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According to the National Health Council, nearly 40% of the adult population in the US is diagnosed with at least one chronic condition, such as diabetes, asthma, and hypertension. The annual cost burden associated with chronic diseases in the US is around USD 3.8 trillion. A major share of expenses incurred by patients living with a chronic disorder is driven by the frequent need to medicate, which may require repeated visits to hospitals / out-patient clinics. This has prompted stakeholders to develop devices that enable self-administration of drugs. Amongst the various self-injection devices, autoinjectors have steadily gained prominence, especially for the treatment of emergency and chronic conditions. However, there are several challenges associated with such devices, such as safety issues, human errors and dosing errors.
In order to overcome the aforementioned challenges, players are engaged in the development of cutting-edge technology, such as feedback systems in autoinjectors that track the injection process of therapies. Presently, close to 80 autoinjectors are available in the market, of which nearly 40% are accessible in combination with therapeutics and have disposable components. In addition, a significant increase in the research and development efforts for reusable autoinjectors has been observed. This can be attributed to the fact that the development of reusable or multi chambered autoinjectors is likely to reduce the cost of therapy for patients. It is worth mentioning that more than 11,000 patents have been filed / granted by the stakeholders related to autoinjectors. As a result, the intellectual capital around reusable components, reusable autoinjectors and associated methods of use has also grown, indicating the cost saving potential of the autoinjectors. Given these trends, it is crucial to monitor both the pockets of creativity and the white spaces that signal areas with a high potential for innovation. This report captures some of the key R&D-related trends and provides competitive intelligence on intellectual property in the field of autoinjectors.
Scope of the Report
The “Autoinjectors: Intellectual Property Landscape” report features an extensive study of some of the key historical and contemporary intellectual property (IP) documents (featuring granted patents, patent applications and other documents), describing the various types of autoinjectors and their components intended to offer safety features and patient compliance during self-administration of drugs. The insights generated in this report have been presented across two deliverables, namely a MS Excel workbook and a MS PowerPoint deck, summarizing the ongoing activity in this domain. Key inclusions are briefly described below:
Sheet 1 features details regarding how the input data for this project was collated, including the search strings used to query a popular, public patent database (namely lens.org), and data segregation notes.
Sheet 2 is a summary MS Excel dashboard, offering a detailed, graphical perspective of the intellectual property landscape of autoinjectors. It includes pictorial representations of the overall patent landscape, IP documents representing patent families that describe unique innovations related to autoinjectors, trends related to patent applications (including insights on patentability and freedom to operate), trends related to granted patents (including insights on patentability and freedom to operate), key inferences from a proprietary claims analysis, list of popular CPC symbols (featuring key pockets of innovation), list of popular applicants (shortlisted based on number of published IP documents), and list of non-English patents (including IP documents with undecipherable language and script as downloaded from lens.org)
Sheet 3 is an elaborate tabular representation of the overall IP landscape, featuring information on the various types of IP documents, related to autoinjectors, which have been published since 1973.
Sheet 4 is an excerpt of the data presented in the previous sheet, featuring published IP documents, that represent unique patent families across various global jurisdictions. This dataset has been analyzed in detail, in the report.
Sheet 5 includes a tabular representation of the key words and phrases (prior art search expressions) that are used to describe autoinjectors and affiliated intellectual capital.
Sheet 6 is a subset of sheet 4, featuring all the patent applications, covering innovations related to autoinjectors and affiliated methods / products.
Sheet 7 is a subset of sheet 4, featuring all the granted patents, covering innovations related to autoinjectors and affiliated methods / products.
Sheet 8 is an insightful summary of key inferences from the independent claims of the granted, active patents in the dataset. It involves the use of a systematic segregation approach to analyze key trends associated with the preamble, type of patent (product patent or method patent), type of claim (open ended claim or closed ended claim) and key elements of a claim (individual aspects of an innovation that are covered in a singular claim).
Sheet 9 provides insights related to some of the key applicants that are active in this field of research, featuring company-specific details (such as year of establishment, and location of headquarters), and inputs on their respective IP publication trends.
Sheet 10 features an analysis of the most popular CPC symbols and CPC families (in terms of frequency of appearance in the dataset), related to autoinjectors that are either already available in the market, or under development.
Sheet 11 is a subset of Sheet 4 and features a list of IP documents with non-English patent literature, with the contents of the documents filed in the native language of the applicants’ country and have not been analyzed in the research report.
Sheet 12 is an appendix, which includes pivot tables that drive the charts and interactive elements for the complete IP landscape depicted in sheet 2 of the deliverable.
Sheet 13 is an appendix, featuring details related to the categorization done in the report, and important abbreviations used in reference to the data categories mentioned in the document.
Chapter 1 briefly describes of the need for autoinjectors interventions, and the key advantages associated with the use of such devices. Further, it provides an overview of the intellectual property landscape related to this important yet relatively less explored domain of autoinjectors.
Chapter 2 and 3 feature brief (pictorial) summaries of the approach used during data collection for this project, and the key objectives of the study.
Chapter 4 is an executive summary of the important insights and key takeaways, generated from analyzing the IP landscape of autoinjectors.
Chapter 5 offers an informed perspective on the core concepts of autoinjectors. It also includes brief descriptions of autoinjectors and important milestones in this field of research, highlighting some of the important breakthrough approvals in the field of autoinjectors. Further, it provides a summary of the important application areas, advantages and existing limitations of autoinjectors. Finally, the chapter also features a discussion on recent developments, providing an informed future outlook backed by informed opinions and insights from several key senior stakeholders in the industry.
Chapter 6 includes a review of the various patents and affiliated IP documents that have been published related to autoinjectors, since the year 1973. It also features an in-depth analysis of published IP documents, representing unique patent families across various global jurisdictions, and includes insightful inferences related to both historical and recent R&D trends within this niche, but rapidly evolving segment of the biopharmaceutical industry.
Chapter 7 features an insightful examination of IP literature, highlighting key words and phrases that are used to describe different autoinjectors, including information on historical usage in IP filings, key affiliated terms (which can be used to identify other relevant IP search terms and establish relationships between prior art search expressions), and other related trends.
Chapter 8 offers insights from a competitive benchmarking and valuation analysis of the key members of unique patent families that have been captured in the report. It takes into consideration important parameters, such as type of IP document, year of application, time to expiry, number of citations and jurisdiction (factoring in value associated with the gross domestic product (GDP) of a particular region).
Chapter 9 provides a detailed summary of the patent applications (representative of unique patent families) that were filed across different jurisdictions and their relative value in the IP ecosystem. The analysis segregates the captured intellectual capital in terms of type of innovation and the specific innovation (novel products, enabling technologies or methods of use), thereby, offering the means to identify active arenas of research and assess innovation-specific IP filing trends. Further, it features an analysis that helps identify relevant areas of innovation by analyzing published IP documents (representative of unique patent families), defining the uniqueness of patent pending innovations, in order to assess the scope of patentability in this domain, and pinpoint jurisdictions where new and / or modified claims may be filed without infringing on existing IP.
Chapter 10 is an elaborate summary of the granted patents (representative of unique patent families) across different global jurisdictions and their relative value in the IP ecosystem. The analysis also features a meaningful classification system, segregating granted IP into relevant categories (namely type of innovation and innovation) to help develop a detailed perspective on the diversity of intellectual capital (having marketing exclusivity) related to autoinjectors, and the assessing likelihood for innovators to enter into promising product markets, once active patents expire. Further, it features an analysis that helps identify relevant areas of innovation by analyzing published IP documents (representative of unique patent families), defining the uniqueness of patented innovations, in order to assess the scope of patentability in this domain, and pinpoint jurisdictions where new and / or modified claims may be filed without infringing on existing IP.
Chapter 11 features profiles of some of the most popular applicant companies, which were shortlisted based on their respective patent filing activities. Each profiles includes a brief overview of the company, information on annual revenues (wherever available), details of its autoinjector portfolio, names of key management team members and recent developments.
Chapter 12 includes an insightful analysis of the various CPC symbols mentioned in published IP literature (representative of unique patent families) and their affiliated families, offering the means to identify historical and existing pockets of innovation (based on the functional area / industry described by the elaborate and systematic IP classification approach, mentioned earlier); the analysis also features a discussion on prevalent white spaces (based on innovation and type of innovation) in this field of research.
Chapter 13 concludes the report by summarizing publicly available insights on the anticipated developments in this domain (taking into consideration the perspectives of eminent representatives of stakeholder companies in this industry), and trends that are likely to shape the future of autoinjectors.
Chapter 14 is a set of appendices, entailing an overview of the excel research report, a list of IP documents featuring the identified white spaces, table of contents, list of figures, list of tables, glossary, and list of all the applicant companies and organizations.
1. Research Notes
2. Summary Dashboard
A. Overall Intellectual Property Landscape
B. Intellectual Property Landscape (Grouped by Simple Families)
C. Key Prior Art Search Expressions
D. Key Trends related to Patent Applications (featuring Patentability and Freedom-to-Operate)
E. Key Trends related to Granted Patents (featuring Patentability and Freedom-to-Operate)
F. Claim Analysis
G. Key CPC Symbols
H. Key Applicants
3. Overall Intellectual Property Landscape Dataset
4. Prior Art Search Expressions (Keyword Analysis)
5. Patent Applications Dataset
6. Granted Patents Dataset
7. Claim Analysis
8. Key Applicants Analysis
9. CPC Analysis
10. Appendix I: Pivot Tables
11. Appendix II: Country / Geography Codes
12. Appendix III: Innovation Categories
2. Project Approach
3. Project Objectives
4. Executive Summary
5.2. Limitations of Conventional Methods
5.3. Need of Self Administrations
5.5. Classification of Autoinjectors
5.6. Benefits of Autoinjectors
6. Overall Intellectual Property Landscape
6.2. Analysis of Simple Patent Families
6.3. Key Innovation Categories
6.4. Insights from Patent Applications
6.5. Insights from Granted Patents
7. Key Prior Art Search Expressions
7.2. Analysis of Prior Art Search Expressions
8. Intellectual Property Valuation Analysis
8.1. Valuation Overview
8.2. Analysis of Individual Value Ranks
8.2.1. Rank 1 IP Documents
8.2.2. Rank 2 IP Documents
8.2.3. Rank 3 IP Documents
8.2.4. Rank 4 IP Documents
8.2.5. Rank 5 IP Documents
8.3. Concluding Remarks
8.4. List of Rank 1 IP Documents
9. Analysis of Patent Applications
9.2. Relative Valuation of Patent Applications
9.3. Patentability & Freedom-to-Operate
10. Analysis of Granted Patents
10.2. Relative Valuation of Grated Patents
10.3. Patentability & Freedom-to-Operate
10.4. Analysis of Patent Claims (Granted Active Patents)
11. Key Applicants
11.2. Analysis of Key Applicants
11.2.1. Company A
11.2.2. Company B
11.2.3. Company C
11.2.4. Company D
11.2.5. Company E
11.2.6. Company F
11.2.7. Company G
11.2.8. Company H
11.2.9. Company I
11.2.10. Company J
12. Pockets of Innovation and White Spaces
12.2. Pockets of Innovation
12.3. White Spaces
12.4. Concluding Remarks
13. Future Outlook
13.2. Contemporary Sentiments & Expert Opinions
13.3. Future Perspectives
The following companies / institutes / government bodies and organizations have been mentioned in this report:
Source 1: https://www.sciencedirect.com/science/article/pii/S1319016421001821
Source 2: https://aapsopen.springeropen.com/articles/10.1186/s41120-018-0027-z