{"id":34874,"date":"2026-02-13T08:33:36","date_gmt":"2026-02-13T13:33:36","guid":{"rendered":"https:\/\/www.drugpatentwatch.com\/blog\/?p=34874"},"modified":"2026-02-13T09:14:09","modified_gmt":"2026-02-13T14:14:09","slug":"leveraging-alternative-data-to-complement-drug-patent-intelligence-for-pharmaceutical-stock-investors","status":"publish","type":"post","link":"https:\/\/www.drugpatentwatch.com\/blog\/leveraging-alternative-data-to-complement-drug-patent-intelligence-for-pharmaceutical-stock-investors\/","title":{"rendered":"Leveraging Alternative Data to Complement Drug Patent Intelligence for Pharmaceutical Stock Investors"},"content":{"rendered":"\n

I. The Bedrock of Valuation: A Deep Dive into Drug Patent Data<\/strong> <\/h2>\n\n\n\n
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In biopharmaceutical investments the intellectual property (IP) portfolio is not merely a legal detail; it is the fundamental currency of a company’s valuation. Unlike sectors where brand recognition or network effects might provide a sustainable competitive advantage, the pharmaceutical industry operates on a distinct principle: a molecule’s structure can be reverse-engineered with relative ease [1]. Consequently, a patent serves as the primary “moat” or a defensible, revenue-generating asset that underpins a company’s market worth [2]. For investors, especially those evaluating small biotechnology firms, the strength and remaining term of a single, early-stage composition of matter patent on a promising new chemical entity (NCE) are often the most significant drivers of its financial valuation [2]. A deep understanding of this asset is the first, but not the only, step toward credible analysis.<\/p>\n\n\n\n

Beyond the initial composition of matter patent, pharmaceutical companies meticulously build a comprehensive IP fortress around their core product through a strategic process known as Life Cycle Management (LCM) [2]. This involves creating a “thicket” or “picket fence” of secondary patents, such as method-of-use patents that cover specific treatments for a disease, or formulation patents that protect the drug’s delivery mechanism [2]. For investors, an examination of this strategy is critical for predicting the inevitable \u201cpatent cliff\u201d [2]. This infamous event marks the steep decline in revenue that occurs when a drug’s core patent expires and is exposed to generic competition [2]. A company with a weak secondary patent portfolio is highly vulnerable to this decline [2].<\/p>\n\n\n\n

The financial consequences of the patent cliff are severe and widely documented [3]. The ability to predict this event with remarkable accuracy is a core tenet of sophisticated pharmaceutical forecasting [2]. As a company’s top-selling drug, which can generate billions of dollars annually, loses its market exclusivity, its revenue can plummet by 80-90% within just 24 months of generic entry [3]. This translates directly into significant stock price pressure and a rapid erosion of shareholder value [3]. While patent data provides an essential guide to a company’s ownership of IP, it presents a limited, and potentially dangerous, view when used in isolation. The core protection, its breadth, global strategy, and remaining lifespan are all crucial variables [2]. However, patents are a map of what a company owns<\/em>\u2014they do not account for a myriad of other non-patent factors that are equally vital for commercial success and future market potential [4].<\/p>\n\n\n\n

II. The Imperative for a Multi-layered Approach: Why Patents are Not Enough<\/strong><\/h2>\n\n\n\n

A comprehensive investment analysis in the biopharmaceutical sector necessitates a perspective that extends beyond the patent portfolio. While patents provide a crucial legal layer of protection, they do not guarantee commercial viability. A thorough evaluation requires a deeper understanding of the various non-patent factors that can significantly influence a drug’s success and ultimately, a company’s stock performance [4]. The following factors provide a powerful intellectual rationale for incorporating alternative data into a strategic investment framework.<\/p>\n\n\n\n

A broader view of market exclusivity reveals that government regulatory agencies provide various forms of protection that can be independent of a drug’s patent status [3, 4]. These regulatory exclusivities are designed to incentivize the development of new medicines and can provide a significant market advantage [3, 4]. Examples include New Chemical Entity (NCE) exclusivity, which grants five years of protection to a new drug, or Orphan Drug exclusivity, which provides seven years of market exclusivity for treatments addressing rare diseases [3, 4]. These periods of exclusivity are a critical variable in financial forecasting and can extend a drug\u2019s revenue stream long after its composition of matter patent has expired [3, 4].<\/p>\n\n\n\n

De-risking the pipeline is another critical factor where a patent-centric analysis falls short. The pharmaceutical R&D process is a capital-intensive gauntlet fraught with scientific, regulatory, and financial hurdles [5]. The overall likelihood of approval for a drug entering Phase 1 trials is distressingly low, consistently ranging between 7.9% and 12% [5]. High attrition rates act as a significant “sinkhole” for investment, with Phase 3 trials alone accounting for up to 40% of a drug’s total R&D costs [6]. While a company may possess a robust patent on a molecule, the immense financial risk associated with its passage through clinical trials can fundamentally alter its valuation [4]. The ability to gain early insight into the progress and success rates of these trials is paramount for making data-driven “go\/no-go” decisions [7].<\/p>\n\n\n\n

Finally, navigating the competitive arena and securing market access are non-patent factors that directly influence a drug’s commercial potential [4]. A drug with strong IP protection and positive clinical data can still fail if it does not address a true unmet clinical need, or if it faces restricted reimbursement from payers [4, 8]. Poor market access can lead to lower-than-expected prices and limited adoption by physicians, severely impeding a drug’s commercial success [4]. A comprehensive analysis of the competitive landscape, including an evaluation of competitors\u2019 existing products, sales trajectories, and pipelines, is a critical forward-looking component of any market forecast [5]. This multi-layered approach moves the analysis from a static view of a company’s IP to a dynamic, predictive model of its future commercial viability.<\/p>\n\n\n\n

III. A Taxonomy of Complementary Alternative Data: A Guide for the Sophisticated Investor<\/strong><\/h2>\n\n\n\n

The inherent limitations of a patent-only analysis create an urgent need for a more comprehensive, multi-source intelligence framework. Alternative data, which refers to non-traditional sources of information such as social media, satellite imagery, and regulatory filings, provides the critical data points necessary to construct this framework [9, 10]. These datasets are increasingly utilized by sophisticated investors to complement traditional sources like financial statements and market reports, offering unique perspectives and predictive power [9, 10]. The following is a detailed taxonomy of the most valuable complementary alternative data sources for pharmaceutical stock investors.<\/p>\n\n\n\n

1. Clinical Trial and Pipeline Data<\/strong><\/h3>\n\n\n\n

The R&D pipeline represents the future of a biopharmaceutical company, and a robust analysis of its progression is a powerful predictive tool [7]. Clinical trial data, sourced from a wide array of public and proprietary platforms, provides a detailed view of a drug’s journey from Phase 1 to regulatory approval [11]. This data includes specific metrics such as study design, enrollment rates, and key endpoints, which are crucial for assessing a company’s standing relative to its peers [12].<\/p>\n\n\n\n

A critical aspect of analyzing clinical trial data is understanding the market’s asymmetric reaction to new announcements. Research indicates that the release of clinical trial results is an economically significant event that can have a meaningful effect on a company’s market value [6]. However, stock price underperformance due to negative events is greater in magnitude and persists longer than abnormal returns due to positive events [6]. This suggests that the market may have already priced in a high probability of success for a promising drug. A failure, on the other hand, comes as an unexpected surprise, leading to a sharp and lasting repricing of the asset. For this reason, the early detection of negative signals is disproportionately valuable as a risk-mitigation tool.<\/p>\n\n\n\n

Beyond the raw data, sophisticated investors are assessing a company\u2019s preparedness and readiness to adapt [13]. Due diligence extends to understanding the “who, why, and when” of the clinical trial setup [13]. Investors want to see a clear pivot strategy for when something changes or goes wrong, demonstrating a willingness to adapt based on trial findings [13]. This level of detail moves analysis beyond simple metrics to a more nuanced assessment of the management team’s competence and foresight. A powerful example of this approach is the case of hedge fund SAC Capital Advisors, which was considering an investment in Vertex Pharmaceuticals [14]. To validate its thesis, the fund used a Freedom of Information Act (FOIA) request to the U.S. Food and Drug Administration (FDA) to acquire adverse event reports on a new drug [14]. The discovery that no adverse reports existed confirmed their investment, demonstrating the value of leveraging publicly available but unstructured regulatory data to gain an information edge [14].<\/p>\n\n\n\n

2. Litigation and Regulatory Filings Data<\/strong><\/h3>\n\n\n\n

Litigation data represents a powerful, yet often underutilized, predictive tool for forecasting generic drug launches and understanding the competitive landscape [3]. The foundation for this analysis lies in the Hatch-Waxman Act, which created a system of \u201cpatent linkage\u201d where the FDA cannot grant final approval for a generic drug until the relevant patents on the branded drug have expired or been successfully challenged [1]. A generic challenger\u2019s “Paragraph IV certification,” which claims a branded drug\u2019s patent is invalid, unenforceable, or will not be infringed, automatically triggers a 30-month stay of FDA approval while the dispute is litigated [1, 2, 3].<\/p>\n\n\n\n

This system creates a direct cause-and-effect chain that can be modeled and analyzed. By tracking court dockets, legal filings, and the outcomes of these high-stakes patent lawsuits, an investor can develop a probabilistic model to predict the generic launch date more accurately than by relying solely on the core patent expiry date [3]. This goes beyond simply identifying a risk; it provides a framework for predicting a specific financial event. The analysis can extend to the strength of a generic company\u2019s non-infringement arguments, previous litigation outcomes with similar patents, and even international invalidation decisions that may signal potential outcomes in the U.S. [3].<\/p>\n\n\n\n

Complementing this, data on \u201cpatent pending\u201d applications holds immense strategic value even before a patent is granted [15]. While not legally enforceable, a “patent pending” status functions as a strategic flag, deterring competitors from investing substantial resources in developing a similar product [15]. For investors, this data is a leading indicator for M&A due diligence and can even reveal strategic opportunities, often called “white spaces,” which are areas with limited patent activity but significant therapeutic potential [15].<\/p>\n\n\n\n

3. Patient-Generated and Social Media Data<\/strong><\/h3>\n\n\n\n

Information generated by patients and consumers online provides a real-time, unfiltered view of sentiment and behavior that is impossible to capture through traditional means [9, 16]. Sourced from online health communities, forums, and social media platforms, this data is a critical barometer for assessing the “unmet need” for a drug, a core criterion for market success [5]. The true value of this data lies in its ability to inform key variables in a market forecast [17].<\/p>\n\n\n\n

A traditional market forecast relies on inputs like awareness, acceptance, and compliance, which are often derived from surveys and expert interviews [17]. Social media and patient-generated data provide a scalable, real-time proxy for these variables [9]. A drug with high social media buzz and positive sentiment, as measured by tools that use artificial intelligence (AI) and natural language processing (NLP), may signal strong future market adoption, even before sales data is available [16, 18]. This moves the analysis from a static, backward-looking exercise to a dynamic, forward-looking one. For example, a company can use this data to track shifts in customer sentiment over days or weeks, allowing for more strategic business decisions and an enhanced understanding of audience responses [16].<\/p>\n\n\n\n

4. Commercial, Prescribing, and Expert Data<\/strong><\/h3>\n\n\n\n

Beyond the public sphere, other forms of alternative data provide real-time commercial signals. Aggregated transaction data, such as anonymized and aggregated credit card or online account transactions, can be used to track drug sales and other transaction information in near-real-time, providing a pre-earnings signal of a company’s commercial performance [19].<\/p>\n\n\n\n

Another powerful source of complementary data is human intelligence itself. Expert networks and one-on-one consultations with medical professionals and key opinion leaders (KOLs) are a form of alternative data increasingly leveraged by sophisticated investors [8, 20]. A study of hedge fund strategies found that funds that integrate medical expertise consistently outperform their peers [20]. These experts provide real-time analysis of emerging trends, help evaluate a drug’s efficacy and safety profile against market expectations, and can provide due diligence on a company’s clinical trial data [20]. Their insights on pricing and payer policies are also crucial inputs for a market forecast [17].<\/p>\n\n\n\n

Finally, competitive intelligence can be derived from specialized databases that track mergers and acquisitions (M&A), licensing deals, and funding rounds [21]. Analyzing this data reveals competitor strategies, provides benchmarks for valuing a company’s own assets, and helps identify potential acquisition targets [21].<\/p>\n\n\n\n

The following table summarizes these complementary data sources and their strategic application in investment analysis.<\/p>\n\n\n\n

Table 1: The Complementary Data Matrix<\/strong><\/p>\n\n\n\n

Alternative Data Category<\/strong><\/td>Specific Data Points<\/strong><\/td>Unique Insights Provided<\/strong><\/td>How it Complements Patent Analysis<\/strong><\/td><\/tr>
Clinical Trial & Pipeline<\/strong><\/td>Phase success rates, enrollment numbers, trial design.<\/td>De-risks investment by assessing clinical viability; provides early signal of efficacy and safety.<\/td>Patent indicates what’s owned; clinical data assesses whether it can be brought to market.<\/td><\/tr>
Litigation & Regulatory Filings<\/strong><\/td>Paragraph IV filings, litigation outcomes, regulatory exclusivity dates.<\/td>Predicts the timing of generic entry; quantifies the value of regulatory protection.<\/td>Patent expiration is a fixed date; litigation and regulatory exclusivity can delay or extend market exclusivity.<\/td><\/tr>
Patient-Generated & Social Media<\/strong><\/td>Sentiment scores, online mentions, forum discussions, patient reviews.<\/td>Gauges true unmet medical need and patient acceptance; forecasts drug adoption and compliance.<\/td>Patent provides a legal shield; sentiment data predicts market demand and commercial success.<\/td><\/tr>
Commercial & Prescribing<\/strong><\/td>Aggregated credit card transactions, pharmacy receipts, prescribing trends.<\/td>Provides real-time sales signals ahead of earnings reports; validates or disproves commercial forecasts.<\/td>Patent value is a function of projected revenue; real-time data validates if those projections are materializing.<\/td><\/tr>
Expert & Deal Data<\/strong><\/td>Expert interview transcripts, M&A records, licensing deal terms.<\/td>Offers deep, qualitative due diligence; provides benchmarks for asset valuation; reveals competitor strategy.<\/td>Patent is an asset; expert and deal data provides the context for its strategic value to a partner or acquirer.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

IV. A Strategic Framework for Integration and Analysis<\/strong><\/h2>\n\n\n\n

The effective use of alternative data requires more than simply subscribing to a new data feed; it demands a robust strategic framework and a cultural shift within an organization [22]. The goal is to build a “system of integrated intelligence” that transforms unstructured data into actionable insights [5]. The integration process can be viewed as a multi-stage playbook that moves from data acquisition to model building and, finally, to the generation of actionable signals [23].<\/p>\n\n\n\n

The first step is a collaborative effort to clearly define data needs, involving portfolio managers, analysts, and data scientists [23]. This requires answering fundamental questions about the specific data required, its format, and its frequency, all of which are tailored to the investment strategy [23]. Once a dataset is identified, a rigorous testing and vetting process is essential [23]. This is where the challenges of alternative data become apparent: it is often “messy and unstructured,” created differently by various providers, and may lack the accuracy and reliability of traditional sources [24, 25].<\/p>\n\n\n\n

After a successful trial, the next step is data ingestion and structuring [23]. This is a significant technical challenge, as datasets, even from public sources like the FDA, may lack time-variant consistency and require significant structuring [25]. Many companies do not have the internal resources to clean and link these disparate datasets, which is why data providers are increasingly doing the “heavy lifting” upfront [25]. This process transforms raw data into a usable format, ready for analysis and model building [23].<\/p>\n\n\n\n

Once the data is structured, the final and most critical step is signal extraction and model building [23]. This is where hypotheses are tested and predictive models are developed to find buy\/sell signals or forecasts [23]. The most forward-looking investors recognize that AI and machine learning are not just analytical tools but are the engine that makes this entire multi-data strategy possible [26]. AI-powered tools can use natural language processing (NLP) to read and understand the technical content of millions of patents, classify them into specific categories, and identify semantically similar patents, even without exact keyword matches [2]. More broadly, supervised learning algorithms can be trained on extensive datasets to predict outcomes in clinical trials, identify potential drug targets, and even detect adverse events [26].<\/p>\n\n\n\n

A significant implication of this technological shift is that the most sophisticated investors are not just using AI to analyze data; they are investing in companies that are using AI to de-risk their own R&D pipelines [7]. This fundamentally shifts the investment thesis from “what drug is this company developing?” to “how is this company’s use of AI giving them a competitive advantage in drug development?” [7]. This strategy enables companies to embrace a \u201cfail fast\u201d and \u201cfail cheap\u201d philosophy, freeing up resources for more promising ventures and ultimately accelerating the pace of innovation [7].<\/p>\n\n\n\n

V. The Challenges and Ethical Landscape<\/strong><\/h2>\n\n\n\n

The promise of alternative data is immense, but its adoption is not without significant challenges and ethical considerations that must be carefully managed [15, 22]. No expert report is complete without a candid discussion of these risks.<\/p>\n\n\n\n

The first major challenge is data quality and availability. Alternative data sources are not typically collected for evidence-building or policymaking purposes and may not support a ready measurement of data quality [24]. As one analysis noted, while these sources may be strong on timeliness, they can be weak on accuracy and reliability [24]. Furthermore, the data is often “messy and unstructured,” created and organized differently depending on the source, which requires significant time and resources to clean and integrate [12, 25].<\/p>\n\n\n\n

Beyond the data itself, the adoption of alternative data introduces specific organizational and operational risks [22]. Two of the most common are model risk and vendor risk [22, 23]. Model risk is the potential for making inconsistent or irregular investment decisions due to complications introduced by a new, poorly integrated, or irregularly updated dataset [22]. Vendor risk arises from the entrepreneurial nature of the alternative data industry, where changes in data collection methodology or terms of use can impact the effectiveness of the data [22]. Mitigating these risks requires a comprehensive model risk management framework and a documented plan for the periodic reassessment of data vendors [22, 23].<\/p>\n\n\n\n

Finally, the use of alternative data, especially in the healthcare sector, is fraught with privacy, regulatory, and ethical considerations [27, 28]. The Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule protects \u201cindividually identifiable health information\u201d (PHI) [29, 30]. While the Rule permits the use and disclosure of de-identified data, it is important to note that even when properly applied, the risk of identification is not zero [29]. Removing names is not always sufficient to protect confidentiality, as other attributes like geographical information can lead to “deductive identification” when cross-referenced with other data sources [31]. The ethical principles of informed consent, data minimization, and equity are paramount [31]. Investors have an ethical obligation to ensure that their data sources adhere to these principles and do not contribute to exploitation or the reinforcement of biases [28, 31].<\/p>\n\n\n\n

The following table outlines these key risks and provides corresponding mitigation strategies.<\/p>\n\n\n\n

Table 2: Key Risks and Mitigation Strategies<\/strong><\/p>\n\n\n\n

Risk Category<\/strong><\/td>Description of Risk<\/strong><\/td>Mitigation Strategy<\/strong><\/td><\/tr>
Data Quality & Availability<\/strong><\/td>Data is often messy, unstructured, and lacks consistency; sources may not be reliable or complete.<\/td>Implement a rigorous data vetting process; invest in data structuring and linking tools; partner with vendors who pre-clean data.<\/td><\/tr>
Model Risk<\/strong><\/td>Poorly integrated or maintained data leads to inconsistent and faulty investment models.<\/td>Establish a comprehensive Model Risk Management (MRM) framework; document model assumptions and updates; ensure proper data lineage.<\/td><\/tr>
Vendor Risk<\/strong><\/td>Changes in data collection methodology or terms of use by providers can invalidate an investment thesis.<\/td>Document and periodically reassess data vendors; establish clear escalation protocols for adverse events.<\/td><\/tr>
Privacy, Regulation, & Ethics<\/strong><\/td>Non-compliance with regulations like HIPAA; using data without informed consent; risk of re-identification.<\/td>Adhere to legal frameworks (e.g., HIPAA); ensure data is properly anonymized; invest in ethical review processes and transparent communication with data sources.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

VI. Conclusion: The Future of Pharmaceutical Investment Intelligence<\/strong><\/h2>\n\n\n\n

The evidence overwhelmingly suggests that a patent-only view of pharmaceutical valuation is insufficient for generating alpha and managing risk. While intellectual property remains the bedrock of a company’s market-exclusive revenue stream, the true drivers of commercial success and stock performance are determined by a complex, multi-variable equation. The most sophisticated investors are already moving beyond a superficial analysis of patent expiry dates to leverage a diverse and interconnected ecosystem of alternative data.<\/p>\n\n\n\n

The future of pharmaceutical investment intelligence is not about replacing traditional analysis with new data, but about a powerful convergence of intelligence from disparate sources. This involves seamlessly integrating insights from clinical trial progression, litigation outcomes, patient-generated content, and human expertise with a foundational understanding of a company\u2019s patent portfolio. The ability to connect a Paragraph IV filing to a projected generic launch date, to validate a market forecast with real-time patient sentiment data, and to de-risk an investment by identifying management’s preparedness for trial setbacks is the new frontier of competitive advantage.<\/p>\n\n\n\n

This multi-data strategy is made possible by the accelerating adoption of AI and machine learning. These technologies are not just tools for analysis; they are the engine that transforms unstructured data into actionable intelligence, enabling a predictive and forward-looking investment approach. As the industry continues to evolve, the most valuable investment thesis will be centered not just on the molecules a company is developing, but on its strategic use of data and technology to navigate the immense scientific and commercial uncertainties that define the biopharmaceutical landscape.<\/p>\n\n\n\n

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