Dataset Information

Inference of synergy/antagonism between anticancer drugs from the pooled analysis of clinical trials.

ABSTRACT:

Background

Drug interactions can have a significant impact on the response to combinatorial therapy for anticancer treatment. In some instances these interactions can be anticipated based on pre-clinical models. However, the anticipation of drug interactions in the clinical context is in general a challenging task.

Methods

Here we propose the pooled analysis of clinical trials as a mean to investigate drug interactions in anticancer therapy. To this end we collected 1,163 Phase II clinical trials with response data on over 53,745 subjects.

Results

We provide statistical definitions of drugs resulting in clinical synergy and antagonism and identify drug combinations in each group. We also quantify the possibility of inferring interactions between three or more drugs from parameters characterizing the action of single and two-drugs combinations.

Conclusions

Our analysis provides a statistical methodology to track the performance of drug combinations in anticancer therapy and to quantify drug interactions in the clinical context.

SUBMITTER: Kang W

PROVIDER: S-EPMC3694030 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

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Publications

Inference of synergy/antagonism between anticancer drugs from the pooled analysis of clinical trials.

Kang Wenfeng W DiPaola Robert S RS Vazquez Alexei A

BMC medical research methodology 20130612

<h4>Background</h4>Drug interactions can have a significant impact on the response to combinatorial therapy for anticancer treatment. In some instances these interactions can be anticipated based on pre-clinical models. However, the anticipation of drug interactions in the clinical context is in general a challenging task.<h4>Methods</h4>Here we propose the pooled analysis of clinical trials as a mean to investigate drug interactions in anticancer therapy. To this end we collected 1,163 Phase II ...[more]

PMID: 23758906

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Project description:BackgroundPatients with moderate-to-severe atopic dermatitis (AD) have increased infection risk, including skin infections and systemic infections. Immunomodulators (e.g., anti-tumor necrosis factors, anti-interleukin [anti-IL]-23, anti-IL-17, Janus kinase inhibitors) increase risk of infections. Dupilumab (a monoclonal antibody blocking the shared receptor component for IL-4 and IL-13) is approved for inadequately controlled moderate-to-severe AD and for moderate-to-severe eosinophilic or oral corticosteroid-dependent asthma.ObjectiveThe aim was to determine the impact of dupilumab on infection rates in patients with moderate-to-severe AD.MethodsThis analysis pooled data from seven randomized, placebo-controlled dupilumab trials in adults with moderate-to-severe AD. Exposure-adjusted analyses assessed infection rates.ResultsOf 2932 patients, 1091 received placebo, 1095 dupilumab 300 mg weekly, and 746 dupilumab 300 mg every 2 weeks. Treatment groups had similar infection rates overall per 100 patient-years (placebo, 155; dupilumab weekly, 150; dupilumab every 2 weeks, 156; dupilumab combined, 152), and similar non-skin infection rates. Serious/severe infections were reduced with dupilumab (risk ratio 0.43; p < 0.05), as were bacterial and other non-herpetic skin infections (risk ratio 0.44; p < 0.001). Although herpesviral infection rates overall were slightly higher with dupilumab than placebo, clinically important herpesviral infections (eczema herpeticum, herpes zoster) were less common with dupilumab (risk ratio 0.31; p < 0.01). Systemic anti-infective medication use was lower with dupilumab.ConclusionsDupilumab is associated with reduced risk of serious/severe infections and non-herpetic skin infections and does not increase overall infection rates versus placebo in patients with moderate-to-severe AD. CLINICALTRIALS.Gov identifiersNCT01548404, NCT02210780, NCT01859988, NCT02277743, NCT02277769, NCT02260986, and NCT02755649.

Dataset Information

Inference of synergy/antagonism between anticancer drugs from the pooled analysis of clinical trials.

Background

Methods

Results

Conclusions

Publications

Inference of synergy/antagonism between anticancer drugs from the pooled analysis of clinical trials.

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