A five-part series exploring the foundational quantitative analysis methodologies that shape clinical pharmacology strategy and pharmacometrics approaches.

At A2-Ai, we believe robust drug development decisions start with asking the right question and a shared understanding of what approaches to take and how the models are built and applied. Modeling Monday is a five-part blog series that highlights the core considerations and modeling methodologies used across drug discovery and development, with a focus of “demystifying” them.

The series is authored by Rosa Luo, MS, SVP of Clinical Pharmacology, and Sean McCann, PhD, Associate Pharmacometrician, who bring complementary perspectives from clinical pharmacology and pharmacometrics. Together, they will walk through commonly used quantitative analysis and modeling techniques, when they are most impactful, and how they inform real-world decision making.

This week, we begin with noncompartmental analysis (NCA), which serves as the indispensable cornerstone of clinical pharmacology, offering a robust and model-independent method to describe a drug’s behavior within the body and potentially gain insights for more advanced, population-based analyses.

Noncompartmental Analysis (NCA)

Empirical-based investigation of pharmacokinetic (PK) profiles may be conducted with limited assumptions regarding expected drug behavior. Noncompartmental analysis, or NCA, assumes first-order processes for elimination and absorption (where applicable) to describe the shape of the observed drug concentration over time (i.e., concentration-time profile or “PK curves”). This method provides ability to analyze individual profiles within or across populations of interest and at different doses, regimens, or specific intrinsic and extrinsic factors independent of complex mathematics.

Key PK parameters for drug, prodrug, or metabolite are calculated to assess systemic and tissue exposures, such as maximum concentration (C_max), time of maximum concentration (T_max), apparent elimination rate and half-life, and area under the concentration time curve (AUC) that represents the total exposure. This data is then summarized for comparisons between cohorts and/or periods based on study specific objectives. Primary considerations for NCA output include proportionality of dose-exposure, observed effects of relevant intrinsic and extrinsic factors, including drug interactions, organ impairments, influence from disease conditions (e.g., cancer type), formulations, route of administrations, fed/fasted state, or the presence of anti-drug/neutralizing antibodies, etc. NCA results and interpretation are not only one of the key components of clinical study reports (CSR), they are also the building blocks to refine clinical pharmacology strategy and provide the empirical evidence needed to develop population PK models.

Expert Perspective Use of NCA

NCA is the “gold standard” for toxicokinetic analysis and Phase 1 clinical pharmacology studies where PK is the primary objective. Its application is also essential in the regulatory landscape because it is highly reproducible and requires no prior assumptions about drug disposition. NCA is the vital first step in any program, providing the empirical “ground truth” that informs more complex downstream pharmacometrics and guides critical strategic decisions in drug development. Phase 1 clinical pharmacology studies address specific questions that delineate if and how various intrinsic and extrinsic factors would meaningfully impact PK. While NCA calculation is straightforward, pertinent interpretation of the NCA results requires comprehensive understanding of PK principles and the absorption, distribution, metabolism, and excretion (ADME) property of the therapeutic modality. The integrity of NCA is greatly dependent on the quality and timing of the PK blood draws during the study design and protocol development stage, as NCA is “data-driven” rather than “model-driven.” Robust NCA requires a strategic balance of frequent early sampling to define the absorption profile and sufficiently late sampling to capture the apparent terminal elimination phase.

Once sufficient PK data has been amassed, population-based analyses should be considered and utilized to make more robust assessments, especially for relevant intrinsic factors that could not be properly assessed in individual studies.