Clustering and Kernel Density Estimation for Assessment of Measurable Residual Disease by Flow Cytometry

peer reviewedStandardization, data mining techniques, and comparison to normality are changing the landscape of multiparameter flow cytometry in clinical hematology. On the basis of these principles, a strategy was developed for measurable residual disease (MRD) assessment. Herein, suspicious cell clusters are first identified at diagnosis using a clustering algorithm. Subsequently, automated multidimensional spaces, named “Clouds”, are created around these clusters on the basis of density calculations. This step identifies the immunophenotypic pattern of the suspicious cell clusters. Thereafter, using reference samples, the “Abnormality Ratio” (AR) of each Cloud is calculated, and major malignant Clouds are retained, known as “Leukemic Clouds” (L-Clouds). In follow-up samples, MRD is identified when more cells fall into a patient’s L-Cloud compared to reference samples (AR concept). This workflow was applied on simulated data and real-life leukemia flow cytometry data. On simulated data, strong patient-dependent positive correlation (R2 = 1) was observed between the AR and spiked-in leukemia cells. On real patient data, AR kinetics was in line with the clinical evolution for five out of six patients. In conclusion, we present a convenient flow cytometry data analysis approach for the follow-up of hematological malignancies. Further evaluation and validation on more patient samples and different flow cytometry panels is required before implementation in clinical practice

Efficacy and safety of NI-0101, an anti-toll-like receptor 4 monoclonal antibody, in patients with rheumatoid arthritis after inadequate response to methotrexate: a phase II study

Objectives Anti-citrullinated protein antibodies (ACPAs) form immune complexes with citrullinated proteins binding toll-like receptor (TLR) 4, which has been proposed as a mediator of rheumatoid arthritis (RA). NI-0101 is a first-in-class humanised monoclonal antibody blocking TLR4, as confirmed by inhibition of in vivo lipopolysaccharide-induced cytokine release in healthy volunteers. This study was design to confirm preclinical investigations supporting a biomarker-driven approach for treatment of patients with RA who present positive for these immune complexes. Methods Placebo-controlled, double-blind, randomised (2:1) trial of the tolerability and efficacy of NI-0101 (5 mg/kg, every 2 weeks for 12 weeks) versus placebo in ACPA-positive RA patients with inadequate response to methotrexate. Efficacy measures included Disease Activity Score (28-joint count) with C reactive protein (DAS28-CRP), European League Against Rheumatism (EULAR) good and moderate responses, and American College of Rheumatology (ACR) 20, ACR50 and ACR70 responses. Subgroup analyses defined on biomarkers were conducted. Pharmacokinetics, pharmacodynamics and safety were reported. Results 90 patients were randomised (NI-0101 (61) and placebo (29)); 86 completed the study. No significant between-group difference was observed for any of the efficacy endpoints. Subgroup analyses using baseline parameters as covariants did not reveal any population responding to NI-0101. Treatment-emergent adverse events occurred in 51.7% of patients who received placebo versus 52.5% for NI-0101. Conclusions We demonstrate for the first time that in RA, a human immune-mediated inflammatory disease, blocking the TLR4 pathway alone does not improve disease parameters. Successful targeting of innate immune pathways in RA may require broader and/or earlier inhibitory approaches

A Comprehensive Hepatitis C Viral Kinetic Model Explaining Cure

International audienceWe propose a model that characterizes and links the complexity and diversity of clinically observed hepatitis C viral kinetics to sustained virologic response (SVR)--the primary clinical end point of hepatitis C treatment, defined as an undetectable viral load at 24 weeks after completion of treatment)--in patients with chronic hepatitis C (CHC) who have received treatment with peginterferon α-2a ± ribavirin. The new attributes of our hepatitis C viral kinetic model are (i) the implementation of a cure/viral eradication boundary, (ii) employment of all hepatitis C virus (HCV) RNA measurements, including those below the lower limit of quantification (LLOQ), and (iii) implementation of a population modeling approach. The model demonstrated excellent positive (99.3%) and negative (97.1%) predictive values for SVR as well as high sensitivity (96.6%) and specificity (99.4%). The proposed viral kinetic model provides a framework for mechanistic exploration of treatment outcome and permits evaluation of alternative CHC treatment options with the ultimate aim of developing and testing hypotheses for personalizing treatments in this disease

The use of the SAEM algorithm in MONOLIX software for estimation of population pharmacokinetic-pharmacodynamic-viral dynamics parameters of maraviroc in asymptomatic HIV subjects

Using simulated viral load data for a given maraviroc monotherapy study design, the feasibility of different algorithms to perform parameter estimation for a pharmacokinetic-pharmacodynamic-viral dynamics (PKPD-VD) model was assessed. The assessed algorithms are the first-order conditional estimation method with interaction (FOCEI) implemented in NONMEM VI and the SAEM algorithm implemented in MONOLIX version 2.4. Simulated data were also used to test if an effect compartment and/or a lag time could be distinguished to describe an observed delay in onset of viral inhibition using SAEM. The preferred model was then used to describe the observed maraviroc monotherapy plasma concentration and viral load data using SAEM. In this last step, three modelling approaches were compared; (i) sequential PKPD-VD with fixed individual Empirical Bayesian Estimates (EBE) for PK, (ii) sequential PKPD-VD with fixed population PK parameters and including concentrations, and (iii) simultaneous PKPD-VD. Using FOCEI, many convergence problems (56%) were experienced with fitting the sequential PKPD-VD model to the simulated data. For the sequential modelling approach, SAEM (with default settings) took less time to generate population and individual estimates including diagnostics than with FOCEI without diagnostics. For the given maraviroc monotherapy sampling design, it was difficult to separate the viral dynamics system delay from a pharmacokinetic distributional delay or delay due to receptor binding and subsequent cellular signalling. The preferred model included a viral load lag time without inter-individual variability. Parameter estimates from the SAEM analysis of observed data were comparable among the three modelling approaches. For the sequential methods, computation time is approximately 25% less when fixing individual EBE of PK parameters with omission of the concentration data compared with fixed population PK parameters and retention of concentration data in the PD-VD estimation step. Computation times were similar for the sequential method with fixed population PK parameters and the simultaneous PKPD-VD modelling approach. The current analysis demonstrated that the SAEM algorithm in MONOLIX is useful for fitting complex mechanistic models requiring multiple differential equations. The SAEM algorithm allowed simultaneous estimation of PKPD and viral dynamics parameters, as well as investigation of different model sub-components during the model building process. This was not possible with the FOCEI method (NONMEM version VI or below). SAEM provides a more feasible alternative to FOCEI when facing lengthy computation times and convergence problems with complex models

Efficacy and safety of emapalumab in macrophage activation syndrome

OBJECTIVES: Macrophage activation syndrome (MAS) is a severe, life-threatening complication of systemic juvenile idiopathic arthritis (sJIA) and adult-onset Still's disease (AOSD). The objective of this study was to confirm the adequacy of an emapalumab dosing regimen in relation to interferon-γ (IFNγ) activity by assessing efficacy and safety. The efficacy outcome was MAS remission by week 8, based on clinical and laboratory criteria. METHODS: We studied emapalumab, a human anti-IFNγ antibody, administered with background glucocorticoids, in a prospective single-arm trial involving patients who had MAS secondary to sJIA or AOSD and had previously failed high-dose glucocorticoids, with or without anakinra and/or ciclosporin. The study foresaw 4-week treatment that could be shortened or prolonged based on investigator's assessment of response. Patients entered a long-term (12 months) follow-up study. RESULTS: Fourteen patients received emapalumab. All patients completed the trial, entered the long-term follow-up and were alive at the end of follow-up. The investigated dosing regimen, based on an initial loading dose followed by maintenance doses, was appropriate, as shown by rapid neutralisation of IFNγ activity, demonstrated by a prompt decrease in serum C-X-C motif chemokine ligand 9 (CXCL9) levels. By week 8, MAS remission was achieved in 13 of the 14 patients at a median time of 25 days. Viral infections and positive viral tests were observed. CONCLUSIONS: Neutralisation of IFNγ with emapalumab was efficacious in inducing remission of MAS secondary to sJIA or AOSD in patients who had failed high-dose glucocorticoids. Screening for viral infections should be performed, particularly for cytomegalovirus. TRIAL REGISTRATION NUMBER: NCT02069899 and NCT03311854

Modelling and Clinical Trials in Paediatrics

Clinical trials are more difficult to conduct in children, but they are even more necessary than in adults – their scarcity is an ethical scandal. Mathematical models can be built that can describe both the disease process and the mechanism of action of drugs. These models can then be used to simulate the outcome of clinical trials. Inspection of the simulated results then facilitates optimisation of the trial design and proposed methods of analysis. Validation is a crucial issue for the good practice of modelling and simulation. The participants of Round Table No. 6 recommend: (i) that modelling be systematically employed; (ii) that all the required professional personnel be involved, at all phases; (iii) that all data needed are made accessible; (iv) that clinicians be trained; (v) that specialists develop training tool kits; and (vi) that universities provide appropriate training