ABSTRACT Quantitative modeling can guide drug development and regulatory decisions by providing insights into pharmacokinetics, pharmacodynamics, efficacy, and safety. Integrating models allows simulations that support dosing considerations. A simulation framework for belantamab mafodotin (B) in relapsed/refractory multiple myeloma was developed using data from monotherapy studies and combination studies with bortezomib/dexamethasone (BVd) and pomalidomide/dexamethasone (BPd). Individual changes from baseline in serum M‐protein concentrations were modeled using a tumor growth inhibition model. Changes in ophthalmic exam findings (OEFs) grades per the Keratopathy and Visual Acuity (KVA) scale were modeled using a continuous‐time Markov model. Belantamab mafodotin concentrations were modeled with a population pharmacokinetic model. All final models adequately described the clinical data. The M‐protein/KVA/pharmacokinetic models were integrated in a simulation framework, and simulations of treatment outcomes were performed. Simulated regimens included dose modifications (holds/reductions) for OEFs. Simulations of protocol dosing and dose modifications from the phase 3 DREAMM‐7 (BVd) and DREAMM‐8 (BPd) trials predicted similar results to the respective trials (for BVd simulations/DREAMM‐7 data: very good partial response rates [VGPR+] 62.0%/62.0%, median progression‐free survival [mPFS] 39.4/36.6 months, and grade ≥ 3 OEFs 87.5%/76.5%; for BPd simulations/DREAMM‐8 data: VGPR+ rates 60.2%/68.0%, mPFS not reached/not reached, and grade ≥ 3 OEFs 77.2%/82.5%). Simulations of alternative BVd regimens indicated that a 2.5 mg/kg starting dose produces the highest efficacy, and schedule extensions after the first BVd dose can improve tolerability while continuing to demonstrate efficacy. This framework adequately predicted outcomes from DREAMM‐7/DREAMM‐8, supporting its use to predict benefit–risk profiles of alternate belantamab mafodotin dosing strategies.

ABSTRACT Tumor size–overall survival (TS‐OS) models can support decision‐making in oncology drug development by predicting long‐term OS based on TS data from early data cuts and baseline patient factors. The current work describes the development of a TS‐OS framework capable of predicting OS across a variety of treatment modalities and mechanisms of action in patients with non‐small cell lung cancer from seven clinical studies. The presented framework jointly models TS with a bi‐exponential Stein model and OS with an accelerated failure time log‐normal survival model. In the corresponding link function between TS and OS, the most significant predictor of OS was the tumor growth rate (kg), applied via an Emax function. Time to tumor growth and baseline TS were additional TS predictors informing OS. Albumin, total protein, and neutrophil‐to‐lymphocyte ratio were selected from the tested baseline factors as the most significant predictors of OS. Significant baseline covariates for the TS model included number of target lesions on baseline TS, tumor PD‐L1 expression on tumor shrinkage rate, and lactate dehydrogenase levels on kg. The TS‐OS framework model adequately describes the OS distributions within this specific set of treatment modalities—chemotherapies, immuno‐oncology treatments, and combinations thereof—using a single treatment‐independent link function, supporting the use of the framework to support evaluation and design of future studies. Our findings contribute to a body of literature exploring and qualifying TS‐OS modeling as a methodology capable of supporting and accelerating oncology drug development.

The gold standard for regulatory approval in oncology is overall survival (OS). Because OS data are initially limited, early drug development decisions are often based on early efficacy endpoints, such as objective response rate and progression-free survival. Tumor size (TS)-OS models provide a framework to support decision-making on potential late-stage success based on early readouts, through leveraging TS data with limited follow-up and treatment-agnostic TS-OS link functions, to predict longer-term OS. Conditional simulations (also known as Bayesian forecasting) with TS-OS models can be used to simulate long-term OS outcomes for an ongoing study, conditional on the available TS and OS data at interim data cuts of the same study. This tutorial provides a comprehensive overview of the steps involved in using such conditional simulations to support better informed drug development decisions in oncology. The tutorial covers the selection of the TS-OS framework model; applying the TS-OS model to the interim data; performing conditional simulations; generating relevant output; as well as correct interpretation and communication of the output for decision making.

Daprodustat, a novel oral hypoxia-inducible factor prolyl hydroxylase inhibitor is approved in the United States for the treatment of anemia due to chronic kidney disease (CKD) in adults receiving dialysis for at least 4 months. Pharmacodynamic dose-hemoglobin (Dose-Hgb) models were developed as daprodustat progressed through development. To support global phase III development, a dose-titration algorithm, guided by simulations from the initial Dose-Hgb model based on phase II clinical data, was implemented. This work was to update and re-calibrate this model to support the dose titration algorithm. Data from five pivotal phase III studies in CKD patients with anemia treated with daprodustat once daily (q.d.) and/or three times a week (t.i.w.) using a titration dosing schedule were included. The data comprised 2,770 CKD patients with anemia providing 53,535 Hgb observations over a period of 6 months up to 4 years. This final Dose-Hgb model consisted of a precursor cell compartment and 12 transit compartments to describe the red blood cell (RBC) lifespan. Treatment increased the precursor cell production rate (Kin) by a power of allometrically scaled dose. Disease progression, as an exponential decline of Hgb production rate over time, varied with dialysis status. The dose-titration algorithm resulted in comparable response for t.i.w. dosing relative to q.d. dosing. Titration-based visual predictive checks for Hgb target criteria for the analysis dataset and the prediction dataset showed that the model adequately predicted the observed data. This re-calibrated Dose-Hgb model will provide further support for the individualized dosing strategy in CKD patients with anemia treated with daprodustat.

Model-informed drug development (MIDD) played a crucial role in the successful development and regulatory approval of daprodustat, a novel oral hypoxia-inducible factor prolyl hydroxylase (HIF-PHI) inhibitor aimed at treating anemia in chronic kidney disease patients. MIDD was pivotal in optimizing dosing strategies and enabling effective dose individualization, ensuring the right dose for the right patient. This tutorial illustrates how integrated quantitative approaches, including longitudinal hemoglobin response modeling, population pharmacokinetics (PopPK), and clinical trial simulations, were applied to guide phase III dose selection, inform trial design, and support regulatory interactions. We highlight the evolution and application of these models, emphasizing key development decisions, challenges encountered, and insights gained.

Daprodustat is a first-in-class hypoxia-inducible factor prolyl hydroxylase inhibitor (HIF-PHI) approved in the USA for treatment of anemia owing to chronic kidney disease (CKD) in dialysis-dependent adults and in Japan for treatment of CKD in dialysis- and non-dialysis dependent adults. This analysis characterized the population pharmacokinetics (PopPK) of daprodustat in adults with CKD and evaluated the influence of intrinsic and extrinsic factors. This PopPK analysis included data from one phase 2B and four phase 3 studies comprising 707 CKD subjects dose titrated to prespecified target hemoglobin levels with daprodustat doses ranging from 1 to 24 mg once daily and 2 to 48 mg given three times a week (TIW). Model development leveraged a previous phase 1/2 PopPK model. Stepwise covariate analysis included 20 extrinsic and intrinsic factors. Model evaluation used standard goodness-of-fit and visual predictive checks. Daprodustat PopPK was adequately characterized using a three-compartment distribution model with first-order elimination. The absorption phase was described using five transit compartments. Oral clearance and volume of distribution was 24.6 L/h and 26.9 L, respectively. Body weight dependence (with fixed allometric coefficients) of clearance and volume terms was a statistically significant covariate. Concomitant use of clopidogrel (moderate CYP2C8 inhibitor) decreased oral clearance, resulting in higher area under the plasma concentration-time curve (AUC) ratio of 1.59 (90

ABSTRACT With over 30 years of use, levonorgestrel-releasing intrauterine systems (LNG-IUSs) have proven to be highly effective methods of contraceptives. The active ingredient in LNG-IUS, levonorgestrel (LNG), is released directly into the uterine cavity, which causes suppression of endometrial maturation and thickened cervical mucus. A variety of LNG-IUS options exhibit well-established safety profiles and efficacy, providing anywhere between 2 and 18 times lower systemic exposure when compared with LNG-containing pills or implants. Patient-centered decision-making regarding product choice may be encouraged through standardized comparisons of LNG release and exposure during the time of their respective usage periods. Evaluation of the efficacy, safety, and PK of extended Mirena use from beyond 5 years to the conclusion of 8 years was designed in the Mirena Extension Trial (MET), thereby facilitating comparisons with LNG-IUS 19.5 mg and LNG-IUS 13.5 mg. Using validated liquid chromatography-tandem mass spectrometry methods and SHBG concentrations, the authors determined concentrations of plasma LNG levels. Dichloromethane was used for extracting residual LNG from the elastomer material from removed IUS devices, which was then quantified via liquid chromatography and external calibration on a reversed-phase column. Although previous models used measured LNG and SHBG (serum) concentrations, the population pharmacokinetics (popPK) allows for a uniform method of comparison among different pharmacologic studies. The popPK approach allows for a reliable estimates of in vivo LNG exposure and release during the entirety of the 8-year use period. The MET enrolled premenopausal women aged 18–35 years for a multicenter, single-arm study regarding patients who had used LNG-IUS 52 mg for the last 4.5–5 years for up to 8 years. The study took place from December 22, 2016 to May 28, 2021 throughout 54 US centers, with the primary outcome of contraceptive efficacy and failure rate. Participants each provided written, informed consent. Study strengths of the popPK approach included its robust methodology, its ability to provide in vivo release rate estimates, a stepwise demo of model suitability from use years 6 to 8, model-based estimation of unbound LNG, and the ability to provide estimates for both population and individual PK profiles from sparse available sampling. Limitations include the possibility of findings being group effects possibly inapplicable to all LNG-IUS users. In addition, the data fail to provide local endometrial and cervical LNG concentrations due to the systemic nature of the LNG exposure. Finally, the popPK Mondale should not be used for theoretical question addressing (such as creating rate release estimates outside of available data sets). In conclusion, a broad data set indicates that reliable LNG exposure is delivered by 8-year popPK and release models for up to 7 years of LNG-IUs 52 mg use. Efficacy is data-supported for 6–8 years of use, with constant LNG release throughout this time frame. Systemic LNG exposure from LNG-IUD 52 mg proved both comparable to other LNG-IUS devices and significantly lower than oral and implanted LNG-containing contraceptives.

Establishing a dosing regimen that maximizes clinical benefit and minimizes adverse effects for novel therapeutics is a key objective for drug developers. Finding an optimal dose and schedule can be particularly challenging for compounds with a narrow therapeutic window such as in oncology. Modeling and simulation tools can be valuable to conduct in silico evaluations of various dosing scenarios with the goal to identify those that could minimize toxicities, avoid unscheduled dose interruptions, or minimize premature discontinuations, which all could limit the potential for therapeutic benefit. In this tutorial, we present a stepwise development of an adaptive dose simulation framework that can be used for dose optimization simulations. The tutorial first describes the general workflow, followed by a technical description with basic to advanced practical examples of its implementation in mrgsolve and is concluded with examples on how to use this in decision-making around dose and schedule optimization. The adaptive simulation framework is built with pharmacokinetic, pharmacodynamic (i.e., biomarkers, activity markers, target engagement markers, efficacy markers), and safety models that include evaluations of unexplained interindividual and intraindividual variability and covariate impact, which can be replaced and expanded (e.g., combination setting, comparator setting) with user-defined models. Subsequent adaptive simulations allow investigation of the impact of starting dose, dosing intervals, and event-driven (exposure or effect) dose modifications on any end point. The resulting simulation-derived insights can be used in quantitatively proposing dose and regimens that better balance benefit and adverse effects for further evaluation, aiding dose selection discussions, and designing dose modification recommendations, among others.

Abstract Belantamab mafodotin, a monomethyl auristatin F (MMAF)–containing monoclonal antibody‐drug conjugate (ADC), demonstrated deep and durable responses in the DRiving Excellence in Approaches to Multiple Myeloma (DREAMM)‐1 and pivotal DREAMM‐2 studies in patients with relapsed/refractory multiple myeloma. As with other MMAF‐containing ADCs, ocular adverse events were observed. To predict the effects of belantamab mafodotin dosing regimens and dose‐modification strategies on efficacy and ocular safety end points, DREAMM‐1 and DREAMM‐2 data across a range of doses were used to develop an integrated simulation framework incorporating two separate longitudinal models and the published population pharmacokinetic model. A concentration‐driven tumor growth inhibition model described the time course of serum M‐protein concentration, a measure of treatment response, whereas a discrete time Markov model described the time course of ocular events graded with the GSK Keratopathy and Visual Acuity scale. Significant covariates included baseline β2‐microglobulin on growth rate, baseline M‐protein on kill rate, extramedullary disease on the effect compartment rate constant, and baseline soluble B cell maturation antigen on maximal effect. Efficacy and safety end points were simulated for various doses with dosing intervals of 1, 3, 6, and 9 weeks and various event‐driven dose‐modification strategies. Simulations predicted that lower doses and longer dosing intervals were associated with lower probability and lower overall time with Grade 3+ and Grade 2+ ocular events compared with the reference regimen (2.5 mg/kg every 3 weeks), with a less‐than‐proportional reduction in efficacy. The predicted improved benefit–risk profiles of certain dosing schedules and dose modifications from this integrated framework has informed trial designs for belantamab mafodotin, supporting dose‐optimization strategies.

Abstract Rivaroxaban is approved in various regions for the treatment of acute venous thromboembolism (VTE) in children aged between 0 and 18 years and was recently investigated for thromboprophylaxis in children aged between 2 and 8 years (with body weights <30 kg) with congenital heart disease who had undergone the Fontan procedure. In the absence of clinical data, rivaroxaban doses for thromboprophylaxis in post‐Fontan children aged 9 years and older or ≥30 kg were derived by a bridging approach that used physiologically‐based pharmacokinetic (PBPK) and population pharmacokinetic (popPK) models based on pharmacokinetic (PK) data from 588 pediatric patients and from adult patients who received 10 mg once daily for thromboprophylaxis after major orthopedic surgeries as a reference. Both models showed a tendency toward underestimating rivaroxaban exposure in post‐Fontan patients aged between 2 and 5 years but accurately described rivaroxaban PK in post‐Fontan patients aged between 5 and 8 years. Under the assumption that hepatic function is not impaired in post‐Fontan patients, PBPK and popPK simulations indicated that half of the rivaroxaban doses for the same body weight given to pediatric patients treated for acute VTE would yield in pediatric post‐Fontan patients exposures similar to the exposure observed in adult patients receiving 10 mg rivaroxaban once daily for thromboprophylaxis. Simulation‐derived doses (7.5 mg rivaroxaban once daily for body weights 30–<50 kg and 10 mg once daily for body weights ≥50 kg) were therefore included in the recent US label of rivaroxaban for thromboprophylaxis in children aged 2 years and older with congenital heart disease who have undergone the Fontan procedure.

Uprifosbuvir is a uridine nucleoside monophosphate prodrug inhibitor of the hepatitis C virus NS5B RNA polymerase. To quantitatively elucidate key metabolic pathways, assess the link between unmeasurable effect site concentrations and viral load reduction, and evaluate the influence of intrinsic and extrinsic factors on pharmacokinetics and pharmacodynamics, a model-informed drug development (MIDD) framework was initiated at an early stage. Originally scoped as a modeling effort focused on minimal physiologically based pharmacokinetic and covariate analyses, this project turned into a collaborative effort focused on gaining a deeper understanding of the data from drug metabolism, biopharmaceutics, pharmacometrics, and clinical pharmacology perspectives. This article presents an example of the practical execution of a MIDD-based, cooperative multidisciplinary modeling approach, creating a model that grows along with the team's integrated knowledge. Insights gained from this process could be used in forming optimal collaborations between disciplines in drug development for other investigative compounds.

Once-daily two 600 mg tablets (1200 mg q.d.) raltegravir offers an easier treatment option compared to the twice-daily regimen of one 400 mg tablet. No pharmacokinetic, efficacy, or safety data of the 1200 mg q.d. regimen have been reported in pregnant women to date as it is challenging to collect these clinical data. This study aimed to develop a population pharmacokinetic (PopPK) model to predict the pharmacokinetic profile of raltegravir 1200 mg q.d. in pregnant women and to discuss the expected pharmacodynamic properties of raltegravir 1200 mg q.d. during pregnancy based on previously reported concentration-effect relationships. Data from 11 pharmacokinetic studies were pooled (n = 221). A two-compartment model with first-order elimination and absorption through three sequential transit compartments best described the data. We assessed that the bio-availability of the 600 mg tablets was 21% higher as the 400 mg tablets, and the bio-availability in pregnant women was 49% lower. Monte-Carlo simulations were performed to predict the pharmacokinetic profile of 1200 mg q.d. in pregnant and nonpregnant women. The primary criteria for efficacy were that the lower bound of the 90% confidence interval (CI) of the concentration before next dose administration (C-trough) geometric mean ratio (GMR) of simulated pregnant/nonpregnant women had to be greater than 0.75. The simulated raltegravir C-trough GMR (90% CI) was 0.51 (0.41-0.63), hence not meeting the primary target for efficacy. Clinical data from two pregnant women using 1200 mg q.d. raltegravir showed a similar C-trough ratio pregnant/nonpregnant. Our pharmacokinetic results support the current recommendation of not using the raltegravir 1200 mg q.d. regimen during pregnancy until more data on the exposure-response relationship becomes available.

The free and open-source package nlmixr implements pharmacometric nonlinear mixed effects model parameter estimation in R. It provides a uniform language to define pharmacometric models using ordinary differential equations. Performances of the stochastic approximation expectation-maximization (SAEM) and first order-conditional estimation with interaction (FOCEI) algorithms in nlmixr were compared with those found in the industry standards, Monolix and NONMEM, using the following two scenarios: a simple model fit to 500 sparsely sampled data sets and a range of more complex compartmental models with linear and nonlinear clearance fit to data sets with rich sampling. Estimation results obtained from nlmixr for FOCEI and SAEM matched the corresponding output from NONMEM/FOCEI and Monolix/SAEM closely both in terms of parameter estimates and associated standard errors. These results indicate that nlmixr may provide a viable alternative to existing tools for pharmacometric parameter estimation.

nlmixr is a free and open-source R package for fitting nonlinear pharmacokinetic (PK), pharmacodynamic (PD), joint PK-PD, and quantitative systems pharmacology mixed-effects models. Currently, nlmixr is capable of fitting both traditional compartmental PK models as well as more complex models implemented using ordinary differential equations. We believe that, over time, it will become a capable, credible alternative to commercial software tools, such as NONMEM, Monolix, and Phoenix NLME.

Drug dosing regimen can significantly impact drug effect and, thus, the success of treatments. Nevertheless, trial and error is still the most commonly used method by conventional pharmacometric approaches to optimize dosing regimen. In this tutorial, we utilize four distinct classes of quantitative systems pharmacology models to introduce frequency‐domain response analysis, a method widely used in electrical and control engineering that allows the analytical optimization of drug treatment regimen from the dynamics of the model.

Reliance on modeling and simulation in drug discovery and development has dramatically increased over the past decade. Two disciplines at the forefront of this activity, pharmacometrics and systems pharmacology (SP), emerged independently from different fields; consequently, a perception exists that only few examples integrate these approaches. Herein, we review the state of pharmacometrics and SP integration and describe benefits of combining these approaches in a model-informed drug discovery and development framework.

Objective: Nomegestrol acetate (NOMAC), a selective progestogen, and 17 beta-estradio1 (E2), which is identical to endogenous oestrogen, are components of a new monophasic combined oral contraceptive NOMAC/E2. This study aimed to compare pharmacokinetics (PK) of NOMAC in adolescent and adult women following a single dose of NOMAC/E2.Study design: Healthy postmenarcheal adolescent (14-17 years) and adult (18-50 years) women received a single dose of NOMAC/E2 (2.5 mg/l.5 mg) in this single-centre, open-label, parallel-group Phase 1 study (EudraCT#2008-002142-38). Blood samples were obtained for PK analysis, and concentrations of NOMAC, E2 and its metabolite estrone (El) were determined for up to 129 h following dosing to obtain PK data. An independent whole-body physiology-based pharmacokinetic (WB-PBPK) simulation model of NOMAC based on an independent Phase 3 dataset was used to scale NOMAC concentration time plots to adolescents.Results: Overall, 52 women were screened, of whom 30 (15 adolescents and 15 adults) were enrolled. No statistically significant differences were observed between the adolescent and adult groups for the clinically evaluated NOMAC PK parameters [maximum concentration (C-max), area under the curve (AUC) and half-life (t(1/2))] The PK of E2 and El showed extensive overlap between both age groups. The WB-PBPK model accurately predicted NOMAC AUC and C-max values in both groups.Conclusions: No differences were observed in the clinically evaluated PK parameters for NOMAC between adolescent and adult women after a single dose of NOMAC/E2. The WB-PBPK model accurately predicted NOMAC PK data (EudraCT# 2008-002142-38).Implications: PK studies in adolescents are challenging because of ethical considerations. The whole-body physiology-based model described here complements classic noncompartmental and population PK approaches. The utility of this method is its ability to expand to adolescent postmenarcheal girls by using virtual postmenarcheal adolescent population data and applying physiological scaling. (C) 2016 Elsevier Inc. All rights reserved.