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Longitudinal pharmacometric models offer many advantages in the analysis of clinical trial data, but potentially inflated type I error and biased drug effect estimates, as a consequence of model misspecifications and multiple testing, are main drawbacks. In this work, we used real data to compare these aspects for a standard approach (STD) and a new one using mixture models, called individual model averaging (IMA). Placebo arm data sets were obtained from three clinical studies assessing ADAS-Cog scores, Likert pain scores, and seizure frequency. By randomly (1:1) assigning patients in the above data sets to “treatment” or “placebo,” we created data sets where any significant drug effect was known to be a false positive. Repeating the process of random assignment and analysis for significant drug effect many times (N = 1000) for each of the 40 to 66 placebo-drug model combinations, statistics of the type I error and drug effect bias were obtained. Across all models and the three data types, the type I error was (5th, 25th, 50th, 75th, 95th percentiles) 4.1, 11.4, 40.6, 100.0, 100.0 for STD, and 1.6, 3.5, 4.3, 5.0, 6.0 for IMA. IMA showed no bias in the drug effect estimates, whereas in STD bias was frequently present. In conclusion, STD is associated with inflated type I error and risk of biased drug effect estimates. IMA demonstrated controlled type I error and no bias. Supplementary Information The online version contains supplementary material available at 10.1208/s12248-021-00596-8.

Background: Overall survival (OS) in adults with ALL remains around 40%, but treatment with asparaginase (ASP)-containing pediatric-inspired regimens demonstrated a 3- or 4-year OS of 67-76%, including in patients with T-cell ALL, with manageable toxicities (Geyer 2020, Stock 2019, DeAngelo 2015). Recent studies in adults with reduced pegaspargase (PEG-ASP) doses showed survival outcomes similar to those with standard doses, with reduced toxicities, including pancreatitis, hepatic and thromboembolic events (Derman 2020, Patel 2020). Calaspargase pegol (ASPARLAS; Cal-PEG), similar to PEG-ASP, is an E. coli L-asparaginase covalently conjugated to monomethoxy polyethylene glycol, but with a more chemically stable succinimidyl carbonate linker, providing sustained asparagine depletion with less frequent dosing (every 21 days). The efficacy and safety of Cal-PEG 2500 U/m 2, vs PEG-ASP 2500 U/m 2, was evaluated in 2 multicenter, randomized clinical trials (AALL07P4 [n=166] and DFCI 11-001 [n=239]) in newly diagnosed patients with ALL ≤21 years. Complete remission (CR), minimal residual disease (MRD), event-free survival (EFS), and OS were similar in both arms, with a 94% 5-year OS for Cal-PEG. The safety profile was consistent with that of PEG-ASP, with similar rates of hypersensitivity, pancreatitis, thrombosis, and hyperbilirubinemia (Vrooman 2021). Plasma asparaginase activity (PAA) levels were ≥0.1 U/mL 25 days after the induction dose in 95% of patients (Angiolillo 2014). The absence of a standardized treatment and the inadequate outcome of adult treatment regimens, as well as a lack of targeted immunotherapies for T-ALL, highlight a critical unmet need for improved therapeutic approaches. A treatment protocol with less frequent Cal-PEG administration due to a more sustained asparaginase activity as well as age- and weight-based dose adjustments may improve clinical outcomes without added toxicities, and possibly extend the upper age limit of ASP-containing regimen for older adults. Trial Design: We present a clinical trial design of an ongoing, multicenter, phase 2/3 study (NCT04817761) assessing the safety and anti-leukemic activity of Cal-PEG in newly diagnosed patients with Philadelphia-negative B- or T-cell ALL. Patients aged ≥22 years are eligible with ECOG performance status 0-2, no known history of pancreatitis, coagulopathy, CNS thrombosis or severe hepatic impairment. This trial comprises two parts: dose confirmation run-in (part 1) and the expansion phase (part 2). Part 1 is open for enrollment and is the focus of this abstract. Starting doses of Cal-PEG are based on the patients' age and BMI, with older patient groups assigned to lower doses (Table 1). A minimum of 4 (initial cohort) and up to 8 patients will be admitted per patient group. A total of 6 Cal-PEG doses will be administered during the treatment period as part of a multiagent chemotherapy regimen based on CALGB 10403 trial (Figure 1; Table 2), with end of treatment visit 1 year after the induction dose, and an additional 2 years of survival follow-up. The primary endpoints in part 1 are 1) the safety of Cal-PEG, including incidence of pre-defined unacceptable toxicities (UT) within 30 days after the induction dose and 2) PAA profiles, including achieving PAA ≥0.1 U/mL at pre-specified time points. Secondary endpoints include immunogenicity, CR, end of induction and consolidation MRD, and 1-, 2- and 3-year EFS, disease-free survival, and OS. Bayesian optimal interval design (BOIN) will be used to evaluate UT with specified boundaries for dose confirmation, expansion, or reduction. For each patient group, the observed UT rate at a dose level will be compared with the safety boundary (23.6%) and toxicity boundary (35.8%) to guide safety confirmation. PAA data from previous pediatric studies and each run-in cohort will also be used to confirm the tested dose through population PK modeling-based predictions. The decision to confirm or adjust the dose will be based on the accumulated safety and induction PAA data at end-of-cohort meetings with the investigators, sponsor and in consult with DSMB recommendations. Part 1 started in July 2021 and will enroll ≈16-32 patients at multiple sites in the US. Part 2 will commence once the safety and PAA data from part 1 are analyzed and the expansion doses are confirmed. Up to 122 patients are expected to be enrolled in both phases of the trial. Clinical trial information: NCT04817761 Figure 1 Figure 1. Disclosures Stock: Pfizer: Consultancy, Honoraria, Research Funding; amgen: Honoraria; agios: Honoraria; jazz: Honoraria; kura: Honoraria; kite: Honoraria; morphosys: Honoraria; servier: Honoraria; syndax: Consultancy, Honoraria; Pluristeem: Consultancy, Honoraria. Park: Kura Oncology: Consultancy; Autolus: Consultancy; Intellia: Consultancy; Affyimmune: Consultancy; Amgen: Consultancy; Novartis: Consultancy; Kite Pharma: Consultancy; BMS: Consultancy; PrecisionBio: Consultancy; Minerva: Consultancy; Innate Pharma: Consultancy; Curocel: Consultancy; Servier: Consultancy; Artiva: Consultancy. Emadi: Secura Bio.: Consultancy; Servier: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Servier: Research Funding; NewLink Genetics: Research Funding; Jazz Pharmaceuticals: Research Funding; KinaRx, Inc.: Membership on an entity's Board of Directors or advisory committees, Other: Co-founder. Abdul-Hay: Jazz: Other: Advisory Board, Speakers Bureau; Servier: Other: Advisory Board, Speakers Bureau; Amgen: Membership on an entity's Board of Directors or advisory committees; Abbvie: Consultancy; Takeda: Speakers Bureau. Cassaday: Kite/Gilead: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Research Funding; Vanda Pharmaceuticals: Research Funding; Pepromene Bio: Membership on an entity's Board of Directors or advisory committees; Servier: Research Funding; Merck: Research Funding; Amgen: Consultancy, Research Funding. Pullarkat: Amgen, Dova, and Novartis: Consultancy, Honoraria; AbbVie, Amgen, Genentech, Jazz Pharmaceuticals, Novartis, Pfizer, and Servier: Membership on an entity's Board of Directors or advisory committees. Webster: AmGen: Consultancy; Pfizer: Consultancy. Pandya: Servier: Current Employment; Agios: Current equity holder in publicly-traded company, Ended employment in the past 24 months. Mogul: Servier Pharmaceuticals: Current Employment. Shvenke: Servier Pharmaceuticals: Current Employment. Zhu: Servier Pharmaceuticals: Current Employment. Tessier: Servier: Current Employment. DeAngelo: Abbvie: Research Funding; Incyte Corporation: Consultancy; Takeda: Consultancy; Novartis: Consultancy, Research Funding; Shire: Consultancy; Pfizer: Consultancy; Amgen: Consultancy; Agios: Consultancy; Autolus: Consultancy; Forty-Seven: Consultancy; Blueprint Medicines Corporation: Consultancy; Jazz: Consultancy; GlycoMimetics: Research Funding. OffLabel Disclosure: Calaspargase pegol is an asparagine specific enzyme indicated as a component of a multi-agent chemotherapeutic regimen for the treatment of acute lymphoblastic leukemia in pediatric and young adult patients age 1 month to 21 years, with recommended dosage of 2,500 units/m2 intravenously no more frequently than every 21 days

We show through a simulation study how the joint analysis of data from phase I and phase II studies enhances the power of pharmacogenetic tests in pharmacokinetic (PK) studies. PK profiles were simulated under different designs along with 176 genetic markers. The null scenarios assumed no genetic effect, while under the alternative scenarios, drug clearance was associated with six genetic markers randomly sampled in each simulated dataset. We compared penalized regression Lasso and stepwise procedures to detect the associations between empirical Bayes estimates of clearance, estimated by nonlinear mixed effects models, and genetic variants. Combining data from phase I and phase II studies, even if sparse, increases the power to identify the associations between genetics and PK due to the larger sample size. Design optimization brings a further improvement, and we highlight a direct relationship between η-shrinkage and loss of genetic signal.

Nevirapine is metabolized by several hepatic cytochrome P450 (CYP) isoforms to generate four primary hydroxylated metabolites: 2-hydroxynevirapine, 3-hydroxynevirapine, 8-hydroxynevirapine, and 12-hydroxynevirapine. The present study characterized associations between genetic polymorphisms and metabolite ratios in HIV-infected Cambodians. We demonstrate associations between CYP2B6 polymorphisms and metabolite ratios for both 3-hydroxynevirapine and 8-hydroxynevirapine, suggesting involvement of CYP2B6 in generating these metabolites.

International audience; Genetic data is now collected in many clinical trials, especially in population pharmacokinetic studies. There is no consensus on methods to test the association between pharmacokinetics and genetic covariates. We performed a simulation study inspired by real clinical trials, using the PK of a compound under development having a nonlinear bioavailability along with genotypes for 176 Single Nucleotide Polymorphisms (SNPs). Scenarios included 78 subjects extensively sampled (16 observations per subject) to simulate a phase I study, or 384 subjects with the same rich design. Under the alternative hypothesis (H 1), 6 SNPs were drawn randomly to affect the log-clearance under an additive linear model. For each scenario 200 PK data sets were simulated under the null hypothesis (no gene effect) and H1. We compared 16 combinations of four association tests, a stepwise procedure and three penalised regressions (ridge regression, Lasso, HyperLasso), applied to four pharmacokinetic phenotypes, two observed concentrations, area under the curve estimated by noncompartmental analysis and model-based clearance. The different combinations were compared in terms of true and false positives and probability to detect the genetic effects. In presence of nonlinearity and/or variability in bioavailability, model-based phenotype allowed a higher probability to detect the SNPs than other phenotypes. In a realistic setting with a limited number of subjects, all methods showed a low ability to detect genetic effects. Ridge regression had the best probability to detect SNPs, but also a higher number of false positives. No association test showed a much higher power than the others.

To face the new challenges of production metering and back allocation, TOTAL is making an important effort to develop and test new approaches to improve its current production metering systems particularly for multiphase conditions. New technologies are tested through pilot applications within different subsidiaries worldwide. Thanks to these pilots, the suitability of various approaches to the needs of each asset can be analyzed and tested in detail. The SPE-149908 (M. Haouche 2012) paper presented a Virtual Flow Meter (VFM) field pilot in Qatar. The approach used on this VFM is the Data Validation and Reconciliation (DVR). The single Well model of the VFM is an assembly of three main units: Reservoir unit, Electrical Submersible Pump (ESP) unit and Production Tubing unit. The conclusion was that the VFM was catching-up the production trends of all the Wells of the pilot. However, the VFM results are not quantitatively comparable to the production measurements, especially when the VFM is running far away from the calibration period. A recommendation was to enhance the ESP model that controls mainly the information on the flow rate. The present paper describes a new method for modeling the ESP performances in the VFM. This method takes into account the impact of the Gas on the ESP performances. Results are presented for three operational cases to see the benefits of this method and where it is applicable. The ESP operational data have been considered for a long time period in order to get a representative range of operational conditions. Conclusions are made on the capabilities of the new method to assess the impact of Gas on the ESP operational performances.

The availability of reliable data is obviously a prerequisite to good production optimization. So far, many tools have been developed to increase the availability of data by using Smart Completions, Downhole Measurements and Remote control Sensors installed along the production chain. The progress in production models has also contributed to this improvement. Nevertheless, in term of reliability, the collected data are affected by uncertainties, which must be taken into account. The process of Data Validation and Reconciliation (DVR) considers all the relevant information (measures/ models) of the production plant as one integral set of parameters. Furthermore, the originality of DVR as compared to equivalent systems is the use of a statistical model to control uncertainties associated with each parameter, and calculating the resulting error propagation. As results and thanks to the information redundancy, DVR allows an automatic real-time correction of each parameter based on its allocated uncertainty. In this paper, we focused on a real time application with 16 Wells belonging to 3 offshore Platforms. The single-Well model is initially validated by using the Multiphase Flow Meter (MPFM) measurements. The global results analysis is discussed by comparing the online application outputs with production tests using MPFM, in terms of Oil flow-rate and Water Liquid Ratio (WLR). Also, the monitoring of each information is highlighted in order to detect a model deviation or instruments failure. Furthermore, the error distribution is used to identify the impact of any deviation occurring in the application. Conclusions are drawn on the performance of the virtual metering system in terms of online availability, results reliability and global consistency between data and results. The DVR application is considered as an operational monitoring tool thanks to alarms which are customized by operators, and as an analysis tool for investigation and diagnosis studies.

To face new hydrocarbon production metering systems and back allocation challenges, Total E&P Qatar and its Research Center (TRC-Q), located in the Qatar Science and Technology Park (QSTP), test novel approaches to improve current production metering system in the Al-Khalij field. This field is operated under a Production Sharing Agreement (PSA) with Qatar Petroleum (QP). A field pilot, which involves three platforms offshore, is currently conducted to test a Data Validation and Reconciliation (DVR) software. This tool is used for production accounting, monitoring and as an alarm system related to equipments’ failure. The main outcome to be expected from this pilot will be an automatic validation of on-line production data before their final recording in the database. The key advantage of the DVR approach is to take into account all the information redundancy and data uncertainties. In the DVR approach, a realistic uncertainty is associated with the information (measurement / model parameter) and a statistical method is applied to re-estimate and improve simultaneously the information and its subsequent uncertainty. The current status of this study is presented along with improvements and challenges. A few examples of on line monitoring of Al-Khalij Wells’ production are presented through a comparison between the DVR on-line virtual meter outputs and the on-site production tests. The results, obtained from sensitivity analysis are also discussed to assess the improvements achieved by applying the DVR approach.

Multiphase flow occurs in almost all oil and gas wells and production installations. The past three decades have seen intense efforts to develop empirical correlations and modelling tools to improve the ability to predict multiphase-flow behaviour with greater accuracy. The empirical approach typically involves flowing fluids at carefully measured flow rates through a pipe, observing the flow pattern, and measuring liquid holdup and pressure drop. Using the measured data, empirical correlations are developed for predicting flow patterns, liquid holdup, and friction factor, and a pressure gradient equation is developed that uses these empirical correlations. In its operations on Al-Khalij field, which is operated under a Production Sharing Agreement (PSA) with Qatar Petroleum (QP), Total E&P Qatar experience multiphase flow in main production lines, with a resulting pressure loss which has to be accurately predicted. As part of the Qatar Science & Technology Park (QSTP), Total Research Centre - Qatar (TRC-Q) has research collaborations with different partners. In this work, we present the results of a joint research project between the Petroleum Engineering program at Texas A&M University in Qatar and the Smart Metering Project team from TRC-Q. A literature review of available multiphase flow correlations has been performed first. Secondly, limitations and range of applicability of these correlations were evaluated. We then identified the most applicable correlation to specific conditions of a pipeline in Qatar's Al-Khalij field. This included gas-oil-water flow in a large-diameter multi-kilometre-long pipeline which passes through a hilly terrain (slightly deviated from horizontal). Our Analyses show that Beggs and Brill set of equations estimates pressure gradient with the least error among other correlations for these conditions. To improve the correlation's accuracy, we divided the pipeline into several segments with an optimum length to minimize the error and ensure computation efficiency. We developed a code to implement this correlation to simulate the multiphase flow inside the pipe. We could obtain pressure and phase flow rates along the pipeline. Also, a sensitivity analysis of some key parameters was performed. The model provided good agreement with measured pressure at the end of the pipeline.

Total E&P Qatar seeks to constantly improve its operations performances. These performances are associated with such indicators as health, safety & environment (HSE) performance, oil production, water management, production costs and energy efficiency. A key tool for reaching this goal is the enhancement of data monitoring and management technology. The smart metering pilot project is run at Total Research Centre - Qatar (TRC-Q) at Qatar Science and Technology Park in close cooperation with Total E&P Qatar Operations and TOTAL Research in France. At TRC-Q, we are testing a game-changing technology based on data validation and reconciliation (DVR). We integrate real-time measurements and virtual metering in an online monitoring system on our production platforms. The originality compared to equivalent systems is the use of the DVR approach, which consists of using a statistical model to manage uncertainties associated with each measured parameter and of quantifying error propagation. The DVR process allows an automatic real-time correction of both measurements and model parameters, on the basis of their allocated uncertainty and thanks to the information redundancy. Consequently, the resulting output data is more consistent with the available data and associated with a reduced uncertainty. So, for the DVR to be effective we seek to increase the data redundancy and provide high-quality modelling of physical and thermodynamic phenomena occurring over the whole production process. The achievement so far is a pilot hourly metering system based on online measurements. These measurements and modelling parameters are automatically linked on the basis of their redundancy and the Operations Department is provided with a customised report containing the corrected parameters and their associated uncertainties. We also keep looking for new relevant information to incorporate into the system and for improvement in the quality of the mathematical model associated with each equipment (pumps, flow meters, etc.) model in order to improve the reliability of the output. The results that will be presented cover the initial promising results of the pilot for the virtual metering of oil, water and gas production and the associated uncertainty quantification.

International audience; We show through a simulation study how the joint analysis of data from phase I and phase II studies enhances the power of pharmacogenetic tests in pharmacokinetic (PK) studies. PK profiles were simulated under different designs along with 176 genetic markers. The null scenarios assumed no genetic effect, while under the alternative scenarios, drug clearance was associated to 6 genetic markers randomly sampled in each simulated dataset. We compared penalised regression Lasso and stepwise procedures to detect the associations between empirical Bayes estimates of clearance, estimated by nonlinear mixed effects models, and genetic variants. Combining data from phase I and phase II studies, even sparse, increases the power to identify the associations between genetics and PK due to the larger sample size. Design optimisation brings a further improvement, and we highlight a direct relationship between η-shrinkage and loss of genetic signal.