ReviewFuture medicinal chemistry2025
Expecting medication misuse: a proactive approach to drug discovery to prevent fatal overdose.
Review in Future medicinal chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Authors and funding
4 authors.
Funding
Abstract
Misuse of central nervous system (CNS) depressants (alprazolam, fentanyl, etc.) is a major cause of fatal overdose, with a high prevalence of deaths involving polydrug interactions from the victim's own prescriptions. Thus, there is an urgent need to improve the safety of CNS depressants to prevent fatalities. Pharmacological pursuits aiming to prevent harm through the design of non-addictive alternatives have either failed before clinical trials or produced mediocre treatment alternatives. Therefore, we propose a new perspective for medicinal chemists: rather than aiming to prevent misuse, we must design new central nervous system (CNS) depressants under the expectation of misuse. By shifting the design focus to partial modulators rather than full agonists, we can develop novel chemical entities (NCEs) that intrinsically minimize physical harm caused by misuse without sacrificing therapeutic efficacy. In this perspective, we provide an overview of the two most widely misused classes of medications (opioid and GABA
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.