FDA's 2026 CGM and Immunogenicity-PK Specifications: Preserve the Path from Source to Analysis

TL;DR

FDA's May 2026 CGM and June 2026 immunogenicity–pharmacokinetics technical specifications address different data problems with a shared operational need: reviewers should be able to connect an analysis value to its observations, timing, interpretation and derivation. Both documents contain nonbinding recommendations, subject to any specifically cited legal requirements. Their variable tables should be read within the applicable study and submission context. [1] [2]

For CGM, the consequential distinction is between a reading, a device-generated missing-data record and an interval for which the device collected no record. For immunogenicity-PK analysis, screening, confirmatory results, titers and the overall ADA conclusion need their distinct meanings retained, with consistent timing across the relevant datasets. A single generic result field or an undifferentiated missing value can lose information that later derivation needs. [1] [2]

EClinCloud's proposed acceptance exercise follows representative source records through tabulation, analysis and reviewer documentation. Define the scientific and data-standard decisions first; then test that the interfaces and derivations preserve them. A successful file transfer alone provides limited evidence about that chain.

Determine the applicable specifications and versions

The CGM document expressly excludes technical specifications for closed-loop systems that couple a CGM sensor, insulin pump and dosing algorithm. It should not be treated as a complete data specification for such a combined system. Its stated scope also does not establish device validation or a trial endpoint's clinical interpretation. [1]

A submission's electronic-data obligations depend on the application, study and applicable standards framework. FDA's standardized-study-data guidance, Data Standards Catalog and Study Data Technical Conformance Guide provide the broader context. The two modality-specific documents add detailed recommendations. Those layers should be recorded separately when planning the study's data deliverables. [1] [2] [3] [4]

The CGM document concerns data submitted from clinical trials supporting a marketing application for a drug or biological product. It discusses epoch-level observations, intermediate summaries, final analysis data and paired glucose analysis. It also provides examples and recommendations concerning device-level information, disposition and missingness. [1]

The immunogenicity-PK document discusses ADIS, ADPC and ADSL. It recognizes that not every variable is appropriate for every trial and describes documenting omitted or added variables in the data-definition file and reviewer's guide. That express allowance belongs beside any table of desired variables. [2]

A study-data plan should therefore state the chosen source versions, applicable scope and any issue requiring discussion with the review division. Distinguish a standard's required/expected/permissible terminology from an agency-specific statement and from a study team's implementation choice. A table copied out of context can erase all three distinctions. [2]

The modality specifications are dated May and June 2026. The general conformance source inspected for this publication is October 2026 Version 6.3. Its Section 4.1.2.11 calls for ADLB results in SI units and a correspondingly structured ADLC in conventional units, including the specified laboratory-result coverage and ADRG reference-range information. Preserving a device’s original unit is therefore an input to the applicable analysis representations, rather than the sole submission-unit decision. Section 7.1.4’s directory table separately addresses combining subject narratives/profiles in a USUBJID-bookmarked PDF. These changes affect downstream representations and packaging; the examples below address upstream result states and sample/time/parameter relationships, rather than a complete submission package. [4]

CGM: distinguish missing observations from missing source records

The CGM specification recommends representing missing readings in the laboratory dataset, LB, with the reason where that information was collected by the device. A warmup period or another device-generated error can have a timestamped record even when it has no glucose result. If the device collected no record during nonwear or a gap between sensors, the tabulation representation differs. The specification addresses representation of intermittent missing data in the analysis epoch-level dataset. [1]

This distinction changes the source-to-analysis handoff. A source record reporting an error should retain that status and its human-readable reason. An analysis row constructed to represent an expected but absent epoch should remain identifiable as derived. Converting both to an ordinary glucose observation would misstate the evidence.

Operational synthesis of the CGM specification, including its scenario-specific LB recommendations and ADaM discussion. The applicable SAP governs subsequent imputation decisions. [1]

A sensor's silence also leaves the cause uncertain unless another source supplies it. A data pipeline can detect a gap. It cannot establish from that absence alone whether the participant forgot to replace the sensor, the device failed or a transfer failed. Preserve an unknown reason when the evidence is insufficient.

The specification's example records illustrate possible source and derived representations. They are examples, rather than default values to assign to every study. A replacement workflow should use the actual device information, source records and approved missing-data plan. [1]

Preserve time at the resolution the analysis uses

The CGM specification explicitly discusses assigning epoch-level data to analysis timing variables using full timestamps. A date-only comparison can mishandle observations later on the same day. The analysis-window rule and its implementation consequently deserve a targeted boundary test. [1]

For an illustrative five-minute sampling schedule, a complete 24-hour interval contains 288 planned epochs, calculated as 24 × 60 ÷ 5. This is arithmetic for a defined interval, rather than a measured wear-completeness result. Real expected counts depend on the protocol's interval boundaries, sampling behavior, discontinuation and other applicable rules.

A useful test includes observations immediately before, exactly at and immediately after an analysis-window boundary. It also includes a sensor replacement, a gap and an out-of-window observation. The expected assignment should follow the prespecified rule, with source timestamps retained for reconciliation.

Time zones and source clocks need a declared treatment in a real integration. The appropriate plan depends on the collected data and study. EClinCloud recommends confirming that treatment with the sponsor's data and analysis owners before implementation, then testing it against the actual transfer specification. This is an implementation recommendation, rather than a universal time-zone rule attributed to FDA.

Device identity has a similar source boundary. The CGM guidance encourages sensor-level information and linkage using the relevant device identifier. A system should preserve what the actual device and study collect. A lot number, software version or serial number should be interpreted according to its real definition, rather than inferred from the appearance of an example identifier. [1]

Immunogenicity-PK: retain the result hierarchy

The June 2026 document describes an immunogenicity analysis dataset with parameter-specific records. Its examples distinguish ADA screening, confirmatory results, titers, neutralizing-antibody information and an ADA conclusion. Those results have different meanings. The final conclusion may depend on preceding results and the applicable interpretation rules. [2]

A data transfer that replaces the hierarchy with a single positive/negative field can discard the evidence needed to understand that conclusion. Conversely, keep screening status separate from the final confirmed ADA status. The source laboratory's result and the sponsor's derived analysis result need their respective definitions preserved.

The specification also discusses consistency of visit and time-point terminology across ADIS and ADPC, supporting analysis of the relationship between immunogenicity and pharmacokinetic concentrations. A join based on inconsistent labels can create a plausible-looking dataset with the wrong pairing. [2]

EClinCloud's proposed acceptance questions, informed by the June 2026 specification. They supply no assay procedure, diagnostic interpretation or universal positivity threshold. [2]

Baseline selection and treatment-emergent classifications require the approved definitions and data. A generic interface rule should not invent a baseline from the first available row or assign a subject-level category from an isolated result without the prescribed context. The analysis team determines those rules; the implementation evidence should show how they were executed.

The same discipline applies when data were never collected. The specification permits appropriate omissions and additions with documentation. That is a reason to explain the actual dataset, rather than create unsupported values merely to fill every listed variable. [2]

Compare two illustrative transfers

The following records are synthetic illustrations of the handoff, not FDA example values, patient data or prescribed dataset variables. The output labels describe review questions; the actual SDTM/ADaM mapping follows the applicable specification and approved study plan. [1] [2]

EClinCloud's hypothetical transfer comparison, informed by the CGM specification's missingness and traceability recommendations. Later exclusion or imputation requires its own approved analysis rule. [1]

For an immunogenicity-PK example, suppose synthetic subject P01 has ADA specimen A1 at 09:02, with separate screening-positive and confirmatory-negative records, and PK specimen P1 at 08:58 with a concentration of 10 ng/mL. Keep A1's sample/time/assay-parameter relationships when preparing ADIS, and P1's sample/time/concentration relationships when preparing ADPC. Use ADSL for the applicable subject-level context. Match records through their actual sample/time/parameter relationships, preserving multiple assay and PK rows within a nominal visit. These labels and numbers are illustrative inputs, rather than a universal result-classification or join algorithm. [2]

If A1's confirmatory record is missing, preserve that unresolved component; avoid replacing it with a negative result. If an aliquot or identifier relationship is unresolved, reconcile it against the laboratory transfer definition before creating an analysis association. The approved assay interpretation and SAP determine any final positivity, adjustment or imputation rule. This distinction protects both the source hierarchy and the meaning of an absent result.

EClinCloud's standards-version report covers general version planning. The current conformance guide is October 2026 Version 6.3. Confirm the applicable standards, dataset organization and reviewer documentation without treating every illustrative specification field as mandatory. [3] [4]

Method and limits

This report reads the May 2026 CGM and June 2026 immunogenicity-PK specifications, particularly their scope statements, missing-data recommendations, dataset descriptions and traceability provisions. It uses the broader standardized-study-data and conformance resources for context. The five-minute example is explicitly calculated; it represents no observed trial. [1] [2] [3] [4]

The analysis supplies no new clinical efficacy, safety or assay result. The two source documents have their own stated purposes, which define the scope used here.

For an EClinCloud implementation discussion, bring the device or laboratory transfer definition, protocol timing, applicable standard versions and analysis rules. Ask the team to trace one valid record, one missing or indeterminate case and one boundary case through the proposed pipeline. A reconciled example gives the sponsor a concrete basis for accepting the handoff while preserving scientific and regulatory ownership.

Sources

1. FDA. Submitting Continuous Glucose Monitoring Data in Clinical Trials, technical specifications, Version 1.0, May 2026. Scope, LB missingness recommendations and ADaM timing/analysis discussion. Nonbinding recommendations.

2. FDA. Submitting Clinical Trial Datasets to Evaluate the Impact of Immunogenicity on the Pharmacokinetics of a Drug, technical specifications, June 2026. ADIS, ADPC, ADSL, terminology and documented omissions/additions. Nonbinding recommendations.

3. FDA. Data Standards Catalog and Providing Regulatory Submissions in Electronic Format — Standardized Study Data. Consult the applicable current standards and submission conditions.

4. FDA. Study Data Technical Conformance Guide, Version 6.3, October 2026, checked 17 October 2026. General dataset, traceability and reviewer-documentation context; check the current version for a submission.