Population pharmacokinetic and pharmacodynamic analysis of osimertinib.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e20536-e20536
Author(s):  
Martin Johnson ◽  
Henning Schmidt ◽  
Mikael Sunnaker ◽  
Anthony F Nash ◽  
Suman Nayak ◽  
...  
Keyword(s):  
Exposure Model ◽  
Population Pharmacokinetic ◽  
Baseline Serum ◽  
Model Based ◽  
Exposure Response ◽  
Number Of Patients ◽  
Population Pk ◽  
The Impact ◽  
Egfr Tki ◽  
Model Based Analysis

e20536 Background: Osimertinib is an oral, potent, irreversible, CNS active EGFR-TKI, selective for sensitizing (EGFRm) and T790M resistance mutations, indicated for the treatment of patients with T790M positive advanced non-small cell lung cancer who have progressed on or after EGFR-TKI therapy. Osimertinib pharmacokinetics (PK) were evaluated using a population approach and pharmacodynamic (PD) relationships using appropriate modeling approaches. Methods: To understand the impact of covariates on osimertinib PK, a population PK analysis was performed using data from patients who received osimertinib (20–240 mg) during the AURA studies. Exposure metrics were derived from a PK model and used to assess the exposure-response (safety/efficacy) relationship. Efficacy analysis included patients who were T790M positive (n = 710) and safety analysis included all dosed patients (n = 1088). The impact of covariates on exposure-response was assessed. Models accounting for rare safety events were applied to quantify the association between events and exposure. Results: Population PK analyses supported dose- and time-independent PK of osimertinib with no clinically meaningful covariates identified. Patients in the highest exposure quartile (Q4) had a numerically shorter median progression-free survival (8.3 months [95% CI 6.9, 10.5]) compared with patients in Q1, Q2 and Q3 (all 11.2 months [95% CIs 9.7, 12.7; 8.5, 15.6 and 8.7, 13.7, respectively]). A model-based analysis indicated that this effect is likely due to a larger number of patients in Q4 with poor prognostic features, i.e. worse performance status (WHO 1 or 2) and lower baseline serum albumin compared with Q1, Q2 and Q3, rather than to osimertinib exposure. Model-predicted probability of a relationship between osimertinib exposure and LVEF changes was not evident. Model-based analysis predicted that, compared with the median probability (0.03), the probability of a patient experiencing interstitial lung disease may increase with increasing osimertinib exposure (Q1 probability 0.01 [steady-state AUC 6361 nM*h] vs Q4 0.06 [24460 nM*h]) at the 80 mg dose. Conclusions: Population PK and PK-PD analysis is supportive of 80 mg as an appropriate dose for osimertinib. Clinical trial information: NCT01802632; NCT01802632; NCT02094261; NCT02151981.

scholarly journals Population Pharmacokinetic Model-Based Evaluation of Intact Oxaliplatin in Rats with Acute Kidney Injury

Cancers ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 6382
Author(s):  
Shinji Kobuchi ◽  
Miyu Kai ◽  
Yukako Ito
Keyword(s):  
Acute Kidney Injury ◽  
Renal Function ◽  
Dose Reduction ◽  
Ischemia Reperfusion ◽  
Kidney Injury ◽  
Population Pharmacokinetic ◽  
Time Curve ◽  
Model Based ◽  
Population Pk ◽  
The Impact

Acute kidney injury (AKI) complicates the dosing strategies of oxaliplatin (L-OHP) and the requirement for L-OHP dose reduction in patients with renal failure remains controversial. The objective of this study is to assess the impact of AKI on the pharmacokinetics (PK) of intact L-OHP and simulate the relationship between the degree of renal function and intact L-OHP exposures using a population PK model. Intact L-OHP concentrations in plasma and urine after L-OHP administration were measured in mild and severe AKI models established in rats through renal ischemia-reperfusion. Population PK modeling and simulation were performed. There were no differences among rats in the area under the plasma concentration–time curve of intact L-OHP after intravenous L-OHP administrations. Nevertheless, the amount of L-OHP excretion after administration of 8 mg/kg L-OHP in mild and severe renal dysfunction rats was 63.5% and 37.7%, respectively, and strong correlations were observed between biochemical renal function markers and clearance of intact L-OHP. The population PK model simulated well the observed levels of intact L-OHP in AKI model rats. The population PK model-based simulation suggests that dose reduction is unnecessary for patients with mild to moderate AKI.

The impact of global change on the hydropower potential of Europe: a model-based analysis

Energy Policy ◽  
2005 ◽  
Vol 33 (7) ◽  
pp. 839-855 ◽  
Author(s):  
Bernhard Lehner ◽  
Gregor Czisch ◽  
Sara Vassolo
Keyword(s):  
Global Change ◽  
Model Based ◽  
Hydropower Potential ◽  
The Impact ◽  
Model Based Analysis

scholarly journals Model-Based Analysis of Feedback Control Strategies in Aerobic Biotrickling Filters for Biogas Desulfurization

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 208
Author(s):  
Luis Rafael López ◽  
Mabel Mora ◽  
Caroline Van der Heyden ◽  
Juan Antonio Baeza ◽  
Eveline Volcke ◽  
...  
Keyword(s):  
Closed Loop ◽  
Control Strategies ◽  
Biotrickling Filter ◽  
Process Performance ◽  
Control Loops ◽  
Biotrickling Filters ◽  
Model Based ◽  
Outlet Concentration ◽  
The Impact ◽  
Model Based Analysis

Biotrickling filters are one of the most widely used biological technologies to perform biogas desulfurization. Their industrial application has been hampered due to the difficulty to achieve a robust and reliable operation of this bioreactor. Specifically, biotrickling filters process performance is affected mostly by fluctuations in the hydrogen sulfide (H2S) loading rate due to changes in the gas inlet concentration or in the volumetric gas flowrate. The process can be controlled by means of the regulation of the air flowrate (AFR) to control the oxygen (O2) gas outlet concentration ([O2]out) and the trickling liquid velocity (TLV) to control the H2S gas outlet concentration ([H2S]out). In this work, efforts were placed towards the understanding and development of control strategies in biological H2S removal in a biotrickling filter under aerobic conditions. Classical proportional and proportional-integral feedback controllers were applied in a model of an aerobic biotrickling filter for biogas desulfurization. Two different control loops were studied: (i) AFR Closed-Loop based on AFR regulation to control the [O2]out, and (ii) TLV Closed-Loop based on TLV regulation to control the [H2S]out. AFR regulation span was limited to values so that corresponds to biogas dilution factors that would give a biogas mixture with a minimum methane content in air, far from those values required to obtain an explosive mixture. A minimum TLV of 5.9 m h−1 was applied to provide the nutrients and moisture to the packed bed and a maximum TLV of 28.3 m h−1 was set to prevent biotrickling filter (BTF) flooding. Control loops were evaluated with a stepwise increase from 2000 ppmv until 6000 ppmv and with changes in the biogas flowrate using stepwise increments from 61.5 L h−1 (EBRT = 118 s) to 184.5 L h−1 (EBRT = 48.4 s). Controller parameters were determined based on time-integral criteria and simple criteria such as stability and oscillatory controller response. Before implementing the control strategies, two different mass transfer correlations were evaluated to study the effect of the manipulable variables. Open-loop behavior was also studied to determine the impact of control strategies on process performance variables such as removal efficiency, sulfate and sulfur selectivity, and oxygen consumption. AFR regulation efficiently controlled [O2]out; however, the impact on process performance parameters was not as great as when TLV was regulated to control [H2S]out. This model-based analysis provided valuable information about the controllability limits of each strategy and the impact that each strategy can have on the process performance.

scholarly journals Pharmacometrics-Based Considerations for the Design of a Pharmacogenomic Clinical Trial Assessing Irinotecan Safety

2021 ◽  
Author(s):  
Iris K. Minichmayr ◽  
Mats O. Karlsson ◽  
Siv Jönsson
Keyword(s):  
Statistical Power ◽  
Rational Design ◽  
Total Sample ◽  
Individual Variability ◽  
Statistical Testing ◽  
Poor Metabolizers ◽  
Model Based ◽  
Exposure Response ◽  
Study Power ◽  
Model Based Analysis

Abstract Purpose Pharmacometric models provide useful tools to aid the rational design of clinical trials. This study evaluates study design-, drug-, and patient-related features as well as analysis methods for their influence on the power to demonstrate a benefit of pharmacogenomics (PGx)-based dosing regarding myelotoxicity. Methods Two pharmacokinetic and one myelosuppression model were assembled to predict concentrations of irinotecan and its metabolite SN-38 given different UGT1A1 genotypes (poor metabolizers: CLSN-38: -36%) and neutropenia following conventional versus PGx-based dosing (350 versus 245 mg/m2 (-30%)). Study power was assessed given diverse scenarios (n = 50–400 patients/arm, parallel/crossover, varying magnitude of CLSN-38, exposure-response relationship, inter-individual variability) and using model-based data analysis versus conventional statistical testing. Results The magnitude of CLSN-38 reduction in poor metabolizers and the myelosuppressive potency of SN-38 markedly influenced the power to show a difference in grade 4 neutropenia (<0.5·109 cells/L) after PGx-based versus standard dosing. To achieve >80% power with traditional statistical analysis (χ2/McNemar’s test, α = 0.05), 220/100 patients per treatment arm/sequence (parallel/crossover study) were required. The model-based analysis resulted in considerably smaller total sample sizes (n = 100/15 given parallel/crossover design) to obtain the same statistical power. Conclusions The presented findings may help to avoid unfeasible trials and to rationalize the design of pharmacogenetic studies.

scholarly journals Estimation of the economic burden of COVID-19 using disability-adjusted life years (DALYs) and productivity losses in Kerala, India: a model-based analysis

BMJ Open ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. e049619
Author(s):  
Denny John ◽  
M S Narassima ◽  
Jaideep Menon ◽  
Jammy Guru Rajesh ◽  
Amitava Banerjee
Keyword(s):  
Economic Burden ◽  
Premature Mortality ◽  
Secondary Outcome ◽  
Years Of Life Lost ◽  
Disability Adjusted Life Years ◽  
Model Based ◽  
Disability Adjusted Life ◽  
Life Years ◽  
The Impact ◽  
Model Based Analysis

ObjectivesFrom the beginning of the COVID-19 pandemic, clinical practice and research globally have centred on the prevention of transmission and treatment of the disease. The pandemic has had a huge impact on the economy and stressed healthcare systems worldwide. The present study estimates disability-adjusted life years (DALYs), years of potential productive life lost (YPPLL) and cost of productivity lost (CPL) due to premature mortality and absenteeism secondary to COVID-19 in the state of Kerala, India.SettingDetails on sociodemographics, incidence, death, quarantine, recovery time, etc were derived from public sources and the Collective for Open Data Distribution-Keralam. The working proportion for 5-year age–gender cohorts and the corresponding life expectancy were obtained from the 2011 Census of India.Primary and secondary outcome measuresThe impact of the disease was computed through model-based analysis on various age–gender cohorts. Sensitivity analysis was conducted by adjusting six variables across 21 scenarios. We present two estimates, one until 15 November 2020 and later updated to 10 June 2021.ResultsSeverity of infection and mortality were higher among the older cohorts, with men being more susceptible than women in most subgroups. DALYs for males and females were 15 954.5 and 8638.4 until 15 November 2020, and 83 853.0 and 56 628.3 until 10 June 2021. The corresponding YPPLL were 1323.57 and 612.31 until 15 November 2020, and 6993.04 and 3811.57 until 10 June 2021, and the CPL (premature mortality) were 263 780 579.94 and 41 836 001.82 until 15 November 2020, and 1 419 557 903.76 and 278 275 495.29 until 10 June 2021.ConclusionsMost of the COVID-19 burden was contributed by years of life lost. Losses due to YPPLL were reduced as the impact of COVID-19 infection was lesser among the productive cohorts. The CPL values for individuals aged 40–49 years old were the highest. These estimates provide the data necessary for policymakers to work on reducing the economic burden of COVID-19 in Kerala.

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