Completed Public Health & Healthcare Infection & Immunity

Forensic epidemiology and impact of substandard and falsified antimicrobials on public health

Funder Wellcome Charity-funded
Associated organisations Edmund Mach Foundation · LexiGraph · University of Edinburgh · University of Oxford Europe PMC affiliations are not treated as award recipients or mapped locations.
Funding £2.9M
Period Oct 2021 — Sept 2025

In plain English

AI plain-English summary

Counterfeit and substandard antibiotics are flooding global supply chains, and researchers are now using forensic genomics and chemistry to track where they come from and how they move. This matters because poor-quality antimicrobials are a hidden driver of drug-resistant infections. When patients take weak or fake antibiotics, the drugs fail to clear the infection, allowing resistant bacteria to survive and spread. The scale of the problem is unknown, and current surveillance methods cannot reliably trace the origins or trade routes of these products. The research fills a critical gap by combining high-throughput DNA sequencing, chemical analysis, and social network mapping to characterise the “pharmabiome” of genuine versus falsified drugs and link them to their sources. If successful, this work could transform how regulators and international bodies monitor pharmaceutical supply chains. It would provide forensic tools to identify production hubs and trafficking networks, enabling targeted enforcement and policy changes. The project also models the public health impact of substandard antimicrobials—particularly their role in accelerating antimicrobial resistance—and identifies which pathogen-drug combinations are most at risk. This is not a laboratory curiosity; it directly addresses a systemic failure in global health infrastructure that quietly undermines treatment for millions of patients.

View original technical description
Substandard and falsified (SF) antimicrobials are a massive but underappreciated global health challenge in great need of innovative research to inform interventions. We will build on our collective pioneering research, to construct an innovative, multidisciplinary research hub that will improve understanding and inform global policy and action. Leading specialists investigating illegal wildlife trade, forensic genomics and chemistry, social network analysis and modelling, will work together to answer two main aims: 1. How can innovative forensic tools be used to identify sources and trade routes? How can novel genomic (‘pharmabiome’), chemical and isotopic analysis with social network techniques be used to characterise the epidemiology of SF antimicrobials to inform policy and action to improve our global pharmaceutical supply quality? We will conduct high-throughput sequencing and novel chemical analysis of falsified and genuine antimicrobials to determine their comparative pharmabiome/chemical spectra, followed by social network analysis of origins and trade routes. 2. What are the public health impacts of SF antimicrobials? What are the modelled impacts of SF antimicrobials on patient outcome, global public health, especially engendering antimicrobial resistance and how can these be minimised? Using a One Health approach, which pathogen-antimicrobial pairs are at greatest risk of SF antimicrobials?

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Researchers

Ben Cooper (EPMC Awardee)Clark Freifeld (EPMC Awardee)Federico Varese (EPMC Awardee)Heather Hamill (EPMC Awardee)Luana Bontempo (EPMC Awardee)Paul Newton (EPMC Awardee)Rob Ogden (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

Anti-Microbials In Society (AMIS): a Global Interdisciplinary Research Hub
Modelling the impact of poor quality antimicrobials on patient outcome and drug resistance – a pilot study to inform policy in the absence of empirical data
Foresight Study on European Stakeholder Appraisal of Diagnostics to Manage Anti-Microbial Resistance
Understanding the political barriers to tackling sub-standard and falsified medicines
The Global Governance of Antimicrobial Resistance: An Empirical Analysis of Participation and Effectiveness

Original classification

Collaborative Award in Science

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.