Individual Parameter Estimates Research Articles

Does difference between label and actual potency of factor VIII concentrate affect pharmacokinetic-guided dosing of replacement therapy in haemophilia A?

BackgroundTo account for interindividual variability in the pharmacokinetics (PK) of factor concentrates, PK‐guided dosing is increasingly implemented in haemophilia patients. Calculations are based on provided label potency, but legislation allows a potency difference of ±20% between label and actual potency. It is unknown if these differences affect PK guidance.AimExplore the effects of potency differences on individual factor VIII (FVIII) PK parameters and the prediction of FVIII trough levels of dosing regimens.MethodsWe analyzed individual preoperative PK profiling data from severe and moderate haemophilia A patients included in the OPTI‐CLOT randomized controlled trial. Label and actual potency were compared, with data on potency provided by pharmaceutical companies. For both potencies, individual PK parameters were estimated and concentration‐time curves were constructed by nonlinear mixed‐effects modelling. Finally, we explored the effect of both the identified and the maximum legislated potency difference on predicted FVIII trough levels infused in a low and high dose regimen.ResultsIn 45/50 included patients, actual potency was higher than its label potency. The median potency difference was 6.0% (range ‐9.2% to 18.4%) and resulted in varying individual PK parameter estimates but practically identical FVIII concentration‐time curves. As expected, predicted FVIII trough levels were linearly correlated to the actual dose.ConclusionIt is not necessary to take potency differences into account when applying PK guidance of FVIII concentrates in haemophilia A patients. However, when the patient is switched to another FVIII batch after PK‐guided dosing, trough levels may deviate ±20% from calculations based on label dose.

Population PBPK modeling using parametric and nonparametric methods of the Simcyp Simulator, and Bayesian samplers

Physiologically‐based pharmacokinetic (PBPK) models usually include a large number of parameters whose values are obtained using in vitro to in vivo extrapolation. However, such extrapolations can be uncertain and may benefit from inclusion of evidence from clinical observations via parametric inference. When clinical interindividual variability is high, or the data sparse, it is essential to use a population pharmacokinetics inferential framework to estimate unknown or uncertain parameters. Several approaches are available for that purpose, but their relative advantages for PBPK modeling are unclear. We compare the results obtained using a minimal PBPK model of a canonical theophylline dataset with quasi‐random parametric expectation maximization (QRPEM), nonparametric adaptive grid estimation (NPAG), Bayesian Metropolis‐Hastings (MH), and Hamiltonian Markov Chain Monte Carlo sampling. QRPEM and NPAG gave consistent population and individual parameter estimates, mostly agreeing with Bayesian estimates. MH simulations ran faster than the others methods, which together had similar performance.

Free and Open-Source Posologyr Software for Bayesian Dose Individualization: An Extensive Validation on Simulated Data.

Model-informed precision dosing is being increasingly used to improve therapeutic drug monitoring. To meet this need, several tools have been developed, but open-source software remains uncommon. Posologyr is a free and open-source R package developed to enable Bayesian individual parameter estimation and dose individualization. Before using it for clinical practice, performance validation is mandatory. The estimation functions implemented in posologyr were benchmarked against reference software products on a wide variety of models and pharmacokinetic profiles: 35 population pharmacokinetic models, with 4.000 simulated subjects by model. Maximum A Posteriori (MAP) estimates were compared to NONMEM post hoc estimates, and full posterior distributions were compared to Monolix conditional distribution estimates. The performance of MAP estimation was excellent in 98.7% of the cases. Considering the full posterior distributions of individual parameters, the bias on dosage adjustment proposals was acceptable in 97% of cases with a median bias of 0.65%. These results confirmed the ability of posologyr to serve as a basis for the development of future Bayesian dose individualization tools.

Imputation and system modeling of acid-base state parameters for different groups of patients

The paper investigated the possibility of correct replacement of missing values in sets of acid-base state in the artery and vein in different groups of patients with different outcomes of the disease: “discharged”, “died”, “transferred to another medical institution”, as well as prospects for the application of individual optimization multidimensional estimates of these biomedical parameters in the form of projections on a one-dimensional space. The relevance of the above tasks is determined by the need for the full use of medical data in the analysis of large repositories of information of medical organizations and the provision of verified multidimensional assessments of biomedical systems to doctors from a large range of patient health indicators. A statistical method has been applied to verify the correctness of imputation data sets using discriminant analysis procedures. Further, the imputed data set was processed to obtain a symmetric correlation matrix optimized in a certain way and the accompanying logarithms of criterion functions that are individual system assessments of the condition of each patient in different groups of patients at a certain point in the study. After that, to identify differences in the logarithms of the criterion functions of the acid-base state parameters between groups of patients with different outcomes, the authors used the method of calculating multidimensional Hotelling T2 statistics. The correctness of the application of discriminant analysis procedures to verify the imputation of data sets is shown. Differences in the logarithms of the criteria functions of the acid-base state indicators between venous and arterial blood by patient outcome groups were revealed. Significant differences in the parameters of acid-base state based on the multidimensional statistics of T2 Hotelling between groups of patients with different outcomes were revealed. It is found that data imputation significantly increases the volume and representativeness of the sample under study. It is demonstrated that the substituted data make it possible to carry out a systematic statistical assessment of the totality of body parameters based on the calculation of the logarithms of the criterion functions of the acid-base state. Such logarithms make it possible to reliably distinguish patients in different groups of patients by outcomes in three groups: “discharged”, “deceased”, “transferred to another medical institution”. 100 % differences of biochemical parameters according to the multivariate T2 Hotelling statistics between these three groups of patients with COVID-19 are shown. The results of the study can be applied in the development of information systems of individual medical biochemical and hematological devices and analyzers and corresponding artificial intelligence systems in the future.

Consumer preference for fish safety inspection in Bangladesh

Consumers are entitled to eat safe food, so authorities should ensure that this right is preserved by enacting regulations and ensuring compliance through enforcement activities. Safety inspection is key to the enforcement system. Therefore, this paper presents an analysis of consumer responses to a regulatory scheme for safe seafood. The regulations consist of national and local authority enforcement and subsequent follow up activities to ensure that all wild and farmed fish in all product formats are safe. We collected primary data from two major cities in Bangladesh, Dhaka and Chittagong. The data were analysed using conditional and generic multinomial logit models to identify different utility ratios. We find that consumers expect safety control information at a low mental cost or effort. They value fish safety inspection highly in their affective reaction, whereas this value is lacking in their cognitive response. The individual parameter estimates show that consumers' preferences for both wild and farmed fish are significantly positive. They are most likely to reject frozen fish and be willing to pay less for it. Wild-caught fish creates utility for consumers without any food safety inspection, but this is not the case for farmed, frozen fish. The lack of authorised food safety inspection significantly decreases utility, suggesting a positive market potential, particularly for farmed fish with local authority safety certification.

Population Pharmacokinetic and Pharmacodynamic Analysis of Polmacoxib in Healthy Volunteers and Patients With Osteoarthritis

PurposePolmacoxib, a new coxib dually inhibiting cyclooxygenase-2 and carbonic anhydrase I/II, was recently approved for osteoarthritis treatment in South Korea. This study explored the population pharmacokinetic and pharmacodynamic characteristics of polmacoxib. MethodsNonlinear mixed-effects modeling was performed using pooled pharmacokinetic data from a Phase I study in healthy individuals and pharmacokinetic properties and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) data from a Phase IIb study in patients with osteoarthritis. Pharmacodynamic models for WOMAC were sequentially fit using individual pharmacokinetic parameter estimates. FindingsPolmacoxib concentrations in whole blood were adequately described by the 2-compartment model, with mixed zero- and first-order absorption kinetics. Iron concentration was the significant covariate associated with clearance of polmacoxib. The relationship between the whole blood concentration of polmacoxib and WOMAC was best described by a 2-effect compartment model that consisted of central and peripheral compartments with the rate constant of 0.408 min−1 for distribution to the central effect compartment. A decrease in WOMAC was linked to the central effect site compartment concentration through an ordinary maximum effect model with an effect site concentration needed to achieve 50% of the maximum effect of 508 ng/mL. ImplicationsThe current model accurately characterized the pharmacokinetic and pharmacodynamic properties of polmacoxib and could provide a basis for individualized drug therapy.

Robust K-PD model for activated clotting time prediction and UFH dose individualisation during cardiopulmonary bypass

Background and objectiveActivated clotting time (ACT) is a point-of-care test used to monitor the effect of unfractionated heparin (UFH) during cardiopulmonary bypass (CPB). This test sometimes returns aberrant values, which can lead to the administration of an inappropriate dosing regimen. The development of a population-robust K-PD model of UFH could allow the individualisation and automation of UFH therapy during CPB. MethodsWe conducted a prospective observational study to collect ACT measurements from patients undergoing surgery using CPB. The ACT data were split into a development and validation cohort. The development cohort was used to estimate a standard and robust population K-PD model characterised by a residual error following a normal distribution and student's t-distribution. The ACT prediction performance using Bayesian estimates of individual K-PD parameters was evaluated by comparing predicted versus observed ACTs. Using estimates of the robust K-PD model, a Bayesian individualisation strategy to automate UFH administration was proposed and evaluated using Monte Carlo simulations. ResultsA total of 295 patients were included in the study, and 1561 ACTs were collected. In patients without outlier values, Bayesian estimates (based on four ACT measurements) from both standard and robust K-PD models had similar performances, with a median prediction bias close to 0 s. In patients with outlier measurements, the use of the robust K-PD model greatly improved the prediction bias and root-mean-square error (RMSE), with a mean prediction bias of 3.25 s, IQR = [-19.9; 46.03] versus -86 s IQR = [-135.7; -63.8] for the standard model. Monte Carlo simulations showed that the robust Bayesian individualisation strategy allowed the ACT to be maintained above the target using only two to three ACT measurements. ConclusionsThe use of a robust K-PD model reduced prediction bias and RMSE in patients with outlier ACT measurements. The Bayesian individualisation strategy using robust estimates of individual parameters may help automate UFH dosing regimens. Proper clinical validation is warranted before its use in daily clinical practice.

A note on out-of-sample prediction, marginal effects computations, and temporal testing with random parameters crash-injury severity models

Random parameters logit models have become an increasingly popular method to investigate crash-injury severities in recent years. However, there remain potential elements of the approach that need clarification including out-of-sample prediction, the calculation of marginal effects, and temporal instability testing. In this study, four models are considered for comparison: a fixed parameters multinomial logit model; a random parameters logit model; a random parameters logit model with heterogeneity in means; and a random parameters logit model with heterogeneity in means and variances. A full simulation of random parameters is undertaken for out-of-sample injury-severity predictions, and the prediction accuracy of the estimated models was assessed. Results indicate, not surprisingly, that the random parameters logit model with heterogeneity in the means and variances outperformed other models in predictive performance. Following this, two alternative methods for computing marginal effects are considered: one using Monte Carlo simulation and the other using individual estimates of random parameters. The empirical results indicate that both methods produced defensible results since the full distributions of random parameters are considered. Finally, two testing alternatives for temporal instability are evaluated: a global test across all time periods being considered, and a pairwise time-period to time-period comparison. It is shown that the pairwise comparison can provide more detailed insights into possible temporal variability.

Call (and pay) the midwife: A discrete choice experiment on mothers’ preferences and their willingness to pay for midwifery care

BackgroundMothers in Germany are entitled to midwifery care; however, they face a lack of skilled professionals. While the reliability of the access to midwifery is of great public interest, we know little about clients’ preferences. ObjectivesWe conduct a discrete choice experiment to study preferences and willingness to accept copayment for the entire scope of midwifery care (pregnancy, delivery, and postnatal). Thereby, we aim to provide policy recommendations for priority settings in times of scarcity. Furthermore, we evaluate to what extent midwives’ education matters to parents and assess the degree of support for the latest Midwifery Reform Act that transfers education from vocational schools to universities. DesignDiscrete choice experiment with separated adaptive dual response. SettingsOnline Survey promoted through Facebook to parents in Germany. Respondents2080 respondents completed the experiment. They all have or are expecting at least one natural child, mainly born between 2018 and 2020 (87%). The average respondent is female (99%), 33 years old, with a university degree (50%). MethodsWe use a d-optimal fractional factorial design and obtain individual parameter estimates through a Multinomial Logit analysis with Hierarchical Bayes estimation techniques. We calculate willingness to pay and importance weights and simulate uptake probabilities for different packages of care. To avoid extreme choice behavior, we apply separated adaptive dual response. ResultsHome visits during the postnatal phase are most important (importance weight 50%); online support is demanded when no personal support is available. We find that 1:1 care during delivery is highly preferred, but one midwife supporting two women intrapartum is still acceptable. The midwife´s education plays a minor role with an importance weight of 3%; however, we find a preference for midwives trained at vocational schools rather than at universities. ConclusionsIn times of scarcity, postnatal care in the form of home visits should be prioritized over pregnancy counseling, and online services should be promoted as an add-on but not as a substitute for personal support. There is a high level of willingness to accept co-financing to ensure the availability of services usually covered by health insurance.

Validity and Reliability of Hydraulic-Analogy Bioenergetic Models in Sprint Roller Skiing.

Purpose: To develop a method for individual parameter estimation of four hydraulic-analogy bioenergetic models and to assess the validity and reliability of these models’ prediction of aerobic and anaerobic metabolic utilization during sprint roller-skiing.Methods: Eleven elite cross-country skiers performed two treadmill roller-skiing time trials on a course consisting of three flat sections interspersed by two uphill sections. Aerobic and anaerobic metabolic rate contributions, external power output, and gross efficiency were determined. Two versions each (fixed or free maximal aerobic metabolic rate) of a two-tank hydraulic-analogy bioenergetic model (2TM-fixed and 2TM-free) and a more complex three-tank model (3TM-fixed and 3TM-free) were programmed into MATLAB. The aerobic metabolic rate (MRae) and the accumulated anaerobic energy expenditure (Ean,acc) from the first time trial (STT1) together with a gray-box model in MATLAB, were used to estimate the bioenergetic model parameters. Validity was assessed by simulation of each bioenergetic model using the estimated parameters from STT1 and the total metabolic rate (MRtot) in the second time trial (STT2).Results: The validity and reliability of the parameter estimation method based on STT1 revealed valid and reliable overall results for all the four models vs. measurement data with the 2TM-free model being the most valid. Mean differences in model-vs.-measured MRae ranged between -0.005 and 0.016 kW with typical errors between 0.002 and 0.009 kW. Mean differences in Ean,acc at STT termination ranged between −4.3 and 0.5 kJ and typical errors were between 0.6 and 2.1 kJ. The root mean square error (RMSE) for 2TM-free on the instantaneous STT1 data was 0.05 kW for MRae and 0.61 kJ for Ean,acc, which was lower than the other three models (all P < 0.05). Compared to the results in STT1, the validity and reliability of each individually adapted bioenergetic model was worse during STT2 with models underpredicting MRae and overpredicting Ean,acc vs. measurement data (all P < 0.05). Moreover, the 2TM-free had the lowest RMSEs during STT2.Conclusion: The 2TM-free provided the highest validity and reliability in MRae and Ean,acc for both the parameter estimation in STT1 and the model validity and reliability evaluation in the succeeding STT2.

Using Observed Residual Error Structure Yields the Best Estimates of Individual Growth Parameters

Obtaining the best possible estimates of individual growth parameters is essential in studies of physiology, fisheries management, and conservation of natural resources since growth is a key component of population dynamics. In the present work, we use data of an endangered fish species to demonstrate the importance of selecting the right data error structure when fitting growth models in multimodel inference. The totoaba (Totoaba macdonaldi) is a fish species endemic to the Gulf of California increasingly studied in recent times due to a perceived threat of extinction. Previous works estimated individual growth using the von Bertalanffy model assuming a constant variance of length-at-age. Here, we reanalyze the same data under five different variance assumptions to fit the von Bertalanffy and Gompertz models. We found consistent significant differences between the constant and nonconstant error structure scenarios and provide an example of the consequences using the growth performance index ϕ′ to show how using the wrong error structure can produce growth parameter values that can lead to biased conclusions. Based on these results, for totoaba and other related species, we recommend using the observed error structure to obtain the individual growth parameters.

Exploring Factors Associated with Cyclist Injury Severity in Vehicle-Electric Bicycle Crashes Based on a Random Parameter Logit Model

Electric bicyclists are vulnerable road users and play an important role in traffic safety. The focus of this research is on analyzing cyclists’ injury severity in vehicle-electric bicycle collisions. It is an exploratory analysis that was conducted based on samples obtained from video data provided by the police of Xi’an China. Three types of severity include fatal, injury, and property-damage-only (PDO). A random parameter logit (RPL) model was specified to gain more insights into factors related to the injury severity level, including human behaviors, vehicle characteristics, roadway attributes, and environmental conditions. Some factors not included in previous research were introduced into this study, especially precrash behaviors of drivers and cyclists. The direct pseudo-elasticity effects of variables were compared to investigate the stability of individual parameter estimates on the severity categories. The results indicated that variables that significantly increment the probability of fatal accidents were as follows: driver violation behaviors (speeding, red-light violation, driving in the opposite direction), cyclist violation behaviors (speeding, red-light violation), day of time (nighttime), visibility restrictions (fixed obstacles), and vehicle type (larger bus, small truck, and larger truck). Based on these findings, we suggested measures such as strengthening law enforcement by installing cameras, implementing zero tolerance for cyclist violations, promoting education by completing training courses for cyclists, and enhancing traffic safety awareness through educational activities. The research results can provide a theoretical basis for formulating strategies to improve cyclist safety.

Predicting Inter-individual Variability During Lipid Resuscitation of Bupivacaine Cardiotoxicity in Rats: A Virtual Population Modeling Study.

IntroductionIntravenous lipid emulsions (ILE) have been credited for successful resuscitation in drug intoxication cases where other cardiac life-support methods have failed. However, inter-individual variability can function as a confounder that challenges our ability to define the scope of efficacy for lipid interventions, particularly as relevant data are scarce. To address this challenge, we developed a quantitative systems pharmacology model to predict outcome variability and shed light on causal mechanisms in a virtual population of rats subjected to bupivacaine toxicity and ILE intervention.Materials and MethodsWe combined a physiologically based pharmacokinetic–pharmacodynamic model with data from a small study in Sprague-Dawley rats to characterize individual-specific cardiac responses to lipid infusion. We used the resulting individual parameter estimates to posit a population distribution of responses to lipid infusion. On that basis, we constructed a large virtual population of rats (N = 10,000) undergoing lipid therapy following bupivacaine cardiotoxicity.ResultsUsing unsupervised clustering to assign resuscitation endpoints, our simulations predicted that treatment with a 30% lipid emulsion increases bupivacaine median lethal dose (LD50) by 46% when compared with a simulated control fluid. Prior experimental findings indicated an LD50 increase of 48%. Causal analysis of the population data suggested that muscle accumulation rather than liver accumulation of bupivacaine drives survival outcomes.ConclusionOur results represent a successful prediction of complex, dynamic physiological outcomes over a virtual population. Despite being informed by very limited data, our mechanistic model predicted a plausible range of treatment outcomes that accurately predicts changes in LD50 when extrapolated to putatively toxic doses of bupivacaine. Furthermore, causal analysis of the predicted survival outcomes indicated a critical synergy between scavenging and direct cardiotonic mechanisms of ILE action.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40268-021-00353-4.

Abstract 1366: Integrated population modeling of loncastuximab tesirine (Lonca) exposure in B-cell non-Hodgkin lymphoma (B-NHL)

Abstract Lonca is an antibody (Ab) drug conjugate comprising a humanized monoclonal Ab directed against B-cell antigen CD19, conjugated with a potent pyrrolobenzodiazepine (PBD) dimer toxin. In a Phase 1 (NCT02669017) study in relapsed or refractory (R/R) B-NHL, a recommended Phase 2 dosing regimen for Lonca of 150 µg/kg once every 3 weeks (Q3W) for two doses, followed by 75 µg/kg Q3W was identified then evaluated in a Phase 2 (NCT03589469) study in R/R diffuse large B-cell lymphoma (DLBCL). A population pharmacokinetic (PPK) model was developed to characterize the pharmacokinetics (PK) of the Lonca dosing regimen and evaluate exposure covariates. The integrated PPK model was used to describe drug concentrations for Lonca PBD-conjugated Ab (5,301 samples), total Ab (5,241 samples), and unconjugated warhead SG3199 (239 samples) in serum from 328 patients. Samples were taken pre-dose, at end of infusion and at pre-determined timepoints post-infusion throughout the trials. Analysis was performed using non-linear mixed-effects modeling (NONMEM, v7.4 [ICON Solutions]). Subgroup analyses were conducted on model-predicted exposure metrics derived from simulation of Lonca PK profiles based on empirical Bayesian estimates of individual PK parameters. Concentration-time data of Lonca PBD-conjugated Ab and total Ab were best characterized by a two-compartment model with parallel linear clearance (0.218 L/day), and a time-dependent clearance component which approached zero by ~15 weeks. The estimated volume of distribution of central compartment was 3.86 L and the estimated typical half-life at steady state of Lonca is ~3 weeks. The effect of body weight, age, sex, race, renal impairment, drug formulation, anti-drug Ab, ECOG, and concomitant P-gp inhibitors did not show clinically important influence on exposure (&amp;lt; ±30% change relative to reference). Based on model-predicted Cycle 1 average concentration (Cavg) patients with low albumin (&amp;lt;35 g/L, n=49) had 52% lower Cavg than patients with normal albumin levels (≥35 g/L, n=279). Patients with non-DLBCL (n=44) had 61% lower Cavg than patients with DLBCL (n=284). Trends of lower exposures with baseline mild/moderate hepatic impairment and ECOG status &amp;gt;1 were noted but did not appear to be of clinical relevance. In conclusion, Lonca exposure was well described by a two-compartment model with linear and time-dependent clearance; the latter is thought to reflect reduction of tumor cells. Lonca exposure was lower in non-DLBCL patients, and lower in patients with hypoalbuminemia secondary to enhanced protein clearance in patients with critical illness. Overall, modeling demonstrates rapid attainment of steady-state exposure for Lonca given 150 µg/kg Q3W for two doses, and a sustaining profile for Lonca given 75 µg/kg Q3W thereafter for patients with DLBCL. Citation Format: Melhem Solh, Juan Pablo Alderuccio, Anastasios Stathis, David Ungar, Sam Liao, Lisa Khouri, Xiaoyan Zhang, Joseph Boni. Integrated population modeling of loncastuximab tesirine (Lonca) exposure in B-cell non-Hodgkin lymphoma (B-NHL) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1366.

Nyström-based spectral clustering using airborne LiDAR point cloud data for individual tree segmentation

ABSTRACT The spectral clustering method has notable advantages in segmentation. But the high computational complexity and time consuming limit its application in large-scale and dense airborne Light Detection and Ranging (LiDAR) point cloud data. We proposed the Nyström-based spectral clustering (NSC) algorithm to decrease the computational burden. This novel NSC method showed accurate and rapid in individual tree segmentation using point cloud data. The K-nearest neighbour-based sampling (KNNS) was proposed for the Nyström approximation of voxels to improve the efficiency. The NSC algorithm showed good performance for 32 plots in China and Europe. The overall matching rate and extraction rate of proposed algorithm reached 69% and 103%. For all trees located by Global Navigation Satellite System (GNSS) calibrated tape-measures, the tree height regression of the matching results showed an value of 0.88 and a relative root mean square error (RMSE) of 5.97%. For all trees located by GNSS calibrated total-station measures, the values were 0.89 and 4.49%. The method also showed good performance in a benchmark dataset with an improvement of 7% for the average matching rate. The results demonstrate that the proposed NSC algorithm provides an accurate individual tree segmentation and parameter estimation using airborne LiDAR point cloud data.

Population pharmacokinetics (Pop PK) of MYL-1402O (a proposed biosimilar to bevacizumab) and reference product (Avastin) in patients with nonsquamous non-small cell lung cancer (nsNSCLC).

e21087 Background: MYL-1402O (MYL) is a proposed biosimilar to bevacizumab reference product. A multicenter, double blind randomized, phase 3 study compared the efficacy, safety, PK, and immunogenicity of MYL and Avastin in patients with Stage IV metastatic nsNSCLC. Patients received either MYL or reference product, in combination with carboplatin-paclitaxel up to 18 weeks (6 cycles) followed by monotherapy for up to an additional 24 weeks (8 cycles). The objective was to develop a Pop PK model based on data from a phase 3 study pooled with a single dose healthy volunteer phase 1 study data; to assess PK linearity across the dose levels of 1 mg/kg to 15 mg/kg in 2 clinical studies; to assess the PK similarity of MYL and reference product in patients with nsNSCLC; and to explore potential covariates to account for variability in Pop PK model parameters. Methods: A Pop PK model was developed based on preliminary analyses of MYL phase 1 data and published population analyses of reference product using a 2-compartment linear model (Han K et al., 2016). Individual empiric Bayesian parameter estimates of nsNSCLC patients were used to predict PK measures reflecting exposure to drug and were compared qualitatively between treatments. Results: The data subset used for model development consisted of 8724 records from 771 subjects. Population PK analyses indicated no differences between PK profiles of patients in the MYL and reference product arms. Importantly, treatment was not a significant covariate of clearance ( P = 0.453) or volume of the central compartment ( P = 0.161) using the likelihood ratio χ2 test. Model-based steady state exposure measures, predicted based on the final model for all patients, were also similar between treatment arms (Table). Conclusions: The model supported linear PK at clinical doses in patients with nsNSCLC; there were no clinically relevant/significant differences between the PK of MYL and reference product; and the findings were consistent with the PK study in normal, healthy volunteers. Bayesian Parameter Clinical trial information: 2015-005141-32. [Table: see text]

An Analysis of Glucose Effectiveness in Subjects With or Without Type 2 Diabetes via Hierarchical Modeling.

Glucose effectiveness, defined as the ability of glucose itself to increase glucose utilization and inhibit hepatic glucose production, is an important mechanism maintaining normoglycemia. We conducted a minimal modeling analysis of glucose effectiveness at zero insulin (GEZI) using intravenous glucose tolerance test data from subjects with type 2 diabetes (T2D, n=154) and non-diabetic (ND) subjects (n=343). A hierarchical statistical analysis was performed, which provided a formal mechanism for pooling the data from all study subjects, to yield a single composite population model that quantifies the role of subject specific characteristics such as weight, height, age, sex, and glucose tolerance. Based on the resulting composite population model, GEZI was reduced from 0.021 min–1 (standard error – 0.00078 min–1) in the ND population to 0.011 min–1 (standard error – 0.00045 min–1) in T2D. The resulting model was also employed to calculate the proportion of the non–insulin-dependent net glucose uptake in each subject receiving an intravenous glucose load. Based on individual parameter estimates, the fraction of total glucose disposal independent of insulin was 72.8% ± 12.0% in the 238 ND subjects over the course of the experiment, indicating the major contribution to the whole-body glucose clearance under non-diabetic conditions. This fraction was significantly reduced to 48.8% ± 16.9% in the 30 T2D subjects, although still accounting for approximately half of the total in the T2D population based on our modeling analysis. Given the potential application of glucose effectiveness as a predictor of glucose intolerance and as a potential therapeutic target for treating diabetes, more investigations of glucose effectiveness in other disease conditions can be conducted using the hierarchical modeling framework reported herein.

Investigating the Impact of Gastric Emptying on Pharmacokinetic Parameters Using Delay Differential Equations and Principal Component Analysis.

Losartan presents multiple peaks after single oral administration, which can be attributed to gastric emptying. The aim of this study was to describe the multiple peak phenomenon of losartan using a delay differential model and a model with sine function. The impact of gastric emptying on pharmacokinetic parameters was investigated by applying principal component analysis to the individual parameter estimates. Using MonolixTM, two population pharmacokinetic models were developed to describe the multiple peak phenomenon; the first using delay differential equations and the second using a sine function. Matlab® delay differential equation solver was used to arithmetically solve both functions. Principal component analysis and all statistical analyses were performed in the R language. The description of losartan multiple peaks can be achieved by the use of either delay differential equations or typical sine wave functions. Principal component analysis unveiled the impact of gastric emptying on the pharmacokinetic parameters. In the case of the delay differential equation model, a negative relationship was found between the constant delay tau1 and the parameters reflecting rate and extent of absorption (i.e., area under the curve [AUC], peak plasma concentration [Cmax], and the absorption rate constant). Similar results were obtained from the sine model, where a higher amplitude and lower period (i.e., higher frequency) of gastric emptying were associated with higher AUC and Cmax values. The observed multiple peaks for certain drugs like losartan can be attributed to gastric emptying. Parameters describing gastric emptying can be associated with pharmacokinetic metrics like AUC and Cmax.

Abstract PS18-23: Population pharmacokinetics (Pop PK) of MYL-1401O (a trastuzumab biosimilar) and reference trastuzumab (Herceptin®) in patients with HER2-positive metastatic breast cancer (mBC)

Abstract Background: Mylan trastuzumab (MYL-1401O) is a biosimilar to trastuzumab (Herceptin®). A multicenter, double blind, randomized, phase III study (HERITAGE) compared the efficacy, safety, Pop PK and immunogenicity of MYL-1401O and trastuzumab in patients with HER2-positive mBC. Patients were randomized 1:1 to receive either MYL-1401O or Herceptin®, in combination with taxane Q3W for 24 weeks followed by monotherapy until unacceptable toxicity, disease progression or early discontinuation.Objectives: The objectives of the Pop PK analysis were to compare the Pop PK derived AUC, Cmax, clearance, Vd, and T1/2 profiles of MYL1401O and Herceptin® and to perform an exploratory assessment of the impact of shed extracellular domain (ECD) fragments of the HER2 receptor (HER2/ ECD) on PK parameters.Methods: One end of infusion PK sample was collected at Cycle 1 and Cycle 6, and 1 trough sample per cycle from all patients; additional samples were taken in a Pop PK subset (MYL-1401O: 45; Herceptin®: 37) in the first dosing interval and at subsequent times. Pop PK modeling was performed using NONMEM. Individual patient empiric Bayesian parameter estimates were used to estimate PK measures. The impact of HER2/ECD presence on PK levels was evaluated in the primary covariate analysis. Results: Two hundred forty-five (245) patients in the PK population received MYL-1401O, while 240 received Herceptin® of which 482 were included in the base model Pop PK analysis. 3170 concentration records with sufficient information were included in the Pop PK analysis. There were no notable demographic differences between the treatment groups. Bayesian parameter based exposure estimates at or near steady-state dosing were comparable between treatments, confirming similar PK (Table 1). Treatment was not a significant covariate of clearance (p=0.177) or volume of the central compartment (p=0.584) using the likelihood ratio Chi-square test.The test-to reference mean ratios of Cmin for Cycle 1 and Cycle 6 (end of cycle) were 103.11(90% CI = 90.61, 117.33) and 104.16 (90% CI = 94.00, 115.42), respectively.The HER2/ECD concentrations were a significant covariate of trastuzumab clearance. Conclusion: Pop PK profiles of MYL-1401O vs. Herceptin® were similar in patients with HER2positive mBC. Model-based exposure measures were similar between treatments. HER2/ECD concentrations were a strong determinant of trastuzumab clearance, and clearance was similar between treatments. The observed trough concentrations were similar between treatments at the end of Cycle 1 and at Cycle 6.Clinical trial identification: EudraCT Number: 2011-001965-42Legal entity responsible for the study: Mylan GmbHFunding: Mylan GmbH Table 1:Bayesian Parameter Based Exposure Estimates at Cycle 6MYL-1401O (N=245)Herceptin®(N=240)Total(N=485)Parametern*213202415Dose (mg)Mean (SD)420.70 (90.46)421.25 (97.67)420.97 (93.92)Clearance (L/day)0.27 (0.10)0.28 (0.08)0.27 (0.09)Volume of Central Compartment (L)3.16 (0.60)3.20 (0.60)3.18 (0.60)Volume at Steady State (L)6.36 (1.19)6.32 (1.14)6.34 (1.16)AUC (ug*day/mL)40501.40 (13037.04)38816.90 (11966.26)39681.40 (12540.58)Dose-normalized AUC (ug*day/mL/mg)98.50 (30.56)94.41 (28.90)96.51 (29.80)Cmax,ss (ug/mL)177.00 (37.76)171.52 (34.61)174.33 (36.32)Dose-normalized Cmax,ss (ug/mL/mg)0.43 (0.10)0.42 (0.09)0.43 (0.10)Half-life (day)Median (SD)25.12 (7.50)24.34 (6.89)24.74 (7.21)*concentrations below lower limit of quantification and samples before first dose with values &amp;gt;zero were excluded from Pop PK analysis, as were some patients with incomplete information for covariates significant in the final model. Citation Format: Joel Owen, Russell Rackley, Mark Liu, Adolfo Fuentes-Alburo, Tazeen Idris, Subramanian Loganathan, Abhijit Barve, Cornelius F. Waller, Hope S. Rugo. Population pharmacokinetics (Pop PK) of MYL-1401O (a trastuzumab biosimilar) and reference trastuzumab (Herceptin®) in patients with HER2-positive metastatic breast cancer (mBC) [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS18-23.

Individual Transition Label Noise Logistic Regression in Binary Classification for Incorrectly Labeled Data

We consider a binary classification problem in the case where some observations in the training data are incorrectly labeled. In the presence of such label noise, conventional classification fails to obtain a classifier to be generalized to a population. In this work, we investigate label noise logistic regression and explain how it works with noisy training data. We demonstrate that, when label transition probabilities are correctly provided, label noise logistic regression satisfies the Fisher consistency and enjoys the property of robustness. To accommodate various label noise mechanisms that occur in practice, we propose a flexible label noise model in a nonparametric way. We propose an efficient algorithm under the thresholding rule for individual parameter estimation. We demonstrate its performance under synthetic and real examples. We discuss the proposed flexible transition model is also useful for robust classification.