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Background: UCART191 is an “off-the-shelf” genome-edited anti-CD19 chimeric antigen receptor (CAR)-T cell product, manufactured from unrelated healthy donor cells. Methods: UCART19 was administered to 25 adult patients with relapsed or refractory (R/R) B-cell acute lymphoblastic leukemia (B-ALL) in the CALM trial. All patients underwent lymphodepletion with fludarabine and cyclophosphamide ± alemtuzumab and received one of three ascending doses of UCART19. Given the allogeneic nature of UCART19, we analyzed the impact of lymphodepletion, HLA disparities, and host immune system reconstitution on its kinetics, along with other factors known to affect autologous CAR-T cell clinical pharmacology. Results: Responder patients (12/25) had higher UCART19 expansion (Cmax) and exposure (AUCTlast) than nonresponders (13/25), as measured by transgene levels in peripheral blood. The persistence of CAR+ T cells did not exceed 28 days in 10/25 patients and lasted beyond 42 days in 4/25. No significant correlation was found between UCART19 kinetics and administered cell dose, patient and product characteristics or HLA disparities. However, the number of prior lines of therapy and absence of alemtuzumab negatively impacted UCART19 expansion and persistence. Alemtuzumab exposure positively affected IL7 and UCART19 kinetics, while negatively correlating with host T lymphocyte AUC0-28. Conclusions: UCART19 expansion is a driver of response in adult patients with R/R B-ALL. These results shed light on the factors associated with UCART19 kinetics, which remain highly affected by the impact of alemtuzumab on IL7 and host-versus-graft rejection. Significance: First description of the clinical pharmacology of a genome-edited allogeneic anti-CD19 CAR-T cell product showing the crucial role of an alemtuzumab-based regimen in sustaining UCART19 expansion and persistence through increased IL7 availability and decreased host T lymphocyte population.

Chimeric antigen receptor (CAR)-T cell therapies have shown tremendous results against various hematologic cancers. Prior to cell infusion, a host preconditioning regimen is required to achieve lymphodepletion and improve CAR-T cell pharmacokinetic exposure, leading to greater chances of therapeutic success. To better understand and quantify the impact of the preconditioning regimen, we built a population-based mechanistic pharmacokinetic-pharmacodynamic model describing the complex interplay between lymphodepletion, host immune system, homeostatic cytokines, and pharmacokinetics of UCART19, an allogeneic product developed against CD19+ B cells. Data were collected from a phase I clinical trial in adult relapsed/refractory B-cell acute lymphoblastic leukemia and revealed three different UCART19 temporal patterns: (i) expansion and persistence, (ii) transient expansion with subsequent rapid decline, and (iii) absence of observed expansion. On the basis of translational assumptions, the final model was able to capture this variability through the incorporation of IL-7 kinetics, which are thought to be increased owing to lymphodepletion, and through an elimination of UCART19 by host T cells, which is specific to the allogeneic context. Simulations from the final model recapitulated UCART19 expansion rates in the clinical trial, confirmed the need for alemtuzumab to observe UCART19 expansion (along with fludarabine cyclophosphamide), quantified the importance of allogeneic elimination, and suggested a high impact of multipotent memory T-cell subpopulations on UCART19 expansion and persistence. In addition to supporting the role of host cytokines and lymphocytes in CAR-T cell therapy, such a model could help optimizing the preconditioning regimens in future clinical trials. Significance: A mathematical mechanistic pharmacokinetic/pharmacodynamic model supports and captures quantitatively the beneficial impact of lymphodepleting patients before the infusion of an allogeneic CAR-T cell product. Mediation through IL-7 increase and host T lymphocytes decrease is underlined, and the model can be further used to optimize CAR-T cell therapies lymphodepletion regimen.

Background:UCART191 is an “off-the-shelf” genome-edited anti-CD19 chimeric antigen receptor (CAR)-T cell product, manufactured from unrelated healthy donor cells.Methods:UCART19 was administered to 25 adult patients with relapsed or refractory (R/R) B-cell acute lymphoblastic leukemia (B-ALL) in the CALM trial. All patients underwent lymphodepletion with fludarabine and cyclophosphamide ± alemtuzumab and received one of three ascending doses of UCART19. Given the allogeneic nature of UCART19, we analyzed the impact of lymphodepletion, HLA disparities, and host immune system reconstitution on its kinetics, along with other factors known to affect autologous CAR-T cell clinical pharmacology.Results:Responder patients (12/25) had higher UCART19 expansion (Cmax) and exposure (AUCTlast) than nonresponders (13/25), as measured by transgene levels in peripheral blood. The persistence of CAR+ T cells did not exceed 28 days in 10/25 patients and lasted beyond 42 days in 4/25. No significant correlation was found between UCART19 kinetics and administered cell dose, patient and product characteristics or HLA disparities. However, the number of prior lines of therapy and absence of alemtuzumab negatively impacted UCART19 expansion and persistence. Alemtuzumab exposure positively affected IL7 and UCART19 kinetics, while negatively correlating with host T lymphocyte AUC0-28.Conclusions:UCART19 expansion is a driver of response in adult patients with R/R B-ALL. These results shed light on the factors associated with UCART19 kinetics, which remain highly affected by the impact of alemtuzumab on IL7 and host-versus-graft rejection.Significance:First description of the clinical pharmacology of a genome-edited allogeneic anti-CD19 CAR-T cell product showing the crucial role of an alemtuzumab-based regimen in sustaining UCART19 expansion and persistence through increased IL7 availability and decreased host T lymphocyte population.

Chimeric antigen receptor (CAR)-T cell therapies have shown tremendous results against various hematologic cancers. Prior to cell infusion, a host preconditioning regimen is required to achieve lymphodepletion and improve CAR-T cell pharmacokinetic exposure, leading to greater chances of therapeutic success. To better understand and quantify the impact of the preconditioning regimen, we built a population-based mechanistic pharmacokinetic-pharmacodynamic model describing the complex interplay between lymphodepletion, host immune system, homeostatic cytokines, and pharmacokinetics of UCART19, an allogeneic product developed against CD19+ B cells. Data were collected from a phase I clinical trial in adult relapsed/refractory B-cell acute lymphoblastic leukemia and revealed three different UCART19 temporal patterns: (i) expansion and persistence, (ii) transient expansion with subsequent rapid decline, and (iii) absence of observed expansion. On the basis of translational assumptions, the final model was able to capture this variability through the incorporation of IL-7 kinetics, which are thought to be increased owing to lymphodepletion, and through an elimination of UCART19 by host T cells, which is specific to the allogeneic context. Simulations from the final model recapitulated UCART19 expansion rates in the clinical trial, confirmed the need for alemtuzumab to observe UCART19 expansion (along with fludarabine cyclophosphamide), quantified the importance of allogeneic elimination, and suggested a high impact of multipotent memory T-cell subpopulations on UCART19 expansion and persistence. In addition to supporting the role of host cytokines and lymphocytes in CAR-T cell therapy, such a model could help optimizing the preconditioning regimens in future clinical trials.Significance:A mathematical mechanistic pharmacokinetic/pharmacodynamic model supports and captures quantitatively the beneficial impact of lymphodepleting patients before the infusion of an allogeneic CAR-T cell product. Mediation through IL-7 increase and host T lymphocytes decrease is underlined, and the model can be further used to optimize CAR-T cell therapies lymphodepletion regimen.

Table S1: Representativeness of CALM study participants; Table S2: UCART19 dose and cellular kinetics analysis; Table S3: Characteristics of UCART19 PBMC donors; Table S4: Impact of UCART19 and alemtuzumab doses on UCART19 expansion.

Fig.S1: Schematic diagram of CALM study design; Fig.S2: Correlation of UCART19 transgene levels evaluated by qPCR in paired peripheral blood and bone marrow aspirate samples; Fig.S3 & S4: Impact of demographic characteristics, prior therapies and tumor burden on UCART19 in vivo expansion (Cmax) or persistence (AUCTlast), respectively; Fig.S5: Impact of tumor burden at the time of UCART19 infusion on UCART19 kinetics based on response status; Fig.S6: UCART19 product characteristics; Fig.S7: Scatter plots of UCART19 cellular kinetic parameters by qPCR vs transduction efficiency and cell viability; Fig.S8: Impact of lymphodepletion on homeostatic cytokines (IL-7 and IL-15) and UCART19 cellular kinetics; Fig.S9: Impact of number of prior treatment lines on IL-7 exposure (AUC28).

Additional information regarding the CALM study (including study population and summary results) and UCART19 product characterization.

Supplementary Data

Background Nonalcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease, which is associated with features of metabolic syndrome. NAFLD may progress in a subset of patients into nonalcoholic steatohepatitis (NASH) with liver injury resulting ultimately in cirrhosis and potentially hepatocellular carcinoma. Today, there is no approved treatment for NASH due to, at least in part, the lack of preclinical models recapitulating features of human disease. Here, we report the development of a dietary model of NASH in the Gottingen minipig. Methods First, we performed a longitudinal characterization of diet-induced NASH and fibrosis using biochemical, histological, and transcriptional analyses. We then evaluated the pharmacological response to Obeticholic acid (OCA) treatment for 8 weeks at 2.5mg/kg/d, a dose matching its active clinical exposure. Results Serial histological examinations revealed a rapid installation of NASH driven by massive steatosis and inflammation, including evidence of ballooning. Furthermore, we found the progressive development of both perisinusoidal and portal fibrosis reaching fibrotic septa after 6 months of diet. Histological changes were mechanistically supported by well-defined gene signatures identified by RNA Seq analysis. While treatment with OCA was well tolerated throughout the study, it did not improve liver dysfunction nor NASH progression. By contrast, OCA treatment resulted in a significant reduction in diet-induced fibrosis in this model. Conclusions These results, taken together, indicate that the diet-induced NASH in the Gottingen minipig recapitulates most of the features of human NASH and may be a model with improved translational value to prioritize drug candidates toward clinical development

International audience; INTRODUCTION:The aim of this study was to evaluate episiotomy technique, in particular suture angles, and any correlation between suture angle and severe perineal tears.MATERIAL AND METHODS:An observational questionnaire-based study was conducted between 01 August 2015 and 30 April 2016 among accoucheurs performing episiotomies in a French maternity unit with facilities for high-risk pregnancies. For each patient included, accoucheurs were asked to measure the episiotomy suture angle, and to record the angle at which they thought they had cut, the length of the episiotomy, its distance from the anus, and whether the woman sustained a sphincter injury.RESULTS:The centre's episiotomy rate during the study period was 15%. We analyzed the characteristics of episiotomies performed on 89 women (68 by doctors and 21 by midwives). Only 43% of suture angles were between 45° and 60° (45.6% of those performed by doctors vs 38.1% by midwives, p=0.8623), whereas 91% of accoucheurs thought they had cut within the correct range. Doctors made longer incisions than midwives (4 [4.2-5.0] vs 3 [2.5-3.5] cm, p=0.0006). Only 40.5% of accoucheurs correctly estimated the incision angle. Twelve (13.64%) of the 88 women sustained a third-degree perineal tear. The risk of sphincter injury was higher with suture angles

International audience; Repeated measures over time are common in the biomedical field, and widely used to analyze the link between covariates and a clinical criterion. In a longitudinal context, a high number of variables associated with the presence of missing data, are complex issues to be resolved. We deal with several types of covariates, some suffer from haphazard missingness, and others are subject to detection thresholds. For the latter, Tobit regression combined with bootstrap is an unbiased approach, but it needs complete predictors for the mean model. An adaptation of the wellknown multivariate imputation by chained equation is proposed. We use the Tobit model as the imputation method for covariates below the detection limit, predictive mean matching and logistic regression for others. Variable selection is done by using MI-PGEE which consists in the following ingredients: a) a group LASSO penalty is imposed on the group of estimated regression coefficients of the same variable across multiplyimputed datasets leading to a consistent selection. The optimal shrinkage parameter is chosen by minimizing a BIC-like criterion. b) GEE allows integrating correlations due to the longitudinal context. The usefulness of the new method is illustrated by an application on the FNIH project of the Osteoarthritis Initiative.

International audience; The generalized estimating equations (GEE) are a useful tool for marginal regression analysis with repeated measurements and longitudinal data. Penalized regressions, such as LASSO, have been extended to GEE to allow shrinkage and dimension reduction but can't handle missingness. Missing data as well as a large number of variables combined with small sample size are usual issues faced with longitudinal data. Multiple imputation is a popular tool for handling missing data and in particular, the MI-GEE can be used for inference. Although methods to handle missing data such as MI-GEE have been improved, variable selection for GEE has not been systematically processed to integrate missing data. The multiple imputation-least absolute shrinkage and selection operator (MI-LASSO) proposes a consistent selection through the multiple-imputed datasets but cannot handle correlation among individual observations. We present MI-PGEE, a new multiple imputation-penalized generalized estimating equations as an extension of the MI-LASSO to be applied on longitudinal data. MI-PGEE applies the group LASSO penalty to the group of estimated regression coefficients of the same variable, across multiple-imputed datasets. Estimates are computed using a Local Quadratic Approach and an algorithm based on a modified Newton-Raphson method ; a new BIC-like criterion is presented in order to select the tuning parameter. MI-PGEE yields a consistent variable selection across multiple-imputed datasets, making this a selection method for longitudinal data able to manage missing data. We simulated different patterns of correlation structure between continuous as well as binary variables : our results demonstrate advantages of MI-PGEE compared to simple imputation PGEE such as mean imputation. The usefulness of the new method is illustrated by an application on osteoarthritis of the knee to identify important biomarkers and magnetic resonance imaging criteria that are associated with joint space width.

International audience; Clinical research is often interested in longitudinal follow-up over several visits. All scheduled visits are not carried out and it is not unusual to have a different number of visits by patient. The Generalized Estimating Equations can handle continuous or discrete autocorrelated response. The method allows a different number of visits by patients. The GEE are robust to missing completely at random data, but when the last visits are fewer, the estimator may be biased. We propose a simulation study to investigate the impact of missing visits on the estimators of the model parameters under different missing data patterns. Different types of responses are studied with an exchangeable or autoregressive of order one structure. The number of subjects affected by the missing data and the number of visits removed, vary in order to assess the impact of the missing data. Our simulations show that the esti-mators obtained by GEE are resistant to a certain rate of missing data. The results are homogeneous regardless to the imposed missing data structure.

International audience; Clinical research is regularly interested in longitudinal follow-up over several visits. All scheduled visits are not carried out and it is not unusual to have a different number of visits by patient. The Generalized Estimating Equations can handle continuous or discrete autocorrelated response. The method allows a different number of visits by patients. The GEE are robust to missing completely at random data. However when the last visits are fewer, the estimator may be biased. We propose a simulation study to investigate the impact of missing visits on the GEE estimators under different missing data pattern. Different types of responses are studied with an exchangeable or autore-gressive of order one structure. The number of subjects affected by the missing data and the number of visits removed vary in order to assess their impact. Our simulations show that the estimators obtained by GEE are resistant to a certain rate of missing data. The results are homogeneous regardless to the imposed missing data structure.; La recherche clinique s'intéresse régulièrement au suivi longitudinal du patient au cours de plusieurs visites. Toutes les visites prévues ne sont pas effectuées et il n'est pas rare d'avoir un nombre de visites différent selon les individus. Les Generalized Estimating Equations permettent d'étudier une réponse continue ou discrète autocorrélée. Cette méthode permet un nombre de visites qui diffère selon les patients. Les GEE sont robustes aux données manquantes complètement aléatoires. Cependant dans le cas où les visites de fin d'étude sont moins nombreuses, l'estimateur peutêtre biaisé. Nous proposons uneétude par simulation pourétudier l'impact de visites non effectuées sur les estimateurs obtenus par GEE sous divers schéma de données manquantes. Deux types de réponses sontétudiées avec une structureéchangeable ou auto-régressive d'ordre un. Le nombre de sujets touchés et le nombre de visites supprimées varient afin d'évaluer leur impact. Nos simulations montrent que les estimateurs calculés par GEE sont résistants jusqu'à un certain taux de données manquantes. Les résultats sont homogènes quelle que soit la structure de données manquantes imposée.

Generalized estimating equations (GEE) are a useful tool for marginal regression analysis with repeated measurements. Missing data as well as a large number of variables combined with small sample size are usual issues faced with longitudinal data. Multiple imputation is a popular tool for handling missing data and in particular , the MI-GEE can be used for inference. The multiple imputation-least absolute shrinkage and selection operator (MI-LASSO) proposes a consistent selection through the multiply-imputed datasets but cannot handle correlation among individual observations. 1 We present MI-PGEE, a new multiple imputation-penalized generalized estimating equations as an extension of the MI-LASSO to be applied on longitudinal data. MI-PGEE applies the penalized GEE with ridge penalty and adaptive weights that are common to the group of estimated regression coefficients of the same variable across multiply-imputed datasets. In order to select the tuning parameter, a new BIC-like criterion is presented. MI-PGEE yields a consistent variable selection across multiply-imputed datasets, making this a selection method for longitudinal data able to manage missing data and within subject correlation. The usefulness of the new method is illustrated by an application on the placebo arm of the Strontium ranelate Efficacy in Knee OsteoarthrItis triAl (SEKOIA) study., Les Generalized estimating equations (GEE) sont une méthode de régression utile pour l'analyse marginale en présence de mesures répétées. Dans le contexte longi-tudinale, il est fréquent de faire face aux données manquantes ainsi qu'à de nombreuses variables mesurées au cours du temps. L'imputation multiple, outil populaire pour le traitement des données manquantes et plus particulièrement les MI-GEE peuventêtre utilisés pour l'inférence. Bien que les méthodes pour traiter les données manquantes telles que les MI-GEE aientété mises place, la sélection de variables pour GEE n'a pasété systématiquement développée pour intégrer les données manquantes. Le multiple imputation-least absolute shrinkage and selection operator (MI-LASSO) propose une sélection consistante au sein des jeux de données imputés, mais ne permet pas de prendre en compte les corrélations intra-patient. Nous présentons le MI-PGEE, multiple imputation-penalized generalized estimating equations, extension du MI-LASSO pour les données longitudinales. Cette méthode utilise les GEE pénalisés par une pénalité ridge et des poids adaptatifs qui sont communsà l'ensemble des coefficients de régression estimés de la même variable sur leséchantillons multi-imputés. Nous présentons un critère de type BIC pour le choix du paramètre de régularisation. Le MI-PGEE fournit une sélection consistante sur l'ensemble des imputations, ce qui en fait une méthode de sélection pour données longitudinales capable d'intégrer les données manquantes et les corrélations intra-sujet. Une application sur le sous groupe placebo de la base de données Strontium ranelate Efficacy in Knee OsteoarthrItis triAl (SEKOIA) est présentée.