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Aims.In recent years several warnings have been issued by regulatory authorities on the risk of electrocardiogram abnormalities in individuals exposed to psychotropic drugs. As a consequence of these warnings, monitoring of the QT interval corrected for heart rate (QTc) has become increasingly common. This study was conducted to measure the frequency of QTc prolongation in unselected psychiatric patients, and to document the associated factors using a cross-sectional approach.Method.The study was carried out in 35 Italian psychiatric services that are part of the STAR (Servizi Territoriali Associati per la Ricerca) Network, a research group established to produce scientific knowledge by collecting data under ordinary circumstances. During a three-month period, a consecutive unselected series of both in- and out-patients were enrolled if they performed an ECG during the recruitment period and were receiving psychotropic drugs on the day ECG was recorded.Results.During the recruitment period a total of 2411 patients were included in the study. The prevalence of QTc prolongation ranged from 14.7% (men) and 18.6% (women) for the cut-off of 450 ms, to 1.26% (men) and 1.01% (women) for the cut-off of 500 ms. In the multivariate model conducted in the whole sample of patients exposed to psychotropic drugs, female sex, age, heart rate, alcohol and/or substance abuse, cardiovascular diseases and cardiovascular drug treatment, and drug overdose were significantly associated with QTc prolongation. In patients exposed to antipsychotic drugs, polypharmacy was positively associated with QTc prolongation, whereas use of aripiprazole decreased the risk. In patients exposed to antidepressant drugs, use of citalopram, citalopram dose and use of haloperidol in addition to antidepressant drugs, were all positively associated with QTc prolongation.Conclusions.The confirmation of a link between antipsychotic polypharmacy and QTc prolongation supports the current guidelines that recommend avoiding the concurrent use of two or more antipsychotic drugs, and the confirmation of a link between citalopram and QTc prolongation supports the need for routine QTc monitoring. The relatively low proportion of patients with QTc prolongation not only suggests compliance with current safety warnings issued by regulatory authorities, but also casts some doubts on the clinical relevance of QTc prolongation related to some psychotropic drugs.

Antipsychotic (AP) drugs have the potential to cause prolongation of the QT interval corrected for heart rate (QTc). As this risk is dose-dependent, it may be associated with the number of AP drugs concurrently prescribed, which is known to be associated with increased cumulative equivalent AP dosage. This study analysed whether AP dose mediates the relationship between polypharmacy and QTc interval. We used data from a cross-sectional survey that investigated the prevalence of QTc lengthening among people with psychiatric illnesses in Italy. AP polypharmacy was tested for evidence of association with AP dose and QTc interval using the Baron and Kenny mediational model. A total of 725 patients were included in this analysis. Of these, 186 (26%) were treated with two or more AP drugs (AP polypharmacy). The mean cumulative AP dose was significantly higher in those receiving AP polypharmacy (prescribed daily dose/defined daily dose = 2.93, standard deviation 1.31) than monotherapy (prescribed daily dose/defined daily dose = 0.82, standard deviation 0.77) (z = -12.62, p < 0.001). Similarly, the mean QTc interval was significantly longer in those receiving AP polypharmacy (mean = 420.86 milliseconds, standard deviation 27.16) than monotherapy (mean = 413.42 milliseconds, standard deviation 31.54) (z = -2.70, p = 0.006). The Baron and Kenny mediational analysis showed that, after adjustment for confounding variables, AP dose mediates the association between polypharmacy and QTc interval. The present study found that AP polypharmacy is associated with QTc interval, and this effect is mediated by AP dose. Given the high prevalence of AP polypharmacy in real-world clinical practice, clinicians should consider not only the myriad risk factors for QTc prolongation in their patients, but also that adding a second AP drug may further increase risk as compared with monotherapy.

Besozzi, D., Manzoni, L., Nobile, M. S., Spolaor, S., Castelli, M., Vanneschi, L., ... Tangherloni, A. (2020). Computational Intelligence for Life Sciences. Fundamenta Informaticae, 171(1-4), 57-80. https://doi.org/10.3233/FI-2020-1872 Computational Intelligence (CI) is a computer science discipline encompassing the theory, design, development and application of biologically and linguistically derived computational paradigms. Traditionally, the main elements of CI are Evolutionary Computation, Swarm Intelligence, Fuzzy Logic, and Neural Networks. CI aims at proposing new algorithms able to solve complex computational problems by taking inspiration from natural phenomena. In an intriguing turn of events, these nature-inspired methods have been widely adopted to investigate a plethora of problems related to nature itself. In this paper we present a variety of CI methods applied to three problems in life sciences, highlighting their effectiveness: we describe how protein folding can be faced by exploiting Genetic Programming, the inference of haplotypes can be tackled using Genetic Algorithms, and the estimation of biochemical kinetic parameters can be performed by means of Swarm Intelligence. We show that CI methods can generate very high quality solutions, providing a sound methodology to solve complex optimization problems in life sciences. authorsversion published

Background:Whether arrhythmia risks will increase if drugs with electrocardiographic (ECG) QT-prolonging properties are combined is generally supposed but not well studied. Based on available evidence, the Arizona Center for Education and Research on Therapeutics (AZCERT) classification defines the risk of QT prolongation for exposure to single drugs. We aimed to investigate how combining AZCERT drug categories impacts QT duration and how relative drug exposure affects the extent of pharmacodynamic drug–drug interactions.Methods:In a cohort of 2558 psychiatric inpatients and outpatients, we modeled whether AZCERT class and number of coprescribed QT-prolonging drugs correlates with observed rate-corrected QT duration (QTc) while also considering age, sex, inpatient status, and other QTc-prolonging risk factors. We concurrently considered administered drug doses and pharmacokinetic interactions modulating drug clearance to calculate individual weights of relative exposure with AZCERT drugs. Because QTc duration is concentration-dependent, we estimated individual drug exposure with these drugs and included this information as weights in weighted regression analyses.Results:Drugs attributing a ‘known’ risk for clinical consequences were associated with the largest QTc prolongations. However, the presence of at least two versus one QTc-prolonging drug yielded nonsignificant prolongations [exposure-weighted parameter estimates with 95% confidence intervals for ‘known’ risk drugs + 0.93 ms (–8.88;10.75)]. Estimates for the ‘conditional’ risk class increased upon refinement with relative drug exposure and co-administration of a ‘known’ risk drug as a further risk factor.Conclusions:These observations indicate that indiscriminate combinations of QTc-prolonging drugs do not necessarily result in additive QTc prolongation and suggest that QT prolongation caused by drug combinations strongly depends on the nature of the combination partners and individual drug exposure. Concurrently, it stresses the value of the AZCERT classification also for the risk prediction of combination therapies with QT-prolonging drugs.

This paper introduces the concepts of error vector and error space, directly bound to semantics, one of the hottest topics in genetic programming. Based on these concepts, we introduce the notions of optimally aligned individuals and optimally coplanar individuals. We show that, given optimally aligned, or optimally coplanar, individuals, it is possible to construct a globally optimal solution analytically. Thus, we introduce a genetic programming framework for symbolic regression called Error Space Alignment GP (ESAGP) and two of its instances: ESAGP-1, whose objective is to find optimally aligned individuals, and ESAGP-2, whose objective is to find optimally coplanar individuals. We also discuss how to generalize the approach to any number of dimensions. Using two complex real-life applications, we provide experimental evidence that ESAGP-2 outperforms ESAGP-1, which in turn outperforms both standard GP and geometric semantic GP. This suggests that “adding dimensions” is beneficial and encourages us to pursue the study in many different directions, that we summarize in the final part of the manuscript