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Abstract With the International Conference on Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) E17 guidelines in effect from 2018, the design of Asia‐inclusive multiregional clinical trials (MRCTs) has been streamlined, thereby enabling efficient simultaneous global development. Furthermore, with the recent regulatory reforms in China and its drug administration joining the ICH as a full regulatory member, early participation of China in the global clinical development of novel investigational drugs is now feasible. This would also allow for inclusion of the region in the geographic footprint of pivotal MRCTs leveraging principles of the ICH E5 and E17. Herein, we describe recent case examples of model‐informed Asia‐inclusive global clinical development in the EMD Serono portfolio, as applied to the ataxia telangiectasia and Rad3‐related inhibitors, tuvusertib and berzosertib (oncology), the toll‐like receptor 7/8 antagonist, enpatoran (autoimmune diseases), the mesenchymal–epithelial transition factor inhibitor tepotinib (oncology), and the antimetabolite cladribine (neuroimmunological disease). Through these case studies, we illustrate pragmatic approaches to ethnic sensitivity assessments and the application of a model‐informed drug development toolkit including population pharmacokinetic/pharmacodynamic modeling and pharmacometric disease progression modeling and simulation to enable early conduct of Asia‐inclusive MRCTs. These examples demonstrate the value of a Totality of Evidence approach where every patient's data matter for de‐risking ethnic sensitivity to inter‐population variations in drug‐ and disease‐related intrinsic and extrinsic factors, enabling inclusive global development strategies and timely evidence generation for characterizing benefit/risk of the proposed dosage in Asian populations.

Abstract Tepotinib is approved for the treatment of patients with non‐small‐cell lung cancer harboring MET exon 14 skipping alterations. While edema is the most prevalent adverse event (AE) and a known class effect of MET inhibitors including tepotinib, there is still limited understanding about the factors contributing to its occurrence. Herein, we apply machine learning (ML)‐based approaches to predict the likelihood of occurrence of edema in patients undergoing tepotinib treatment, and to identify factors influencing its development over time. Data from 612 patients receiving tepotinib in five Phase I/II studies were modeled with two ML algorithms, Random Forest, and Gradient Boosting Trees, to predict edema AE incidence and severity. Probability calibration was applied to give a realistic estimation of the likelihood of edema AE. Best model was tested on follow‐up data and on data from clinical studies unused while training. Results showed high performances across all the tested settings, with F1 scores up to 0.961 when retraining the model with the most relevant covariates. The use of ML explainability methods identified serum albumin as the most informative longitudinal covariate, and higher age as associated with higher probabilities of more severe edema. The developed methodological framework enables the use of ML algorithms for analyzing clinical safety data and exploiting longitudinal information through various covariate engineering approaches. Probability calibration ensures the accurate estimation of the likelihood of the AE occurrence, while explainability tools can identify factors contributing to model predictions, hence supporting population and individual patient‐level interpretation.

Abstract Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long‐term tolerability beyond dose‐limiting toxicities, and the lack of reliable biomarkers for long‐term efficacy. Through the lens of Totality of Evidence and with the mindset of model‐informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.

Abstract Brigatinib is an oral anaplastic lymphoma kinase (ALK) inhibitor approved for the treatment of ALK‐positive metastatic non‐small cell lung cancer. In vitro studies indicated that brigatinib is primarily metabolized by CYP2C8 and CYP3A4 and inhibits P‐gp, BCRP, OCT1, MATE1, and MATE2K. Clinical drug–drug interaction (DDI) studies with the strong CYP3A inhibitor itraconazole or the strong CYP3A inducer rifampin demonstrated that CYP3A‐mediated metabolism was the primary contributor to overall brigatinib clearance in humans. A physiologically‐based pharmacokinetic (PBPK) model for brigatinib was developed to predict potential DDIs, including the effect of moderate CYP3A inhibitors or inducers on brigatinib pharmacokinetics (PK) and the effect of brigatinib on the PK of transporter substrates. The developed model was able to predict clinical DDIs with itraconazole (area under the plasma concentration–time curve from time 0 to infinity [AUC∞] ratio [with/without itraconazole]: predicted 1.86; observed 2.01) and rifampin (AUC∞ ratio [with/without rifampin]: predicted 0.16; observed 0.20). Simulations using the developed model predicted that moderate CYP3A inhibitors (e.g., verapamil and diltiazem) may increase brigatinib AUC∞ by ~40%, whereas moderate CYP3A inducers (e.g., efavirenz) may decrease brigatinib AUC∞ by ~50%. Simulations of potential transporter‐mediated DDIs predicted that brigatinib may increase systemic exposures (AUC∞) of P‐gp substrates (e.g., digoxin and dabigatran) by 15%–43% and MATE1 substrates (e.g., metformin) by up to 29%; however, negligible effects were predicted on BCRP‐mediated efflux and OCT1‐mediated uptake. The PBPK analysis results informed dosing recommendations for patients receiving moderate CYP3A inhibitors (40% brigatinib dose reduction) or inducers (up to 100% increase in brigatinib dose) during treatment, as reflected in the brigatinib prescribing information.

Abstract Several investigational agents are under evaluation in systemic lupus erythematosus (SLE) clinical trials but quantitative frameworks to enable comparison of their efficacy to reference benchmark treatments are lacking. To benchmark SLE treatment effects and identify clinically important covariates, we developed a model‐based meta‐analysis (MBMA) within a latent variable model framework for efficacy end points and SLE composite end point scores (BILAG‐based Composite Lupus Assessment and Systemic Lupus Erythematosus Responder Index) using aggregate‐level data on approved and investigational therapeutics. SLE trials were searched using PubMed and www.clinicaltrials.gov for treatment name, SLE and clinical trial as search criteria that resulted in four data structures: (1) study and investigational agent, (2) dose and regimen, (3) baseline descriptors, and (4) outcomes. The final dataset consisted of 25 studies and 81 treatment arms evaluating 16 different agents. A previously developed (K Goteti et al. 2022) SLE latent variable model of data from placebo arms (placebo + standard of care treatments) was used to describe aggregate SLE end points over time for the various SLE placebo and treatment arms in a Bayesian MBMA framework. Continuous dose‐effect relationships using a maximum effect model were included for anifrolumab, belimumab, CC‐220 (iberdomide), epratuzumab, lulizumab pegol, and sifalimumab, whereas the remaining treatments were modeled as discrete dose effects. The final MBMA model was then used to benchmark these compounds with respect to the maximal efficacy on the latent variable compared to the placebo. This MBMA illustrates the application of latent variable models in understanding the trajectories of composite end points in chronic diseases and should enable model‐informed development of new investigational agents in SLE.

Abstract This analysis aimed to quantify tumor dynamics in patients receiving either bintrafusp alfa (BA) or pembrolizumab, by population pharmacokinetic (PK)‐pharmacodynamic modeling, and investigate clinical and molecular covariates describing the variability in tumor dynamics by pharmacometric and machine‐learning (ML) approaches. Data originated from two clinical trials in patients with biliary tract cancer (BTC; NCT03833661) receiving BA and non‐small cell lung cancer (NSCLC; NCT03631706) receiving BA or pembrolizumab. Individual drug exposure was estimated from previously developed population PK models. Population tumor dynamics models were developed for each drug‐indication combination, and covariate evaluations performed using nonlinear mixed‐effects modeling (NLME) and ML (elastic net and random forest models) approaches. The three tumor dynamics’ model structures all included linear tumor growth components and exponential tumor shrinkage. The final BTC model included the effect of drug exposure (area under the curve) and several covariates (demographics, disease‐related, and genetic mutations). Drug exposure was not significant in either of the NSCLC models, which included two, disease‐related, covariates in the BA arm, and none in the pembrolizumab arm. The covariates identified by univariable NLME and ML highly overlapped in BTC but showed less agreement in NSCLC analyses. Hyperprogression could be identified by higher tumor growth and lower tumor kill rates and could not be related to BA exposure. Tumor size over time was quantitatively characterized in two tumor types and under two treatments. Factors potentially related to tumor dynamics were assessed using NLME and ML approaches; however, their net impact on tumor size was considered as not clinically relevant.

Abstract Like other monoclonal antibodies, immune checkpoint inhibitors may be immunogenic in some patients, potentially affecting pharmacokinetics (PKs) and clinical outcomes. In post hoc analyses, we characterized antidrug antibody (ADA) development with avelumab monotherapy in patients with metastatic Merkel cell carcinoma (mMCC) from the JAVELIN Merkel 200 trial (first‐line [1L; N = 116] and second‐line or later [≥2L; N = 88] cohorts) or with advanced urothelial carcinoma (aUC) from the JAVELIN Bladder 100 (1L maintenance [N = 350]) and JAVELIN Solid Tumor (≥2L [N = 249]) trials. Treatment‐emergent ADAs developed in a numerically higher proportion of patients with aUC (1L maintenance, 19.1%; ≥2L, 18.1%) versus mMCC (1L, 8.2%; ≥2L, 8.9%); incidences within tumor types were similar by line of therapy. In PK analyses, numerically lower avelumab trough concentration and higher baseline clearance were observed in treatment‐emergent ADA+ versus ADA− subgroups; however, differences were not clinically relevant. Numerical differences in overall survival, progression‐free survival, or objective response rate by ADA status were observed; however, no clinically meaningful trends were identified. Proportions of patients with treatment‐emergent adverse events (TEAEs; any grade or grade 3/4), serious TEAEs, TEAEs leading to treatment discontinuation, or infusion‐related reactions were similar, with overlapping 80% confidence intervals between ADA subgroups. Efficacy and safety observations were similar in subgroups defined by early development of ADA+ status during treatment. In conclusion, no meaningful differences in PKs, efficacy, and safety were observed between subgroups of avelumab‐treated patients with different ADA status. Overall, these data suggest that ADAs are not relevant for treatment decisions with avelumab.

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

Abstract The development of immune checkpoint inhibitors (ICIs) has revolutionized cancer therapy but only a fraction of patients benefits from this therapy. Model‐informed drug development can be used to assess prognostic and predictive clinical factors or biomarkers associated with treatment response. Most pharmacometric models have thus far been developed using data from randomized clinical trials, and further studies are needed to translate their findings into the real‐world setting. We developed a tumor growth inhibition model based on real‐world clinical and imaging data in a population of 91 advanced melanoma patients receiving ICIs (i.e., ipilimumab, nivolumab, and pembrolizumab). Drug effect was modeled as an ON/OFF treatment effect, with a tumor killing rate constant identical for the three drugs. Significant and clinically relevant covariate effects of albumin, neutrophil to lymphocyte ratio, and Eastern Cooperative Oncology Group (ECOG) performance status were identified on the baseline tumor volume parameter, as well as NRAS mutation on tumor growth rate constant using standard pharmacometric approaches. In a population subgroup (n = 38), we had the opportunity to conduct an exploratory analysis of image‐based covariates (i.e., radiomics features), by combining machine learning and conventional pharmacometric covariate selection approaches. Overall, we demonstrated an innovative pipeline for longitudinal analyses of clinical and imaging RWD with a high‐dimensional covariate selection method that enabled the identification of factors associated with tumor dynamics. This study also provides a proof of concept for using radiomics features as model covariates.

Abstract Systemic lupus erythematosus (SLE) is an autoimmune disease affecting multiple organ systems. Many investigational agents have failed or shown only modest effects when added to standard of care (SoC) therapy in placebo‐controlled trials, and only two therapies have been approved for SLE in the last 60 years. Clinical trial outcomes have shown discordance in drug effects between clinical endpoints. Herein, we characterized longitudinal disease activity in the SLE population and the sources of variability by developing a latent disease trajectory model for SLE component endpoints (Systemic Lupus Erythematosus Disease Activity Index [SLEDAI], Physician's Global Assessment [PGA], British Isles Lupus Assessment Group Index [BILAG]) and composite endpoints (Systemic Lupus Erythematosus Responder Index [SRI], BILAG‐based Composite Lupus Assessment [BICLA], and Lupus Low Disease Activity State [LLDAS]) using patient‐level historical SoC data from nine phase II and III studies. Across all endpoints, in predictions up to 52 weeks from the final disease trajectory model, the following baseline covariates were associated with a greater decrease in SLE disease activity and higher response to placebo + SoC: Hispanic ethnicity from Central/South America, absence of hypocomplementemia, recent SLE diagnosis, and high baseline disease activity score using SLEDAI and BILAG separately. No discernible differences were observed in the trajectory of response to placebo + SoC across different SoC medications (antimalarial and immunosuppressant such as mycophenolate, methotrexate, and azathioprine). Across all endpoints, disease trajectory showed no difference in Asian versus non‐Asian patients, supporting Asia‐inclusive global SLE drug development. These results describe the first population approach to support a model‐informed drug development framework in SLE.

Abstract Key elements of scientific writing—consistency and clarity—can be compromised in case of inaccurate use of methodological terms, especially in complex and multidisciplinary scientific fields. Such is the case in reports of pharmacometrics exposure–response analyses with the use of the terms univariate/multivariate and univariable/multivariable. This perspective outlines the issues in the use of these terms, clarifies their definitions, provides examples, and makes recommendations for authors, reviewers, and journals in the fields of clinical pharmacology and pharmacometrics.

Abstract Bintrafusp alfa, a first‐in‐class bifunctional fusion protein composed of the extracellular domain of TGF‐βRII (a TGF‐β “trap”) fused to a human IgG1 mAb blocking PD‐L1, is being evaluated for efficacy and safety in solid tumor indications as monotherapy and in combination with small‐molecule drugs. We evaluated the perpetrator drug–drug interaction (DDI) potential of bintrafusp alfa via cytochrome P4503A4 (CYP3A4) enzyme modulation, which is responsible for the metabolism of a majority of drugs. The holistic approach included (1) evaluation of longitudinal profiles of cytokines implicated in CYP3A4 modulation and serum 4β‐hydroxycholesterol, an endogenous marker of CYP3A4 activity, in a phase I clinical study, and (2) transcriptomics analysis of the CYP3A4 mRNA levels vs the TGFB gene expression signature in normal hepatic tissues. Bintrafusp alfa was confirmed not to cause relevant proinflammatory cytokine modulation or alterations in 4β‐hydroxycholesterol serum concentrations in phase I studies. Transcriptomics analyses revealed no meaningful correlations between TGFB gene expression and CYP3A4 mRNA expression, supporting the conclusion that the risk of CYP3A4 enzyme modulation due to TGF‐β neutralization by bintrafusp alfa is low. Thus, bintrafusp alfa is not expected to have DDI potential as a perpetrator with co‐administered drugs metabolized by CYP3A4; this information is relevant to clinical evaluations of bintrafusp alfa in combination settings.

Abstract One of the objectives of oncology phase I dose‐escalation studies has been to determine the maximum tolerated dose (MTD). Although MTD is no longer set as the dose for further development in contemporary oncology drug development, MTD determination is still important for informing the therapeutic index. Bayesian adaptive model‐based designs are becoming mainstream in oncology first‐in‐human trials. Herein, we illustrate via simulations the use of systemic exposure in Bayesian adaptive dose–toxicity models to estimate MTD. We extend traditional dose–toxicity models to incorporate pharmacokinetic exposure, which provides information on exposure–toxicity relationships. We pursue dose escalation until the maximum tolerated exposure (corresponding to the MTD) is reached. By leveraging pharmacokinetics, dose escalation considers exposure and interindividual variability on a continuous rather than discrete domain, offering additional information for dose‐escalation decisions. To demonstrate this, we generated 1000 simulations (starting dose of 1/25th the reference dose and six dose levels) for several different scenarios. Both rule‐based and model‐based designs were compared using metrics of potential safety, accuracy, and reliability. The mean results over simulations and different toxicity scenarios showed that model‐based designs were better than rule‐based methods and that exposure–toxicity model‐based methods have the potential to valuably complement dose–toxicity model‐based methods. Exposure–toxicity model‐based methods had decreased underdose risk accompanied by a relatively smaller increase in overdose risk, resulting in improved net reliability. MTD estimation accuracy was compromised when exposure variability was large, emphasizing the importance of appropriate control of pharmacokinetic variability in phase I dose‐escalation studies.

Abstract Enhancing resiliency should elevate innovation and efficiency in biomedical research and development (R&D); however, compared with other professions, data on practice of resilience is lacking. Using the Benatti Resiliency Model (5 anchors: Well‐Being, Self‐Awareness, Brand, Connection, and Innovation), we surveyed professionals, including those in biomedical and pharmaceutical R&D. A structured LinkedIn questionnaire (March 16–May 23, 2021), surveyed each model anchor using five categories. One hundred fifty‐eight participants (~6% student/trainee, 18%, 27%, and 49% in 1–5, 5–15 or >15 years post‐terminal degree) took the survey (90 in biomedical and pharmaceutical R&D). Over 50% chose “always”/“often” across questions, except external influence or engagement. The question with one of the lowest “always” scores (~15%) was “I get feedback on my influence and impact in my career” in Brand, highlighting areas for leadership development and coaching. In the anchor of Well‐being, nutrition and stress management also received some lowest “always” scores (~15% for both). Connection and Innovation scores trended slightly higher in biomedical and pharmaceutical R&D. No students/trainees chose “always” in Brand, indicating evolution of brand maturity over time. Self‐ and survey‐assessed resiliency scores were associated (rs = 0.37, p

Abstract N‐Nitrosamine (NA) impurities are considered genotoxic and have gained attention due to the recall of several marketed drug products associated with higher‐than‐permitted limits of these impurities. Rifampicin is an index inducer of multiple cytochrome P450s (CYPs) including CYP2B6, 2C8, 2C9, 2C19, and 3A4/5 and an inhibitor of OATP1B transporters (single dose). Hence, rifampicin is used extensively in clinical studies to assess drug–drug interactions (DDIs). Despite NA impurities being reported in rifampicin and rifapentine above the acceptable limits, these critical anti‐infective drugs are available for therapeutic use considering their benefit–risk profile. Reports of NA impurities in rifampicin products have created uncertainty around using rifampicin in clinical DDI studies, especially in healthy volunteers. Hence, a systematic investigation through a literature search was performed to determine possible alternative index inducer(s) to rifampicin. The available strong CYP3A inducers were selected from the University of Washington DDI Database and their in vivo DDI potential assessed using the data from clinical DDI studies with sensitive CYP3A substrates. To propose potential alternative CYP3A inducers, factors including lack of genotoxic potential, adequate safety, feasibility of multiple dose administration to healthy volunteers, and robust in vivo evidence of induction of CYP3A were considered. Based on the qualifying criteria, carbamazepine, phenytoin, and lumacaftor were identified to be the most promising alternatives to rifampicin for conducting CYP3A induction DDI studies. Strengths and limitations of the proposed alternative CYP3A inducers, the magnitude of in vivo CYP3A induction, appropriate study designs for each alternative inducer, and future perspectives are presented in this paper.

Abstract Ixazomib is an oral proteasome inhibitor approved in combination with lenalidomide and dexamethasone for the treatment of relapsed/refractory multiple myeloma (MM). Approval in the United States, Europe, and additional countries was based on results from the phase III TOURMALINE‐MM1 (C16010) study. Here, joint population pharmacokinetic/pharmacodynamic time‐to‐event (TTE) and discrete time Markov models were developed to describe key safety (rash and diarrhea events, and platelet counts) and efficacy (myeloma protein [M‐protein] and progression‐free survival [PFS]) outcomes observed in TOURMALINE‐MM1. Models reliably described observed safety and efficacy results; prior immunomodulatory drug therapy and race were significant covariates for diarrhea and rash events, respectively, whereas M‐protein dynamics were sufficiently characterized using TTE models of relapse and dropout. Moreover, baseline M‐protein was identified as a significant covariate for observed PFS. The developed framework represents an integrated approach to describing safety and efficacy with MM therapy, enabling the simulation of prospective trials and potential alternate dosing regimens.

Abstract The ALK in Lung Cancer Trial of brigAtinib in First Line (ALTA‐1L) compared brigatinib versus crizotinib in anaplastic lymphoma kinase (ALK) inhibitor‐naive patients with ALK+ non‐small cell lung cancer (NSCLC). A population pharmacokinetic (PK) model was used to estimate brigatinib exposures for exposure‐efficacy and exposure‐safety analyses in ALTA‐1L. A previously developed population PK model for brigatinib was applied to estimate brigatinib PK parameters. Relationships between static (time‐independent) and dynamic (time‐varying) exposure metrics and efficacy (progression‐free survival [PFS], objective response rate [ORR], and intracranial ORR [iORR]) and safety outcomes (selected grade ≥2 and grade ≥3 adverse events [AEs]) were evaluated using logistic regression and time‐to‐event analyses. There were no meaningful differences in brigatinib PK in the first‐line and second‐line settings, supporting use of the previous population PK model for the first‐line population. Exposure‐response analyses showed no significant effect of time‐varying brigatinib exposure on PFS. Brigatinib exposure was not significantly related to ORR, but higher exposure was associated with higher iORR (odds ratio: 1.13, 95% confidence interval: 1.01–1.28, p = 0.049). Across the observed median exposure (5th–95th percentile) at steady state for 180 mg once daily, the predicted probability of iORR was 0.83 (0.58–0.99). AEs significantly associated with higher exposure were elevated lipase (grade ≥3) and amylase (grade ≥2). Time to first brigatinib dose reduction was not related to exposure. These results support the benefit‐risk profile of first‐line brigatinib 180 mg once daily (7‐day lead‐in dose at 90 mg once daily) in patients with ALK+ NSCLC.

Abstract Avelumab (anti–PD‐L1) is an approved anticancer treatment for several indications. The JAVELIN Gastric 100 phase III trial did not meet its primary objective of demonstrating superior overall survival (OS) with avelumab maintenance versus continued chemotherapy in patients with advanced gastric cancer/gastroesophageal junction cancer; however, the OS rate was numerically higher with avelumab at timepoints after 12 months. Machine learning (random forests, SIDEScreen, and variable‐importance assessments) was used to build models to identify prognostic/predictive factors associated with long‐term OS and tumor growth dynamics (TGDs). Baseline, re‐baseline, and longitudinal variables were evaluated as covariates in a parametric time‐to‐event model for OS and Gompertzian population model for TGD. The final OS model incorporated a treatment effect on the log‐logistic shape parameter but did not identify a treatment effect on OS or TGD. Variables identified as prognostic for longer OS included older age; higher gamma‐glutamyl transferase (GGT) or albumin; absence of peritoneal carcinomatosis; lower neutrophil‐lymphocyte ratio, lactate dehydrogenase, or C‐reactive protein (CRP); response to induction chemotherapy; and Eastern Cooperative Oncology Group performance status of 0. Among baseline and time‐varying covariates, the largest effects were found for GGT and CRP, respectively. Liver metastasis at re‐baseline predicted higher tumor growth. Tumor size after induction chemotherapy was associated with number of metastatic sites and stable disease (vs. response). Asian region did not impact OS or TGD. Overall, an innovative workflow supporting pharmacometric modeling of OS and TGD was established. Consistent with the primary trial analysis, no treatment effect was identified. However, potential prognostic factors were identified.

Abstract Cladribine tablets have been approved in many countries for the treatment of patients with various forms of relapsing multiple sclerosis (MS). Cladribine has a unique pharmacokinetic/pharmacodynamic (PK/PD) profile with a short elimination half‐life (~ 1 day) relative to a prolonged PD effect on specific immune cells (most notably a reversible reduction in B and T lymphocyte counts). This results in a short dosing schedule (up to 20 days over 2 years of treatment) to sustain efficacy for at least another 2 years. Global clinical studies were conducted primarily in White patients, in part due to the distinctly higher prevalence of MS in White patients. Given the very low prevalence in Asian countries, MS is considered as a rare disease there. In spite of the limited participation of Asian patients, to demonstrate favorable benefit/risk profile in the treatment of MS demanded application of a Totality of Evidence approach to assess ethnic sensitivity for informing regulatory filings in Asian countries and supporting clinical use of cladribine in Asian patients. Population PD modeling and simulation of treatment‐related reduction in absolute lymphocyte count, as a mechanism‐related biomarker of drug effect, confirmed consistent PDs in Asian and non‐Asian patients with MS, supporting absence of ethnic sensitivity and a common dosage across populations. Through this example, we demonstrate the value of holistic integration of all available data using a model‐informed drug development (MIDD) framework and a Totality of Evidence mindset to evaluate ethnic sensitivity in support of Asia‐inclusive development and use of the drug across populations.

Abstract The investigational NEDD8‐activating enzyme inhibitor pevonedistat is being evaluated in combination with azacitidine versus single‐agent azacitidine in patients with higher‐risk myelodysplastic syndrome (higher‐risk MDS), higher‐risk chronic myelomonocytic leukemia (higher‐risk CMML), or low‐blast acute myeloid leukemia (AML) in a Phase 3 trial PANTHER. To support Asia‐inclusive global development, we applied multiregional clinical trial (MRCT) principles of the International Conference on Harmonisation E17 guidelines by evaluating similarity in drug‐related and disease‐related intrinsic and extrinsic factors. A PubMed literature review (January 2000–November 2019) supported similarity in epidemiology of higher‐risk MDS, AML, and CMML in Western and East Asian populations. Furthermore, the treatment of MDS/AML was similar in both East Asian and Western regions, with the same dose of azacitidine being the standard of care. Median overall survival in MDS following azacitidine treatment was generally comparable across regions, and the types and frequencies of molecular alterations in AML and MDS were comparable. Dose‐escalation studies established the same maximum tolerated dose of pevonedistat in combination with azacitidine in Western and East Asian populations. Pevonedistat clearance was similar across races. Taken together, conservation of drug‐related and disease‐related intrinsic and extrinsic factors supported design of an Asia‐inclusive Phase 3 trial and a pooled East Asian region. A sample size of ~ 30 East Asian patients (of ~ 450 randomized) was estimated as needed to demonstrate consistency in efficacy relative to the global population. This analysis is presented as an exemplar to illustrate application of clinical pharmacology and translational science principles in designing Asia‐inclusive MRCTs. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? Azacitidine is the standard of care for myelodysplastic syndromes/low‐blast acute myeloid leukemia (AML) across Western and East Asian patients. The first‐in‐class small‐molecule inhibitor of NEDD8‐activating enzyme, pevonedistat, has been investigated as a single agent in multiple studies of hematologic and nonhematologic malignancies and in combination with azacitidine in elderly patients with untreated AML. WHAT QUESTION DID THIS STUDY ADDRESS? By applying clinical pharmacology and translational science and International Conference on Harmonisation E17 principles, this study designed an East Asian‐inclusive global pivotal Phase 3 trial of pevonedistat, taking into consideration drug‐related and disease‐related intrinsic and extrinsic factors. WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE? These analyses provide scientific rationale for Asia‐inclusive globalization of the pivotal, Phase 3 PANTHER trial and for pooling clinical data across the East Asian region for assessing consistency in efficacy. HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE? We developed a framework to facilitate efficient global clinical development of investigational therapies for rare cancers and orphan diseases in Asia‐inclusive multiregional clinical trials.