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The Value in Decolonising Drug Discovery

Why Partnership Beats Extraction

Jesse Grey Eagle, Founder of Indigenous Futures OS and Author of Indigenous Systems Thinking

Dr. Sabah Ul-Hasan, Lead Informatics & Bioinformatics Consultant

The history of drug discovery is permeated with extractive practices. At a glance, we may not see how these widely accepted approaches are scientifically narrow and unsustainable. We thus posit sovereign partnerships, from sample collection to data analyses, as a strategic necessity that ensure the long-term resilience of pharmaceutical research.

Introduction

The Failure of Extraction 

The simplest way to discuss extraction in drug discovery, or taking knowledge from a source without attribution, is that it’s a bad habit. 

To frame it as a historical relic is to misunderstand its persistent and modern reality (1). The taking of cells from Henrietta Lacks's tumor without consent in 1951 remains a topic of contention more than 70 years later (2,3). This extends into multinational corporations actively pursuing biopiracy today (4). Not only is extraction a bad habit, it’s a failure in governance. Extraction is a reflection of how research systems organise authority, access, and decision-making in ways that enable value to be taken from the same communities and ecosystems from which the value originates (1,5,6).

Table 1. Core Definitions

Core Definitions

We argue this extractive approach is scientifically unsustainable, ethically bankrupt, and self-defeating. It limits the pool of actionable knowledge by ignoring context (7,8,9), severs long-term research access by eroding trust (1), and produces incomplete or flawed conclusions (6). The path to more resilient and innovative drug discovery requires a fundamental paradigm shift: From a one-way transaction to a cyclical, accountable partnership.

The Extractive Model

A Structural Habit with Consequences

What makes a discovery novel? We credit Charles Darwin as a pioneer for his findings in the sciences (10), yet know little to nothing of the contributions made by Indigenous people along his journey (11). This pattern of overlooking source communities continues today (1), and the history of many pharmaceutical compounds reflects this extractive pattern. As early as 3400 B.C.E., the opium poppy was cultivated in Mesopotamia (12). India is a present-day primary supplier of morphine derived from the opium poppy (13), yet the majority of profits are concentrated in the United States and Europe (14). This uneven distribution of value isn’t an inevitable outcome of history, it’s a structural feature of a system where benefits don’t flow back to the source communities. There are over 19,800 drugs in the global pipeline, with 4,788 of those approved (15). Although 4.8 billion people of the 8 billion global population live in Asia (16), over 70% of pharma profits occur in North America and Europe (17) whereas upwards of 40% are suspected to be derived from traditional knowledge (18).

Table 2. Examples of Pharmaceutical Breakthroughs from Traditional Knowledge

Examples of Pharmaceutical Breakthroughs from Traditional Knowledge

The consequences of this sustained extractive model are severe:

  1. Scientifically Incomplete: Without deep, contextual knowledge, research misses crucial nuances. For instance, after a US patent was granted on the use of turmeric for wound healing, the Government of India's Council of Scientific and Industrial Research successfully challenged it by providing evidence of the knowledge's long history in Ayurvedic practice (25). Such cases represent dead ends and legal battles that could be avoided.
  2. Digital Flaws: Extraction continues in the digital age. The genetic data of Middle Eastern/North African (MENA) populations is often misclassified or erased in genomic datasets (26). This "data extraction" without community-specific framing leads to poor health outcomes, as drugs developed on incomplete data may fail for these populations, effectively excluding them from the benefits (6,26).
  3. Trust-Eroding: Each act of extraction burns a bridge. The history of mishandling data from Indigenous American populations, for example, has led to justifiable resistance and reluctance to participate in research (27). Communities lock away future discoveries and stewarded knowledge, eroding the very foundation of long-term research access (1,28).

Approaching extraction from an Indigenous systems perspective highlights a different logic. Non-extractive practice is grounded in continuity. Authority, responsibility, and benefit are understood as extending over time, rather than being severed once materials or data are transferred (8,9,29). Knowledge is brought into shared systems with an expectation of relationship and accountability.

The Partnership Model

Architecting Continuity

The alternative isn’t “ethical charity”, it’s robust governance. It requires architectural frameworks that embed continuity (29,30). A successful partnership fundamentally alters the incentive structure, leading to better science (28,31,32,33). As the pharmaceutical industry increasingly embraces open innovation to access external knowledge and cut costs (32), sovereign partnerships must be the guiding framework for engagement with communities rich in traditional knowledge and biodiversity.

A Practical Framework: The Four Pillars of Digital Sovereignty

This framework operationalises the principle of continuity from sample collection to data governance, providing a clear blueprint for alignment with agreements like the Nagoya Protocol (34), attributed to the governance design work of Jesse Grey Eagle (35).

  1. Data Visibility: Who controls the genetic sequences, chemical data, and research findings? Partnership requires shared data access, clear provenance tracked through systems like blockchain, and community co-authorship on knowledge produced from their resources (36,37).
  2. Infrastructure Control: Are the labs, databases, and/or AI tools owned solely by the corporation? Partnership invests in shared capacity, building local lab infrastructure, training community researchers, and ensuring technological tools are accessible and governed jointly. This moves beyond "local students" to supporting community-defined education and career pathways (27,38).
  3. Consent Renewal: Consent is not a single transaction but a continuous governance commitment. It is structured around the Accountability Rhythm: Request → Review → Renewal. Communities must be continually informed and retain the right to withdraw or re-negotiate terms as a project evolves (34,36).
  4. Economy Return: How are benefits shared? This must be defined upfront and include fair royalties, non-monetary benefits (jobs, training), and intellectual equity (patent co-ownership). The economic model must sustain the community, not just the corporation (31,34).

Table 3. Examples of Partnership in Pharma

Examples of Partnership in Pharma

A Blueprint for Implementation

From Theory to Practice 

Operationalising this shift requires redistributing authority through structured accountability. This can be applied at every stage of a project (36,41,42).

Accountability in Action

  • Request: The initial proposal is co-designed with the community, aligning research questions with community priorities (28,32).
  • Review: Joint committees regularly evaluate data, progress, and partnership health. This is a shared checkpoint, not a corporate report (36,38).
  • Renewal: Formal opportunities are built in to re-establish consent, update terms, and ensure the relationship remains equitable (32,36).

Concrete Steps for Researchers & Companies

  • Co-Design from the Start: Involve community representatives in drafting research proposals and methodologies (28,38).
  • Formalise Agreements: Use Mutually Agreed Terms (MAT) and Prior Informed Consent (PIC) as living documents, subject to the Accountability Rhythm (32).
  • Invest in Capacity Building: Budget for training community members and building in-country research infrastructure as a core project cost (27,42).
  • Plan for Data Return & Governance: Ensure all research results and data are returned in accessible formats, with clear, community-held agreements on future use, following the CARE Principles for Indigenous Data (36).

The incentive for this deep partnership is resilient, breakthrough science. When partnerships are sovereign, they yield richer data and more sustainable access (43,44,45). The agreement that led to Jeevani was hailed internationally as a pioneering model of benefit-sharing, establishing a trust fund for the Kani community from license fees and royalties (39,46). However, its long-term trajectory became a cautionary tale as the fund later collapsed due to mismanagement and the community's traditional healers saw little lasting benefit, underscoring that genuine partnership requires sustained accountability, not just initial promises (47). In a data-driven future, the most resilient pipelines will be those built on trust and continuity. In contrast, organisations governed by Indigenous data sovereignty principles such as the Native BioData Consortium, embed community authority into their operational structure and ensure partnerships remain equitable and sustainable (48).

Conclusion

The Symbiotic Potential of Medicine 

Ethical partnership isn’t a barrier to innovation. On the contrary, it’s the foundation for sustainability. With data-driven and AI-enabled discovery systems on the rise (32,39,49), without explicit reframing of governance at the level of system architecture extractive dynamics are likely to accelerate at digital speed. The pharmaceutical industry stands at a crossroads. It can cling to the short-term, extractive model that collapses trust and opportunity or it can invest in the long-term, resilient model of partnership. Every institution trains AI. The deeper question is whether we are also training accountability (35,37).

The next paradigm-shifting medicine may indeed be found in a rare plant, but its true key lies in the generations-deep wisdom of its stewards. The goal is not to find a key to a lock, but to be invited to build the door together (8,9,29).

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Acknowledgements

This article serves as one of many ongoing conversational pieces for discussion in the pharmaceutical and big data spheres. We deeply thank Rohith Nuguri for the invitation along with the following individuals for their input and insight on the development of this piece: Kamran Anwar, Nancy Fernandes, Nur Shahir, Priyanka Kushwaha, Sarah Alnahari, Sarah Price, and Sarvani Chadalapaka. 

Jesse Grey Eagle

Jesse Grey Eagle (Oglala Lakota) is the founder of Indigenous Futures OS and a governance designer for Tribal nations. He works at the nexus of ethical data, AI sovereignty, and long-view institutional systems, advocating for partnership architectures that move beyond extraction.

Dr. Sabah Ul-Hasan

Sabah Ul-Hasan is a Ph.D. scientist with over 15 years of practice in the life sciences. An Indian-Pakistani American based in Albuquerque (New Mexico), Dr. Ul-Hasan centers on ethical data practices that bridge science with community-based knowledge systems.