Key Lessons & Opportunities in applying NCA and PopPK to ADCs

Unlike small molecules or antibodies alone, ADCs are made up of several interdependent components:

• Conjugated antibody (ADC): the intact antibody with attached payloads, usually the main driver of efficacy.
• Total antibody: includes both conjugated and unconjugated (“naked”) antibody.
• Free payload or unconjucated payload: the small molecule released from the ADC, often responsible for off-target toxicity. May influence efficacy, if there’s extracellular release.
• Antibody-conjugated payloads: reflects how much payload is attached to the antibody.

There are tight inter-analyte PK relationships. For example, free payload exposure depends on release from the ADC, while naked antibody arises from deconjugation. Treating ADCs as a single analyte risk missing these critical dynamics.

One of the most common pitfalls in ADC analysis is reporting only in mass units. Because antibodies, payloads, and linkers differ greatly in molecular weight, comparisons in ng/mL can be misleading.

Switching to molar units makes it possible to:

• Perform mole-balance checks.
• Calculate clearance and volume accurately.
• Normalize doses across analytes.
• Compare constructs with different DAR values (e.g., DAR 4 vs DAR 8), since DAR is defined as a mole ratio.

Without this step, dose–exposure relationships and cross-study comparisons may be unreliable.

DAR distribution shapes nearly every aspect of ADC behavior. ADC is a mixture of molecules with varying number of attached payloads, DAR species, each with a distinct PK profile.

• Higher DAR species tend to clear more quickly and often show nonlinear clearance
• Lower DAR species circulate longer, behaving more like unconjugated antibodies.
• Balance is key: while higher DAR increases potency by delivering more payload per antibody, it can also reduce stability and increase off-target effects.

Accounting for DAR during NCA and popPK analysis can help understand the PK and ensures more accurate exposure metrics and avoids flawed interpretations.

NCA remains a trusted tool for early PK assessment—it is fast, transparent, reproducible, and regulator-accepted. But for ADCs, the standard outputs need to go further.

• Overlay plots of antibody vs ADC concentrations can confirm stability.
• Correlation plots (e.g., payload vs ADC post-Tmax) show whether one analyte can reliably predict another.
• Exposure ratio tables highlight how payload exposure compares to ADC exposure, offering insights into safety and stability.
• Nonlinearity checks help identify target-mediated drug disposition (TMDD), which often presents as a change in half-life with concentration.

These tailored outputs provide a clearer, more complete picture of how an ADC behaves in vivo.

NCA offers a snapshot of “what happened,” but more complex questions require models that can simulate “what if.”Population PK modeling becomes essential when:

• Optimizing or comparing dosing regimens (e.g., every six vs every eight weeks).
• Projecting dosing for pediatrics or special populations.
• Separating efficacy drivers (intact ADC) from safety drivers (free payload).
• Capturing nonlinearities like TMDD.
• Assessing the impact of anti-drug antibodies (ADAs) on different analytes.

Recognizing these triggers early allows teams to move from descriptive analyses to predictive insights, supporting more confident development and regulatory decisions.

Applying these lessons not only improves how ADCs are analyzed today, but also opens the door to new opportunities:

• Closer assay–model integration: ensuring that what is measured in the lab maps directly to analytes needed in model development.
• Faster pediatric access: using popPK models with allometric scaling and maturation functions.

ADCs hold tremendous therapeutic potential but require tailored pharmacokinetic approaches. Noncompartmental analysis (NCA) provides the foundation when adapted for multi-analyte systems, molar units, and DAR. Knowing when to advance to population PK modeling is equally important to refine dosing, balance safety and efficacy, and build regulatory confidence.

Success starts with understanding the ADC—its mechanism, analyte profiles, and DAR dynamics—to create models that are both robust and actionable. Applying these principles helps developers bring ADCs to patients faster and extend their benefits beyond oncology.