New automated dried blood spot analysis successfully detects GHRP-2 and 45 other doping agents
Background
In elite sports, anti-doping agencies face a constant challenge to detect a growing list of performance-enhancing drugs, including small peptides. Traditional sample collection methods have limitations, and athletes may use novel substances like glycine derivatives of Growth Hormone-Releasing Peptides (GHRPs) to evade detection. Dried Blood Spot (DBS) sampling offers a less invasive and more stable alternative to urine or whole blood, but its quantitative accuracy has been hampered by the "hematocrit effect"—variations in red blood cell volume affecting sample consistency. Developing a robust, automated, and high-throughput DBS method that corrects for hematocrit is crucial for expanding the scope and reliability of sports drug testing, especially for lower molecular mass (<2 kDa) compounds like ghrelin receptor agonists (GHSR) and GnRH receptor agonists (GnRHR).
Study Design
Researchers developed a fully automated sample preparation workflow for Dried Blood Spot (DBS) analysis. The process began with a robotic-assisted, non-destructive hematocrit measurement from the DBS using near-infrared spectroscopy. This was followed by an automated sample preparation pipeline involving strong cation exchange solid-phase extraction and evaporation. The final extracts were analyzed using liquid chromatography-high-resolution mass spectrometry (LC-HRMS) to detect 46 different lower molecular mass peptide and non-peptide drugs. The assay was validated according to World Anti-Doping Agency (WADA) guidelines. As a proof of concept, the method was tested on authentic post-administration DBS samples containing GHRP-2 and GHRP-6, and also on samples collected via a microneedle device from the upper arm.
Results
The fully automated DBS analysis method successfully enabled the detection of 46 different low molecular mass (<2 kDa) doping agents. The target analytes included agonists for the gonadotropin-releasing hormone receptor, the ghrelin receptor (e.g., GHRP-2, GHRP-6), the human growth hormone receptor, and the antidiuretic hormone receptor. The method was sensitive, achieving estimated Limits of Detection (LODs) between 0.5 ng/mL and 20 ng/mL across the panel of substances.
As a proof of concept, the assay successfully analyzed authentic post-administration specimens containing GHRP-2 and GHRP-6, confirming its real-world applicability. The method was also able to detect glycine derivatives of GHRPs, which are specifically designed to evade standard anti-doping tests. Cross-validation using DBS samples from a microneedle-based collection device confirmed the robustness of the matrix for selected parameters.
Key Findings
- A new automated method detected 46 lower molecular mass (<2 kDa) peptide and non-peptide doping agents.
- The method achieved estimated Limits of Detection (LODs) between 0.5 and 20 ng/mL.
- The assay successfully identified GHRP-2 and GHRP-6 in authentic post-administration human samples.
- The system integrates a non-destructive hematocrit measurement to improve quantitative accuracy of DBS analysis.
- The protocol can detect glycine derivatives of GHRPs, designed to undermine current testing methods.
Why It Matters
This automated system significantly enhances the capabilities of anti-doping programs. By enabling reliable, quantitative analysis from Dried Blood Spot samples, it makes collection less invasive and sample handling more efficient. For athletes and biohackers using peptides like GHRP-2, this technology closes a potential loophole; the method's ability to detect novel GHRP derivatives means that even modified versions of common peptides can be identified. The key takeaway is that DBS is becoming a viable primary matrix for routine doping control, increasing the likelihood of detection for a wide range of substances. This technology moves sports drug testing closer to a more comprehensive, higher-throughput, and harder-to-evade screening protocol. The integration of hematocrit measurement is a critical step towards making quantitative DBS analysis a standard procedure, not just a supplementary one.
