Moving Beyond Serendipity to Mechanism-Driven Psychiatric Therapeutics

Pieper, Andrew A.; Baraban, Jay M.

doi:10.1007/s13311-017-0547-6

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…5 Recognized biological heterogeneity, also adds to the difficulty of identifying reliable biological markers associated with these conditions. 6 Treatments for psychiatric disorders have emerged largely as a result of serendipitous observations 7 with an unfortunate range of side-effects 8 and this may be why mortality and prevalence rates associated with psychiatric illnesses have not decreased in past years, 9 as compared to other medical conditions such as certain types of cancer 10 or heart diseases. 11…”

Section: Introductionmentioning

confidence: 99%

Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning

et al. 2019

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In the literature, there are substantial machine learning attempts to classify schizophrenia based on alterations in resting-state (RS) brain patterns using functional magnetic resonance imaging (fMRI). Most earlier studies modelled patients undergoing treatment, entailing confounding with drug effects on brain activity, and making them less applicable to real-world diagnosis at the point of first medical contact. Further, most studies with classification accuracies >80% are based on small sample datasets, which may be insufficient to capture the heterogeneity of schizophrenia, limiting generalization to unseen cases. In this study, we used RS fMRI data collected from a cohort of antipsychotic drug treatment-naive patients meeting DSM IV criteria for schizophrenia ( N = 81) as well as age- and sex-matched healthy controls ( N = 93). We present an ensemble model -- EMPaSchiz (read as ‘Emphasis’; standing for ‘Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction’) that stacks predictions from several ‘single-source’ models, each based on features of regional activity and functional connectivity, over a range of different a priori parcellation schemes. EMPaSchiz yielded a classification accuracy of 87% (vs. chance accuracy of 53%), which out-performs earlier machine learning models built for diagnosing schizophrenia using RS fMRI measures modelled on large samples ( N > 100). To our knowledge, EMPaSchiz is first to be reported that has been trained and validated exclusively on data from drug-naive patients diagnosed with schizophrenia. The method relies on a single modality of MRI acquisition and can be readily scaled-up without needing to rebuild parcellation maps from incoming training images.

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Section: Introductionmentioning

confidence: 99%

Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning

et al. 2019

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“…It is clear that during the 1950s and 1960s serendipity played an important role in the process of building modern psychopharmacology in general and the first groups of families of antidepressant drugs in particular giving way in later decades to another way of understanding scientific research in this field, namely the systematic and rational planning of projects to be developed. In recent decades, psychopharmacology is moving away from the influence of serendipity towards new scientific approaches, although this is a gradual process[ 98 ] as can be seen with the serendipitous introduction of ketamine into the antidepressant arsenal. In any event, the results of this work confirm that serendipity should be understood as more of an eminently scientific construct than a literary curiosity.…”

Section: Discussionmentioning

confidence: 99%

Role of serendipity in the discovery of classical antidepressant drugs: Applying operational criteria and patterns of discovery

López‐Muñoz¹,

D’Ocón²,

Romero³

et al. 2022

WJP

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The role played by serendipity in the origin of modern psychopharmacology has proven to be controversial in scientific literature. In its original meaning (Walpole), serendipity refers to discoveries made through a combination of accidents and sagacity. We have implemented an operational definition of serendipity based on finding something unexpected or unintended, regardless of the systematic process that led to the accidental observation, and we have established four different patterns of serendipitous attributability. In this paper, we have analyzed the role of serendipity in the discovery and development of classical antidepressant drugs, tricyclic antidepressants and monoamine oxidase inhibitors as well as heterocyclic, “atypical” or “second generation” antidepressants. The discovery of the antidepressant properties of imipramine and iproniazid, the prototypes of tricyclic antidepressants and monoamine oxidase inhibitors, respectively, fits the mixed type II pattern; initial serendipitous discoveries (imipramine was an antipsychotic and iproniazid was an anti-tuberculosis agent) led secondarily to non-serendipitous discoveries. But the other components of these two families of drugs were developed specifically as antidepressants, modifying the chemical structure of the series leaders, thereby allowing all of them to be included in the type IV pattern, characterized by the complete absence of serendipity. Among the heterocyclic drugs, mianserin (originally developed as an antihistamine) also falls into the type II pattern.

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“…Novel therapeutics are severely lacking for patients suffering from neuropsychiatric and neurodegenerative diseases 1 – 4 . One promising avenue for central nervous system (CNS) drug development is to address alterations in the magnitude of postnatal hippocampal neurogenesis and hippocampal volume reported for many CNS disorders, including Alzheimer’s disease, schizophrenia, major depression, addiction and anxiety 5 , 6 .…”

Section: Introductionmentioning

confidence: 99%

Neuroprotective efficacy of P7C3 compounds in primate hippocampus

et al. 2018

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There is a critical need for translating basic science discoveries into new therapeutics for patients suffering from difficult to treat neuropsychiatric and neurodegenerative conditions. Previously, a target-agnostic in vivo screen in mice identified P7C3 aminopropyl carbazole as capable of enhancing the net magnitude of postnatal neurogenesis by protecting young neurons from death. Subsequently, neuroprotective efficacy of P7C3 compounds in a broad spectrum of preclinical rodent models has also been observed. An important next step in translating this work to patients is to determine whether P7C3 compounds exhibit similar efficacy in primates. Adult male rhesus monkeys received daily oral P7C3-A20 or vehicle for 38 weeks. During weeks 2–11, monkeys received weekly injection of 5′-bromo-2-deoxyuridine (BrdU) to label newborn cells, the majority of which would normally die over the following 27 weeks. BrdU+ cells were quantified using unbiased stereology. Separately in mice, the proneurogenic efficacy of P7C3-A20 was compared to that of NSI-189, a proneurogenic drug currently in clinical trials for patients with major depression. Orally-administered P7C3-A20 provided sustained plasma exposure, was well-tolerated, and elevated the survival of hippocampal BrdU+ cells in nonhuman primates without adverse central or peripheral tissue effects. In mice, NSI-189 was shown to be pro-proliferative, and P7C3-A20 elevated the net magnitude of hippocampal neurogenesis to a greater degree than NSI-189 through its distinct mechanism of promoting neuronal survival. This pilot study provides evidence that P7C3-A20 safely protects neurons in nonhuman primates, suggesting that the neuroprotective efficacy of P7C3 compounds is likely to translate to humans as well.

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Moving Beyond Serendipity to Mechanism-Driven Psychiatric Therapeutics

Cited by 5 publications

References 19 publications

Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning

Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning

Role of serendipity in the discovery of classical antidepressant drugs: Applying operational criteria and patterns of discovery

Neuroprotective efficacy of P7C3 compounds in primate hippocampus

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