Model-informed Precision Dosing Research Articles

Model-Informed Precision Dosing of Isoniazid: Parametric Population Pharmacokinetics Model Repository.

Isoniazid (INH) is a crucial first-line anti tuberculosis (TB) drug used in adults and children. However, various factors can alter its pharmacokinetics (PK). This article aims to establish a population pharmacokinetic (popPK) models repository of INH to facilitate clinical use. A literature search was conducted until August 23, 2022, using PubMed, Embase, and Web of Science databases. We excluded published popPK studies that did not provide full model parameters or used a non-parametric method. Monte Carlo simulation works was based on RxODE. The popPK models repository was established using R. Non-compartment analysis was based on IQnca. Fourteen studies included in the repository, with eleven studies conducted in adults, three studies in children, one in pregnant women. Two-compartment with allometric scaling models were commonly used as structural models. NAT2 acetylator phenotype significantly affecting the apparent clearance (CL). Moreover, postmenstrual age (PMA) influenced the CL in pediatric patients. Monte Carlo simulation results showed that the geometric mean ratio (95% Confidence Interval, CI) of PK parameters in most studies were within the acceptable range (50.00-200.00%), pregnant patients showed a lower exposure. After a standard treatment strategy, there was a notable exposure reduction in the patients with the NAT2 RA or nonSA (IA/RA) phenotype, resulting in a 59.5% decrease in AUC0-24 and 83.2% decrease in Cmax (Infants), and a 49.3% reduction in AUC0-24 and 73.5% reduction in Cmax (Adults). Body weight and NAT2 acetylator phenotype are the most significant factors affecting the exposure of INH. PMA is a crucial factor in the pediatric population. Clinicians should consider these factors when implementing model-informed precision dosing of INH. The popPK model repository for INH will aid in optimizing treatment and enhancing patient outcomes.

Subgroup identification-based model selection to improve the predictive performance of individualized dosing.

Currently, model-informed precision dosing uses one population pharmacokinetic model that best fits the target population. We aimed to develop a subgroup identification-based model selection approach to improve the predictive performance of individualized dosing, using vancomycin in neonates/infants as a test case. Data from neonates/infants with at least one vancomycin concentration was randomly divided into training and test dataset. Population predictions from published vancomycin population pharmacokinetic models were calculated. The single best-performing model based on various performance metrics, including median absolute percentage error (APE) and percentage of predictions within 20% (P20) or 60% (P60) of measurement, were determined. Clustering based on median APEs or clinical and demographic characteristics and model selection by genetic algorithm was used to group neonates/infants according to their best-performing model. Subsequently, classification trees to predict the best-performing model using clinical and demographic characteristics were developed. A total of 208 vancomycin treatment episodes in training and 88 in test dataset was included. Of 30 identified models from the literature, the single best-performing model for training dataset had P20 26.2-42.6% in test dataset. The best-performing clustering approach based on median APEs or clinical and demographic characteristics and model selection by genetic algorithm had P20 44.1-45.5% in test dataset, whereas P60 was comparable. Our proof-of-concept study shows that the prediction of the best-performing model for each patient according to the proposed model selection approaches has the potential to improve the predictive performance of model-informed precision dosing compared with the single best-performing model approach.

Better Rejection-Free Survival at Three Years in Kidney Transplant Recipients With Model-Informed Precision Dosing of Mycophenolate Mofetil.

The clinical impact of individual dose adjustment of mycophenolate mofetil is still debated, due to conflicting results from randomized clinical trials. This retrospective study aimed to compare 3-year rejection-free survival and adverse effects between adult kidney transplant recipients (KTRs) with or without mycophenolate mofetil model-informed precision dosing (MIPD). MIPD is defined here as mycophenolic acid area under the curve (AUC0-12h) estimation using a limited sampling strategy, pharmacokinetic models and Bayesian estimators; dose recommendation to reach AUC0-12h = 45 mg.h/L; using a widely used online expert system. The study, nested in two multicenter prospective cohort studies, focused on patients who received a mycophenolate drug and were followed up for 1-3 years. Mycophenolate mofetil MIPD was prescribed as per local practice, on a regular basis, when deemed necessary, or not at all. The MIPD group included 341 KTRs and the control group 392. At 3 years, rejection-free survival was respectively 91.2% and 80.6% (P < 0.001) and the cumulative incidence of rejection 5.08% vs. 12.7% per patient × year (hazard ratio = 0.49 (0.34, 0.71), P < 0.001), corresponding to a 2.5-fold reduction. Significant association with rejection-free survival was confirmed in patients at low or high risk of rejection (P = 0.017 and 0.013) and in patients on tacrolimus, but not on cyclosporine (P < 0.001 and 0.205). The mycophenolate mofetil MIPD group had significantly more adverse effects, but most occurred before the first AUC0-12h, suggesting some may be the reason why MIPD was ordered.

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 registrationEudraCT number: 2021-003670-31. Registered June 28, 2021. ClinicalTrials.gov identifier: NCT05535075. Registered September 10, 2022. Protocol version 3, protocol date: April 21, 2023.

Exploring the Impact of Model-Informed Precision Dosing on Procalcitonin Concentrations in Critically Ill Patients: A Secondary Analysis of the DOLPHIN Trial.

Model-informed precision dosing (MIPD) might be used to optimize antibiotic treatment. Procalcitonin (PCT) is a biomarker for severity of infection and response to antibiotic treatment. The aim of this study was to assess the impact of MIPD on the course of PCT and to investigate the association of PCT with pharmacodynamic target (PDT) attainment in critically ill patients. This is a secondary analysis of the DOLPHIN trial, a multicentre, open-label, randomised controlled trial. Patients with a PCT value available at day 1 (T1), day 3 (T3), or day 5 (T5) after randomisation were included. The primary outcome was the absolute difference in PCT concentration at T1, T3, and T5 between the MIPD and the standard dosing group. In total, 662 PCT concentrations from 351 critically ill patients were analysed. There was no statistically significant difference in PCT concentration between the trial arms at T1, T3, or T5. The median PCT concentration was highest in patients who exceeded 10× PDT at T1 [13.15 ng/mL (IQR 5.43-22.75)]. In 28-day non-survivors and in patients that exceeded PDT at T1, PCT decreased significantly between T1 and T3, but plateaued between T3 and T5. PCT concentrations were not significantly different between patients receiving antibiotic treatment with or without MIPD guidance. The potential of PCT to guide antibiotic dosing merits further investigation.

A simulation study on model-informed precision dosing of amikacin for achieving target area under the concentration-time curve.

Amikacin requires therapeutic drug monitoring for optimum efficacy; however, the optimal model-informed precision dosing strategy for the area under the concentration-time curve (AUC) of amikacin is uncertain. This simulation study aimed to determine the efficient blood sampling points using the Bayesian forecasting approach for early achievement of the target AUC range for amikacin in critically ill patients. We generated a virtual population of 3000 individuals using 2 validated population pharmacokinetic models identified using a systematic literature search. AUC for each blood sampling point was evaluated using the probability of achieving a ratio of estimated/reference AUC at steady state in the 0.8-1.2 range. On day 1, the 1-point samplings for population pharmacokinetic models showed a priori probabilities of 26.3 and 45.6%, which increased to 47.3 and 94.4% at 23 and 15 h, respectively. Using 2-point sampling at the peak (3 and 4h) and trough (24 h) on day 1, these probabilities further increased to 72.3 and 99.5%, respectively. These probabilities were comparable on days 2 and 3, regardless of 3 and 6 sampling points or estimated glomerular filtration rate. These results indicated the higher predictive accuracy of 2-point sampling than 1-point sampling on day 1 for amikacin AUC estimation. Moreover, 2-point sampling was a more reasonable approach than rich sampling. This study contributes to the development of an efficient model-informed precision dosing strategy for early targeting of amikacin AUC in critically ill patients.

Predictive Performance of a Gentamicin Pharmacokinetic Model in Term Neonates with Perinatal Asphyxia Undergoing Controlled Therapeutic Hypothermia.

Model validation procedures are crucial when population pharmacokinetic (PK) models are used to develop dosing algorithms and to perform model-informed precision dosing. We have previously published a population PK model describing the PK of gentamicin in term neonates with perinatal asphyxia during controlled therapeutic hypothermia (TH), which showed altered gentamicin clearance during the hypothermic phase dependent on gestational age and weight. In this study, the predictive performance and generalizability of this model were assessed using an independent data set of neonates with perinatal asphyxia undergoing controlled TH. The external data set contained a subset of neonates included in the prospective observational multicenter PharmaCool Study. Predictive performance was assessed by visually inspecting observed-versus-predicted concentration plots and calculating bias and precision. In addition, simulation-based diagnostics, model refitting, and bootstrap analyses were performed. The external data set included 323 gentamicin concentrations of 39 neonates. Both the model-building and external data set included neonates from multiple centers. The original gentamicin PK model predicted the observed gentamicin concentrations with adequate accuracy and precision during all phases of controlled TH. Model appropriateness was confirmed with prediction-corrected visual predictive checks and normalized prediction distribution error analyses. Model refitting to the merged data set (n = 86 neonates with 935 samples) showed accurate estimation of PK parameters. The results of this external validation study justify the generalizability of the gentamicin dosing recommendations made in the original study for neonates with perinatal asphyxia undergoing controlled TH (5 mg/kg every 36 or 24 h with gestational age 36-41 and 42 wk, respectively) and its applicability in model-informed precision dosing.

Virtual twins for model-informed precision dosing of clozapine in patients with treatment-resistant schizophrenia.

Model-informed precision dosing using virtual twins (MIPD-VTs) is an emerging strategy to predict target drug concentrations in clinical practice. Using a high virtualization MIPD-VT approach (Simcyp version 21), we predicted the steady-state clozapine concentration and clozapine dosage range to achieve a target concentration of 350 to 600 ng/mL in hospitalized patients with treatment-resistant schizophrenia (N = 11). We confirmed that high virtualization MIPD-VT can reasonably predict clozapine concentrations in individual patients with a coefficient of determination (R2 ) ranging between 0.29 and 0.60. Importantly, our approach predicted the final dosage range to achieve the desired target clozapine concentrations in 73% of patients. In two thirds of patients treated with fluvoxamine augmentation, steady-state clozapine concentrations were overpredicted two to four-fold. This work supports the application of a high virtualization MIPD-VT approach to inform the titration of clozapine doses in clinical practice. However, refinement is required to improve the prediction of pharmacokinetic drug-drug interactions, particularly with fluvoxamine augmentation.

Model-Informed Precision Dosing Improves Outcomes in Patients Receiving Vancomycin for Gram-Positive Infections.

Consensus guidelines for dosing and monitoring of vancomycin recommend collection of 2 serum concentrations to estimate an area under the curve/minimum inhibitory concentration ratio (AUC/MIC). Use of Bayesian software for AUC estimation and model-informed precision dosing (MIPD) enables pre-steady state therapeutic drug monitoring using a single serum concentration; however, data supporting this approach are limited. Adult patients with culture-proven gram-positive infections treated with vancomycin ≥72 hours receiving either trough-guided or AUC-guided therapy were included in this retrospective study. AUC-guided therapy was provided using MIPD and single-concentration monitoring. Treatment success, vancomycin-associated acute kidney injury (VA-AKI), and inpatient mortality were compared using a desirability of outcome ranking analysis. The most desirable outcome was survival with treatment success and no VA-AKI, and the least desirable outcome was death. The study population (N = 300) was comprised of an equal number of patients receiving AUC-guided or trough-guided therapy. More patients experienced the most desirable outcome in the AUC-guided group compared to the trough-guided group (58.7% vs 46.7%, P = .037). Rates of VA-AKI were lower (21.3% vs 32.0%, P = .037) and median hospital length of stay was shorter (10 days [interquartile range {IQR}, 8-20] vs 12 days [IQR, 8-25]; P = .025) among patients receiving AUC-guided therapy. AUC-guided vancomycin therapy using MIPD and single-concentration monitoring improved outcomes in patients with culture-proven gram-positive infections. Safety was improved with reduced incidence of VA-AKI, and no concerns for reduced efficacy were observed. Moreover, MIPD allowed for earlier assessment of AUC target attainment and greater flexibility in the collection of serum vancomycin concentrations.

Initial dosage optimization of olanzapine in patients with bipolar disorder based on model-informed precision dosing: a study from the real world.

Olanzapine is used for treating bipolar disorder (BPD); however, the optimal initial dosing regimen is unclear. The present study aimed to investigate the optimal olanzapine initial dosage in patients with BPD via model-informed precision dosing (MIPD) based on a real-world study. Thirty-nine patients with BPD from the real-world study were collected to construct the MIPD model. Weight, combined used quetiapine influenced olanzapine clearances in patients with BPD, where the clearance rates were 0.152:1 in patients with or without quetiapine under the same weight. We simulated olanzapine doses once a day or twice a day, of which twice a day was optimal. Without quetiapine, for twice-a-day olanzapine doses, 0.80, 0.70, and 0.60mg/kg/day were suitable for 40- to 56-kg BPD patients, 56- to 74-kg BPD patients, and 74- to 100-kg BPD patients, respectively. With quetiapine, for twice-a-day olanzapine doses, 0.05mg/kg/day was suitable for 40- to 100-kg BPD patients. This study was the first to investigate the optimal olanzapine initial dosage in patients with BPD via MIPD based on a real-world study, providing clinical reference for the precision medication of olanzapine in BPD patients.

Model-Informed Precision Dosing to Reduce Vincristine-Induced Peripheral Neuropathy in Pediatric Patients: A Pharmacokinetic and Pharmacodynamic Modeling and Simulation Analysis.

Vincristine-induced peripheral neuropathy (VIPN) is a common adverse effect of vincristine, a drug often used in pediatric oncology. Previous studies demonstrated large inter- and intrapatient variability in vincristine pharmacokinetics (PK). Model-informed precision dosing (MIPD) can be applied to calculate patient exposure and individualize dosing using therapeutic drug monitoring (TDM) measurements. This study set out to investigate the PK/pharmacodynamic (PKPD) relationship of VIPN and determine the utility of MIPD to support clinical decisions regarding dose selection and individualization. Data from 35 pediatric patients were utilized to quantify the relationship between vincristine dose, exposure and the development of VIPN. Measurements of vincristine exposure and VIPN (Common Terminology Criteria for Adverse Events [CTCAE]) were available at baseline and for each subsequent dosing occasions (1-5). A PK and PKPD analysis was performed to assess the inter- and intraindividual variability in vincristine exposure and VIPN over time. In silico trials were performed to portray the utility of vincristine MIPD in pediatric subpopulations with a certain age, weight and cytochrome P450 (CYP)3A5 genotype distribution. A two-compartmental model with linear PK provided a good description of the vincristine exposure data. Clearance and distribution parameters were related to bodyweight through allometric scaling. A proportional odds model with Markovian elements described the incidence of Grades 0, 1 and ≥2 VIPN overdosing occasions. Vincristine area under the curve (AUC) was the most significant exposure metric related to the development of VIPN, where an AUC of 50 ng⋅h/mL was estimated to be related to an average VIPN probability of 40% over five dosing occasions. The incidence of Grade ≥2 VIPN reduced from 62.1 to 53.9% for MIPD-based dosing compared with body surface area (BSA)-based dosing in patients. Dose decreases occurred in 81.4% of patients with MIPD (vs. 86.4% for standard dosing) and dose increments were performed in 33.4% of patients (no dose increments allowed for standard dosing). The PK and PKPD analysis supports the use of MIPD to guide clinical dose decisions and reduce the incidence of VIPN. The current work can be used to support decisions with respect to dose selection and dose individualization in children receiving vincristine.

Toward Model-Based Informed Precision Dosing of Vancomycin in Hematologic Cancer Patients: A First Step.

There is no consensus on the optimal vancomycin dose to achieve pharmacokinetic/pharmacodynamic (PK/PD) target in patients with hematologic cancer or in hematopoietic stem cell transplant (HSCT) recipients. A 24-h area under the concentration-time curve (AUC) >400 mg*h/L must be achieved early for successful treatment of severe methicillin-resistant Staphylococcus aureus (MRSA) infections. Current nomograms derived from general population data are not sufficiently accurate to allow AUC-based model-informed precision dosing. The objective of this study was to characterize vancomycin PK in patients with hematologic cancer or in HSCT recipients and to develop a model-informed dosing tool based on PK/PD target requirements. Pooled retrospective and prospective vancomycin serum concentrations were analyzed using NONMEM® to evaluate the performance of previously published population PK (popPK) models built from hematologic cancer datasets and to develop a novel Bayesian PK model. Patients' characteristics and clinical data were tested as potential covariates. The popPK model was validated internally and externally. Predictions of vancomycin concentrations for different dosing regimens were made using Monte-Carlo simulations, and a nomogram strategy was proposed according to selected probability of target attainment (PTA). The predictive performance of the published popPK models was found to be suboptimal for our population. A novel popPK model was developed using 240 vancomycin concentrations (60 patients). A two-compartment structural model with an additive error model best described the data. Ideal body weight and estimated glomerular filtration rate (eGFR) [Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI)] were selected as covariates for volume of distribution (V) and clearance (CL). Bootstrapping confirmed the stability and precision of the popPK parameters. The volume of distribution was V1=46.8 L and V2=56.1L, while CL=5.63L/h. External validation using 107 vancomycin concentrations (24 patients) demonstrated the predictivity of the model. A nomogram was developed to reach minimally PTA >50% for 400 < AUC < 600 mg*h/L. To our knowledge, this study provides the first model-informed AUC-based strategy in North American hematologic cancer patients with or without HSCT. The resulting nomogram generated provides a simplified approach to improving the accuracy of initial vancomycin dosing in this population.

Pharmacokinetic and Pharmacodynamic Considerations of Antibiotic Use in Neonates.

The selection of an appropriate dose of a given antibiotic for a neonate not only requires knowledge of the drug's basic pharmacokinetic (PK) and pharmacodynamic (PD) properties but also the profound effects that organ development might have on the volume of distribution and clearance, both of which may affect the PK/PD of a drug. Interest has grown in alternative antibiotic dosing strategies that are better aligned with the antibiotic's PK and PD properties. These strategies should be used in conjunction with minimum inhibitory concentration measurements and therapeutic drug monitoring to measure their potential success. They can also guide the clinician in tailoring the delivery of antibiotics to suit an individual patient's needs. Model-informed precision dosing, such as Bayesian forecasting dosing software (which incorporates PK/PD population models), may be utilized to optimize antibiotic exposure in neonatal populations. Consequently, optimizing the antibiotic dose and exposure in each newborn requires expertise in different fields. It drives the collaboration of physicians together with lab technicians and quantitative clinical pharmacologists.

Rationale, Development, and Validation of HdxSim, a Clinical Decision Support Tool for Model-Informed Precision Dosing of Hydroxyurea for Children with Sickle Cell Anemia.

Hydroxyurea treatment for children with sickle cell anemia (SCA) is effective and life-saving. Stepwise escalation to maximum tolerated dose (MTD) provides optimal benefits, but is logistically challenging and time-consuming, especially in low-income countries where most people with SCA live. Model-informed precision dosing (MIPD) of hydroxyurea expedites MTD determination and improves outcomes compared with trial-and-error dose adjustments. HdxSim, a user-friendly, online, clinical decision support tool was developed to facilitate hydroxyurea MIPD and evaluated using real-world pharmacokinetic (PK) data. First-dose hydroxyurea PK profiles were analyzed from two clinical trial datasets (Hydroxyurea Study of Long-Term Effects (HUSTLE), NCT00305175 and Therapeutic Response Evaluation and Adherence Trial (TREAT), NCT02286154). Areas under the concentration-time curve (AUC) estimated by HdxSim were compared with those determined using traditional trapezoidal methodology and PK software (MWPharm-DOS). The doses predicted by HdxSim and MWPharm-DOS were compared with the observed clinical MTD. For HUSTLE participants, HdxSim accurately estimated hydroxyurea AUC compared with the trapezoidal method, with < 20% variance. The average (mean ± SD) AUC for TREAT participants estimated with HdxSim (68.6 ± 18.0 mg*hour/L) was lower than MWPharm-DOS (78.6 ± 20.7 mg*hour/L, P = 0.012), but the average recommended doses were not different (425 vs. 423 mg/day, P = 0.97). Moreover, HdxSim was non-inferior to MWPharm-DOS at predicting clinical MTD (absolute difference 3.9 ± 5.8 vs. 4.9 ± 8.2 mg/kg/day, P = 0.19). HdxSim accurately estimates hydroxyurea exposure and is noninferior to traditional PK approaches at predicting the clinical hydroxyurea MTD. Hydroxyurea dosing based on target exposure leads to improved outcomes in children with SCA, and has the potential to make PK-guided hydroxyurea dosing more accessible to this neglected population globally.

2539. Towards Biomarker-Guided Model Informed Precision Dosing – A Case Study With Continuously Infused Vancomycin in Sepsis

Abstract Background Model-informed precision dosing can streamline the dosing individualization of vancomycin in patients with severe bacterial infections. Yet, currently utilized models are limited to predicting the pharmacokinetics (PK). In this study, a pharmacometric model was developed that described the longitudinal relationship between PK and CRP after continuous infusion of vancomycin. Subsequently, its forecasting performance was evaluated. Methods In a clinical dataset in 67 patients with sepsis, the PK was linked to the CRP time courses using a turnover model with an effect compartment. For the forecasting analysis, scenarios with different numbers of TDM samples were tested to investigate how many are needed for valid predictions of future (unobserved) PK and CRP samples. Results Overall, the developed pharmacometric model described the PK and CRP data well, except sudden CRP increases potentially occurring due to other effects than the treated infection. The half maximal effective concentration of vancomycin was estimated to be 20.3 mg/L (95 % CI= 18.9 - 21.5 mg/L). The usage of just one CRP concentration from routine clinical data led to imprecise predictions. The inclusion of a single vancomycin concentration improved the predictive performance, but a second plasma sample of vancomycin and CRP was required to increase accuracy and precision significantly.Figure 3.Comparison of the predictive performance of the model for the pharmacokinetics (PK) and pharmacodynamics (PD) of future (unobserved) data using the relative bias and the relative root mean squared error when different amounts of information (observed data from 0-3 or all occasions) were provided to the model. Conclusion The present study corroborates the currently used therapeutic range for continuously infused vancomycin of 20-25 mg/L. The future tendency of the time course of up to 14 (range: 1-14) CRP measurements and vancomycin concentrations with a given vancomycin therapy regime was adequately predicted when at least two measurements of CRP and vancomycin were provided to the model, in particular if the tendency of CRP was increasing or decreasing was predicted well. These predictions are only trustworthy if there are no other events with an influence on the CRP concentration. Further biomarkers with a higher specificity for bacterial infections should be investigated or the model needs to be refined to handle such events. Disclosures All Authors: No reported disclosures

External Evaluation of Population Pharmacokinetics Models of Lithium in the Bipolar Population.

Lithium has been used in the treatment of bipolar disorder for several decades. Treatment optimization is recommended for this drug, due to its narrow therapeutic range and a large pharmacokinetics (PK) variability. In addition to therapeutic drug monitoring, attempts have been made to predict individual lithium doses using population pharmacokinetics (popPK) models. This study aims to assess the clinical applicability of published lithium popPK models by testing their predictive performance on two different external datasets. Available PopPK models were identified and their predictive performance was determined using a clinical dataset (46 patients/samples) and the literature dataset (89 patients/samples). The median prediction error (PE) and median absolute PE were used to assess bias and inaccuracy. The potential factors influencing model predictability were also investigated, and the results of both external evaluations compared. Only one model met the acceptability criteria for both datasets. Overall, there was a lack of predictability of models; median PE and median absolute PE, respectively, ranged from -6.6% to 111.2% and from 24.4% to 111.2% for the literature dataset, and from -4.5% to 137.6% and from 24.9% to 137.6% for the clinical dataset. Most models underpredicted the observed concentrations (7 out of 10 models presented a negative bias). Renal status was included as a covariate of lithium's clearance in only two models. To conclude, most of lithium's PopPK models had limited predictive performances related to the absence of covariates of interest included, such as renal status. A solution to this problem could be to improve the models with methodologies such as metamodeling. This could be useful in the perspective of model-informed precision dosing.

Drug Clearance in Patients with Inflammatory Bowel Disease Treated with Biologics.

Biological therapy is very effective for treating patients with moderate to severe inflammatory bowel disease (IBD). However, up to 40% can have primary non-response, and up to 50% of the patients can experience a loss of response to anti-tumor necrosis factor therapy. These undesirable outcomes can be attributed to either a mechanistic failure or pharmacokinetic (PK) issues characterized by an inadequate drug exposure and a high drug clearance. There are several factors associated with accelerated clearance of biologics including increased body weight, low serum albumin and immunogenicity. Drug clearance has gained a lot of attention recently as cumulative data suggest that there is an association between drug clearance and therapeutic outcomes in patients with IBD. Moreover, clearance is used by model informed precision dosing (MIDP) tools, or PK dashboards, to adjust the dosing for reaching a target drug concentration threshold towards a more personalized application of TDM. However, the role of drug clearance in clinical practice is yet to be determined. This comprehensive review aims to present data regarding the variables affecting the clearance of specific biologics, the association of clearance with therapeutic outcomes and the role of clearance monitoring and MIPD in patients with IBD.

Adult and pediatric physiologically-based biopharmaceutics modeling to explain lamotrigine immediate release absorption process.

Physiologically-based biopharmaceutics modeling (PBBM) has potential to accelerate the development of new drug and formulations. An important application of PBBM is for special populations such as pediatrics that have pharmacokinetics dependent on the maturation process. Lamotrigine (LTG) is a Biopharmaceutics Classification System (BCS) II drug and is widely prescribed. Therefore, the goal of this study was to assess the biopharmaceutics risk of the low-soluble drug LTG when the ontogeny on gastrointestinal tract (GIT) physiological parameters are considered. An oral physiologically-based pharmacokinetic model and a PBBM were developed and verified using GastroPlus™ software for both adults and children (2-12 years old, 12-52 kg). The biopharmaceutics properties and GIT physiological parameters were evaluated by sensitivity analysis. High doses were simulated assuming a worst case scenario, that is, the dose of 200 mg for adults and 5 mg/kg (up to the maximum of 200 mg) for 2-year-old children. Although several authors have suggested that ontogeny may have an effect on gastrointestinal fluid volume, our study found no evidence of interference between fluid and dose volumes with invivo dissolution of LTG. The most impactful parameter was found to be the gastric transit time. Therefore, the hypothesis is developed to examine whether LTG exhibits characteristics of a BCS II classification invitro while showing BCS I-like behavior invivo. This hypothesis could act as a base for conducting novel studies on model-informed precision dosing, tailored to specific populations and clinical conditions. In addition, it could be instrumental in assessing the influence of various release profiles on invivo performance for both adult and pediatric populations.

Retrospective Assessment of an Adalimumab Model-Informed Precision Dosing Support Tool for Use in Pediatric Inflammatory Bowel Disease.

Therapeutic drug monitoring in pediatric inflammatory bowel disease (IBD) has been used to achieve and maintain remission. Few guidelines exist to aid clinicians in the adjustment of anti-tumor necrosis factor therapies. The objective was to assess the agreement between real-world postinduction and posteriori analysis of retrospective data, using 2 novel pharmacokinetic (PK) models for adalimumab. A retrospective chart review was conducted in pediatric IBD patients treated with adalimumab. A Bayesian clinical decision support tool (InsightRX) was used. Postinduction serum concentration measurements of adalimumab were performed by drug-tolerant, homogenous shift mobility assay. Predicted serum adalimumab concentrations from both models were compared to the actual serum concentrations through a Bland-Altman analysis. Paired sample t test was used for equivalence. A total of 47 patients were included. Forty-one patients (87%) had Crohn disease, and 30 (64%) were male. Most were induced with 160 mg of adalimumab and maintained on 40 mg biweekly. No significant difference resulted between the de Klaver average prediction and mean population concentration (p = 0.294). Significant difference was observed between Ternant and mean population serum adalimumab concentration (p < 0.001). The Bland-Altman plot for the de Klaver method showed no proportional bias. Additionally, 49% of patients required a dose adjustment during maintenance therapy. The de Klaver model was able to provide less bias than the Ternant model and may aid in predicting serum adalimumab concentrations. Approximately half of the patients required dose adjustment during maintenance therapy to obtain a therapeutic drug concentration or achieve clinical remission.

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.