Clinical Decision Support Tools: The Evolution of a Revolution

Therapeutic Drug Monitoring of Biologics: Current Practice, Challenges and Opportunities - a Workshop Report

Therapeutic drug monitoring (TDM) for dose modification of biologics has the potential to improve patient outcomes. The US Food and Drug Administration (FDA) and the American Association of Pharmaceutical Scientists (AAPS) hosted the first US-based public workshop on TDM of biologics with contributions from a broad array of interested parties including healthcare providers, clinical pharmacologists, test developers, bioanalysis and immunogenicity scientists, health economics and outcomes research (HEOR) experts and regulators. The key insight was that despite a body of evidence to support TDM in certain therapeutic areas, there remain substantial challenges to widespread clinical implementation. There is a lack of consensus regarding the integration of TDM in clinical guidelines, and a lack of consensus on the cost-effectiveness of TDM; both factors contribute to the difficulty that healthcare providers face in obtaining reimbursement for TDM (both coverage of testing itself, and coverage of potential dosing modifications). The HEOR experts outlined alternative routes to obtaining reimbursement and suggested advocating for changes in coverage policies to promote TDM use in the clinic. Reaching alignment across policy makers, patients and advocacy groups, payers, and healthcare providers, on specific treatment settings where TDM will be clearly beneficial, was identified as an important step to advancing TDM implementation for the benefit of patients.

Melphalan 140 mg/m2 is Safe and Effective for Frail and Older Multiple Myeloma Patients With Comparable Rates of Minimal Residual Disease Negativity

BACKGROUND

Despite therapeutic advances, multiple myeloma (MM) remains challenging to treat effectively. High-dose melphalan (Mel200) with autologous stem cell transplantation (ASCT) is the standard treatment for transplant-eligible patients. Reduced-dose melphalan (Mel140) is an alternative for older or frail patients, yet its efficacy data remain unclear.

METHODS

We retrospectively analyzed 233 MM patients undergoing first ASCT between 2014 and 2022, comparing outcomes between Mel140 (n = 82) and Mel200 (n = 151). We assessed patient demographics, disease characteristics, progression-free survival (PFS), and overall survival (OS). In an exploratory subset analysis achievement of MRD from bone marrow samples after ASCT was compared between the 2 groups.

RESULTS

As expected, patients who received Mel 140 were significantly older with a higher KPS. Median follow-up was 47.7 months. Both groups had similar rates of readmissions and infections within the first 100 days after transplant despite Mel140 group being older with more comorbidities. No significant difference in PFS or OS was observed between Mel140 and Mel200 groups (P > .05). MRD negativity rates at sensitivity levels of 10-5 and 10-6 were comparable (64% vs. 60%, P = .7). Patients achieving sustained MRD negativity demonstrated improved PFS regardless of melphalan dose.

CONCLUSION

Our findings suggest equivalent efficacy and safety profiles between Mel140 and Mel200, supporting Mel140 as a viable option for older or frail MM patients. In a subset analysis equivalent rates of MRD were achieved between the groups and remained a highly significant predictor of PFS, highlighting its relevance regardless of dosing strategies.

Integrating early response biomarkers in pharmacokinetic models: A novel method to individualize the initial infliximab dose in patients with Crohn's disease

The use of model-informed precision dosing to personalize infliximab has been shown to improve both the acquisition of concentration targets and clinical outcomes during maintenance. Current iterations of infliximab pharmacokinetic models include time-varying covariates of drug clearance, however, not accounting for the expected improvements in the covariates can lead to indiscriminate use of higher infliximab doses and imprecise drug exposure. The aim was to identify changes in the four biomarkers associated with infliximab clearance (Xiong et al. model) and determine if integration of these dynamic changes would improve model performance during induction and early maintenance. We analyzed two cohorts of children receiving infliximab for Crohn's Disease. The Emax method was used to assess time-varying changes in covariates. Model performance (observed vs. predicted infliximab concentrations) was evaluated using median percentage error (bias) and median absolute percentage error (precision). The combined cohorts included 239 Crohn's disease patients. We found from baseline to dose 4, the maximum changes in weight, albumin, erythrocyte sedimentation rate, and neutrophil CD64 were 4.7%, +11.7%, -62.4%, and -26.5%, respectively. We also found the use of baseline covariates alone to forecast future trough concentration was inferior to the Emax time-varying method with a significant improvement observed in bias (doses 2, 3, and 4) and precision (doses 2 and 4). The integration of the four time-varying biomarkers of drug clearance with pharmacokinetic modeling improved the accuracy and precision of the predictions. This novel strategy may be key to improving drug exposure, minimizing indiscriminate dosing strategies, and reducing healthcare costs.

"Getting the Dose Right"-Revisiting the Topic With Focus on Biologic Agents

Nearly two decades after the Peck and Cross article '"Getting the dose right: facts, a blueprint, and encouragements" was published, a review of dose recommendations for biologics shows that the success in getting the dose right appears to have improved given the relatively low incidence of drug withdrawals and dosing/label changes. However, the clinical experience with monoclonal antibodies (MAbs) following approval has been less than perfect. In inflammatory diseases, the disease burden changes with time and high treatment failure rates have been reported. In addition, the use of concomitant steroids and immunosuppressant drugs with MAbs is common. These concomitant agents have their own safety issues and many immunosuppressant agents are not well-tolerated although they have been shown to reduce the incidence of anti-drug antibodies (ADA). This same complexity is seen in MAbs used in oncology as well, although with these agents the doses appear to be higher than needed, which results in high treatment costs and incidence of adverse events. Given the complexity of MAb pharmacokinetics, which makes providing a detailed description of dose options difficult, product labeling should include the options for alternative dose strategies and potentially include the use of therapeutic drug monitoring with dose individualization which have been shown to improve clinical response and reduce the incidence of ADA. So, while the recommended dosing for biologics seems improved over the issues noted 17 years ago, we still have some work to do.

External validation of population pharmacokinetic models of tacrolimus in Thai adult liver transplant recipients

OBJECTIVE

Several population pharmacokinetic models of tacrolimus in liver transplant patients were built, and their predictability was evaluated in their settings. However, the extrapolation in the prediction was unclear. This study aimed to evaluate the predictive performance of published tacrolimus models in adult liver transplant recipients using data from the Thai population as an external dataset.

METHODS

The selected published models were systematically searched and evaluated for their quality. The external dataset of patients who underwent the first liver transplant and received immediate-release tacrolimus was used to assess the predictive performance of each selected model. Trough concentrations between 3 and 6 months were retrospectively collected to evaluate the predictability of each model using prediction-based diagnostics, simulation-based diagnostics, and Bayesian forecasting.

RESULTS

Sixty-seven patients with 360 trough concentrations and eight selected published models were included in this study. None of the models met the predictive precision criteria in prediction-based diagnostics. Meanwhile, four published population pharmacokinetic models showed a normal distribution in NPDE testing. Regarding Bayesian forecasting, all models improved their forecasts with at least one prior information data point.

CONCLUSION

Bayesian forecasting is more accurate and precise than other testing methods for predicting drug concentrations. However, none of the evaluated models provides satisfactory predictive performance for generalization to Thai liver transplant patients. This underscores the need for future research to develop population PK models tailored to the Thai population. Such efforts should consider the inclusion of nonlinear pharmacokinetics and region-specific factors, including genetic variability, to improve model accuracy and applicability.

HLA-DQA1*05 correlates with increased risk of anti-drug antibody development and reduced response to infliximab in Chinese patients with Crohn's disease

Background

The efficacy of anti-TNF therapy in Crohn's disease (CD), such as infliximab, is often compromised by the development of anti-drug antibodies (ADAs). The genetic variation HLA-DQA1*05 has been linked to the immunogenicity of biologics, influencing ADA formation. This study investigates the correlation between HLA-DQA1*05 and ADA formation in CD patients treated with infliximab in a Chinese Han population and assesses clinical outcomes.

Methods

In this retrospective cohort study, 345 infliximab-exposed CD patients were genotyped for HLADQ A1*05A > G (rs2097432). We evaluated the risk of ADA development, loss of infliximab response, adverse events, and treatment discontinuation among variant and wild-type allele individuals.

Results

A higher percentage of patients with ADAs formation was observed in HLA-DQA1*05 G variant carriers compared with HLA-DQA1*05 wild-type carriers (58.5% vs 42.9%, P = 0.004). HLA-DQA1*05 carriage significantly increased the risk of ADAs development (adjusted hazard ratio = 1.65, 95% CI 1.18-2.30, P = 0.003) and was associated with a greater likelihood of infliximab response loss (adjusted HR = 2.55, 95% CI 1.78-3.68, P < 0.0001) and treatment discontinuation (adjusted HR = 2.21, 95% CI 1.59-3.06, P < 0.0001). Interestingly, combined therapy with immunomodulators increased the risk of response loss in HLA-DQA1*05 variant carriers.

Conclusions

HLA-DQA1*05 significantly predicts ADAs formation and impacts treatment outcomes in infliximab-treated CD patients. Pre-treatment screening for this genetic factor could therefore be instrumental in personalizing anti-TNF therapy strategies for these patients.

Understanding the factors that influence CT utilization for mild traumatic brain injury in a low resource setting - a qualitative study using the Theoretical Domains Framework

Introduction

In low resource settings (LRS), utilization of Computed Tomography scan (CTS) for mild traumatic brain injuries (mTBIs) presents unique challenges and considerations given the limited infrastructure, financial resources, and trained personnel. The Theoretical Domains Framework (TDF) offers a comprehensive theoretical lens to explore factors influencing the decision-making to order CTS for mTBI by imaging referrers (IRs).

Objectives

The primary objective was to explore IRs' beliefs about factors influencing CT utilization in mTBIs using TDF in Uganda.Differences in the factors influencing CTS ordering behavior across specialties, levels of experience, and hospital category were also explored.

Materials and Methods

In-depth semi-structured interviews guided by TDF were conducted among purposively selected IRs from 6 tertiary public and private hospitals with functional CTS services. A thematic analysis was performed with codes and emerging themes developed based on the TDF.

Results

Eleven IRs including medical officers, non-neurosurgeon specialists and neurosurgeons aged on average 42 years (SD+/-12.3 years) participated.Identified factors within skills domain involved IRs' clinical assessment and decision-making abilities, while beliefs about capabilities and consequences encompassed their confidence in diagnostic abilities and perceptions of CTS risks and benefits. The environmental context and resources domain addressed the availability of CT scanners and financial constraints. The knowledge domain elicited IRs' understanding of clinical guidelines and evidence-based practices while social influences considered peer influence and institutional culture. For memory, attention & decision processes domain, IRs adherence to guidelines and intentions to order CT scans were cited.

Conclusion

Using TDF, IRs identified several factors believed to influence decision making to order CTS in mTBI in a LRS. The findings can inform stakeholders to develop targeted strategies and evidence-based interventions to optimize CT utilization in mTBI such as; educational programs, workflow modifications, decision support tools, and infrastructure improvements, among others.

Development of an exercise therapy referral clinical support tool for patients with osteoporosis at risk for falls

BACKGROUND

The purpose of this study was to develop a clinical support tool for osteoporosis clinic providers to support risk assessment and referrals for evidence-based exercise therapy programs.

METHODS

A sequential Delphi method was used with a multidisciplinary group of national falls experts, to provide consensus on referral to exercise therapy for patients at risk for falls. The Delphi study included a primary research team, expert panel, and clinical partners to answer the questions: (1) "What patient characteristics are needed to develop a clinical support tool?"; (2) "What are the recommended exercise referrals for patients with osteoporosis at risk for falls?" The consensus process consisted of two rounds with 8 weeks between meetings. Two qualitative researchers analyzed the data using a modified version of a matrix analysis approach.

RESULTS

The following were the most important variables to include when determining exercise therapy referrals for patients with osteoporosis: Patient history and demographics, falls history over the last year, current physical function and balance, caregiver and transportation status, socioeconomic and insurance status, and patient preference. Potential exercise therapy referrals included one-on-one physical therapy, group physical therapy, home health, community-based exercise programs, and not acceptable for exercise therapy.

CONCLUSIONS

Patient characteristics including patient history, physical function and balance performance, socioeconomic and insurance status, and patient preference for exercise therapy are important to inform both the medical provider and patient with osteoporosis to choose the most appropriate exercise therapy referral. Adoption of the algorithmic suggestions may have a significant impact on uptake and adherence to exercise therapy, ultimately improving patient physical function and reducing falls risk.

Population Health Management in Radiology: Economic Considerations

There is a growing emphasis on population health management (PHM) in the United States, in part because it has the worst health outcomes indices among high-income countries despite spending by far the most on health care. Successful PHM is expected to lead to a healthier population with reduced health care utilization and cost. The role of radiology in PHM is increasingly being recognized, including efforts in care coordination, secondary prevention, and appropriate imaging utilization, among others. To further discuss economic considerations for PHM, we must understand the evolving health care payer environment, which combines fee-for-service and increasingly, an alternative payment model framework developed by the Health Care Payment Learning and Action Network. In considering the term "value-based care," perceived value needs to accrue to those who ultimately pay for care, which is more commonly employers and the government. This perspective drives the design of alternative payment models and thus should be taken into consideration to ensure sustainable practice models.

The Role of Therapeutic Drug Monitoring in Children

The use of biologic therapies has changed the treatment landscape for children with inflammatory bowel disease. While the novel biologics have improved clinical outcomes, there remains a significant gap in achieving endoscopic remission, prolonged steroid-free remission, and drug durability. Contributing to this gap is the paucity of real-world pharmacokinetic studies in children and a failure to dose optimize therapy during induction. Emerging data from a pediatric clinical trial and several observational studies have shown that the combination of proactive therapeutic drug monitoring and achievement of early therapeutic concentrations is effective in achieving improved outcomes. The next steps will be to leverage these past studies to develop more innovative clinical trials to properly assess the safety and effectiveness of proactive therapeutic drug monitoring in children.

Bayesian prediction-based individualized dosing of anti-methicillin-resistant Staphylococcus aureus treatment: Recent advancements and prospects in therapeutic drug monitoring

As one of the efficient techniques for TDM, the population pharmacokinetic (popPK) model approach for dose individualization has been developed due to the rapidly growing innovative progress in computer technology and has recently been considered as a part of model-informed precision dosing (MIPD). Initial dose individualization and measurement followed by maximum a posteriori (MAP)-Bayesian prediction using a popPK model are the most classical and widely used approach among a class of MIPD strategies. MAP-Bayesian prediction offers the possibility of dose optimization based on measurement even before reaching a pharmacokinetically steady state, such as in an emergency, especially for infectious diseases requiring urgent antimicrobial treatment. As the pharmacokinetic processes in critically ill patients are affected and highly variable due to pathophysiological disturbances, the advantages offered by the popPK model approach make it highly recommended and required for effective and appropriate antimicrobial treatment. In this review, we focus on novel insights and beneficial aspects of the popPK model approach, especially in the treatment of infectious diseases with anti-methicillin-resistant Staphylococcus aureus agents represented by vancomycin, and discuss the recent advancements and prospects in TDM practice.

Personalized Dosing of Infliximab in Patients With Inflammatory Bowel Disease Using a Bayesian Approach: A Next Step in Therapeutic Drug Monitoring

Although biological agents have revolutionized the management of inflammatory bowel diseases (IBDs), a significant proportion of patients show primary non-response or develop secondary loss of response. Therapeutic drug monitoring (TDM) is advocated to maintain the efficacy of biologic agents. Reactive TDM can rationalize the management of primary non-response and secondary loss of response and has shown to be more cost-effective compared with empiric dose escalation. Proactive TDM is shown to increase clinical remission and the durability of the response to a biologic agent. However, the efficacy of proactive and reactive TDM has been questioned in recent studies and meta-analyses. Hence, we need a different approach to TDM, which addresses inflammatory burden, the individual patient, and disease factors. Bayesian approaches, which use population pharmacokinetic models, enable clinicians to make better use of TDM for dose adjustment. With rapid improvement in computer technology, these Bayesian model-based software packages are now available for clinical use. Bayesian dashboard systems allow clinicians to apply model-based dosing to understand an individual's pharmacokinetics and achieve a target serum drug concentration. The model is updated using previously measured drug concentrations and relevant patient factors, such as body weight, C-reactive protein, and serum albumin concentration, to maintain effective drug concentrations in the serum. Initial studies have found utility for the Bayesian approach in induction and maintenance, in adult and pediatric patients, in clinical trials, and in real-life situations for patients with IBD treated with infliximab. This needs confirmation in larger studies. This article reviews the Bayesian approach to therapeutic drug monitoring in IBD.

External Evaluation of Population Pharmacokinetic Models of Busulfan in Chinese Adult Hematopoietic Stem Cell Transplantation Recipients

Objective: Busulfan (BU) is a bi-functional DNA-alkylating agent used in patients undergoing hematopoietic stem cell transplantation (HSCT). Over the last decades, several population pharmacokinetic (pop PK) models of BU have been established, but external evaluation has not been performed for almost all models. The purpose of the study was to evaluate the predictive performance of published pop PK models of intravenous BU in adults using an independent dataset from Chinese HSCT patients, and to identify the best model to guide personalized dosing.

Methods: The external evaluation methods included prediction-based diagnostics, simulation-based diagnostics, and Bayesian forecasting. In prediction-based diagnostics, the relative prediction error (PE%) was calculated by comparing the population predicted concentration (PRED) with the observations. Simulation-based diagnostics included the prediction- and variability-corrected visual predictive check (pvcVPC) and the normalized prediction distribution error (NPDE). Bayesian forecasting was executed by giving prior one to four observations. The factors influencing the model predictability, including the impact of structural models, were assessed.

Results: A total of 440 concentrations (110 patients) were obtained for analysis. Based on prediction-based diagnostics and Bayesian forecasting, preferable predictive performance was observed in the model developed by Huang et al. The median PE% was -1.44% which was closest to 0, and the maximum F₂₀ of 57.27% and F₃₀ of 72.73% were achieved. Bayesian forecasting demonstrated that prior concentrations remarkably improved the prediction precision and accuracy of all models, even with only one prior concentration.

Conclusion: This is the first study to comprehensively evaluate published pop PK models of BU. The model built by Huang et al. had satisfactory predictive performance, which can be used to guide individualized dosage adjustment of BU in Chinese patients.

Ordering of computed tomography scans for head and cervical spine: a qualitative study exploring influences on doctors' decision-making

BACKGROUND

Ordering of computed tomography (CT) scans needs to consideration of diagnostic utility as well as resource utilisation and radiation exposure. Several factors influence ordering decisions, including evidence-based clinical decision support tools to rule out serious disease. The aim of this qualitative study was to explore factors influencing Emergency Department (ED) doctors' decisions to order CT of the head or cervical spine.

METHODS

In-depth semi-structured interviews were conducted with purposively selected ED doctors from two affiliated public hospitals. An interview tool with 10 questions, including three hypothetical scenarios, was developed and validated to guide discussions. Interviews were audio recorded, transcribed verbatim, and compared with field notes. Transcribed data were imported into NVivo Release 1.3 to facilitate coding and thematic analysis.

RESULTS

In total 21 doctors participated in semi-structured interviews between February and December 2020; mean interview duration was 35 min. Data saturation was reached. Participants ranged from first-year interns to experienced consultants. Five overarching emerging themes were: 1) health system and local context, 2) work structure and support, 3) professional practices and responsibility, 4) reliable patient information, and 5) holistic patient-centred care. Mapping of themes and sub-themes against a behaviour change model provided a basis for future interventions.

CONCLUSIONS

CT ordering is complex and multifaceted. Multiple factors are considered by ED doctors during decisions to order CT scans for head or c-spine injuries. Increased education on the use of clinical decision support tools and an overall strategy to improve awareness of low-value care is needed. Strategies to reduce low-yield CT ordering will need to be sustainable, sophisticated and supportive to achieve lasting change.

Optimized sampling to estimate vancomycin drug exposure: Comparison of pharmacometric and equation-based approaches in a simulation-estimation study

Vancomycin dosing should be accompanied by area under the concentration‐time curve (AUC)–guided dosing using model‐informed precision dosing software according to the latest guidelines. Although a peak plus a trough sample is considered the gold standard to determine the AUC, single‐sample strategies might be more economic. Yet, optimal sampling times for AUC determination of vancomycin have not been systematically evaluated. In the present study, automated one‐ or two‐sample strategies were systematically explored to estimate the AUC with a model averaging and a model selection algorithm. Both were compared with a conventional equation‐based approach in a simulation‐estimation study mimicking a heterogenous patient population (n = 6000). The optimal single‐sample timepoints were identified between 2–6.5 h post dose, with varying bias values between −2.9% and 1.0% and an imprecision of 23.3%–24.0% across the population pharmacokinetic approaches. Adding a second sample between 4.5–6.0 h improved the predictive performance (−1.7% to 0.0% bias, 17.6%–18.6% imprecision), although the difference in the two‐sampling strategies were minor. The equation‐based approach was always positively biased and hence inferior to the population pharmacokinetic approaches. In conclusion, the approaches always preferred samples to be drawn early in the profile (<6.5 h), whereas sampling of trough concentrations resulted in a higher imprecision. Furthermore, optimal sampling during the early treatment phase could already give sufficient time to individualize the second dose, which is likely unfeasible using trough sampling.

Development and Validation of Open-Source R Package HMCtdm for Therapeutic Drug Monitoring

Most therapeutic drug monitoring (TDM) packages are based on the maximum a posteriori (MAP) estimation. In this study, HMCtdm, a new TDM package, was developed using a Hamiltonian Monte Carlo (HMC) simulation. The estimation process of HMCtdm for the drugs amikacin, vancomycin, theophylline, and phenytoin was based on the R package Torsten. The prior pharmacokinetic (PK) models of the drugs were derived from the Abbottbase® pharmacokinetics systems (PKS) program. The performance of HMCtdm for each drug was assessed through internal and external validations. The internal validation results of the HMCtdm were compared with those of a MAP-based estimation. The developed open-source HMCtdm package is user friendly. The validation results were reviewed and interpreted using the mean percentage error and root mean squared error. The successful transplantation of the prior PK structures (used in PKS) was confirmed by comparing the validation results with a MAP estimation. An open-source HMC-based TDM package was also successfully developed in this study, and its performance was evaluated. This package can be operated by users unfamiliar with C++ and can be further developed for various applications.

External evaluation of population pharmacokinetic models of imatinib in adults diagnosed with chronic myeloid leukaemia

AIMS

Imatinib is considered the standard first-line treatment in newly diagnosed patients with chronic-phase myeloid leukaemia (CML). Several imatinib population pharmacokinetic (popPK) models have been developed. However, their predictive performance has not been well established when extrapolated to different populations. Therefore, this study aimed to perform an external evaluation of available imatinib popPK models developed mainly in adult patients, and to evaluate the improvement in individual model-based predictions through Bayesian forecasting computed by each model at different treatment occasions.

METHODS

A literature review was conducted through PubMed and Scopus to identify popPK models. Therapeutic drug monitoring data collected in adult CML patients treated with imatinib was used for external evaluation, including prediction- and simulated-based diagnostics together with Bayesian forecasting analysis.

RESULTS

Fourteen imatinib popPK studies were included for model-performance evaluation. A total of 99 imatinib samples were collected from 48 adult CML patients undergoing imatinib treatment with a minimum of one plasma concentration measured at steady-state between January 2016 and December 2020. The model proposed by Petain et al showed the best performance concerning prediction-based diagnostics in the studied population. Bayesian forecasting demonstrated a significant improvement in predictive performance at the second visit. Inter-occasion variability contributed to reducing bias and improving individual model-based predictions.

CONCLUSIONS

Imatinib popPK studies developed in Caucasian subjects including α1-acid glycoprotein showed the best model performance in terms of overall bias and precision. Moreover, two imatinib samples from different visits appear sufficient to reach an adequate model-based individual prediction performance trough Bayesian forecasting.

Monoclonal Antibody Monitoring: Clinically Relevant Aspects, A Systematic Critical Review

Monoclonal antibody (mAb) therapy does not usually lead to a clinical response in all patients and resistance may increase over time after repeated mAb administration. This lack or loss of response to the treatment may originate from different and little-known epigenetic, biomolecular, or pathophysiological mechanisms, although an inadequate serum concentration is perhaps the most likely cause, even if not widely recognized and investigated yet. Patient factors that influence the pharmacokinetics (PK) of a mAb should be taken into account. Multiple analyses of patient-derived PK data have identified various factors influencing the clearance of mAbs. These factors include the presence of antidrug antibodies, low serum albumin, high serum levels of C-reactive protein, high body weight, and gender differences among others. The same clearance processes involved in systemic clearance after intravenous administration are also involved in local first-pass catabolism after subcutaneous administration of mAbs. Therapeutic drug monitoring has been proposed as a way to understand and respond to the variability in clinical response and remission. For both classes of mAbs with anti-inflammatory and antitumor effects, dose-guided optimization based on the measurement of serum concentrations in individual patients could be the next step for a personalized and targeted mAb therapy.

Evaluation of the MeroRisk Calculator, A User-Friendly Tool to Predict the Risk of Meropenem Target Non-Attainment in Critically Ill Patients

BACKGROUND

The MeroRisk-calculator, an easy-to-use tool to determine the risk of meropenem target non-attainment after standard dosing (1000 mg; q8h), uses a patient's creatinine clearance and the minimum inhibitory concentration (MIC) of the pathogen. In clinical practice, however, the MIC is rarely available. The objectives were to evaluate the MeroRisk-calculator and to extend risk assessment by including general pathogen sensitivity data.

METHODS

Using a clinical routine dataset (155 patients, 891 samples), a direct data-based evaluation was not feasible. Thus, in step 1, the performance of a pharmacokinetic model was determined for predicting the measured concentrations. In step 2, the PK model was used for a model-based evaluation of the MeroRisk-calculator: risk of target non-attainment was calculated using the PK model and agreement with the MeroRisk-calculator was determined by a visual and statistical (Lin's concordance correlation coefficient (CCC)) analysis for MIC values 0.125-16 mg/L. The MeroRisk-calculator was extended to include risk assessment based on EUCAST-MIC distributions and cumulative-fraction-of-response analysis.

RESULTS

Step 1 showed a negligible bias of the PK model to underpredict concentrations (-0.84 mg/L). Step 2 revealed a high level of agreement between risk of target non-attainment predictions for creatinine clearances >50 mL/min (CCC = 0.990), but considerable deviations for patients <50 mL/min. For 27% of EUCAST-listed pathogens the median cumulative-fraction-of-response for the observed patients receiving standard dosing was < 90%.

CONCLUSIONS

The MeroRisk-calculator was successfully evaluated: For patients with maintained renal function it allows a reliable and user-friendly risk assessment. The integration of pathogen-based risk assessment substantially increases the applicability of the tool.

External evaluation of the predictive performance of seven population pharmacokinetic models for phenobarbital in neonates

AIM

Several studies have reported population pharmacokinetic models for phenobarbital (PB), but the predictive performance of these models has not been well documented. This study aims to do external evaluation of the predictive performance in published pharmacokinetic models.

METHODS

Therapeutic drug monitoring data collected in neonates and young infants treated with PB for seizure control was used for external evaluation. A literature review was conducted through PubMed to identify population pharmacokinetic models. Prediction- and simulation-based diagnostics, and Bayesian forecasting were performed for external evaluation. The incorporation of allometric scaling for body size and maturation factors into the published models was also tested for prediction improvement.

RESULTS

A total of 79 serum concentrations from 28 subjects were included in the external dataset. Seven population pharmacokinetic studies of PB were identified as relevant in the literature search and included for our evaluation. The model by Voller et al showed the best performance concerning prediction-based evaluation. In simulation-based analyses, the normalized prediction distribution error of two models (those of Shellhaas et al and Marsot et al) obeyed a normal distribution. Bayesian forecasting with more than one observation improved predictive capability. Incorporation of both allometric size scaling and maturation function generally enhanced the predictive performance, with improvement as observed in the model of Vucicevic et al. CONCLUSIONS: The predictive performance of published pharmacokinetic models of PB was diverse. Bayesian forecasting and incorporation of both size and maturation factors could improve the predictability of the models for neonates.

Real-World Infliximab Pharmacokinetic Study Informs an Electronic Health Record-Embedded Dashboard to Guide Precision Dosing in Children with Crohn's Disease

Standard-of-care infliximab dosing regimens were developed prior to the routine use of therapeutic drug monitoring and identification of target concentrations. Not surprisingly, subtherapeutic infliximab concentrations in pediatric Crohn's disease (CD) are common. The primary aim was to conduct a real-world pharmacokinetic (PK) evaluation to discover blood biomarkers of rapid clearance, identify exposure targets, and a secondary aim to translate PK modeling to the clinic. In a multicenter observational study, 671 peak and trough infliximab concentrations from 78 patients with CD were analyzed with a drug-tolerant assay (Esoterix; LabCorp, Calabasas, CA). Individual area under the curve (AUC) estimates were generated as a measure of drug exposure over time. Population PK modeling (nonlinear mixed-effect modeling) identified serum albumin, antibody to infliximab, erythrocyte sedimentation rate (ESR), and neutrophil CD64 as biomarkers for drug clearance. Week 14 and week 52 biochemical remitters (fecal calprotectin < 250 µg/g) had higher infliximab exposure (AUC) throughout induction. The optimal infliximab AUC target during induction for week 14 biochemical remission was 79,348 µg*h/mL (area under the receiver operating characteristic curve (AUROC) 0.77, [0.63-0.90], 85.7% sensitive, and 64.3% specific) with those exceeding the AUC target more likely to achieve a surgery-free week 52 biochemical remission (OR 4.3, [1.2-14.6]). Pretreatment predictors for subtherapeutic week 14 AUC included neutrophil CD64 > 6 (OR 4.5, [1.4-17.8]), ESR > 30 mm/h (OR 3.8, [1.4-11]), age < 10 years old (OR 4.2, [1.2-20]), and weight < 30 kg (OR 6.6, [2.1-25]). We created a decision-support PK dashboard with an iterative process and embedded the modeling program within the electronic health record. Model-informed precision dosing guided by real-world PKs is now available at the bedside in real-time.

Area under the curve achievement of once daily tobramycin in children with cystic fibrosis during clinical care

BACKGROUND

The area under the concentration-time curve over 24 hours (AUC₂₄ ) is frequently utilized to monitor tobramycin exposure in children with cystic fibrosis (CF). An understanding of exposure target achievement during clinical implementation of an AUC₂₄ based approach in children is limited.

METHODS

A retrospective chart review was performed in children with CF treated with once daily tobramycin and drug concentration monitoring at a pediatric CF center. During clinical care AUC₂₄ was estimated using a traditional log-linear regression approach (LLR). AUC₂₄ was also estimated retrospectively using a pharmacokinetic model-based Bayesian forecasting approach (BF). AUC₂₄ achievement after both approaches were compared.

RESULTS

In 77 treatment courses (mean age, 12.7 ± 5.0 years), a target AUC₂₄ 100 to 125 mg h/L was achieved after starting dose in 21 (27%) and after initial dose adjustment in 35 (45%). In the first 7 days of treatment, 24 (32%) required ≥3 dose adjustments, and the mean number of drug concentrations measured was 7.1 ± 3.2. Examination of a BF approach demonstrated adequate prediction of measured tobramycin concentrations (median bias -2.1% [95% CI -3.1 to -1.4]; median precision 7.6% [95% CI, 7.1%-8.2%]). AUC₂₄ estimates utilizing the BF approach were higher than the LLR approach with a mean difference of 6.4 mg h/L (95% CI, 4.8 to 8.0 mg h/L).

CONCLUSIONS

Achievement of a narrow AUC₂₄ target is challenging during clinical care, and dose individualization is needed in most children with CF. Implementing a BF approach for estimating AUC₂₄ in children with CF is supported.

A Model Averaging/Selection Approach Improves the Predictive Performance of Model-Informed Precision Dosing: Vancomycin as a Case Study

Many important drugs exhibit substantial variability in pharmacokinetics and pharmacodynamics leading to a loss of the desired clinical outcomes or significant adverse effects. Forecasting drug exposures using pharmacometric models can improve individual target attainment when compared with conventional therapeutic drug monitoring (TDM). However, selecting the "correct" model for this model-informed precision dosing (MIPD) is challenging. We derived and evaluated a model selection algorithm (MSA) and a model averaging algorithm (MAA), which automates model selection and finds the best model or combination of models for each patient using vancomycin as a case study, and implemented both algorithms in the MIPD software "TDMx." The predictive performance (based on accuracy and precision) of the two algorithms was assessed in (i) a simulation study of six distinct populations and (ii) a clinical dataset of 180 patients undergoing TDM during vancomycin treatment and compared with the performance obtained using a single model. Throughout the six virtual populations the MSA and MAA (imprecision: 9.9-24.2%, inaccuracy: less than ± 8.2%) displayed more accurate predictions than the single models (imprecision: 8.9-51.1%; inaccuracy: up to 28.9%). In the clinical dataset, the predictive performance of the single models applying at least one plasma concentration varied substantially (imprecision: 28-62%, inaccuracy: -16 to 25%), whereas the MSA or MAA utilizing these models simultaneously resulted in unbiased and precise predictions (imprecision: 29% and 30%, inaccuracy: -5% and 0%, respectively). MSA and MAA approaches implemented in TDMx might thereby lower the burden of fit-for-purpose validation of individual models and streamline MIPD.

Fused Deposition Modeling (FDM), the new asset for the production of tailored medicines

Over the last few years, conventional medicine has been increasingly moving towards precision medicine. Today, the production of oral pharmaceutical forms tailored to patients is not achievable by traditional industrial means. A promising solution to customize oral drug delivery has been found in the utilization of 3D Printing and in particular Fused Deposition Modeling (FDM). Thus, the aim of this systematic literature review is to provide a synthesis on the production of pharmaceutical solid oral forms using FDM technology. In total, 72 relevant articles have been identified via two well-known scientific databases (PubMed and ScienceDirect). Overall, three different FDM methods have been reported: "Impregnation-FDM", "Hot Melt Extrusion coupled with FDM" and "Print-fill", which yielded to the formulation of thermoplastic polymers used as main component, five families of other excipients playing different functional roles and 47 active ingredients. Solutions are underway to overcome the high printing temperatures, which was the initial brake on to use thermosensitive ingredients with this technology. Also, the moisture sensitivity shown by a large number of prints in preliminary storage studies is highlighted. FDM seems to be especially fitted for the treatment of rare diseases, and particular populations requiring tailored doses or release kinetics. For future use of FDM in clinical trials, an implication of health regulatory agencies would be necessary. Hence, further efforts would likely be oriented to the use of a quality approach such as "Quality by Design" which could facilitate its approval by the authorities, and also be an aid to the development of this technology for manufacturers.

Can Population Pharmacokinetics of Antibiotics be Extrapolated? Implications of External Evaluations

BACKGROUND AND OBJECTIVE

External evaluation is an important issue in the population pharmacokinetic analysis of antibiotics. The purpose of this review was to summarize the current approaches and status of external evaluations and discuss the implications of external evaluation results for the future individualization of dosing regimens.

METHODS

We systematically searched the PubMed and EMBASE databases for external evaluation studies of population analysis and extracted the relevant information from these articles. A total of 32 studies were included in this review.

RESULTS

Vancomycin was investigated in 17 (53.1%) articles and was the most studied drug. Other studied drugs included gentamicin, tobramycin, amikacin, amoxicillin, ceftaroline, meropenem, fluconazole, voriconazole, and rifampicin. Nine (28.1%) studies were prospective, and the sample size varied widely between studies. Thirteen (40.6%) studies evaluated the population pharmacokinetic models by systematically searching for previous studies. Seven (21.9%) studies were multicenter studies, and 27 (84.4%) adopted the sparse sampling strategy. Almost all external evaluation studies of antibiotics (93.8%) used metrics for prediction-based diagnostics, while relatively fewer studies were based on simulations (46.9%) and Bayesian forecasting (25.0%).

CONCLUSION

The results of external evaluations in previous studies revealed the poor extrapolation performance of existing models of prediction- and simulation-based diagnostics, whereas the posterior Bayesian method could improve predictive performance. There is an urgent need for the development of standards and guidelines for external evaluation studies.

Optimization of infliximab therapy in inflammatory bowel disease using a dashboard approach-an Indian experience

PURPOSE

Infliximab (IFX) therapy in inflammatory bowel disease (IBD) is associated with loss of response in half the patients, due to complex pharmacokinetic and immunological factors. Dashboard's Bayesian algorithms use information from model and individual multivariate determinants of IFX concentration and can predict dose and dosing interval.

AIM

To compare measured IFX concentrations in our laboratory with values predicted by iDose dashboard system and report its efficacy in managing patients not responding to conventional dosing schedule.

METHOD

Clinical history, demographic details, and laboratory findings such as albumin and C-reactive protein (CRP) data of IBD patients (n = 30; median age 23 years (IQR: 14.25 - 33.5)) referred for IFX drug monitoring in our laboratory from November 2017 to November 2019 were entered in iDose software. The IFX concentration predicted by iDose based on this information was compared with that measured in our laboratory. In addition, a prospective dashboard-guided dosing was prescribed in 11 of these 30 patients not responding to conventional dosing and was followed to assess their clinical outcome.

RESULT

IFX monitoring in our 30 patients had shown therapeutic concentration in 12, supratherapeutic in 2 and subtherapeutic concentration in 16 patients. The iDose predicted concentration showed concordance in 21 of these 30 patients. Of 11 patients managed with iDose-assisted prospective dosing, 8 achieved clinical remission, 2 showed partial response, and one developed antibodies.

CONCLUSION

Retrospective data analysis showed concordance between laboratory measured and iDose-predicted IFX level in 70% of patients. iDose-assisted management achieved clinical remission and cost reduction.

MTXPK.org: A Clinical Decision Support Tool Evaluating High-Dose Methotrexate Pharmacokinetics to Inform Post-Infusion Care and Use of Glucarpidase

Methotrexate (MTX), an antifolate, is administered at high doses to treat malignancies in children and adults. However, there is considerable interpatient variability in clearance of high‐dose (HD) MTX. Patients with delayed clearance are at an increased risk for severe nephrotoxicity and life‐threatening systemic MTX exposure. Glucarpidase is a rescue agent for severe MTX toxicity that reduces plasma MTX levels via hydrolysis of MTX into inactive metabolites, but is only indicated when MTX concentrations are > 2 SDs above the mean excretion curve specific for the given dose together with a significant creatinine increase (> 50%). Appropriate administration of glucarpidase is challenging due to the ambiguity in the labeled indication. A recent consensus guideline was published with an algorithm to provide clarity in when to administer glucarpidase, yet clinical interpretation of laboratory results that do not directly correspond to the algorithm prove to be a limitation of its use. The goal of our study was to develop a clinical decision support tool to optimize the administration of glucarpidase for patients receiving HD MTX. Here, we describe the development of a novel 3‐compartment MTX population pharmacokinetic (PK) model using 31,672 MTX plasma concentrations from 772 pediatric patients receiving HD MTX for the treatment of acute lymphoblastic leukemia and its integration into the online clinical decision support tool, MTXPK.org. This web‐based tool has the functionality to utilize individualized demographics, serum creatinine, and real‐time drug concentrations to predict the elimination profile and facilitate model‐informed administration of glucarpidase.

Clinical implementation of pharmacogenetics and model-informed precision dosing to improve patient care

Providing maximal therapeutic efficacy without toxicity is a universal goal of rational drug therapy. However, substantial between-patient variability in drug response often impedes such successful treatments and brings the necessity of tailoring drug dose to individual needs for more precise therapy. In many cases plenty of patient characteristics, such as body size, genetic makeup and environmental factors, need to be taken into consideration to find the optimal dose in clinical practice. A pharmacokinetics and pharmacodynamics (PK/PD) model-informed approach offers integration of various patient information to provide an expectation of drug response and derive practical dose estimates to support clinicians' dosing decisions. Such an approach was pioneered in the late 1970s, but its broad clinical acceptance and implementation have been hampered by the lack of widespread computer technology, including user-friendly software tools. This has significantly changed in recent years. With the advent of electronic health records (EHRs) and the ubiquity of user-friendly software tools, we now experience a convergence of clinical information, pharmacogenetics, systems pharmacology and pharmacometrics, and technology. Advanced pharmacometrics research is now more appliable and implementable to improve health care. This article presents examples of successful development and implementation of pharmacogenetics-guided and PK/PD model-informed decision support to facilitate precision dosing, including the development of an EHR-embedded decision support tool. Through the integration of clinical decision support tools in EHRs, clinical pharmacometrics support can be brought directly to the clinical team and the bedside.

Model-Informed Precision Dosing of Vancomycin in Hospitalized Children: Implementation and Adoption at an Academic Children's Hospital

Background

Model-informed precision dosing (MIPD) can serve as a powerful tool during therapeutic drug monitoring (TDM) to help individualize dosing in populations with large pharmacokinetic variation. Yet, adoption of MIPD in the clinical setting has been limited. Overcoming technologic hurdles that allow access to MIPD at the point-of-care and placing it in the hands of clinical specialists focused on medication dosing may encourage adoption.

Objective

To describe the hospital implementation and usage of a MIPD clinical decision support (CDS) tool for vancomycin in a pediatric population.

Methods

Within an academic children’s hospital, MIPD for vancomycin was implemented via a commercial cloud-based CDS tool that utilized Bayesian forecasting. Clinical pharmacists were recognized as local champions to facilitate adoption of the tool and operated as end-users. Integration within the electronic health record (EHR) and automatic transmission of patient data to the tool were identified as important requirements. A web-link icon was developed within the EHR which when clicked sends users and needed patient-level clinical data to the CDS platform. Individualized pharmacokinetic predictions and exposure metrics for vancomycin are then presented in the form of a web-based dashboard. Use of the CDS tool as part of TDM was tracked and users were surveyed on their experience.

Results

After a successful pilot phase in the neonatal intensive care unit, implementation of MIPD was expanded to the pediatric intensive care unit, followed by availability to the entire hospital. During the first 2+ years since implementation, a total of 853 patient-courses (n = 96 neonates, n = 757 children) and 2,148 TDM levels were evaluated using the CDS tool. For the most recent 6 months, the CDS tool was utilized to support 79% (181/230) of patient-courses in which TDM was performed. Of 26 users surveyed, > 96% agreed or strongly agreed that automatic transmission of patient data to the tool was a feature that helped them complete tasks more efficiently; 81% agreed or strongly agreed that they were satisfied with the CDS tool.

Conclusions

Integration of a vancomycin CDS tool within the EHR, along with leveraging the expertise of clinical pharmacists, allowed for successful adoption of MIPD in clinical care.

The Steps to Therapeutic Drug Monitoring: A Structured Approach Illustrated With Imatinib

Pharmacometric methods have hugely benefited from progress in analytical and computer sciences during the past decades, and play nowadays a central role in the clinical development of new medicinal drugs. It is time that these methods translate into patient care through therapeutic drug monitoring (TDM), due to become a mainstay of precision medicine no less than genomic approaches to control variability in drug response and improve the efficacy and safety of treatments. In this review, we make the case for structuring TDM development along five generic questions: 1) Is the concerned drug a candidate to TDM? 2) What is the normal range for the drug's concentration? 3) What is the therapeutic target for the drug's concentration? 4) How to adjust the dosage of the drug to drive concentrations close to target? 5) Does evidence support the usefulness of TDM for this drug? We exemplify this approach through an overview of our development of the TDM of imatinib, the very first targeted anticancer agent. We express our position that a similar story shall apply to other drugs in this class, as well as to a wide range of treatments critical for the control of various life-threatening conditions. Despite hurdles that still jeopardize progress in TDM, there is no doubt that upcoming technological advances will shape and foster many innovative therapeutic monitoring methods.

Clinical Pharmacokinetic and Pharmacodynamic Considerations in the Treatment of Ulcerative Colitis

Ulcerative colitis (UC) is an inflammatory bowel disease (IBD) of unknown etiology, probably caused by a combination of genetic and environmental factors. The treatment of patients with active UC depends on the severity, localization and history of IBD medication. According to the classic step-up approach, treatment with 5-aminosalicylic acid compounds is the first step in the treatment of mild to moderately active UC. Corticosteroids, such as prednisolone are used in UC patients with moderate to severe disease activity, but only for remission induction therapy because of side effects associated with long-term use. Thiopurines are the next step in the treatment of active UC but monotherapy during induction therapy in UC patients is not preferred because of their slow onset. Therapeutic drug monitoring (TDM) of the pharmacologically active metabolites of thiopurines, 6-thioguanine nucleotide (6-TGN), has proven to be beneficial. Thiopurine S-methyltransferase (TMPT) plays a role in the metabolic conversion pathway of thiopurines and exhibits genetic polymorphism; however, the clinical benefit and relevance of TPMT genotyping is not well established. In patients with severely active UC refractory to corticosteroids, calcineurin inhibitors such as ciclosporin A (CsA) and tacrolimus are potential therapeutic options. These agents usually have a rather rapid onset of action. Monoclonal antibodies (anti-tumor necrosis factor [TNF] agents, vedolizumab) are the last pharmacotherapeutic option for UC patients before surgery becomes inevitable. Body weight, albumin status and antidrug antibodies contribute to the variability in the pharmacokinetics of anti-TNF agents. Additionally, the use of concomitant immunomodulators (thiopurines/methotrexate) lowers the rate of immunogenicity, and therefore the concomitant use of anti-TNF therapy with an immunomodulator may confer some advantage compared with monotherapy in certain patients. TDM of anti-TNF agents could be beneficial in patients with primary nonresponse and secondary loss of response. The potential benefit of applying TDM during vedolizumab treatment has yet to be determined.

Population Pharmacokinetics of Alemtuzumab (Campath) in Pediatric Hematopoietic Cell Transplantation: Towards Individualized Dosing to Improve Outcome

BACKGROUND AND OBJECTIVE

Alemtuzumab (Campath^®) is used to prevent graft-versus-host disease and graft failure following pediatric allogeneic hematopoietic cell transplantation. The main toxicity includes delayed immune reconstitution, subsequent viral reactivations, and leukemia relapse. Exposure to alemtuzumab is highly variable upon empirical milligram/kilogram dosing.

METHODS

A population pharmacokinetic (PK) model for alemtuzumab was developed based on a total of 1146 concentration samples from 206 patients, aged 0.2-19 years, receiving a cumulative intravenous dose of 0.2-1.5 mg/kg, and treated between 2003 and 2015 in two centers.

RESULTS

Alemtuzumab PK were best described using a two-compartment model with a parallel saturable and linear elimination pathway. The linear clearance pathway, central volume of distribution, and intercompartmental distribution increased with body weight. Blood lymphocyte counts, a potential substrate for alemtuzumab, did not impact clearance.

CONCLUSION

The current practice with uniform milligram/kilogram doses leads to highly variable exposures in children due to the non-linear relationship between body weight and alemtuzumab PK. This model may be used for individualized dosing of alemtuzumab.

Nonlinear Mixed-Effects Model Development and Simulation Using nlmixr and Related R Open-Source Packages

nlmixr is a free and open-source R package for fitting nonlinear pharmacokinetic (PK), pharmacodynamic (PD), joint PK-PD, and quantitative systems pharmacology mixed-effects models. Currently, nlmixr is capable of fitting both traditional compartmental PK models as well as more complex models implemented using ordinary differential equations. We believe that, over time, it will become a capable, credible alternative to commercial software tools, such as NONMEM, Monolix, and Phoenix NLME.

Dried blood samples can support monitoring of infliximab concentrations in patients with inflammatory bowel disease: A clinical validation

Aims

Therapeutic drug monitoring (TDM) can optimize the efficacy of infliximab (IFX) in patients with inflammatory bowel disease (IBD). Because of the delay between blood samples taken at trough and availability of results, dose adjustments can only be carried out at the next infusion, typically 8 weeks later. Dried blood samples (DBS) performed at home to measure IFX concentrations can reduce the time to adapt dose/dosing interval. Here, we aimed to validate the clinical application of DBS for IFX in IBD patients and to evaluate the feasibility of home sampling.

Methods

DBS results from 40 IBD patients on IFX treatment were compared to serum sample results at trough, peak, and 3–5 weeks after IFX infusion. Subsequently, patients performed DBS home sampling one week before the next IFX infusion. These were compared to serum concentrations as predicted by Bayesian analysis.

Results

IFX concentrations from finger prick and venous puncture correlate well. DBS IFX concentrations showed high correlation with serum IFX concentrations (Spearman correlation: ≥0.965), without bias. Passing‐Bablok regression for IFX concentrations in DBS from home sampling also showed no bias (intercept: 1.02 mg L⁻¹ (95% CI −1.77–2.04 mg L⁻¹), slope: 0.82 (95% CI 0.63–1.40)), with reasonable correlation (Spearman correlation: 0.671).

Conclusions

Timely adjustment of IFX dose/dosing interval can be facilitated by IFX concentration measurement in home‐sampled DBS. DBS is a reliable method to measure IFX and can be used to predict IFX trough concentrations.

Optimising infliximab induction dosing for patients with ulcerative colitis

AIMS

The therapeutic failure of infliximab therapy in patients with ulcerative colitis remains a challenge even 2 decades after its approval. Therapeutic drug monitoring (TDM) has shown value during maintenance therapy, but induction therapy has still not been explored. Patients may be primary nonresponders or underexposed with the standard dosing regimen. We aimed to: (i) develop a population pharmacokinetic-pharmacodynamic model; (ii) identify the best exposure metric that predicts mucosal healing; and (iii) build an exposure-response (ER) model to demonstrate model-based dose finding during induction therapy with infliximab.

METHODS

Data were retrospectively collected from a clinical database. A total of 583 samples, from 204 patients, was used to develop a population pharmacokinetic model to generate exposure metrics for subsequent ER modelling. A subset of 159 patients was used to develop a logistic regression ER model, describing the relationship between infliximab exposure and ordered transitions between Mayo endoscopic subscore (MES) 3, 2 and ≤1 (baseline to post-induction).

RESULTS

A 1-compartment population pharmacokinetic model with interindividual and interoccasion variability was found to fit the data best. Covariates influencing exposure were C-reactive protein, albumin, baseline MES, fat-free mass, concomitant corticosteroid use and pancolitis. The cumulative area under the infliximab concentration-time curve until endoscopy (CAUC_endoscopy ) was found to be the best exposure metric for predicting mucosal healing (baseline MES >1 and post-induction MES ≤1). The model predicted that 70% of patients will attain mucosal healing with infliximab administered at days 0, 14 and 42 and a target CAUC_endoscopy of 3752 mg/L*day at day 84.

CONCLUSIONS

TDM-based dose individualisation targeting CAUC_endoscopy has the potential to improve the effectiveness of infliximab during induction therapy.

Clinical dashboard development and use for academic detailing in the U.S. Department of Veterans Affairs

OBJECTIVE

To describe the U.S. Department of Veterans Affairs (VA) Academic Detailing Service's (ADS) experience with the development and use of clinical dashboards across the VA's national clinical campaigns. We focused only on dashboards developed by the VA ADS national clinical program managers.

SETTING

U.S. Department of Veterans Affairs Pharmacy Benefits Management National Academic Detailing Service.

PRACTICE DESCRIPTION

Academic detailing is a multifaceted, educational outreach intervention that services providers through interactions with academic detailers (at the VA, these are specially trained clinical pharmacy specialists) using evidence-based research, educational brochures, and clinical dashboards to align prescribing behavior with best practices. The VA ADS developed clinical dashboards to benchmark and monitor academic detailing activities and performance and to identify opportunities for redistributing resources. We used the opioid crisis as an example to highlight key steps in the development of a clinical dashboard.

EVALUATION

Testing is an important part of clinical dashboard development. Evaluations of the users perceptions contributed to lessons learned.

RESULTS

Data validation, missing data, data availability, standardization, user engagement, and technical limitations were among several challenges the VA ADS encountered during dashboard development. Stakeholder engagement, communication, and flexibility with development time allowed us to develop efficient dashboards.

CONCLUSIONS

Health care data and health analytics have transformed the type of clinical care that can be practiced by creating the ability to implement system-wide processes for both population management and quality improvement processes. End users of these VA ADS clinical dashboards can generate priority panel reports and data visualization of key performance indicators to identify areas for improvement or action.

What Does it Take to Make Model-Informed Precision Dosing Common Practice? Report from the 1st Asian Symposium on Precision Dosing

Model-informed precision dosing (MIPD) is modeling and simulation in healthcare to predict the drug dose for a given patient based on their individual characteristics that is most likely to improve efficacy and/or lower toxicity in comparison to traditional dosing. This paper describes the background and status of MIPD and the activities at the 1st Asian Symposium of Precision Dosing. The theme of the meeting was the question, "What does it take to make MIPD common practice?" Formal presentations highlighted the distinction between genetic and non-genetic sources of variability in drug exposure and response, the use of modeling and simulation as decision support tools, and the facilitators to MIPD implementation. A panel discussion addressed the types of models used for MIPD, how the pharmaceutical industry views MIPD, ways to upscale MIPD beyond academic hospital centers, and the essential role of healthcare professional education as a way to progress. The meeting concluded with an ongoing commitment to use MIPD to improve patient care.

Suggestions for Model-Informed Precision Dosing to Optimize Neonatal Drug Therapy

Evidence for dosing, efficacy, and safety of most medications used to treat neonates is sparse. Thus, dosing is usually derived by extrapolation from adult and pediatric pharmacologic data with scaling by body weight or body surface area. This may lead to drug dosing that is unsafe or ineffective. However, new strategies are being developed and studied to dose medications in critically ill neonates. Mass spectroscopy technology capable of quickly analyzing drug levels is readily available. Software that integrates population pharmacokinetics and pharmacodynamics with data from sparse samples from neonates allows for timely adjustments of dosing to achieve the desired effect while minimizing adverse outcomes. Some genetic polymorphisms that affect drug response in neonates have also been reported. This review highlights aspects of drug response and how it is impacted by prematurity, assesses pharmacogenomic studies in neonates, and offers suggestions for innovative pharmacokinetic/pharmacodynamic model-based approaches that combine population- or physiology-based pharmacology data, Bayesian analysis, and electronic decision support tools for precision dosing in neonates while illustrating examples where this approach can be used to optimize medical therapy in neonates. Barriers to implementing precision dosing in neonates and how to overcome them are also discussed.

Individualized Dosing of Therapeutic Monoclonal Antibodies-a Changing Treatment Paradigm?

The introduction of monoclonal antibodies (mAbs) to the treatment of inflammatory bowel disease (IBD) was an important medical milestone. MAbs have been demonstrated as safe and efficacious treatments of IBD. However, a large percentage of patients either fail to respond initially or lose response to therapy after a period of treatment. Although there are factors associated with poor treatment outcomes in IBD, one cause for treatment failure may be low mAb exposure. Consequently, gastroenterologists have begun using therapeutic drug monitoring (TDM) to guide dose adjustment. However, while beneficial, TDM does not provide sufficient information to effectively adjust doses. The pharmacokinetics (PK) and pharmacodynamics (PD) of mAbs are complex, with numerous factors impacting on mAb PK and PD. The concept of dashboard-guided dosing based on Bayesian PK models allows physicians to combine TDM with factors influencing mAb PK to individualize therapy more effectively. One issue with TDM has been the slow turnaround of assay results, either necessitating an additional clinic visit for a sample or reacting to TDM results at a subsequent, rather than the current, dose. New point-of-care (POC) assays for mAbs are being developed that would potentially allow physicians to determine drug concentration quickly. However, work remains to understand how to determine what target exposure is needed for an individual patient, and whether the combination of POC assays and dashboards presents a safe approach with substantial outcome benefit over the current standard of care.

Leveraging Big Data in Pediatric Development Programs: Proceedings From the 2016 American College of Clinical Pharmacology Annual Meeting Symposium

This article discusses the use of big data in pediatric drug development. The article covers key topics discussed at the ACCP annual meeting symposium in 2016 including the extent to which big data or real-world data can inform clinical trial design and substitute for efficacy and safety data typically obtained in clinical trials. The current states of use, opportunities, and challenges with the use of big data in future pediatric drug development are discussed.

Application of Population Pharmacokinetic Modeling for Individualized Infliximab Dosing Strategies in Crohn Disease

OBJECTIVES

The pharmacokinetics of infliximab (IFX) is highly variable in children with Crohn disease (CD), and a one-size-fits-all approach to dosing is inadequate. Model-based drug dosing can help individualize dosing strategies. We evaluated the predictive performance and clinical utility of a published population pharmacokinetic model of IFX in children with CD.

METHODS

Within a cohort of 34 children with CD who had IFX trough concentrations measured, the pharmacokinetics of each patient was estimated in NONMEM using a published population pharmacokinetic model. Infliximab concentrations were then predicted based on each patient's dosing history and compared with actual measured concentrations (n = 59). In addition, doses 5 to 10 mg/kg and dosing intervals every 4 to 8 weeks were simulated in each patient to examine dose-trough relationships.

RESULTS

Predicted concentrations were within ±1.0 μg/mL of actual measured concentrations for 88% of measurements. The median prediction error (ie, measure of bias) was -0.15 μg/mL (95% confidence interval -0.37 to -0.05 μg/mL) and absolute prediction error (ie, measure of precision) was 0.26 μg/mL (95% confidence interval 0.15 to 0.40 μg/mL). At standard maintenance dosing of 5 mg/kg every 8 weeks, a trough >3 μg/mL was predicted to be achieved in 32% of patients. To achieve a trough >3 μg/mL, a dosing interval ≤every 6 weeks was predicted to be required in 29% of patients.

CONCLUSIONS

A published IFX population pharmacokinetic model demonstrated accurate predictive performance in a pediatric CD population. Individualized IFX dosing strategies in children with CD will be critical to consistently achieve trough concentrations associated with optimal outcomes.

External evaluation of population pharmacokinetic models for ciclosporin in adult renal transplant recipients

AIMS

Several population pharmacokinetic (popPK) models for ciclosporin (CsA) in adult renal transplant recipients have been constructed to optimize the therapeutic regimen of CsA. However, little is known about their predictabilities when extrapolated to different clinical centres. Therefore, this study aimed to externally evaluate the predictive ability of CsA popPK models and determine the potential influencing factors.

METHODS

A literature search was conducted and the predictive performance was determined for each selected model using an independent data set of 62 patients (471 predose and 500 2-h postdose concentrations) from our hospital. Prediction-based diagnostics and simulation-based normalized prediction distribution error were used to evaluate model predictability. The influence of prior information was assessed using Bayesian forecasting. Additionally, potential factors influencing model predictability were investigated.

RESULTS

Seventeen models extracted from 17 published popPK studies were assessed. Prediction-based diagnostics showed that ethnicity potentially influenced model transferability. Simulation-based normalized prediction distribution error analyses indicated misspecification in most of the models, especially regarding variance. Bayesian forecasting demonstrated that the predictive performance of the models substantially improved with 2-3 prior observations. The predictability of nonlinear Michaelis-Menten models was superior to that of linear compartmental models when evaluating the impact of structural models, indicating the underlying nonlinear kinetics of CsA. Structural model, ethnicity, covariates and prior observations potentially affected model predictability.

CONCLUSIONS

Structural model is the predominant factor influencing model predictability. Incorporation of nonlinear kinetics in CsA popPK modelling should be considered. Moreover, Bayesian forecasting substantially improved model predictability.

The challenges of implementing pharmacogenomic testing in the clinic

INTRODUCTION

Pharmacogenomic testing has the potential to greatly benefit patients by enabling personalization of medication management, ensuring better efficacy and decreasing the risk of side effects. However, to fully realize the potential of pharmacogenomic testing, there are several important issues that must be addressed. Areas covered: In this expert review we discuss current challenges impacting the implementation of pharmacogenomic testing in the clinical practice. We emphasize issues related to testing methods, reporting of the results, test selection, clinical interpretation of the results, cost-effectiveness, and the long-term use of pharmacogenomic results in clinical practice. We identify opportunities and future directions to facilitate clinical implementation. Expert commentary: Several key elements are necessary to optimally integrate pharmacogenomic testing into clinical practice. Collaborative efforts among laboratories are needed to improve standardization of testing and reporting of the results. Clinicians need educational opportunities to improve understanding of which test to order and how to interpret the results. The electronic health records and other clinical systems need to improve their storage of the pharmacogenomics test results and interoperability to facilitate the use of clinically actionable results to improve patient care.