Interactive applications, developed using Shiny for the R programming language, have the potential to revolutionize the sharing and communication of pharmacometric model simulations. Shiny allows customization of the application's user-interface to provide an elegant environment for displaying user-input controls and simulation output–where the latter simultaneously updates with changing input. The flexible nature of the R language makes simulations of population variability possible thus promoting the combination of Shiny with R in model visualization.

Abstract. In recent years, there has been increasing interest in the development of medical decisionsupport 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.

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.

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 (C max ) 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 C max and AUC, respectively. The overall distribution of exposures (C max 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

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.

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.

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.

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. http://clinicaltrials.gov identifiers: NCT01616277 and NCT02310763.