Proactive Drug Monitoring Boosts Adalimumab Effectiveness in Psoriasis

Proactive drug monitoring for adalimumab: a PKPD study that could change psoriasis care

Over the past two decades, biologic therapies have reshaped how we treat immune-mediated inflammatory diseases, offering targeted options for conditions such as psoriasis, rheumatoid arthritis, and inflammatory bowel disease (Source: Kuek et al.).

Despite these advances, many people treated with tumor necrosis factor (TNF) inhibitors like adalimumab either don’t get a good long-term response or lose benefit over time, leaving clinicians to decide whether to escalate dose, add therapies, or switch drug classes (Source: Pan S et al.).

Why monitoring drug levels matters

For several biologics, higher blood concentrations generally correlate with better clinical outcomes until a plateau is reached; this observation underlies the idea of measuring drug levels to guide treatment (Source: Pan S et al.).

In psoriasis, a proposed therapeutic range for adalimumab has been suggested previously as roughly 3.2–7.0 μg/mL, but robust prospective trials validating routine monitoring are still limited (Source: Pan S et al.).

What the new study did

Researchers used a combined pharmacokinetic-pharmacodynamic (PKPD) model to simulate the effect of a proactive therapeutic drug monitoring strategy versus standard dosing for patients with psoriasis receiving adalimumab (Source: Pan S et al.).

The analysis drew on real-world data from 544 patients treated with adalimumab monotherapy across UK cohorts including BSTOP and PSORT-Discovery, with registry support from BADBIR centers (Source: Pan S et al.; Source: BADBIR).

The investigators modeled drug levels, the presence of anti-drug antibodies, Psoriasis Area and Severity Index responses, and patient characteristics such as body weight and comorbidities to predict outcomes under different monitoring strategies (Source: Pan S et al.).

How the PKPD model worked

The team first characterized the pharmacokinetics of adalimumab, confirming linear behavior consistent with prior studies across immune-mediated diseases (Source: Pan S et al.).

The model then linked drug exposure to clinical effect using a turnover model that simulates how psoriasis lesions improve or recur over time — a way to estimate how long it takes the skin to respond after changing drug dosing (Source: Pan S et al.).

Key pharmacokinetic findings

The analysis found that increased drug clearance was associated with several measurable factors, including higher body weight, larger waist circumference, female sex, hypertension, and detectable anti-drug antibodies, each of which can reduce circulating adalimumab concentrations (Source: Pan S et al.).

The relationship between antibodies and clearance supports the role of immunogenicity — the immune system forming antibodies against the biologic — in secondary loss of response for some patients (Source: Pan S et al.).

Pharmacodynamics: how quickly lesions change

Using the turnover approach, investigators estimated a lesion turnover half-life of about 17 days, a time scale that aligns with other biologic studies in psoriasis and helps set expectations for when clinical change might be seen after a dosing adjustment (Source: Pan S et al.).

Despite fitting the model to many clinical variables, substantial between-patient variability remained, emphasizing that measurable factors don’t fully explain why some people respond much better than others (Source: Pan S et al.).

The proactive TDM algorithm tested in simulations

Investigators simulated a proactive therapeutic drug monitoring strategy where trough adalimumab levels were checked at week 5 and week 17 after treatment start, with predefined dose changes based on those results (Source: Pan S et al.).

Under the simulated algorithm, patients with week 5 troughs below 3.2 μg/mL were escalated to weekly dosing beginning at week 6; decisions at week 17 used both PASI responses and measured drug levels to either continue, escalate, de-escalate, or consider switching (Source: Pan S et al.).

What the simulations showed

Compared with standard every-2-week dosing, the proactive TDM simulation improved the estimated 6-month PASI90 rate from 28.3% to 38.9%, a relative improvement of about 37.5% (Source: Pan S et al.).

Similarly, simulated PASI75 rates rose from 62.4% to 70.4% under the proactive strategy, though these gains came with a roughly 25.9% increase in total adalimumab exposure across the modeled population (Source: Pan S et al.).

Distinct patient subgroups emerged

The simulations highlighted at least two clinically important subgroups: patients with persistently low trough concentrations even after dose escalation, and patients with high trough concentrations who were doing very well clinically (Source: Pan S et al.).

Patients who remained with low troughs despite escalation were unlikely to reach PASI90, indicating that further escalation might have limited value and that switching to a different biologic class could be more appropriate (Source: Pan S et al.).

Conversely, a subgroup with troughs above 7 μg/mL who had already achieved PASI90 were modeled to have their dosing reduced to every 3 weeks; in this simulation their PASI90 rates fell modestly from 100% to 86.5%, suggesting selective de‑escalation could be feasible for some well-controlled patients (Source: Pan S et al.).

Clinical implications: optimize dosing or switch drug class?

Investigators suggest that proactive TDM could be used both to optimize dosing in patients likely to benefit and to identify those who are unlikely to achieve remission with further dose increases and who may need a different mechanism of action, such as IL-17 or IL-23 inhibitors (Source: Pan S et al.).

Using therapeutic drug monitoring in this way could help avoid unnecessary drug exposure, reduce ineffective escalation, and potentially speed appropriate switches for patients showing immunogenicity or extremely rapid clearance (Source: Pan S et al.).

How this fits with existing practice and evidence

Reactive TDM — testing drug levels after a patient loses response — is already common in conditions such as inflammatory bowel disease, where clinicians use levels to guide adjustments (Source: Wang et al.).

By contrast, evidence supporting routine proactive TDM (testing before clinical failure to guide preventative dose adjustments) remains mixed, and guideline panels have been cautious: some guidance supports proactive monitoring for maintenance infliximab but is less enthusiastic about routine proactive monitoring for adalimumab because high-quality trial data are limited (Source: Zeraatkar et al.).

The current study is simulation-based rather than a randomized controlled trial, but authors argue it provides useful evidence that individualized PKPD-guided dosing strategies deserve prospective testing in psoriasis (Source: Pan S et al.).

Strengths and limitations to keep in mind

A major strength of the work was the use of large, real-world registry data across multiple UK dermatology centers, which helps the findings reflect routine clinical practice rather than highly selected trial populations (Source: Pan S et al.; Source: BADBIR).

However, limitations include pragmatic drug sampling — a mix of trough and non‑trough specimens — and some missing dosing information that required assumptions about adherence and injection timing (Source: Pan S et al.).

The proactive algorithm tested was intentionally simplified and evaluated over a relatively short window (6 months), so the long-term consequences of proactive TDM on sustained remission, safety, and cost-effectiveness remain unknown (Source: Pan S et al.).

What might come next: tools and trials

Investigators envision that future clinical care could be supported by Bayesian forecasting models and digital dashboards that integrate drug levels and patient data to make individualized dosing recommendations in near real time (Source: Pan S et al.).

Ultimately, prospective randomized studies will be needed to determine whether proactive TDM can consistently improve long-term remission rates, reduce unnecessary exposure, and represent a cost-effective alternative to empiric escalation or premature switching (Source: Pan S et al.; Source: Zeraatkar et al.).

Sources

1. Kuek A, Hazleman BL, Ostör AJ. Immune-mediated inflammatory diseases (IMIDs) and biologic therapy: a medical revolution. 2007. (Source: Kuek et al.)
2. Pan S, Tsakok T, Wei R, et al. Evaluation of a therapeutic drug monitoring strategy for adalimumab in psoriasis: a prospective pharmacokinetic-pharmacodynamic study. doi:10.1111/cts.70563 (Source: Pan S et al.).
3. Wang MY, Zhao JW, Zheng CQ, Sang LX. Therapeutic drug monitoring in inflammatory bowel disease treatments. World J Gastroenterol. 2022;28(15):1604-1607. doi:10.3748/wjg.v28.i15.1604 (Source: World Journal of Gastroenterology).
4. Zeraatkar D, Pitre TS, Kirsh S, et al. Proactive therapeutic drug monitoring of biologic drugs in patients with inflammatory bowel disease, inflammatory arthritis, and psoriasis: systematic review and meta-analysis. Published 2024 Oct 28. doi:10.1136/bmjmed-2024-000998 (Source: BMJ Medicine).
5. BSTOP and PSORT-Discovery cohorts; BADBIR registry data used in the PKPD analysis (Source: BSTOP, PSORT-Discovery, BADBIR).