Laboratory techniques for molecular surveillance of P. falciparum antimalarial drug resistance markers: a review to support decision-making

Background Antimalarial drug resistance (AMDR) continues to threaten malaria control and elimination, underscoring the need for timely and reliable malaria molecular surveillance. Data generation through therapeutic efficacy studies (TES) and independent surveys is critical, yet limited guidance exists to support policymakers and National Malaria Control Programs (NMCPs) in selecting appropriate laboratory techniques for different surveillance objectives and operational contexts. This descriptive technical review aimed to assess the currently used laboratory techniques based on their technical and operational characteristics, identify knowledge gaps and provide decision support for Malaria Molecular Surveillance (MMS) of Plasmodium falciparum . Methods The literature search was conducted in PubMed and supplemented by citation searching. Following title and abstract screening, data were extracted on predefined performance, operational, and technical characteristics. A non-exclusionary assessment of reporting quality was also performed. The results were reviewed by field experts, who added additional information, based on the findings. Results Thirty-nine studies published between 1998 and 2025 met the inclusion criteria. Performance varied between PCR-based and sequencing-based techniques, with analytical sensitivity ranging from 1–1000 parasites/µL for PCR-based techniques, and 1–2000 parasites/µL for sequencing-based techniques. Clinical sensitivity and specificity were high across all techniques, though highest for sequencing-based methods. PCR-based techniques provided rapid results and required less infrastructure for the targeted detection of known SNPs but could not identify novel markers. Sequencing-based techniques offered high multiplexing potential and could often detect minority variants and discover novel markers. However, they required greater capital investment, more extensive training, and substantial bioinformatics capacity. Cost data were highly variable and often incompletely reported, highlighting a knowledge gap. We developed a structured decision-support framework, illustrated through three hypothetical country scenarios reflecting differing surveillance needs, laboratory capacities, and resource constraints. These examples demonstrate that no single method is universally optimal and that technique selection should be tailored to specific surveillance objectives, epidemiological context, existing infrastructure, budget, and long-term capacity-building goals. Conclusion This review provides an updated overview of available techniques and practical guidance to support stakeholders in selecting accurate, feasible, and sustainable approaches for monitoring AMDR through MMS, while also highlighting key knowledge gaps.