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The tumor size ratio (TSR), time‐to‐tumor growth (TTG), and tumor growth rate (kG) are frequently suggested as model‐based predictors of overall survival (OS) for different types of tumors. When the tumor metrics are applied in forecasting of the outcome for individual patients at an early stage, the tumor data might be sparse resulting in imprecise prediction. This simulation study aimed to investigate how the tumor follow‐up data and estimation approaches influence the accuracy in the tumor size metrics and the predicted hazard of death for individual patients. Longitudinal tumor size and OS data were simulated using tumor growth inhibition and Weibull distribution models, respectively. Based on the model and increasing measurement durations, the accuracy (defined as 80–125% of the simulated "true" value) in individual metrics and hazard was computed. TSR week 6 (TSRw6) accuracy was adequate for 91% of the patients when tumor size was measured up to 12 weeks. For TTG and kG metrics, the highest accuracy observed was lower (43 and 77%, respectively) and occurred later (42 and 60 weeks, respectively). The simultaneous (joint) and sequential estimation approaches resulted in similar accuracies, however, in general, the sequential approach where individual tumor size parameters are fixed, demonstrated inferior estimation properties. The TSRw6 and the model‐predicted tumor time course (absolute or relative change) had better forecasting properties than TTG or kG. The population pharmacokinetic (PK) parameters and data approach performed similarly or better than the simultaneous approach and had a better accuracy in estimating individuals' hazard of death than the individual PK parameters method. [ABSTRACT FROM AUTHOR]

• We studied whether meropenem (MEM) stability allows for continuous infusion (CI). • High- and low-dose CI regimens were simulated with a PK-PD model on P. aeruginosa. • Meropenem degradation affected plasma concentrations and bacterial load. • Infusion bag changes appeared particularly beneficial for 50 mg/mL MEM solutions. • Stability and consequently PK/PD measures improved for less concentrated infusions. Continuous infusion of meropenem has been proposed to increase target attainment in critically ill patients, although stability might limit its practical use. This study investigated the impact of meropenem degradation and infusion bag changes on the concentration-time profiles and bacterial growth and killing of P. aeruginosa given different continuous-infusion solutions. A semi-mechanistic pharmacokinetic-pharmacodynamic (PK-PD) model quantifying meropenem concentrations (C MEM) and bacterial counts of a resistant P. aeruginosa strain (ARU552, MIC = 16 mg/L) over 24 h was used to translate in vitro antibiotic effects to patients with severe infections. Concentration-dependent drug degradation of saline infusion solutions was considered using an additional compartment in the population PK model. C MEM , f T >MIC (time that concentrations exceed the MIC) and total bacterial load (B TOT) after 24 h were simulated for different scenarios (n = 144), considering low- and high-dose regimens (3000/6000 mg/day±loading dose), clinically relevant infusion solutions (20/40/50 mg/mL), different intervals of infusion bag changes (every 8/24 h, q8/24 h), and varied renal function (creatinine clearance 40/80/120 mL/min) and MIC values (8/16 mg/L). Highest deviations between changing infusion bags q8h and q24h were observed for 50 mg/mL solutions and scenarios with C MEM_24 h close to the MIC, with differences (Δ) in C MEM_24 h up to 4.9 mg/L, Δ f T >MIC ≤65.7%, and ΔB TOT_24h ≤1.1 log10 CFU/mL, thus affecting conclusions on whether bacteriostasis was reached. In summary, this study indicated that for continuous infusion of meropenem, eight-hourly infusion bag changes improved PK/PD target attainment and might be beneficial particularly for high meropenem concentrations of saline infusion solutions and for plasma concentrations in close proximity to the MIC. [Display omitted] [ABSTRACT FROM AUTHOR]

The aim of this study was to develop a multistate model for overall survival (OS) analysis, based on parametric hazard functions and combined with an investigation of predictors derived from a longitudinal tumor size model on the transition hazards. Different states – stable disease, tumor response, progression, second‐line treatment, and death following docetaxel treatment initiation (stable state) in patients with HER2‐negative breast cancer (n = 183) were used in model building. Past changes in tumor size prospectively predicts the probability of state changes. The hazard of death after progression was lower for subjects who had longer treatment response (i.e., longer time‐to‐progression). Young age increased the probability of receiving second‐line treatment. The developed multistate model adequately described the transitions between different states and jointly the overall event and survival data. The multistate model allows for simultaneous estimation of transition rates along with their tumor model derived metrics. The metrics were evaluated in a prospective manner so not to cause immortal time bias. Investigation of predictors and characterization of the time to develop response, the duration of response, the progression‐free survival, and the OS can be performed in a single multistate modeling exercise. This modeling approach can be applied to other cancer types and therapies to provide a better understanding of efficacy of drug and characterizing different states, thereby facilitating early clinical interventions to improve anticancer therapy. [ABSTRACT FROM AUTHOR]

PurposeAn item response theory (IRT) pharmacometric framework is presented to characterize Functional Assessment of Cancer Therapy-Breast (FACT-B) data in locally-advanced or metastatic breast cancer patients treated with ado-trastuzumab emtansine (T-DM1) or capecitabine-plus-lapatinib.MethodsIn the IRT model, four latent well-being variables, based on FACT-B general subscales, were used to describe the physical, social/family, emotional and functional well-being. Each breast cancer subscale item was reassigned to one of the other subscales. Longitudinal changes in FACT-B responses and covariate effects were investigated.ResultsThe IRT model could describe both item-level and subscale-level FACT-B data. Non-Asian patients showed better baseline social/family and functional well-being than Asian patients. Moreover, patients with Eastern Cooperative Oncology Group performance status of 0 had better baseline physical and functional well-being. Well-being was described as initially increasing or decreasing before reaching a steady-state, which varied substantially between patients and subscales. T-DM1 exposure was not related to any of the latent variables. Physical well-being worsening was identified in capecitabine-plus-lapatinib-treated patients, whereas T-DM1-treated patients typically stayed stable.ConclusionThe developed framework provides a thorough description of FACT-B longitudinal data. It acknowledges the multi-dimensional nature of the questionnaire and allows covariate and exposure effects to be evaluated on responses. [ABSTRACT FROM AUTHOR]

Background: Pharmacokinetic/pharmacodynamic (PKPD) models developed based on data from in vitro time-kill experiments have been suggested to contribute to more efficient drug development programmes and better dosing strategies for antibiotics. However, for satisfactory predictions such models would have to show good extrapolation properties.Objectives: To evaluate if a previously described mechanism-based PKPD model was able also to predict drug efficacy for higher bacterial densities and across bacterial strains.Methods: A PKPD model describing the efficacy of ciprofloxacin on Escherichia coli was evaluated. The predictive performance of the model was evaluated across several experimental conditions with respect to: (i) bacterial start inoculum ranging from the standard of ∼106 cfu/mL up to late stationary-phase cultures; and (ii) efficacy for seven additional strains (three laboratory and four clinical strains), not included during the model development process, based only on information regarding their MIC. Model predictions were performed according to the intended experimental protocol and later compared with observed bacterial counts.Results: The mechanism-based PKPD model structure developed based on data from standard start inoculum experiments was able to accurately describe the inoculum effect. The model successfully predicted the time course of drug efficacy for additional laboratory and clinical strains based on only the MIC values. The model structure was further developed to better describe the stationary phase data.Conclusions: This study supports the use of mechanism-based PKPD models based on preclinical data for predictions of untested scenarios. [ABSTRACT FROM AUTHOR]

Neutrophil granulocytes are key components of the host response against pathogens, and severe neutropenia, with neutrophil counts below 0.5 × 106 cells/mL, renders patients increasingly vulnerable to infections. Published in vitro (n = 7) and in vivo (n = 5) studies with time‐course information on bacterial and neutrophil counts were digitized to characterize the kinetics of neutrophil‐mediated bacterial killing and inform on the immune systems' contribution to the clearance of bacterial infections. A mathematical model for the in vitro dynamics of bacteria and the kinetics of neutrophil‐mediated phagocytosis and digestion was developed, which was extended to in vivo studies in immune‐competent and immune‐compromised mice. Neutrophil‐mediated bacterial killing was described by two first‐order processes—phagocytosis and digestion—scaled by neutrophil concentration, where 50% of the maximum was achieved at neutrophil counts of 1.19 × 106 cells/mL (phagocytosis) and 6.55 × 106 cells/mL (digestion). The process efficiencies diminished as the phagocytosed bacteria to total neutrophils ratio increased (with 50% reduction at a ratio of 3.41). Neutrophil in vivo dynamics were captured through the characterization of myelosuppressive drug effects and postinoculation neutrophil influx into lungs and by system differences (27% bacterial growth and 9.3% maximum capacity, compared with in vitro estimates). Predictions showed how the therapeutically induced reduction of neutrophil counts enabled bacterial growth, especially when falling below 0.5 × 106 cells/mL, whereas control individuals could deal with all tested bacterial burdens (up to 109 colony forming units/g lung). The model‐based characterization of neutrophil‐mediated bacterial killing simultaneously predicted data across in vitro and in vivo studies and may be used to inform the capacity of host–response at the individual level. [ABSTRACT FROM AUTHOR]

Traditionally, reduced daily doses of colistin methanesulphonate (CMS) in critically ill patients receiving continuous venovenous haemodiafiltration (CVVHDF) have resulted in suboptimal colistin concentrations. The necessity of a loading dose (LD) at treatment initiation has been proposed. A LD of 9 million IU (MU) [ca. 270 mg of colistin base activity (CBA)] was administrated with a maintenance dose of 4.5 MU (ca. 140 mg CBA) every 12 h (q12h) to eight critically ill patients receiving renal replacement therapy. Blood samples were collected immediately before and at different time intervals after the LD and the fourth dose, whilst pre-filter and post-filter blood samples were also collected. CMS and colistin concentrations were determined using an LC-MS/MS assay. Median maximum observed concentrations after the LD were 22.1 mg/L for CMS and 1.55 mg/L for colistin, whereas during maintenance dosing the corresponding values were 12.6 mg/L and 1.72 mg/L, respectively. CVVHDF clearance was determined as 2.98 L/h for colistin, equivalent to 62% of total apparent colistin clearance in CVVHDF patients. Both CMS and colistin were cleared by CVVHDF. Application of a LD of 9 MU CMS resulted in more rapid achievement of the target colistin concentration. Following implementation of a predicted pharmacokinetic model on plasma CMS/colistin concentrations, a LD of 12 MU CMS appears more appropriate, whilst a CMS maintenance dosage of at least 6.5–7.5 MU q12h is suggested in patients undergoing CVVHDF. However, further clinical studies are warranted to assess the safety of a LD of 12 MU CMS in patients receiving CVVHDF. [ABSTRACT FROM AUTHOR]

Objectives: For antibiotics, extensive animal PKPD studies are often performed to evaluate the PK/PD driver for subsequent use when recommending dosing regimens. The aim of this work was to evaluate a PKPD model, developed based on in vitro time-kill data for colistin, in predicting the relationships between PK/PD indices and the bacterial killing previously observed in mice.Methods: An in silico PKPD model for Pseudomonas aeruginosa exposed to colistin was previously developed based on static in vitro time-kill data. The model was here applied to perform an in silico replication of an in vivo study where the effect of colistin on P. aeruginosa was studied in the thigh infection model. Concentration-time profiles of unbound colistin were predicted and used as input to drive the bacterial killing in the PKPD model. The predicted bacterial count at 24 h was related to each of the PK/PD indices and the results were compared with reported observations in vivo.Results: The model was found to adequately predict in vivo results from mice; both in terms of which PK/PD index best correlates to effect (fAUC/MIC) as well as the magnitude needed for a 2 log kill. The fAUC/MIC needed to achieve a 2 log reduction in bacterial counts after 24 h was here predicted to be 9 compared with 31 previously reported in vivo.Conclusions: This study provides further support that PKPD models based on longitudinal data can be a useful tool to make drug development more efficient within the infectious diseases area. [ABSTRACT FROM AUTHOR]

Ibrutinib is a Bruton tyrosine kinase (Btk) inhibitor for treating chronic lymphocytic leukemia (CLL). It has also been associated with hypertension. The optimal dosing schedule for mitigating this adverse effect is currently under discussion. A quantification of relationships between systemic ibrutinib exposure and efficacy (i.e., leukocyte count and sum of the product of perpendicular diameters [SPD] of lymph nodes) and hypertension toxicity (i.e., blood pressure), and their association with overall survival is needed. Here, we present a semi‐mechanistic pharmacokinetic‐pharmacodynamic modeling framework to characterize such relationships and facilitate dose optimization. Data from a phase Ib/II study were used, including ibrutinib plasma concentrations to derive daily 0–24‐h area under the concentration‐time curve, leukocyte count, SPD, survival, and blood pressure measurements. A nonlinear mixed effects modeling approach was applied, considering ibrutinib's pharmacological action and CLL cell dynamics. The final framework included (i) an integrated model for SPD and leukocytes consisting of four CLL cell subpopulations with ibrutinib inhibiting phosphorylated Btk production, (ii) a turnover model in which ibrutinib stimulates an increase in blood pressure, and (iii) a competing risk model for dropout and death. Simulations predicted that the approved dosing schedule had a slightly higher efficacy (24‐month, progression‐free survival [PFS] 98%) than de‐escalation schedules (24‐month, average PFS ≈ 97%); the latter had, on average, ≈20% lower proportions of patients with hypertension. The developed modeling framework offers an improved understanding of the relationships among ibrutinib exposure, efficacy and toxicity biomarkers. This framework can serve as a platform to assess dosing schedules in a biologically plausible manner. [ABSTRACT FROM AUTHOR]

Our professional and academic community lost a great pioneer in the field of pharmacokinetics and pharmacometrics when Dr. Hartmut Derendorf, distinguished professor at the University of Florida College of Pharmacy passed away unexpectedly on November 23, 2020, at the age of 67. A native of Germany, Dr. Derendorf received his BS and PhD in pharmacy from the University of Münster and then joined University of Florida (UF) as a postdoctoral fellow in 1981. Dr. Hartmut Derendorf, PhD, a world-renowned expert in pharmacokinetics and pharmacometrics (1953-2020). [Extracted from the article]

Dr. Reddy's Laboratories rituximab (DRL_RI; Dr. Reddy's Laboratories SA, Basel, Switzerland) is under development as a rituximab biosimilar. Study RI‐01‐002 (Clinical Trials Registry ‐ India/2012/11/003129), comparing DRL_RI to the reference medicinal product (RMP) MabThera® (Roche, Grenzach‐Wyhlen, Germany), demonstrated pharmacokinetic (PK) equivalence and showed comparable pharmacodynamic, efficacy, safety, and immunogenicity profiles. We used data from the same study to perform population PK and PK–pharmacodynamic analyses: first exploring possible factors influencing the PK similarity assessment between products and then performing simulations to investigate the impact of tumor size on rituximab PK. Nonlinear mixed‐effects models for PK, tumor size, tumor size–PK, and tumor response were developed independently. The final PK model included drug product as a dose‐scaling parameter and predicted a 6.75% higher dose reaching the system in RMP‐treated patients. However, when tumor size was included in the tumor size–PK model, the drug product effect was no longer observed. The model rather indicated that patients with larger tumor size have higher clearance. Further simulations confirmed that higher baseline tumor size is associated to slightly lower rituximab exposure. Tumor response, described by a continuous‐time Markov model, did not differ between drug products. Both had higher effects during the first 20 weeks of treatment. Also, the model described a subpopulation of nonresponders to treatment (42%) with faster transitions to a worse state. The different rituximab exposure initially detected between drug products (6.75%) was shown using PK/PK–pharmacodynamic analysis to be attributed to a tumor size imbalance between treatment groups. PK/PK–pharmacodynamic analyses may contribute to PK similarity assessments. [ABSTRACT FROM AUTHOR]

The article presents a study that compares the efficiency of the agonist-antagonist interaction (AAI) model and prolactin pool model for assessment of the effect of remoxipride on prolactin. The study assesses the two models in eight male subjects, who were infused with remoxipride with criteria such as goodness-of-fit (GOF) and objective function values (OFV). Result shows that pool model is better to use for circadian rhythm function, while AAI describes better circadian rhythm of prolactin.

One of the most employed approaches to reduce severe neutropenia following anticancer drug regimens is to reduce the consecutive dose in fixed steps, commonly by 25%. Another approach has been to use pharmacokinetic (PK) sampling to tailor dosing, but only rarely have model-based computer approaches utilizing collected PK and/or pharmacodynamic (PD) data been used. A semi-mechanistic model for myelosuppression that can characterize the interindividual and interoccasion variability in the time-course of neutrophils following administration of a wide range of anticancer drugs may be used in a clinical setting for model-based dose individualization. The aim of this study was to compare current stepwise procedures to model-based dose adaptation by simulations, and investigate if the overall dose intensity in the population could be increased without increasing the risk of severe toxicity. The value of various amounts of PK- and/or PD-information was compared to standard dosing strategies using a maximum a posteriori procedure in NONMEM. The results showed that when information on neutrophil counts was available, the additional improvement from PK sampling was negligible. Using neutrophil sampling at baseline and an observation near the predicted nadir increased the number of patients in the target range by 27% in comparison with a one-sided 25% dose adjustment schedule, while keeping the number of patients experiencing severe toxicity at a comparable low level after five courses of treatment. High interindividual variability did not limit the benefit of model-based dose adaptation, whereas high interoccasion variability was predicted to make any dose adaptation method less successful. This study indicates that for successful model-based dose adaptation clinically, there is no need for drug concentration sampling, and that one extra neutrophil measurement in addition to the pre-treatment value is sufficient to limit severe neutropenia while increasing dose intensity. [ABSTRACT FROM AUTHOR]

Abstract: Chemotherapy dosing in anticancer treatment is a balancing act between achieving concentrations that are effective towards the malignancy and that result in acceptable side-effects. Neutropenia is one major side-effect of many antitumor agents, and is related to an increased risk of infection. A model capable of describing the time-course of myelosuppression from administered drug could be used in individual dose selection. In this paper we describe the transfer of a previously developed semi-mechanistic model for myelosuppression from NONMEM to a dosing tool in MS Excel, with etoposide as an example. The tool proved capable to solve a differential equation system describing the pharmacokinetics and pharmacodynamics, with estimation performance comparable to NONMEM. In the dosing tool the user provides neutrophil measures from a previous treatment course and request for the dose that results in a desired nadir in the upcoming course through a Bayesian estimation procedure. [Copyright &y& Elsevier]

Aims To develop a pharmacokinetic–pharmacodynamic model describing the time-course of QT interval prolongation after citalopram overdose and to evaluate the effect of charcoal on the relative risk of developing abnormal QT and heart-rate combinations. Methods Plasma concentrations and electrocardiograph (ECG) data from 52 patients after 62 citalopram overdose events were analysed in WinBUGS using a Bayesian approach. The reported doses ranged from 20 to 1700 mg and on 17 of the events a single dose of activated charcoal was administered. The developed pharmacokinetic–pharmacodynamic model was used for predicting the probability of having abnormal combinations of QT-RR, which was assumed to be related to an increased risk for torsade de pointes (TdP). Results The absolute QT interval was related to the observed heart rate with an estimated individual heart-rate correction factor [α = 0.36, between-subject coefficient of variation (CV) = 29%]. The heart-rate corrected QT interval was linearly dependent on the predicted citalopram concentration (slope = 40 ms l mg−1, between-subject CV = 70%) in a hypothetical effect-compartment (half-life of effect-delay = 1.4 h). The heart-rate corrected QT was predicted to be higher in women than in men and to increase with age. Administration of activated charcoal resulted in a pronounced reduction of the QT prolongation and was shown to reduce the risk of having abnormal combinations of QT-RR by approximately 60% for citalopram doses above 600 mg. Conclusion Citalopram caused a delayed lengthening of the QT interval. Administration of activated charcoal was shown to reduce the risk that the QT interval exceeds a previously defined threshold and therefore is expected to reduce the risk of TdP. [ABSTRACT FROM AUTHOR]

Abstract: N-(6-Chlorophenoxyhexyl)-N′-cyano-N″-4-pyridylguanidine (CHS 828) is a novel anticancer agent that shows schedule-dependent effects in vitro and in vivo, as well as in Phase I clinical trials. A rat hollow fibre model was used to investigate whether this dependency is due to pharmacokinetic and/or pharmacodynamic factors. The effect on two cell lines, MDA-MB-231 (breast cancer) and CCRF-CEM (leukaemia) were studied after CHS 828 was administered orally as a single dose or in a 5-day schedule, at different total dose levels. The 5-day schedules were associated with greater effects on both cell lines compared with single doses. A one-compartment pharmacokinetic model, with a half-life of 2.3h and a consecutive zero- and first-order process to describe dissolution and absorption, fit the concentration data. Pharmacokinetics were dose-dependent, as the fraction absorbed decreased with increasing dose. Clearance increased with accumulative exposure. Twenty hours after administration, concentrations started to increase again, probably due to coprophagy. Pharmacokinetic–pharmacodynamic models characterized the cell growth and cell kill over time and showed that schedule-dependent antitumour effects were present also when the dose-dependent pharmacokinetics were accounted for. The prolonged schedules of CHS 828 were therefore associated with greater antitumour effects than single doses of the same total exposure. [Copyright &y& Elsevier]

The AIDA randomized clinical trial found no significant difference in clinical failure or survival between colistin monotherapy and colistin–meropenem combination therapy in carbapenem‐resistant Gram‐negative infections. The aim of this reverse translational study was to integrate all individual preclinical and clinical pharmacokinetic–pharmacodynamic (PKPD) data from the AIDA trial in a pharmacometric framework to explore whether individualized predictions of bacterial burden were associated with the trial outcomes. The compiled dataset included for each of the 207 patients was (i) information on the infecting Acinetobacter baumannii isolate (minimum inhibitory concentration, checkerboard assay data, and fitness in a murine model), (ii) colistin plasma concentrations and colistin and meropenem dosing history, and (iii) disease scores and demographics. The individual information was integrated into PKPD models, and the predicted change in bacterial count at 24 h for each patient, as well as patient characteristics, was correlated with clinical outcomes using logistic regression. The in vivo fitness was the most important factor for change in bacterial count. A model‐predicted growth at 24 h of ≥2‐log10 (164/207) correlated positively with clinical failure (adjusted odds ratio, aOR = 2.01). The aOR for one unit increase of other significant predictors were 1.24 for SOFA score, 1.19 for Charlson comorbidity index, and 1.01 for age. This study exemplifies how preclinical and clinical anti‐infective PKPD data can be integrated through pharmacodynamic modeling and identify patient‐ and pathogen‐specific factors related to clinical outcomes – an approach that may improve understanding of study outcomes. [ABSTRACT FROM AUTHOR]

Background: Pharmacogenetic profiling and therapeutic drug monitoring (TDM) have both been proposed to manage inter-individual variability (IIV) in drug exposure. However, determining the most effective approach for estimating exposure for a particular drug remains a challenge. This study aimed to quantitatively assess the circumstances in which pharmacogenetic profiling may outperform TDM in estimating drug exposure, under three sources of variability (IIV, inter-occasion variability [IOV], and residual unexplained variability [RUV]). Methods: Pharmacokinetic models were selected from the literature corresponding to drugs for which pharmacogenetic profiling and TDM are both clinically considered approaches for dose individualization. The models were used to simulate relevant drug exposures (trough concentration or area under the curve [AUC]) under varying degrees of IIV, IOV, and RUV. Results: Six drug cases were selected from the literature. Model-based simulations demonstrated that the percentage of patients for whom pharmacogenetic exposure prediction is superior to TDM differs for each drug case: tacrolimus (11.0%), tamoxifen (12.7%), efavirenz (49.2%), vincristine (49.6%), risperidone (48.1%), and 5-fluorouracil (5-FU) (100%). Generally, in the presence of higher unexplained IIV in combination with lower RUV and IOV, exposure was best estimated by TDM, whereas, under lower unexplained IIV in combination with higher IOV or RUV, pharmacogenetic profiling was preferred. Conclusions: For the drugs with relatively low RUV and IOV (e.g., tamoxifen and tacrolimus), TDM estimated true exposure the best. Conversely, for drugs with similar or lower unexplained IIV (e.g., efavirenz or 5-FU, respectively) combined with relatively high RUV, pharmacogenetic profiling provided the most accurate estimate for most patients. However, genotype prevalence and the relative influence of genotypes on the PK, as well as the ability of TDM to accurately estimate AUC with a limited number of samples, had an impact. The results could be used to support clinical decision making when considering other factors, such as the probability for severe side effects. [ABSTRACT FROM AUTHOR]

Insight into the development of treatment resistance can support the optimization of anticancer treatments. This study aims to characterize the tumor dynamics and development of drug resistance in patients with non‐small cell lung cancer treated with erlotinib, and investigate the relationship between baseline circulating tumor DNA (ctDNA) data and tumor dynamics. Data obtained for the analysis included (1) intensively sampled erlotinib concentrations from 29 patients from two previous pharmacokinetic (PK) studies, and (2) tumor sizes, ctDNA measurements, and sparsely sampled erlotinib concentrations from 18 patients from the START‐TKI study. A two‐compartment population PK model was first developed which well‐described the PK data. The PK model was subsequently applied to investigate the exposure‐tumor dynamics relationship. To characterize the tumor dynamics, models accounting for intra‐tumor heterogeneity and acquired resistance with or without primary resistance were investigated. Eventually, the model assumed acquired resistance only resulted in an adequate fit. Additionally, models with or without exposure‐dependent treatment effect were explored, and no significant exposure‐response relationship for erlotinib was identified within the observed exposure range. Subsequently, the correlation of baseline ctDNA data on EGFR and TP53 variants with tumor dynamics' parameters was explored. The analysis indicated that higher baseline plasma EGFR mutation levels correlated with increased tumor growth rates, and the inclusion of ctDNA measurements improved model fit. This result suggests that quantitative ctDNA measurements at baseline have the potential to be a predictor of anticancer treatment response. The developed model can potentially be applied to design optimal treatment regimens that better overcome resistance. [ABSTRACT FROM AUTHOR]

Appropriate antibiotic dosing to ensure early and sufficient target attainment is crucial for improving clinical outcome in critically ill patients. Parametric survival analysis is a preferred modeling method to quantify time‐varying antibiotic exposure – response effects, whereas bias may be introduced in hazard functions and survival functions when competing events occur. This study investigated predictors of in‐hospital mortality in critically ill patients treated with meropenem by pharmacometric multistate modeling. A multistate model comprising five states (ongoing meropenem treatment, other antibiotic treatment, antibiotic treatment termination, discharge, and death) was developed to capture the transitions in a cohort of 577 critically ill patients treated with meropenem. Various factors were investigated as potential predictors of the transitions, including patient demographics, creatinine clearance calculated by Cockcroft–Gault equation (CLCRCG), time that unbound concentrations exceed the minimum inhibitory concentration (fT>MIC), and microbiology‐related measures. The probabilities to transit to other states from ongoing meropenem treatment increased over time. A 10 mL/min decrease in CLCRCG was found to elevate the hazard of transitioning from states of ongoing meropenem treatment and antibiotic treatment termination to the death state by 18%. The attainment of 100% fT>MIC significantly increased the transition rate from ongoing meropenem treatment to antibiotic treatment termination (by 9.7%), and was associated with improved survival outcome. The multistate model prospectively assessed predictors of death and can serve as a useful tool for survival analysis in different infection scenarios, particularly when competing risks are present. [ABSTRACT FROM AUTHOR]

Background: 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. Methods: 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. Results: 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). Conclusions: 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. [ABSTRACT FROM AUTHOR]

Objectives Combination therapy is often used for carbapenem-resistant Gram-negative bacteria. We previously demonstrated synergy of polymyxin B and minocycline against carbapenem-resistant Klebsiella pneumoniae in static time–kill experiments and developed an in silico pharmacokinetic/pharmacodynamic (PK/PD) model. The present study assessed the synergistic potential of this antibiotic combination in dynamic experiments. Methods Two clinical K. pneumoniae isolates producing KPC-3 and OXA-48 (polymyxin B MICs 0.5 and 8 mg/L, and minocycline MICs 1 and 8 mg/L, respectively) were included. Activities of the single drugs and the combination were assessed in 72 h dynamic time–kill experiments mimicking patient pharmacokinetics. Population analysis was performed every 12 h using plates containing antibiotics at 4× and 8× MIC. WGS was applied to reveal resistance genes and mutations. Results The combination showed synergistic and bactericidal effects against the KPC-3-producing strain from 12 h onwards. Subpopulations with decreased susceptibility to polymyxin B were frequently detected after single-drug exposures but not with the combination. Against the OXA-48-producing strain, synergy was observed between 4 and 8 h and was followed by regrowth. Subpopulations with decreased susceptibility to polymyxin B and minocycline were detected throughout experiments. For both strains, the observed antibacterial activities showed overall agreement with the in silico predictions. Conclusions Polymyxin B and minocycline in combination showed synergistic effects, mainly against the KPC-3-producing K. pneumoniae. The agreement between the experimental results and in silico predictions supports the use of PK/PD models based on static time–kill data to predict the activity of antibiotic combinations at dynamic drug concentrations. [ABSTRACT FROM AUTHOR]

FAP‐4‐1BBL is a bispecific antibody exerting 4‐1BB‐associated T‐cell activation only while simultaneously bound to the fibroblast activation protein (FAP) receptor, expressed on the surface of cancer‐associated fibroblasts. The trimeric complex formed when FAP‐4‐1BBL is simultaneously bound to FAP and 4‐1BB represents a promising mechanism to achieve tumor‐specific 4‐1BB stimulation. We integrated in vitro data with mathematical modeling to characterize the pharmacology of FAP‐4‐1BBL as a function of trimeric complex formation when combined with the T‐cell engager cibisatamab. This relationship was used to prospectively predict a range of clinical doses where trimeric complex formation is expected to be at its maximum. Depending on the dosing schedule and FAP‐4‐1BBL plasma: tumor distribution, doses between 2 and 145 mg could lead to maximum trimeric complex formation in the clinic. Due to the expected variability in both pharmacokinetic and FAP expression in the patient population, we predict that detecting a clear dose–response relationship would remain difficult without a large number of patients per dose level, highlighting that mathematical modeling techniques based on in vitro data could aid dose selection. [ABSTRACT FROM AUTHOR]

The dose/exposure‐efficacy analyses are often conducted separately for oncology end points like best overall response, progression‐free survival (PFS) and overall survival (OS). Multistate models offer to bridge these dose‐end point relationships by describing transitions and transition times from enrollment to response, progression, and death, and evaluating transition‐specific dose effects. This study aims to apply the multistate pharmacometric modeling and simulation framework in a dose optimization setting of bintrafusp alfa, a fusion protein targeting TGF‐β and PD‐L1. A multistate model with six states (stable disease [SD], response, progression, unknown, dropout, and death) was developed to describe the totality of endpoints data (time to response, PFS, and OS) of 80 patients with non‐small cell lung cancer receiving 500 or 1200 mg of bintrafusp alfa. Besides dose, evaluated predictor of transitions include time, demographics, premedication, disease factors, individual clearance derived from a pharmacokinetic model, and tumor dynamic metrics observed or derived from tumor size model. We found that probabilities of progression and death upon progression decreased over time since enrollment. Patients with metastasis at baseline had a higher probability to progress than patients without metastasis had. Despite dose failed to be statistically significant for any individual transition, the combined effect quantified through a model with dose‐specific transition estimates was still informative. Simulations predicted a 69.2% probability of at least 1 month longer, and, 55.6% probability of at least 2‐months longer median OS from the 1200 mg compared to the 500 mg dose, supporting the selection of 1200 mg for future studies. [ABSTRACT FROM AUTHOR]

Aims: Cladribine tablets have shown significant efficacy for the treatment of relapsing multiple sclerosis, a chronic and debilitating immune‐mediated disorder. This study was conducted to examine acute and/or cumulative effects of cladribine tablets 10 mg (3.5 or 5.25 mg/kg cumulative dose over 2 years) on heart rate, AV conduction and cardiac repolarization in patients with relapsing–remitting multiple sclerosis (RRMS). Methods: CLARITY was a 96‐week, double‐blind, placebo‐controlled, multicentre trial which evaluated the safety and efficacy of cladribine tablets 3.5 and 5.25 mg/kg body weight in patients with RRMS. A total of 135 patients were included in the ECG substudy, providing a total of 1534 post‐dose ECGs. ECG data were collected 15 minutes pre‐dose and between 0.5 and 3 hours post‐dose at pre‐study evaluation, study Day 1 and Weeks 5, 9, 13, 48 and 52. Results: For cladribine tablets 3.5 mg/kg, the maximum change in placebo‐adjusted post‐dose QTcF vs. visit‐baseline (BL) was −0.42 ms (90% CI: −3.61–4.44) at Week 1 (acute effects), and 3.20 ms (90% CI: −0.08–6.33) for cladribine tablets 5.25 mg/kg. The greatest observed differences in post‐dose QTcF vs. study BL occurred at Week 48 for both the 3.5 and 5.25 mg/kg doses of cladribine tablets with 5.99 ms (90% CI: 0.53–11.44) and 8.74 ms (90% CI: 3.18–14.31), respectively. No significant changes were observed in T‐wave morphology in either treatment group. Conclusions: Cladribine tablets 3.5 mg/kg (approved dose in Europe/other regions) did not confer clinically meaningful effects on heart rate, AV conduction and ventricular repolarization. [ABSTRACT FROM AUTHOR]

Background:In view of the paucity of clinical evidence,in vitrostudies are needed to find antibiotic combinations effective against multidrug-resistant Gram-negative bacteria. Interpretation ofin vitroeffects is usually based on bacterial growth after 24 h in time-kill and checkerboard experiments. However, the clinical relevance of the effects observedin vitrois not established. In this study we explored alternative output parameters to assess the activities of colistin and meropenem againstPseudomonas aeruginosaandAcinetobacter baumannii. Methods:Four strains each ofP. aeruginosaandA. baumanniiwere exposed to colistin and meropenem, alone and in combination, in 8 h dynamic time-kill experiments. Initial (1 h), maximum and 8 h bacterial reductions and the area under the bacterial time-kill curve were evaluated. Checkerboards, interpreted based on fractional inhibitory concentration indices after 24 h, were performed for comparison. Results:In the time-kill experiments, the combination resulted in enhanced 1 h, maximum and 8 h bacterial reductions against 2, 3 and 5 of 8 strains, respectively, as compared to the single drugs. A statistically significant reduction in the area under the time-kill curve was observed for three strains. In contrast, the checkerboards did not identify synergy for any of the strains. Conclusions:Combination effects were frequently found with colistin and meropenem againstP. aeruginosaand A.baumanniiin time-kill experiments but were not detected with the checkerboard method. We propose that the early dynamics of bacterial killing and growth, which may be of great clinical importance, should be considered in futurein vitrocombination studies. [ABSTRACT FROM PUBLISHER]

Blood pressure measurements form a critical component of adverse event monitoring for tyrosine kinase inhibitors, but might also serve as a biomarker for dose titrations. This study explored the impact of various sources of within-individual variation on blood pressure readings to improve measurement practices and evaluated the utility for individual-and population-level dose selection. A pharmacokinetic-pharmacodynamic modeling framework was created to describe circadian blood pressure changes, inter-and intra-day variability, changes from dipper to non-dipper profiles, and the relationship between drug exposure and blood pressure changes over time. The framework was used to quantitatively evaluate the influence of physiological and pharmacological aspects on blood pressure measurements, as well as to compare measurement techniques, including office-based, home-based, and ambulatory 24-h blood pressure readings. Circadian changes, as well as random intra-day and inter-day variability, were found to be the largest sources of within-individual variation in blood pressure. Office-based and ambulatory 24-h measurements gave rise to potential bias (>5 mmHg), which was mitigated by model-based estimations. Our findings suggest that 5-8 consecutive, home-based, measurements taken at a consistent time around noon, or alternatively within a limited time frame (e.g., 8.00 a.m. to 12.00 p.m. or 12.00 p.m. to 5.00 p.m.), will give rise to the most consistent blood pressure estimates. Blood pressure measurements likely do not represent a sufficiently accurate method for individual-level dose selection, but may be valuable for population-level dose identification. A user-friendly tool has been made available to allow for interactive blood pressure simulations and estimations for the investigated scenarios. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to an article about application of pharmacokinetic-pharmacodynamic modeling in management of QT abnormalities after citalopram overdose.

A response by Isbister, Duffull and Friberg to a letter to the editor about their article on the modeling of cardiac toxicity for venlafaxine overdose in the November 2006 issue is presented.

New drugs and methods to efficiently fight carbapenem-resistant gram-negative pathogens are sorely needed. In this study, we characterized the preclinical pharmacokinetics (PK) and pharmacodynamics of the clinical stage drug candidate apramycin in time kill and mouse lung infection models. Based on in vitro and in vivo data, we developed a mathematical model to predict human efficacy. Three pneumonia-inducing gram-negative species Acinetobacter baumannii , Pseudomonas aeruginosa , and Klebsiella pneumoniae were studied. Bactericidal kinetics were evaluated with time-kill curves; in vivo PK were studied in healthy and infected mice, with sampling in plasma and epithelial lining fluid after subcutaneous administration; in vivo efficacy was measured in a neutropenic mouse pneumonia model. A pharmacokinetic-pharmacodynamic model, integrating all the data, was developed and simulations were performed. Good lung penetration of apramycin in epithelial lining fluid (ELF) was shown (area under the curve (AUC) ELF /AUC plasma = 88%). Plasma clearance was 48% lower in lung infected mice compared to healthy mice. For two out of five strains studied, a delay in growth (∼5 h) was observed in vivo but not in vitro. The mathematical model enabled integration of lung PK to drive mouse PK and pharmacodynamics. Simulations predicted that 30 mg/kg of apramycin once daily would result in bacteriostasis in patients. Apramycin is a candidate for treatment of carbapenem-resistant gram-negative pneumonia as demonstrated in an integrated modeling framework for three bacterial species. We show that mathematical modelling is a useful tool for simultaneous inclusion of multiple data sources, notably plasma and lung in vivo PK and simulation of expected scenarios in a clinical setting, notably lung infections. [ABSTRACT FROM AUTHOR]

Background: Apramycin is under development for human use as EBL-1003, a crystalline free base of apramycin, in face of increasing incidence of multidrug-resistant bacteria. Both toxicity and cross-resistance, commonly seen for other aminoglycosides, appear relatively low owing to its distinct chemical structure.Objectives: To perform a population pharmacokinetic (PPK) analysis and predict an efficacious dose based on data from a first-in-human Phase I trial.Methods: The drug was administered intravenously over 30 min in five ascending-dose groups ranging from 0.3 to 30 mg/kg. Plasma and urine samples were collected from 30 healthy volunteers. PPK model development was performed stepwise and the final model was used for PTA analysis.Results: A mammillary four-compartment PPK model, with linear elimination and a renal fractional excretion of 90%, described the data. Apramycin clearance was proportional to the absolute estimated glomerular filtration rate (eGFR). All fixed effect parameters were allometrically scaled to total body weight (TBW). Clearance and steady-state volume of distribution were estimated to 5.5 L/h and 16 L, respectively, for a typical individual with absolute eGFR of 124 mL/min and TBW of 70 kg. PTA analyses demonstrated that the anticipated efficacious dose (30 mg/kg daily, 30 min intravenous infusion) reaches a probability of 96.4% for a free AUC/MIC target of 40, given an MIC of 8 mg/L, in a virtual Phase II patient population with an absolute eGFR extrapolated to 80 mL/min.Conclusions: The results support further Phase II clinical trials with apramycin at an anticipated efficacious dose of 30 mg/kg once daily. [ABSTRACT FROM AUTHOR]

• One hundred and thirty-two publications on pharmacokinetic/pharmacodynamic (PKPD) models for time courses of antibiotic effects were reviewed. • Points to consider when developing such PKPD models are discussed. • Models describing bacterial growth, regrowth, drug effects and interactions are shown. • An interactive online table enables the review of PKPD models from 1963 to 2021. Pharmacokinetic/pharmacodynamic (PKPD) models have emerged as valuable tools for the characterization and translation of antibiotic effects, and consequently for drug development and therapy. In contrast to traditional PKPD concepts for antibiotics such as minimum inhibitory concentration and PKPD indices, PKPD models enable description of the continuous, often species- or population-dependent time course of antimicrobial effects, commonly considering mechanistic pathogen- and drug-related knowledge. This review presents a comprehensive overview of previously published PKPD models describing repeated measurements of antibiotic effects. A literature review was conducted to identify PKPD models based on: (i) antibiotic compounds; (ii) Gram-positive or Gram-negative pathogens; and (iii) in-vitro or in-vivo longitudinal colony-forming unit data. In total, 132 publications were identified that were released between 1963 and 2021, including models based on exposure to single antibiotics (n =92) and drug combinations (n =40), as well as different experimental settings (e.g. static/traditional dynamic/hollow-fibre/animal time-kill models, n =90/27/32/11). An interactive, fully searchable table summarizes the details of each model, namely variants and mechanistic elements of PKPD submodels capturing observed bacterial growth, regrowth, drug effects and interactions. Furthermore, the review highlights the main purposes of PKPD model development, including the translation of preclinical PKPD to clinical settings, and the assessment of varied dosing regimens and patient characteristics for their impact on clinical antibiotic effects. In summary, this comprehensive overview of PKPD models will assist in identifying PKPD modelling strategies to describe growth, killing, regrowth and interaction patterns for pathogen–antibiotic combinations over time, and ultimately facilitate model-informed antibiotic translation, dosing and drug development. [ABSTRACT FROM AUTHOR]

Purpose: Flucloxacillin is a β-lactam penicillin commonly used in the treatment of bone and soft tissue infections. In a recent porcine study, we found surprisingly low time for which the free concentration was maintained above the minimal inhibitory concentration (fT>MIC) in bone and soft tissue, following flucloxacillin oral (PO) and intravenous (IV) administration at 1g every 6h (q6h). In addition to plasma, sampling was obtained from subcutaneous tissue, knee joint, cancellous bone and cortical bone, using microdialysis. To identify flucloxacillin dosing regimens that result in theoretically therapeutic concentrations, we developed a population pharmacokinetic (PK) model for the porcine data, and combined it with a human flucloxacillin population PK model for simulations. Methods: A four-compartment model was developed, and various dosing regimens and modes of administration were simulated. Predicted concentrations were compared to %fT>MIC (0.5 mg/L and 2 mg/L). Results: Continuous infusion (CI) resulted in higher %fT>MIC compared to intermittent administration. For intermittent IV dosing (4, 8 and 12g/24h), fT>MIC (0.5 mg/L) was ≥70% in plasma, and ranged between 42-96% in the sampled tissue in a typical individual. By applying CI, 4g/day was sufficient to achieve ≥98% fT>MIC (0.5 mg/L) in all sampled tissues. For MIC 2 mg/L, ≥50% fT>MIC was only achieved in plasma at CI 8 and 12g/24h and IV 3g q6h. Conclusions: To reach efficacious flucloxacillin bone and tissue concentrations, dose increment or continuous infusion needs to be considered. [ABSTRACT FROM AUTHOR]

Astegolimab is a fully human immunoglobulin G2 monoclonal antibody that binds to the ST2 receptor and blocks the interleukin‐33 signaling. It was evaluated in patients with uncontrolled severe asthma in the phase 2b study (Zenyatta) at doses of 70, 210, and 490 mg subcutaneously every 4 weeks for 52 weeks. This work aimed to characterize astegolimab pharmacokinetics, identify influential covariates contributing to its interindividual variability, and make a descriptive assessment of the exposure‐response relationships. A population pharmacokinetic model was developed using data from 368 patients in the Zenyatta study. Predicted average steady‐state concentration was used in the subsequent exposure‐response analyses, which evaluated efficacy (asthma exacerbation rate) and biomarker end points including forced expiratory volume in 1 second, fraction exhaled nitric oxide, blood eosinophils, and soluble ST2. A 2‐compartment disposition model with first‐order elimination and first‐order absorption best described the astegolimab pharmacokinetics. The relative bioavailability for the 70‐mg dose was 15.3% lower. Baseline body weight, estimated glomerular filtration rate, and eosinophils were statistically correlated with pharmacokinetic parameters, but only body weight had a clinically meaningful influence on the steady‐state exposure (ratios exceeding 0.8‐1.25). The exposure‐response of efficacy and biomarkers were generally flat with a weak trend in favor of the highest dose/exposure. This study characterized astegolimab pharmacokinetics in patients with asthma and showed typical pharmacokinetic behavior as a monoclonal antibody–based drug. The exposure‐response analyses suggested the highest dose tested in the Zenyatta study (490 mg every 4 weeks) performed close to the maximum effect, and no additional response may be expected above it. [ABSTRACT FROM AUTHOR]

In oncology trials, overall survival ( OS) is considered the most reliable and preferred endpoint to evaluate the benefit of drug treatment. Other relevant variables are also collected from patients for a given drug and its indication, and it is important to characterize the dynamic effects and links between these variables in order to improve the speed and efficiency of clinical oncology drug development. However, the drug-induced effects and causal relationships are often difficult to interpret because of temporal differences. To address this, population pharmacokinetic-pharmacodynamic ( PKPD) modelling and parametric time-to-event ( TTE) models are becoming more frequently applied. Population PKPD and TTE models allow for exploration towards describing the data, understanding the disease and drug action over time, investigating relevance of biomarkers, quantifying patient variability and in designing successful trials. In addition, development of models characterizing both desired and adverse effects in a modelling framework support exploration of risk-benefit of different dosing schedules. In this review, we have summarized population PKPD modelling analyses describing tumour, tumour marker and biomarker responses, as well as adverse effects, from anticancer drug treatment data. Various model-based metrics used to drive PD response and predict OS for oncology drugs and their indications are also discussed. [ABSTRACT FROM AUTHOR]

Objectives: To describe the population genetics and antibiotic resistance gene distribution of carbapenem-resistant Acinetobacter baumannii (CRAB) isolates causing infections in three Mediterranean countries.Methods: Isolates were collected during the 2013-17 AIDA clinical trial in six hospitals in Israel, Greece and Italy. WGS, bioinformatic characterization and antibiotic resistance profiling were performed.Results: In the 247 CRAB isolates characterized in this study, ST distribution varied by country: 29/31 (93.5%) Greek isolates, 34/41 (82.9%) Italian isolates and 70/175 (40.0%) Israeli isolates belonged to ST2. The identified ST2 isolates included eight distinct clades: 2C, 2D and 2H were significantly more common in Italy, while 2F was unique to Greece. The uncommon ST3 was not present among Greek isolates and constituted only 5/41 (12%) Italian isolates. On the other hand, it was much more common among Israeli isolates: 78/175 (44.6%) belonged to ST3. The vast majority of isolates, 240/247 (97.2%), were found to harbour acquired carbapenemases, primarily blaOXA-23. The chromosomal oxaAb (blaOXA-51-like) and ampC genes characteristic of this organism were also ubiquitous. Most (96.4%) ST3 isolates carried a broad-host-range plasmid IncP1α.Conclusions: The geographical differences in CRAB populations support the theory that clonal spread of CRAB leads to endemicity in hospitals and regions. The close association between antibiotic resistance genes and clades, and between plasmids and STs, suggest that de novo creation of MDR A. baumannii is rare. The clustering of antibiotic resistance genes and plasmids that is unique to each clade/ST, and nearly uniform within clades/STs, suggests that horizontal transmission is rare but crucial to the clade's/ST's success. [ABSTRACT FROM AUTHOR]

Aims Although genetic, clinical and demographic factors have been shown to explain approximately half of the inter-individual variability in warfarin dose requirement in adults, less is known about causes of dose variability in children. This study aimed to identify and quantify major genetic, clinical and demographic sources of warfarin dose variability in children using modelling and simulation. Methods Clinical, demographic and genetic data from 163 children with a median age of 6.3 years (range 0.06-18.9 years), covering over 183 years of warfarin therapy and 6445 INR observations were used to update and optimize a published adult pharmacometric warfarin model for use in children. Results Genotype effects in children were found to be comparable with what has been reported for adults, with CYP2C9 explaining up to a four-fold difference in dose ( CYP2C9 *1/*1 vs. *3/*3) and VKORC1 explaining up to a two-fold difference in dose ( VKORC1 G/G vs. A/A), respectively. The relationship between bodyweight and warfarin dose was non-linear, with a three-fold difference in dose for a four-fold difference in bodyweight. In addition, age, baseline and target INR, and time since initiation of therapy, but not CYP4F2 genotype, had a significant impact on typical warfarin dose requirements in children. Conclusions The updated model provides quantitative estimates of major clinical, demographic and genetic factors impacting on warfarin dose variability in children. With this new knowledge more individualized dosing regimens can be developed and prospectively evaluated in the pursuit of improving both efficacy and safety of warfarin therapy in children. [ABSTRACT FROM AUTHOR]

Objectives An optimized dosing regimen of the prodrug of colistin, colistin methanesulphonate (CMS), against resistant Pseudomonas aeruginosa is needed to ensure effective bacterial killing. The objectives of this study were to develop a pharmacokinetic (PK)/pharmacodynamic (PD) model that characterizes the time course of the antibacterial activity of colistin against P. aeruginosa in a static in vitro system and to perform simulations of different dosing regimens and dosing algorithms to evaluate the effect of interindividual variability and interoccasion variability in PK on bacterial killing. Methods Static in vitro time–kill curve experiments were conducted on two different strains of P. aeruginosa (MIC 1 and 1.5 mg/L). Mechanism-based PK/PD models were fitted in NONMEM7 and the final model was combined with a previously developed population PK model of CMS and colistin to perform simulations of variability based on different dosing algorithms. Results A model with compartments for growing and resting bacteria, with a function allowing the maximal bacterial killing of colistin to reduce upon increasing colistin exposure, characterized both the fast bactericidal effect and the adaptive resistance. The variability in PK was shown to translate into pronounced interoccasion variability in bacterial killing. A flat fixed loading dose was demonstrated to result in less variability than an algorithm based on weight. Conclusions The developed PK/PD model described the growth, death and resistance development of P. aeruginosa in response to colistin for two different strains. Based on simulations, a flat fixed loading dose followed by an 8 or 12 hourly maintenance dose with an infusion duration of up to 2 h appeared adequate. [ABSTRACT FROM PUBLISHER]

Purpose: Model-based methods can predict pediatric exposure and support initial dose selection. The aim of this study was to evaluate the performance of allometric scaling of population pharmacokinetic (popPK) versus physiologically based pharmacokinetic (PBPK) models in predicting the exposure of tyrosine kinase inhibitors (TKIs) for pediatric patients (≥ 2 years), based on adult data. The drugs imatinib, sunitinib and pazopanib were selected as case studies due to their complex PK profiles including high inter-patient variability, active metabolites, time-varying clearances and non-linear absorption.Pediatric concentration measurements and adult popPK models were derived from the literature. Adult PBPK models were generated in PK-Sim® using available physicochemical properties, calibrated to adult data when needed. PBPK and popPK models for the pediatric populations were translated from the models for adults and were used to simulate concentration-time profiles that were compared to the observed values.Ten pediatric datasets were collected from the literature. While both types of models captured the concentration-time profiles of imatinib, its active metabolite, sunitinib and pazopanib, the PBPK models underestimated sunitinib metabolite concentrations. In contrast, allometrically scaled popPK simulations accurately predicted all concentration-time profiles. Trough concentration (Ctrough) predictions from the popPK model fell within a 2-fold range for all compounds, while 3 out of 5 PBPK predictions exceeded this range for the imatinib and sunitinib metabolite concentrations.Based on the identified case studies it appears that allometric scaling of popPK models is better suited to predict exposure of TKIs in pediatric patients ≥ 2 years. This advantage may be attributed to the stable enzyme expression patterns from 2 years old onwards, which can be easily related to adult levels through allometric scaling. In some instances, both methods performed comparably. Understanding where discrepancies between the model methods arise, can further inform model development and ultimately support pediatric dose selection.Methods: Model-based methods can predict pediatric exposure and support initial dose selection. The aim of this study was to evaluate the performance of allometric scaling of population pharmacokinetic (popPK) versus physiologically based pharmacokinetic (PBPK) models in predicting the exposure of tyrosine kinase inhibitors (TKIs) for pediatric patients (≥ 2 years), based on adult data. The drugs imatinib, sunitinib and pazopanib were selected as case studies due to their complex PK profiles including high inter-patient variability, active metabolites, time-varying clearances and non-linear absorption.Pediatric concentration measurements and adult popPK models were derived from the literature. Adult PBPK models were generated in PK-Sim® using available physicochemical properties, calibrated to adult data when needed. PBPK and popPK models for the pediatric populations were translated from the models for adults and were used to simulate concentration-time profiles that were compared to the observed values.Ten pediatric datasets were collected from the literature. While both types of models captured the concentration-time profiles of imatinib, its active metabolite, sunitinib and pazopanib, the PBPK models underestimated sunitinib metabolite concentrations. In contrast, allometrically scaled popPK simulations accurately predicted all concentration-time profiles. Trough concentration (Ctrough) predictions from the popPK model fell within a 2-fold range for all compounds, while 3 out of 5 PBPK predictions exceeded this range for the imatinib and sunitinib metabolite concentrations.Based on the identified case studies it appears that allometric scaling of popPK models is better suited to predict exposure of TKIs in pediatric patients ≥ 2 years. This advantage may be attributed to the stable enzyme expression patterns from 2 years old onwards, which can be easily related to adult levels through allometric scaling. In some instances, both methods performed comparably. Understanding where discrepancies between the model methods arise, can further inform model development and ultimately support pediatric dose selection.Results: Model-based methods can predict pediatric exposure and support initial dose selection. The aim of this study was to evaluate the performance of allometric scaling of population pharmacokinetic (popPK) versus physiologically based pharmacokinetic (PBPK) models in predicting the exposure of tyrosine kinase inhibitors (TKIs) for pediatric patients (≥ 2 years), based on adult data. The drugs imatinib, sunitinib and pazopanib were selected as case studies due to their complex PK profiles including high inter-patient variability, active metabolites, time-varying clearances and non-linear absorption.Pediatric concentration measurements and adult popPK models were derived from the literature. Adult PBPK models were generated in PK-Sim® using available physicochemical properties, calibrated to adult data when needed. PBPK and popPK models for the pediatric populations were translated from the models for adults and were used to simulate concentration-time profiles that were compared to the observed values.Ten pediatric datasets were collected from the literature. While both types of models captured the concentration-time profiles of imatinib, its active metabolite, sunitinib and pazopanib, the PBPK models underestimated sunitinib metabolite concentrations. In contrast, allometrically scaled popPK simulations accurately predicted all concentration-time profiles. Trough concentration (Ctrough) predictions from the popPK model fell within a 2-fold range for all compounds, while 3 out of 5 PBPK predictions exceeded this range for the imatinib and sunitinib metabolite concentrations.Based on the identified case studies it appears that allometric scaling of popPK models is better suited to predict exposure of TKIs in pediatric patients ≥ 2 years. This advantage may be attributed to the stable enzyme expression patterns from 2 years old onwards, which can be easily related to adult levels through allometric scaling. In some instances, both methods performed comparably. Understanding where discrepancies between the model methods arise, can further inform model development and ultimately support pediatric dose selection.Conclusion: Model-based methods can predict pediatric exposure and support initial dose selection. The aim of this study was to evaluate the performance of allometric scaling of population pharmacokinetic (popPK) versus physiologically based pharmacokinetic (PBPK) models in predicting the exposure of tyrosine kinase inhibitors (TKIs) for pediatric patients (≥ 2 years), based on adult data. The drugs imatinib, sunitinib and pazopanib were selected as case studies due to their complex PK profiles including high inter-patient variability, active metabolites, time-varying clearances and non-linear absorption.Pediatric concentration measurements and adult popPK models were derived from the literature. Adult PBPK models were generated in PK-Sim® using available physicochemical properties, calibrated to adult data when needed. PBPK and popPK models for the pediatric populations were translated from the models for adults and were used to simulate concentration-time profiles that were compared to the observed values.Ten pediatric datasets were collected from the literature. While both types of models captured the concentration-time profiles of imatinib, its active metabolite, sunitinib and pazopanib, the PBPK models underestimated sunitinib metabolite concentrations. In contrast, allometrically scaled popPK simulations accurately predicted all concentration-time profiles. Trough concentration (Ctrough) predictions from the popPK model fell within a 2-fold range for all compounds, while 3 out of 5 PBPK predictions exceeded this range for the imatinib and sunitinib metabolite concentrations.Based on the identified case studies it appears that allometric scaling of popPK models is better suited to predict exposure of TKIs in pediatric patients ≥ 2 years. This advantage may be attributed to the stable enzyme expression patterns from 2 years old onwards, which can be easily related to adult levels through allometric scaling. In some instances, both methods performed comparably. Understanding where discrepancies between the model methods arise, can further inform model development and ultimately support pediatric dose selection. [ABSTRACT FROM AUTHOR]

Quantitative characterization of evolving tumor resistance under targeted treatment could help identify novel treatment schedules, which may improve the outcome of anti-cancer treatment. In this study, a mathematical model which considers various clonal populations and evolving treatment resistance was developed. With parameter values fitted to the data or informed by literature data, the model could capture previously reported tumor burden dynamics and mutant KRAS levels in circulating tumor DNA (ctDNA) of patients with metastatic colorectal cancer treated with panitumumab. Treatment schedules, including a continuous schedule, intermittent schedules incorporating treatment holidays, and adaptive schedules guided by ctDNA measurements were evaluated using simulations. Compared with the continuous regimen, the simulated intermittent regimen which consisted of 8-week treatment and 4-week suspension prolonged median progression-free survival (PFS) of the simulated population from 36 to 44 weeks. The median time period in which the tumor size stayed below the baseline level (TTS

Mortality among patients with carbapenem-resistant Acinetobacter baumannii (CRAB) infections varies between studies. We examined whether in vivo fitness of CRAB strains is associated with clinical outcomes in patients with CRAB infections. Isolates were collected from patients enrolled in the AIDA trial with hospital-acquired pneumonia, bloodstream infections and/or urinary tract infections caused by CRAB. The primary outcome was 14-day clinical failure, defined as failure to meet all criteria: alive; haemodynamically stable; improved or stable Sequential Organ Failure Assessment (SOFA) score; improved or stable oxygenation; and microbiological cure of bacteraemia. The secondary outcome was 14-day mortality. We tested in vivo growth using a neutropenic murine thigh infection model. Fitness was defined based on the CFU count 24 hours after injection of an inoculum of 105 CFU. We used mixed-effects logistic regression to test the association between fitness and the two outcomes. The sample included 266 patients; 215 (80.8%) experienced clinical failure. CRAB fitness ranged from 5.23 to 10.08 log CFU/g. The odds of clinical failure increased by 62% for every 1-log CFU/g increase in fitness (OR 1.62, 95% CI 1.04–2.52). After adjusting for age, Charlson score, SOFA score and acquisition in the intensive care unit, fitness remained significant (adjusted OR 1.63, 95% CI 1.03–2.59). CRAB fitness had a similar effect on 14-day mortailty, although the association was not statistically significant (OR 1.56, 95% CI 0.95–2.57). It became significant after adjusting for age, Charlson score, SOFA score and recent surgery (adjusted OR 1.88, 95% CI 1.09–3.25). In vivo CRAB fitness was associated with clinical failure in patients with CRAB infection. [ABSTRACT FROM AUTHOR]

Background. Methyl parathion is classed as an extremely hazardous pesticide with a rodent LD50 of 6 to 24 mg/kg. It has been banned in numerous countries, but there are few reports of acute methyl parathion poisoning. Methods. Plasma cholinesterase and acetylcholinesterase were measured in blood. Methyl parathion and the major metabolite 4-nitrophenol where measured in serum and urine. Based on the available concentration-time data, the pharmacokinetic parameters of methyl parathion were estimated for this patient. Case Report and Results. A 29-year-old male ingested 50 to 100mL (12 to 24 g) of methyl parathion causing delayed and prolonged suppression of acetylcholinesterase but almost no clinical effects. Absorption was predicted to last for 30 hours and the bioavailability appeared to be very low. Conclusions. Although it is feasible the patient ingested much less, a tenth of his alleged ingestion dose is more than the oral LD50 in rats. Methyl parathion appears to be less toxic in humans than parathion for similar amounts ingested, which is not consistent with the two pesticides having similar rodent LD50. [ABSTRACT FROM AUTHOR]

For many oncological agents, myelosuppression is the dose-limiting toxicity and the quantitative characterisation of the relationship between drug dose, plasma concentration and haematological toxicity is of importance in the drug development. Mechanism-based population pharmacokinetic-pharmacodynamic models have been developed for this purpose and the applications of these in candidate selection, first-in-man studies, prodrug and formulation development, dose finding, schedule optimisation, assessing influence of modifying agents, drug combination studies, subgroup identification and feedback individualisation are reviewed. [ABSTRACT FROM AUTHOR]

Novel therapeutics are urgently required for the treatment of carbapenem-resistant Acinetobacter baumannii (CRAB) causing critical infections with high mortality. Here we assessed the therapeutic potential of the clinical-stage drug candidate EBL-1003 (crystalline free base of apramycin) in the treatment of CRAB lung infections. The genotypic and phenotypic susceptibility of CRAB clinical isolates to aminoglycosides and colistin was assessed by database mining and broth microdilution. The therapeutic potential was assessed by target attainment simulations on the basis of time–kill kinetics, a murine lung infection model, comparative pharmacokinetic analysis in plasma, epithelial lining fluid (ELF) and lung tissue, and pharmacokinetic/pharmacodynamic (PKPD) modelling. Resistance gene annotations of 5451 CRAB genomes deposited in the National Database of Antibiotic Resistant Organisms (NDARO) suggested >99.9% of genotypic susceptibility to apramycin. Low susceptibility to standard-of-care aminoglycosides and high susceptibility to EBL-1003 were confirmed by antimicrobial susceptibility testing of 100 A. baumannii isolates. Time–kill experiments and a mouse lung infection model with the extremely drug-resistant CRAB strain AR Bank #0282 resulted in rapid 4-log CFU reduction both in vitro and in vivo. A single dose of 125 mg/kg EBL-1003 in CRAB-infected mice resulted in an AUC of 339 h × μg/mL in plasma and 299 h × μg/mL in ELF, suggesting a lung penetration of 88%. PKPD simulations suggested a previously predicted dose of 30 mg/kg in patients (creatinine clearance (CLCr) = 80 mL/min) to result in >99% probability of –2 log target attainment for MICs up to 16 μg/mL. This study provides proof of concept for the efficacy of EBL-1003 in the treatment of CRAB lung infections. Broad in vitro coverage, rapid killing, potent in vivo efficacy, and a high probability of target attainment render EBL-1003 a strong therapeutic candidate for a priority pathogen for which treatment options are very limited. [ABSTRACT FROM AUTHOR]

Information on individual lesion dynamics and organ location are often ignored in pharmacometric modeling analyses of tumor response. Typically, the sum of their longest diameters is utilized. Herein, a tumor growth inhibition model was developed for describing the individual lesion time‐course data from 183 patients with metastatic HER2‐negative breast cancer receiving docetaxel. The interindividual variability (IIV), interlesion variability (ILV), and interorgan variability of parameters describing the lesion time‐courses were evaluated. Additionally, a model describing the probability of new lesion appearance and a time‐to‐event model for overall survival (OS), were developed. Before treatment initiation, the lesions were largest in the soft tissues and smallest in the lungs, and associated with a significant IIV and ILV. The tumor growth rate was 2.6 times higher in the breasts and liver, compared with other metastatic sites. The docetaxel drug effect in the liver, breasts, and soft tissues was greater than or equal to 1.2 times higher compared with other organs. The time‐course of the largest lesion, the presence of at least 3 liver lesions, and the time since study enrollment, increased the probability of new lesion appearance. New lesion appearance, along with the time to growth and time‐course of the largest lesion at baseline, were identified as the best predictors of OS. This tumor modeling approach, incorporating individual lesion dynamics, provided a more complete understanding of heterogeneity in tumor growth and drug effect in different organs. Thus, there may be potential to tailor treatments based on lesion location, lesion size, and early lesion response to provide better clinical outcomes. [ABSTRACT FROM AUTHOR]

• The mechanism-based model characterized UV1-induced tumor shrinkage over time. • The model quantified relationships between SLD, immune response and OS. • Increased model-predicted CD4+ T cells, increased the probability of immune response. • High baseline SLD and its relative change from nadir were predictors of reduced OS. • The framework can serve as a model-informed drug development tool via simulations. Therapeutic cancer vaccines are novel immuno-therapeutics, aiming to improve clinical outcomes with other immunotherapies. However, obstacles to their successful clinical development remain, which model-informed drug development approaches may address. UV1 is a telomerase based therapeutic cancer vaccine candidate being investigated in phase I clinical trials for multiple indications. We developed a mechanism-based model structure, using a nonlinear mixed‐effects modeling techniques, based on longitudinal tumor sizes (sum of the longest diameters, SLD), UV1-specific immunological assessment (stimulation index, SI) and overall survival (OS) data obtained from a UV1 phase I trial including non-small cell lung cancer (NSCLC) patients and a phase I/IIa trial including malignant melanoma (MM) patients. The final structure comprised a mechanistic tumor growth dynamics (TGD) model, a model describing the probability of observing a UV1-specific immune response (SI ≥ 3) and a time-to-event model for OS. The mechanistic TGD model accounted for the interplay between the vaccine peptides, immune system and tumor. The model-predicted UV1-specific effector CD4+ T cells induced tumor shrinkage with half-lives of 103 and 154 days in NSCLC and MM patients, respectively. The probability of observing a UV1-specific immune response was mainly driven by the model-predicted UV1-specific effector and memory CD4+ T cells. A high baseline SLD and a high relative increase from nadir were identified as main predictors for a reduced OS in NSCLC and MM patients, respectively. Our model predictions highlighted that additional maintenance doses, i.e. UV1 administration for longer periods, may result in more sustained tumor size shrinkage. [ABSTRACT FROM AUTHOR]

Background We evaluated whether carbapenem-colistin combination therapy reduces the emergence of colistin resistance, compared to colistin monotherapy, when given to patients with infections due to carbapenem-resistant Gram-negative organisms. Methods This is a pre-planned analysis of a secondary outcome from a randomized, controlled trial comparing colistin monotherapy with colistin-meropenem combination for the treatment of severe infections caused by carbapenem-resistant, colistin-susceptible Gram-negative bacteria. We evaluated rectal swabs taken on Day 7 or later for the presence of new colistin-resistant (ColR) isolates. We evaluated the emergence of any ColR isolate and the emergence of ColR Enterobacteriaceae (ColR-E). Results Data were available for 214 patients for the primary analysis; emergent ColR organisms were detected in 22 (10.3%). No difference was observed between patients randomized to treatment with colistin monotherapy (10/106, 9.4%) versus patients randomized to colistin-meropenem combination therapy (12/108, 11.1%; P =.669). ColR-E organisms were detected in 18/249 (7.2%) patients available for analysis. No difference was observed between the 2 treatment arms (colistin monotherapy 6/128 [4.7%] vs combination therapy 12/121 [9.9%]; P =.111). Enterobacteriaceae, as the index isolate, was found to be associated with development of ColR-E (hazard ratio, 3.875; 95% confidence interval, 1.475–10.184; P =.006). Conclusions Carbapenem-colistin combination therapy did not reduce the incidence of colistin resistance emergence in patients with infections due to carbapenem-resistant organisms. Further studies are necessary to elucidate the development of colistin resistance and methods for its prevention. [ABSTRACT FROM AUTHOR]

This mini-review discusses the pharmacodynamics of immune-related biomarkers in the area of bacterial infectious diseases that could be of interest from a pharmacokinetic (PK) and pharmacokinetic/pharmacodynamic (PK/PD) perspective in the evaluation of treatment effects. The host response to an infection is often poorly defined both in preclinical assessments and in clinical practice when it comes to characterisation of PK and PK/PD relationships. Through population modelling, the time courses and variability of immune response variables can be quantified. Incorporation of such biomarker information into PK and PK/PD models may guide the evaluation of individual response to treatment (right antibiotic, more antibiotic, less antibiotic) and when to stop treatment. Furthermore, translation of results from preclinical systems to clinical scenarios may be improved with the incorporation of biomarker information. Potential biomarkers for these purposes are discussed and a few modelling examples are provided. [ABSTRACT FROM AUTHOR]