he process of designing clinical studies today entails the manual compilation of the protocol document. Several people work on building the protocol by passing document versions back and forth, and spending hours sifting through past protocols, scientific literature, and regulatory guidelines to inform the new study design. This approach comes with several limitations

hile HER2 overexpression has been observed in various solid tumors, HER2-targeted therapies have been largely limited to use in patients with HER2-positive (HER2+) breast and gastric cancers. Emerging clinical data have demonstrated that HER2-targeted therapies, like T-DXd, zanidatamab, and tucatinib, may offer clinical benefit for patients with any HER2-overexpessing cancer. As the use of HER2-targeted therapies expands into other solid tumors, in which tissue samples may be more limited and HER2 expression may be more heterogenous, more sensitive and less invasive diagnostic methods are needed. Liquid biopsies and advances in ctDNA analysis offer a wide range of potential clinical applications, complementing standard clinical, imaging, and tissue assessments in order to optimize tumor-agnostic cancer diagnosis and patient treatment management.

ecentralized clinical trials (DCTs) are a paradigm shift from the more traditional site-centric models to more patient-centric approaches. A new development within DCTs is “smart” trial protocols that adapt dynamically to participant needs and data patterns. These protocols apply artificial intelligence toward real-time optimization of study design, thus allowing, in some cases, shorter trial durations, accelerated patient timelines, and generally better logistical outcomes. This is also where blockchain technology is increasingly coming to prominence, as it offers a secure method to manage participant consent and data sharing, long a bugbear for decentralized research in terms of data privacy and integrity.

ith all the excitement around artificial intelligence (AI), many in healthcare are exploring ways to use this technology to improve how we discover, develop, and test new medicines. While the hype around AI can be somewhat overblown, there are real ways it can significantly improve clinical research, specifically with decision-making for trial design and execution.

n the evolving landscape of scientific research and healthcare, technologies like artificial intelligence (AI), machine learning (ML), and biosimulation are reshaping how data is managed, processed, and utilized. In silico technologies (IST) represent a key component of this transformation, providing a groundbreaking acceleration of predictive intelligence, insights, and evidence, revolutionizing traditional research and development (R&D) by leveraging advanced computational techniques.

Clinical Outcome Assessment (COA) is defined by the US FDA as a method to capture clinical outcomes reported by a patient, observer, clinician, or via performance-based testing. COAs reflect how an individual feels, functions, or survives, providing essential insights into the effects of treatments. In clinical research, multiple types of COAs may be used, including:

ne of the key components of FDA’s regulatory oversight of the quality, safety, and efficacy of pharmaceutical products, both domestically and internationally, is the issuance of Certificates of Pharmaceutical Product (CPP), which are critical in the approval process for pharmaceuticals entering foreign (e.g., non-US) markets.

he Certificate of Pharmaceutical Product (CPP) is a document issued by a regulatory authority at the request of a product owner to support the registration process of that pharmaceutical product by another regulatory authority in a different country. Historically, one of the most in-demand CPP-issuing authorities has been the European Medicines Agency (EMA).

he World Health Organization (WHO) Certification Scheme on the quality of pharmaceutical products moving into international commerce, in effect since 1969, is an international voluntary agreement to provide assurance to countries participating in the Scheme about the quality of pharmaceutical products. The primary document of the Scheme is the Certificate of Pharmaceutical Product (CPP). Since its inception, the Scheme has undergone revisions (most recently, in 2021) to adapt to the changing global regulatory environment, including the rapid globalization of the pharmaceutical manufacturing sector. In this article, voices from regulatory authorities from three different regions explain how they have been adapting their CPP-related practices while ensuring that this Certificate retains its usefulness and supports more efficient regulatory decision-making.

he WHO Certification Scheme for CPP, an international voluntary agreement that provides assurance about the quality of pharmaceutical products moving into international commerce to countries participating in the Scheme, was established in the 1960s as one of the first regulatory reliance tools.

rtificial Intelligence (AI) is on the cusp of quickly becoming a large component of the healthcare industry (broadly defined), with the emergence of generative AI and sophisticated predictive models making significant inroads across various domains. At this point, it is crucial to carefully consider and plan for the responsible use of AI in healthcare, as it presents both numerous potential benefits and diverse risks to society.

The global AI in healthcare market was valued at approximately USD 19.27 billion in 2023, and it is projected to grow at a compound annual growth rate of 38.5% from 2024 to 2030. This market expansion is largely driven by the healthcare sector’s growing demand for improved efficiency, accuracy, and enhanced patient outcomes. According to a Premier, Inc. survey of 752 healthcare business decision-makers in November 2023, 81.5% said that AI was part of their organization’s strategic direction.

fter my diagnosis of breast cancer at age 35, I struggled to wrap my head around what life would look like as a young woman with breast cancer. It also coincided with the pandemic, so there weren’t a ton of resources for me to utilize during my treatment,” explained DIA Patient Partner and cancer survivor Courtney Shihabuddin (BJE Consultants, The Pink Warrior) in this DIA 2024 interview. “I felt, once I made it to the other side, that I needed to utilize my knowledge and my voice to help other young women not feel so afraid and scared in a time that is so terrifying to a young woman with a new diagnosis.

lobal Forum founding Editor-in-Chief and former DIA board member Andrzej Czarnecki (Eli Lilly and Company) shares his reflections on DIA’s 60-year anniversary and prospects for the future with his successor, current Editor-in-Chief Alberto Grignolo.

rofessor Lo Chung-mau, Hong Kong’s secretary of health, delves into some of the key strategic initiatives for advancing Hong Kong’s healthcare and biomedical innovation. Key topics include the integration of the Greater Bay Area (GBA) for enhanced clinical trials, addressing the increasing needs of the Hong Kong healthcare system, and leveraging international partnerships for global benchmarking. Lo emphasizes, “Seeing is believing,” highlighting the importance of firsthand experience to understand the vast opportunities within the GBA.