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JNJ-73763989 is an N-acetylgalactosamine conjugated short interfering RNA combination product consisting of two triggers in clinical development for chronic hepatitis B virus (HBV) infection treatment that induces a selective degradation of all HBV mRNA transcripts. Our aim is to characterize the plasma and liver pharmacokinetics (PK) of JNJ-73763989 after intravenous and subcutaneous administration in recombinant adeno-associated (rAAV) HBV infected mice. Forty-two male rAAV-HBV infected C57Bl/6 mice received JNJ-73763989 doses of 10 mg/kg i.v. or 1, 3 and 10 mg/kg s.c. Plasma and liver concentrations were analyzed simultaneously using nonlinear mixed-effects modeling with the NONMEM 7.4. A population PK model consisting of a two-compartment disposition model with transporter-mediated drug disposition, including internalization to the liver compartment, linear elimination from plasma and liver, and first-order absorption following subcutaneous administration, was suitable to describe both plasma and liver PK. After subcutaneous dosing, absolute bioavailability was complete and flip-flop kinetics were observed. JNJ-73763989 distributes from plasma to liver via transporter-mediated liver internalization in less than 24 hours, with sustained (gt;42 days) liver exposure. The saturation of transporter-mediated liver internalization was hypothesized to be due to asialoglycoprotein receptor saturation. Increasing the dose decreased the relative liver uptake efficiency in mice for intravenously and, to a lesser extent, subcutaneously administered JNJ-73763989. Lower dose levels administered subcutaneously in mice can maximize the proportion of the dose reaching the liver. SIGNIFICANCE STATEMENT: Pharmacokinetic modeling of JNJ-73763989 liver and plasma concentration-time data in mice indicated that the proportion of JNJ-73763989 reaching the liver may be increased by administering lower subcutaneous doses compared to higher intravenous doses. Model-based simulations can be applied to optimize the dose and regimen combination.

Background Exposure–response analyses were conducted to explore the relationship between selected efficacy and safety endpoints and serum phosphate (PO4) concentrations, a potential biomarker of efficacy and safety, in locally advanced or metastatic urothelial carcinoma patients with FGFR alterations treated with erdafitinib. Methods Data from two dosing regimens of erdafitinib in a phase 2 study (NCT02365597), 6 and 8-mg/day with provision for pharmacodynamically guided titration per serum PO4 levels, were analyzed using Cox proportional hazard or logistic regression models. Efficacy endpoints were overall survival (OS), progression-free survival (PFS), and objective response rate (ORR). Safety endpoints were adverse events typical for FGFR inhibitors. Results Exposure-efficacy analyses on 156 patients (6-mg = 68; 8-mg = 88) showed that patients with higher serum PO4 levels within the first 6 weeks showed better OS (hazard ratio 0.57 [95% CI 0.46–0.72] per mg/dL of PO4; p = 0.01), PFS (hazard ratio 0.80 [0.67–0.94] per mg/dL of PO4; p = 0.01), and ORR (odds ratio 1.38 [1.02–1.86] per mg/dL of PO4; p = 0.04). Exposure-safety analyses on 177 patients (6-mg = 78; 8-mg = 99) showed that the incidence of selected adverse events associated with on-target off-tumor effects significantly rose with higher PO4. Conclusions The exploratory relationship between serum PO4 levels and efficacy/safety outcomes supported the use of pharmacodynamically guided dose titration to optimize erdafitinib’s therapeutic benefit/risk ratio. Clinical trial registration number NCT02365597.

A population pharmacokinetic (PK)-pharmacodynamic (PD) model was developed using data from 345 patients with cancer. The population PK-PD model evaluated the effect of erdafitinib total and free plasma concentrations on serum phosphate concentrations after once-daily oral continuous (0.5-12 mg) and intermittent (10-12 mg for 7 days on/7 days off) dosing, and investigated the potential covariates affecting erdafitinib-related changes in serum phosphate levels. Phosphate is used as a biomarker for erdafitinib's efficacy and safety: increases in serum phosphate were observed after dosing with erdafitinib, which were associated with fibroblast growth factor receptor target engagement via inhibition of renal fibroblast growth factor 23-mediated signaling. PK-PD model-based simulations were performed to assess the approved PD-guided dosing algorithm of erdafitinib (8 mg once-daily continuous dosing, with up-titration to 9 mg based on phosphate levels [5.5 mg/dl] and tolerability at 14-21 days of treatment). The serum phosphate concentrations increased after the first dose and reached near maximal level after 14 days of continuous treatment. Serum phosphate increased with erdafitinib free drug concentrations: doubling the free concentration resulted in a 1.8-fold increase in drug-related phosphate changes. Dose adjustment after at least 14 days of dosing was supported by achievement of95% maximal serum phosphate concentration. The peak-to-trough fluctuation within a dosing interval was limited for serum phosphate concentrations (5.68-5.65 mg/dl on Day 14), supporting phosphate monitoring at any time relative to dosing. Baseline phosphate was higher in women, otherwise, none of the investigated covariate-parameter relationships were considered clinically relevant. Simulations suggest that the starting dose of 8-mg with up-titration to 9-mg on Days 14-21 maximized the number of patients within the target serum phosphate concentrations (5.5-7 mg/dl) while limiting the number of treatment interruptions. The findings from the PK-PD model provided a detailed understanding of the erdafitinib concentration-related phosphate changes over time, which supports erdafitinib's dosing algorithm.

Erdafitinib (JNJ-42756493, BALVERSA) is a tyrosine kinase inhibitor indicated for the treatment of advanced urothelial carcinoma. In this work, a translational model-based approach to inform the choice of the doses in phase 1 trials is illustrated. A pharmacokinetic (PK) model was developed to describe the time course of erdafitinib plasma concentrations in mice and rats. Data from multiple xenograft studies in mice and rats were analyzed using the Simeoni tumor growth inhibition (TGI) model. The model parameters were used to derive a range of erdafitinib exposures that might inform the choice of the doses in oncology phase 1 trials. Conversion of exposures to doses was based on preliminary PK assessments from the first-in human (FIH) study. A one-compartment PK disposition model, with linear absorption and dose-dependent clearance, adequately described the PK data in both mice and rats via an allometric scaling approach. The TGI model was able to describe tumor growth dynamics, providing quantitative measurements of erdafitinib antitumor potency in mice and rats. Based on these estimates, ranges of efficacious unbound concentration were identified for erdafitinib in mice (0.642–5.364 μg/L) and rats (0.782–2.565 μg/L). Based on the FIH data, it was possible to transpose exposures into doses and doses of above 4 mg/day provided erdafitinib exposures associated with significant TGI in animals. The findings were in agreement with the results of the FIH trial, in which the first hints of clinical activities were observed at 6 mg. The successful modeling exercise of erdafitinib preclinical data showed how translational PK-PD modeling might be a tool to help to inform the choice of the doses in FIH studies.

Macitentan and its active metabolite, aprocitentan, are non-peptide, potent, dual endothelin receptor antagonists. Macitentan is approved for the treatment of pulmonary arterial hypertension in adults, at a dose of 10 mg/day. The objective of this study was to develop a comprehensive population model to describe the pharmacokinetics of macitentan and aprocitentan in healthy adults and adult subjects with pulmonary arterial hypertension. Pharmacokinetic data of 452 subjects in nine studies, after single and repeated doses (dose range 0.2–600 mg), were pooled for a non-linear mixed-effects analysis and the assessment of covariates, i.e., body weight, age, sex, race, renal and hepatic impairment, health status (healthy volunteers vs patients with pulmonary arterial hypertension), and formulation (capsules vs tablets) on pharmacokinetic parameters. The final model was an open one-compartment disposition model, with linear elimination for macitentan and linear formation and elimination for aprocitentan. A semi-mechanistic absorption model described the dose dependency and multiple peaks observed for macitentan. For a female patient with pulmonary arterial hypertension after oral administration at 10 mg, macitentan reached a maximum concentration after 9 h and, following daily dosing, reached steady state after 3 days with a twofold accumulation factor. The apparent volume of distribution was 34 L and clearance was 1.39 L/h. Aprocitentan reached maximum concentration after 51 h and steady state after 9 days, with a 12.5-fold accumulation factor. Body weight, sex, race, renal impairment, health status, and formulation were statistically significant covariates on pharmacokinetic parameters. The comprehensive population pharmacokinetic model adequately described the pharmacokinetics of macitentan and aprocitentan across different dose concentrations, regimens, and formulations. Several covariates significantly influenced the pharmacokinetics of macitentan and aprocitentan, but none was considered clinically relevant.

A population pharmacokinetic (PK) model was developed using data pooled from 6 clinical studies (3 in healthy volunteers and 3 in cancer patients) to characterize total and free plasma concentrations of erdafitinib following single- and multiple-dose administration, to understand clinically relevant covariates, and to quantify the inter- and intraindividual variability in erdafitinib PK. An open, linear, 3-compartment disposition model with first-order absorption and a lag time was used to describe the PK profile of total and free erdafitinib plasma concentrations. The PK of erdafitinib were linear and time independent. After oral administration, erdafitinib was rapidly absorbed, with a time to maximum concentration between 2 and 4 hours. In patients, erdafitinib total apparent oral clearance was 0.200 L/h (median free fraction = 0.24%), and the effective terminal half-life of total drug was 76.4 hours. Interindividual variability in PK parameters was moderate for oral clearance and central volume of distribution, and large for absorption rate and peripheral volume of distribution. Sex and renal function were significant covariates on free oral clearance, while weight, sex, and α1 -acid-glycoprotein were significant on oral central volume of distribution. Age, race, and mild hepatic impairment were not significant covariates of erdafitinib exposure. Given that the magnitude of the covariate effects were within 25% of reference values and that the recommended dosing regimen of erdafitinib comprises individual dose up-titrations and reductions based on presence or absence of toxicities, the clinical relevance of the investigated covariates is expected to be limited, and no dose adjustments are warranted.

Assessing the QT prolongation potential of a drug is typically done based on pivotal safety studies called thorough QT studies. Model-based estimation of the drug-induced QT prolongation at the estimated mean maximum drug concentration could increase efficiency over the currently used intersection-union test. However, robustness against model misspecification needs to be guaranteed in pivotal settings. The objective of this work was to develop an efficient, fully prespecified model-based inference method for thorough QT studies, which controls the type I error and provides satisfactory test power. This is achieved by model averaging: The proposed estimator of the concentration-response relationship is a weighted average of a parametric (linear) and a nonparametric (monotonic I-splines) estimator, with weights based on mean integrated square error. The desired properties of the method were confirmed in an extensive simulation study, which demonstrated that the proposed method controlled the type I error adequately, and that its power was higher than the power of the nonparametric method alone. The method can be extended from thorough QT studies to the analysis of QT data from pooled phase I studies.

To characterize the effect of erdafitinib on electrocardiogram (ECG) parameters and the relationship between erdafitinib plasma concentrations and QTc interval changes in patients with advanced or refractory solid tumors. Triplicate ECGs and continuous 12-lead Holter data were collected in the dose escalation part (Part 1) of the first-in-human study, with doses ranging from 0.5 to 12 mg. Triplicate ECG monitoring continued in Parts 2–4 where 2 dose regimens selected from Part 1 were expanded in prespecified tumor types. Analyses of ECG data included central tendency analyses, identification of categorical outliers and morphological assessment. A concentration–QTc analysis was conducted using a linear mixed-effect model based on extracted time matching Holter data. Central tendency, categorical outlier, and ECG morphologic analyses from 187 patients revealed no clinically significant effect of erdafitinib on heart rate, atrioventricular conduction or cardiac depolarization (PR and QRS), and no effect on cardiac repolarization (QTc). Concentration–QTc analysis from 62 patients indicated that the slopes of relationship between total and free erdafitinib plasma concentrations and QTcI (mean exponent of 0.395) were estimated as − 0.00269 ms/(ng/mL) and − 1.138 ms/(ng/mL), respectively. The predicted change in QTcI at the observed geometric mean of total and free concentration at the highest therapeutic erdafitinib dose (9 mg daily) was

Albumin concentration and body weight are altered in patients with multidrug-resistant tuberculosis (MDR-TB) and change during the long treatment period, potentially affecting drug disposition. We here describe the pharmacokinetics (PKs) of the novel anti-TB drug bedaquiline and its metabolite M2 in 335 patients with MDR-TB receiving 24 weeks of bedaquiline on top of a longer individualized background regimen. Semiphysiological models were developed to characterize the changes in weight and albumin over time. Bedaquiline and M2 disposition were well described by three and one-compartment models, respectively. Weight and albumin were correlated, typically increasing after the start of treatment, and significantly affected bedaquiline and M2 plasma disposition. Additionally, age and race were significant covariates, whereas concomitant human immunodeficiency virus (HIV) infection, sex, or having extensively drug-resistant TB was not. This is the first population model simultaneously characterizing bedaquiline and M2 PKs in its intended use population. The developed model will be used for efficacy and safety exposure-response analyses. Title in Thesis list of papers: Population pharmacokinetics of bedaquiline and metabolite M2 in drug-resistant tuberculosis patients – the effect of time-varying weight and albumin

Knowledge of the uncertainty in model parameters is essential for decision-making in drug development. Contrarily to other aspects of nonlinear mixed effects models (NLMEM), scrutiny towards assumptions around parameter uncertainty is low, and no diagnostic exists to judge whether the estimated uncertainty is appropriate. This work aims at introducing a diagnostic capable of assessing the appropriateness of a given parameter uncertainty distribution. The new diagnostic was applied to case bootstrap examples in order to investigate for which dataset sizes case bootstrap is appropriate for NLMEM. The proposed diagnostic is a plot comparing the distribution of differences in objective function values (dOFV) of the proposed uncertainty distribution to a theoretical Chi square distribution with degrees of freedom equal to the number of estimated model parameters. The uncertainty distribution was deemed appropriate if its dOFV distribution was overlaid with or below the theoretical distribution. The diagnostic was applied to the bootstrap of two real data and two simulated data examples, featuring pharmacokinetic and pharmacodynamic models and datasets of 20–200 individuals with between 2 and 5 observations on average per individual. In the real data examples, the diagnostic indicated that case bootstrap was unsuitable for NLMEM analyses with around 70 individuals. A measure of parameter-specific “effective” sample size was proposed as a potentially better indicator of bootstrap adequacy than overall sample size. In the simulation examples, bootstrap confidence intervals were shown to underestimate inter-individual variability at low sample sizes. The proposed diagnostic proved a relevant tool for assessing the appropriateness of a given parameter uncertainty distribution and as such it should be routinely used.

Quantifying the uncertainty around endpoints used for decision-making in drug development is essential. In nonlinear mixed-effects models (NLMEM) analysis, this uncertainty is derived from the uncertainty around model parameters. Different methods to assess parameter uncertainty exist, but scrutiny towards their adequacy is low. In a previous publication, sampling importance resampling (SIR) was proposed as a fast and assumption-light method for the estimation of parameter uncertainty. A non-iterative implementation of SIR proved adequate for a set of simple NLMEM, but the choice of SIR settings remained an issue. This issue was alleviated in the present work through the development of an automated, iterative SIR procedure. The new procedure was tested on 25 real data examples covering a wide range of pharmacokinetic and pharmacodynamic NLMEM featuring continuous and categorical endpoints, with up to 39 estimated parameters and varying data richness. SIR led to appropriate results after 3 iterations on average. SIR was also compared with the covariance matrix, bootstrap and stochastic simulations and estimations (SSE). SIR was about 10 times faster than the bootstrap. SIR led to relative standard errors similar to the covariance matrix and SSE. SIR parameter 95% confidence intervals also displayed similar asymmetry to SSE. In conclusion, the automated SIR procedure was successfully applied over a large variety of cases, and its user-friendly implementation in the PsN program enables an efficient estimation of parameter uncertainty in NLMEM. Electronic supplementary material The online version of this article (doi:10.1007/s10928-017-9542-0) contains supplementary material, which is available to authorized users.

Background Erdafitinib (Erda), a pan-FGFR tyrosine kinase inhibitor, recently received accelerated approval by US FDA for treatment of post-platinum advanced UC pts with certain FGFR3 or 2 alterations. Erda induces dose dependent hyperphosphatemia via inhibition of PO4 excretion. Methods Ph1 data and PK/PD modeling identified a serum PO4 level of ≥ 5.5 mg/dL as a PD target with acceptable tolerability. In a ph2 study (NCT02365597) pts started Erda at 8mg/day, serum PO4 was assessed every cycle, and Cycle 1 Day 14 (C1D14). Up-titration on C1D15 to 9mg/day occurred if serum PO4 was Results 99 patients started 8 mg Erda and 41% were up-titrated to 9 mg based on C1D14 PO4 levels. The ORR and DCR were higher (42.7% and 85.3%) when maximum PO4 levels were >5.5 mg/dL as compared to below (34.8% and 65.2%, respectively). Median OS and PFS were prolonged with levels >5.5 mg/dL as compared to below with hazard ratio of 0.35 (median not reached vs 6.7 months) and 0.68 (5.6 vs 3.8 months), respectively. Among patients that could not be up-titrated due to concurrent toxicities, ORR was 27.3% (n = 22; 95% CI- 8.7%, 45.9%) as compared to 48.8% (n = 41; 95% CI-33.5%, 64.1%) for patients who were up-titrated. Logistic regression analysis (ORR, DCR), and Cox regression (PFS, OS) showed that maximum phosphate levels positively correlated with ORR, DCR, PFS, and OS (Table). Table . 932P ORR, DCR, PFS, OS and maximum on-treatment serum PO4 * ORR DCR PFS OS ORb, (95% CI), p-valuec ORb, (95% CI), p-valuec HR, (95% CI), p-valuee HR, (95% CI), p-valuee Prognostic variablea Maximum PO4: per 1 mg/dL increase 1.25, (0.86, 1.81), 0.247 2.31, (1.31, 4.08), 0.004 0.78, (0.63, 0.96), 0.021 0.58, (0.43, 0.80), 0.001 Simulated outcomes via PK/PD modeling of up-titrating from 8 mg to 9 mg QDf 1.10 (0.93, 1.29), - 1.43 (1.12, 1.83), - 0.90 (0.82, 0.98), - 0.79 (0.70, 0.91), - * The median time to maximum = 1.3m. Note: a. variable in model. b. odds ratio c. Pearson’s chi-square p-value e. Pearson’s chi-square p-value f. derived from first row using OR0.43 and HR0.43, based on PKPD simulations predicting average 0.43 mg/dL increase in maximum PO4 from 8 to 9 mg at steady state. Conclusions Hyperphosphatemia in Erda treated pts is associated with improved outcomes, supporting optimization of Erda dosing via uptitration based on serum PO4. Clinical trial identification NCT02365597, February 19, 2015. Editorial acknowledgement Himabindu Gutha (SIRO Clinpharm Pvt. Ltd.) writing assistance, Dr. Harry Ma (Janssen Research & Development, LLC) additional editorial support. Legal entity responsible for the study Janssen Research & Development. Funding Janssen Research & Development. Disclosure S.T. Tagawa: Advisory / Consultancy: Medivation, Astellas Pharma, Dendreon, Janssen, Bayer, Genentech, Endocyte, Immunomedics, Karyopharm Therapeutics, AbbVie, Tolmar, QED, Amgen, Sanofi; Research grant / Funding (institution): Lily, Sanofi, Janssen, Astellas Pharma, Progenics, Millennium, Amgen, Bristol-Myers Squibb, Dendreon, Rexahn Pharmaceuticals, Bayer, Genentech, Newlink Genetics, Inovio Pharmaceuticals, AstraZeneca, Immunomedics, Novartis, AVEO, Boehringer Ingelheim, Merc; Travel / Accommodation / Expenses: Sanofi, Immunomedics, Amgen. A.O. Siefker-Radtke: Advisory / Consultancy: Janssen, Threshold Pharmaceuticals, Merck, National Comprehensive Cancer Network, Eisai, Genentech, Vertex, AstraZeneca, EMD Serono, Bristol-Myers Squibb; Speaker Bureau / Expert testimony: Genentech; Research grant / Funding (self): National Institutes of Health, Genentech, Janssen Pharmaceuticals, Millennium, Michael and Sherry Sutton Fund for Urothelial Cancer. A. Dosne: Full / Part-time employment: Janssen Research and Development. B. Zhong: Full / Part-time employment: Janssen Research and Development. W.S. Shalaby: Full / Part-time employment: Janssen Research and Development. A. O’Hagan: Full / Part-time employment: Janssen Research and Development. K. Qi: Stockholder / Stock options, Full / Parttime employment: Janssen Research and Development. P. De Porre: Full / Part-time employment: Janssen Research and Development. P. Mahadevia: Full / Part-time employment: Janssen Research and Development. Y. Loriot: Advisory / Consultancy: Astellas Oncology; AstraZeneca; Ipsen; Janssen; MSD; Roche; Sanofi; Research grant / Funding (self): Sanofi (Inst); Travel / Accommodation / Expenses: AstraZeneca; MSD; Roche. All other authors have declared no conflicts of interest.

Taking parameter uncertainty into account is key to make drug development decisions such as testing whether trial endpoints meet defined criteria. Currently used methods for assessing parameter uncertainty in NLMEM have limitations, and there is a lack of diagnostics for when these limitations occur. In this work, a method based on sampling importance resampling (SIR) is proposed, which has the advantage of being free of distributional assumptions and does not require repeated parameter estimation. To perform SIR, a high number of parameter vectors are simulated from a given proposal uncertainty distribution. Their likelihood given the true uncertainty is then approximated by the ratio between the likelihood of the data given each vector and the likelihood of each vector given the proposal distribution, called the importance ratio. Non-parametric uncertainty distributions are obtained by resampling parameter vectors according to probabilities proportional to their importance ratios. Two simulation examples and three real data examples were used to define how SIR should be performed with NLMEM and to investigate the performance of the method. The simulation examples showed that SIR was able to recover the true parameter uncertainty. The real data examples showed that parameter 95 % confidence intervals (CI) obtained with SIR, the covariance matrix, bootstrap and log-likelihood profiling were generally in agreement when 95 % CI were symmetric. For parameters showing asymmetric 95 % CI, SIR 95 % CI provided a close agreement with log-likelihood profiling but often differed from bootstrap 95 % CI which had been shown to be suboptimal for the chosen examples. This work also provides guidance towards the SIR workflow, i.e.,which proposal distribution to choose and how many parameter vectors to sample when performing SIR, using diagnostics developed for this purpose. SIR is a promising approach for assessing parameter uncertainty as it is applicable in many situations where other methods for assessing parameter uncertainty fail, such as in the presence of small datasets, highly nonlinear models or meta-analysis. Electronic supplementary material The online version of this article (doi:10.1007/s10928-016-9487-8) contains supplementary material, which is available to authorized users.

Nonlinear mixed effects models parameters are commonly estimated using maximum likelihood. The properties of these estimators depend on the assumption that residual errors are independent and normally distributed with mean zero and correctly defined variance. Violations of this assumption can cause bias in parameter estimates, invalidate the likelihood ratio test and preclude simulation of real-life like data. The choice of error model is mostly done on a case-by-case basis from a limited set of commonly used models. In this work, two strategies are proposed to extend and unify residual error modeling: a dynamic transform-both-sides approach combined with a power error model (dTBS) capable of handling skewed and/or heteroscedastic residuals, and a t-distributed residual error model allowing for symmetric heavy tails. Ten published pharmacokinetic and pharmacodynamic models as well as stochastic simulation and estimation were used to evaluate the two approaches. dTBS always led to significant improvements in objective function value, with most examples displaying some degree of right-skewness and variances proportional to predictions raised to powers between 0 and 1. The t-distribution led to significant improvement for 5 out of 10 models with degrees of freedom between 3 and 9. Six models were most improved by the t-distribution while four models benefited more from dTBS. Changes in other model parameter estimates were observed. In conclusion, the use of dTBS and/or t-distribution models provides a flexible and easy-to-use framework capable of characterizing all commonly encountered residual error distributions. Electronic supplementary material The online version of this article (doi:10.1007/s10928-015-9460-y) contains supplementary material, which is available to authorized users.

411 Background: Although immune checkpoint inhibitors (ICI) have improved outcomes in some pts with platinum-resistant mUC, many pts (eg, pts with TCGA luminal 1 tumors, many of whom are FGFRa) may not benefit. ERDA, a pan-FGFR (1-4) inhibitor, demonstrated promising phase 1 activity: 11 partial responses among 24 FGFRa mUC pts. We report efficacy and safety of ERDA in the ongoing global open-label phase 2 study BLC2001 (NCT02365597). Methods: Pts had measurable mUC with specific FGFR2/ FGFR3 mutations or translocations per central lab Janssen assay, ECOG 0-2, and were chemorefractory (progressed during/following ≥ 1 line of prior systemic chemo or ≤ 12 mos of [neo]adjuvant chemo). Cisplatin-ineligible, chemo-naïve pts, and prior ICI treatment were allowed. Pts were randomized 1:1 to 28-d cycles of oral 6 mg/d continuous dosing (6 C) or 10 mg/d intermittent 7 d on/7 d off dosing (10 I) ERDA; the dose was further uptitrated if no significant treatment-related adverse events (TRAEs) were observed. The primary end point was ORR. Results: 78 pts received 6 C and 33 pts received 10 I (10 I cohort stopped early) ERDA. 31 pts in 6 C arm were further uptitrated. Across arms, 50% had ≥ 2 prior lines of therapy; 93% were chemorefractory. Confirmed ORRs (RECIST 1.1) were 35% and 24%, and disease control rates (CR+PR+SD) were 74% and 73% in the 6 C and 10 I arms, respectively. Adverse events (AEs) were manageable, and there were no treatment-related deaths (Table). Treatment is ongoing in 10 pts. Conclusions: ERDA (6 C or 10 I) has promising efficacy and tolerability in pts with FGFRa mUC. Based on these results and ERDA pharmacometric modeling, dosing was optimized at 8 mg/d (continuous), and this cohort is ongoing. Phase 3 study is planned. Clinical trial information: NCT02365597. [Table: see text]