Individualised medicine: why we need Bayesian dosing

Population pharmacokinetic analyses of methotrexate in pediatric patients: a systematic review

BACKGROUND AND OBJECTIVES

Methotrexate is widely utilized in the chemotherapy of malignant tumors and autoimmune diseases in the pediatric population, but dosing can be challenging. Several population pharmacokinetic models were developed to characterize factors influencing variability and improve individualization of dosing regimens. However, significant covariates included varied across studies. The primary objective of this review was to summarize and discuss population pharmacokinetic models of methotrexate and covariates that influence pharmacokinetic variability in pediatric patients.

METHODS

Systematic searches were conducted in the PubMed and EMBASE databases from inception to 7 July 2023. Reporting Quality was evaluated based on a checklist with 31 items. The characteristics of studies and information for model construction and validation were extracted, summarized, and discussed.

RESULTS

Eighteen studies (four prospective studies and fourteen retrospective studies with sample sizes of 14 to 772 patients and 2.7 to 93.1 samples per patient) were included in this study. Two-compartment models were the commonly used structural models for methotrexate, and the clearance range of methotrexate ranged from 2.32 to 19.03 L/h (median: 6.86 L/h). Body size and renal function were found to significantly affect the clearance of methotrexate for pediatric patients. There were limited reports on the role of other covariates, such as gene polymorphisms and co-medications, in the pharmacokinetic parameters of methotrexate pediatric patients. Internal and external evaluations were used to assess the performance of the population pharmacokinetic models.

CONCLUSION

A more rigorous external evaluation needs to be performed before routine clinical use to select the appropriate PopPK model. Further research is necessary to incorporate larger cohorts or pool analyses in specific susceptible pediatric populations to improve the understanding of predicted exposure profiles and covariate identification.

N-of-1 medicine

ABSTRACT

The fields of precision and personalised medicine have led to promising advances in tailoring treatment to individual patients. Examples include genome/molecular alteration-guided drug selection, single-patient gene therapy design and synergy-based drug combination development, and these approaches can yield substantially diverse recommendations. Therefore, it is important to define each domain and delineate their commonalities and differences in an effort to develop novel clinical trial designs, streamline workflow development, rethink regulatory considerations, create value in healthcare and economics assessments, and other factors. These and other segments are essential to recognise the diversity within these domains to accelerate their respective workflows towards practice-changing healthcare. To emphasise these points, this article elaborates on the concept of digital health and digital medicine-enabled N-of-1 medicine, which individualises combination regimen and dosing using a patient's own data. We will conclude with recommendations for consideration when developing novel workflows based on emerging digital-based platforms.

Model-Informed Precision Dosing of Imipenem in an Obese Adolescent Patient with Augmented Renal Clearance and History of Schizophrenia

Imipenem is a broad-spectrum antibiotic that has been used in treating severe infections and exhibits a time-dependent PK/PD profile. Its dose should be adjusted based on renal function. However, there is little experience with imipenem dosing in obese adolescent patients with augmented renal clearance (ARC) and history of schizophrenia. This case reported successful dosing of imipenem in an obese adolescent patient with ARC based on therapeutic drug monitoring (TDM) and model-informed precision dosing (MIPD). A 15-year-old male adolescent patient with history of schizophrenia was diagnosed with ventilator-associated pneumonia due to carbapenem-susceptible Klebsiella pneumoniae and received imipenem treatment (0.5 g every 8 hours with a 1-hour infusion). However, the exposure of imipenem was suboptimal due to ARC, and there is no available model for MIPD in this patient. Thus, we utilized prediction error to find a population pharmacokinetic model that fit this patient and ran Maximum a posteriori Bayesian estimation and Monte Carlo simulation based on screened models to predict changes in drug concentrations. The dose of imipenem was adjusted to 0.5 g every 6 hours with a 2-hour infusion, and subsequent TDM revealed that dosing adjustment was accurate and successful. Finally, the patient's status of infection improved. This study will be beneficial to imipenem dosing in similar cases in the future, thereby improving the safety and effectiveness of imipenem or other antibiotics.

Towards Model-Informed Precision Dosing of Voriconazole: Challenging Published Voriconazole Nonlinear Mixed-Effects Models with Real-World Clinical Data

BACKGROUND AND OBJECTIVES

Model-informed precision dosing (MIPD) frequently uses nonlinear mixed-effects (NLME) models to predict and optimize therapy outcomes based on patient characteristics and therapeutic drug monitoring data. MIPD is indicated for compounds with narrow therapeutic range and complex pharmacokinetics (PK), such as voriconazole, a broad-spectrum antifungal drug for prevention and treatment of invasive fungal infections. To provide guidance and recommendations for evidence-based application of MIPD for voriconazole, this work aimed to (i) externally evaluate and compare the predictive performance of a published so-called 'hybrid' model for MIPD (an aggregate model comprising features and prior information from six previously published NLME models) versus two 'standard' NLME models of voriconazole, and (ii) investigate strategies and illustrate the clinical impact of Bayesian forecasting for voriconazole.

METHODS

A workflow for external evaluation and application of MIPD for voriconazole was implemented. Published voriconazole NLME models were externally evaluated using a comprehensive in-house clinical database comprising nine voriconazole studies and prediction-/simulation-based diagnostics. The NLME models were applied using different Bayesian forecasting strategies to assess the influence of prior observations on model predictivity.

RESULTS

The overall best predictive performance was obtained using the aggregate model. However, all NLME models showed only modest predictive performance, suggesting that (i) important PK processes were not sufficiently implemented in the structural submodels, (ii) sources of interindividual variability were not entirely captured, and (iii) interoccasion variability was not adequately accounted for. Predictive performance substantially improved by including the most recent voriconazole observations in MIPD.

CONCLUSION

Our results highlight the potential clinical impact of MIPD for voriconazole and indicate the need for a comprehensive (pre-)clinical database as basis for model development and careful external model evaluation for compounds with complex PK before their successful use in MIPD.

The Significance of Bayesian Pharmacokinetics in Dosing for Critically Ill Patients: A Primer for Clinicians Using Vancomycin as an Example

Antibiotic use is becoming increasingly challenging with the emergence of multidrug-resistant organisms. Pharmacokinetic (PK) alterations result from complex pathophysiologic changes in some patient populations, particularly those with critical illness. Therefore, antibiotic dose individualization in such populations is warranted. Recently, there have been advances in dose optimization strategies to improve the utilization of existing antibiotics. Bayesian-based dosing is one of the novel approaches that could help clinicians achieve target concentrations in a greater percentage of their patients earlier during therapy. This review summarizes the advantages and disadvantages of current approaches to antibiotic dosing, with a focus on critically ill patients, and discusses the use of Bayesian methods to optimize vancomycin dosing. The Bayesian method of antibiotic dosing was developed to provide more precise predictions of drug concentrations and target achievement early in therapy. It has benefits such as the incorporation of personalized PK/PD parameters, improved predictive abilities, and improved patient outcomes. Recent vancomycin dosing guidelines emphasize the importance of using the Bayesian method. The Bayesian method is able to achieve appropriate antibiotic dosing prior to the patient reaching the steady state, allowing the patient to receive the right drug at the right dose earlier in therapy.

Population pharmacokinetic dosimetry model using imaging data to assess variability in pharmacokinetics of 177 Lu-PSMA-617 in prostate cancer patients

Studies to evaluate and optimize [177 Lu]Lu-PSMA treatment focus primarily on individual patient data. A population pharmacokinetic (PK) dosimetry model was developed to explore the potential of using imaging data as input for population PK models and to characterize variability in organ and tumor uptake of [177 Lu]Lu-PSMA-617 in patients with low volume metastatic prostate cancer. Simulations were performed to identify the effect of dose adjustments on absorbed doses in salivary glands and tumors. A six-compartment population PK model was developed, consisting of blood, salivary gland, kidneys, liver, tumor, and a lumped compartment representing other tissue (compartment 1-6, respectively), based on data from 10 patients who received [177 Lu]Lu-PSMA-617 (2 cycles, ~ 3 and ~ 6 GBq). Data consisted of radioactivity levels (decay corrected) in blood and tissues (9 blood samples and 5 single photon emission computed tomography/computed tomography scans). Observations in all compartments were adequately captured by individual model predictions. Uptake into salivary glands was saturable with an estimated maximum binding capacity (Bmax ) of 40.4 MBq (relative standard error 12.3%) with interindividual variability (IIV) of 59.3% (percent coefficient of variation [CV%]). IIV on other PK parameters was relatively minor. Tumor volume was included as a structural effect on the tumor uptake rate constant (k15 ), where a two-fold increase in tumor volume resulted in a 1.63-fold increase in k15 . In addition, interoccasion variability on k15 improved the model fit (43.5% [CV%]). Simulations showed a reduced absorbed dose per unit administered activity for salivary glands after increasing radioactivity dosing from 3 to 6 GBq (0.685 Gy/GBq vs. 0.421 Gy/GBq, respectively). All in all, population PK modeling could help to improve future radioligand therapy research.

Population Pharmacokinetic Analysis of Perampanel in Portuguese Patients Diagnosed with Refractory Epilepsy

Perampanel is a promising antiepileptic drug (AED) for refractory epilepsy treatment due to its innovative mechanism of action. This study aimed to develop a population pharmacokinetic (PopPK) model to be further used in initial dose optimization of perampanel in patients diagnosed with refractory epilepsy. A total of seventy-two plasma concentrations of perampanel obtained from forty-four patients were analyzed through a population pharmacokinetic approach by means of nonlinear mixed effects modeling (NONMEM). A one-compartment model with first-order elimination best described the pharmacokinetic profiles of perampanel. Interpatient variability (IPV) was entered on clearance (CL), while the residual error (RE) was modeled as proportional. The presence of enzyme-inducing AEDs (EIAEDs) and body mass index (BMI) were found as significant covariates for CL and volume of distribution (V), respectively. The mean (relative standard error) estimates for CL and V of the final model were 0.419 L/h (5.56%) and 29.50 (6.41%), respectively. IPV was 30.84% and the proportional RE was 6.44%. Internal validation demonstrated an acceptable predictive performance of the final model. A reliable population pharmacokinetic model was successfully developed, and it is the first enrolling real-life adults diagnosed with refractory epilepsy.

Pharmacokinetics in Critically Ill Children with Acute Kidney Injury

Acute kidney injury (AKI) is a commonly encountered comorbidity in critically ill children. The coexistence of AKI disturbs drug pharmacokinetics and pharmacodynamics, leading to clinically significant consequences. This can complicate an already critical clinical scenario by causing potential underdosing or overdosing giving way to possible therapeutic failures and adverse reactions. Current available studies offer little guidance to help maneuver such complex dosing regimens and decision-making in pediatric patients as most of them are done on heterogeneous groups of adult populations. Though there are some studies on drug dosing during continuous renal replacement therapy (CRRT), their utility is in question because of the recent advances in CRRT technology. Our review aims to discuss the principles of pharmacokinetics pertinent for honing the existing practices of drug dosing in critically ill children with AKI, and the various complexities and intricate challenges involved. This in turn will provide a framework to help enable caretakers to tailor dosing regimens in complex clinical setups with further ease and precision.

The Current Status and Future Perspectives of Beta-Lactam Therapeutic Drug Monitoring in Critically Ill Patients

Beta-lactams (BL) are the first line agents for the antibiotic management of critically ill patients with sepsis or septic shock. BL are hydrophilic antibiotics particularly subject to unpredictable concentrations in the context of critical illness because of pharmacokinetic (PK) and pharmacodynamics (PD) alterations. Thus, during the last decade, the literature focusing on the interest of BL therapeutic drug monitoring (TDM) in the intensive care unit (ICU) setting has been exponential. Moreover, recent guidelines strongly encourage to optimize BL therapy using a PK/PD approach with TDM. Unfortunately, several barriers exist regarding TDM access and interpretation. Consequently, adherence to routine TDM in ICU remains quite low. Lastly, recent clinical studies failed to demonstrate any improvement in mortality with the use of TDM in ICU patients. This review will first aim at explaining the value and complexity of the TDM process when translating it to critically ill patient bedside management, interpretating the results of clinical studies and discussion of the points which need to be addressed before conducting further TDM studies on clinical outcomes. In a second time, this review will focus on the future aspects of TDM integrating toxicodynamics, model informed precision dosing (MIPD) and “at risk” ICU populations that deserve further investigations to demonstrate positive clinical outcomes.

Implementing Vancomycin Population Pharmacokinetic Models: An App for Individualized Antibiotic Therapy in Critically Ill Patients

In individualized therapy, the Bayesian approach integrated with population pharmacokinetic models (PopPK) for predictions together with therapeutic drug monitoring (TDM) to maintain adequate objectives is useful to maximize the efficacy and minimize the probability of toxicity of vancomycin in critically ill patients. Although there are limitations to implementation, model-informed precision dosing (MIPD) is an approach to integrate these elements, which has the potential to optimize the TDM process and maximize the success of antibacterial therapy. The objective of this work was to present an app for individualized therapy and perform a validation of the implemented vancomycin PopPK models. A pragmatic approach was used for selecting the models of Llopis, Goti and Revilla for developing a Shiny app with R. Through ordinary differential equation (ODE)-based mixed effects models from the mlxR package, the app simulates the concentrations' behavior, estimates whether the model was simulated without variability and predicts whether the model was simulated with variability. Moreover, we evaluated the predictive performance with retrospective trough concentration data from patients admitted to the adult critical care unit. Although there were no significant differences in the performance of the estimates, the Llopis model showed better accuracy (mean 80.88%; SD 46.5%); however, it had greater bias (mean -34.47%, SD 63.38%) compared to the Revilla et al. (mean 10.61%, SD 66.37%) and Goti et al. (mean of 13.54%, SD 64.93%) models. With respect to the RMSE (root mean square error), the Llopis (mean of 10.69 mg/L, SD 12.23 mg/L) and Revilla models (mean of 10.65 mg/L, SD 12.81 mg/L) were comparable, and the lowest RMSE was found in the Goti model (mean 9.06 mg/L, SD 9 mg/L). Regarding the predictions, this behavior did not change, and the results varied relatively little. Although our results are satisfactory, the predictive performance in recent studies with vancomycin is heterogeneous, and although these three models have proven to be useful for clinical application, further research and adaptation of PopPK models is required, as well as implementation in the clinical practice of MIPD and TDM in real time.

The Bayesian-Based Area under the Curve of Vancomycin by Using a Single Trough Level: An Evaluation of Accuracy and Discordance at Tertiary Care Hospital in KSA

The AUC_0-24 is the most accurate way to track the vancomycin level while the C_min is not an accurate surrogate. Most hospitals in Saudi Arabia are under-practicing the AUC-guided vancomycin dosing and monitoring. No previous work has been conducted to evaluate such practice in the whole kingdom. The current study objective is to calculate the AUC_0-24 using the Bayesian dosing software (PrecisePK), identify the probability of patients who receive the optimum dose of vancomycin, and evaluate the accuracy and precision of the Bayesian platform. This retrospective study was conducted at King Abdulaziz medical city, Jeddah. All adult patients treated with vancomycin were included. Pediatric patients, critically ill patients requiring ICU admission, patients with acute renal failure or undergoing dialysis, and febrile neutropenic patients were excluded. The AUC_0-24 was predicted using the PrecisePK platform based on the Bayesian principle. The two-compartmental model by Rodvold et al. in this platform and patients' dose data were utilized to calculate the AUC_0-24 and trough level. Among 342 patients included in the present study, the mean of the estimated vancomycin AUC_0-24 by the posterior model of PrecisePK was 573 ± 199.6 mg, and the model had a bias of 16.8%, whereas the precision was 2.85 mg/L. The target AUC_0-24 (400 to 600 mg·h/L) and measured trough (10 to 20 mg/L) were documented in 127 (37.1%) and 185 (54%), respectively. Furthermore, the result demonstrated an increase in odds of AUC_0-24 > 600 mg·h/L among trough level 15-20 mg/L group (OR = 13.2, p < 0.05) as compared with trough level 10-14.9 mg/L group. In conclusion, the discordance in the AUC_0-24 ratio and measured trough concentration may jeopardize patient safety, and implantation of the Bayesian approach as a workable alternative to the traditional trough method should be considered.

Personalized tobramycin dosing in children with cystic fibrosis: a comparative clinical evaluation of log-linear and Bayesian methods

BACKGROUND

Children with cystic fibrosis (CF) pulmonary exacerbations receive IV tobramycin therapy, with dosing guided by either log-linear regression (LLR) or Bayesian forecasting (BF).

OBJECTIVES

To compare clinical and performance outcomes for LLR and BF.

PATIENTS AND METHODS

A quasi-experimental intervention study was conducted at a tertiary children's hospital. Electronic medical records were extracted (from January 2015 to September 2021) to establish a database consisting of pre-intervention (LLR) and post-intervention (BF) patient admissions and relevant outcomes. All consecutive patients treated with IV tobramycin for CF pulmonary exacerbations guided by either LLR or BF were eligible.

RESULTS

A total of 376 hospital admissions (LLR = 248, BF = 128) for CF pulmonary exacerbations were included. Patient demographics were similar between cohorts. There were no significant differences found in overall hospital length of stay, rates of re-admission within 1 month of discharge or change in forced expiratory volume in the first second (Δ FEV1) at the end of tobramycin treatment. Patients treated with LLR on average had twice the number of therapeutic drug monitoring (TDM) blood samples collected during a single hospital admission. The timeframe for blood sampling was more flexible with BF, with TDM samples collected up to 16 h post-tobramycin dose compared with 10 h for LLR. The tobramycin AUC0-24 target of ≥100 mg/L·h was more frequently attained using BF (72%; 92/128) compared with LLR (50%; 124/248) (P < 0.001). Incidence of acute kidney injury was rare in both groups.

CONCLUSIONS

LLR and BF result in comparable clinical outcomes. However, BF can significantly reduce the number of blood collections required during each admission, improve dosing accuracy, and provide more reliable target concentration attainment in CF children.

Bayesian method application: Integrating mathematical modeling into clinical pharmacy through vancomycin therapeutic monitoring

The most recent consensus guidelines for dosing and monitoring vancomycin recommended the use of area-under-the-curve with Bayesian estimation for therapeutic monitoring. As this is a modern concept in the practice of clinical pharmacy, the main objective of this review is to introduce the fundamentals of Bayesian estimation and its mathematical application as it relates to vancomycin therapeutic drug monitoring. In addition, we aim to identify pharmacokinetic (PK) software programs that incorporate Bayesian estimation for vancomycin dosing and to describe the PK models utilized in those software programs for the adult population. Twelve software programs that utilize Bayesian estimation were identified, which included: Adult and Pediatric Kinetics, Best Dose, ClinCalc, DoseMeRx, ID-ODS, InsightRx, MwPharm++, NextDose, PrecisePK, TDMx, Tucuxi, and VancoCalc. The software programs varied in the population PK models used as the Bayesian a priori. With the presence of various vancomycin Bayesian software programs, it is important to choose those that utilize PK models reflective of the specific patient population.

A Free Open-Source Bayesian Vancomycin Dosing App for Adults: Design and Evaluation Study

Background

It has been suggested that Bayesian dosing apps can assist in the therapeutic drug monitoring of patients receiving vancomycin. Unfortunately, Bayesian dosing tools are often unaffordable to resource-limited hospitals. Our aim was to improve vancomycin dosing in adults. We created a free and open-source dose adjustment app, VancoCalc, which uses Bayesian inference to aid clinicians in dosing and monitoring of vancomycin.

Objective

The aim of this paper is to describe the design, development, usability, and evaluation of a free open-source Bayesian vancomycin dosing app, VancoCalc.

Methods

The app build and model fitting process were described. Previously published pharmacokinetic models were used as priors. The ability of the app to predict vancomycin concentrations was performed using a small data set comprising of 52 patients, aged 18 years and over, who received at least 1 dose of intravenous vancomycin and had at least 2 vancomycin concentrations drawn between July 2018 and January 2021 at Lakeridge Health Corporation Ontario, Canada. With these estimated and actual concentrations, median prediction error (bias), median absolute error (accuracy), and root mean square error (precision) were calculated to evaluate the accuracy of the Bayesian estimated pharmacokinetic parameters.

Results

A total of 52 unique patients’ initial vancomycin concentrations were used to predict subsequent concentration; 104 total vancomycin concentrations were assessed. The median prediction error was –0.600 ug/mL (IQR –3.06, 2.95), the median absolute error was 3.05 ug/mL (IQR 1.44, 4.50), and the root mean square error was 5.34.

Conclusions

We described a free, open-source Bayesian vancomycin dosing calculator based on revisions of currently available calculators. Based on this small retrospective preliminary sample of patients, the app offers reasonable accuracy and bias, which may be used in everyday practice. By offering this free, open-source app, further prospective validation could be implemented in the near future.

Chinese ICU physicians' knowledge of antibiotic pharmacokinetics/pharmacodynamics (PK/PD): a cross-sectional survey

BACKGROUND

Patients with sepsis have a high mortality rate, accumulated evidences suggest that an optimal antibiotic administration strategy based on pharmacokinetics/pharmacodynamics (PK/PD) can improve the prognosis of septic patients. Therefore, we assessed Chinese intensive care unit (ICU) physicians' knowledge about PK/PD.

METHODS

In December 2019, we designed a questionnaire focused on Chinese ICU physicians' knowledge about PK/PD and collected the questionnaires after 3 months. The questionnaire was distributed via e-mail and WeChat, and was distributed to ICU doctors in 31 administrative regions of China except Hong Kong, Macao and Taiwan. The passing score was corrected by the Angoff method, and the ICU physicians' knowledge about PK/PD was analysed accordingly.

RESULTS

We received a total of 1,309 questionnaires and retained 1,240 valid questionnaires. The passing score was 90.8, and the overall pass rate was 56.94%. The pass rate for tertiary and secondary hospitals was 59.07% and 37.19%, respectively. ICU physicians with less than 5 years of work experience and resident physician accounted for the highest pass rate, while those with between 5 to 10 years of work experience and attending accounted for the lowest pass rate. The majority of participants in the Chinese Critical Care Certified Course (5C) were from Jiangsu and Henan provinces, and they had the highest average scores (125.8 and 126.5, respectively). For Beijing and Shanghai, the average score was only 79.4 and 90.9, respectively.

CONCLUSIONS

Chinese ICU physicians' knowledge about PK/PD is unsatisfactory. Therefore, it is essential to strengthen ICU physicians' knowledge about PK/PD.

"De-Shrinking" EBEs: The Solution for Bayesian Therapeutic Drug Monitoring

BACKGROUND

Therapeutic drug monitoring (TDM) aims at individualising a dosage regimen and is increasingly being performed by estimating individual pharmacokinetic parameters via empirical Bayes estimates (EBEs). However, EBEs suffer from shrinkage that makes them biased. This bias is a weakness for TDM and probably a barrier to the acceptance of drug dosage adjustments by prescribers.

OBJECTIVE

The aim of this article is to propose a methodology that allows a correction of EBE shrinkage and an improvement in their precision.

METHODS

As EBEs are defined, they can be seen as a special case of ridge estimators depending on a parameter usually denoted λ. After a bias correction depending on λ, we chose λ so that the individual pharmacokinetic estimations have minimal imprecision. Our estimate is by construction always better than EBE with respect to bias (i.e. shrinkage) and precision.

RESULTS

We illustrate the performance of this approach with two different drugs: iohexol and isavuconazole. Depending on the patient's actual pharmacokinetic parameter values, the improvement given by our approach ranged from 0 to 100%.

CONCLUSION

This innovative methodology is promising since, to the best of our knowledge, no other individual shrinkage correction has been proposed.

Assessing the predictive performance of population pharmacokinetic models for intravenous polymyxin B in critically ill patients

Polymyxin B (PMB) has reemerged as a last‐line therapy for infections caused by multidrug‐resistant gram‐negative pathogens, but dosing is challenging because of its narrow therapeutic window and pharmacokinetic (PK) variability. Population PK (POPPK) models based on suitably powered clinical studies with appropriate sampling strategies that take variability into consideration can inform PMB dosing to maximize efficacy and minimize toxicity and resistance. Here we reviewed published PMB POPPK models and evaluated them using an external validation data set (EVD) of patients who are critically ill and enrolled in an ongoing clinical study to assess their utility. Seven published POPPK models were employed using the reported model equations, parameter values, covariate relationships, interpatient variability, parameter covariance, and unexplained residual variability in NONMEM (Version 7.4.3). The predictive ability of the models was assessed using prediction‐based and simulation‐based diagnostics. Patient characteristics and treatment information were comparable across studies and with the EVD (n = 40), but the sampling strategy was a main source of PK variability across studies. All models visually and statistically underpredicted EVD plasma concentrations, but the two‐compartment models more accurately described the external data set. As current POPPK models were inadequately predictive of the EVD, creation of a new POPPK model based on an appropriately powered clinical study with an informed PK sampling strategy would be expected to improve characterization of PMB PK and identify covariates to explain interpatient variability. Such a model would support model‐informed precision dosing frameworks, which are urgently needed to improve PMB treatment efficacy, limit resistance, and reduce toxicity in patients who are critically ill.

A Call to Action Toward Optimizing the Electrical Dose Received by Neural Targets in Spinal Cord Stimulation Therapy for Neuropathic Pain

Spinal cord stimulation has seen unprecedented growth in new technology in the 50 years since the first subdural implant. As we continue to grow our understanding of spinal cord stimulation and relevant mechanisms of action, novel questions arise as to electrical dosing optimization. Programming adjustment — dose titration — is often a process of trial and error that can be time-consuming and frustrating for both patient and clinician. In this report, we review the current preclinical and clinical knowledge base in order to provide insights that may be helpful in developing more rational approaches to spinal cord stimulation dosing. We also provide key conclusions that may help in directing future research into electrical dosing, given the advent of newer waveforms outside traditional programming parameters.

Therapeutic drug monitoring of immunosuppressive drugs in hepatology and gastroenterology

Immunosuppressive drugs have been key to the success of liver transplantation and are essential components of the treatment of inflammatory bowel disease (IBD) and autoimmune hepatitis (AIH). For many but not all immunosuppressants, therapeutic drug monitoring (TDM) is recommended to guide therapy. In this article, the rationale and evidence for TDM of tacrolimus, mycophenolic acid, the mammalian target of rapamycin inhibitors, and azathioprine in liver transplantation, IBD, and AIH is reviewed. New developments, including algorithm-based/computer-assisted immunosuppressant dosing, measurement of immunosuppressants in alternative matrices for whole blood, and pharmacodynamic monitoring of these agents is discussed. It is expected that these novel techniques will be incorporate into the standard TDM in the next few years.

Therapeutic Drug Monitoring Can Improve Linezolid Dosing Regimens in Current Clinical Practice: A Review of Linezolid Pharmacokinetics and Pharmacodynamics

Linezolid is an antibiotic used to treat infections caused by drug-resistant gram-positive organisms, including vancomycin-resistant Enterococcus faecium, multi-drug resistant Streptococcus pneumoniae, and methicillin-resistant Staphylococcus aureus. The adverse effects of linezolid can include thrombocytopenia and neuropathy, which are more prevalent with higher exposures and longer treatment durations. Although linezolid is traditionally administered at a standard 600 mg dose every 12 hours, the resulting exposure can vary greatly between patients and can lead to treatment failure or toxicity. The efficacy and toxicity of linezolid are determined by the exposure achieved in the patient; numerous clinical and population pharmacokinetics (popPK) studies have identified threshold measurements for both parameters. Several special populations with an increased need for linezolid dose adjustments have also been identified. Therapeutic Drug Monitoring (TDM) is a clinical strategy that assesses the response of an individual patient and helps adjust the dosing regimen to maximize efficacy while minimizing toxicity. Adaptive feedback control and model-informed precision dosing are additional strategies that use Bayesian algorithms and PK models to predict patient-specific drug exposure. TDM is a very useful tool for patient populations with sparse clinical data or known alterations in pharmacokinetics, including children, patients with renal insufficiency or those receiving renal replacement therapy, and patients taking co-medications known to interact with linezolid. As part of the clinical workflow, clinicians can use TDM with the thresholds summarized from the current literature to improve linezolid dosing for patients and maximize the probability of treatment success.

Bayesian estimation of pharmacokinetic parameters: an important component to include in the teaching of clinical pharmacokinetics and therapeutic drug monitoring

Bayesian estimation of pharmacokinetic parameters (PKP), as discussed in this review, provides a powerful approach towards the individualization of dosing regimens. The method was first described by Lewis Sheiner and colleagues and it is well suited in clinical environs where few blood fluid measures of drugs are available in the clinic. This makes it a valuable tool in the effective implementation of therapeutic drug monitoring. The principle behind the method is Bayes theorem, which incorporates elements of variability in a priori-known population estimates and variability in the pharmacokinetic parameters, and known errors intrinsic to the assay method used to estimate the blood fluid drug concentrations. This manuscript reviews the Bayesian method. The literature was scanned using Pubmed to provide background into the Bayesian method. An Add-in for Excel program was used to show the ability of the method to estimate PKP using sparse blood fluid concentration vs time data. Using a computer program, the method was able to find reasonable estimates of individual pharmacokinetic parameters, assessed by comparing the estimated data to the true PKP. Education of students in clinical pharmacokinetics is incomplete without some mention and instruction of the Bayesian forecasting method. For a complete understanding, a computer program is needed to demonstrate its utility.

Therapeutic drug monitoring of oral targeted antineoplastic drugs

PURPOSE

This review provides an overview of the current challenges in oral targeted antineoplastic drug (OAD) dosing and outlines the unexploited value of therapeutic drug monitoring (TDM). Factors influencing the pharmacokinetic exposure in OAD therapy are depicted together with an overview of different TDM approaches. Finally, current evidence for TDM for all approved OADs is reviewed.

METHODS

A comprehensive literature search (covering literature published until April 2020), including primary and secondary scientific literature on pharmacokinetics and dose individualisation strategies for OADs, together with US FDA Clinical Pharmacology and Biopharmaceutics Reviews and the Committee for Medicinal Products for Human Use European Public Assessment Reports was conducted.

RESULTS

OADs are highly potent drugs, which have substantially changed treatment options for cancer patients. Nevertheless, high pharmacokinetic variability and low treatment adherence are risk factors for treatment failure. TDM is a powerful tool to individualise drug dosing, ensure drug concentrations within the therapeutic window and increase treatment success rates. After reviewing the literature for 71 approved OADs, we show that exposure-response and/or exposure-toxicity relationships have been established for the majority. Moreover, TDM has been proven to be feasible for individualised dosing of abiraterone, everolimus, imatinib, pazopanib, sunitinib and tamoxifen in prospective studies. There is a lack of experience in how to best implement TDM as part of clinical routine in OAD cancer therapy.

CONCLUSION

Sub-therapeutic concentrations and severe adverse events are current challenges in OAD treatment, which can both be addressed by the application of TDM-guided dosing, ensuring concentrations within the therapeutic window.

Model based dose personalization in clinical trials

BACKGROUND AND OBJECTIVE

Personalized medicine is an important area of medical research which consists of designing therapies specifically for a patient or a group of patients. For drugs having a narrow therapeutic index or for vulnerable patients, methods such as therapeutic drug monitoring are used in a hospital setting to ensure that the blood concentration of the drug is maintained within a pre-decided range. However, such methods can not be used for drugs which are still in the developmental phase since, generally, insufficient information is available about the pharmacokinetic behaviour of the drug.

METHODS

In this paper, we present a new methodology for explicit optimization of dose regimens during the course of the pharmacokinetic studies such that the resultant blood concentration of the drug in each subject is maintained around a desired target concentration or within a target range.

RESULTS

We demonstrate that our algorithm is able to achieve the clinical objective of PK estimation while simultaneously individualizing the dose to every subject in the trial. Our algorithm computes dose regimens that, on average, have a relative efficiency of 97% with a standard deviation of less than 5%. The results show that the algorithm can be relied upon to ensure that the subjects in the trial are minimally over- and under-exposed to the test therapy.

CONCLUSIONS

The proposed methodology can assist in ensuring correct dosing to each subject in a clinical trial so that each subject receives only the intended exposure to the drug while simultaneously estimating the PK profile of the drug. Our methodology can also be applied in randomized concentration-controlled trials where maintenance of the target concentration in the subjects is a fundamental requirement for conducting these trials.

A Model-Informed Method for the Purpose of Precision Dosing of Isoniazid in Pulmonary Tuberculosis

Background and Objective

This study aimed to develop and evaluate a population pharmacokinetic model and limited sampling strategy for isoniazid to be used in model-based therapeutic drug monitoring.

Methods

A population pharmacokinetic model was developed based on isoniazid and acetyl-isoniazid pharmacokinetic data from seven studies with in total 466 patients from three continents. Three limited sampling strategies were tested based on the available sampling times in the dataset and practical considerations. The tested limited sampling strategies sampled at 2, 4, and 6 h, 2 and 4 h, and 2 h after dosing. The model-predicted area under the concentration–time curve from 0 to 24 h (AUC₂₄) and the peak concentration from the limited sampling strategies were compared to predictions using the full pharmacokinetic curve. Bias and precision were assessed using the mean error (ME) and the root mean square error (RMSE), both expressed as a percentage of the mean model-predicted AUC₂₄ or peak concentration on the full pharmacokinetic curve.

Results

Performance of the developed model was acceptable and the uncertainty in parameter estimations was generally low (the highest relative standard error was 39% coefficient of variation). The limited sampling strategy with sampling at 2 and 4 h was determined as most suitable with an ME of 1.1% and RMSE of 23.4% for AUC₂₄ prediction, and ME of 2.7% and RMSE of 23.8% for peak concentration prediction. For the performance of this strategy, it is important that data on both isoniazid and acetyl-isoniazid are used. If only data on isoniazid are available, a limited sampling strategy using 2, 4, and 6 h can be employed with an ME of 1.7% and RMSE of 20.9% for AUC₂₄ prediction, and ME of 1.2% and RMSE of 23.8% for peak concentration prediction.

Conclusions

A model-based therapeutic drug monitoring strategy for personalized dosing of isoniazid using sampling at 2 and 4 h after dosing was successfully developed. Prospective evaluation of this strategy will show how it performs in a clinical therapeutic drug monitoring setting.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40262-020-00971-2.

Feasibility of Extrapolating Randomly Taken Plasma Samples to Trough Levels for Therapeutic Drug Monitoring Purposes of Small Molecule Kinase Inhibitors

Small molecule kinase inhibitors (SMKIs) are widely used in oncology. Therapeutic drug monitoring (TDM) for SMKIs could reduce underexposure or overexposure. However, logistical issues such as timing of blood withdrawals hamper its implementation into clinical practice. Extrapolating a random concentration to a trough concentration using the elimination half-life could be a simple and easy way to overcome this problem. In our study plasma concentrations observed during 24 h blood sampling were used for extrapolation to trough levels. The objective was to demonstrate that extrapolation of randomly taken blood samples will lead to equivalent estimated trough samples compared to measured C_min values. In total 2241 blood samples were analyzed. The estimated C_trough levels of afatinib and sunitinib fulfilled the equivalence criteria if the samples were drawn after T_max. The calculated C_trough levels of erlotinib, imatinib and sorafenib met the equivalence criteria if they were taken, respectively, 12 h, 3 h and 10 h after drug intake. For regorafenib extrapolation was not feasible. In conclusion, extrapolation of randomly taken drug concentrations to a trough concentration using the mean elimination half-life is feasible for multiple SMKIs. Therefore, this simple method could positively contribute to the implementation of TDM in oncology.

Optimizing the Use of Antibiotic Agents in the Pediatric Intensive Care Unit: A Narrative Review

Antibiotics are one of the most prescribed drug classes in the pediatric intensive care unit, yet the incidence of inappropriate antibiotic prescribing remains high in critically ill children. Optimizing the use of antibiotics in this population is imperative to guarantee adequate treatment, avoid toxicity and the occurrence of antibiotic resistance, both on a patient level and on a population level. Antibiotic stewardship encompasses all initiatives to promote responsible antibiotic usage and the PICU represents a major target environment for antibiotic stewardship programs. This narrative review provides a summary of the available knowledge on the optimal selection, duration, dosage, and route of administration of antibiotic treatment in critically ill children. Overall, more scientific evidence on how to optimize antibiotic treatment is warranted in this population. We also give our personal expert opinion on research priorities.

Therapeutic drug monitoring of commonly used anti-infective agents: A nationwide cross-sectional survey of Australian hospital practices

When performed according to best-practice principles, therapeutic drug monitoring (TDM) can optimise anti-infective treatment and directly benefit clinical outcomes. We evaluated TDM performance and clinical decision-making for established anti-infective agents amongst Australian hospitals. A nationwide cross-sectional survey was conducted between August and September 2019. The survey consisted of multiple-choice questions regarding TDM of anti-infective agents in general as well as clinical vignettes specific to vancomycin, gentamicin and voriconazole. We sought to survey all Australian hospitals operating both in the public and private health sectors. Responses were captured from 85 unique institutions, from all Australian states and territories. Regarding guidelines, 26% of hospitals did not have endorsed guidelines to advise on the ordering, sampling and interpretation of TDM for any anti-infective agent. Admitting teams were predominantly responsible for ordering TDM (85%) and interpreting results (76%). Only 51% of hospitals had access to dose prediction software, with access generally better amongst principal referral (69%) (P = 0.01) and children's hospitals (100%) (P = 0.04). Whenever a laboratory-derived minimum inhibitory concentration (MIC) was not available to guide dosing decisions, a surrogate target MIC was assumed in 77% of hospitals. This was based on a 'worst-case' scenario infection in 11% of hospitals. The rates of clinical practice consistent with current guideline recommendations across all aspects of TDM were demonstrated to be 0% for vancomycin, 4% for gentamicin and 35% for voriconazole. At present, there is significant institutional variability in the clinical practice of TDM for anti-infective agents in Australia for established TDM drugs.

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.

Personalized Tuberculosis Treatment Through Model-Informed Dosing of Rifampicin

BACKGROUND AND OBJECTIVE

This study proposes a model-informed approach for therapeutic drug monitoring (TDM) of rifampicin to improve tuberculosis (TB) treatment.

METHODS

Two datasets from pulmonary TB patients were used: a pharmacokinetic study (34 patients, 373 samples), and TDM data (96 patients, 391 samples) collected at Radboud University Medical Center, The Netherlands. Nine suitable population pharmacokinetic models of rifampicin were identified in the literature and evaluated on the datasets. A model developed by Svensson et al. was found to be the most suitable based on graphical goodness of fit, residual diagnostics, and predictive performance. Prediction of individual area under the concentration-time curve from time zero to 24 h (AUC₂₄) and maximum concentration (C_max) employing various sampling strategies was compared with a previously established linear regression TDM strategy, using sampling at 2, 4, and 6 h, in terms of bias and precision (mean error [ME] and root mean square error [RMSE]).

RESULTS

A sampling strategy using 2- and 4-h blood collection was selected to be the most suitable. The bias and precision of the two strategies were comparable, except that the linear regression strategy was more biased in prediction of the AUC₂₄ than the model-informed approach (ME of 9.9% and 1.5%, respectively). A comparison of resulting dose advice, using predictions on a simulated dataset, showed no significant difference in sensitivity or specificity between the two methods. The model was successfully implemented in the InsightRX precision dosing platform.

CONCLUSION

Blood sampling at 2 and 4 h, combined with model-based prediction, can be used instead of the currently used linear regression strategy, shortening the sampling by 2 h and one sampling point without performance loss while simultaneously offering flexibility in sampling times.

Assessing the accuracy of two Bayesian forecasting programs in estimating vancomycin drug exposure

Background

Current guidelines for intravenous vancomycin identify drug exposure (as indicated by the AUC) as the best pharmacokinetic (PK) indicator of therapeutic outcome.

Objectives

To assess the accuracy of two Bayesian forecasting programs in estimating vancomycin AUC0–∞ in adults with limited blood concentration sampling.

Methods

The application of seven vancomycin population PK models in two Bayesian forecasting programs was examined in non-obese adults (n = 22) with stable renal function. Patients were intensively sampled following a single (1000 mg or 15 mg/kg) dose. For each patient, AUC was calculated by fitting all vancomycin concentrations to a two-compartment model (defined as AUCTRUE). AUCTRUE was then compared with the Bayesian-estimated AUC0–∞ values using a single vancomycin concentration sampled at various times post-infusion.

Results

Optimal sampling times varied across different models. AUCTRUE was generally overestimated at earlier sampling times and underestimated at sampling times after 4 h post-infusion. The models by Goti et al. (Ther Drug Monit 2018;

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212–21) and Thomson et al. (J Antimicrob Chemother 2009;

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1050–7) had precise and unbiased sampling times (defined as mean imprecision &lt;25% and &lt;38 mg·h/L, with 95% CI for mean bias containing zero) between 1.5 and 6 h and between 0.75 and 2 h post-infusion, respectively. Precise but biased sampling times for Thomson et al. were between 4 and 6 h post-infusion.

Conclusions

When using a single vancomycin concentration for Bayesian estimation of vancomycin drug exposure (AUC), the predictive performance was generally most accurate with sample collection between 1.5 and 6 h after infusion, though optimal sampling times varied across different population PK models.

What Are the Current Approaches to Optimising Antimicrobial Dosing in the Intensive Care Unit?

Antimicrobial dosing in the intensive care unit (ICU) can be problematic due to various challenges including unique physiological changes observed in critically ill patients and the presence of pathogens with reduced susceptibility. These challenges result in reduced likelihood of standard antimicrobial dosing regimens achieving target exposures associated with optimal patient outcomes. Therefore, the aim of this review is to explore the various methods for optimisation of antimicrobial dosing in ICU patients. Dosing nomograms developed from pharmacokinetic/statistical models and therapeutic drug monitoring are commonly used. However, recent advances in mathematical and statistical modelling have resulted in the development of novel dosing software that utilise Bayesian forecasting and/or artificial intelligence. These programs utilise therapeutic drug monitoring results to further personalise antimicrobial therapy based on each patient’s clinical characteristics. Studies quantifying the clinical and cost benefits associated with dosing software are required before widespread use as a point-of-care system can be justified.

Dosage of phenytoin in neurocritical patients using Bayesian algorithms: a pilot study

Phenytoin is widely used in neurocritical patients. Owing to its high pharmacokinetic variability and narrow therapeutic range, plasma level-guided dosing has become the standard. Bayesian prediction (BP) is considered the most flexible and precise pharmacokinetic strategy among several options. A retrospective study of BP dosage adjustment in 20 patients (35 plasma measures) was developed. Results indicated that 70% of phenytoin plasma levels of first plasma samples were beyond the therapeutic range. Phenytoin doses were also estimated according to BP for all patients. The measurements confirmed the ability of the strategy to lead to optimal dosage in 80% of patients, thus indicating a three-fold improvement over the basing dosage adjustment recommended in the literature.

An evaluation of the user-friendliness of Bayesian forecasting programs in a clinical setting

AIMS

To evaluate 3 Bayesian forecasting (BF) programs-TDMx, InsightRx and DoseMe-on their user-friendliness and common liked and disliked features through a survey of hospital pharmacists.

METHODS

Clinical pharmacists across 3 Australian hospitals that did not use a BF program were invited to a BF workshop and complete a survey on programs they trialled. Participants were given 4 case scenarios to work through and asked to complete a 5-point Likert scale survey evaluating the program's user-friendliness. Liked and disliked features of each program were ascertained through written responses to open-ended questions. Survey results were compared using a χ² test of equal or given proportions to identify significant differences in response.

RESULTS

Twenty-seven pharmacists, from hospitals, participated. BF programs were rated overall as user-friendly with 70%, 41% and 37% (P = .02) of participants recording a Likert score of 4 or 5 for DoseMe, TDMx and InsightRx, respectively. Participants found it easy to access all required information to use the programs, understood dosing recommendations and visualisations given by each program, and thought programs supported decision-making with >50% of participants scoring a 4 or 5 across the programs in these categories. Common liked features across all programs were the graphical displays and ease of data entry, while common disliked features were related to the units, layout and information display.

CONCLUSION

Although differences exist between programs, all 3 programs were most commonly rated as user-friendly across all themes evaluated, which provides useful information for healthcare facilities wanting to implement a BF program.

Evaluation of the predictive performance of Bayesian dosing for warfarin in Chinese patients

Aim: To evaluate the accuracy and predictive performance of Bayesian dosing for warfarin in Chinese patients. Materials & methods: Six multiple linear regression algorithms (Wei, Lou, Miao, Huang, Gage and IWPC) and a Bayesian method implemented in Warfarin Dose Calculator were compared with each other. Results: Six multiple linear regression warfarin dosing algorithms had similar predictive ability, except Miao and Lou. The mean prediction error of Bayesian priori and posteriori method were 0.01 mg/day (95% CI: -0.18 to 0.19) and 0.17 mg/day (95% CI: -0.05 to 0.29), respectively, and Bayesian posteriori method demonstrated better performance in all dose ranges. Conclusion: The Bayesian method showed a good potential for warfarin maintenance dose prediction in Chinese patients requiring less than 6 mg/day.