Beyond Psoriasis: Why Biologics Are Reshaping Disease-Specific Dermatology
Introduction
Biologics have transformed the therapeutic landscape of chronic inflammatory dermatoses, demonstrating remarkable efficacy and safety profiles in conditions previously challenging to manage. These targeted immunomodulators have revolutionized treatment approaches for moderate-to-severe psoriasis, hidradenitis suppurativa, and atopic dermatitis. Their ability to precisely target specific inflammatory pathways represents a fundamental shift in dermatological care.
What are biologics? These sophisticated pharmaceutical agents are engineered proteins that selectively modulate immune system components involved in disease pathogenesis. Unlike conventional systemic medications, biologics for psoriasis have demonstrated superior clinical outcomes, with newer oral therapies showing promising PASI 90 and PASI 100 response rates that surpass those of current treatment options. Furthermore, patients with hidradenitis suppurativa experience a substantial reduction in inflammation and painful nodule formation when treated with appropriate biologic therapies.
Despite these advances, current biologic management faces notable challenges. Therapeutic Drug Monitoring (TDM) offers reactive or proactive dose optimization based on serum concentrations, yet its threshold-based approach fails to account for individual pharmacokinetic variability. Therefore, the emerging concept of Model-Informed Precision Dosing (MIPD) represents a critical paradigm shift, transitioning from standardized protocols to dynamic, patient-centric dosing strategies. This evolution toward personalized medicine in dermatology acknowledges the complex interplay between drug pharmacokinetics and patient-specific factors.
Limitations of Standard Biologic Dosing in Dermatology
Standard biologic dosing protocols present multiple challenges in dermatological practice, particularly for psoriasis management. Although these medications offer substantial benefits, current administration protocols often fail to account for the complex interplay among patient-specific factors, resulting in suboptimal outcomes in clinical settings.
Fixed Dosing vs Patient Variability in Psoriasis
Most biologics employ standardized dosing regimens that inadequately account for individual patient characteristics. While infliximab is fully weight-dosed, most other agents follow fixed protocols with minimal adjustments. Ustekinumab offers higher dosing for patients weighing more than 100 kg; bimekizumab may be administered every 4 weeks for those weighing more than 120 kg; and tildrakizumab provides a higher dose option for patients with severe disease or those weighing more than 90 kg in the European Union (though not in the US) [1].
Consequently, patients with high body weight experience lower drug concentrations when administered standard doses, potentially resulting in subtherapeutic exposure [1]. Clinical evidence consistently demonstrates that high body mass index (BMI) correlates with reduced treatment responsiveness [2]. In a Danish nationwide study, patients weighing 90–110 kg were 8.2% less likely to achieve treatment response than patients weighing under 70 kg, while those weighing over 110 kg showed a 17.0% reduction in response probability [2].
Additionally, smoking status significantly impacts treatment efficacy. Smokers demonstrated a 6.2% lower probability of achieving response on biologic treatment compared with non-smokers [2]. This relationship has been consistently observed across multiple inflammatory conditions beyond psoriasis.
Primary Non-Response and Secondary Loss of Response
Treatment failures manifest in two distinct patterns: primary non-response (no initial improvement) and secondary loss of response (diminishing effectiveness after initial success) [3]. Approximately 6% of patients exhibit treatment-refractory psoriasis, defined as treatment failure with at least 3 different biologics targeting at least 2 distinct pathways [3].
Notably, patients switching between drug classes due to inadequate responses tend to experience lower success rates with subsequent treatments [3]. However, emerging evidence suggests that switching within the same drug class can remain effective, underscoring the importance of individualized treatment [3].
Several factors contribute to treatment non-response:
- Genetic predisposition affecting drug metabolism
- High body mass index leading to suboptimal drug exposure
- Metabolic or cardiovascular comorbidities altering drug pharmacokinetics
- Smoking status impacts inflammatory pathways
- Development of neutralizing antibodies (immunogenicity)
The development of anti-drug antibodies represents a particularly challenging mechanism of secondary failure. In clinical practice, temporary improvements followed by quick relapses often indicate immunogenic responses that neutralize biologic effects [3].
Off-Label Adjustments Without Pharmacokinetic Justification
In response to treatment failures, clinicians frequently resort to empirical dose adjustments lacking robust pharmacokinetic justification [4]. These modifications often involve increased doses above guideline recommendations, modified dosing frequency, or switching between agents [4].
Nevertheless, off-label dosing adjustments remain commonplace, with one study finding that dose escalation is particularly common with etanercept [4]. For secukinumab, researchers observed an increase in median accumulated syringes above recommendations during maintenance therapy, potentially reflecting responses to a gradual loss of efficacy [4].
Moreover, these adjustments carry significant economic implications. The accumulated costs in secukinumab-treated patients were notably higher than expected based on standard dosing, highlighting the financial impact of dose escalation [4].
Meanwhile, international surveys also reveal widespread use of dose-reduction strategies. Among 53 dermatologists surveyed, 66% performed dose reductions, primarily for adalimumab, etanercept, ustekinumab, and secukinumab [5]. However, the criteria for implementing these adjustments varied widely across practitioners, reflecting the lack of standardized approaches [5].
These limitations underscore the need for more personalized therapeutic approaches that account for individual pharmacokinetic profiles, clinical characteristics, and biomarker data to optimize biologic dosing in dermatological practice.
Therapeutic Drug Monitoring (TDM) in Clinical Practice 
Therapeutic drug monitoring (TDM) has emerged as an instrumental approach for optimizing biologic therapy in dermatology, especially for psoriasis management. This methodology—encompassing measurement of trough concentrations and anti-drug antibodies—provides objective data to guide clinical decision-making beyond standardized protocols.
Threshold-Based Dose Adjustments Using Serum Levels
TDM enables personalized dosing by assessing serum drug concentrations within established therapeutic ranges. For adalimumab in psoriasis, the most recent target therapeutic range for trough levels is 3.51-7.00 mg/L [6]. When concentrations fall below this threshold, clinicians can implement rational dose adjustments rather than making empirical changes without pharmacokinetic justification.
The clinical relevance of TDM stems from well-documented exposure-response relationships across biologic classes. Initially established for adalimumab, these relationships have subsequently been identified for newer biologics [7]. For instance, research indicates that higher serum infliximab concentrations correlate with superior mucosal healing rates, as demonstrated in IBD studies, where concentrations above 7 μg/mL increased the likelihood of healing [1].
The practical implementation of threshold-based dosing requires consideration of medication-specific factors. Etanercept exhibits lower immunogenicity than other biologics, with anti-etanercept antibodies observed in only 0%-18.3% of patients [6]. Conversely, adalimumab shows higher immunogenicity, with anti-adalimumab antibodies detected in 6.5%-45% of patients [6].
Reactive vs Proactive TDM Strategies
Two distinct approaches characterize TDM implementation in clinical practice:
Reactive TDM involves measuring drug concentrations and antibody levels in response to treatment failure—either primary non-response (lack of initial effect) or secondary loss of response (diminishing efficacy after initial success) [5]. This approach helps determine whether inadequate response stems from insufficient drug exposure or immunogenicity.
Proactive TDM entails scheduled measurement of drug concentrations and antibodies to optimize drug exposure before clinical failure [5]. This preventive strategy aims to maintain drug levels within therapeutic windows throughout treatment.
Clinical evidence increasingly favours proactive monitoring. A systematic review and meta-analysis found that proactive TDM of anti-TNF therapy, compared with standard care, was associated with reduced treatment failure and surgical rates, as well as improved endoscopic outcomes [5]. Furthermore, a large retrospective study demonstrated that proactive TDM of infliximab was associated with greater drug durability, fewer IBD-related hospitalizations, and lower risk of antibody formation or infusion reactions [1].
Limitations in Predicting Long-Term Outcomes
Even as TDM offers advantages over empirical dosing, several limitations hinder its predictive capacity for long-term outcomes. First, turnaround time with many TDM assays creates a substantial challenge—clinicians often must base current dosing decisions on trough levels from previous infusions weeks earlier [8]. This temporal disconnect complicates real-time optimization.
Additionally, variability between assay methods introduces potential inconsistency. Although evidence suggests that inter-assay variability for infliximab concentrations is likely not clinically significant, comparable data for other biologics remain insufficient [1].
Target thresholds also differ based on disease phenotype and desired outcome. For infliximab in the post-induction phase, minimal trough concentrations should exceed 3 μg/ml, whereas concentrations above 7 μg/ml correlate with increased mucosal healing [1]. These varying thresholds complicate the standardization of monitoring protocols.
Furthermore, the threshold-based nature of conventional TDM inherently limits the prediction of individual pharmacokinetic variability and future outcomes [9]. This static approach fails to account for dynamic changes in drug metabolism and disease activity that occur over time.
Finally, practical implementation barriers persist, including costs of repeated testing, limited point-of-care availability, and challenges in patient adherence to monitoring schedules [6]. These factors collectively constrain TDM’s ability to consistently predict and optimize long-term treatment trajectories.
Model-Informed Precision Dosing (MIPD) Framework
Model-Informed Precision Dosing (MIPD) represents an advanced evolution beyond conventional therapeutic drug monitoring in dermatology practice. This approach extends traditional methods by integrating mathematical models with patient-specific data to generate dynamic, individualized dosing recommendations [9].
Population PK/PD Models in Biologic Therapy
Population pharmacokinetic/pharmacodynamic (PK/PD) models serve as the foundation for precision dosing of biologics in dermatology. These mathematical frameworks characterize the relationships among dose, drug concentration, and clinical response across patient populations. In psoriasis management, PK/PD models can quantify the relationship between adalimumab concentrations and PASI score improvements, with studies reporting an IC50 of 1.19 mg/L [10]. Such models incorporate various covariates—including concomitant medications, antibody formation, and patient characteristics—to explain interindividual variability in drug clearance. For instance, research demonstrates that co-administration of methotrexate reduces adalimumab clearance, potentially extending its therapeutic effect [10].
Bayesian Forecasting for Sparse Data Scenarios
Bayesian methods offer crucial advantages for precision dosing when limited data is available. Through Bayesian forecasting, clinicians can combine population-level data (prior information) with individual patient measurements to generate personalized parameter estimates [2]. This mathematical approach enables accurate predictions even with sparse sampling—a common scenario in clinical dermatology. External validation of a risankizumab pharmacokinetic model using this approach demonstrated excellent predictive power (R² = 0.86) compared to basic models [2]. Bayesian methods are particularly valuable for their ability to propagate uncertainty, handle sparse data, and address high-dimensional parameter spaces [11].
Adaptive Dosing Algorithms for Non-Steady-State Conditions
Adaptive algorithms enable dosing recommendations even before steady-state conditions are achieved—a distinct advantage over threshold-based TDM. These computational approaches can forecast future drug concentrations and clinical responses from early measurements, enabling proactive dose adjustments. For ustekinumab in psoriasis, MIPD analysis predicted that 35% and 26% of patients would require optimized or intensified dosing regimens, respectively, compared with standard clinical protocols [12]. Properly calibrated concentration ranges (1.6–1.8 mg/L for ustekinumab) help achieve optimal clinical targets such as PASI ≤1 in 90% of patients [12].
Integration with Electronic Health Records
Practical implementation of MIPD requires seamless integration with clinical workflows. Interactive dashboards embedded in electronic health records enable real-time entry of patient data and the rapid generation of dosing recommendations. User testing of a precision dosing dashboard for risankizumab demonstrated strong acceptability among healthcare professionals, with dosing calculations averaging 2 minutes [2]. Such tools facilitate the transition from theoretical models to practical clinical application, ultimately enhancing the effectiveness of biologic therapies across dermatological conditions.
Patient-Specific Factors in Biologic Optimisation
Optimization of biologic therapy requires careful consideration of individual characteristics that influence treatment efficacy. Recent advances in personalized medicine have established several patient-specific variables as crucial determinants for tailoring biologic treatment in psoriasis.
Pharmacogenomics in Biologic Response Prediction
Genetic variants play a vital role in predicting biologic treatment outcomes. Polymorphisms in the TNF-α gene, including rs1800629, rs361525, and rs1799724, directly influence response to TNF inhibitors in European populations [13]. Likewise, specific variants in TNFAIP3 (rs610604, rs6920220) and IL-12B (rs2546890) modify treatment efficacy [13]. For secukinumab response, recent research identified three key genetic predictors: rs9267325 (MICB-DT), rs34085293 (DDX58), and rs2304255 (TYK2) [13]. Interestingly, the HLA-Cw6 status—traditionally considered important—has shown inconsistent predictive value across studies, with evidence suggesting that secukinumab remains effective regardless of HLA-Cw6 status [14].
Biomarker Profiling for IL-17 and IL-23 Inhibitors
Beyond genetic markers, molecular response patterns offer valuable predictive insights. Patients who achieve PASI90 at Week 52 demonstrate improvement in psoriasis-associated gene expression as early as Week 12 [3]. Conversely, those showing suboptimal cellular and molecular responses at Week 12 typically fail to attain PASI90 at Week 52 [3]. Advanced biomarker technologies now allow comprehensive profiling—one patented dermal biomarker patch extracts mRNA from skin, capturing over 7,000 biomarkers per sample [15]. Through transcriptomic analysis and machine learning algorithms, these platforms provide actionable results with >92% positive predictive value [15].
Body Weight and Age as Dosing Modifiers
Patient demographics substantially impact biologic efficacy. A recent meta-analysis revealed four factors negatively associated with PASI90 achievement at 6 months: older age, previous biologic exposure, higher body mass index, and smoking [16]. Indeed, each 1-unit increase in BMI is associated with reduced treatment response (OR 0.96; 95% CI, 0.94-0.99) [16].
The effects of body weight vary by medication class. Anti-IL drugs appear more affected by body weight than anti-TNF agents [17]. After 12 and 24 weeks of therapy, the proportion of patients achieving PASI90 was higher in individuals with BMI < 30 kg/m² than in those with BMI ≥ 30 kg/m² (54.90% vs 43.45% and 66.84% vs 56.55%, respectively) [17].
Since most biologics employ fixed dosing—except infliximab (fully weight-dosed), ustekinumab (adjusted for >100kg), and select others—patients with higher body weights often experience lower drug concentrations [18]. Accordingly, weight reduction or dose adjustment for patients with elevated BMI may increase the likelihood of response by addressing potential underdosing [16].
Digital Tools and Point-of-Care Implementation
Recent technological advances enable the practical application of model-informed precision dosing (MIPD) in clinical dermatology settings. These innovations bridge the gap between theoretical models and real-world implementation.
Precision Dosing Dashboards in Dermatology Clinics
Interactive dashboards now facilitate MIPD implementation in dermatology practices. A recently validated online TDM dashboard for risankizumab demonstrated excellent predictive power (R² = 0.86) when compared to basic pharmacokinetic models [19]. During beta testing with 29 clinicians across 8 UK dermatology centres, this tool achieved above-average usability scores (72/100) [4]. Healthcare professionals rated the dashboard as “user-friendly” and “acceptable,” with a mean calculation time of only 1.9 minutes per dosing recommendation [4]. Hence, these interfaces effectively translate complex pharmacokinetic data into actionable clinical decisions.
Mobile Health Apps for Real-Time Monitoring
Mobile applications currently enhance patient engagement and treatment optimization for biologics. Disease management apps with medication reminders increase adherence by 41% overall (52% in patients under 35 years, 29% in those 65 and older) [20]. The Smart Skin Monitor, utilizing AI imaging technology, achieves 90.2% agreement with clinician PASI scores while reducing assessment time from 15 to 4 minutes per patient [20]. Furthermore, AI-assisted biological selection has increased complete clearance rates from 39% to 61% compared with traditional protocols [20].
Barriers to Clinical Adoption of MIPD Software
Despite promising results, several obstacles hinder widespread implementation. Clinician scepticism is a major challenge—72% of clinicians distrust opaque AI decision-making processes [20]. Additionally, prohibitive costs limit accessibility in resource-constrained settings [20]. Technical barriers also exist, including limited data for model validation, unclear regulatory pathways for model endorsement, and challenges with integration into electronic health records [21]. Essentially, non-diverse AI training datasets (as low as 12% representation of varied skin tones) limit applicability across global populations [20].
Conclusion
The evolution of biologics in dermatology marks a fundamental shift from conventional therapeutic approaches toward precision medicine. These targeted immunomodulators now offer dermatologists powerful tools for managing previously challenging inflammatory conditions. Nevertheless, standard dosing protocols continue to fall short for many patients due to individual variability in drug metabolism, body composition, genetic factors, and immunogenicity profiles.
Therapeutic Drug Monitoring provides valuable insights through measurement of trough concentrations and anti-drug antibodies, though its threshold-based approach remains reactive rather than truly predictive. Therefore, Model-Informed Precision Dosing emerges as the logical next step in biologic optimization. This mathematical modelling approach integrates population pharmacokinetic data with individual patient measurements, enabling dynamic dose adjustments before clinical failure.
Patient-specific factors undoubtedly influence treatment outcomes. Genetic variants, especially within TNF-α pathways, predict response to specific biologics. Similarly, early molecular response patterns help identify patients likely to achieve long-term clearance. Body weight particularly impacts drug exposure with fixed-dose regimens, explaining reduced efficacy among patients with higher BMI values.
Digital implementation tools now bridge theoretical models with clinical practice. Precision dosing dashboards with excellent predictive power require minimal calculation time, while mobile health applications enhance monitoring accuracy and patient engagement. Despite these advances, barriers to widespread adoption persist—including clinician scepticism, cost constraints, and technical limitations.
Future dermatology practice will likely combine these approaches into unified clinical pathways. Pharmacogenomic testing at baseline could guide initial biologic selection, followed by early biomarker assessment to confirm appropriate molecular response. Subsequently, model-informed dosing algorithms would optimize maintenance therapy based on individual patient characteristics. This tailored approach aims to maximize therapeutic efficacy while minimizing side effects and healthcare costs.
Ultimately, the transition from standardized protocols to personalized biologic therapy represents not merely a technical advancement but a philosophical reconceptualization of dermatologic care. Physicians must now consider each patient’s unique biological characteristics when designing treatment strategies. Though implementation challenges remain, the potential benefits—higher response rates, improved sustainability, and enhanced quality of life—certainly justify continued investment in precision dermatology.
Key Takeaways
Biologics are revolutionizing dermatology treatment, but current approaches need refinement to maximize their potential for individual patients.
- Standard fixed-dose biologic protocols fail 6% of psoriasis patients, with body weight and smoking significantly impacting treatment success rates.
- Model-Informed Precision Dosing (MIPD) uses mathematical models and patient data to predict optimal dosing before treatment failure occurs.
- Genetic variants in TNF-α and IL-12B genes can predict biologic response, enabling personalized treatment selection from the start.
- Digital dashboards now make precision dosing practical, requiring only 2 minutes to generate personalized recommendations in clinical settings.
- Early biomarker profiling at 12 weeks accurately predicts long-term treatment success with over 92% positive predictive value.
The future of dermatology lies in combining pharmacogenomics, biomarker testing, and AI-powered dosing algorithms to create truly personalized treatment pathways that maximize efficacy while minimizing costs and side effects.
Frequently Asked Questions:
FAQs
Q1. How do biologics differ from traditional psoriasis treatments? Biologics are engineered proteins that selectively target specific components of the immune system involved in psoriasis. Unlike conventional systemic medications, they offer superior clinical outcomes and can achieve higher rates of skin clearance in moderate-to-severe cases.
Q2. Why might some patients not respond well to biologic treatments? Several factors can contribute to poor response, including high body mass index leading to suboptimal drug exposure, genetic predisposition affecting drug metabolism, development of neutralizing antibodies, and lifestyle factors like smoking that impact inflammatory pathways.
Q3. What is Model-Informed Precision Dosing (MIPD) and how does it improve biologic therapy? MIPD is an advanced approach that uses mathematical models and patient-specific data to generate personalized dosing recommendations. It allows for dynamic dose adjustments based on individual pharmacokinetics, potentially improving treatment outcomes compared to standard dosing protocols.
Q4. How do genetic factors influence the effectiveness of biologic treatments? Genetic variants, particularly in genes related to TNF-α and interleukin pathways, can predict a patient’s response to specific biologics. Pharmacogenomic testing can help guide initial biologic selection, potentially leading to more effective personalized treatment strategies.
Q5. What role do digital tools play in optimizing biologic treatments for psoriasis? Digital tools, such as precision dosing dashboards and mobile health apps, are making personalized biologic therapy more accessible in clinical practice. These technologies can provide real-time monitoring, facilitate rapid dosing calculations, and improve patient engagement, ultimately enhancing treatment effectiveness.
References:
[1] – https://pmc.ncbi.nlm.nih.gov/articles/PMC6661210/
[2] – https://kclpure.kcl.ac.uk/portal/en/publications/personalizing-biologic-therapy-in-psoriasis-development-validatio/
[3] – https://www.sciencedirect.com/science/article/pii/S0923181123000038
[4] – https://academic.oup.com/bjd/article/191/Supplement_3/ljae360.030/7916062
[5] – https://pmc.ncbi.nlm.nih.gov/articles/PMC10497208/
[6] – https://www.centerforbiosimilars.com/view/therapeutic-drug-monitoring-has-promise-for-improving-psoriasis-outcomes-study-suggests
[7] – https://eadv.org/2024/07/19/psoriasis-therapeutic-drug-monitoring-in-your-practice/
[8] – https://academic.oup.com/crohnscolitis360/article/1/3/otz049/5646641
[9] – https://pubmed.ncbi.nlm.nih.gov/41235748/
[10] – https://www.jidonline.org/article/S0022-202X(23)03103-2/fulltext
[11] – https://www.sciencedirect.com/science/article/pii/S175543652500043X
[12] – https://www.mdpi.com/1999-4923/16/10/1295
[13] – https://pmc.ncbi.nlm.nih.gov/articles/PMC8470543/
[14] – https://www.mdpi.com/1422-0067/24/8/7329
[15] – https://thedermdigest.com/precision-medicine-in-action-mindera-health-and-liviniti-join-forces-to-predict-biologic-response-in-pso-patients/
[16] – https://jamanetwork.com/journals/jamadermatology/fullarticle/2819898
[17] – https://pmc.ncbi.nlm.nih.gov/articles/PMC8481196/
[18] – https://www.mdpi.com/2075-1729/11/12/1316
[19] – https://pubmed.ncbi.nlm.nih.gov/39983977/
[20] – https://pmc.ncbi.nlm.nih.gov/articles/PMC12641434/
[21] – https://pmc.ncbi.nlm.nih.gov/articles/PMC11835426/
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