Pharmacokinetics as a Biomarker

ABSTRACT

Pharmacokinetics (PK) has long been differentiated from pharmacodynamics (PD) and biomarkers, yet this distinction undervalues PK's translational relevance. In this Perspective, we propose that PK itself functions as a biomarker that bridges dose, exposure, and response. Using examples from target‐mediated drug disposition, antidrug antibodies, cerebrospinal fluid PK, high‐dose methotrexate therapy, and anti‐infective pharmacology, we illustrate how PK serves as a measurable, predictive, and actionable biomarker that informs drug development, guides decisions, and advances precision medicine.

The traditional distinction of pharmacokinetics (PK) versus pharmacodynamics (PD) or biomarkers is an anachronism that limits the full utility of quantitative clinical translational pharmacology. Simplistically, that dogma posits a pharmacologic intervention as a cascade of events from dosing to PK (exposure) to PD (biomarker response), resulting in efficacy (clinical outcome). PK and PD are depicted as conceptually separate in typical PK/PD analyses, but, in fact, PK as the measurement of drug concentration serves as the most proximal and reliably quantifiable indicator of target exposure, providing an essential early link in the translational chain (Figure 1).

FIGURE 1.

Pharmacokinetics as a biomarker.

The definition of a biomarker [1] describes it as “a defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or biological responses to an exposure or intervention, including therapeutic interventions. Biomarkers may include molecular, histologic, radiographic, or physiologic characteristics. A biomarker is not a measure of how an individual feels, functions, or survives.” One typical biomarker type is a pharmacodynamic or response biomarker [1], which is defined as “a biomarker used to show that a biological response, potentially beneficial or harmful, has occurred in an individual who has been exposed to a medical product or an environmental agent.” More generally, in clinical pharmacology, PD is the study of how drugs interact with the body to produce their molecular, biochemical, and physiological effects [2]. PK is the study of the time course of drug absorption, distribution, metabolism, and excretion [3]. Commonly, PK is understood as what the body does to drugs and PD is what the drug does to the body. That said, drug PK, used to inform or predict efficacy or safety, appears to fall within the category of a biomarker, particularly when there is a robust evidentiary link between exposure and clinical outcome. The value of PK data, and its role as a biomarker, evolves throughout drug development. In early‐phase clinical studies, PK is typically valued as PK (i.e., defining the time course of drug disposition, establishing safe and tolerable exposures, and determining dose regimens for subsequent trials). As development progresses and clinical data accumulates, the value of PK transforms. PK becomes the link between exposure and therapeutic or safety outcomes, establishing exposure–response relationships that inform decision‐making. Through this evolution, PK transitions from a descriptive measure of disposition to a biomarker of clinical effect, ultimately guiding regulatory approval and precision dosing in practice. In selected cases, such as infectious diseases drug development, as discussed below, PK serves as a response biomarker linking target drug exposure to antimicrobial activity and therapeutic success, often from the early‐stage onset of human testing. PK parameters are not merely data points; they are predictive, prognostic, and monitoring biomarkers that drive critical decisions in drug discovery, development, and patient care. The BEST biomarker definition does not necessarily align with the traditional PK/PD dogma.

From the perspective of clinical translational pharmacology, the distinction between PK and PD is fundamentally a semantic boundary imposed for historical and analytical convenience, not a reflection of a different biological reality. The bedrock principle of pharmacology—that drug effect is a function of drug concentration at the site of action—elevates the measurement of concentration from a simple description of “what the body does to the drug” to a critical, objectively measured indicator of a therapeutic intervention's potential or realized response. Therefore, a modern, mechanism‐informed perspective should embrace the position: PK is a biomarker. Why does it matter? Recognition of PK as an essential class of biomarkers and critical intermediary between dose and therapeutic response helps the field of translational science to further enhance the efficiency of drug development, guides therapeutic decisions, better utilizes model‐informed drug development, and, most importantly, delivers on the promise of precision medicine by using the patient's PK profile to optimize their therapeutic regimen.

Target‐mediated drug disposition (TMDD) is an example of PK as a PD or response biomarker. A response biomarker indicates biologic activity of a medical product or environmental agent without necessarily drawing conclusions about efficacy or disease outcome or necessarily linking this activity to an established mechanism of action [1] TMDD is essentially a target occupancy/target engagement biomarker, a type of response biomarker, and measures drug‐target interaction (Table 1). In the context of biologics drug development (e.g., monoclonal antibodies), the nonlinear relationship between dose and concentration often reflects the drug binding and clearing its molecular target [4] The specific PK profile in this context serves as a pharmacodynamic or response biomarker of drug‐target binding and saturation.

TABLE 1.

Examples of pharmacokinetics as a biomarker across therapeutic areas.

PK measure	Therapeutic area/context	Biomarker category	Utility	References
TMDD	Biologics, Immuno‐oncology	Response biomarker (target engagement)	Indicates receptor binding and target saturation; informs dose selection and mechanistic proof of concept	Dua et al., 2015 [4]
ADA	Biologics	Response and safety biomarker	Detects antidrug antibodies that alter PK, reduce efficacy or increase risk of hypersensitivity; informs immunogenicity risk assessment and dose/regimen strategy	Shankar et al., 2014 [5]
CSF	CNS drug development	Response biomarker (target site exposure)	Serves as a surrogate for brain interstitial fluid exposure; supports go/no‐go decisions for CNS penetration	de Lange et al., 2002 [6]
TDM	Oncology, transplant, anti‐seizure, antibiotics	Response and safety biomarker	Guides individualized dosing, ensures therapeutic range, and prevents toxicity	Taylor et al., 2020 [7]
T > MIC	Infectious diseases	Predictive and response biomarker	Links exposure to antimicrobial activity and clinical outcomes; defines susceptibility breakpoints and regimen design	Craig, 1998 [8]
Organ Insufficiency	Cross‐therapeutic (renal/hepatic impairment studies)	Predictive and safety biomarker	Identifies altered exposure in impaired organ function; supports dose adjustment and labeling	U.S. FDA, 2003 [9], 2024 [10]

Abbreviations: %T > MIC, time above minimum inhibitory concentration; ADA, antidrug antibodies; CNS, central nervous system; CSF, cerebrospinal fluid; PK, pharmacokinetics.

In biologics development, antidrug antibodies (ADAs) are another example of the use of PK as a biomarker that integrates drug exposure, immunogenicity, and clinical response. For biologics, one of the critical aspects is to maintain adequate systemic exposure for sustained target engagement. When ADAs are formed, they can potentially bind to the therapeutic protein, resulting in increased clearance, reduced exposure, and/or neutralization of activity [5]. ADA‐related effects are commonly detected first as changes in PK, making PK a sensitive, early indicator of attenuated target engagement, and during clinical development, ADA‐associated reductions in exposures often define a critical go/no‐go biomarker threshold, since dose escalation is generally ineffective at overcoming immunogenicity (Table 1). Thus, PK assessments in the context of ADA formation serve as both response and safety biomarkers, directly informing the feasibility of achieving and sustaining the required therapeutic exposure.

Cerebrospinal fluid (CSF) PK is another compelling example of PK functioning as a target site exposure/target engagement biomarker for drugs targeting the central nervous system (CNS). The site of action of a CNS‐acting agent is the brain interstitial fluid (ISF), where the drug must interact with its target receptor or enzyme. Direct sampling of brain ISF in humans is generally invasive and impractical. However, the unbound drug concentration in CSF is similar to the unbound drug concentration in the brain ISF [6]. Thus, CSF drug PK is not just a PK measurement; it is a biomarker of target site exposure that informs dose selection and confirms target engagement potential (Table 1). For a new CNS drug candidate, demonstrating adequate CSF penetration using PK is often a go/no‐go biomarker decision point. If the CSF‐to‐plasma unbound concentration ratio is too low, the program should likely be terminated, as the biomarker indicates that sufficient central exposure is not attainable, irrespective of systemic plasma concentrations. CSF PK, in this context, entirely fulfills the definition of a response biomarker that suggests achieving a necessary therapeutic threshold in a difficult‐to‐access tissue.

Therapeutic drug monitoring (TDM) is another example in which PK functions as a real‐time response or PD biomarker. In clinical practice, TDM is typically employed for drugs with a narrow therapeutic index, where the dose required for efficacy has little margin to the dose that causes toxicity, and where there is high interindividual PK variability. In TDM, the downstream PD effect is not measured; rather, the trough concentration or steady‐state concentration is used as a clinically actionable biomarker to keep the patient within the therapeutic window. In the TDM context of use, the measured drug concentration is a response biomarker because it is an objective, measurable characteristic that is evaluated as an indicator of the pharmacologic response (i.e., whether the patient has achieved the predetermined exposure necessary for efficacy and safety). TDM could also be construed as a safety or predictive biomarker (Table 1) [1]. A safety biomarker is measured before or after an exposure to a medical product or an environmental agent to indicate the likelihood, presence, or extent of toxicity as an adverse effect. A predictive biomarker is used to identify individuals who are more likely than similar individuals without the biomarker to experience a favorable or unfavorable effect from exposure to a medical product or an environmental agent. Using TDM as a response biomarker, the clinical decision to increase, decrease, or maintain the dose is directly triggered by the PK value relative to its established therapeutic reference range.

TDM in high‐dose methotrexate (HDMTX) therapy exemplifies the use of PK as a response biomarker that directly guides individualized patient management. HDMTX is a cornerstone treatment for several pediatric and adult malignancies but requires careful post‐infusion monitoring to mitigate the risk of delayed drug elimination and associated toxicities. In this setting, the measured methotrexate concentration at defined time points (e.g., 24, 48, and 72 h after infusion) serves as an actionable response and safety biomarker that informs the timing, intensity, and duration of leucovorin rescue, hydration, urine alkalinization, and, when indicated, the administration of the enzyme rescue agent glucarpidase. Clinical decision support tools such as MTXPK.org [7] further operationalize this PK‐biomarker framework by integrating patient‐specific PK data into model‐informed estimates of a patient's methotrexate excretion curve. These quantitative forecasts allow clinicians to interpret visual and numeric information related to a patient's exposure, thereby supporting precise and timely supportive care interventions. In this way, methotrexate TDM transforms PK from a retrospective descriptor of exposure into a prospective response biomarker that drives real‐time, individualized clinical decision making.

Similarly, in the treatment of infectious diseases, PK serves as a response biomarker linking drug exposure to antimicrobial activity and therapeutic success. For antibiotics and antifungals, parameters such as the ratio of the area under the concentration‐time curve to the minimum inhibitory concentration (AUC/MIC), peak concentration to MIC (Cmax/MIC), or the percentage of time the concentration remains above the MIC (%T > MIC) are established biomarkers of pharmacologic response (Table 1) [8]. These PK metrics reliably predict microbial eradication, resistance suppression, and clinical outcomes across drug classes. In both preclinical and clinical settings, they are used to define susceptibility breakpoints, guide dosing regimens, and individualize therapy in critically ill or special populations. Thus, the PK measurement in anti‐infective therapy does not merely describe drug disposition but rather functions as a validated, mechanistic biomarker that bridges exposure and response, enabling model‐informed dosing strategies that optimize efficacy while minimizing toxicity and resistance.

In organ impairment clinical trials, such as renal or hepatic insufficiency, PK acts as a predictive or safety biomarker (Table 1). PK parameters are crucial predictive biomarkers of toxicity risk, most notably in regulatory‐mandated studies assessing the impact of hepatic [9] and renal insufficiency [10] during drug development.

In conclusion, the clinical translational pharmacology perspective should recognize PK as an essential modality of biomarkers. Whether predicting a patient's unique safety risk due to organ insufficiency, confirming site‐specific exposure via CSF analysis, or guiding real‐time dosing through TDM, PK is the measurable indicator that bridges dose and effect. The statement “pharmacokinetics is a biomarker” is not a hypothesis; it is the operational reality of modern, quantitative, precision medicine.

Funding

The authors have nothing to report.

Conflicts of Interest

J.A.W. is an employee of Aditum Bio, Tempero Bio, and Trames Bio and may own stock and/or stock options. S.S. is an employee of Merck & Co and may own stock and/or stock options. The other author declared no competing interests for this work.

Wagner J. A., Singh S., and Taylor Z. L., “Pharmacokinetics as a Biomarker,” Clinical and Translational Science 19, no. 2 (2026): e70486, 10.1111/cts.70486.