Model-based assessment of the benefits and risks of recombinant tissue plasminogen activator treatment in acute ischaemic stroke

AIMS

Recombinant tissue plasminogen activator (rt-PA) is the only first-line agent approved by the US Food and Drug Administration to treat acute ischaemic stroke. However, it often causes the serious adverse event (AE) of haemorrhagic transformation. The present study developed a pharmacometric model for the rt-PA treatment effect and AE and, using the developed model, proposed a benefit-to-risk ratio assessment scheme as a supportive tool to optimize treatment outcome.

METHODS

The data from 336 acute ischaemic stroke patients were used. The treatment effect was assessed based on an improvement in National Institutes of Health Stroke Scale (NIHSS) scores, which were described using an item response theory (IRT)-based disease progression model. Treatment failure and AE probabilities, and their occurrence times, were described by incidence and time-to-event models. Using the developed model, benefit-to-risk ratios were simulated under various scenarios using the global benefit-to-risk trade-off ratio (GBR).

RESULTS

High initial NIHSS score and middle cerebral artery (MCA) stroke were risk factors for treatment failure, where the failure rate with MCA stroke was 2.87-fold higher than with non-MCA stroke. The haemorrhagic transformation time was associated with longitudinal changes in NIHSS scores. The benefit-to-risk ratio simulated was highest in minor stroke severity, with GBR >1 in all scenarios, and the ratio with non-MCA stroke was 2-3 fold higher than with MCA stroke.

CONCLUSIONS

The study demonstrated the feasibility of applying an IRT model to describing the time course of the rt-PA treatment effect and AE. Benefit-to-risk ratio analyses showed that the treatment was optimal in non-MCA stroke with minor stroke severity.

Joint modeling of efficacy, dropout, and tolerability in flexible-dose trials: a case study in depression

Many difficulties may arise during the modeling of the time course of Hamilton Rating Scale for Depression (HAM D)scores in clinical trials for the evaluation of antidepressant drugs: (i) flexible designs, used to increase the chance of selecting more efficacious doses, (ii) dropout events, and (iii) adverse effects related to the experimental compound.It is crucial to take into account all these factors when designing an appropriate model of the HAM D time course and to obtain a realistic description of the dropout process. In this work, we propose an integrated approach to the modeling of a double-blind, flexible-dose, placebo-controlled, phase II depression trial that comprises response,tolerability, and dropout. We investigate three different dropout mechanisms in terms of informativeness. Goodness of fit is quantitatively assessed with respect to response (HAM D score) and dropout data. We show that dropout is a complex phenomenon that may be influenced by HAM D evolution, dose changes, and occurrence of drug-related adverse effects.