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A mechanistic, model-based approach to safety assessment in clinical development.

ABSTRACT: Assessing the safety of pharmacotherapies is a primary goal of clinical trials in drug development. The low frequency of relevant side effects, however, often poses a significant challenge for risk assessment. Methodologies allowing robust extrapolation of safety statistics based on preclinical data and information from clinical trials with limited numbers of patients are hence needed to further improve safety and efficacy in the drug development process. Here, we present a generic systems pharmacology approach integrating prior physiological and pharmacological knowledge, preclinical data, and clinical trial results, which allows predicting adverse event rates related to drug exposure. Possible fields of application involve high-risk populations, novel drug candidates, and different dosing scenarios. As an example, the approach is applied to simvastatin and pravastatin and the prediction of myopathy rates in a population with a genotype leading to a significantly increased myopathy risk.CPT: Pharmacometrics & Systems Pharmacology (2012) 1, e13; doi:10.1038/psp.2012.14; advance online publication 7 November 2012.

SUBMITTER: Lippert J

PROVIDER: S-EPMC3600730 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

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A mechanistic, model-based approach to safety assessment in clinical development.

Lippert J J Brosch M M von Kampen O O Meyer M M Siegmund H-U HU Schafmayer C C Becker T T Laffert B B Görlitz L L Schreiber S S Neuvonen P J PJ Niemi M M Hampe J J Kuepfer L L

CPT: pharmacometrics & systems pharmacology 20121107

Assessing the safety of pharmacotherapies is a primary goal of clinical trials in drug development. The low frequency of relevant side effects, however, often poses a significant challenge for risk assessment. Methodologies allowing robust extrapolation of safety statistics based on preclinical data and information from clinical trials with limited numbers of patients are hence needed to further improve safety and efficacy in the drug development process. Here, we present a generic systems pharm ...[more]

PMID: 23835795

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Project description:BackgroundNon-attendance to scheduled hospital outpatient appointments may compromise healthcare resource planning, which ultimately reduces the quality of healthcare provision by delaying assessments and increasing waiting lists. We developed a model for predicting non-attendance and assessed the effectiveness of an intervention for reducing non-attendance based on the model.MethodsThe study was conducted in three stages: (1) model development, (2) prospective validation of the model with new data, and (3) a clinical assessment with a pilot study that included the model as a stratification tool to select the patients in the intervention. Candidate models were built using retrospective data from appointments scheduled between January 1, 2015, and November 30, 2018, in the dermatology and pneumology outpatient services of the Hospital Municipal de Badalona (Spain). The predictive capacity of the selected model was then validated prospectively with appointments scheduled between January 7 and February 8, 2019. The effectiveness of selective phone call reminders to patients at high risk of non-attendance according to the model was assessed on all consecutive patients with at least one appointment scheduled between February 25 and April 19, 2019. We finally conducted a pilot study in which all patients identified by the model as high risk of non-attendance were randomly assigned to either a control (no intervention) or intervention group, the last receiving phone call reminders one week before the appointment.ResultsDecision trees were selected for model development. Models were trained and selected using 33,329 appointments in the dermatology service and 21,050 in the pneumology service. Specificity, sensitivity, and accuracy for the prediction of non-attendance were 79.90%, 67.09%, and 73.49% for dermatology, and 71.38%, 57.84%, and 64.61% for pneumology outpatient services. The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) for pneumology, respectively. The effectiveness of the intervention was assessed on 1,311 individuals identified as high risk of non-attendance according to the selected model. Overall, the intervention resulted in a significant reduction in the non-attendance rate to both the dermatology and pneumology services, with a decrease of 50.61% (p<0.001) and 39.33% (p=0.048), respectively.ConclusionsThe risk of non-attendance can be adequately estimated using patient information stored in medical records. The patient stratification according to the non-attendance risk allows prioritizing interventions, such as phone call reminders, to effectively reduce non-attendance rates.

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A mechanistic, model-based approach to safety assessment in clinical development.

Publications

A mechanistic, model-based approach to safety assessment in clinical development.

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