Who Benefits From Medical Innovation? Global Representation, Affordability, Safety and Regulatory Equity in Alzheimer's Disease and Cancer Research

Modern clinical research has produced striking advances in both Alzheimer's disease and cancer: the first disease-modifying Alzheimer's therapies, and an accelerating shift toward biomarker-gated precision oncology. Whether these advances reflect the global populations who carry the burden of these diseases, however, has rarely been measured directly rather than assumed. This monograph investigates that question using an original, purpose-built dataset: 400 interventional trials (200 per disease) randomly sampled from ClinicalTrials.gov, coded against a six-category exclusion-criteria taxonomy and scored using TREI-AC, an adaptation of the Trial Representation Equity Index built specifically for this comparison. Across the sample, 99.5% of Alzheimer's disease trials and 97.8% of cancer trials had no site in a country classified as low- or lower-middle-income by the World Bank; a two-tier comparison against disease-burden data shows trial-site presence in these countries running roughly 44 to 54 percentage points below their share of global disease burden for both conditions. Comorbidity and prior-treatment exclusion criteria were significantly more common in cancer trials than in Alzheimer's disease trials. In contrast, biomarker or imaging diagnostic requirements present in 15 to 16% of trials in both diseases structurally gate a meaningful share of research access behind infrastructure much of the world lacks. A dedicated case study of India-sited trials further distinguishes between an extractive pattern, in which a country functions only as a recruitment site within externally designed trials, and a domestic-research pattern largely disconnected from that same pipeline. Building on these findings, the monograph traces the same structural gap through diagnosis, treatment, affordability, pharmacovigilance, regulatory science, and the emerging role of artificial intelligence across the clinical research lifecycle, before proposing a ten-pillar Equity-by-Design framework and a disease-specific research agenda aimed at closing the gap this evidence documents. The central argument is that a therapy's value cannot be judged solely by whether it succeeds in the trial that produced it, if that trial was never positioned to reflect how it would perform for most of the people who will eventually need it.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-18
DOI
https://doi.org/10.5281/zenodo.22837502
Primary Topic
Ethics in Clinical Research
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
preprint

Who Benefits From Medical Innovation? Global Representation, Affordability, Safety and Regulatory Equity in Alzheimer's Disease and Cancer Research

Khan Gulrez Shagufa Fazal Ahmed
Zenodo (CERN European Organization for Nuclear Research)
Ethics in Clinical Research
preprint

Who Benefits From Medical Innovation? Global Representation, Affordability, Safety and Regulatory Equity in Alzheimer's Disease and Cancer Research

Khan Gulrez Shagufa Fazal Ahmed
preprint en

Abstract

Modern clinical research has produced striking advances in both Alzheimer's disease and cancer: the first disease-modifying Alzheimer's therapies, and an accelerating shift toward biomarker-gated precision oncology. Whether these advances reflect the global populations who carry the burden of these diseases, however, has rarely been measured directly rather than assumed. This monograph investigates that question using an original, purpose-built dataset: 400 interventional trials (200 per disease) randomly sampled from ClinicalTrials.gov, coded against a six-category exclusion-criteria taxonomy and scored using TREI-AC, an adaptation of the Trial Representation Equity Index built specifically for this comparison. Across the sample, 99.5% of Alzheimer's disease trials and 97.8% of cancer trials had no site in a country classified as low- or lower-middle-income by the World Bank; a two-tier comparison against disease-burden data shows trial-site presence in these countries running roughly 44 to 54 percentage points below their share of global disease burden for both conditions. Comorbidity and prior-treatment exclusion criteria were significantly more common in cancer trials than in Alzheimer's disease trials. In contrast, biomarker or imaging diagnostic requirements present in 15 to 16% of trials in both diseases structurally gate a meaningful share of research access behind infrastructure much of the world lacks. A dedicated case study of India-sited trials further distinguishes between an extractive pattern, in which a country functions only as a recruitment site within externally designed trials, and a domestic-research pattern largely disconnected from that same pipeline. Building on these findings, the monograph traces the same structural gap through diagnosis, treatment, affordability, pharmacovigilance, regulatory science, and the emerging role of artificial intelligence across the clinical research lifecycle, before proposing a ten-pillar Equity-by-Design framework and a disease-specific research agenda aimed at closing the gap this evidence documents. The central argument is that a therapy's value cannot be judged solely by whether it succeeds in the trial that produced it, if that trial was never positioned to reflect how it would perform for most of the people who will eventually need it.

Zenodo (CERN European Organization for Nuclear Research)
Anusandhan Trust (IN)
Ethics in Clinical Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.