Clozapine: A special case of an atypical antipsychotic

The treatment of psychiatric disorders is still an unresolved problem in our society and schizophrenia, the 12th cause of disability in the world, is an important and serious mental disorder. This short review presents a comprehensive overview of the gold standard clozapine developed approximately seven decades ago, which can be considered a milestone in the development of new antipsychotics. It is the first atypical antipsychotic to enter clinical use and is recognized for the treatment of psychiatric illnesses such as schizophrenia and bipolar disorder, as its efficacy is superior to other antipsychotic agents like chlorpromazine. Clozapine is a 5-HT2A and D2 receptor antagonist, as well as a 5-HT1A receptor agonist

Assessment of neuroleptic-induced movement disorders in a naturalistic schizophrenia population

The prevalence and assessment of neuroleptic-induced movement disorders (NIMDs) in a naturalistic schizophrenia population that uses conventional neuroleptics were studied. We recruited 99 chronic schizophrenic institutionalized adult patients from a state nursing home in central Estonia. The total prevalence of NIMDs according to the diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) was 61.6%, and 22.2% had more than one NIMD. We explored the reliability and validity of different instruments for measuring these disorders. First, we compared DSM-IV with the established observer rating scales of Barnes Akathisia Rating Scale (BARS), Simpson-Angus Scale (SAS) (for neuroleptic-induced parkinsonism, NIP) and Abnormal Involuntary Movement Scale (AIMS) (for tardive dyskinesia), all three of which have been used for diagnosing NIMD. We found a good overlap of cases for neuroleptic-induced akathisia (NIA) and tardive dyskinesia (TD) but somewhat poorer overlap for NIP, for which we suggest raising the commonly used threshold value of 0.3 to 0.65. Second, we compared the established observer rating scales with an objective motor measurement, namely controlled rest lower limb activity measured by actometry. Actometry supported the validity of BARS and SAS, but it could not be used alone in this naturalistic population with several co-existing NIMDs. It could not differentiate the disorders from each other. Quantitative actometry may be useful in measuring changes in NIA and NIP severity, in situations where the diagnosis has been made using another method. Third, after the relative failure of quantitative actometry to show diagnostic power in a naturalistic population, we explored descriptive ways of analysing actometric data, and demonstrated diagnostic power pooled NIA and pseudoakathisia (PsA) in our population. A subjective question concerning movement problems was able to discriminate NIA patients from all other subjects. Answers to this question were not selective for other NIMDs. Chronic schizophrenia populations are common worldwide, NIMD affected two-thirds of our study population. Prevention, diagnosis and treatment of NIMDs warrant more attention, especially in countries where typical antipsychotics are frequently used. Our study supported the validity and reliability of DSM-IV diagnostic criteria for NIMD in comparison with established rating scales and actometry. SAS can be used with minor modifications for screening purposes. Controlled rest lower limb actometry was not diagnostically specific in our naturalistic population with several co-morbid NIMDs, but it may be sensitive in measuring changes in NIMDs.Väitoskirjatyö "Assessment of neuroleptic-induced movement disorders in a naturalistic schizophrenia population" osoittaa, että kroonista skitsofreniaa sairastavilla, vanhoja psykoosilääkkeitä saavilla potilailla on runsaasti liikehäirioitä ja usein useampi liikehäiriö samanaikasesti. Tutkimus vertaili ja arvioi eri mittauskeinojen käyttökelpoisuutta vanhojen psykoosilääkkeiden aiheuttamien neurologisten haittavaikutusten arvioinnissa. Psykoosien lääkehoidon ensimmäistä kehitysvaihetta edustavat, 1950-luvulla käyttöön tulleet tavanomaiset psykoosilääkkeet ovat tehokkaita psykoosin ns. positiivisen oireisiin, mutta niiden käyttöä ovat rajoittaneet erilaiset haittavaikutukset. Keskeisimpiä näistä ovat mm. pakkoliikkeinä, vapinana ja jäykkyytenä ilmenevät neurologiset oireet, jotka esiintyessään heikentävät potilaan hoitomyöntyvyyttä, mikä vaarantaa pitkällä aikavälillä hoidon toteutumisen. Tästä johtuen kahden viimeisen vuosikymmenen aikana on pyritty kehittämään tehokkaampia ja paremmin siedettyjä psykoosilääkkeitä, joiden käyttömahdollisuudet kuitenkin vaihtelevat maasta toisen niiden korkeasta hinnasta johtuen. Tutkimus toteutettiin poikkileikkaustutkimuksena Viron suurimmassa psykiatrisessa hoitokodissa. Tutkimuksen osallistuneet 99 potilasta olivat keski-ikäisiä, pitkään skitsofrenian takia hoidettavina olleita potilaita, joista noin 80 prosenttia käytti vanhoja/ tavanomaisia psykoosilääkkeitä. Heistä noin 60 prosentilla oli todettavissa jokin neurologinen haittaoire, jonka esiintyvyyttä ja haittaavuutta arviointiin eri arviointimenetelmin sekä uutta tekniikkaa edustavalla aktometrialla. Tutkimuksessa käytettiin haastatteluun ja havainnointiin perustuvina mittausmenetelminä nykyiseen diagnostiseen järjestelmään perustuvaa liikehäiriöiden arvioitia, Barnesin akatisia-astekkoa (BARS), Simpson-Angusin parkinsonismiastekkoa (SAS) ja tardiivin dyskinesian arvioimiseksi epänormaalien tahattomien liikkeiden asteikkoa (AIMS). Aktometria osoittautui arviointiastekkojen rinnalla hyödylliseksi lisäarviointimenetelmäksi, joka ei kuitenkaan yksinään osoittautunut riittäväksi em. liikehäiriöiden alamuotojen erotusdiagnostiikassa

Safety of Antipsychotic Medication in Individuals Diagnosed with Autism Spectrum Disorder (ASD)

Background: Autism spectrum disorder (ASD) is a lifelong neurodevelopmental condition which presents in childhood. In the UK, risperidone is the only antipsychotic drug approved for the management of behavioural disturbance in children and adolescents with ASD. Aim: To explore the safety of antipsychotic medication use in people with ASD. Method: Four observational studies using a UK primary care database as a data source. The first study was a descriptive study to provide up-to-date information on the prevalence of ASD and psychotropic medication prescribing. Next, two analytical studies, of different designs, to investigate the risk of incident seizure associated with antipsychotic use, were conducted. A cohort study comparing the risk of incident seizure in people using antipsychotics with the users of other psychotropics; followed by a selfcontrolled case series analysis on the risk of incident seizure associated with antipsychotic use. Lastly, a cohort study to investigate the relationship between the risk of cardiac events and antipsychotic exposure, compared to other psychotropics, was conducted. Results: There has been a noticeable increase (3.3-fold) in the prevalence of ASD over the period from 2009 to 2016. Over this period, 12.4% of the treated ASD patients had been prescribed antipsychotics; 50.7% of antipsychotic prescriptions was for risperidone and 49.3% was for other antipsychotics. The hazard ratios of the risk of incident seizure and cardiac events associated with antipsychotic use were 1.28 (95% CI: 0.74-2.19) and 1.27 (95% CI: 0.62-2.62), respectively. During the first month of other psychotropic medication treatment, the incidence rate ratio of seizure was 1.57, 95% CI:1.03-2.38. Conclusion: This research found no evidence of an increased risk of incident seizure or cardiac outcomes associated with antipsychotic use compared to other psychotropics ASD patients. A short term increase in the risk of incident seizure was noted with the use of psychotropics other than antipsychotics

PROBABILISTIC LATENT FACTOR MODELS FOR TRANSFORMATIVE DRUG DISCOVERY

The cost of discovering a new drug has doubled every 9 years since the 1950s. This can change by using machine learning to guide experimentation. The idea I have developed over the course of my PhD is that using latent factor modeling (LFM) of the drug-target interaction network, we can guide drug repurposable efforts to achieve transformative improvements. By better characterizing the drug-target interaction network, it is possible to use currently approved drugs to achieve therapies for diseases that currently are not optimally treated. These drugs might be directly used through repurposing, or they can serve as a starting point for new drug discovery efforts where they are optimized through medicinal chemistry methods. To achieve this goal, I have developed LFM-based techniques applicable to existing databases of drug-target interaction networks. Specifically, I have started out by establishing that probabilistic matrix factorization (PMF; one type of LFM algorithm) can be used as descriptors by showing they capture therapeutic function similarities that state-of-the-art 3D chemical similarity methods could not capture. Then I have shown that PMF can effectively predict unknown drug-target interactions. Furthermore, I have used newly developed computational techniques for discovering repurposable drugs for two diseases, α1 antitrypsin (1-AT) deficiency (ATD) and Huntington’s disease (HD) leading to successful discoveries in both. For ATD, two sets of data generated by the David Perlmutter and Gary Silverman laboratories have been used as input to deduce potential targets and repurposable drugs: (i) a high throughput screening data from a genome-wide RNAi knockdown in a C. elegans model for studying ATZ (Z-allele of 1-AT), and (ii) data from Prestwick library screen for the same model. We have predicted that the antidiabetic drug glibenclamide would be beneficial against ATZ aggregation, and data collected to date in Mus musculus models are promising. We have worked on HD with the Robert Friedlander lab, by examining the potential drugs and implicated pathways for 15 neuroprotective (repurposable) drugs that they have identified in a two-stage screening study. Based on LFM-based analysis of the targets of these drugs, we have developed a number of hypotheses to be tested. Among them, the antihypertensive drug sodium nitroprusside appears to be effective against HD based on neuronal cell death inhibition experiments that were conducted at the University of Pittsburgh Drug Discovery Institute as well as the Friedlander lab. Finally, we have built a web server, named BalestraWeb, for facilitating the use of PMF in repurposable drug identification by the broader community. BalestraWeb enables users to extract information on known and potential targets (or drugs) for any approved drug (or target), simply by entering the name of the query drug (or target). I have also laid out the framework for developing an integrated resource for quantitative systems pharmacology, Balestra toolkit (BalestraTK), which would take advantage of existing databases such as STITCH, UniProt, and PubChem. Collectively, our results provide firm evidence for the potential utility of machine learning techniques for assisting in drug discovery