Long-acting injectable (LAI) antipsychotic formulations are increasingly used for improving patient compliance and long-term outcomes. Transitioning to LAIs raises questions regarding how optimum efficacy can be rapidly achieved while minimizing potential efficacy and safety concerns related to overlapping plasma levels of prior treatments and the new LAI. Ideally, randomized clinical trials would provide guidance regarding transition algorithms, but the number of studies and sample size required to address relevant questions makes this approach unachievable. We have used quantitative systems pharmacology, a clinically calibrated, mechanism-based computer model for schizophrenia to identify optimal switching scenarios to injectable paliperidone palmitate once-monthly (PP1M) from oral antipsychotics. We show that starting PP1M 1day after the last oral medication dose or 4weeks after the last LAI injection provides optimal benefit-risk compared to a delayed PP1M start after 1week with either a 1- or 2-week overlap with oral paliperidone. Although a similar or better therapeutic effect can be achieved within 2weeks for oral medications and LAI haloperidol decanoate and 8weeks for LAI aripiprazole, we identified a potential transient undertreatment liability in all cases except for risperidone. Switching from oral olanzapine may lead to a small reduction of antipsychotic efficacy in some patients. Switching to PP1M decreases extrapyramidal symptom liability in most cases, but increased dopamine D receptor inhibition (except for haloperidol) might potentially increase prolactin synthesis. Overall, these results suggest time-windows for which the treating clinician must be most vigilant for potential efficacy and safety signals when switching to PP1M.
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http://dx.doi.org/10.1016/j.schres.2017.11.016 | DOI Listing |
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Department of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, 1218 S 5th Ave, Monrovia, California 91016, United States.
Bayesian network modeling (BN modeling, or BNM) is an interpretable machine learning method for constructing probabilistic graphical models from the data. In recent years, it has been extensively applied to diverse types of biomedical data sets. Concurrently, our ability to perform long-time scale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially.
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Division of Hospital Internal Medicine, Mayo Clinic, Rochester, MN, USA.
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MethodsX
June 2025
Faculty of Design and Art, University of Wuppertal, 42119 Wuppertal, Germany.
Project-based learning, with its emphasis on 'learning by doing', is the dominant teaching method in industrial design. Learners are supposed to be motivated to tackle complex problems such as those in the dynamic field of sustainability. However, it is still unclear how the process of increasing motivation within projects can be systematically targeted for specific sustainability challenges and directed towards potential later pro-environmental behavior.
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