Intended use and target population

Quantitative PCR was defined as a surrogate marker of parasitological response applicable across a broad range of patients, including infants with congenital CD, pediatric and adult patients with acute or chronic infection, and immunosuppressed individuals. However, while qPCR has demonstrated reliability for detecting treatment failure, its role as a surrogate for sustained parasitological response requires further validation through correlation with clinical and serological outcomes in longer-term studies (Table 2).

Trial duration and qPCR timelines

Consensus was reached on acceptable and optimal sampling points (screening, pre-treatment, end-of-treatment, and follow-up) and trial duration (Table 3).

Defining treatment failure on the basis of qPCR positivity supports the use of a 12-month post-treatment follow-up as a pragmatic interim window, given the timing of most documented relapses. Available evidence indicates that most qPCR-detectable treatment failures occur within the first 3–6 months after treatment completion, with nearly all molecular relapses becoming evident during this early period [6,7,10,14,27].

However, qPCR negativity in peripheral blood should not be interpreted as evidence of sterilizing cure or complete parasite clearance. Rather, qPCR is best framed as a tool primarily validated for the early detection of treatment failure, defined as the reappearance of detectable parasite DNA in blood. Its negative predictive value for persistent tissue-level infection, and therefore for sustained parasitological response, remains uncertain.

On this basis, efficacy assessment could be organized in two complementary stages: first, a 12-month interim endpoint aimed at detecting early treatment failure; and second, a 24–36-month confirmatory endpoint to evaluate sustained parasitological clearance. This stratified approach is consistent with the framework summarized in Table 3.

Extending follow-up beyond 12 months would strengthen the validation of qPCR as a surrogate marker of sustained treatment response [5]. Nevertheless, longer follow-up also introduces important feasibility challenges, including increased logistical complexity, higher costs, and greater risk of patient attrition. Loss to follow-up may bias relapse estimates, particularly if it is substantial or differs between treatment groups.

Laboratory requirements

Acceptable laboratory settings include GLP-compliant facilities with physical separation of pre- and post-PCR areas, ideally with controlled pressure workflows. Although qPCR can be performed by trained technicians, the most appropriate standard is demonstrated competence validated through: (1) participation in harmonization studies that demonstrate proficiency in performing the specific qPCR protocols to be used in the trial; (2) successful performance in proficiency testing programs (internal or external quality assessment schemes) with documented acceptable results; (3) Good Laboratory Practice (GLP) training or equivalent quality management system training appropriate to the regulatory context of the trial; and (4) documented validation experience with the specific qPCR assay platform and protocols being implemented. This competency-based approach: (i) ensures quality through objective performance metrics rather than relying on proxy measures like years of experience; (ii) accommodates diverse settings including sites in endemic countries where formal certification programs may differ; (iii) focuses on demonstrated ability to perform the specific techniques required for the trial; and (iv) aligns with regulatory expectations for operator qualification in clinical trials.

Participating laboratories should implement quality systems, SOPs, and international certification where possible (Table 4).

Sample type and processing

Consensus recommendations were established for blood collection, anticoagulants, stabilizers, sample volume, transport, processing, and replicate sampling (Table 5).

Serial sampling and replicate testing can enhance detection sensitivity, but they should be considered as trade-offs within a TPP, given their implications for sample volume, laboratory workload, cost, and turnaround time. Because sampling frequency and assay sensitivity are key determinants of the ability to detect intermittent parasitemia in chronic CD, trial protocols should explicitly define and justify both parameters.

Molecular target analyte

Nuclear satellite DNA (satDNA) remains the most widely used target. Single extraction/qPCR is acceptable for acute cases, whereas triplicate testing is recommended for chronic infection. Minicircle DNA (kDNA) assays show promise for higher sensitivity but require further validation.

Ct thresholds and assay variability

Variation among thermocyclers and assays complicates definition of the upper Ct cutoff. Laboratories must validate assay-specific Ct thresholds using appropriate controls, particularly for post-treatment samples.

Analytical performance

Consensus criteria for analytical sensitivity, specificity, and parasite load quantification are summarized in Table 6.

A conservative, achievable limit of detection (LoD₉₅) target of 1 parasite-equivalent per mL (par. eq./mL) was established to balance analytical sensitivity with inter-laboratory reproducibility while maintaining clinical utility. Following CLSI guidelines, reported LoDs exhibit variability attributable to multiple methodological factors, including: (i) qPCR target selection (satDNA versus kDNA), (ii) T. cruzi strain or DTU used for sample spiking (e.g., TcI, TcII, TcVI), (iii) blood processing methods and reagents (anticoagulants/stabilizers such as EDTA or guanidine-containing buffers, boiled versus nonboiled sample treatment), and (iv) DNA extraction platform utilized (e.g., Roche, Qiagen, MagMax).

S1 Table summarizes LoD₉₅ values from different laboratories using varied protocols, demonstrating the range of achievable analytical sensitivity and the factors contributing to variability.

LoD₉₅ thresholds are based primarily on validation studies using reference strains, which are the most common DTUs in clinical studies from South America. Universal application of parasitic load determination across all DTUs (TcI through TcVI and TcBat) without correction may result in systematic under- or over-estimation of parasite loads depending on the DTU’s actual satDNA copy number. The implementation of a synthetic satDNA standard, such as the one developed by Muñoz-Calderón et al. [28] is a promising solution that was specifically designed to address DTU-independent quantification. This synthetic standard provides a uniform reference point that is not biased toward any particular DTU’s copy number variation, potentially enabling more consistent quantification across different parasite populations.

Quantification requires standardized calibration curves; optimal practice involves stable international reference materials.

Quality assurance

qPCR laboratories must implement validated and periodically re-validated quality assurance procedures, including instrument calibration, reagent lot verification, use of positive and negative controls, technical replicates, and full documentation and traceability.

Distinct control functions are essential for quantitative comparability: (a) internationally accepted internal reference standards to be included in each qPCR assay run to control for inter-laboratory operational variability, instrument performance differences, and run-to-run variation, and (b) endogenous patient control genes (e.g., human housekeeping genes) to control for multiple quality control functions: detection of PCR inhibition, confirmation of adequate sample collection and DNA input, assessment of DNA extraction efficiency, and normalization for patient-to-patient variability in sample quality (Table 6).

Each laboratory should include reference standards at defined parasite load levels in every qPCR run to enable normalization and cross-laboratory comparability.

Moreover, endogenous controls can be used, beyond their current framing as inhibition detectors. This multi-functional quality control framework is essential for reliable quantification and result interpretation.

Clinical sensitivity and specificity

In acute, congenital, and reactivation cases, qPCR sensitivity of ~98% is acceptable. In chronic CD, sensitivity of 60–70% compared with serology has been accepted, although >75% is the optimal target. Optimal thresholds should be targeted for: (i) registration trials intended for regulatory approval, where maximizing detection of treatment failures is critical for safety and efficacy determination; (ii) late-phase confirmatory studies; (iii) post-market surveillance programs; and (iv) clinical settings where treatment decisions depend heavily on qPCR results. Acceptable thresholds may be appropriate for: (i) early-phase exploratory trials (Phase I/II) where the primary objective is preliminary efficacy signal detection rather than definitive demonstration; (ii) proof-of-concept studies; (iii) situations where qPCR is used as one component of a composite endpoint rather than the sole outcome measure; and (iv) resource-limited settings where achieving optimal performance may not be feasible, provided that the limitations are explicitly acknowledged in result interpretation. It must be pointed out that these are general guidelines and that the appropriate threshold should be determined based on the specific study objectives, regulatory requirements, patient population characteristics, and risk-benefit considerations. Transparency in reporting actual achieved sensitivity (with confidence intervals) in trial publications is essential for proper interpretation of results and for meta-analyses combining data across studies. This is also relevant for sample size calculations that can incorporate realistic assumptions about qPCR sensitivity based on the specific patient population (acute versus chronic infection, disease stage, baseline parasitemia levels) and that sensitivity analyses should explore the impact of varying detection rates on statistical power.

Clinical sensitivity can be improved by serial sampling and replicate testing as strategies to improve detection sensitivity. Clinical specificity should remain ≥98%.

Data interpretation and reporting

Standardized SOPs are required to harmonize data interpretation across trials, including Ct cutoffs, fluorescence thresholds, and quantification methods. Acceptable reporting times range from 48 hours to 1 week, with an optimal target of one working day.

Genotyping

DTU genotyping should be regarded as a complementary analysis, particularly because it is difficult to resolve in blood samples from chronic CD patients, in whom parasitemia is generally below 5 par. eq./mL. This limitation reflects the fact that the molecular targets currently used to discriminate among DTUs are represented at substantially lower copy numbers than the targets used for T. cruzi detection.

Biological Constraints on Blood-Based Parasitological Response Endpoints in T. cruzi Infection

A sustained parasitological response paradigm is difficult to translate to T. cruzi infection because of parasite dormancy and tissue reservoirs in multiple compartments, including heart, skeletal muscle, adipose tissue, and gastrointestinal tract. Reactivation and replication are not synchronized across reservoirs, making parasite activity within a patient spatially and temporally heterogeneous. Therefore, clearance from peripheral blood does not ensure clearance from tissues and challenges the concept of declaring systemic “cure” using blood-based measurements alone.

A second limitation is that peripheral blood qPCR cannot detect or quantify tissue persistence. Parasites may persist with intermittent or subclinical parasitemia that escapes sampling, or remain confined to inaccessible reservoirs. Thus, a negative blood qPCR cannot be interpreted as evidence of systemic cure or sterilizing clearance.

Third, the relationship between blood-detectable parasites and total tissue parasite burden is uncertain and likely varies among individuals. Host immunity, tissue perfusion, parasite tropism, and treatment history may all influence this relationship, limiting the validity of blood-based surrogates as universal cure indicators.

Finally, these constraints make it difficult to distinguish sterilizing cure from temporary suppression of blood-detectable parasitemia. True sterilizing cure would require elimination of parasites from all reservoirs, which cannot currently be verified with blood-based diagnostics alone. Post-treatment blood negativity may therefore reflect suppression rather than eradication, with relapse possible after reservoir reactivation.

The implications for trial endpoints are clear: endpoints should be framed around early failure detection and relapse risk, not cure confirmation. True cure endpoints will require tissue-based diagnostics or multi-compartment biomarkers that better reflect total body parasite burden. Accordingly, the performance thresholds in our TPP should be interpreted as targets for detecting failure, not definitive cure criteria. Future work should prioritize multi-marker approaches, harmonized sampling where feasible, longer-term or nested analyses, tissue-based or multi-compartment biomarkers, imaging modalities, and longitudinal designs able to distinguish transient suppression from sustained clearance.

Issues related to parasite genetic diversity and qPCR quantification

Most qPCR assays used in CD clinical trials target T. cruzi satDNA. However, satDNA copy number varies substantially across DTUs, which may affect parasite load quantification when parasite equivalents are estimated by assuming a fixed target copy number. Consequently, the LoD95 and quantification thresholds presented in Table 6 should not be interpreted as uniformly applicable across all DTUs.

In principle, DTU-specific correction factors could improve the accuracy of parasite load estimates when the infecting DTU is known or can be reliably determined. However, DTU genotyping is not routinely performed in most clinical or trial settings and is technically difficult to resolve in blood samples from chronic CD patients. Thus, although DTU-specific correction factors may be analytically relevant, their routine implementation is currently impractical. Accordingly, the thresholds proposed in Table 6 should be regarded as functional performance targets derived from the DTUs represented in validation studies, while recognizing that analytical sensitivity and quantification accuracy may vary according to regional DTU distribution [35]. For registration trials and regulatory submissions, this source of variability should be addressed by using DTU-independent reference standards, such as synthetic satDNA controls, characterizing DTUs in a representative subset of samples whenever feasible, and explicitly acknowledging parasite genetic diversity as a source of measurement uncertainty in the interpretation of qPCR results.

Cost considerations

For CD clinical trials conducted in endemic countries, acceptable cost thresholds should prioritize accessibility, feasibility, and long-term sustainability over purely technical optimality, provided that the assay meets predefined and acceptable performance criteria. In this context, the most technically advanced option may not always be the most appropriate if its cost, infrastructure requirements, or supply-chain demands limit implementation in the settings where trials are conducted.

We do not propose specific prices as acceptable or optimal, given the marked heterogeneity in the economic conditions of affected countries and health systems. Actual costs may vary substantially according to geographic region, procurement volume, local taxation and import policies, shipping and customs procedures, availability of laboratory infrastructure, and institutional or public–private agreements. Therefore, a single universal cost threshold would be unlikely to reflect real-world implementation conditions across endemic settings.

Instead, we recommend that trial sponsors and implementing partners conduct region-specific cost assessments during trial planning. These assessments should consider not only reagent and consumable costs, but also equipment, maintenance, quality assurance, personnel training, sample transport, data management, and external quality assessment. Such an approach would help ensure that molecular monitoring strategies are both analytically reliable and operationally sustainable in the diverse contexts in which CD clinical trials are performed.

Harmonization strategies for multi-center implementation

Thermocycler-related Ct variability limits the use of universal Ct cutoffs and complicates cross-trial comparability. Therefore, harmonization should rely on biological calibration rather than fixed Ct values. Each laboratory should validate instrument-specific Ct thresholds using common reference materials with defined parasite loads. These thresholds should then be used to translate platform-specific Ct values into standardized parasite-equivalent concentrations through calibration curves included in each qPCR run.

Before trial initiation, and periodically during the study, participating laboratories should complete inter-laboratory proficiency testing using identical blinded panels. Concordance should be assessed against predefined criteria, such as acceptable quantitative variation and agreement in positive/negative classification. Additional controllable variables should also be standardized across sites, including the use of a same SOP, same DNA extraction and amplification kits and quality control criteria.

For multi-center trials, centralized re-testing of a representative sample subset could provide an external quality check and identify systematic site- or instrument-related bias. Statistical analysis plans should prospectively assess inter-site variability and adjust for laboratory or equipment effects when needed. Trial reports should disclose thermocycler models, validation-derived Ct thresholds, quantitative results in parasite equivalents rather than raw Ct values, and proficiency testing concordance metrics. Overall, harmonization should prioritize standardized biological performance measures over instrument-dependent Ct values, while acknowledging residual equipment-related variability through predefined sensitivity analyses.

Finally, integrating qPCR endpoints with serological and emerging biomarkers will provide the most robust evidence of efficacy. In the interim, harmonized qPCR standards represent the most immediate path forward. Establishing a TPP for qPCR in CD will reduce variability, facilitate regulatory alignment, and accelerate development of urgently needed therapies.