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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]

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]

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]

Prolactin elevation is a side effect of all currently available D2 receptor antagonists used in the treatment of schizophrenia. Prolactin elevation is the result of a direct antagonistic D2 effect blocking the tonic inhibition of prolactin release by dopamine. The aims of this work were to assess the correlation between in vitro estimates of D2 receptor affinity and pharmacokinetic-pharmacodynamic model-based estimates obtained from analysis of clinical data using an agonist-antagonist interaction (AAI) model and to assess the value of such a correlation in early prediction of full prolactin time profiles. A population model describing longitudinal prolactin data was fitted to clinical data from 16 clinical phases 1 and 3 trials including five different compounds. Pharmacokinetic data were modeled for each compound and the prolactin model was both fitted in per-compound fits aswell as simultaneously to all prolactin data. Estimates of prolactin elevating potency were compared to corresponding in vitro values and their predictability was evaluated through model-based simulations. The model successfully described the prolactin time course for all compounds. Estimates derived from experimental preclinical data and the model fit of the clinical data were strongly correlated (p<0.001), and simulations adequately predicted the prolactin elevation in five out of six compounds. The AAI model has the potential to be used in drug development to predict prolactin response for a given exposure of D2 antagonists using routinely produced preclinical data. [ABSTRACT FROM AUTHOR]

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]

A previously developed semi-physiological model of chemotherapy-induced myelosuppression has shown consistent system-related parameter and inter-individual variability (IIV) estimates across drugs. A requirement for dose individualization to be useful is relatively low variability between treatment courses (inter-occasion variability [IOV]) in relation to IIV. The objective of this study was to evaluate and compare magnitudes of IOV and IIV in myelosuppression model parameters across six different anti-cancer drug treatments. Neutrophil counts from several treatment courses following therapy with docetaxel, paclitaxel, epirubicin-docetaxel, 5-fluorouracil–epirubicin–cyclophosphamide, topotecan, and etoposide were included in the analysis. The myelosuppression model was fitted to the data using NONMEM VI. IOV in the model parameters baseline neutrophil counts (ANC0), mean transit time through the non-mitotic maturation chain (mean transit time [MTT]), and the parameter describing the concentration–effect relationship (slope), were evaluated for statistical significance ( P < 0.001). Inter-occasion variability in MTT was significant for all the investigated datasets, except for topotecan, and was of similar magnitude (8–16 CV%). IOV in slope was significant for docetaxel, topotecan, and etoposide (19–39 CV%). For all six investigated datasets, the IOV in myelosuppression parameters was lower than the IIV. There was no indication of systematic shifts in the system- or drug sensitivity-related parameters over time across datasets. This study indicates that the semi-physiological model of chemotherapy-induced myelosuppression has potential to be used for prediction of the time-course of myelosuppression in future courses and is, thereby, a valuable step towards individually tailored anticancer drug therapy. [ABSTRACT FROM AUTHOR]

Simple Summary: This study investigated the correlation between scalp cooling used to prevent chemotherapy-induced alopecia and the pharmacokinetics of paclitaxel in female cancer patients with a solid tumor. In a prospective cohort study, 14 patients who were treated with weekly paclitaxel and scalp cooling were able to undergo pharmacokinetic sampling of paclitaxel during one cycle of treatment. In comparison to a control cohort of 24 patients treated with weekly paclitaxel without scalp cooling, our data showed that scalp cooling used concomitantly with one course of paclitaxel did not reduce or increase the clearance of paclitaxel. Therefore, it is unlikely that scalp cooling influences paclitaxel efficacy or toxicity. Finally, despite scalp cooling, half of the patients in our study developed a form of hair loss. Importantly, neither an association with difference in paclitaxel clearance nor change in hair loss was found. Chemotherapy-induced alopecia (CIA), a side effect with high impact, can be prevented by cooling the scalp during the administration of some cytotoxic drugs. However, the effects of this prolonged scalp cooling on the pharmacokinetics of chemotherapy have never been investigated. In this study, we compared the pharmacokinetics of the widely used chemotherapeutic agent paclitaxel (weekly dose of 80–100 mg/m2) in female patients with solid tumors using concomitant scalp cooling (n = 14) or not (n = 24). Blood samples were collected in all patients for pharmacokinetic analyses up to 6 h after one course of paclitaxel administration. The primary endpoint was the clearance (L/h) of paclitaxel. Paclitaxel clearance—expressed as relative difference in geometric means—was 6.8% (90% CI: −16.7% to 4.4%) lower when paclitaxel was administered with concomitant scalp cooling versus paclitaxel infusions without scalp cooling. Within the subgroup of patients using scalp cooling, paclitaxel clearance was not statistically significantly different between patients with CIA (alopecia grade 1 or 2) and those without CIA. Hence, scalp cooling did not negatively influence the clearance of paclitaxel treatment. [ABSTRACT FROM AUTHOR]

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]

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]

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]