally, the Latin American region accounts for the lowest share of the global pharmaceutical market’s revenues.

Top pharmaceuticals globally

The top pharmaceutical products sold globally include Humira, Eliquis and Revlimid. Oncology is the op therapeutic area for drug sales globally and it is expected to show the largest growth over the next years. It is followed by drug spending for autoimmune diseases and diabetes. During the height of the COVID-19 pandemic, covid vaccine Comirnaty was the world's top revenue generating pharmaceutical product.

Data from pharmaceutical paper. This dataset is associated with the following publication: Furlong, E., A. Batt, S. Glassmeyer, M. Noriega, D. Kolpin, H. Mash, and K. Schenck. Nationwide reconnaissance of contaminants of emerging concern in source and treated drinking waters of the United States: Pharmaceuticals. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 579: 1629–1642, (2017).

Study design We conducted a cross-sectional study to quantify the number and describe the characteristics of prescription drugs on the Canadian market as of August 23, 2022 with a patient support program defined as services (including but not limited to financial assistance) offered to patients prescribed a specific drug and initiated and funded by the manufacturer. We then conducted a structured content analysis of web-based sources to understand the types and range of supports provided to patients through these programs. We chose to rely exclusively on publicly available data sources to both identify and describe manufacturer-sponsored patient support programs as these are sources currently available to patients when making program enrolment decisions and policymakers seeking to understand the extent and impact of this model of care. Sampling frame Because the European Medicines Association defines a patient support program as services for a specific drug offered by the company holding the marketing authorization, we first sought to identify all drug companies with currently marketed, prescription products in Canada. Between June 27, 2022 and August 23, 2022, two investigators independently extracted the names of all member companies listed on the websites of the three main trade associations for the Canadian pharmaceutical industry (Innovative Medicines Canada, representing the research-based pharmaceutical industry; BIOTECanada, representing the biotechnology industry; and the Canadian Generic Pharmaceutical Association, representing generic drug manufacturers). Because trade association membership is voluntary, we supplemented this list with non-member drug manufacturers identified in previous research. Using the Health Canada Drug Product Database,two investigators independently screened the list of companies and included those with marketed, prescription products and excluded companies that were not drug manufacturers (e.g., law firms) or without currently marketed prescription drugs (e.g., products under development). Discrepancies were resolved through discussion or adjudication by a third author. Sample and variables Using the Health Canada Drug Product Database, one investigator searched each identified drug manufacturer and extracted the product and active ingredient name(s) for all marketed, prescription drugs. We counted a single “drug” as all dosages, formulations, or routes of administration with the same active ingredients and manufacturer since industry patient support programs are brand-specific and do not typically differentiate among these factors. We selected variables that reflect known characteristics of drugs that may be associated with having a patient support program, and for which data were publicly available. One investigator also extracted Schedule D (biologic) status, route(s) of administration, and Level 1 Anatomical Therapeutic Chemical (ATC) code from the Drug Product Database and Product Monograph and identified whether the drug had Orphan Drug Status using the searchable United States database. On the basis of type of Health Canada regulatory review (i.e. innovator or subsequent entry), clinical expertise, and knowledge about the manufacturer, two investigators independently identified the brand status of each drug as brand (i.e. “innovator” products first to market); branded generic (i.e. “subsequent entry” products which are bioequivalent or biosimilar to an existing product on the market, but given a proprietary name); or generic (i.e. “subsequent entry” products which are bioequivalent to an existing product on the market). We classified biosimilars as branded generics. We resolved discrepancies through discussion, and/or adjudication by a third author. Identifying patient support programs and their characteristics Our primary outcome was whether a sampled drug had an associated manufacturer-sponsored patient support program. We defined a patient support program as any combination of services or resources related to medication access, administration, adherence, education, storage, or disposal for patients prescribed a specific product and initiated, sponsored and/or operated by the company holding the product’s marketing authorization. We distinguished patient support programs from “patient assistance programs,” excluding programs that exclusively provided financial assistance (e.g., coupons, co-pay coverage, etc.); expanded or compassionate access programs; risk management programs outlined in the Product Monograph (initiated by the regulator rather than the manufacturer); and programs delivered solely for a clinical study. Two investigators independently performed structured searches on Google (“[company name] AND patient support program AND Canada” and “[drug brand name] AND patient support program AND Canada”) to identify industry sponsored patient support programs in Canada and resolving discrepancies through discussion. Using Zotero, a reference management...

The Pharmaceutical Real-World Evidence (RWE) market is a dynamic and rapidly evolving sector that leverages real-world data to enhance decision-making across the healthcare landscape. By analyzing data from multiple sources, including electronic health records, insurance claims, and patient-reported outcomes, RWE pr

Argentina AR: Pharmaceutical Industry: Total Exports data was reported at 811.470 USD mn in 2021. This records an increase from the previous number of 708.230 USD mn for 2020. Argentina AR: Pharmaceutical Industry: Total Exports data is updated yearly, averaging 556.943 USD mn from Dec 1993 (Median) to 2021, with 29 observations. The data reached an all-time high of 1.137 USD bn in 2015 and a record low of 88.062 USD mn in 1993. Argentina AR: Pharmaceutical Industry: Total Exports data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Argentina – Table AR.OECD.MSTI: Trade Statistics: Non OECD Member: Annual.

In Argentina, the coverage of the business enterprises was expanded in 2015. BERD data are derived from a new survey from 2009. Since 1997, data for human resources relate to R&D. Before that, human resources data were expressed in terms of Science and Technology Activities (STA), involving R&D and diffusion activities of S&T (library services, training services, conferences, etc.). These have not been transferred to the OECD database. Since 2002, the source of funds data for private non-profit organisations, universities and S&T public organisations are requested for R&D. Before 2002, these sources of funds data were requested in terms of STA. These data were converted into R&D by means of a coefficient for each sector of performance. The main source of funds for science and technology activities in Argentina is the National Budget.

GlobalData's “Partnerships, Licensing, Investments and M&A Deals and Trends for March 2020 in Pharmaceuticals” report is an essential source of data and trend analysis on partnerships, licensing, mergers and acquisitions (M&As) and financings in the pharmaceuticals industry. The report provides detailed information on partnership and licensing transactions, M&As, equity/debt offerings, private equity, and venture financing registered in the pharmaceuticals industry in March 2020. The report portrays detailed comparative data on the number of deals and their value in the last six months, subdivided by deal types, various therapy areas, and geographies. Additionally, the report provides information on the top financial advisory firms in the pharmaceuticals industry.
Data presented in this report is derived from GlobalData’s proprietary in-house Pharma Intelligence Center deals database and primary and secondary research. Read More

Real-world Data (RWD) Market by Source (EMR, Claims, Pharmacy, Disease Registries), Application [Market Access, Drug Development & Approvals (Oncology, Neurology), Post Market Surveillance], End User (Pharma, Payers, Providers) - Global Forecast to 2029

Objectives: This study examines financial conflict-of-interest (FCOI) of clinicians who made submissions to the panCanadian Oncology Drug Review (pCODR), the arm of the Canadian Agency for Drugs and Technology in Health that recommends whether oncology drug-indications should be publicly funded. Final reports from pCODR published between October 2016 and February 2019 were examined. Design: Descriptive study. Data sources: Website of panCanadian Oncology Drug Review. Interventions: None. Primary and secondary outcomes: The primary outcome is the number of submissions declaring FCOI. Secondary outcomes are the number of times where clinicians agreed and disagreed with preliminary recommendation from pCODR and the association between the distribution of individual clinicians’ FCOI and pCODR’s funding recommendations. Results: There were 46 drug-indication reports from pCODR. Clinicians made 261 submissions. Clinicians declared they received payments from companies 323 times and named 38 different companies making those payments a total of 500 times. Financial conflicts with drug companies were declared in 176 (66.3%) of all submissions. In 21 (45.7%) of the 46 drug-indications, 50% or more of the clinicians had a conflict with the company making the drug. Clinicians commented on 37 preliminary recommendations. In all 25 where pCODR recommended funding or conditional funding the clinicians either agreed or agreed in part. pCODR recommended that the drug-indication not be funded 12 times and 9 times clinicians disagreed with that recommendation. The distribution of clinician responses was statistically significantly different depending on whether pCODR recommended funding/conditional funding or do not fund p < 0.0001 (Fisher exact test). The distribution of clinicians’ FCOI differed depending on whether the recommendation was fund/conditional fund or do not fund p = 0.027 (Fisher exact test). Conclusion: Financial conflicts with pharmaceutical companies are widespread among experts making submissions to the pCODR. Financial COIs of cliniciansDryad table.xlsx

Background: Patient groups represent the interest of their members when it comes to drug funding. Many patient groups receive grants from pharmaceutical companies that make products being considered for funding. This research examines whether there is an association between the positions that Canadian groups take about the products and conflicts of interest with the companies.

Methods: The Common Drug Review (CDR) and panCanadian Oncology Drug Review (pCODR) make recommendations to Canadian provincial and federal drug plans about funding particular drug-indications. Both utilize input from patient groups in making their recommendations. Patient group submissions are available from both organizations and these submissions contain statements about conflicts of interest. Views of the patient groups, with and without a conflict with the company making the drug under consideration and without any conflicts at all, were assessed and then compared with the recommendations from CDR and pCODR.

Results: There was a total of 222 reports for drug-indications. There were 372 submissions from 93 different patient groups. Groups declared a total of 1896 conflicts with drug companies in 324 (87.1%) individual submissions. There were 268 submissions where groups declared a conflict with the company making the product or said they had no conflict. Irrespective of whether there was a conflict, the views of patient groups about the drug-indications under consideration were the same. There was no statistically significant difference between views of patient groups and the recommendations from CDR and/or pCODR.

Conclusions: The large majority of patient groups making submissions about funding of particular drug-indications had conflicts with the companies making the products and their views about the products were almost always positive. This association between funding and views needs to be further investigated to determine if a true cause and effect exists.