Dataset Information

Analysis of biomarker utility using a PBPK/PD model for carbaryl.

ABSTRACT: There are many types of biomarkers; the two common ones are biomarkers of exposure and biomarkers of effect. The utility of a biomarker for estimating exposures or predicting risks depends on the strength of the correlation between biomarker concentrations and exposure/effects. In the current study, a combined exposure and physiologically-based pharmacokinetic/pharmacodynamic (PBPK/PD) model of carbaryl was used to demonstrate the use of computational modeling for providing insight into the selection of biomarkers for different purposes. The Cumulative and Aggregate Risk Evaluation System (CARES) was used to generate exposure profiles, including magnitude and timing, for use as inputs to the PBPK/PD model. The PBPK/PD model was then used to predict blood concentrations of carbaryl and urine concentrations of its principal metabolite, 1-naphthol (1-N), as biomarkers of exposure. The PBPK/PD model also predicted acetylcholinesterase (AChE) inhibition in red blood cells (RBC) as a biomarker of effect. The correlations of these simulated biomarker concentrations with intake doses or brain AChE inhibition (as a surrogate of effects) were analyzed using a linear regression model. Results showed that 1-N in urine is a better biomarker of exposure than carbaryl in blood, and that 1-N in urine is correlated with the dose averaged over the last 2 days of the simulation. They also showed that RBC AChE inhibition is an appropriate biomarker of effect. This computational approach can be applied to a wide variety of chemicals to facilitate quantitative analysis of biomarker utility.

SUBMITTER: Phillips MB

PROVIDER: S-EPMC4235294 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Analysis of biomarker utility using a PBPK/PD model for carbaryl.

Phillips Martin B MB Yoon Miyoung M Young Bruce B Tan Yu-Mei YM

Frontiers in pharmacology 20141118

There are many types of biomarkers; the two common ones are biomarkers of exposure and biomarkers of effect. The utility of a biomarker for estimating exposures or predicting risks depends on the strength of the correlation between biomarker concentrations and exposure/effects. In the current study, a combined exposure and physiologically-based pharmacokinetic/pharmacodynamic (PBPK/PD) model of carbaryl was used to demonstrate the use of computational modeling for providing insight into the sele ...[more]

PMID: 25477820

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Project description:BackgroundImmune checkpoint inhibitors (ICPI) are a tumor agnostic treatment. However, trials of their use have been site specific. Here we summarize the trial data and explore the utility of programmed death-ligand 1 (PD-L1) expression as a biomarker to direct their pan-cancer use.MethodA systematic review of literature, following PRISMA guidelines, was performed. Medline, Embase, Cochrane CENTRAL, NHS Health and Technology, and Web of Science were searched from their conception to June 2022 limited to the English language. The search terms and method were devised by a specialist medical librarian. Studies were limited to adults with solid cancers (excluding melanomas) treated with ICPIs. Only phase III randomized control trials (RCT) were included. The primary outcome was overall survival and secondary outcomes were progression free survival, PD-L1 expression, quality of life outcomes and adverse event data. Where present in eligible clinical trials, hazard ratios (HR), risk ratios (RR), standard error (SE) and 95% confidence intervals (CI) were extracted or calculated. Heterogeneity across studies was described with the use of an I2 score (Low: 25, 50%: moderate, 75% low heterogeneity). HR pools inverse variance methods were adopted by Random Effects (RE). Means were standardized across any heterogenous scale limits.ResultsIn total 46,510 participants were included in the meta-analysis. Overall, meta-analysis favored the use of ICPIs with an overall survival (OS) HR of 0.74 (95% CI 0.71 to 0.78). Lung cancers showed the most benefit in OS [HR 0.72 (95% 0.66-0.78)] followed by head and neck cancers [HR 0.75 (95% CI 0.66-0.84)] and gastroesophageal junction cancers [HR 0.75 (95% CI 0.61-0.92)]. ICPIs seem to be efficacious at both primary presentation and recurrence [OS HR 0.73 (95% CI 0.68-0.77)] vs. [OS HR 0.79 (95% CI 0.72 to 0.87)] respectively. Interestingly, subgroup analysis comparing studies in which most cancers demonstrated PD-L1 expression vs. those studies in which a minority of cancer demonstrated PD-L1 expression reported similar effect of ICPI use on OS; oddly the data favored ICPI use in studies with a minority of PD-L1 expression. Specifically, studies with minority PD-L1 expression had an HR 0.73 (95% CI 0.68-0.78) vs. studies with majority PD-L1 expression HR 0.76 (95% CI 0.70-0.84). This was maintained even when studies exploring the same cancer site were directly compared. Subgroup analysis was performed comparing the impact on OS subdivided by the specific ICPI used. Where meta-analysis was performed, Nivolumab led to the greatest impact [HR 0.70 (95% CI 0.64-0.77)] with Avelumab failing to reach significance [HR 0.93 (95% CI 0.80-1.06)]. However, overall heterogenicity was high (I2 = 95%). Finally, the use of ICPIs led to an improved side effect profile when compared with standard chemotherapy [RR 0.85 (95% CI 0.73-0.98)].ConclusionICPIs improve survival outcomes in all cancer types. These effects are seen in the primary, recurrent, chemotherapy sensitive, chemotherapy resistant disease. These data support their use as a tumor agnostic therapy. Furthermore, they are well tolerated. However, PD-L1 as a biomarker for the targeting of ICPI use seems problematic. Other biomarkers such as mismatch repair or tumor mutational burden should be explored in randomized trials. In addition, there are still limited trials looking at ICPI use outside of lung cancer.

Dataset Information

Analysis of biomarker utility using a PBPK/PD model for carbaryl.

Publications

Analysis of biomarker utility using a PBPK/PD model for carbaryl.

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