Using machine learning techniques to characterize sleep-deprived driving behavior.

OBJECTIVE: Sleep deprivation is known to affect driving behavior and may lead to serious car accidents similar to the effects from e.g., alcohol. In a previous study, we have demonstrated that the use of machine learning techniques allows adequate characterization of abnormal driving behavior after alprazolam and/or alcohol intake. In the present study, we extend this approach to sleep deprivation and test the model for characterization of new interventions. We aimed to classify abnormal driving behavior after sleep deprivation, and, by using a machine learning model, we tested if this model could also pick up abnormal driving behavior resulting from other interventions. METHODS: Data were collected during a previous study, in which 24 subjects were tested after being sleep-deprived and after a well-rested night. Features were calculated from several driving parameters, such as the lateral position, speed of the car, and steering speed. In the present study, we used a gradient boosting model to classify sleep deprivation. The model was validated using a 5-fold cross validation technique. Next, probability scores were used to identify the overlap of driving behavior after sleep deprivation and driving behavior affected by other interventions. In the current study alprazolam, alcohol, and placebo are used to test/validate the approach. RESULTS: The sleep deprivation model detected abnormal driving behavior in the simulator with an accuracy of 77 ± 9%. Abnormal driving behavior after alprazolam, and to a lesser extent also after alcohol intake, showed remarkably similar characteristics to sleep deprivation. The average probability score for alprazolam and alcohol measurements was 0.79, for alcohol 0.63, and for placebo only 0.27 and 0.30, matching the expected relative drowsiness. CONCLUSION: We developed a model detecting abnormal driving induced by sleep deprivation. The model shows the similarities in driving characteristics between sleep deprivation and other interventions, i.e., alcohol and alprazolam. Consequently, our model for sleep deprivation may serve as a next reference point for a driving test battery of newly developed drugs.

The use of machine learning improves the assessment of drug-induced driving behaviour.

RATIONALE: Car-driving performance is negatively affected by the intake of alcohol, tranquillizers, sedatives and sleep deprivation. Although several studies have shown that the standard deviation of the lateral position on the road (SDLP) is sensitive to drug-induced changes in simulated and real driving performance tests, this parameter alone might not fully assess and quantify deviant or unsafe driving. OBJECTIVE: Using machine learning we investigated if including multiple simulator-derived parameters, rather than the SDLP alone would provide a more accurate assessment of the effect of substances affecting driving performance. We specifically analysed the effects of alcohol and alprazolam. METHODS: The data used in the present study were collected during a previous study on driving effects of alcohol and alprazolam in 24 healthy subjects (12 M, 12 F, mean age 26 years, range 20-43 years). Various driving features, such as speed and steering variations, were quantified and the influence of administration of alcohol or alprazolam was assessed to assist in designing a predictive model for abnormal driving behaviour. RESULTS: Adding additional features besides the SDLP increased the model performance for prediction of drug-induced abnormal driving behaviour (from an accuracy of 65 %-83 % after alprazolam intake and from 50 % to 76 % after alcohol ingestion). Driving behaviour influenced by alcohol and alprazolam was characterised by different feature importance, indicating that the two interventions influenced driving behaviour in a different way. CONCLUSION: Machine learning using multiple driving features in addition to the state-of-the-art SDLP improves the assessment of drug-induced abnormal driving behaviour. The created models may facilitate quantitative description of abnormal driving behaviour in the development and application of psychopharmacological medicines. Our models require further validation using similar and unknown interventions.

Glutathione-PEGylated liposomal methylprednisolone in comparison to free methylprednisolone: slow release characteristics and prolonged lymphocyte depression in a first-in-human study.

AIMS: Intravenous high-dose free methylprednisolone (MP) hemisuccinate is the primary treatment for an acute relapse in relapsing-remitting multiple sclerosis. However, it is inconvenient and its side effects are undesirable. Both dose and dosing frequency can be reduced by incorporating free MP in glutathione-PEGylated liposomes, creating a slow-release formulation with reduced toxicity and prolonged peripheral efficacy. This first-in-human study was designed to assess the safety, pharmacokinetics and pharmacodynamics of glutathione-PEGylated liposomes containing MP (2B3-201). METHODS: The first part was a double-blind, three-way cross over study in 18 healthy male subjects, receiving ascending doses of 2B3-201, active comparator (free MP) or placebo. Part 2 of the study was an open-label infusion of 2B3-201 (different doses), exploring pretreatment with antihistamines and different infusion schedules in another 18 healthy male subjects, and a cross-over study in six healthy female subjects. MP plasma concentrations, lymphocyte counts, adrenocorticotropic hormone, osteocalcin and fasting glucose were determined. Safety and tolerability profiles were assessed based on adverse events, safety measurements and central nervous system tests. RESULTS: The most frequent recorded AE related to 2B3-201 was an infusion related reaction (89%). 2B3-201 was shown to have a plasma half-life between 24 and 37 h and caused a prolonged decrease in the lymphocyte count, adrenocorticotropic hormone and osteocalcin, and a rise in fasting glucose. CONCLUSION: 2B3-201 is considered safe, with no clinically relevant changes in central nervous system safety parameters and no serious adverse events. In addition, 2B3-201 shows a long plasma half-life and prolonged immunosuppressive effects.

The perception and pharmacokinetics of a 20-mg dose of escitalopram orodispersible tablets in a relative bioavailability study in healthy men.

BACKGROUND: Rapidly dissolving oral (orodispersible) drug formulations have been developed to overcome problems related to swallowing. OBJECTIVE: The aim of this study was to compare the bioavailability of orodispersible and conventional immediate-release (IR) escitalopram tablets. METHODS: This was a randomized, open-label, 3-way crossover trial in which healthy men received single doses of orodispersible escitalopram formulations (2 ×10 mg and 1 × 20 mg) and conventional (2 × 10 mg) oral escitalopram tablets. Blood samples for pharmacokinetic analysis were obtained during a 168-hour period after dosing. The safety profile and tolerability were assessed by monitoring of adverse events, physical examinations, ECGs, and clinical laboratory and vital signs assessments. A questionnaire was used to assess the perception of the orodispersible tablet (ODT). RESULTS: The assumed bioequivalence assessment was based on pharmacokinetic and statistical analysis of data from the 29 men who completed the 3 treatments. The serum concentration-time profiles of escitalopram were similar after intake of the 3 treatments. The 90% CI for the mean treatment ratios of the log-transformed C(max), AUC(0-t), and AUC(0-∞) were all within the predefined equivalence range of 80% to 125%. Most subjects (87%) thought that the ODT was pleasant to take, and 85% of subjects thought that it was convenient to take the tablet without water. Most subjects (67%-90%) reported adverse events, with a similar incidence for all treatments. Most adverse events were mild, with somnolence and nausea being the most frequently reported. No clinically relevant changes were observed in physical, biochemical, hematologic, or urinalysis variables during the study. CONCLUSIONS: In this small study population of fasting healthy male volunteers, 2 × 10-mg ODTs or 1 × 20-mg ODT and 2 × 10-mg conventional IR escitalopram tablets met the regulatory criteria for assumed bioequivalence. CLINICALTRIALS.GOV IDENTIFIER: NCT 01395433.