he identification of the gene and causative mutation for a human disease is an important breakthrough for patients and clinicians. It provides a DNA diagnosis, which can help with family planning, connecting families affected by the same condition or clinicians who could better define or study the condition. However, it is fundamentally a descriptive, hypothesis-generating milestone. The question immediately becomes how knowledge of the causative mutation can be used to better treat the patient. The answer almost invariably lies in understanding the disease gene’s function and how it is regulated within its biological pathway. This is where the experimental power of model organisms plays such an important role.

onsistently, patients participate in clinical trials for altruistic reasons, and this is never more true than when the clinical trial targets a disease with no cure, or a life-limiting diagnosis. Many are placing their hope in the trial that is running: that it will prolong survival and at the same time afford, critically, an increased quality of life and a delay of symptoms. As the industry tries to find cures for these illnesses, death naturally occurs. As many have said, life is a terminal condition; there is nothing new there, but how do we, as clinical researchers, design trials that are compassionate, can help patients have a good death, and can help caregivers both support their loved ones and themselves feel supported?

he life sciences industry has long waited for big data to transform how new medicines are developed and delivered. With artificial intelligence (AI) and machine learning (ML) now coming of age, research and development teams can finally seize the opportunity if their data is clean, standardized, interoperable, and secure.

ith the continued implementation of technology and remote services in clinical trials, there is a persistent lack of clarity and understanding around regulatory oversight. Investigators and clinical research site staff have not been properly guided or coached on how to ensure continued oversight when these new trial execution elements are utilized. This ambiguity is unfortunately causing undue hesitation and reluctance to accept trials that incorporate remote services and technology, not to mention the serious consequences that this lack of clarity brings with it.

here is a clear conceptual link between Quality Management and Analytics, encapsulated in the maxim “You can’t manage what you can’t measure.” In practice, however, many pharmaceutical organizations struggle to operationalize this idea: Quantifying quality is filled with challenges, both technical and organizational, including a lack of essential data, siloed teams, decentralized systems, and lack of a unified roadmap for uplifting quality reporting and analytics.

hese case studies serve as practical illustrations of how an analytical approach can make a meaningful impact in ensuring process adherence and enabling study quality oversight throughout the clinical trial lifecycle and across audience tiers within the organization. However, as alluded to in the introduction, analytical use cases always require collaboration and—importantly—sponsorship from other areas of the business. This section will outline a range of strategies for gaining endorsement for and removing barriers to implementation.

egulators worldwide recently convened in San Diego for DIA’s Global Annual Meeting 2024 to discuss respective countries’ priorities and chart new horizons in reimagining regulatory processes, alignment, and collaboration. Regulatory representation at several Town Halls included intergovernmental bodies like the World Health Organization (WHO), international coalitions such as the International Coalition of Medicines Regulatory Authorities (ICMRA), and regional agencies including the US FDA, European Medicines Agency (EMA), Health Canada, ANVISA (Brazil), PMDA (Japan), and NMPA (China). The meeting also featured the Access Consortium, a coalition of regulatory authorities from Australia, Canada, Singapore, Switzerland, and the United Kingdom.

nly a few months remain before developers must follow a new market access route for new cancer products and advanced therapy medicinal products (ATMPs) in the European Union (EU). Traditionally, health technology assessments (HTAs)–evaluations of the clinical effectiveness and value of new medicines and devices–were conducted by individual EU member states. This approach led to redundancies and potential delays in bringing new treatments to patients, particularly on clinical benefit assessment. The EU HTA Regulation and the introduction of Joint Clinical Assessments (JCAs) aim to streamline these evaluations.

o meet the needs of patients and to drive competitiveness, Europe needs a consistent and predictable regulatory framework to attract investment and expertise and support innovation. Here’s how we can do it together.

n 2013, Margaret Chan, who served as Director-General of the World Health Organization (WHO) from 2006 until 2017, invited civil society to reflect on why all ministers around the world could be, to some extent, ministers of health: “Prevention must be the cornerstone of the global response to … diseases. Their root causes reside in non-health sectors. Collaboration among multiple sectors is imperative.” This disruptive approach is grounded on the “health in all policies” (HiAP) principle outlined in the 1993 Helsinki Declaration.

t is estimated that more than 350 million people worldwide, approximately 10% of the global population, half of whom are children, live with a rare disease. Of these rare diseases, fewer than 10% have approved treatments. This significant unmet treatment need has been met with unprecedented response within the global scientific, biopharmaceutical, regulatory, and patient communities, including unprecedented growth in rare disease drug and gene therapy development and approvals. However, the well-known difficulties of conducting clinical trials (e.g., small samples, wide geographic distribution, heterogenous symptoms) and developing products for these rare indications persist, and identifying outcomes remains a hurdle. For efficacy endpoints based on clinical outcome assessments, COAs have not yet been identified, developed, or modified for most rare diseases.

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