Model-Informed individualized dosage regimen of sirolimus in pediatric patients with intractable lymphatic malformations

Intractable lymphatic malformations (iLM) pose a significant threat to affected children, demonstrating limited responses to conventional treatments. Sirolimus, effectively inhibiting endothelial cell proliferation in lymphatic vessels, plays a crucial role in iLM treatment. However, the drug's narrow therapeutic window and substantial interindividual variability necessitate customized dosing strategies. This study aims to establish a Population Pharmacokinetic Model (PopPK model) for sirolimus in pediatric iLM patients, identifying quantitative relationships between covariates and sirolimus clearance and volume of distribution. Initial dosages are recommended based on a target concentration range of 5-15 ng/mL. Retrospective data from our institution, encompassing 53 pediatric patients with 275 blood concentration results over the past five years (average age: 4.64 ± 4.19 years), constituted the foundation of this analysis. The final model, adopting a first-order absorption and elimination single-compartment model, retained age as the sole covariate. Results indicated a robust correlation between apparent clearance (CL/F) at 5.56 L/h, apparent volume of distribution (V/F) at 292.57 L, and age. Monte Carlo simulation guided initial dosages for patients aged 0-18 years within the target concentration range. This study presents the first PopPK model using a large Therapeutic Drug Monitoring (TDM) database to describe personalized sirolimus dosing for pediatric iLM patients, contributing to pharmacokinetic guidance and potentially improving long-term clinical outcomes.

A prediction method for the individual serum concentration and therapeutic effect for optimizing adalimumab therapy in inflammatory bowel disease

OBJECTIVES

Adalimumab (ADM) therapy is effective for inflammatory bowel disease (IBD), but a significant number of IBD patients lose response to ADM. Thus, it is crucial to devise methods to enhance ADM's effectiveness. This study introduces a strategy to predict individual serum concentrations and therapeutic effects to optimize ADM therapy for IBD during the induction phase.

METHODS

We predicted the individual serum concentration and therapeutic effect of ADM during the induction phase based on pharmacokinetic and pharmacodynamic (PK/PD) parameters calculated using the empirical Bayesian method. We then examined whether the predicted therapeutic effect, defined as clinical remission or treatment failure, matched the observed effect.

RESULTS

Data were obtained from 11 IBD patients. The therapeutic effect during maintenance therapy was successfully predicted at 40 of 47 time points. Moreover, the predicted effects at each patient's final time point matched the observed effects in 9 of the 11 patients.

CONCLUSION

This is the inaugural report predicting the individual serum concentration and therapeutic effect of ADM using the Bayesian method and PK/PD modelling during the induction phase. This strategy may aid in optimizing ADM therapy for IBD.

Perceived barriers and facilitators for model-informed dosing in pregnancy: a qualitative study across healthcare practitioners and pregnant women

BACKGROUND

Most women use medication during pregnancy. Pregnancy-induced changes in physiology may require antenatal dose alterations. Yet, evidence-based doses in pregnancy are missing. Given historically limited data, pharmacokinetic models may inform pregnancy-adjusted doses. However, implementing model-informed doses in clinical practice requires support from relevant stakeholders.

PURPOSE

To explore the perceived barriers and facilitators for model-informed antenatal doses among healthcare practitioners (HCPs) and pregnant women.

METHODS

Online focus groups and interviews were held among healthcare practitioners (HCPs) and pregnant women from eight countries across Europe, Africa and Asia. Purposive sampling was used to identify pregnant women plus HCPs across various specialties prescribing or providing advice on medication to pregnant women. Perceived barriers and facilitators for implementing model-informed doses in pregnancy were identified and categorised using a hybrid thematic analysis.

RESULTS

Fifty HCPs and 11 pregnant women participated in 12 focus groups and 16 interviews between January 2022 and March 2023. HCPs worked in the Netherlands (n = 32), the UK (n = 7), South Africa (n = 5), Uganda (n = 4), Kenya, Cameroon, India and Vietnam (n = 1 each). All pregnant women resided in the Netherlands. Barriers and facilitators identified by HCPs spanned 14 categories across four domains whereas pregnant women described barriers and facilitators spanning nine categories within the same domains. Most participants found current antenatal dosing information inadequate and regarded model-informed doses in pregnancy as a valuable and for some, much-needed addition to antenatal care. Although willingness-to-follow model-informed antenatal doses was high across both groups, several barriers for implementation were identified. HCPs underlined the need for transparent model validation and endorsement of the methodology by recognised institutions. Foetal safety was deemed a critical knowledge gap by both groups. HCPs' information needs and preferred features for model-informed doses in pregnancy varied. Several pregnant women expressed a desire to access information and partake in decisions on antenatal dosing.

CONCLUSIONS

Given the perceived limitations of current pharmacotherapy for pregnant women and foetuses, model-informed dosing in pregnancy was seen as a promising means to enhance antenatal care by pregnant women and healthcare practitioners.

Best Practice for Therapeutic Drug Monitoring of Infliximab: Position Statement from the International Association of Therapeutic Drug Monitoring and Clinical Toxicology

BACKGROUND

Infliximab, an anti-tumor necrosis factor monoclonal antibody, has revolutionized the pharmacological management of immune-mediated inflammatory diseases (IMIDs). This position statement critically reviews and examines existing data on therapeutic drug monitoring (TDM) of infliximab in patients with IMIDs. It provides a practical guide on implementing TDM in current clinical practices and outlines priority areas for future research.

METHODS

The endorsing TDM of Biologics and Pharmacometrics Committees of the International Association of TDM and Clinical Toxicology collaborated to create this position statement.

RESULTS

Accumulating data support the evidence for TDM of infliximab in the treatment of inflammatory bowel diseases, with limited investigation in other IMIDs. A universal approach to TDM may not fully realize the benefits of improving therapeutic outcomes. Patients at risk for increased infliximab clearance, particularly with a proactive strategy, stand to gain the most from TDM. Personalized exposure targets based on therapeutic goals, patient phenotype, and infliximab administration route are recommended. Rapid assays and home sampling strategies offer flexibility for point-of-care TDM. Ongoing studies on model-informed precision dosing in inflammatory bowel disease will help assess the additional value of precision dosing software tools. Patient education and empowerment, and electronic health record-integrated TDM solutions will facilitate routine TDM implementation. Although optimization of therapeutic effectiveness is a primary focus, the cost-reducing potential of TDM also merits consideration.

CONCLUSIONS

Successful implementation of TDM for infliximab necessitates interdisciplinary collaboration among clinicians, hospital pharmacists, and (quantitative) clinical pharmacologists to ensure an efficient research trajectory.

Validation and development of population pharmacokinetic model of vancomycin using a real-world database from a nationwide free web application

INTRODUCTION

Vancomycin requires a population pharmacokinetic (popPK) model to estimate the area under the concentration-time curve (AUC), and an AUC-guided dosing strategy is necessary. This study aimed to develop a popPK model for vancomycin using a real-world database pooled from a nationwide web application (PAT).

METHODS

In this retrospective study, the PAT database between December 14, 2022 and April 6, 2023 was used to develop a popPK model. The model was validated and compared with six existing models based on the predictive performance of datasets from another PAT database and the Kumamoto University Hospital. The developed model determined the dosing strategy for achieving the target AUC.

RESULTS

The modeling populations consisted of 7146 (13,372 concentrations from the PAT database), 3805 (7540 concentrations from the PAT database), and 783 (1775 concentrations from Kumamoto University Hospital) individuals. A two-compartment popPK model was developed that incorporated creatinine clearance as a covariate for clearance and body weight for central and peripheral volumes of distribution. The validation demonstrated that the popPK model exhibited the smallest mean absolute prediction error of 5.07, outperforming others (ranging from 5.10 to 5.83). The dosing strategies suggested a first dose of 30 mg/kg and maintenance doses adjusted for kidney function and age.

CONCLUSIONS

This study demonstrated the updating of PAT through the validation and development of a popPK model using a vast amount of data collected from anonymous PAT users.

Model-Informed Precision Dosing Using Machine Learning for Levothyroxine in General Practice: Development, Validation and Clinical Simulation Trial

Levothyroxine is one of the most prescribed drugs in the western world. Dosing is challenging due to high-interindividual differences in effective dosage and the narrow therapeutic window. Model-informed precision dosing (MIPD) using machine learning could assist general practitioners (GPs), but no such models exist for primary care. Furthermore, introduction of decision-support algorithms in healthcare is limited due to the substantial gap between developers and clinicians' perspectives. We report the development, validation, and a clinical simulation trial of the first MIPD application for primary care. Stable maintenance dosage of levothyroxine was the model target. The multiclass model generates predictions for individual patients, for different dosing classes. Random forest was trained and tested on a national primary care database (n = 19,004) with a final weighted AUC across dosing options of 0.71, even in subclinical hypothyroidism. TSH, fT4, weight, and age were most predictive. To assess the safety, feasibility, and clinical impact of MIPD for levothyroxine, we performed clinical simulation studies in GPs and compared MIPD to traditional prescription. Fifty-one GPs selected starting dosages for 20 primary hypothyroidism cases without and then with MIPD 2 weeks later. Overdosage and underdosage were defined as higher and lower than 12.5 μg relative to stable maintenance dosage. MIPD decreased overdosage in number (30.5 to 23.9%, P < 0.01) and magnitude (median 50 to 37.5 μg, P < 0.01) and increased optimal starting dosages (18.3 to 30.2%, P < 0.01). GPs considered lab results more often with MIPD and most would use the model frequently. This study demonstrates the clinical relevance, safety, and effectiveness of MIPD for levothyroxine in primary care.

CPhaMAS: An online platform for pharmacokinetic data analysis based on optimized parameter fitting algorithm

BACKGROUND AND OBJECTIVE

Clinical pharmacological modeling and statistical analysis software is an essential basic tool for drug development and personalized drug therapy. The learning curve of current basic tools is steep and unfriendly to beginners. The curve is even more challenging in cases of significant individual differences or measurement errors in data, resulting in difficulties in accurately estimating pharmacokinetic parameters by existing fitting algorithms. Hence, this study aims to explore a new optimized parameter fitting algorithm that reduces the sensitivity of the model to initial values and integrate it into the CPhaMAS platform, a user-friendly online application for pharmacokinetic data analysis.

METHODS

In this study, we proposed an optimized Nelder-Mead method that reinitializes simplex vertices when trapped in local solutions and integrated it into the CPhaMAS platform. The CPhaMAS, an online platform for pharmacokinetic data analysis, includes three modules: compartment model analysis, non-compartment analysis (NCA) and bioequivalence/bioavailability (BE/BA) analysis. Our proposed CPhaMAS platform was evaluated and compared with existing WinNonlin.

RESULTS

The platform was easy to learn and did not require code programming. The accuracy investigation found that the optimized Nelder-Mead method of the CPhaMAS platform showed better accuracy (smaller mean relative error and higher R2) in two-compartment and extravascular administration models when the initial value was set to true and abnormal values (10 times larger or smaller than the true value) compared with the WinNonlin. The mean relative error of the NCA calculation parameters of CPhaMAS and WinNonlin was <0.0001 %. When calculating BE for conventional, high-variability and narrow-therapeutic drugs. The main statistical parameters of the parameters Cmax, AUCt, and AUCinf in CPhaMAS have a mean relative error of <0.01% compared to WinNonLin.

CONCLUSIONS

In summary, CPhaMAS is a user-friendly platform with relatively accurate algorithms. It is a powerful tool for analysing pharmacokinetic data for new drug development and precision medicine.

A systematic evaluation of population pharmacokinetic models for polymyxin B in patients with liver and/or kidney dysfunction

Polymyxin B (PMB) is considered a last-line treatment for multidrug-resistant (MDR) gram-negative bacterial infections. Model-informed precision dosing with population pharmacokinetics (PopPK) models could help to individualize PMB dosing regimens and improve therapy. However, the external prediction ability of the established PopPK models has not been fully elaborated. This study aimed to systemically evaluate eleven PMB PopPK models from ten published literature based on a new independent population, which was divided into four different populations, patients with liver dysfunction, kidney dysfunction, liver and kidney dysfunction, and normal liver and kidney function. The whole data set consisted of 146 patients with 391 PMB concentrations. The prediction- and simulation-based diagnostics and Bayesian forecasting were conducted to evaluate model predictability. In the overall evaluation process, none of the models exhibited satisfactory predictive ability in both prediction- and simulation-based diagnostic simultaneously. However, the evaluation of the models in the subgroup of patients with normal liver and kidney function revealed improved predictive performance compared to those with liver and/or kidney dysfunction. Bayesian forecasting demonstrated enhanced predictability with the incorporation of two to three prior observations. The external evaluation highlighted a lack of consistency between the prediction results of published models and the external validation dataset. Nonetheless, Bayesian forecasting holds promise in improving the predictive performance of the models, and feedback from therapeutic drug monitoring is crucial in optimizing individual dosing regimens.

Individualized rabbit anti-thymocyte globulin dosing in adult haploidentical hematopoietic cell transplantation with high-risk hematologic malignancy: Exposure-response analysis and population pharmacokinetics simulations

Hematopoietic cell transplantation (HCT) for hematologic malignancies with non-remission disease and/or prior post-transplant relapse have poor relapse-free survival. We previously demonstrated the efficacy of haploidentical reduced-intensity HCT regimen with glucocorticoid-based graft-versus-host disease (GVHD) prophylaxis. We recently showed a possible association between rabbit antithymocyte globulin (rATG) exposure and acute GVHD (aGVHD) risk, leading to hypothesize that optimization of rATG exposure may further improve this regimen. We retrospectively examined the exposure-response association of rATG and key clinical outcomes post haploidentical HCT. We subsequently developed an individualized rATG dosing that optimizes rATG exposure using a previously developed population pharmacokinetic model. Of the 103 patients analyzed, the median age was 47 years (range: 17-70) and majority had a non-remission disease prior to HCT (88%). rATG concentration on day 0 of HCT (Cday_0 ) was the strongest predictor of Grade 2-4 aGVHD through day +100. Patients with Cday_0  ≥ 20 μg/mL had an approximately 3-fold lower risk of Grade 2-4 aGVHD (hazard ratio [HR]: 0.32, 95% confidence interval [CI]: 0.16, 0.62) and Grade 3-4 aGVHD (HR: 0.33, 95% CI: 0.16, 0.68) as well as an approximately 2-fold lower risk of overall mortality (HR: 0.47, 95% CI: 0.28, 0.77) and relapse (HR: 0.50, 95% CI: 0.26, 0.94). In conclusion, this reduced-intensity haploidentical HCT regimen with exposure-optimized rATG may provide a promising option to patients undergoing high-risk HCT for hematologic malignancy. The developed rATG dosing warrant prospective validation.

Immunosuppressant adherence in adult outpatient hematopoietic cell transplant recipients

INTRODUCTION

Medication nonadherence continues to be challenging for allogeneic hematopoietic cell transplant (HCT) recipients. The risk and severity of chronic graft-versus-host disease (GVHD) are associated with low immunosuppressant concentrations (which can be improved with model-informed precision dosing (MIPD)) and with immunosuppressant nonadherence (which can be improved with acceptable interventions).

METHODS

With the goals of improving adherence and achieving therapeutic concentrations of immunosuppressants to eliminate GVHD, we characterized the feasibility of using the Medication Event Monitoring (MEMS®) Cap in adult HCT recipients.

RESULTS

Of the 27 participants offered the MEMS® Cap at the time of hospital discharge, 7 (25.9%) used it, which is below our a priori threshold of 70%. These data suggest the MEMS® Cap is not feasible for HCT recipients. The MEMS® Cap data were available for a median of 35 days per participant per medication (range: 7-109 days). The average daily adherence per participant ranged from 0 to 100%; four participants had an average daily adherence of over 80%.

CONCLUSIONS

MIPD may be supported by MEMS® technology to provide the precise time of immunosuppressant self-administration. The MEMS® Cap was used by only a small percentage (25.9%) of HCT recipients in this pilot study. In accordance with larger studies using less accurate tools to evaluate adherence, immunosuppressant adherence varied from 0% to 100%. Future studies should establish the feasibility and clinical benefit of combining MIPD with newer technology, specifically the MEMS® Button, which can inform the oncology pharmacist of the time of immunosuppressant self-administration.

Pharmacogenomics - a minor rather than major force in clinical medicine

INTRODUCTION

Pharmacogenomics (PGx) is touted as essential for the future of precision medicine. But the opportunity cost of PGx from the prescribers' perspective is rarely considered. The aim of this article is to critique PGx-guided prescribing using clinical pharmacology principles so that important cases for PGx testing are not missed by doctors responsible for therapeutic decision making.

AREAS COVERED

Three categories of PGx and their limitations are outlined - exposure PGx, response PGx, and immune-mediated safety PGx. Clinical pharmacology reasons are given for the narrow scope of PGx-guided prescribing apart from a few medical specialties. Clinical problems for doctors that may arise from PGx are then explained, including mismatch between patients' expectations of PGx testing and the benefits or answers it provides.

EXPERT OPINION

Contrary to popular opinion, PGx is unlikely to become the cornerstone of precision medicine. Sound clinical pharmacology reasons explain why PGx-guided prescribing is unnecessary for most drugs. Pharmacogenomics is important for niche areas of prescribing but has limited clinical utility more broadly. The opportunity cost of PGx-guided prescribing is currently too great for most doctors.

Kinetic-pharmacodynamic model of warfarin for prothrombin time-international normalized ratio in Japanese patients

AIMS

Genotype-guided dosing algorithms can explain about half of the interindividual variability in prothrombin time-international normalized ratio (PT-INR) under warfarin treatment. This study aimed to refine a published kinetic-pharmacodynamic model and guide warfarin dosage for an optimal PT-INR based on renal function.

METHODS

Using a retrospective cohort of adult patients (>20 years) who were administered warfarin and underwent PT-INR measurements, we refined the kinetic-pharmacodynamic model with age and the genotypes of cytochrome P450 2C9 and vitamin K epoxide reductase complex subunit 1 using the PRIOR subroutine in the nonlinear-mixed-effect modelling programme. We searched the significant covariates for parameters, such as the dose rate for 50% inhibition of coagulation (EDR50 ), using a stepwise forward and backward method. Monte Carlo simulation determined a required daily dose of warfarin with a target range of PT-INR (2.0-3.0 or 1.6-2.6) based on the significant covariates.

RESULTS

A total of 350 patients with 2762 PT-INR measurements were enrolled (estimated glomerular filtration rate [eGFR]: 47.5 [range: 2.6-199.0] mL/min/1.73 m2 ). The final kinetic-pharmacodynamic model showed that the EDR50 changed power functionally with body surface area, serum albumin level and eGFR. Monte Carlo simulation revealed that a lower daily dose of warfarin was required to attain the target PT-INR range as eGFR decreased.

CONCLUSIONS

Model-informed precision dosing of warfarin is a valuable approach for estimating its dosage in patients with renal impairment.

Impact of model-informed precision dosing in adults receiving vancomycin via continuous infusion: a randomized, controlled clinical trial

BACKGROUND

Vancomycin is a commonly prescribed antibiotic to treat gram-positive infections. The efficacy of vancomycin is known to be directly related to the pharmacokinetic/pharmacodynamic (PK/PD) index of the area under the concentration-time curve (AUC) divided by the minimal inhibitory concentration (MIC) of the pathogen. However, in most countries, steady-state plasma concentrations are used as a surrogate parameter of target AUC/MIC, but this practice has some drawbacks. Hence, direct AUC-guided monitoring of vancomycin using model-informed precision dosing (MIPD) tools has been proposed for earlier attainment of target concentrations and reducing vancomycin-related nephrotoxicity. However, solid scientific evidence for these benefits in clinical practice is still lacking. This randomized controlled trial (RCT) aims to investigate the clinical utility of MIPD dosing of vancomycin administered via continuous infusion in hospitalized adults.

METHODS

Participants from 11 wards at two Belgian hospitals are randomly allocated to the intervention group or the standard-of-care comparator group. In the intervention group, clinical pharmacists perform dose calculations using CE-labeled MIPD software and target an AUC24h of 400 to 600 mg × h/L, whereas patients in the comparator group receive standard-of-care dosing and monitoring according to the institutional guidelines. The primary endpoint is the proportion of patients reaching the target AUC24h/MIC of 400-600 between 48 and 72 h after start of vancomycin treatment. Secondary endpoints are the proportion of patients with (worsening) acute kidney injury (AKI) during and until 48 h after stop of vancomycin treatment, the proportion of patients reaching target AUC24h/MIC of 400-600 between 72 and 96 h after start of vancomycin treatment, and the proportion of time within the target AUC24h/MIC of 400-600.

DISCUSSION

This trial will clarify the propagated benefits and provide new insights into how to optimally monitor vancomycin treatment.

TRIAL REGISTRATION

EudraCT number: 2021-003670-31. Registered June 28, 2021.

CLINICALTRIALS

gov identifier: NCT05535075. Registered September 10, 2022. Protocol version 3, protocol date: April 21, 2023.

Beyond Population-Level Targets for Drug Concentrations: Precision Dosing Needs Individual-Level Targets that Include Superior Biomarkers of Drug Responses

The purpose of precision dosing is to increase the chances of therapeutic success in individual patients. This is achieved in practice by adjusting doses to reach precision dosing targets determined previously in relevant populations, ideally with robust supportive evidence showing improved clinical outcomes compared with standard dosing. But is this implicit assumption of translatable population-level precision dosing targets correct and the best for all patients? In this review, the types of precision dosing targets and how they are determined are outlined, problems with the translatability of these targets to individual patients are identified, and ways forward to address these challengers are proposed. Achieving improved clinical outcomes to support precision dosing over standard dosing is currently hampered by applying population-level targets to all patients. Just as "one-dose-fits-all" may be an inappropriate philosophy for drug treatment overall, a "one-target-fits-all" philosophy may limit the broad clinical benefits of precision dosing. Defining individual-level precision dosing targets may be needed for greatest therapeutic success. Superior future precision dosing targets will integrate several biomarkers that together account for the multiple sources of drug response variability.

Training the next generation of pharmacometric modelers: a multisector perspective

The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970's and early 1980's and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.

Insights into the population pharmacokinetics and pharmacodynamics of quetiapine: a systematic review

INTRODUCTION

Quetiapine exhibits notable pharmacokinetic and pharmacodynamic (PK/PD) variability, the origins of which are poorly understood. This systematic review summarizes published population PK/PD studies and identifies significant covariates accounting for this variability to inform precision dosing.

METHODS

We systematically searched the PubMed, Web of Science, and Embase databases and compared study characteristics, model parameters, and covariate effects. Visual predictive distributions were used to compare different models. Forest plots and Monte Carlo simulations were used to assess the influence of covariates.

RESULTS

Six population PK and three population PK/PD studies were included. The median apparent clearance in adults was 87.7 L/h. Strong and moderate cytochrome P450 3A4 inducers increased the apparent clearance approximately fourfold, while strong cytochrome P450 3A4 inhibitors reduced it by 93%. The half-maximum effect concentrations were 82.8 ng/mL for the Brief Psychiatric Rating Scale and 583 ng/mL for dopamine D2 receptor occupancy. Both treatment duration and quetiapine exposure were associated with weight gain.

CONCLUSIONS

Concurrent administration of potent or moderate CYP3A4 inducers and inhibitors need to be avoided in quetiapine-treated patients. When co-medication is required, it is recommended to adjust the dosage based on therapeutic drug monitoring. Additional research is warranted to delineate the dose-exposure-response relationships of quetiapine and active metabolite norquetiapine in pediatrics, geriatrics, hepatically-impaired patients, and women using contraceptives or are pregnant or menopausal.

PROSPERO REGISTRATION

CRD42023446654.

Comparison of the predictive accuracy of the physiologically based pharmacokinetic (PBPK) model and population pharmacokinetic (PPK) model of vancomycin in Japanese patients with MRSA infection

INTRODUCTION

The latest therapeutic drug monitoring guidelines for vancomycin (VCM) recommend that area under the concentration-time curve is estimated based on model-informed precision dosing and used to evaluate efficacy and safety. Therefore, we predicted VCM concentrations in individual methicillin-resistant Staphylococcus aureus-infected patients using existing a physiologically based pharmacokinetic (PBPK) model and 1- and 2-compartment population pharmacokinetic (PPK) models and confirmed and verified the accuracy of the PBPK model in estimating VCM concentrations with the PPK model.

METHODS

The subjects of the study are 20 patients, and the predicted concentrations were evaluated by comparing the observed and predicted trough and peak values of VCM concentrations for individual patients.

RESULTS

The results showed good correlation between the observed and predicted trough and peak concentrations of VCM was observed generally in the PBPK model, R2 values of 0.72, 0.62, and 0.40 with trough values of 0.49, 0.40, and 0.34 with peak values for PBPK model, 1-compartment, and 2-compartment model, respectively.

CONCLUSIONS

Although the performance of the PBPK model is not as predictive as the PPK model, generally similar predictive trends were obtained, suggesting that it may be a valuable tool for rapid and accurate prediction of AUC for VCM.

Tutorial on model selection and validation of model input into precision dosing software for model-informed precision dosing

There has been rising interest in using model-informed precision dosing to provide personalized medicine to patients at the bedside. This methodology utilizes population pharmacokinetic models, measured drug concentrations from individual patients, pharmacodynamic biomarkers, and Bayesian estimation to estimate pharmacokinetic parameters and predict concentration-time profiles in individual patients. Using these individualized parameter estimates and simulated drug exposure, dosing recommendations can be generated to maximize target attainment to improve beneficial effect and minimize toxicity. However, the accuracy of the output from this evaluation is highly dependent on the population pharmacokinetic model selected. This tutorial provides a comprehensive approach to evaluating, selecting, and validating a model for input and implementation into a model-informed precision dosing program. A step-by-step outline to validate successful implementation into a precision dosing tool is described using the clinical software platforms Edsim++ and MwPharm++ as examples.

Identifying Effective Treatments for Dystonia in Patients With Cerebral Palsy: A Precision Therapeutics Approach

Recent focus on improving the recognition of dystonia in cerebral palsy (DCP) has highlighted the need for more effective treatments. Evidence supports improved functional outcomes with early interventions for patients with cerebral palsy, but it is not known which interventions are most effective for DCP. Current pharmacologic recommendations for DCP are based largely on anecdotal evidence, with medications demonstrating minimal to moderate improvements in dystonia and variable efficacy between patients. Patients, families, and clinicians have identified the need for new and improved treatments in DCP, naming this as the top research theme in a recent Neurology® publication. Precision therapeutics focuses on providing early effective interventions that are individualized to every patient and can guide research priorities to improve treatments for DCP. This commentary outlines current obstacles to improving treatment of DCP and addresses how precision therapeutics can address each of these obstacles through 4 key components: (1) identification of predictive biomarkers to select patients likely to develop DCP in the future and for whom early intervention may be appropriate to delay or prevent full manifestation of dystonia, (2) stratification of patients with DCP into subgroups according to shared features (clinical, functional, biochemical, etc) to provide a targeted intervention based on those shared features, (3) administration of an individualized dose of an effective intervention to ensure adequate concentrations of the therapeutic entity at the site of action, and (4) monitoring of objective biomarkers of response to intervention. With implementation of each of these components of precision therapeutics, new and more effective treatments for every person with DCP can be realized.

Simulating clinical trials for model-informed precision dosing: using warfarin treatment as a use case

Treatment response variability across patients is a common phenomenon in clinical practice. For many drugs this inter-individual variability does not require much (if any) individualisation of dosing strategies. However, for some drugs, including chemotherapies and some monoclonal antibody treatments, individualisation of dosages are needed to avoid harmful adverse events. Model-informed precision dosing (MIPD) is an emerging approach to guide the individualisation of dosing regimens of otherwise difficult-to-administer drugs. Several MIPD approaches have been suggested to predict dosing strategies, including regression, reinforcement learning (RL) and pharmacokinetic and pharmacodynamic (PKPD) modelling. A unified framework to study the strengths and limitations of these approaches is missing. We develop a framework to simulate clinical MIPD trials, providing a cost and time efficient way to test different MIPD approaches. Central for our framework is a clinical trial model that emulates the complexities in clinical practice that challenge successful treatment individualisation. We demonstrate this framework using warfarin treatment as a use case and investigate three popular MIPD methods: 1. Neural network regression; 2. Deep RL; and 3. PKPD modelling. We find that the PKPD model individualises warfarin dosing regimens with the highest success rate and the highest efficiency: 75.1% of the individuals display INRs inside the therapeutic range at the end of the simulated trial; and the median time in the therapeutic range (TTR) is 74%. In comparison, the regression model and the deep RL model have success rates of 47.0% and 65.8%, and median TTRs of 45% and 68%. We also find that the MIPD models can attain different degrees of individualisation: the Regression model individualises dosing regimens up to variability explained by covariates; the Deep RL model and the PKPD model individualise dosing regimens accounting also for additional variation using monitoring data. However, the Deep RL model focusses on control of the treatment response, while the PKPD model uses the data also to further the individualisation of predictions.

Population Pharmacokinetic Model of Adalimumab Based on Prior Information Using Real World Data

Adalimumab is a fully human monoclonal antibody used for the treatment of inflammatory bowel disease (IBD). Due to its considerably variable pharmacokinetics and the risk of developing antibodies against adalimumab, it is highly recommended to use a model-informed precision dosing approach. The aim of this study is to develop a population pharmacokinetic (PopPK) model of adalimumab for patients with IBD based on a literature model (reference model) to be used in the clinical setting. A retrospective observational study with 54 IBD patients was used to develop two different PopPK models based on the reference model. One of the developed models estimated the pharmacokinetic population parameters (estimated model), and the other model incorporated informative priors (prior model). The models were evaluated with bias and imprecision. Clinical impact was also assessed, evaluating the differences in dose interventions. The developed models included the albumin as a continuous covariate on apparent clearance. The prior model was superior to the estimated model in terms of bias, imprecision and clinical impact on the target population. In conclusion, the prior model adequately characterized adalimumab PK in the studied population and was better than the reference model in terms of predictive performance and clinical impact.

Treatment of evolving cancers will require dynamic decision support

Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.

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.

Virtual twin for healthcare management

Healthcare is increasingly fragmented, resulting in escalating costs, patient dissatisfaction, and sometimes adverse clinical outcomes. Strategies to decrease healthcare fragmentation are therefore attractive from payer and patient perspectives. In this commentary, a patient-centered smart phone application called Virtual Twin for Healthcare Management (VTHM) is proposed, including its organizational layout, basic functionality, and potential clinical applications. The platform features a virtual twin hub that displays the body and its health data. This is a physiologically based human model that is "virtualized" for the patient based on their unique genetic, molecular, physiological, and disease characteristics. The spokes of the system are a full service and interoperable electronic-health record, accessible to healthcare providers with permission on any device with internet access. Theoretical case studies based on real scenarios are presented to show how VTHM could potentially improve patient care and clinical efficiency. Challenges that must be overcome to turn VTHM into reality are also briefly outlined. Notably, the VTHM platform is designed to operationalize current and future precision medicine initiatives, such as access to molecular diagnostic results, pharmacogenomics-guided prescribing, and model-informed precision dosing.

A Drug Safety Concept (I) to Avoid Polypharmacy Risks in Transplantation by Individual Pharmacotherapy Management in Therapeutic Drug Monitoring of Immunosuppressants

For several, also vital medications, such as immunosuppressants in solid organ and hematopoietic stem cell transplantation, therapeutic drug monitoring (TDM) remains the only strategy for fine-tuning the dosage to the individual patient. Especially in severe clinical complications, the intraindividual condition of the patient changes abruptly, and in addition, drug-drug interactions (DDIs) can significantly impact exposure, due to concomitant medication alterations. Therefore, a single TDM value can hardly be the sole basis for optimal timely dose adjustment. Moreover, every intraindividually varying situation that affects the drug exposure needs synoptic consideration for the earliest adjustment. To place the TDM value in the context of the patient's most detailed current condition and concomitant medications, the Individual Pharmacotherapy Management (IPM) was implemented in the posttransplant TDM of calcineurin inhibitors assessed by the in-house laboratory. The first strategic pillar are the defined patient scores from the electronic patient record. In this synopsis, the Summaries of Product Characteristics (SmPCs) of each drug from the updated medication list are reconciled for contraindication, dosing, adverse drug reactions (ADRs), and DDIs, accounting for defined medication scores as a second pillar. In parallel, IPM documents the resulting review of each TDM value chronologically in a separate electronic Excel file throughout each patient's transplant course. This longitudinal overview provides a further source of information at a glance. Thus, the applied two-arm concept of TDM and IPM ensures an individually tailored immunosuppression in the severely susceptible early phase of transplantation through digital interdisciplinary networking, with instructive and educative recommendations to the attending physicians in real-time. This concept of contextualizing a TDM value to the precise patient's condition and comedication was established at Halle University Hospital to ensure patient, graft, and drug safety.

Model-Informed Precision Dosing Guidance of Ethosuximide Developed from a Randomized Controlled Clinical Trial of Childhood Absence Epilepsy

Ethosuximide was identified as the optimal option for new-onset childhood absence epilepsy (CAE) in a randomized, two-phase dose escalation comparative effectiveness trial of ethosuximide, lamotrigine, and valproic acid. However, 47% of ethosuximide initial monotherapy participants experienced short-term treatment failure. This study aimed to characterize the initial monotherapy ethosuximide exposure-response relationship and to propose model-informed precision dosing guidance. Dose titration occurred over a 16-20-week period until patients experienced seizure freedom or intolerable side effects. Subjects with initial monotherapy failure were randomized to one of the other two medications and dose escalation was repeated. A population pharmacokinetic model was created using plasma concentration data (n = 1,320), collected at 4-week intervals from 211 unique participants during both the initial and second monotherapy phases. A logistic regression analysis was performed on the initial monotherapy cohort (n = 103) with complete exposure-response data. Eighty-four participants achieved seizure freedom with a wide range of ethosuximide area under the curves (AUC) ranging from 420 to 2,420 μg·h/mL. AUC exposure estimates for achieving a 50% and 75% probability of seizure freedom were 1,027 and 1,489 μg·h/mL, respectively, whereas the corresponding cumulative frequency of intolerable adverse events was 11% and 16%. Monte Carlo Simulation indicated a daily dose of 40 and 55 mg/kg to achieve 50% and 75% probability of seizure freedom in the overall population, respectively. We identified the need for adjusted mg/kg dosing in different body weight cohorts. This ethosuximide proposed model-informed precision dosing guidance to achieve seizure freedom carries promise to optimize initial monotherapy success for patients with CAE.

Model-Informed Precision Dosing of Vancomycin in Adult Patients Undergoing Hemodialysis

Model-informed precision dosing (MIPD) maximizes the probability of successful dosing in patients undergoing hemodialysis. In these patients, area under the concentration-time curve (AUC)-guided dosing is recommended for vancomycin. However, this model is yet to be developed. The purpose of this study was to address this issue. The overall mass transfer-area coefficient (KoA) was used for the estimation of vancomycin hemodialysis clearance. A population pharmacokinetic (popPK) model was developed, resulting in a fixed-effect parameter for nonhemodialysis clearance of 0.316 liters/h. This popPK model was externally evaluated, with a resulting mean absolute error of 13.4% and mean prediction error of -0.17%. KoA-predicted hemodialysis clearance was prospectively evaluated for vancomycin (n = 10) and meropenem (n = 10), with a correlation equation being obtained (slope of 1.099, intercept of 1.642; r = 0.927, P < 0.001). An experimental evaluation using an in vitro hemodialysis circuit validated the developed model of KoA-predicted hemodialysis clearance using vancomycin, meropenem, vitamin B6, and inulin in 12 hemodialysis settings. This popPK model indicated a maximum a priori dosing for vancomycin-a loading dose of 30 mg/kg, which achieves the target AUC for 24 h after first dose with a probability of 93.0%, ensured by a predialysis concentration of >15 μg/mL. Maintenance doses of 12 mg/kg after every hemodialysis session could achieve the required exposure, with a probability of 80.6%. In conclusion, this study demonstrated that KoA-predicted hemodialysis clearance may lead to an upgrade from conventional dosing to MIPD for vancomycin in patients undergoing hemodialysis.

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.

Population Pharmacokinetics and AUC-Guided Dosing of Tobramycin in the Treatment of Infections Caused by Glucose-Nonfermenting Gram-Negative Bacteria

PURPOSE

Tobramycin (TOB) exhibits variable pharmacokinetic properties due to the clinical condition of patients. This study aimed to investigate the AUC-guided dosing of TOB based on population pharmacokinetic analysis in the treatment of infections caused by Pseudomonas aeruginosa, Acinetobacter baumannii, and Stenotrophomonas maltophilia.

METHODS

This retrospective study was conducted between January 2010 and December 2020 after obtaining approval from our institutional review board. For 53 patients who received therapeutic drug monitoring of TOB, a population pharmacokinetic model was developed with covariates of estimated glomerular filtration rate using serum creatinine (eGFRcre) on clearance (CL) and weight on both CL and V_d in exponential error modeling (CL = 2.84 × [weight/70] × eGFRcre^0.568, interindividual variability [IIV] = 31.1%; V_d = 26.3 × [weight/70], IIV = 20.2%; residual variability = 28.8%).

FINDINGS

The final regression model for predicting 30-day mortality was developed with risk factors of AUC during a 24-hour period after the first dose to MIC ratio (odds ratio [OR] = 0.996; 95% CI, 0.968-1.003) and serum albumin (OR = 0.137; 95% CI, 0.022-0.632). The final regression model for predicting acute kidney injury was developed with the risk factors of C-reactive protein (OR = 1.136; 95% CI, 1.040-1.266) and AUC during a 72-hour period after the first dose (OR = 1.004; 95% CI, 1.000-1.001). A dose of 8 or 15 mg/kg was beneficial for achievement of AUC during a 24-hour period after the first dose/MIC >80 and trough concentration <1 µg/mL in patients with preserved kidney function and TOB CL >4.47 L/h/70 kg in the events of MIC of 1 or 2 µg/mL, respectively. We propose that the first dose of 15, 11, 10, 8, and 7 mg/kg for eGFRcre >90, 60 to 89, 45 to 59, 30 to 44, and 15 to 29 mL/min/1.73 m² be followed by therapeutic drug monitoring at peak and 24 hours after the first dose.

IMPLICATIONS

This study suggests that TOB use encourages the replacement of trough- and peak-targeted dosing with AUC-guided dosing.

External Evaluation of Population Pharmacokinetic Models for Precision Dosing: Current State and Knowledge Gaps

Predicting drug exposures using population pharmacokinetic models through Bayesian forecasting software can improve individual pharmacokinetic/pharmacodynamic target attainment. However, selecting the most adapted model to be used is challenging due to the lack of guidance on how to design and interpret external evaluation studies. The confusion around the choice of statistical metrics and acceptability criteria emphasises the need for further research to fill this methodological gap as there is an urgent need for the development of standards and guidelines for external evaluation studies. Herein we discuss the scientific challenges faced by pharmacometric researchers and opportunities for future research with a focus on antibiotics.

From Personalized to Precision Medicine in Oncology: A Model-Based Dosing Approach to Optimize Achievement of Imatinib Target Exposure

Imatinib is a targeted cancer therapy that has significantly improved the care of patients with chronic myeloid leukemia (CML) and gastrointestinal stromal tumor (GIST). However, it has been shown that the recommended dosages of imatinib are associated with trough plasma concentration (Cmin) lower than the target value in many patients. The aims of this study were to design a novel model-based dosing approach for imatinib and to compare the performance of this method with that of other dosing methods. Three target interval dosing (TID) methods were developed based on a previously published PK model to optimize the achievement of a target Cmin interval or minimize underexposure. We compared the performance of those methods to that of traditional model-based target concentration dosing (TCD) as well as fixed-dose regimen using simulated patients (n = 800) as well as real patients’ data (n = 85). Both TID and TCD model-based approaches were effective with about 65% of Cmin achieving the target imatinib Cmin interval of 1000–2000 ng/mL in 800 simulated patients and more than 75% using real data. The TID approach could also minimize underexposure. The standard 400 mg/24 h dosage of imatinib was associated with only 29% and 16.5% of target attainment in simulated and real conditions, respectively. Some other fixed-dose regimens performed better but could not minimize over- or underexposure. Model-based, goal-oriented methods can improve initial dosing of imatinib. Combined with subsequent TDM, these approaches are a rational basis for precision dosing of imatinib and other drugs with exposure–response relationships in oncology.

Precision sirolimus dosing in children: The potential for model-informed dosing and novel drug monitoring

The mTOR inhibitor sirolimus is prescribed to treat children with varying diseases, ranging from vascular anomalies to sporadic lymphangioleiomyomatosis to transplantation (solid organ or hematopoietic cell). Precision dosing of sirolimus using therapeutic drug monitoring (TDM) of sirolimus concentrations in whole blood drawn at the trough (before the next dose) time-point is the current standard of care. For sirolimus, trough concentrations are only modestly correlated with the area under the curve, with R 2 values ranging from 0.52 to 0.84. Thus, it should not be surprising, even with the use of sirolimus TDM, that patients treated with sirolimus have variable pharmacokinetics, toxicity, and effectiveness. Model-informed precision dosing (MIPD) will be beneficial and should be implemented. The data do not suggest dried blood spots point-of-care sampling of sirolimus concentrations for precision dosing of sirolimus. Future research on precision dosing of sirolimus should focus on pharmacogenomic and pharmacometabolomic tools to predict sirolimus pharmacokinetics and wearables for point-of-care quantitation and MIPD.

An Overview of Physiologically-Based Pharmacokinetic Models for Forensic Science

A physiologically-based pharmacokinetic (PBPK) model represents the structural components of the body with physiologically relevant compartments connected via blood flow rates described by mathematical equations to determine drug disposition. PBPK models are used in the pharmaceutical sector for drug development, precision medicine, and the chemical industry to predict safe levels of exposure during the registration of chemical substances. However, one area of application where PBPK models have been scarcely used is forensic science. In this review, we give an overview of PBPK models successfully developed for several illicit drugs and environmental chemicals that could be applied for forensic interpretation, highlighting the gaps, uncertainties, and limitations.

Mathematically Modeling the Effect of Endocrine and Cdk4/6 Inhibitor Therapies on Breast Cancer Cells

Mathematical modeling of cancer systems is beginning to be used to design better treatment regimens, especially in chemotherapy and radiotherapy. The effectiveness of mathematical modeling to inform treatment decisions and identify therapy protocols, some of which are highly nonintuitive, is because it enables the exploration of a huge number of therapeutic possibilities. Considering the immense cost of laboratory research and clinical trials, these nonintuitive therapy protocols would likely never be found by experimental approaches. While much of the work to date in this area has involved high-level models, which look simply at overall tumor growth or the interaction of resistant and sensitive cell types, mechanistic models that integrate molecular biology and pharmacology can contribute greatly to the discovery of better cancer treatment regimens. These mechanistic models are better able to account for the effect of drug interactions and the dynamics of therapy. The aim of this chapter is to demonstrate the use of ordinary differential equation-based mechanistic models to describe the dynamic interactions between the molecular signaling of breast cancer cells and two key clinical drugs. In particular, we illustrate the procedure for building a model of the response of MCF-7 cells to standard therapies used in the clinic. Such mathematical models can be used to explore the vast number of potential protocols to suggest better treatment approaches.

Evaluation of vancomycin individualized model-based dosing approach in neonates

BACKGROUND

Vancomycin is commonly used to treat methicillin-resistant staphylococcal infections in neonates. Consensus on its ideal dosing in neonates has not been achieved. Model-based dosing recently has evolved as an important tool to optimize vancomycin initial dosing. The aim of this is to evaluate a population pharmacokinetic model-based approach in achieving the vancomycin therapeutic target of an AUC_0-24 400 as recommended by the recent IDSA treatment guidelines. This model was implemented as a simple Excel calculator to individualize and optimize vancomycin initial dosing in neonates.

METHODS

An Excel calculator was developed using a previously published population pharmacokinetic model in neonates. It was evaluated using retrospectively retrieved data. For each patient, the initial empiric dose was calculated using the proposed Excel model and the most widely used neonatal dosing references. The probability of achieving the target AUC_0-24 of >400 mg h/L using the model-based method was calculated and compared with that of the empiric doses using other references.

RESULTS

This analysis included 225 neonates. The probability of achieving the target AUC_0-24 >400 was 89% using our model-based approach compared with 11%-59% using tertiary neonatal dosing references (p < 0.01 for all comparisons).

CONCLUSION

These innovative personalized dosing calculators are promising to improve vancomycin initial dosing in neonates and are easily applicable in routine practices.

Pharmacokinetics and pharmacodynamics of anti-tuberculosis drugs: An evaluation of in vitro, in vivo methodologies and human studies

There has been an increased interest in pharmacokinetics and pharmacodynamics (PKPD) of anti-tuberculosis drugs. A better understanding of the relationship between drug exposure, antimicrobial kill and acquired drug resistance is essential not only to optimize current treatment regimens but also to design appropriately dosed regimens with new anti-tuberculosis drugs. Although the interest in PKPD has resulted in an increased number of studies, the actual bench-to-bedside translation is somewhat limited. One of the reasons could be differences in methodologies and outcome assessments that makes it difficult to compare the studies. In this paper we summarize most relevant in vitro, in vivo, in silico and human PKPD studies performed to optimize the drug dose and regimens for treatment of tuberculosis. The in vitro assessment focuses on MIC determination, static time-kill kinetics, and dynamic hollow fibre infection models to investigate acquisition of resistance and killing of Mycobacterium tuberculosis populations in various metabolic states. The in vivo assessment focuses on the various animal models, routes of infection, PK at the site of infection, PD read-outs, biomarkers and differences in treatment outcome evaluation (relapse and death). For human PKPD we focus on early bactericidal activity studies and inclusion of PK and therapeutic drug monitoring in clinical trials. Modelling and simulation approaches that are used to evaluate and link the different data types will be discussed. We also describe the concept of different studies, study design, importance of uniform reporting including microbiological and clinical outcome assessments, and modelling approaches. We aim to encourage researchers to consider methods of assessing and reporting PKPD of anti-tuberculosis drugs when designing studies. This will improve appropriate comparison between studies and accelerate the progress in the field.

A Physiologically-Based Pharmacokinetic Model of Ruxolitinib and Posaconazole to Predict CYP3A4-Mediated Drug-Drug Interaction Frequently Observed in Graft versus Host Disease Patients

Ruxolitinib (RUX) is approved for the treatment of steroid-refractory acute and chronic graft versus host disease (GvHD). It is predominantly metabolized via cytochrome P450 (CYP) 3A4. As patients with GvHD have an increased risk of invasive fungal infections, RUX is frequently combined with posaconazole (POS), a strong CYP3A4 inhibitor. Knowledge of RUX exposure under concomitant POS treatment is scarce and recommendations on dose modifications are inconsistent. A physiologically based pharmacokinetic (PBPK) model was developed to investigate the drug-drug interaction (DDI) between POS and RUX. The predicted RUX exposure was compared to observed concentrations in patients with GvHD in the clinical routine. PBPK models for RUX and POS were independently set up using PK-Sim^® Version 11. Plasma concentration-time profiles were described successfully and all predicted area under the curve (AUC) values were within 2-fold of the observed values. The increase in RUX exposure was predicted with a DDI ratio of 1.21 (C_max) and 1.59 (AUC). Standard dosing in patients with GvHD led to higher RUX exposure than expected, suggesting further dose reduction if combined with POS. The developed model can serve as a starting point for further simulations of the implemented DDI and can be extended to further perpetrators of CYP-mediated PK-DDIs or disease-specific physiological changes.

Individualized Patient Care Through Model-Informed Precision Dosing: Reflections on Training Future Practitioners

Prior to his passing, Dr. Roger Jelliffe, expressed the need for educating future physicians and clinical pharmacists on the availability of computer-based tools to support dose optimization in patients in stable or unstable physiological states. His perspectives were to be captured in a commentary for the AAPS J with a focus on incorporating population pharmacokinetic (PK)/pharmacodynamic (PD) models that are designed to hit the therapeutic target with maximal precision. Unfortunately, knowing that he would be unable to complete this project, Dr. Jelliffe requested that a manuscript conveying his concerns be completed upon his passing. With this in mind, this final installment of the AAPS J theme issue titled "Alternative Perspectives for Evaluating Drug Exposure Characteristics in a Population - Avoiding Analysis Pitfalls and Pigeonholes" is an effort to honor Dr. Jelliffe's request, conveying his concerns and the need to incorporate modeling and simulation into the training of physicians and clinical pharmacists. Accordingly, Dr. Jelliffe's perspectives have been integrated with those of the other three co-authors on the following topics: the clinical utility of population PK models; the role of multiple model (MM) dosage regimens to identify an optimal dose for an individual; tools for determining dosing regimens in renal dialysis patients (or undergoing other therapies that modulate renal clearance); methods to analyze and track drug PK in acutely ill patients presenting with high inter-occasion variability; implementation of a 2-cycle approach to minimize the duration between blood samples taken to estimate the changing PK in an acutely ill patient and for the generation of therapeutic decisions in advance for each dosing cycle based on an analysis of the previous cycle; and the importance of expressing therapeutic drug monitoring results as 1/variance rather than as the coefficient of variation. Examples showcase why, irrespective of the overall approach, the combination of therapeutic drug monitoring and computer-informed precision dosing is indispensable for maximizing the likelihood of achieving the target drug concentrations in the individual patient.

An Audit to Evaluate Vancomycin Therapeutic Drug Monitoring in a Neonatal Intensive Care Unit

BACKGROUND

Therapeutic drug monitoring (TDM) is routinely used for optimization of vancomycin therapy, because of exposure-related efficacy and toxicity, in addition to significant variability in pharmacokinetics, which leads to unpredictable drug exposure.

OBJECTIVE

The aim of this study was to evaluate target attainment and TDM of vancomycin in neonates.

METHODS

The authors conducted a retrospective study and collected data from medical records of all neonates who received vancomycin therapy in the neonatal intensive care unit between January 2019 and December 2019. The primary outcome was the proportion of vancomycin courses that reached target trough concentrations of 10-20 mg/L based on appropriate TDM samples collection. Secondary outcomes included proportion of courses with appropriate dose and dose frequency, and proportion of patients who achieved target concentrations after the first dose adjustment.

RESULTS

In total, 69 patients were included, with 129 vancomycin courses. The median initial vancomycin trough concentration was 12 (range: 4-36) mg/L. The target trough concentration was achieved in 75% of courses after the initial dose with appropriate TDM, and 84% of courses after TDM-guided dose adjustments. Patients were dosed appropriately in 121/129 courses and TDM was performed correctly according to protocol in 51/93 courses. A dose adjustment was performed in 18/29 courses, to increase target attainment.

CONCLUSIONS

This study showed that there is a need for an increase in dose to improve target attainment. There is also a need to explore more effective TDM strategies to increase the proportion of neonatal patients attaining vancomycin target trough concentrations.

Ulcerative Colitis and Acute Severe Ulcerative Colitis Patients Are Overlooked in Infliximab Population Pharmacokinetic Models: Results from a Comprehensive Review

Ulcerative colitis (UC) is part of the inflammatory bowels diseases, and moderate to severe UC patients can be treated with anti-tumour necrosis α monoclonal antibodies, including infliximab (IFX). Even though treatment of UC patients by IFX has been in place for over a decade, many gaps in modelling of IFX PK in this population remain. This is even more true for acute severe UC (ASUC) patients for which early prediction of IFX pharmacokinetic (PK) could highly improve treatment outcome. Thus, this review aims to compile and analyse published population PK models of IFX in UC and ASUC patients, and to assess the current knowledge on disease activity impact on IFX PK. For this, a semi-systematic literature search was conducted, from which 26 publications including a population PK model analysis of UC patients receiving IFX therapy were selected. Amongst those, only four developed a model specifically for UC patients, and only three populations included severe UC patients. Investigations of disease activity impact on PK were reported in only 4 of the 14 models selected. In addition, the lack of reported model codes and assessment of predictive performance make the use of published models in a clinical setting challenging. Thus, more comprehensive investigation of PK in UC and ASUC is needed as well as more adequate reports on developed models and their evaluation in order to apply them in a clinical setting.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.

Implementation of Model-Based Dose Adjustment of Tobramycin in Adult Patients with Cystic Fibrosis

Therapeutic drug monitoring (TDM) of tobramycin is widely performed in patients with cystic fibrosis (CF), but little is known about the value of model-informed precision dosing (MIPD) in this setting. We aim at reporting our experience with tobramycin MIPD in adult patients with CF. We analyzed data from adult patients with CF who received IV tobramycin and had model-guided TDM during the first year of implementation of MIPD. The predictive performance of a pharmacokinetic (PK) model was assessed. Observed maximal (Cmax) and minimal (Cmin) concentrations after initial dosing were compared with target values. We compared the initial doses and adjusted doses after model-based TDM, as well as renal function at the beginning and end of therapy. A total of 78 tobramycin courses were administered in 61 patients. After initial dosing set by physicians (mean, 9.2 ± 1.4 mg/kg), 68.8% of patients did not achieve the target Cmax ≥ 30 mg/L. The PK model fit the data very well, with a median absolute percentage error of 4.9%. MIPD was associated with a significant increase in tobramycin doses (p < 0.001) without significant change in renal function. Model-based dose suggestions were wellaccepted by the physicians and the expected target attainment for Cmax was 83%. To conclude, the implementation of MIPD was effective in changing prescribing practice and was not associated with nephrotoxic events in adult patients with CF.

Physiologically based modelling of the antiplatelet effect of aspirin: A tool to characterize drug responsiveness and inform precision dosing

A computational approach involving mathematical modeling and in silico experiments was used to characterize the determinants of extent and duration of platelet cyclooxygenase (COX)-1 inhibition by aspirin and design precision dosing in patients with accelerated platelet turnover or reduced drug bioavailability. To this purpose, a recently developed physiologically-based pharmacokinetics (PK) and pharmacodynamics (PD) model of low-dose aspirin in regenerating platelets and megakaryocytes, was used to predict the main features and determinants of platelet COX-1 inhibition. The response to different aspirin regimens in healthy subjects and in pathological conditions associated with alterations in aspirin PK (i.e., severely obese subjects) or PD (i.e., essential thrombocytemya patients), were simulated. A model sensitivity analysis was performed to identify the main processes influencing COX-1 dynamics. In silico experiments and sensitivity analyses indicated a major role for megakaryocytes and platelet turnover in determining the extent and duration of COX-1 inhibition by once-daily, low-dose aspirin. They also showed the superiority of reducing the dosing interval vs increasing the once-daily dose in conditions of increased platelet turnover, while suggested specific dose adjustments in conditions of possible reduction in drug bioavailability. In conclusion, the consistency of our model-based findings with experimental data from studies in healthy subjects and patients with essential thrombocythemia supports the potential of our approach for describing the determinants of platelet inhibition by aspirin and informing precision dosing which may guide personalized antithrombotic therapy in different patient populations, especially in those under-represented in clinical trials or in those associated with poor feasibility.

Quantitative Assessment of the Impact of Crohn's Disease on Protein Abundance of Human Intestinal Drug-Metabolising Enzymes and Transporters

Crohn's disease affects the mucosal layer of the intestine, predominantly ileum and colon segments, with the potential to affect the expression of intestinal enzymes and transporters, and consequently, oral drug bioavailability. We carried out a quantitative proteomic analysis of inflamed and non-inflamed ileum and colon tissues from Crohn's disease patients and healthy donors. Homogenates from samples in each group were pooled and protein abundance determined by liquid chromatography-mass spectrometry (LC-MS). In inflamed Crohn's ileum, CYP3A4, CYP20A1, CYP51A1, ADH1B, ALPI, FOM1, SULT1A2, SULT1B1 and ABCB7 showed ≥10-fold reduction in abundance compared with healthy baseline. By contrast, only MGST1 showed ≥10 fold reduction in inflamed colon. Ileal UGT1A1, MGST1, MGST2, and MAOA levels increased by ≥2 fold in Crohn's patients, while only ALPI showed ≥2 fold increase in the colon. Counter-intuitively, non-inflamed ileum had a higher magnitude of fold change than inflamed tissue when compared with healthy tissue. Marked but non-uniform alterations were observed in the expression of various enzymes and transporters in ileum and colon compared with healthy samples. Modelling will allow improved understanding of the variable effects of Crohn's disease on bioavailability of orally administered drugs.

Predicting Antibiotic Effect of Vancomycin Using Pharmacokinetic/Pharmacodynamic Modeling and Simulation: Dense Sampling versus Sparse Sampling

This study aimed to investigate the effect of a structural pharmacokinetic (PK) model with fewer compartments developed following sparse sampling on the PK parameter estimation and the probability of target attainment (PTA) prediction of vancomycin. Two- and three-compartment PK models of vancomycin were used for the virtual concentration–time profile simulation. Datasets with reduced blood sampling times were generated to support a model with a lesser number of compartments. Monte Carlo simulation was conducted to evaluate the PTA. For the two-compartment PK profile, the total clearance (CL) of the reduced one-compartment model showed a relative bias (RBias) and relative root mean square error (RRMSE) over 90%. For the three-compartment PK profile, the CL of the reduced one-compartment model represented the largest RBias and RRMSE, while the steady-state volume of distribution of the reduced two-compartment model represented the largest absolute RBias and RRMSE. A lesser number of compartments corresponded to a lower predicted area under the concentration–time curve of vancomycin. The estimated PK parameters and predicted PK/PD index from models built with sparse sampling designs that cannot support the PK profile can be significantly inaccurate and unprecise. This might lead to the misprediction of the PTA and selection of improper dosage regimens when clinicians prescribe antibiotics.

Cost-Effectiveness of Therapeutic Drug Monitoring of Anti-TNF Therapy in Inflammatory Bowel Disease: A Systematic Review

Infliximab and adalimumab are monoclonal antibodies against tumor necrosis factor (anti-TNF) used to manage inflammatory bowel disease (IBD). Therapeutic Drug Monitoring (TDM) has been proven to prevent immunogenicity, to achieve better long-term clinical results and to save costs in IBD treatment. The aim of this study was to conduct a systematic review on cost-effectiveness analyses of studies that apply TDM of anti-TNF in IBD and to provide a critical analysis of the best scientific knowledge available in the literature. The quality of the included studies was assessed using Consolidated Health Economic Evaluation Reporting Standards (CHEERS). Cost-effectiveness of the TDM strategies was presented as total costs, cost savings, quality-adjusted life-years (QALY) and incremental cost-effectiveness ratio (ICER). Thirteen studies that examined the health economics of TDM of anti-TNF in IBD from 2013 to 2021 were included. Eight of them (61.5%) achieved a score between 17 and 23 on the CHEERS checklist. The comparison between the TDM strategy and an empirical strategy was cost saving. The ICER between reactive TDM and an empirical strategy was dominated (favorable) by reactive TDM, whereas the ICER value for proactive TDM compared to an empirical strategy ranged from EUR 56,845 to 3,901,554. This systematic review demonstrated that a TDM strategy is cost-effective or cost-saving in IBD.

Clinical Practice Guidelines for Therapeutic Drug Monitoring of Vancomycin in the Framework of Model-Informed Precision Dosing: A Consensus Review by the Japanese Society of Chemotherapy and the Japanese Society of Therapeutic Drug Monitoring

Background: To promote model-informed precision dosing (MIPD) for vancomycin (VCM), we developed statements for therapeutic drug monitoring (TDM). Methods: Ten clinical questions were selected. The committee conducted a systematic review and meta-analysis as well as clinical studies to establish recommendations for area under the concentration-time curve (AUC)-guided dosing. Results: AUC-guided dosing tended to more strongly decrease the risk of acute kidney injury (AKI) than trough-guided dosing, and a lower risk of treatment failure was demonstrated for higher AUC/minimum inhibitory concentration (MIC) ratios (cut-off of 400). Higher AUCs (cut-off of 600 μg·h/mL) significantly increased the risk of AKI. Although Bayesian estimation with two-point measurement was recommended, the trough concentration alone may be used in patients with mild infections in whom VCM was administered with q12h. To increase the concentration on days 1–2, the routine use of a loading dose is required. TDM on day 2 before steady state is reached should be considered to optimize the dose in patients with serious infections and a high risk of AKI. Conclusions: These VCM TDM guidelines provide recommendations based on MIPD to increase treatment response while preventing adverse effects.