Performance of Cpred/Cobs concentration ratios as a metric reflecting adherence to antidepressant drug therapy

Background: Nonadherence is very common among subjects undergoing pharmacotherapy for schizophrenia and depression. This study aimed to evaluate the performance of the ratio of the nonlinear mixed effects pharmacokinetic model predicted concentration to observed drug concentration (ratio of population predicted to observed concentration (Cpred/Cobs) and ratio of individual predicted to observed concentration (Cipred/Cobs) as a measure of erratic drug exposure, driven primarily by variable execution of the dosage regimen and unknown true dosage history. Methods: Modeling and simulation approaches in conjunction with dosage history information from the Medication Event Monitoring System (MEMS, provided by the "Depression: The search for treatment relevant phenotypes" study), was applied to evaluate the consistency of exposure via simulation studies with scenarios representing a long half-life drug (escitalopram). Adherence rates were calculated based on the percentage of the prescribed doses actually taken correctly during the treatment window of interest. The association between Cpred/Cobs, Cipred/Cobs ratio, and adherence rate was evaluated under various assumptions of known dosing history. Results: Simulations for those scenarios representing a known dosing history were generated from historical MEMS data. Simulations of a long half-life drug exhibited a trend for overprediction of concentrations in patients with a low percentage of doses taken and underprediction of concentrations in patients taking more than their prescribed number of doses. Overall, the ratios did not predict adherence well, except when the true adherence rates were extremely high (greater than 100% of prescribed doses) or extremely low (complete nonadherence). In general, the Cipred/Cobs ratio was a better predictor of adherence rate than the Cpred/Cobs ratio. Correct predictions of extreme (high, low) 7-day adherence rates using Cipred/Cobs were 73.8% and 64.0%. Conclusion: This simulation study demonstrated the limitations of the Cpred/obs and Cipred/obs ratios as metrics for actual dosage intake history, and identified that use of MEMS dosing history monitoring combined with sparse pharmacokinetic sampling is a more reliable approach. © 2011 Feng et al

Artificial Intelligence for In Silico Clinical Trials: A Review

A clinical trial is an essential step in drug development, which is often costly and time-consuming. In silico trials are clinical trials conducted digitally through simulation and modeling as an alternative to traditional clinical trials. AI-enabled in silico trials can increase the case group size by creating virtual cohorts as controls. In addition, it also enables automation and optimization of trial design and predicts the trial success rate. This article systematically reviews papers under three main topics: clinical simulation, individualized predictive modeling, and computer-aided trial design. We focus on how machine learning (ML) may be applied in these applications. In particular, we present the machine learning problem formulation and available data sources for each task. We end with discussing the challenges and opportunities of AI for in silico trials in real-world applications

Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values

This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records. Such data is invariably problematic: noisy, with missing entries, with imbalance in classes of interests, leading to serious bias in predictive modeling. Since standard data mining methods often produce poor performance measures, we argue for development of specialized techniques of data-preprocessing and classification. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values. It is based on a multilevel framework of the cost-sensitive SVM and the expected maximization imputation method for missing values, which relies on iterated regression analyses. We compare classification results of multilevel SVM-based algorithms on public benchmark datasets with imbalanced classes and missing values as well as real data in health applications, and show that our multilevel SVM-based method produces fast, and more accurate and robust classification results.Comment: arXiv admin note: substantial text overlap with arXiv:1503.0625