Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data

Outcomes Associated with Brain Metastases in a Three-Arm Phase III Trial of Gemcitabine-Containing Regimens Versus Paclitaxel Plus Carboplatin for Advanced Non-small Cell Lung Cancer

BACKGROUND: Brain metastases (BMs) are a common complication of non-small cell lung cancer (NSCLC). Because of historical data indicating a poor prognosis for patients with BM, few randomized phase III studies of advanced NSCLC have included patients with BM at presentation. Because the potential benefits of systemic therapy in patients with BM are uncertain, we analyzed data from a recent phase III study. METHODS: One thousand one hundred thirty-five chemonaïve patients with stage IIIB/IV NSCLC were randomized to receive gemcitabine/carboplatin, gemcitabine/paclitaxel, or paclitaxel/carboplatin. Stratification was based on presence or absence of BM, stage, and baseline weight loss. Patients with BM were required to be clinically stable after treatment with radiotherapy or surgery before entry. Results were retrospectively analyzed by presence or absence of BM at study entry. RESULTS: Rate of BM was 17.1% overall. The response rate was 28.9% for patients with BM (n = 194) versus 29.1% without BM (n = 941). Time to progression was 4.3 months with BM and 4.6 months without BM (p = 0.03). Median survival was 7.7 months (95% confidence interval: 6.7-9.3) among patients with BM (n = 194) and 8.6 months (95% confidence interval: 7.9-9.5) for patients without BM (n = 941), p = 0.09. Rates of hematologic adverse events were not different among patients with and without BM. CONCLUSIONS: There were no significant differences in response, survival, or hematologic toxicity for patients with or without BM; however, patients with BM had a small but significantly shorter time to progression. Nonprogressing patients with treated BM are appropriate candidates for systemic therapy and entry into clinical trials

Phase I and Pharmacokinetic Study of Pegylated Liposomal CKD-602 in Patients with Advanced Malignancies

S-CKD602 is a pegylated liposomal formulation of CKD-602, a semi-synthetic camptothecin analogue. Pegylated (STEALTH®) liposomes can achieve extended drug exposure in plasma and tumor. Based on promising preclinical data, the first phase I study of S-CKD602 was performed in patients (pts) with refractory solid tumors

Multicenter, Phase II Trial of Sunitinib in Previously Treated, Advanced Non–Small-Cell Lung Cancer

Aberrant vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF) signaling have been shown to play a role in non–small-cell lung cancer (NSCLC) pathogenesis and are associated with decreased survival. We evaluated the clinical activity and tolerability of sunitinib malate (SU11248), an oral, multitargeted tyrosine kinase inhibitor that blocks the activity of receptors for VEGF and PDGF, as well as related tyrosine kinases in patients with previously treated, advanced NSCLC

Treatment Guidance for Patients With Lung Cancer During the Coronavirus 2019 Pandemic

The global coronavirus disease 2019 pandemic continues to escalate at a rapid pace inundating medical facilities and creating substantial challenges globally. The risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in patients with cancer seems to be higher, especially as they are more likely to present with an immunocompromised condition, either from cancer itself or from the treatments they receive. A major consideration in the delivery of cancer care during the pandemic is to balance the risk of patient exposure and infection with the need to provide effective cancer treatment. Many aspects of the SARS-CoV-2 infection currently remain poorly characterized and even less is known about the course of infection in the context of a patient with cancer. As SARS-CoV-2 is highly contagious, the risk of infection directly affects the cancer patient being treated, other cancer patients in close proximity, and health care providers. Infection at any level for patients or providers can cause considerable disruption to even the most effective treatment plans. Lung cancer patients, especially those with reduced lung function and cardiopulmonary comorbidities are more likely to have increased risk and mortality from coronavirus disease 2019 as one of its common manifestations is as an acute respiratory illness. The purpose of this manuscript is to present a practical multidisciplinary and international overview to assist in treatment for lung cancer patients during this pandemic, with the caveat that evidence is lacking in many areas. It is expected that firmer recommendations can be developed as more evidence becomes available

Phase I Study of Pazopanib in Patients with Advanced Solid Tumors and Hepatic Dysfunction: A National Cancer Institute Organ Dysfunction Working Group Study

Pazopanib is a potent, multi-targeted receptor tyrosine kinase inhibitor; however, there is limited information regarding the effects of liver function on pazopanib metabolism and pharmacokinetics (PK). The objective of this study was to establish the maximum tolerated dose (MTD) and PK profile of pazopanib in patients with varying degrees of hepatic dysfunction

The IASLC Early Lung Imaging Confederation (ELIC) Open-Source Deep Learning and Quantitative Measurement Initiative.

BackgroundWith global adoption of CT lung cancer screening, there is increasing interest to use artificial intelligence (AI) deep learning methods to improve the clinical management process. To enable AI research using an open source, cloud-based, globally distributed, screening CT imaging dataset and computational environment that are compliant with the most stringent international privacy regulations that also protects the intellectual properties of researchers, the International Association of the Study of Lung Cancer (IASLC) sponsored development of the Early Lung Imaging Confederation (ELIC) resource in 2018. The objective of this report is to describe the updated capabilities of ELIC and illustrate how this resource can be utilized for clinically relevant AI research.MethodsIn this second Phase of the initiative, metadata and screening CT scans from two time points were collected from 100 screening participants in seven countries. An automated deep learning AI lung segmentation algorithm, automated quantitative emphysema metrics, and a quantitative lung nodule volume measurement algorithm were run on these scans.ResultsA total of 1,394 CTs were collected from 697 participants. The LAV950 quantitative emphysema metric was found to be potentially useful in distinguishing lung cancer from benign cases using a combined slice thickness ≥ 2.5 mm. Lung nodule volume change measurements had better sensitivity and specificity for classifying malignant from benign lung nodules when applied to solid lung nodules from high quality CT scans.ConclusionThese initial experiments demonstrated that ELIC can support deep learning AI and quantitative imaging analyses on diverse and globally distributed cloud-based datasets