Model-Informed Assessment of Probability of Phase 3 Success for Ritlecitinib in Patients with Moderate-to-Severe Ulcerative Colitis.

Ritlecitinib, an oral Janus kinase 3/tyrosine kinase expressed in hepatocellular carcinoma family inhibitor, was evaluated in patients with ulcerative colitis (UC) in a phase 2b trial. Model-informed drug development strategies were applied to bridge observations from phase 2b to predictions for a proposed phase 3 study design to assess the probability of achieving the target efficacy outcome. A longitudinal exposure-response model of the time course of the 4 Mayo subscores (rectal bleeding, stool frequency, physician's global assessment, and endoscopic subscore) in patients with UC receiving placebo or ritlecitinib was developed using population modeling approaches and an item response theory framework. The quantitative relationships between the 4 Mayo subscores accommodated the prediction of composite endpoints such as total Mayo score and partial Mayo score (key endpoints from phase 2b), and modified clinical remission and endoscopic remission (proposed phase 3 endpoints). Clinical trial simulations using the final model assessed the probability of candidate ritlecitinib dosing regimens (including those tested in phase 2b and alternative) and phase 3 study designs for achieving target efficacy outcomes benchmarked against an approved treatment for moderate-to-severe UC. The probabilities of achieving target modified clinical remission and endoscopic improvement outcomes at both weeks 8 and 52 for ritlecitinib 100 mg once daily was 74.8%. Model-based assessment mitigated some of the risk associated with proceeding to pivotal phase 3 trials with dosing regimens of which there was limited clinical experience.

Moving Beyond Boundaries: Utilization of Longitudinal Exposure-Response Model for Bounded Outcome Score to Inform Decision Making in the Accelerated Drug Development Paradigm.

BACKGROUND AND OBJECTIVES: As drug development scientists strive to accelerate availability of therapies for patients, model-informed drug development (MIDD) plays an important role in contextualizing existing information and facilitating decision making. This paper describes an example of MIDD, where modeling and simulation informed decision making in the circumstance of a combined phase 2b and single pivotal study for ritlecitinib (JAK3/TEC family kinases inhibitor). METHODS: Longitudinal exposure-response (ER) modeling was conducted to describe ritlecitinib efficacy in alopecia areata patients. The Severity of Alopecia Tool (SALT) score (a continuous bounded outcome [CBO] score [0-100]) was used as the efficacy response. The average concentration during the time interval between two adjacent SALT scores was used as the exposure metric driving efficacy. RESULTS: The developed model well described the longitudinal SALT profile of ritlecitinib as well as the frequency of boundary data. The CBO model indicated tested doses in the phase 2b/3 clinical trial are in the ascending region of ER and contextualized a loading dose effect that impacted onset of efficacy without long-term benefit. It also identified disease severity as the only covariate impacting efficacy. The model-based simulation further informed impact of treatment interruption on the loss of efficacy in the absence of a dedicated treatment withdrawal study. Results indicated temporary treatment interruption ≤ 6 weeks is not expected to result in significant loss of efficacy. CONCLUSION: The CBO modeling approach and simulation supported the single pivotal trial strategy and guided dose selection in the accelerated drug development program of ritlecitinib, which can be applied to many indications where efficacy is measured on a bounded scale.

Evolution of Ritlecitinib Population Pharmacokinetic Models During Clinical Drug Development.

BACKGROUND: Ritlecitinib is an oral Janus kinase 3/tyrosine kinase expressed in hepatocellular carcinoma family inhibitor undergoing parallel clinical development for alopecia areata, vitiligo, ulcerative colitis, Crohn's disease, and rheumatoid arthritis. OBJECTIVE: As studies read out simultaneously, strategic planning of population pharmacokinetic model development and evaluation is required to ensure timely decisions. METHODS: Data from healthy participants and patients from 12 clinical trials between December 2014 and July 2021 were included: seven phase I studies in healthy participants and organ impairment, five phase II/III studies in patients with rheumatoid arthritis, ulcerative colitis, alopecia areata, and vitiligo. Population pharmacokinetic models consisted of stepwise procedures to accommodate data availability and the model's application to answering clinical development questions. At each iteration of the model update, parameters of the next model were re-estimated by leveraging previous information and new data. RESULTS: Three model development lifecycle iterations of the ritlecitinib population pharmacokinetic model were conducted to support alopecia areata, vitiligo, and ulcerative colitis study readouts. Initial structural modeling based on healthy participant data (and some rheumatoid arthritis and alopecia areata data) in iteration 1 provided a platform for comprehensive covariate testing during iteration 2, and model evaluation and implementation of the frequentist prior approach in iteration 3. The final model was a two-compartment model with first-order absorption and direct-response non-stationary clearance and bioavailability driven by concentrations in the peripheral compartment. CONCLUSIONS: The present approach demonstrated the evolution of three population pharmacokinetic models with accumulating data, addressed clinical drug development questions related to systemic exposures of ritlecitinib, and informed the approved product label. CLINICAL TRIAL REGISTRATION: NCT02309827, NCT02684760, NCT02958865, NCT02969044, NCT03232905, NCT03732807, NCT04016077, NCT03715829, NCT04037865, NCT04004663, NCT04634565, NCT02974868.

Leveraging Prior Healthy Participant Pharmacokinetic Data to Evaluate the Impact of Renal and Hepatic Impairment on Ritlecitinib Pharmacokinetics.

Ritlecitinib is a selective, covalent, irreversible inhibitor of Janus kinase 3 (JAK3) and the tyrosine kinase expressed in hepatocellular carcinoma (TEC) family kinases. Pharmacokinetics and safety of ritlecitinib in participants with hepatic (Study 1) or renal (Study 2) impairment were to be characterized from two phase I studies. Due to a study pause caused by the COVID-19 pandemic, the study 2 healthy participant (HP) cohort was not recruited; however, the demography of the severe renal impairment cohort closely matched the study 1 HP cohort. We present results from each study and two innovative approaches to utilizing available HP data as reference data for study 2: a statistical approach using analysis of variance and an in silico simulation of an HP cohort created using a population pharmacokinetics (POPPK) model derived from several ritlecitinib studies. For study 1, the observed area under the curve for 24-h dosing interval and maximum plasma concentration for HPs and their observed geometric mean ratios (participants with moderate hepatic impairment vs HPs) were within 90% prediction intervals from the POPPK simulation-based approach, thereby validating the latter approach. When applied to study 2, both the statistical and POPPK simulation approaches demonstrated that patients with renal impairment would not require ritlecitinib dose modification. In both phase I studies, ritlecitinib was generally safe and well tolerated. These analyses represent a new methodology for generating reference HP cohorts in special population studies for drugs in development with well-characterized pharmacokinetics in HPs and adequate POPPK models. TRIAL REGISTRATION: ClinicalTrials.gov NCT04037865 , NCT04016077 , NCT02309827 , NCT02684760 , and NCT02969044 .

Opportunities and Challenges of Disease Progression Modeling in Drug Development - An IQ Perspective.

Disease progression modeling (DPM) represents an important model-informed drug development framework. The scientific communities support the use of DPM to accelerate and increase efficiency in drug development. This article summarizes International Consortium for Innovation & Quality (IQ) in Pharmaceutical Development mediated survey conducted across multiple biopharmaceutical companies on challenges and opportunities for DPM. Additionally, this summary highlights the viewpoints of IQ from the 2021 workshop hosted by the US Food and Drug Administration (FDA). Sixteen pharmaceutical companies participated in the IQ survey with 36 main questions. The types of questions included single/multiple choice, dichotomous, rank questions, and open-ended or free text. The key results show that DPM has different representation, it encompasses natural disease history, placebo response, standard of care as background therapy, and can even be interpreted as pharmacokinetic/pharmacodynamic modeling. The most common reasons for not implementing DPM as frequently seem to be difficulties in internal cross-functional alignment, lack of knowledge of disease/data, and time constraints. If successfully implemented, DPM can have an impact on dose selection, reduction of sample size, trial read-out support, patient selection/stratification, and supportive evidence for regulatory interactions. The key success factors and key challenges of disease progression models were highlighted in the survey and about 24 case studies across different therapeutic areas were submitted from various survey sponsors. Although DPM is still evolving, its current impact is limited but promising. The success of such models in the future will depend on collaboration, advanced analytics, availability of and access to relevant and adequate-quality data, collaborative regulatory guidance, and published examples of impact.

Population PK and PD Analysis of Domagrozumab in Pediatric Patients with Duchenne Muscular Dystrophy.

Myostatin, a negative regulator of skeletal muscle growth, is a therapeutic target in muscle-wasting diseases. Domagrozumab, a humanized recombinant monoclonal antibody, binds myostatin and inhibits activity. Domagrozumab was investigated in a phase II trial (NCT02310763) as a potential treatment for boys with Duchenne muscular dystrophy (DMD). Pharmacokinetic/pharmacodynamic (PK/PD) modeling is vital in clinical trial design, particularly for determining dosing regimens in pediatric populations. This analysis sought to establish the PK/PD relationship between free domagrozumab and total myostatin concentrations in pediatric patients with DMD using a prior semimechanistic model developed from a phase I study in healthy adult volunteers (NCT01616277) and following inclusion of phase II data. The refined model was developed using a multiple-step approach comprising structural, random effects, and covariate model development; assessment of model adequacy (goodness-of-fit); and predictive performance. Differences in PKs/PDs between healthy adult volunteers and pediatric patients with DMD were quantitatively accounted for and evaluated by predicting myostatin coverage (the percentage of myostatin bound by domagrozumab). The final model parameter estimates and semimechanistic target-mediated drug disposition structure sufficiently described both domagrozumab and myostatin concentrations in pediatric patients with DMD, and most population parameters were comparable with the prior model (in healthy adult volunteers). Predicted myostatin coverage for phase II patients with DMD was consistently > 90%. Baseline serum myostatin was ~ 65% lower than in healthy adult volunteers. This study provides insights into the regulation of myostatin in healthy adults and pediatric patients with DMD. Clinicaltrials.gov identifiers: NCT01616277 and NCT02310763.

Population Pharmacokinetic/Pharmacodynamic Modeling of the Effect of Abrocitinib on QT Intervals in Healthy Volunteers.

Abrocitinib is a selective Janus kinase 1 inhibitor for the treatment of moderate to severe atopic dermatitis (AD). To assess the relationship between abrocitinib plasma concentrations and heart rate (HR)-corrected QT (QTc) and HR and calculate the effect of abrocitinib on these parameters at supratherapeutic concentrations, 36 healthy volunteers received single doses of abrocitinib 600 mg, placebo, and moxifloxacin 400 mg in a 3-period crossover study. The relationship between change from baseline in Fridericia-corrected QTc (∆QTcF) values and abrocitinib plasma concentrations was modeled using a prespecified linear mixed-effects model. The 90%CIs for time-matched placebo-corrected ∆QTcF (∆∆QTcF) were calculated from model parameter estimates and assessed against the regulatory threshold (10 millisecond) at the predicted supratherapeutic concentration in patients with atopic dermatitis (2156 ng/mL). Mean (90%CI) time-matched placebo-corrected change from baseline in HR (∆∆HR) was calculated similarly. At the supratherapeutic concentration, mean (90%CI) estimates for ∆∆QTcF and ∆∆HR were 6.00 (4.52-7.49) milliseconds and 6.51 (5.23-7.80) bpm, respectively. Despite a concentration-dependent effect on ∆QTcF and ∆HR, with statistically significant slopes (90%CI) of 0.0026 (0.0018-0.0035) milliseconds/(ng/mL) and 0.0031 (0.0024-0.0038) bpm/(ng/mL), respectively, abrocitinib does not have a clinically significant effect on QTc interval or HR at supratherapeutic exposures.

Population pharmacokinetic-pharmacodynamic modelling of platelet time-courses following administration of abrocitinib.

AIMS: Abrocitinib is a selective Janus kinase 1 inhibitor for the treatment of moderate-to-severe atopic dermatitis. Herein we describe the time-course of drug-induced platelet reduction following abrocitinib administration, identify covariates affecting platelet counts, and determine the probability of patients experiencing thrombocytopaenia while receiving abrocitinib. METHODS: This analysis included data from two Phase 2 and three Phase 3 studies in psoriasis and atopic dermatitis patient populations administered abrocitinib 10-400 mg QD orally for up to 12 weeks, with platelet counts determined up to week 16. A semi-mechanistic model was developed to assess the impact of baseline platelet counts (170, 220 and 270 × 1000/µL), age and race on the platelet nadir and week 12 counts with once-daily abrocitinib 200 mg or 100 mg. RESULTS: Decreases in platelet counts were transient with the nadir occurring on average 24 days (95% prediction interval, 23-24) after continuous administration of abrocitinib 200 mg QD. Following administration of once-daily abrocitinib 200 mg, the probabilities of thrombocytopaenia (<150 × 1000/µL) at the nadir were 8.6% and 95.5% for the typical patient with baseline platelet count of 270 × 1000/µL or 170 × 1000/µL, respectively. Adolescents had a lower probability of thrombocytopaenia compared with adults; platelet count distribution was similar in Asian and Western patients at the nadir and at week 12. CONCLUSION: This analysis supports the safety of once-daily abrocitinib 200 mg and 100 mg dosing regimens, with low probability of thrombocytopaenia during treatment, except for higher risk of low-grade thrombocytopaenia that diminished after 4 weeks in patients with low baseline platelet counts.

Population Pharmacokinetics of Abrocitinib in Healthy Individuals and Patients with Psoriasis or Atopic Dermatitis.

BACKGROUND AND OBJECTIVE: Abrocitinib is a Janus kinase 1 inhibitor in development for the treatment of atopic dermatitis (AD). This work characterized orally administered abrocitinib population pharmacokinetics in healthy individuals, patients with psoriasis, and patients with AD and the effects of covariates on abrocitinib exposure. METHODS: Abrocitinib concentration measurements (n = 6206) from 995 individuals from 11 clinical trials (seven phase I, two phase II, and two phase III) were analyzed, and a non-linear mixed-effects model was developed. Simulations of abrocitinib dose proportionality and steady-state accumulation of maximal plasma drug concentration (Cmax) and area under the curve (AUC) were conducted using the final model. RESULTS: A two-compartment model with parallel zero- and first-order absorption, time-dependent bioavailability, and time- and dose-dependent clearance best described abrocitinib pharmacokinetics. Abrocitinib coadministration with rifampin resulted in lower exposure, whereas Asian/other race coadministration with fluconazole and fluvoxamine, inflammatory skin conditions (psoriasis/AD), and hepatic impairment resulted in higher exposure. After differences in body weight are accounted for, Asian participants demonstrated a 1.43- and 1.48-fold increase in Cmax and AUC, respectively. The overall distribution of exposures (Cmax and AUC) was similar in adolescents and adults after accounting for differences in total body weight. CONCLUSIONS: A population pharmacokinetics model was developed for abrocitinib that can be used to predict abrocitinib steady-state exposure in the presence of drug-drug interaction effects or intrinsic patient factors. Key covariates in the study population accounting for variability in abrocitinib exposures are Asian race and adolescent age, although these factors are not clinically meaningful. CLINICAL TRIAL NUMBERS: NCT01835197, NCT02163161, NCT02201524, NCT02780167, NCT03349060, NCT03575871, NCT03634345, NCT03637790, NCT03626415, NCT03386279, NCT03937258.

Abrocitinib is a drug approved in the UK and Japan for the treatment of atopic dermatitis. A population pharmacokinetic model for abrocitinib was developed based on data from 11 clinical trials that included 995 healthy individuals or patients with atopic dermatitis or psoriasis. Simulations of different patient factors, such as age, race, sex, body weight, liver function, and drug­drug interactions, were tested to examine differences in abrocitinib drug levels achieved in the body. The results of these simulations indicate that although there are some differences in abrocitinib exposure, no dose adjustments of abrocitinib are necessary based on these factors.

Model-Optimized Fluconazole Dose Selection for Critically Ill Patients Improves Early Pharmacodynamic Target Attainment without the Need for Therapeutic Drug Monitoring.

Fluconazole has been associated with higher mortality compared with the echinocandins in patients treated for invasive candida infections. Underexposure from current fluconazole dosing regimens may contribute to these worse outcomes, so alternative dosing strategies require study. The objective of this study was to evaluate fluconazole drug exposure in critically ill patients comparing a novel model-optimized dose selection method with established approaches over a standard 14-day (336-h) treatment course. Target attainment was evaluated in a representative population of 1,000 critically ill adult patients for (i) guideline dosing (800-mg loading and 400-mg maintenance dosing adjusted to renal function), (ii) guideline dosing followed by therapeutic drug monitoring (TDM)-guided dose adjustment, and (iii) model-optimized dose selection based on patient factors (without TDM). Assuming a MIC of 2 mg/liter, free fluconazole 24-h area under the curve (fAUC24) targets of ≥200 mg · h/liter and <800 mg · h/liter were used for assessment of target attainment. Guideline dosing resulted in underexposure in 21% of patients at 48 h and in 23% of patients at 336 h. The TDM-guided strategy did not influence 0- to 48-h target attainment due to inherent procedural delays but resulted in 37% of patients being underexposed at 336 h. Model-optimized dosing resulted in ≥98% of patients meeting efficacy targets throughout the treatment course, while resulting in less overexposure compared with guideline dosing (7% versus 14%) at 336 h. Model-optimized dose selection enables fluconazole dose individualization in critical illness from the outset of therapy and should enable reevaluation of the comparative effectiveness of this drug in patients with severe fungal infections.

Dashboards for Therapeutic Monoclonal Antibodies: Learning and Confirming.

Inflammatory diseases (ID) are incurable, progressive diseases. Literature evidence cites increasing incidence of these diseases worldwide. When treatments with chemical immunosuppressive agents fail, patients are often treated with monoclonal antibodies (MAbs). However, MAb failure rates are generally high, with approximately half the patients being discontinued within 4 years, necessitating switching to another MAb. One potential cause of treatment failure is subtherapeutic exposure. Several studies demonstrated associations between trough MAb concentrations and clinical response, supporting the notion that improving drug exposure may result in improved outcomes. MAbs exhibit complex and highly variable pharmacokinetics in ID patients with numerous factors affecting clearance. Bayesian-guided dosing with dashboard systems is a new tool being investigated in the treatment of ID to reduce variability in exposure. Simulations suggest dashboards will be effective at maintaining patients at target troughs. However, when patients are dosed using doses or intervals outside those listed in prescribing information, there is concern that patients may have drug exposures beyond or below the ranges found to be safe and efficacious. This manuscript reviews the rationale behind dashboard development, evaluations of expected performance, and a simulated assessment of MAb exposure with dashboard-based dosing versus dosing based on the prescribing information. We introduce the concept of pharmacologic equivalence-if patients are dosed based on individual pharmacokinetics, the resulting exposure is consistent with exposures achieved using labeled dosing. We further show that dashboard-based dosing results in observed exposures that are generally contained within the range of exposures achieved with labeled dosing.

Disease activity trajectories in early rheumatoid arthritis following intensive DMARD therapy over 3 years: association with persistence to therapy.

OBJECTIVE: To identify the disease activity trajectories during intensive triple disease modifying anti-rheumatic drug (DMARD) therapy over 3 years in rheumatoid arthritis (RA) patients and to evaluate the association with treatment persistence. METHODS: Disease Activity Score in 28 joints, baseline risk factors and medication usage were obtained from a longitudinal observational cohort of early RA patients, most of whom were treated with combination DMARD therapy consisting of methotrexate, sulfasalazine and hydroxychloroquine. Persistence of each DMARD was defined as the duration of time from initiation to cessation. A group-based trajectory modelling technique was used to identify disease activity trajectories. RESULT: Three disease activity trajectories (good [43.8%], moderate [39.7%] and poor [16.5%]) were identified in a cohort of 297 patients. Most baseline risk factors, medication usage, the rate of treatment persistence and the effect of persistence on disease activity differed among patients in each of the three trajectories. Although the rate of persistence was higher in the trajectory with a good outcome, the association with persistence was more pronounced among patients who were in the poor outcome trajectory. Persistence with at least two or all three baseline DMARDs was associated with a decrease in disease activity to a broadly similar degree in all trajectories. CONCLUSION: After correction for other baseline prognostic factors, persistence with initial DMARDs contributes to heterogeneity in disease activity trajectory and there was an association between persistence with initial DMARD therapy and lower long-term disease activity.

Bayesian Forecasting Tool to Predict the Need for Antidote in Acute Acetaminophen Overdose.

STUDY OBJECTIVE: Acetaminophen (APAP) overdose is the leading cause of acute liver injury in the United States. Patients with elevated plasma acetaminophen concentrations (PACs) require hepatoprotective treatment with N-acetylcysteine (NAC). These patients have been primarily risk-stratified using the Rumack-Matthew nomogram. Previous studies of acute APAP overdoses found that the nomogram failed to accurately predict the need for the antidote. The objectives of this study were to develop a population pharmacokinetic (PK) model for APAP following acute overdose and evaluate the utility of population PK model-based Bayesian forecasting in NAC administration decisions. DESIGN, PATIENTS AND MEASUREMENTS: Limited APAP concentrations from a retrospective cohort of acute overdosed subjects from the Maryland Poison Center were used to develop the population PK model and to investigate the effect of type of APAP products and other prognostic factors. The externally validated population PK model was used a prior for Bayesian forecasting to predict the individual PK profile when one or two observed PACs were available. The utility of Bayesian forecasted APAP concentration-time profiles inferred from one (first) or two (first and second) PAC observations were also tested in their ability to predict the observed NAC decisions. MAIN RESULTS: A one-compartment model with first-order absorption and elimination adequately described the data with single activated charcoal and APAP products as significant covariates on absorption and bioavailability. The Bayesian forecasted individual concentration-time profiles had acceptable bias (6.2% and 9.8%) and accuracy (40.5% and 41.9%) when either one or two PACs were considered, respectively. The sensitivity and negative predictive value of the Bayesian forecasted NAC decisions using one PAC were 84% and 92.6%, respectively. CONCLUSION: The population PK analysis provided a platform for acceptably predicting an individual's concentration-time profile following acute APAP overdose with at least one PAC, and the individual's covariate profile, and can potentially be used for making early NAC administration decisions.

Infliximab Maintenance Dosing in Inflammatory Bowel Disease: an Example for In Silico Assessment of Adaptive Dosing Strategies.

Infliximab is an anti-tumour necrosis factor alpha monoclonal antibody used to treat inflammatory diseases. Many patients fail during induction and others respond initially but relapse during maintenance therapy. Although anti-drug antibodies (ADA) are associated with some clinical failures, there is evidence that some failures may be due to subtherapeutic exposure. Adapting doses based on clinical outcomes and trough concentrations can improve response and reduce the proportion that develop ADA, but identification of appropriate doses in the presence of time-varying patient factors is complicated. Several adaptive dosing strategies (label recommendations versus therapeutic drug monitoring with an established stepwise algorithm or proportional dose adjustments or Bayesian population pharmacokinetic model-based dosing) were simulated on a virtual population (constructed with time-varying covariates and random effects on individual pharmacokinetic parameters) using R to assess their relative performance. Strategies were evaluated on their ability to maintain trough infliximab concentrations above an established target, 3 mg/L, during maintenance phase. Model-based dosing was superior in maintaining target trough concentrations, showing individuals in maintenance achieving concentrations above the target faster and a lower proportion of individuals who developed ADA. Model-based dosing results were consistent across a range of baseline covariate groups. This in silico assessment of adaptive dosing strategies demonstrated that, when challenged with dynamic covariate and random effect changes occurring in individual pharmacokinetic parameters, model-based approaches were superior to other strategies. Model-based dosing has not been tested clinically; however, the potential benefits of model-based dosing for infliximab suggest that it should be investigated to reduce subtherapeutic exposure.

Population Pharmacokinetic Model of Doxycycline Plasma Concentrations Using Pooled Study Data.

The literature presently lacks a population pharmacokinetic analysis of doxycycline. This study aimed to develop a population pharmacokinetic model of doxycycline plasma concentrations that could be used to assess the power of bioequivalence between Doryx delayed-release tablets and Doryx MPC. Doxycycline pharmacokinetic data were available from eight phase 1 clinical trials following single/multiple doses of conventional-release doxycycline capsules, Doryx delayed-release tablets, and Doryx MPC under fed and fasted conditions. A population pharmacokinetic model was developed in a stepwise manner using NONMEM, version 7.3. The final covariate model was developed according to a forward inclusion (P < 0.01) and then backward deletion (P < 0.001) procedure. The final model was a two-compartment model with two-transit absorption compartments. Structural covariates in the base model included formulation effects on relative bioavailability (F), absorption lag (ALAG), and the transit absorption rate (KTR) under the fed status. An absorption delay (lag) for the fed status (FTLAG2 = 0.203 h) was also included in the model as a structural covariate. The fed status was observed to decrease F by 10.5%, and the effect of female sex was a 14.4% increase in clearance. The manuscript presents the first population pharmacokinetic model of doxycycline plasma concentrations following oral doxycycline administration. The model was used to assess the power of bioequivalence between Doryx delayed-release tablets and Doryx MPC, and it could potentially be used to critically examine and optimize doxycycline dose regimens.

A model-based evaluation of single metrics for discriminating changes in rheumatoid arthritis disease activity.

AIMS: Composite indices for quantifying rheumatoid arthritis (RA) disease activity such as the 28-joint disease activity score (DAS28) are comprised of single parameters ('metrics') in various combinations. Population modelling methods were used to evaluate single metrics for their ability to reflect changes in disease activity with a view to understanding and improving composite indices. METHODS: A total of 11 single metrics of RA disease activity (tender and swollen joint counts, acute phase reactants and global health, pain and physical function assessments) were obtained from 203 patients with recent onset RA. Participants received combination disease-modifying anti-rheumatic drugs (DMARDs) according to a treat-to-target approach with a pre-defined protocol for treatment intensification. Models describing each metric's magnitude and variability of change from baseline to a single 'treated' state in the population were developed using nonmem(®) . Measures that displayed uniformly large changes between states across the population were ranked higher in terms of discriminatory capacity. RESULTS: Joint counts demonstrated a greater ability to discriminate changes in RA disease activity than others. Correlations between metrics demonstrated that erythrocyte sedimentation rate (ESR) had limited relationships with others for baseline scores and changes in RA disease activity (r generally < 0.2). However it appeared to be important in describing changes for those individuals where ESR levels were initially elevated. CONCLUSION: It appears unlikely that a single group of metrics may be suitable to capture disease activity changes across all RA patients and defining the most appropriate metric(s) for individual patients will be an important area of future research.

A population model of early rheumatoid arthritis disease activity during treatment with methotrexate, sulfasalazine and hydroxychloroquine.

AIMS: To develop a population model describing the disease activity (DAS28) time course in patients with early rheumatoid arthritis (RA) treated with triple disease-modifying anti-rheumatic drug (DMARD) therapy (methotrexate, sulfasalazine and hydroxychloroquine). METHODS: DAS28 was obtained in 263 patients with early RA from initiation of therapy until 60 weeks. Using NONMEM(®), base models (DAS28 vs. time) and covariate influences were investigated for the population. RESULTS: The best model was an exponential model of DAS28 vs. time that was additive to baseline DAS28, with covariance between parameters, and a combined residual error model. Age and patient smoking history were covariates significantly affecting response to therapy. Population estimates were baseline DAS28 (5.7), extent of change in DAS28 (-2.8) and the half-life of disease activity (6.2 weeks; time to steady disease state achieved within approximately 30 weeks). Older individuals exhibited more severe baseline DAS28, described by a power function centred around 57 years (baseline DAS28 for 40- and 70-year-old patients were 5.4 vs. 5.8, respectively) and current smokers took longer to achieve a steady disease state (approximately 50 weeks). There was considerable within-patient random variability in DAS28 over time (empirical 90% CI for DAS28 in a population typical patient at 60 weeks: 1.8, 4.2 with median value of 2.8). CONCLUSIONS: This is the first report of a disease activity model for early RA treated with triple DMARD therapy. Smoking and age were identified as covariates.

Dashboard systems: implementing pharmacometrics from bench to bedside.

In recent years, there has been increasing interest in the development of medical decision-support tools, including dashboard systems. Dashboard systems are software packages that integrate information and calculations about therapeutics from multiple components into a single interface for use in the clinical environment. Given the high cost of medical care, and the increasing need to demonstrate positive clinical outcomes for reimbursement, dashboard systems may become an important tool for improving patient outcome, improving clinical efficiency and containing healthcare costs. Similarly the costs associated with drug development are also rising. The use of model-based drug development (MBDD) has been proposed as a tool to streamline this process, facilitating the selection of appropriate doses and making informed go/no-go decisions. However, complete implementation of MBDD has not always been successful owing to a variety of factors, including the resources required to provide timely modeling and simulation updates. The application of dashboard systems in drug development reduces the resource requirement and may expedite updating models as new data are collected, allowing modeling results to be available in a timely fashion. In this paper, we present some background information on dashboard systems and propose the use of these systems both in the clinic and during drug development.