The Role of Model Master Files for Sharing, Acceptance, and Communication with FDA.

With the evolving role of Model Integrated Evidence (MIE) in generic drug development and regulatory applications, the need for improving Model Sharing, Acceptance, and Communication with the FDA is warranted. Model Master File (MMF) refers to a quantitative model or a modeling platform that has undergone sufficient model Verification & Validation to be recognized as sharable intellectual property that is acceptable for regulatory purposes. MMF provides a framework for regulatorily acceptable modeling practice, which can be used with confidence to support MIE by both the industry and the U.S. Food and Drug Administration (FDA). In 2022, the FDA and the Center for Research on Complex Generics (CRCG) hosted a virtual public workshop to discuss the best practices for utilizing modeling approaches to support generic product development. This report summarizes the presentations and panel discussions of the workshop symposium entitled "Model Sharing, Acceptance, and Communication with the FDA". The symposium and this report serve as a kick-off discussion for further utilities of MMF and best practices of utilizing MMF in drug development and regulatory submissions. The potential advantages of MMFs have garnered acknowledgment from model developers, industries, and the FDA throughout the workshop. To foster a unified comprehension of MMFs and establish best practices for their application, further dialogue and cooperation among stakeholders are imperative. To this end, a subsequent workshop is scheduled for May 2-3, 2024, in Rockville, Maryland, aiming to delve into the practical facets and best practices of MMFs pertinent to regulatory submissions involving modeling and simulation methodologies.

pyDarwin: A Machine Learning Enhanced Automated Nonlinear Mixed-Effect Model Selection Toolbox.

pyDarwin is an open-source Python package for nonlinear mixed-effect model selection. pyDarwin combines machine-learning algorithms and NONMEM to perform a global search for the optimal model in a user-defined model search space. Compared with traditional stepwise search, pyDarwin provides an efficient platform for conducting an objective, robust, less labor-intensive model selection process without compromising model interpretability. In this tutorial, we will begin by introducing the essential components and concepts within the package. Subsequently, we will provide an overview of the pyDarwin modeling workflow and the necessary files needed for model selection. To illustrate the entire process, we will conclude with an example utilizing quetiapine clinical data.

Population Pharmacokinetic Modeling of Lucitanib in Patients with Advanced Cancer.

BACKGROUND: Lucitanib is an oral, potent, selective inhibitor of the tyrosine kinase activity of vascular endothelial growth factor receptors 1‒3, fibroblast growth factor receptors 1‒3, and platelet-derived growth factor receptors alpha/beta. OBJECTIVE: We aimed to develop a population pharmacokinetics (PopPK) model for lucitanib in patients with advanced cancers. METHODS: PopPK analyses were based on intensive and sparse oral pharmacokinetic data from 5 phase 1/2 clinical studies of lucitanib in a total of 403 patients with advanced cancers. Lucitanib was administered at 5‒30 mg daily doses as 1 of 2 immediate-release oral formulations: a film-coated tablet or a hard gelatin capsule. RESULTS: Lucitanib pharmacokinetics were best described by a 2-compartment model with zero-order release into the dosing compartment, followed by first-order absorption and first-order elimination. Large between-subject pharmacokinetic variability was partially explained by body weight. No effects of demographics or tumor type on lucitanib pharmacokinetics were observed. The model suggested that the formulation impacted release duration (tablet, 0.243 h; capsule, 0.814 h), but the effect was not considered clinically meaningful. No statistically significant effects were detected for concomitant cytochrome P450 (CYP) 3A4 inhibitors or inducers, CYP2C8 or P-glycoprotein inhibitors, serum albumin, mild/moderate renal impairment, or mild hepatic impairment. Concomitant proton pump inhibitors had no clinically significant effect on lucitanib absorption. CONCLUSIONS: The PopPK model adequately described lucitanib pharmacokinetics. High between-subject pharmacokinetic variability supports a safety-based dose-titration strategy currently being used in an ongoing clinical study of lucitanib to optimize drug exposure and clinical benefit. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01283945, NCT02053636, ISRCTN23201971, NCT02202746, NCT02109016.

Pharmacokinetic Drug-Drug Interaction Studies Between Trilaciclib and Midazolam, Metformin, Rifampin, Itraconazole, and Topotecan in Healthy Volunteers and Patients with Extensive-Stage Small-Cell Lung Cancer.

BACKGROUND AND OBJECTIVE: Trilaciclib is a cyclin-dependent kinase 4/6 inhibitor indicated to decrease the incidence of chemotherapy-induced myelosuppression in patients with extensive-stage small-cell lung cancer. Trilaciclib is a substrate and time-dependent inhibitor of cytochrome P450 3A4 and an inhibitor of multidrug and toxin extrusion 1, multidrug and toxin extrusion 2-K, organic cation transporter 1, and organic cation transporter 2. Here, we investigate the pharmacokinetic drug-drug interaction potential of trilaciclib. METHODS: Two phase I studies were conducted as prospective, open-label, fixed-sequence drug-drug interaction studies in healthy subjects (n = 57, n = 20) to investigate potential interactions between intravenously administered trilaciclib (200 or 240 mg/m2) and orally administered midazolam (5 mg), metformin (1000 mg), itraconazole (200 mg), and rifampin (600 mg). A population pharmacokinetic model was fit to phase Ib/IIa data in patients with extensive-stage small-cell lung cancer (n = 114) to assess the impact of trilaciclib dose and exposure (area under the plasma concentration-time curve) on topotecan clearance. RESULTS: Coadministration with trilaciclib had minimal effects on the exposure (area under the plasma concentration-time curve from time 0 to infinity) of midazolam (geometric least-square mean ratio [GMR] vs midazolam alone 1.065; 90% confidence interval [CI] 0.984-1.154) but statistically significantly increased plasma exposure (GMR 1.654; 90% CI 1.472-1.858) and decreased renal clearance (GMR 0.633; 90% CI 0.572-0.701) of metformin. Coadministration of trilaciclib with rifampin or itraconazole decreased trilaciclib area under the plasma concentration-time curve from time 0 to infinity by 17.3% (GMR 0.827; 90% CI 0.785-0.871) and 14.0% (GMR 0.860; 0.820-0.902), respectively, vs trilaciclib alone. Population pharmacokinetic modeling showed no significant effect of trilaciclib on topotecan clearance. CONCLUSIONS: Overall, the drug-drug interaction and safety profiles of trilaciclib in these studies support its continued use in patients with extensive-stage small-cell lung cancer. CLINICAL TRIAL REGISTRATION: Study 106: EudraCT number: 2019-002303-18; Study 114: not applicable; Study 03: Clinicaltrials.org: NCT02514447; August 2015.

Population pharmacokinetic modeling and simulations of berotralstat for prophylactic treatment of attacks of hereditary angioedema.

Hereditary angioedema (HAE) is an autosomal dominant disorder characterized by recurrent episodes of swelling of the skin, larynx, gastrointestinal tract, genitals, and extremities that can be disruptive to patient quality of life. Dysregulation of plasma kallikrein activity leads to increased production and accumulation of bradykinin in HAE and causes attacks of angioedema. Plasma kallikrein is a serine protease essential for the formation of bradykinin. Berotralstat is a potent, highly selective, orally bioavailable small-molecule plasma kallikrein inhibitor that has been approved to prevent attacks of HAE in adults and children 12 years of age and older. Population pharmacokinetic (PK) analyses were conducted to describe the PK of berotralstat (BCX7353; Orladeyo™ ) and to evaluate the covariates that may explain variability in PK. The PK of berotralstat were characterized by population PK modeling of data from 13 clinical studies and a total of 771 healthy subjects and patients with HAE. The PK profile was well described by a three-compartment model with first-order absorption including an absorption lag time and linear elimination. Among the covariates tested, the effects of bilirubin and food were found not to be clinically significant and were removed from the model. Covariate analysis indicated significant effects of dose on bioavailability and weight on berotralstat clearance and volume. Despite the covariate effect of weight, simulations in adolescents and adults who were underweight, low weight, and overweight demonstrated similar predicted exposures to those observed at therapeutic doses in a clinical trial. Therefore, no dose adjustment is required in these HAE patient subpopulations.

Development of a genetic algorithm and NONMEM workbench for automating and improving population pharmacokinetic/pharmacodynamic model selection.

The current approach to selection of a population PK/PD model is inherently flawed as it fails to account for interactions between structural, covariate, and statistical parameters. Further, the current approach requires significant manual and redundant model modifications that heavily lend themselves to automation. Within the discipline of numerical optimization it falls into the "local search" category. Genetic algorithms are a class of algorithms inspired by the mathematics of evolution. GAs are general, powerful, robust algorithms and can be used to find global optimal solutions for difficult problems even in the presence of non-differentiable functions, as is the case in the discrete nature of including/excluding model components in search of the best performing mixed-effects PK/PD model. A genetic algorithm implemented in an R-based NONMEM workbench for identification of near optimal models is presented. In addition to the GA capabilities, the workbench supports modeling efforts by: (1) Organizing and displaying models in tabular format, allowing the user to sort, filter, edit, create, and delete models seamlessly, (2) displaying run results, parameter estimates and precisions, (3) integrating xpose4 and PsN to facilitate generation of model diagnostic plots and run PsN scripts, (4) running regression models between post-hoc parameter estimates and covariates. This approach will further facilitate the scientist to shift efforts to focus on model evaluation, hypotheses generation, and interpretation and applications of resulting models.

Population pharmacokinetics and exposure-response modeling for evinacumab in homozygous familial hypercholesterolemia.

Evinacumab, an angiopoietin-like protein 3 (ANGPTL3) inhibitor, has been shown to significantly reduce low-density lipoprotein cholesterol (LDL-C) in patients with homozygous familial hypercholesterolemia (HoFH). This work characterized the population pharmacokinetics (PK)/pharmacodynamics (PD) of evinacumab using pooled phase III clinical data. Total evinacumab PK were described by a two-compartment model with combined linear and saturable (Michaelis-Menten) elimination, and first-order absorption. At clinically relevant concentrations, plasma drug concentrations were mainly influenced by the linear clearance pathway. Although the maximum target-mediated rate of elimination (Vmax ) parameter for the saturable pathway was found to be positively related to baseline ANGPLTL3, variability in body weight contributed more to the variability in evinacumab exposure than variability in ANGPTL3. An effect of HoFH versus healthy volunteers on Vmax was also identified. Weight-based dosing regimens resulted in consistent evinacumab exposure across weight ranges. An indirect exposure-response model adequately described the relationship between evinacumab and LDL-C, where drug concentration is assumed to inhibit LDL-C production. The final population PK/PD model included two nonclinically significant covariates (race and baseline body weight) on the maximum drug-induced inhibitory effect (Imax ) and one (baseline LDL-C) on the evinacumab concentration inducing 50% of Imax (IC50 ). A smaller IC50 was observed in patients with higher baseline LDL-C, suggesting greater sensitivity to treatment. Population exposure-response analysis permitted estimation of derived PD parameters and individual LDL-C levels over time for patients with HoFH. The model accurately predicted the proportion of patients with HoFH achieving prespecified LDL-C goals with evinacumab during the ELIPSE HoFH study, further supporting a dosing strategy.

Trilaciclib dose selection: an integrated pharmacokinetic and pharmacodynamic analysis of preclinical data and Phase Ib/IIa studies in patients with extensive-stage small cell lung cancer.

PURPOSE: Trilaciclib is a first-in-class CDK4/6 inhibitor that transiently arrests hematopoietic stem and progenitor cells (HSPCs) in the G1 phase of the cell cycle to preserve them from chemotherapy-induced damage (myelopreservation). We report integrated analyses of preclinical and clinical data that informed selection of the recommended Phase II dose (RP2D) used in trilaciclib trials in extensive-stage small cell lung cancer (ES-SCLC). METHODS: A semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model developed from preclinical data guided selection of an optimal dose for G1 bone marrow arrest in a first-in-human Phase I study (G1T28-1-01). PK, PD, safety, and efficacy data from G1T28-1-01 and two Phase Ib/IIa studies (G1T28-02/-03) in ES-SCLC were analyzed to support RP2D selection. RESULTS: Model simulation of bone marrow arrest based on preclinical data predicted that a ≥ 192 mg/m2 dose would induce a 40-50% decrease in total bone marrow proliferation in humans and almost 100% cell cycle arrest of cycling HSPCs. Consistent with this model, analysis of bone marrow aspirates in healthy volunteers after trilaciclib 192 mg/m2 administration demonstrated almost 100% G1 arrest in HSPCs and 40% decrease in total bone marrow proliferation, with minimal toxicity. G1T28-02/-03 reported similar PK parameters with trilaciclib 200 mg/m2 but slightly lower exposures than expected compared with healthy volunteers; consequently, 240 and 280 mg/m2 doses were also tested to match healthy volunteer exposures. Based on PK and relevant safety data, 240 mg/m2 was selected as the RP2D, which was also favored by myelopreservation endpoints in G1T28-02/-03. CONCLUSION: Integrated PK/PD, safety, and efficacy data support 240 mg/m2 as the RP2D for trilaciclib. CLINICALTRIALS. GOV IDENTIFIERS: NCT02243150; NCT02499770; NCT02514447.

Population and Noncompartmental Pharmacokinetics of Sodium Oxybate Support Weight-Based Dosing in Children and Adolescents With Narcolepsy With Cataplexy.

The pharmacokinetics (PKs) of sodium oxybate (SXB) was evaluated in a subset of participants from a study of SXB treatment in children (aged 7-11 years; n = 11) and adolescents (aged 12-17 years; n = 18) with narcolepsy with cataplexy. PK evaluation was conducted over 2 nights during the period when participants received a stable nightly SXB dose. The SXB dose on night 1 was half of night 2 and was administered in two equally divided doses: dose 1 was administered > 2 hours after the evening meal, and dose 2 was administered ≥ 4 hours after dose 1. Noncompartmental PK analysis demonstrated higher plasma concentrations post-dose 2 vs. post-dose 1, higher than dose-proportional increases in area under the concentration-time curve from 0 to 4 hours (AUC0-4h ) after dose 1, indicating nonlinear clearance, and better correlation between exposure and mg/kg than exposure and gram dose. To confirm the noncompartmental findings, identify factors affecting SXB PK, and compare with prior results in adults, a population PK (PopPK) model was established combining PK data from the current study with prior data from adults (132 healthy volunteers and 13 with narcolepsy). A two-compartment PopPK model with first-order absorption and nonlinear clearance from the central compartment described the data well. PopPK identified weight as the main intrinsic factor and food as the main extrinsic factor affecting SXB PK, and predicts similar PK profiles on a mg/kg basis across ages. These results, along with previously reported efficacy and safety outcomes, support weight-based SXB dose initiation in pediatric patients.

Assessment of Demographic and Socio-Behavioral Factors on Adherence to HIV Pre-Exposure Prophylaxis Using a Markov Modeling Approach.

Purpose: Adherence is important for the effectiveness of human immunodeficiency virus (HIV) preexposure prophylaxis (PrEP). The objective of the current work is to assess the impact of multiple demographic and socio-behavioral factors on the adherence to tenofovir-based PrEP among HIV serodiscordant couples in East Africa using Markov mixed-effects modeling approach. Methods: The Partners Demonstration Project was a prospective, open-label, implementation science-driven study of HIV PrEP among heterosexual HIV serodiscordant couples in Kenya and Uganda. The uninfected partner received oral PrEP according to the "bridge to antiretroviral therapy [ART]" strategy (i.e., until the infected partner had been on ART for ≥6 months). Adherence was monitored electronically; demographic and socio-behavioral data were collected during study visits. Analyzed data reflect 12 months of follow-up per participant. A two-state, first-order, discrete time Markov model was developed with longitudinal adherence data characterized by "dose taking (1)" and "dose missing (0)." Covariate effects were linearly added in the logit domain of transition probability parameters (P01 and P10) in the model. The full covariate model was initially developed, followed by backward elimination process to reduce the model. All significant covariates reported by a prior primary statistical analysis of the same data were included in the full covariate model. Results: The model included data from 920 participants, who were predominantly male (65%). Significant covariates associated with higher adherence were 25 years or older [odds ratio (OR) for P10, 0.61], female sex (OR for P10, 0.67), participant wanting the relationship with the partner to succeed (OR for P10, 0.79; OR for P01, 1.45), and sex with partner either with 100% or <100% condom use compared to those reported no sex (OR for P10, 0.84; OR for P01, 1.21). Significant covariates associated with lower adherence were partner on ART >6 months (OR for P01, 0.86; OR for P10, 1.34), subject in the study for >6 months (OR for P01, 0.8; OR for P10, 1.25), and problematic alcohol use (OR for P01, 0.63; OR for P10, 1.16). Conclusion: The developed Markov model provides a mechanistic understanding of relationship between demographic, socio-behavioral covariates, and PrEP adherence, by indicating the pattern of adherence influenced by each factor over time. Such data can be used for further intervention development to promote PrEP adherence.

Tenofovir Plasma Concentration from Preexposure Prophylaxis at the Time of Potential HIV Exposure: a Population Pharmacokinetic Modeling and Simulation Study Involving Serodiscordant Couples in East Africa.

The Partners Demonstration Project was a prospective, open-label, implementation science-driven study of preexposure prophylaxis (PrEP) among heterosexual HIV serodiscordant couples in Kenya and Uganda. Adherence data were collected using the Medication Event Monitoring System (MEMS), and time of sexual activity was collected using the mobile phone short message service (SMS). Two plasma samples were collected at a single study visit. We integrated adherence, pharmacokinetics, and SMS data using a population pharmacokinetic (PopPK) model to simulate tenofovir plasma concentrations from PrEP at the time of sexual activity. In the first stage of this analysis, we used data from the current study to update a prior PopPK model of tenofovir (TFV) developed with data from the Partners PrEP Study (a phase III clinical trial). The second stage involved simulating plasma concentrations at the time of sexual activity using empirical Bayes estimates (EBEs) derived from the final model. In addition, EBEs from a previously published parent metabolite model of TFV (MTN-001, an open-label 3-way crossover study in healthy women) was used to simulate tenofovir diphosphate (TFV-DP) concentrations. We estimated percent PrEP "coverage" as the number of reported sexual events during which simulated concentrations were above an a priori threshold concentrations associated with a high degree of protection from HIV infection: plasma TFV of >40 ng/ml and peripheral blood mononuclear cell (PBMC) TFV-DP concentration of >36 fmol/million cells. The levels of coverage were 72% for TFV and 81% for TFV-DP. These levels are consistent with a high degree of protection against HIV acquisition in this study of a pragmatic delivery model for antiretroviral-based HIV prevention.

Subcutaneous Furosemide in Heart Failure: Pharmacokinetic Characteristics of a Newly Buffered Solution.

Parenteral diuretics form the cornerstone of decongestion in heart failure. However, parenteral therapy routinely requires emergency room or inpatient care. A novel buffered furosemide formulation with neutral pH was developed to offer "hospital-strength" diuresis for outpatient use, including self-administration at home. Subcutaneous infusion using a biphasic delivery profile resulted in complete bioavailability (99.65%) and equivalent diuresis when compared with intravenous administration. Subcutaneous administration of buffered furosemide was well tolerated with no evidence of any drug-induced skin reactions. Subcutaneous infusion of buffered furosemide in the outpatient setting or home may help to reduce the burden of heart failure.

Markov Mixed Effects Modeling Using Electronic Adherence Monitoring Records Identifies Influential Covariates to HIV Preexposure Prophylaxis.

Adherence is a major factor in the effectiveness of preexposure prophylaxis (PrEP) for HIV prevention. Modeling patterns of adherence helps to identify influential covariates of different types of adherence as well as to enable clinical trial simulation so that appropriate interventions can be developed. We developed a Markov mixed-effects model to understand the covariates influencing adherence patterns to daily oral PrEP. Electronic adherence records (date and time of medication bottle cap opening) from the Partners PrEP ancillary adherence study with a total of 1147 subjects were used. This study included once-daily dosing regimens of placebo, oral tenofovir disoproxil fumarate (TDF), and TDF in combination with emtricitabine (FTC), administered to HIV-uninfected members of serodiscordant couples. One-coin and first- to third-order Markov models were fit to the data using NONMEM® 7.2. Model selection criteria included objective function value (OFV), Akaike information criterion (AIC), visual predictive checks, and posterior predictive checks. Covariates were included based on forward addition (α = 0.05) and backward elimination (α = 0.001). Markov models better described the data than 1-coin models. A third-order Markov model gave the lowest OFV and AIC, but the simpler first-order model was used for covariate model building because no additional benefit on prediction of target measures was observed for higher-order models. Female sex and older age had a positive impact on adherence, whereas Sundays, sexual abstinence, and sex with a partner other than the study partner had a negative impact on adherence. Our findings suggest adherence interventions should consider the role of these factors.

Population Pharmacokinetics of Tenofovir in HIV-1-Uninfected Members of Serodiscordant Couples and Effect of Dose Reporting Methods.

Antiretroviral preexposure prophylaxis (PrEP) with once-daily dosing of tenofovir and tenofovir-emtricitabine was shown to be effective for preventing HIV-1 infection in individuals who had HIV-1-seropositive partners (the Partners PrEP Study). We developed a population pharmacokinetic model for tenofovir and investigated the impacts of different dose reporting methods. Dosing information was collected as patient-reported dosing information (PRDI) from 404 subjects (corresponding to 1,280 drug concentration records) from the main trial and electronic monitoring-based adherence data collected from 211 subjects (corresponding to 327 drug concentration records) in an ancillary adherence study. Model development was conducted with NONMEM (7.2), using PRDI with a steady-state assumption or using PRDI replaced with electronic monitoring records where available. A two-compartment model with first-order absorption was the best model in both modeling approaches, with the need for an absorption lag time when electronic monitoring-based dosing records were included in the analysis. Age, body weight, and creatinine clearance were significant covariates on clearance, but only creatinine clearance was retained in the final models per stepwise selection. Sex was not a significant covariate on clearance. Tenofovir population pharmacokinetic parameter estimates and the precisions of the parameters from the two final models were comparable with the point estimates of the parameters, differing from 0% to 35%, and bootstrap confidence intervals widely overlapped. These findings indicate that PRDI was sufficient for population pharmacokinetic model development in this study, with a high level of adherence per multiple measures.

Budesonide Foam Has a Favorable Safety Profile for Inducing Remission in Mild-to-Moderate Ulcerative Proctitis or Proctosigmoiditis.

BACKGROUND: Budesonide foam, a rectally administered, second-generation corticosteroid with extensive hepatic first-pass metabolism, is efficacious for the treatment of mild-to-moderate ulcerative proctitis and ulcerative proctosigmoiditis. AIM: The aim of this study was to comprehensively assess the safety and pharmacokinetic profile of budesonide foam. METHODS: Data from five phase III studies were pooled to further evaluate safety, including an open-label study (once-daily treatment for 8 weeks), an active-comparator study (once-daily treatment for 4 weeks), and two placebo-controlled studies and an open-label extension study (twice-daily treatment for 2 weeks, then once daily for 4 weeks). Data from the placebo-controlled studies and two phase I studies (i.e., patients with mild-to-moderate ulcerative colitis and healthy volunteers) were pooled to evaluate the pharmacokinetics of budesonide foam. RESULTS: A similar percentage of patients reported adverse events in the budesonide foam and placebo groups, with the majority of adverse events being mild or moderate in intensity (93.3 vs 96.0%, respectively). Adverse events occurred in 41.4 and 36.3% of patients receiving budesonide foam and placebo, respectively. Mean morning cortisol concentrations remained within the normal range for up to 8 weeks of treatment; there were no clinically relevant effects of budesonide foam on the hypothalamic-pituitary-adrenal axis. Population pharmacokinetic analysis demonstrated low systemic exposure after budesonide foam administration. CONCLUSIONS: This integrated analysis demonstrated that budesonide foam for the induction of remission of distal ulcerative colitis is safe overall, with no clinically relevant effects on the hypothalamic-pituitary-adrenal axis.

A genetic algorithm based global search strategy for population pharmacokinetic/pharmacodynamic model selection.

The current algorithm for selecting a population pharmacokinetic/pharmacodynamic model is based on the well-established forward addition/backward elimination method. A central strength of this approach is the opportunity for a modeller to continuously examine the data and postulate new hypotheses to explain observed biases. This algorithm has served the modelling community well, but the model selection process has essentially remained unchanged for the last 30 years. During this time, more robust approaches to model selection have been made feasible by new technology and dramatic increases in computation speed. We review these methods, with emphasis on genetic algorithm approaches and discuss the role these methods may play in population pharmacokinetic/pharmacodynamic model selection.

Population pharmacokinetics of ofatumumab in patients with chronic lymphocytic leukemia, follicular lymphoma, and rheumatoid arthritis.

Ofatumumab is a human monoclonal antibody directed at CD20 approved for treatment of chronic lymphocytic leukemia. The population pharmacokinetics of intravenous ofatumumab were characterized in patients with relapsed/refractory chronic lymphocytic leukemia, relapsed/refractory follicular lymphoma, and rheumatoid arthritis, diseases with widely varying CD20⁺ B-cell counts in blood. Serum concentration data from a total of 477 patients who received ofatumumab doses ranging from 100 mg to 2000 mg in different dosing regimens were analyzed to determine the pharmacokinetic characteristics of ofatumumab across different patient groups and to identify factors contributing to the pharmacokinetic variability. Ofatumumab pharmacokinetics were well described by a linear two-compartment model component to represent non-specific monoclonal antibody clearance from the central compartment interacting with a model component representing the target-mediated clearance of ofatumumab by binding to CD20 expressed on B cells. The clearance (7.5 mL/h) and steady-state volume of distribution (5.3 L) for the linear, non-specific component were consistent with results obtained for other monoclonal antibodies. The target-mediated clearance component was related to the disease-specific number of circulating B cells, which will allow simulation of the contribution of target-mediated clearance to ofatumumab pharmacokinetics in untested disease states with data on B-cell counts and turnover.

Genetic algorithm guided population pharmacokinetic model development for simvastatin, concurrently or non-concurrently co-administered with amlodipine.

An automated model development was performed for simvastatin, co-administered with amlodipine concurrently or non-concurrently (i.e., 4 hours later) in 17 patients with coexisting hyperlipidemia and hypertension. The single objective hybrid genetic algorithm (SOHGA) was implemented in the NONMEM software by defining the search space for structural, statistical and covariate models. Candidate models obtained from the SOHGA runs were further assessed for biological plausibility and the precision of parameter estimates, followed by traditional backward elimination process for model refinement. The final population pharmacokinetic model shows that the elimination rate constant for simvastatin acid, the active form by hydrolysis of its lactone prodrug (i.e., simvastatin), is only 44% in the concurrent amlodipine administration group compared with the non-concurrent group. The application of SOHGA for automated model selection, combined with traditional model selection strategies, appears to save time for model development, which also can generate new hypotheses that are biologically more plausible.

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q1 = 4.9 % and q3 = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data.