In clinical trials, Montgomery-Åsberg Depression Rating Scale (MADRS) questionnaire data are added up to total scores before analysis, assuming equal contribution of each separate question. Item... Show moreIn clinical trials, Montgomery-Åsberg Depression Rating Scale (MADRS) questionnaire data are added up to total scores before analysis, assuming equal contribution of each separate question. Item Response Theory (IRT)-based analysis avoids this by using individual question responses to determine the latent variable (ψ), which represents a measure of depression severity. However, utilization of IRT in early phase trials remains difficult, because large datasets are needed to develop IRT models. Therefore, we aimed to evaluate the application and assumptions of a reference IRT model for analysis of an early phase trial. A cross-over, placebo-controlled study investigating the effect of intravenous racemic ketamine on MADRS scores in patients with treatment-resistant major depressive disorder was used as a case study. One hundred forty-seven MADRS responses were measured in 17 patients at five timepoints (predose to 2 weeks after dosing). Two reference IRT models based on different patient populations were selected from literature and used to determine ψ, while testing multiple approaches regarding assumed data distribution. Use of ψ versus total score to determine treatment effect was compared through linear mixed model analysis. Results showed that determined ψ values did not differ significantly between assumed distributions, but were significantly different when changing reference IRT model. Estimated treatment effect size was not significantly affected by chosen approach nor reference population. Finally, increased precision to determine treatment effect was achieved by using IRT versus total scores. This demonstrates the usefulness of reference IRT model application for analysis of questionnaire data in early phase clinical trials. Show less
Otto, M.E.; Burckhardt, M.; Szinnai, G.; Pfister, M.; Gotta, V. 2022
Aims The purpose of this study was to investigate pharmacodynamic effects of drugs targeting cortical excitability using transcranial magnetic stimulation (TMS) combined with electromyography (EMG)... Show moreAims The purpose of this study was to investigate pharmacodynamic effects of drugs targeting cortical excitability using transcranial magnetic stimulation (TMS) combined with electromyography (EMG) and electroencephalography (EEG) in healthy subjects, to further develop TMS outcomes as biomarkers for proof-of-mechanism in early-phase clinical drug development. Antiepileptic drugs presumably modulate cortical excitability. Therefore, we studied effects of levetiracetam, valproic acid and lorazepam on cortical excitability in a double-blind, placebo-controlled, 4-way cross-over study. Methods In 16 healthy male subjects, single- and paired-pulse TMS-EMG-EEG measurements were performed predose and 1.5, 7 and 24 hours postdose. Treatment effects on motor-evoked potential, short and long intracortical inhibition and TMS-evoked potential amplitudes, were analysed using a mixed model ANCOVA and cluster-based permutation analysis. Results We show that motor-evoked potential amplitudes decreased after administration of levetiracetam (estimated difference [ED] -378.4 mu V; 95%CI: -644.3, -112.5 mu V; P < .01), valproic acid (ED -268.8 mu V; 95%CI: -532.9, -4.6 mu V; P = .047) and lorazepam (ED -330.7 mu V; 95%CI: -595.6, -65.8 mu V; P = .02) when compared with placebo. Long intracortical inhibition was enhanced by levetiracetam (ED -60.3%; 95%CI: -87.1%, -33.5%; P < .001) and lorazepam (ED -68.2%; 95%CI: -94.7%, -41.7%; P < .001) at a 50-ms interstimulus interval. Levetiracetam increased TMS-evoked potential component N45 (P = .004) in a central cluster and decreased N100 (P < .001) in a contralateral cluster. Conclusion This study shows that levetiracetam, valproic acid and lorazepam decrease cortical excitability, which can be detected using TMS-EMG-EEG in healthy subjects. These findings provide support for the use of TMS excitability measures as biomarkers to demonstrate pharmacodynamic effects of drugs that influence cortical excitability. Show less
Otto, M.E.; Bergmann, K.R.; Jacobs, G.; Esdonk, M.J. van 2021
Purpose The recent repurposing of ketamine as treatment for pain and depression has increased the need for accurate population pharmacokinetic (PK) models to inform the design of new clinical... Show morePurpose The recent repurposing of ketamine as treatment for pain and depression has increased the need for accurate population pharmacokinetic (PK) models to inform the design of new clinical trials. Therefore, the objectives of this study were to externally validate available PK models on (S)-(nor)ketamine concentrations with in-house data and to improve the best performing model when necessary. Methods Based on predefined criteria, five models were selected from literature. Data of two previously performed clinical trials on (S)-ketamine administration in healthy volunteers were available for validation. The predictive performances of the selected models were compared through visual predictive checks (VPCs) and calculation of the (root) mean (square) prediction errors (ME and RMSE). The available data was used to adapt the best performing model through alterations to the model structure and re-estimation of inter-individual variability (IIV). Results The model developed by Fanta et al. (Eur J Clin Pharmacol 71:441-447, 2015) performed best at predicting the (S)-ketamine concentration over time, but failed to capture the (S)-norketamine C-max correctly. Other models with similar population demographics and study designs had estimated relatively small distribution volumes of (S)-ketamine and thus overpredicted concentrations after start of infusion, most likely due to the influence of circulatory dynamics and sampling methodology. Model predictions were improved through a reduction in complexity of the (S)-(nor)ketamine model and re-estimation of IIV. Conclusion The modified model resulted in accurate predictions of both (S)-ketamine and (S)-norketamine and thereby provides a solid foundation for future simulation studies of (S)-(nor)ketamine PK in healthy volunteers after (S)-ketamine infusion. Show less
