New test methods will help clinicians effectively target treatments in the ongoing fight against antimicrobial resistance
Bacterial resistance to antibiotic treatments is responsible for around 33,000 deaths a year in Europe. These deaths include those due to bacteria that have developed resistance to drugs considered a ‘last resort’ for Antimicrobial Resistance (AMR) infections.
At the international level the World Health Organisation has listed AMR as “one of the top 10 global public health threats facing humanity”.
One of the main drivers of AMR growth has been the excessive use of antibiotics. To help combat this increase rapid and accurate diagnosis of which patients require antibiotics is required, as is detecting infections which are resistant to antibiotics, and guides for clinicians with respect to correct and effective therapies.
Molecular techniques, such as digital PCR (dPCR) and whole genome sequencing (WGS), can identify the presence of pathogen DNA as well as a wide range of potential resistance mutations, indicating which treatments are most suited to combat a particular infection. However, dPCR lacked ratified methodologies for AMR detection and WGS can generate large amounts of data requiring complex informatics pipelines to identify the presence of resistance mutations. No information was available to indicate the measurement errors associated with any one technique or which bioinformatics approach was optimal to ensure the quality of a given test pipeline, limiting the use of such methods in the treatment of AMR.
This project examined a range of measurement procedures to predict AMR and assess viral drug resistance, including a candidate reference method for prolactin, which is elevated in bacterial infections, linking its levels to the SI for the first time. A second candidate reference method using dPCR for quantifying drug-resistant bacteria Methicillin-resistant Staphylococcus Aureus (MRSA) was established and multi-drug-resistant tuberculosis was used as a diagnostic model to produce materials to support point of care analysis. dPCR reference methods were also used for the quantification of anti-viral resistant sequences in HIV and human cytomegalovirus (hCMV).These methods were incorporated into prototype external quality assessment (EQA) schemes for MRSA, tuberculosis and HIV with materials assigned values to evaluate different methods and laboratory performance.
A WGS method to detect a specific form of AMR was also developed and taken up Great Ormand Street Hospital for Children (UK). This helped them to identify drug resistance in a group of rapidly growing, multi-drug resistant bacteria that cause a wide range of skin and soft tissue diseases.
The traceable methodologies established will help to determine the presence and type of infection in patients, allowing better antibiotic targeting as well as improved control and prevention strategies to combat future AMR threats.
The follow-on project Bio-stand 2 incorporated project results into new standards, ensuring the quality of nucleic acid methodologies for monitoring infectious disease.
Journal of Virological Methods
Clinical Infectious Diseases